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Activated by regular exercise, immune cells in muscles found to fend off inflammation, enhance endurance in mice

The connection between exercise and inflammation has captivated the imagination of researchers ever since an  early 20th-century study  showed a spike of white cells in the blood of Boston marathon runners following the race.

Now, a new Harvard Medical School study published Friday in Science Immunology may offer a molecular explanation behind this century-old observation.

The study, done in mice, suggests that the beneficial effects of exercise may be driven, at least partly, by the immune system. It shows that muscle inflammation caused by exertion mobilizes inflammation-countering T cells, or Tregs, which enhance the muscles’ ability to use energy as fuel and improve overall exercise endurance.

Long known for their role in countering the aberrant inflammation linked to autoimmune diseases, Tregs now also emerge as key players in the body’s immune responses during exercise, the research team said.

“The immune system, and the T cell arm in particular, has a broad impact on tissue health that goes beyond protection against pathogens and controlling cancer. Our study demonstrates that the immune system exerts powerful effects inside the muscle during exercise,” said study senior investigator  Diane Mathis , professor of immunology in the Blavatnik Institute at HMS.

Mice are not people, and the findings remain to be replicated in further studies, the researchers cautioned. However, the study is an important step toward detailing the cellular and molecular changes that occur during exercise and confer health benefits.

Understanding the molecular underpinnings of exercise

Protecting from cardiovascular disease, reducing the risk of diabetes, shielding against dementia. The salutary effects of exercise are well established. But exactly how does exercise make us healthy? The question has intrigued researchers for a long time.

The new findings come amid  intensifying efforts  to understand the molecular underpinnings of exercises. Untangling the immune system’s involvement in this process is but one aspect of these research efforts.

“Our research suggests that with exercise, we have a natural way to boost the body’s immune responses to reduce inflammation.” Diane Mathis, professor of immunology in the Blavatnik Institute

“We’ve known for a long time that physical exertion causes inflammation, but we don’t fully understand the immune processes involved,” said study first author Kent Langston, a postdoctoral researcher in the Mathis lab. “Our study shows, at very high resolution, what T cells do at the site where exercise occurs, in the muscle.”

Most previous research on exercise physiology has focused on the role of various hormones released during exercise and their effects on different organs such as the heart and the lungs. The new study unravels the immunological cascade that unfolds inside the actual site of exertion — the muscle.

T cell heroes and inflammation-fueling villains

Exercise is known to cause temporary damage to the muscles, unleashing a cascade of inflammatory responses. It boosts the expression of genes that regulate muscle structure, metabolism, and the activity of mitochondria, the tiny powerhouses that fuel cell function. Mitochondria play a key role in exercise adaptation by helping cells meet the greater energy demand of exercise.

In the new study, the team analyzed what happens in cells taken from the hind leg muscles of mice that ran on a treadmill once and animals that ran regularly. Then, the researchers compared them with muscle cells obtained from sedentary mice.

The muscle cells of the mice that ran on treadmills, whether once or regularly, showed classic signs of inflammation — greater activity in genes that regulate various metabolic processes and higher levels of chemicals that promote inflammation, including interferon.

Both groups had elevated levels of Treg cells in their muscles. Further analyses showed that in both groups, Tregs lowered exercise-induced inflammation. None of those changes were seen in the muscle cells of sedentary mice.

However, the metabolic and performance benefits of exercise were apparent only in the regular exercisers — the mice that had repeated bouts of running. In that group, Tregs not only subdued exertion-induced inflammation and muscle damage, but also altered muscle metabolism and muscle performance, the experiments showed. This finding aligns with well-established observations in humans that a single bout of exercise does not lead to significant improvements in performance and that regular activity over time is needed to yield benefits.

The hind leg muscles of mice lacking Treg cells (right) showed prominent signs of inflammation after regular exercise, compared with those from mice with intact Tregs (left). The research showed such that this uncontrolled inflammation negatively impacted muscle metabolism and function.

Credit: Kent Langston/Mathis Lab, HMS

Further analyses confirmed that Tregs were, indeed, responsible for the broader benefits seen in regular exercisers. Animals that lacked Tregs had unrestrained muscle inflammation, marked by the rapid accumulation of inflammation-promoting cells in their hind leg muscles. Their muscle cells also had strikingly swollen mitochondria, a sign of metabolic abnormality.

More importantly, animals lacking Tregs did not adapt to increasing demands of exercise over time the way mice with intact Tregs did. They did not derive the same whole-body benefits from exercise and had diminished aerobic fitness.

These animals’ muscles also had excessive amounts of interferon, a known driver of inflammation. Further analyses revealed that interferon acts directly on muscle fibers to alter mitochondrial function and limit energy production. Blocking interferon prevented metabolic abnormalities and improved aerobic fitness in mice lacking Tregs.

“The villain here is interferon,” Langston said. “In the absence of guardian Tregs to counter it, interferon went on to cause uncontrolled damage.”

Interferon is known to promote chronic inflammation, a process that underlies many chronic diseases and age-related conditions and has become a tantalizing target for therapies aimed at reducing inflammation. Tregs have also captured the attention of scientists and industry as treatments for a range of immunologic conditions marked by abnormal inflammation.

The study findings provide a glimpse into the cellular innerworkings behind exercise’s anti-inflammatory effects and underscore its importance in harnessing the body’s own immune defenses, the researchers said.

There are efforts afoot to design interventions targeting Tregs in the context of specific immune-mediated diseases. And while immunologic conditions driven by aberrant inflammation require carefully calibrated therapies, exercise is yet another way to counter inflammation, the researchers said.

“Our research suggests that with exercise, we have a natural way to boost the body’s immune responses to reduce inflammation,” Mathis said. “We’ve only looked in the muscle, but it’s possible that exercise is boosting Treg activity elsewhere in the body as well.”

Co-investigators included Yizhi Sun, Birgitta Ryback, Bruce Spiegelman, Amber Mueller, and Christophe Benoist.

The work was funded by National Institutes of Health grants R01 AR070334, F32 AG072874, and F32 AG069363; and by the JPB Foundation.

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  • Research article
  • Open access
  • Published: 16 November 2020

Exercise/physical activity and health outcomes: an overview of Cochrane systematic reviews

  • Pawel Posadzki 1 , 2 ,
  • Dawid Pieper   ORCID: orcid.org/0000-0002-0715-5182 3 ,
  • Ram Bajpai 4 ,
  • Hubert Makaruk 5 ,
  • Nadja Könsgen 3 ,
  • Annika Lena Neuhaus 3 &
  • Monika Semwal 6  

BMC Public Health volume  20 , Article number:  1724 ( 2020 ) Cite this article

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Sedentary lifestyle is a major risk factor for noncommunicable diseases such as cardiovascular diseases, cancer and diabetes. It has been estimated that approximately 3.2 million deaths each year are attributable to insufficient levels of physical activity. We evaluated the available evidence from Cochrane systematic reviews (CSRs) on the effectiveness of exercise/physical activity for various health outcomes.

Overview and meta-analysis. The Cochrane Library was searched from 01.01.2000 to issue 1, 2019. No language restrictions were imposed. Only CSRs of randomised controlled trials (RCTs) were included. Both healthy individuals, those at risk of a disease, and medically compromised patients of any age and gender were eligible. We evaluated any type of exercise or physical activity interventions; against any types of controls; and measuring any type of health-related outcome measures. The AMSTAR-2 tool for assessing the methodological quality of the included studies was utilised.

Hundred and fifty CSRs met the inclusion criteria. There were 54 different conditions. Majority of CSRs were of high methodological quality. Hundred and thirty CSRs employed meta-analytic techniques and 20 did not. Limitations for studies were the most common reasons for downgrading the quality of the evidence. Based on 10 CSRs and 187 RCTs with 27,671 participants, there was a 13% reduction in mortality rates risk ratio (RR) 0.87 [95% confidence intervals (CI) 0.78 to 0.96]; I 2  = 26.6%, [prediction interval (PI) 0.70, 1.07], median effect size (MES) = 0.93 [interquartile range (IQR) 0.81, 1.00]. Data from 15 CSRs and 408 RCTs with 32,984 participants showed a small improvement in quality of life (QOL) standardised mean difference (SMD) 0.18 [95% CI 0.08, 0.28]; I 2  = 74.3%; PI -0.18, 0.53], MES = 0.20 [IQR 0.07, 0.39]. Subgroup analyses by the type of condition showed that the magnitude of effect size was the largest among patients with mental health conditions.

There is a plethora of CSRs evaluating the effectiveness of physical activity/exercise. The evidence suggests that physical activity/exercise reduces mortality rates and improves QOL with minimal or no safety concerns.

Trial registration

Registered in PROSPERO ( CRD42019120295 ) on 10th January 2019.

Peer Review reports

The World Health Organization (WHO) defines physical activity “as any bodily movement produced by skeletal muscles that requires energy expenditure” [ 1 ]. Therefore, physical activity is not only limited to sports but also includes walking, running, swimming, gymnastics, dance, ball games, and martial arts, for example. In the last years, several organizations have published or updated their guidelines on physical activity. For example, the Physical Activity Guidelines for Americans, 2nd edition, provides information and guidance on the types and amounts of physical activity that provide substantial health benefits [ 2 ]. The evidence about the health benefits of regular physical activity is well established and so are the risks of sedentary behaviour [ 2 ]. Exercise is dose dependent, meaning that people who achieve cumulative levels several times higher than the current recommended minimum level have a significant reduction in the risk of breast cancer, colon cancer, diabetes, ischemic heart disease, and ischemic stroke events [ 3 ]. Benefits of physical activity have been reported for numerous outcomes such as mortality [ 4 , 5 ], cognitive and physical decline [ 5 , 6 , 7 ], glycaemic control [ 8 , 9 ], pain and disability [ 10 , 11 ], muscle and bone strength [ 12 ], depressive symptoms [ 13 ], and functional mobility and well-being [ 14 , 15 ]. Overall benefits of exercise apply to all bodily systems including immunological [ 16 ], musculoskeletal [ 17 ], respiratory [ 18 ], and hormonal [ 19 ]. Specifically for the cardiovascular system, exercise increases fatty acid oxidation, cardiac output, vascular smooth muscle relaxation, endothelial nitric oxide synthase expression and nitric oxide availability, improves plasma lipid profiles [ 15 ] while at the same time reducing resting heart rate and blood pressure, aortic valve calcification, and vascular resistance [ 20 ].

However, the degree of all the above-highlighted benefits vary considerably depending on individual fitness levels, types of populations, age groups and the intensity of different physical activities/exercises [ 21 ]. The majority of guidelines in different countries recommend a goal of 150 min/week of moderate-intensity aerobic physical activity (or equivalent of 75 min of vigorous-intensity) [ 22 ] with differences for cardiovascular disease [ 23 ] or obesity prevention [ 24 ] or age groups [ 25 ].

There is a plethora of systematic reviews published by the Cochrane Library critically evaluating the effectiveness of physical activity/exercise for various health outcomes. Cochrane systematic reviews (CSRs) are known to be a source of high-quality evidence. Thus, it is not only timely but relevant to evaluate the current knowledge, and determine the quality of the evidence-base, and the magnitude of the effect sizes given the negative lifestyle changes and rising physical inactivity-related burden of diseases. This overview will identify the breadth and scope to which CSRs have appraised the evidence for exercise on health outcomes; and this will help in directing future guidelines and identifying current gaps in the literature.

The objectives of this research were to a. answer the following research questions: in children, adolescents and adults (both healthy and medically compromised) what are the effects (and adverse effects) of exercise/physical activity in improving various health outcomes (e.g., pain, function, quality of life) reported in CSRs; b. estimate the magnitude of the effects by pooling the results quantitatively; c. evaluate the strength and quality of the existing evidence; and d. create recommendations for future researchers, patients, and clinicians.

Our overview was registered with PROSPERO (CRD42019120295) on 10th January 2019. The Cochrane Handbook for Systematic Reviews of interventions and Preferred Reporting Items for Overviews of Reviews were adhered to while writing and reporting this overview [ 26 , 27 ].

Search strategy and selection criteria

We followed the practical guidance for conducting overviews of reviews of health care interventions [ 28 ] and searched the Cochrane Database of Systematic Reviews (CDSR), 2019, Issue 1, on the Cochrane Library for relevant papers using the search strategy: (health) and (exercise or activity or physical). The decision to seek CSRs only was based on three main aspects. First, high quality (CSRs are considered to be the ‘gold methodological standard’) [ 29 , 30 , 31 ]. Second, data saturation (enough high-quality evidence to reach meaningful conclusions based on CSRs only). Third, including non-CSRs would have heavily increased the issue of overlapping reviews (also affecting data robustness and credibility of conclusions). One reviewer carried out the searches. The study screening and selection process were performed independently by two reviewers. We imported all identified references into reference manager software EndNote (X8). Any disagreements were resolved by discussion between the authors with third overview author acting as an arbiter, if necessary.

We included CSRs of randomised controlled trials (RCTs) involving both healthy individuals and medically compromised patients of any age and gender. Only CSRs assessing exercise or physical activity as a stand-alone intervention were included. This included interventions that could initially be taught by a professional or involve ongoing supervision (the WHO definition). Complex interventions e.g., assessing both exercise/physical activity and behavioural changes were excluded if the health effects of the interventions could not have been attributed to exercise distinctly.

Any types of controls were admissible. Reviews evaluating any type of health-related outcome measures were deemed eligible. However, we excluded protocols or/and CSRs that have been withdrawn from the Cochrane Library as well as reviews with no included studies.

Data analysis

Three authors (HM, ALN, NK) independently extracted relevant information from all the included studies using a custom-made data collection form. The methodological quality of SRs included was independently evaluated by same reviewers using the AMSTAR-2 tool [ 32 ]. Any disagreements on data extraction or CSR quality were resolved by discussion. The entire dataset was validated by three authors (PP, MS, DP) and any discrepant opinions were settled through discussions.

The results of CSRs are presented in a narrative fashion using descriptive tables. Where feasible, we presented outcome measures across CSRs. Data from the subset of homogeneous outcomes were pooled quantitatively using the approach previously described by Bellou et al. and Posadzki et al. [ 33 , 34 ]. For mortality and quality of life (QOL) outcomes, the number of participants and RCTs involved in the meta-analysis, summary effect sizes [with 95% confidence intervals (CI)] using random-effects model were calculated. For binary outcomes, we considered relative risks (RRs) as surrogate measures of the corresponding odds ratio (OR) or risk ratio/hazard ratio (HR). To stabilise the variance and normalise the distributions, we transformed RRs into their natural logarithms before pooling the data (a variation was allowed, however, it did not change interpretation of results) [ 35 ]. The standard error (SE) of the natural logarithm of RR was derived from the corresponding CIs, which was either provided in the study or calculated with standard formulas [ 36 ]. Binary outcomes reported as risk difference (RD) were also meta-analysed if two more estimates were available. For continuous outcomes, we only meta-analysed estimates that were available as standardised mean difference (SMD), and estimates reported with mean differences (MD) for QOL were presented separately in a supplementary Table  9 . To estimate the overall effect size, each study was weighted by the reciprocal of its variance. Random-effects meta-analysis, using DerSimonian and Laird method [ 37 ] was applied to individual CSR estimates to obtain a pooled summary estimate for RR or SMD. The 95% prediction interval (PI) was also calculated (where ≥3 studies were available), which further accounts for between-study heterogeneity and estimates the uncertainty around the effect that would be anticipated in a new study evaluating that same association. I -squared statistic was used to measure between study heterogeneity; and its various thresholds (small, substantial and considerable) were interpreted considering the size and direction of effects and the p -value from Cochran’s Q test ( p  < 0.1 considered as significance) [ 38 ]. Wherever possible, we calculated the median effect size (with interquartile range [IQR]) of each CSR to interpret the direction and magnitude of the effect size. Sub-group analyses are planned for type and intensity of the intervention; age group; gender; type and/or severity of the condition, risk of bias in RCTs, and the overall quality of the evidence (Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria). To assess overlap we calculated the corrected covered area (CCA) [ 39 ]. All statistical analyses were conducted on Stata statistical software version 15.2 (StataCorp LLC, College Station, Texas, USA).

The searches generated 280 potentially relevant CRSs. After removing of duplicates and screening, a total of 150 CSRs met our eligibility criteria [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 , 188 , 189 ] (Fig.  1 ). Reviews were published between September 2002 and December 2018. A total of 130 CSRs employed meta-analytic techniques and 20 did not. The total number of RCTs in the CSRs amounted to 2888; with 485,110 participants (mean = 3234, SD = 13,272). The age ranged from 3 to 87 and gender distribution was inestimable. The main characteristics of included reviews are summarised in supplementary Table  1 . Supplementary Table  2 summarises the effects of physical activity/exercise on health outcomes. Conclusions from CSRs are listed in supplementary Table  3 . Adverse effects are listed in supplementary Table  4 . Supplementary Table  5 presents summary of withdrawals/non-adherence. The methodological quality of CSRs is presented in supplementary Table  6 . Supplementary Table  7 summarises studies assessed at low risk of bias (by the authors of CSRs). GRADE-ings of the review’s main comparison are listed in supplementary Table  8 .

figure 1

Study selection process

There were 54 separate populations/conditions, considerable range of interventions and comparators, co-interventions, and outcome measures. For detailed description of interventions, please refer to the supplementary tables . Most commonly measured outcomes were - function 112 (75%), QOL 83 (55%), AEs 70 (47%), pain 41 (27%), mortality 28 (19%), strength 30 (20%), costs 47 (31%), disability 14 (9%), and mental health in 35 (23%) CSRs.

There was a 13% reduction in mortality rates risk ratio (RR) 0.87 [95% CI 0.78 to 0.96]; I 2  = 26.6%, [PI 0.70, 1.07], median effect size (MES) = 0.93 [interquartile range (IQR) 0.81, 1.00]; 10 CSRs, 187 RCTs, 27,671 participants) following exercise when compared with various controls (Table 1 ). This reduction was smaller in ‘other groups’ of patients when compared to cardiovascular diseases (CVD) patients - RR 0.97 [95% CI 0.65, 1.45] versus 0.85 [0.76, 0.96] respectively. The effects of exercise were not intensity or frequency dependent. Sessions more than 3 times per week exerted a smaller reduction in mortality as compared with sessions of less than 3 times per week RR 0.87 [95% CI 0.78, 0.98] versus 0.63 [0.39, 1.00]. Subgroup analyses by risk of bias (ROB) in RCTs showed that RCTs at low ROB exerted smaller reductions in mortality when compared to RCTs at an unclear or high ROB, RR 0.90 [95% CI 0.78, 1.02] versus 0.72 [0.42, 1.22] versus 0.86 [0.69, 1.06] respectively. CSRs with moderate quality of evidence (GRADE), showed slightly smaller reductions in mortality when compared with CSRs that relied on very low to low quality evidence RR 0.88 [95% CI 0.79, 0.98] versus 0.70 [0.47, 1.04].

Exercise also showed an improvement in QOL, standardised mean difference (SMD) 0.18 [95% CI 0.08, 0.28]; I 2  = 74.3%; PI -0.18, 0.53], MES = 0.20 [IQR 0.07, 0.39]; 15 CSRs, 408 RCTs, 32,984 participants) when compared with various controls (Table 2 ). These improvements were greater observed for health related QOL when compared to overall QOL SMD 0.30 [95% CI 0.21, 0.39] vs 0.06 [− 0.08, 0.20] respectively. Again, the effects of exercise were duration and frequency dependent. For instance, sessions of more than 90 mins exerted a greater improvement in QOL as compared with sessions up to 90 min SMD 0.24 [95% CI 0.11, 0.37] versus 0.22 [− 0.30, 0.74]. Subgroup analyses by the type of condition showed that the magnitude of effect was the largest among patients with mental health conditions, followed by CVD and cancer. Physical activity exerted negative effects on QOL in patients with respiratory conditions (2 CSRs, 20 RCTs with 601 patients; SMD -0.97 [95% CI -1.43, 0.57]; I 2  = 87.8%; MES = -0.46 [IQR-0.97, 0.05]). Subgroup analyses by risk of bias (ROB) in RCTs showed that RCTs at low or unclear ROB exerted greater improvements in QOL when compared to RCTs at a high ROB SMD 0.21 [95% CI 0.10, 0.31] versus 0.17 [0.03, 0.31]. Analogically, CSRs with moderate to high quality of evidence showed slightly greater improvements in QOL when compared with CSRs that relied on very low to low quality evidence SMD 0.19 [95% CI 0.05, 0.33] versus 0.15 [− 0.02, 0.32]. Please also see supplementary Table  9 more studies reporting QOL outcomes as mean difference (not quantitatively synthesised herein).

Adverse events (AEs) were reported in 100 (66.6%) CSRs; and not reported in 50 (33.3%). The number of AEs ranged from 0 to 84 in the CSRs. The number was inestimable in 83 (55.3%) CSRs. Ten (6.6%) reported no occurrence of AEs. Mild AEs were reported in 28 (18.6%) CSRs, moderate in 9 (6%) and serious/severe in 20 (13.3%). There were 10 deaths and in majority of instances, the causality was not attributed to exercise. For this outcome, we were unable to pool the data as effect sizes were too heterogeneous (Table 3 ).

In 38 CSRs, the total number of trials reporting withdrawals/non-adherence was inestimable. There were different ways of reporting it such as adherence or attrition (high in 23.3% of CSRs) as well as various effect estimates including %, range, total numbers, MD, RD, RR, OR, mean and SD. The overall pooled estimates are reported in Table 3 .

Of all 16 domains of the AMSTAR-2 tool, 1876 (78.1%) scored ‘yes’, 76 (3.1%) ‘partial yes’; 375 (15.6%) ‘no’, and ‘not applicable’ in 25 (1%) CSRs. Ninety-six CSRs (64%) were scored as ‘no’ on reporting sources of funding for the studies followed by 88 (58.6%) failing to explain the selection of study designs for inclusion. One CSR (0.6%) each were judged as ‘no’ for reporting any potential sources of conflict of interest, including any funding for conducting the review as well for performing study selection in duplicate.

In 102 (68%) CSRs, there was predominantly a high risk of bias in RCTs. In 9 (6%) studies, this was reported as a range, e.g., low or unclear or low to high. Two CSRs used different terminology i.e., moderate methodological quality; and the risk of bias was inestimable in one CSR. Sixteen (10.6%) CSRs did not identify any studies (RCTs) at low risk of random sequence generation, 28 (18.6%) allocation concealment, 28 (18.6%) performance bias, 84 (54%) detection bias, 35 (23.3%) attrition bias, 18 (12%) reporting bias, and 29 (19.3%) other bias.

In 114 (76%) CSRs, limitation of studies was the main reason for downgrading the quality of the evidence followed by imprecision in 98 (65.3%) and inconsistency in 68 (45.3%). Publication bias was the least frequent reason for downgrading in 26 (17.3%) CSRs. Ninety-one (60.7%) CSRs reached equivocal conclusions, 49 (32.7%) reviews reached positive conclusions and 10 (6.7%) reached negative conclusions (as judged by the authors of CSRs).

In this systematic review of CSRs, we found a large body of evidence on the beneficial effects of physical activity/exercise on health outcomes in a wide range of heterogeneous populations. Our data shows a 13% reduction in mortality rates among 27,671 participants, and a small improvement in QOL and health-related QOL following various modes of physical activity/exercises. This means that both healthy individuals and medically compromised patients can significantly improve function, physical and mental health; or reduce pain and disability by exercising more [ 190 ]. In line with previous findings [ 191 , 192 , 193 , 194 ], where a dose-specific reduction in mortality has been found, our data shows a greater reduction in mortality in studies with longer follow-up (> 12 months) as compared to those with shorter follow-up (< 12 months). Interestingly, we found a consistent pattern in the findings, the higher the quality of evidence and the lower the risk of bias in primary studies, the smaller reductions in mortality. This pattern is observational in nature and cannot be over-generalised; however this might mean less certainty in the estimates measured. Furthermore, we found that the magnitude of the effect size was the largest among patients with mental health conditions. A possible mechanism of action may involve elevated levels of brain-derived neurotrophic factor or beta-endorphins [ 195 ].

We found the issue of poor reporting or underreporting of adherence/withdrawals in over a quarter of CSRs (25.3%). This is crucial both for improving the accuracy of the estimates at the RCT level as well as maintaining high levels of physical activity and associated health benefits at the population level.

Even the most promising interventions are not entirely risk-free; and some minor AEs such as post-exercise pain and soreness or discomfort related to physical activity/exercise have been reported. These were typically transient; resolved within a few days; and comparable between exercise and various control groups. However worryingly, the issue of poor reporting or underreporting of AEs has been observed in one third of the CSRs. Transparent reporting of AEs is crucial for identifying patients at risk and mitigating any potential negative or unintended consequences of the interventions.

High risk of bias of the RCTs evaluated was evident in more than two thirds of the CSRs. For example, more than half of reviews identified high risk of detection bias as a major source of bias suggesting that lack of blinding is still an issue in trials of behavioural interventions. Other shortcomings included insufficiently described randomisation and allocation concealment methods and often poor outcome reporting. This highlights the methodological challenges in RCTs of exercise and the need to counterbalance those with the underlying aim of strengthening internal and external validity of these trials.

Overall, high risk of bias in the primary trials was the main reason for downgrading the quality of the evidence using the GRADE criteria. Imprecision was frequently an issue, meaning the effective sample size was often small; studies were underpowered to detect the between-group differences. Pooling too heterogeneous results often resulted in inconsistent findings and inability to draw any meaningful conclusions. Indirectness and publication bias were lesser common reasons for downgrading. However, with regards to the latter, the generally accepted minimum number of 10 studies needed for quantitatively estimate the funnel plot asymmetry was not present in 69 (46%) CSRs.

Strengths of this research are the inclusion of large number of ‘gold standard’ systematic reviews, robust screening, data extractions and critical methodological appraisal. Nevertheless, some weaknesses need to be highlighted when interpreting findings of this overview. For instance, some of these CSRs analysed the same primary studies (RCTs) but, arrived at slightly different conclusions. Using, the Pieper et al. [ 39 ] formula, the amount of overlap ranged from 0.01% for AEs to 0.2% for adherence, which indicates slight overlap. All CSRs are vulnerable to publication bias [ 196 ] - hence the conclusions generated by them may be false-positive. Also, exercise was sometimes part of a complex intervention; and the effects of physical activity could not be distinguished from co-interventions. Often there were confounding effects of diet, educational, behavioural or lifestyle interventions; selection, and measurement bias were inevitably inherited in this overview too. Also, including CSRs only might lead to selection bias; and excluding reviews published before 2000 might limit the overall completeness and applicability of the evidence. A future update should consider these limitations, and in particular also including non-CSRs.

Conclusions

Trialists must improve the quality of primary studies. At the same time, strict compliance with the reporting standards should be enforced. Authors of CSRs should better explain eligibility criteria and report sources of funding for the primary studies. There are still insufficient physical activity trends worldwide amongst all age groups; and scalable interventions aimed at increasing physical activity levels should be prioritized [ 197 ]. Hence, policymakers and practitioners need to design and implement comprehensive and coordinated strategies aimed at targeting physical activity programs/interventions, health promotion and disease prevention campaigns at local, regional, national, and international levels [ 198 ].

Availability of data and materials

Data sharing is not applicable to this article as no raw data were analysed during the current study. All information in this article is based on published systematic reviews.

Abbreviations

Adverse events

Cardiovascular diseases

Cochrane Database of Systematic Reviews

Cochrane systematic reviews

Confidence interval

Grading of Recommendations Assessment, Development and Evaluation

Hazard ratio

Interquartile range

Mean difference

Prediction interval

Quality of life

Randomised controlled trials

Relative risk

Risk difference

Risk of bias

Standard error

Standardised mean difference

World Health Organization

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Pawel Posadzki

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PP wrote the protocol, ran the searches, validated, analysed and synthesised data, wrote and revised the drafts. HM, NK and ALN screened and extracted data. MS and DP validated and analysed the data. RB ran statistical analyses. All authors contributed to writing and reviewing the manuscript. PP is the guarantor. The authors read and approved the final manuscript.

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Supplementary Information

Additional file 1:.

Supplementary Table 1. Main characteristics of included Cochrane systematic reviews evaluating the effects of physical activity/exercise on health outcomes ( n  = 150). Supplementary Table 2. Additional information from Cochrane systematic reviews of the effects of physical activity/exercise on health outcomes ( n  = 150). Supplementary Table 3. Conclusions from Cochrane systematic reviews “quote”. Supplementary Table 4 . AEs reported in Cochrane systematic reviews. Supplementary Table 5. Summary of withdrawals/non-adherence. Supplementary Table 6. Methodological quality assessment of the included Cochrane reviews with AMSTAR-2. Supplementary Table 7. Number of studies assessed as low risk of bias per domain. Supplementary Table 8. GRADE for the review’s main comparison. Supplementary Table 9. Studies reporting quality of life outcomes as mean difference.

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Posadzki, P., Pieper, D., Bajpai, R. et al. Exercise/physical activity and health outcomes: an overview of Cochrane systematic reviews. BMC Public Health 20 , 1724 (2020). https://doi.org/10.1186/s12889-020-09855-3

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Scientists chart how exercise affects the body

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Exercise is well-known to help people lose weight and avoid gaining it. However, identifying the cellular mechanisms that underlie this process has proven difficult because so many cells and tissues are involved.

In a new study in mice that expands researchers’ understanding of how exercise and diet affect the body, MIT and Harvard Medical School researchers have mapped out many of the cells, genes, and cellular pathways that are modified by exercise or high-fat diet. The findings could offer potential targets for drugs that could help to enhance or mimic the benefits of exercise, the researchers say.

“It is extremely important to understand the molecular mechanisms that are drivers of the beneficial effects of exercise and the detrimental effects of a high-fat diet, so that we can understand how we can intervene, and develop drugs that mimic the impact of exercise across multiple tissues,” says Manolis Kellis, a professor of computer science in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and a member of the Broad Institute of MIT and Harvard.

The researchers studied mice with high-fat or normal diets, who were either sedentary or given the opportunity to exercise whenever they wanted. Using single-cell RNA sequencing, the researchers cataloged the responses of 53 types of cells found in skeletal muscle and two types of fatty tissue.

“One of the general points that we found in our study, which is overwhelmingly clear, is how high-fat diets push all of these cells and systems in one way, and exercise seems to be pushing them nearly all in the opposite way,” Kellis says. “It says that exercise can really have a major effect throughout the body.”

Kellis and Laurie Goodyear, a professor of medicine at Harvard Medical School and senior investigator at the Joslin Diabetes Center, are the senior authors of the study, which appears today in the journal Cell Metabolism . Jiekun Yang, a research scientist in MIT CSAIL; Maria Vamvini, an instructor of medicine at the Joslin Diabetes Center; and Pasquale Nigro, an instructor of medicine at the Joslin Diabetes Center, are the lead authors of the paper.

The risks of obesity

Obesity is a growing health problem around the world. In the United States, more than 40 percent of the population is considered obese, and nearly 75 percent is overweight. Being overweight is a risk factor for many diseases, including heart disease, cancer, Alzheimer’s disease, and even infectious diseases such as Covid-19.

“Obesity, along with aging, is a global factor that contributes to every aspect of human health,” Kellis says.

Several years ago, his lab performed a study on the FTO gene region, which has been strongly linked to obesity risk. In that 2015 study, the research team found that genes in this region control a pathway that prompts immature fat cells called progenitor adipocytes to either become fat-burning cells or fat-storing cells.

That finding, which demonstrated a clear genetic component to obesity, motivated Kellis to begin looking at how exercise, a well-known behavioral intervention that can prevent obesity, might act on progenitor adipocytes at the cellular level.

To explore that question, Kellis and his colleagues decided to perform single-cell RNA sequencing of three types of tissue — skeletal muscle, visceral white adipose tissue (found packed around internal organs, where it stores fat), and subcutaneous white adipose tissue (which is found under the skin and primarily burns fat).

These tissues came from mice from four different experimental groups. For three weeks, two groups of mice were fed either a normal diet or a high-fat diet. For the next three weeks, each of those two groups were further divided into a sedentary group and an exercise group, which had continuous access to a treadmill.

By analyzing tissues from those mice, the researchers were able to comprehensively catalog the genes that were activated or suppressed by exercise in 53 different cell types.

The researchers found that in all three tissue types, mesenchymal stem cells (MSCs) appeared to control many of the diet and exercise-induced effects that they observed. MSCs are stem cells that can differentiate into other cell types, including fat cells and fibroblasts. In adipose tissue, the researchers found that a high-fat diet modulated MSCs’ capacity to differentiate into fat-storing cells, while exercise reversed this effect.

In addition to promoting fat storage, the researchers found that a high-fat diet also stimulated MSCs to secrete factors that remodel the extracellular matrix (ECM) — a network of proteins and other molecules that surround and support cells and tissues in the body. This ECM remodeling helps provide structure for enlarged fat-storing cells and also creates a more inflammatory environment.

“As the adipocytes become overloaded with lipids, there’s an extreme amount of stress, and that causes low-grade inflammation, which is systemic and preserved for a long time,” Kellis says. “That is one of the factors that is contributing to many of the adverse effects of obesity.”

Circadian effects

The researchers also found that high-fat diets and exercise had opposing effects on cellular pathways that control circadian rhythms — the 24-hour cycles that govern many functions, from sleep to body temperature, hormone release, and digestion. The study revealed that exercise boosts the expression of genes that regulate these rhythms, while a high-fat diet suppresses them.

“There have been a lot of studies showing that when you eat during the day is extremely important in how you absorb the calories,” Kellis says. “The circadian rhythm connection is a very important one, and shows how obesity and exercise are in fact directly impacting that circadian rhythm in peripheral organs, which could act systemically on distal clocks and regulate stem cell functions and immunity.”

The researchers then compared their results to a database of human genes that have been linked with metabolic traits. They found that two of the circadian rhythm genes they identified in this study, known as DBP and CDKN1A, have genetic variants that have been associated with a higher risk of obesity in humans.

“These results help us see the translational values of these targets, and how we could potentially target specific biological processes in specific cell types,” Yang says.

The researchers are now analyzing samples of small intestine, liver, and brain tissue from the mice in this study, to explore the effects of exercise and high-fat diets on those tissues. They are also conducting work with human volunteers to sample blood and biopsies and study similarities and differences between human and mouse physiology. They hope that their findings will help guide drug developers in designing drugs that might mimic some of the beneficial effects of exercise.

“The message for everyone should be, eat a healthy diet and exercise if possible,” Kellis says. “For those for whom this is not possible, due to low access to healthy foods, or due to disabilities or other factors that prevent exercise, or simply lack of time to have a healthy diet or a healthy lifestyle, what this study says is that we now have a better handle on the pathways, the specific genes, and the specific molecular and cellular processes that we should be manipulating therapeutically.”

The research was funded by the National Institutes of Health and the Novo Nordisk Research Center in Seattle.

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Researchers from MIT and Harvard Medical School are investigating how exercise and high-fat diets can alter cells, genes and cellular pathways, reports Abby Patkin for Boston.com . “Their research could eventually help develop drugs that would mimic the effects of exercise and combat obesity,” explains Patkin.

Researchers from MIT and Harvard Medical School have conducted a study to see how exercise and high-fat diets can impact cells, reports WCVB. The researchers “say the data could eventually be used to develop drugs that could help enhance or mimic the benefits of exercise,” writes WCVB.

A new study by researchers from MIT and Harvard Medical School has helped identify the impact of exercise and high-fat diets on cells, reports Darren Botelho for NBC Boston 10 . “Years from now, those researchers say the data could lead to a pill that would help not only with weight loss, but with the overall effect from exercise – a better wellbeing,” explains Botelho.

Boston 25 News

Prof. Manolis Kellis speaks with Boston 25 about his team’s work exploring the underlying mechanisms exploring how exercise influences weight loss, findings that could offer potential targets for drugs that could help to enhance or mimic the benefits of exercise. “Such an intervention would be a complete game changer and the reason for that is that the obesity epidemic has led to the U.S. having a decreased life span compared to all other developed countries,” says Kellis.

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Moving Sport and Exercise Science Forward: A Call for the Adoption of More Transparent Research Practices

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A Letter to the Editor to this article was published on 23 May 2020

The primary means of disseminating sport and exercise science research is currently through journal articles. However, not all studies, especially those with null findings, make it to formal publication. This publication bias towards positive findings may contribute to questionable research practices. Preregistration is a solution to prevent the publication of distorted evidence resulting from this system. This process asks authors to register their hypotheses and methods before data collection on a publicly available repository or by submitting a Registered Report. In the Registered Report format, authors submit a stage 1 manuscript to a participating journal that includes an introduction, methods, and any pilot data indicating the exploratory or confirmatory nature of the study. After a stage 1 peer review, the manuscript can then be offered in-principle acceptance, rejected, or sent back for revisions to improve the quality of the study. If accepted, the project is guaranteed publication, assuming the authors follow the data collection and analysis protocol. After data collection, authors re-submit a stage 2 manuscript that includes the results and discussion, and the study is evaluated on clarity and conformity with the planned analysis. In its final form, Registered Reports appear almost identical to a typical publication, but give readers confidence that the hypotheses and main analyses are less susceptible to bias from questionable research practices. From this perspective, we argue that inclusion of Registered Reports by researchers and journals will improve the transparency, replicability, and trust in sport and exercise science research. The preprint version of this work is available on SportR \(\chi \) iv: https://osf.io/preprints/sportrxiv/fxe7a/ .

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The reviewers find that the research question makes some meaningful contribution to the field and that the proposed methods are sound.

While Registered Reports are not meant to replace the current publishing approach, this would be partly appreciated. Such a transition would make the literature homogeneously more rigorous and transparent, properties that are at the heart of good science. This transition would ultimately allow readers of both original studies and meta-analyses to know that the findings have much less bias than they would in a traditional publishing format.

Registered Reports are only one step in a long process for improving sport and exercise science research. In fact, from the email thread used during the creation of this paper, the Society for Transparency, Openness, and Reproducibility in Kinesiology (STORK, http://storkinesiology.org/ ) was formed to help address these issues.

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Acknowledgements

This paper is dedicated to the memory of our co-author Rémi Radel, who unfortunately passed away before this paper reached final publication. Without his dedication, support, and insight, this manuscript would not have been possible. Furthermore, we would like to thank Dr. Matthew Cramer, who provided feedback early on in the writing of this manuscript. We would like to acknowledge the following individuals for their contributions: Ian Boardley (School of Sport, Exercise, & Rehabilitation Sciences, University of Birmingham, Birmingham, USA), Brooke Bouza (Department of Health, Human Performance, and Recreation, University of Arkansas-Fayetteville, Fayetteville, AR, USA), Boris Cheval (Department of Psychology, University of Geneva, Geneva, Switzerland), Zad Rafi Chow (Department of Population Health, NYU Langone Medical Center, New York, NY, USA), Bret Contreras (Sport Performance Research Institute, Auckland University of Technology, Auckland, NZ), Brad Dieter (Washington State University, Pullman, WA, USA; Providence Medical Research Center, Providence Health Care, Spokane, WA, USA), Israel Halperin (School of Public Health, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Sylvan Adams Sports Institute, Tel Aviv University, Tel Aviv, Israel), Cody Haun (Department of Exercise Science, LaGrange College, LaGrange, GA, USA), Duane Knudson (Department of Health and Human Performance, Texas State University, San Marcos, TX, USA), Johan Lahti (Laboratoire LAMHESS, Universite Côte d'Azur, Nice, France), Keith Lohse (Department of Health, Kinesiology, & Recreation; Department of Physical Therapy and Athletic Training; University of Utah, 250 S 1850 E, Room 258, Salt lake City, Utah, 84112), Matthew Miller (School of Kinesiology and Center for Neuroscience, Auburn University, Auburn, AL, USA), Jean-Benoit Morin (Laboratoire LAMHESS, Universite Côte d'Azur, Nice, France), Mitchell Naughton (University of New England, Armidale, New South Wales, Australia), Jason Neva (Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada), Greg Nuckols (Sport and Exercise Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA), David Nunan (Centre for Evidence-Based Medicine, University of Oxford, Nuffield Department of Primary Care Health Science, Woodstock Road, Oxford), Sue Peters (Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada), Brandon Roberts (Department of Cell, Developmental and Integrative Biology, University of Birmingham at Alabama, Birmingham, AL, USA), Megan Rosa-Caldwell (Exercise Science Research Center, University of Arkansas-Fayetteville, Fayetteville, AR, USA), Julia Schmidt (Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada; Department of Occupational Therapy, La Trobe University, Melbourne, Australia), Brad J. Schoenfeld (Health Sciences Department, CUNY Lehman College, Bronx, NY, USA), Richard Severin (Department of Physical Therapy, The University of Illinois at Chicago, Chicago, IL, USA; Doctor of Physical Therapy Program, Baylor University, Waco, TX, USA), Jakob Škarabot (Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK), James Steele (ukactive Research Institute, London, UK; School of Sport, Health, and Social Sciences, Solent University, Southampton, UK), Rosie Twomey (Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada), and Zachary Zenko (Department of Kinesiology, California State University Bakersfield, Bakersfield, CA, USA).

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  • Ian Boardley
  • , Brooke Bouza
  • , Boris Cheval
  • , Zad Rafi Chow
  • , Bret Contreras
  • , Brad Dieter
  • , Israel Halperin
  • , Cody Haun
  • , Duane Knudson
  • , Johan Lahti
  • , Matthew Miller
  • , Jean-Benoit Morin
  • , Mitchell Naughton
  • , Jason Neva
  • , Greg Nuckols
  • , Sue Peters
  • , Brandon Roberts
  • , Megan Rosa-Caldwell
  • , Julia Schmidt
  • , Brad J. Schoenfeld
  • , Richard Severin
  • , Jakob Skarabot
  • , James Steele
  • , Rosie Twomey
  • , Zachary Zenko
  • , Keith R. Lohse
  •  & David Nunan

Contributions

ARC and ADV devised and lead the writing of this manuscript. The co-authors participated in the brainstorming, drafting and editing, or supported the initiatives included within the manuscript. Author order—except for ARC and ADV—was determined via randomization, as per majority vote. The International Committee of Medical Journal Editors (ICMJE) has four requirements for authorship that pertain to this manuscript, which will be used to acknowledge individual contributions: (1) substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; and (2) drafting the work or revising it critically for important intellectual content; and (3) final approval of the version to be published; and (4) agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This manuscript was preprinted and submitted to sports medicine with more authors. However, not all of those authors met the ICMJE guidelines for authorship, thus, the contributions of individuals who did and did not meet authorship guidelines are acknowledged below. All authors—ARC, ADV, MST, RR, DTM, AK, IML, JPM, MPB—made substantial contributions to the conception or design of the work, drafted the work or revised it critically for important intellectual content, provided final approval of the version to be published, and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to Andrew D. Vigotsky .

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Conflict of interest

Aaron R. Caldwell is the current Steering Chair for the preprint server SportR \(\chi \) v, and is on the board for the Society for Transparency, Openness, and Replication in Kinesiology (STORK). David T. Mellor is an employee of the Center for Open Science, a nonprofit organization whose mission includes advocating for increased transparency in scientific research, which includes the Registered Reports format. John P. Mills is the founder of SportR \(\chi \) xiv and the Executive Chair of STORK and Ian M. Lahart is the Editor of Physiology and Nutrition section of Registered Reports in Kinesiology. All other authors—Andrew D. Vigotsky, Matthew S. Tenan, Rémi Radel, Andreas Kreutzer, and Matthieu P. Boisgontier—have no conflicts of interest to declare. No financial support was received for the preparation or publication of this manuscript.

Collaborators

Ian Boardley, Brooke Bouza, Boris Cheval, Zad Rafi Chow, Bret Contreras, Brad Dieter, Israel Halperin, Cody Haun, Duane Knudson, Johan Lahti, Matthew Miller, Jean-Benoit Morin, Mitchell Naughton, Jason Neva, Greg Nuckols, Sue Peters, Brandon Roberts, Megan Rosa-Caldwell, Julia Schmidt, Brad J. Schoenfeld, Richard Severin, Jakob Skarabot, James Steele, Rosie Twomey, Zachary Zenko, Keith Lohse, and David Nunan

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The members of the “Consortium for Transparency in Exercise Science” (COTES) are listed as ‘Collaborators’ at the end of this article.

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Caldwell, A.R., Vigotsky, A.D., Tenan, M.S. et al. Moving Sport and Exercise Science Forward: A Call for the Adoption of More Transparent Research Practices. Sports Med 50 , 449–459 (2020). https://doi.org/10.1007/s40279-019-01227-1

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REVIEW article

How to construct, conduct and analyze an exercise training study.

\r\nAnne Hecksteden&#x;

  • 1 Institute of Sports and Preventive Medicine, Saarland University, Saarbrücken, Germany
  • 2 Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
  • 3 Department of Intervention Research in Exercise Training, German Sport University Cologne, Cologne, Germany

Randomized controlled trials (RCTs) can be regarded as gold standard in investigating dose-response and causal relationships in exercise science. Recommendations for exercise training routines and efficacy analyses of certain training regimen require valid data derived from robust RCTs. Moreover, meta-analyses rely on RCTs and both RCTs and meta-analyses are considered the highest level of scientific evidence. Beyond general study design a variety of methodological aspects and notable pitfalls has to be considered. Therefore, exercise training studies should be carefully constructed focusing on the consistency of the whole design “package” from an explicit hypothesis or research question over study design and methodology to data analysis and interpretation. The present scoping review covers all main aspects of planning, conducting, and analyzing exercise based RCTs. We aim to focus on relevant aspects regarding study design, statistical power, training planning and documentation as well as traditional and recent statistical approaches. We intend to provide a comprehensive hands-on paper for conceptualizing future exercise training studies and hope to stimulate and encourage researchers to conduct sound and valid RCTs in the field of exercise training.

Introduction

The principal study types available for investigating the effects of exercise training range from retrospective epidemiological and cross-sectional research to prospective controlled exercise training trials. The latter can be considered a gold standard to elucidate causal and dose-response relationships in sport specific research, providing the highest level of evidence. Consequently, longitudinal designs with at least two groups, two repeated measures and a randomized allocation of participants are basically required for many research questions. This corresponds to a randomized controlled (training) trial (RCT). Beyond general design, researchers are faced with various conceptual challenges and pitfalls including characteristics of the interventional approach and control condition (e.g., inactive control, work matched control conditions, treatment as usual, social gathering etc.), reliable and valid outcome measures, study population and sample size as well as the statistical approach for data analysis. Importantly, these aspects are tightly interconnected and have to fit with one another. For instance, the conception of the study design determines statistics and study power. Therefore, the whole study “package” has to be thoroughly constructed and outlined prior to the start.

This manuscript addresses critical aspects of the design, realization and analysis of exercise training studies. A variety of conceptual aspects and their interrelations will be discussed in the light of traditional and recent methodological considerations. We are mainly focusing on factors specific to exercise training studies as compared to other e.g., pharmaceutical interventions (for instance, challenges of blinding, standardization and wash-out, low “ n ” in elite athlete studies, etc.). The manuscript intends to provide a concise, structured and comprehensive presentation of sport specific aspects and their interrelations rather than on comprehensively covering the entire theoretical background and can be regarded as a hands-on paper for developing, understanding and applying specific study designs. We deliberately do not deliver solutions to specific issues regarding a particular study “package.” Instead, we aim to sensitize the interested reader to issues and opportunities which are particularly related to exercise science research. For gaining more detailed insight into specific aspects, we would like to refer to the cited literature.

Study Design

General design, randomized controlled trials.

Randomized controlled trials (RCTs) are considered the gold standard for evaluating interventions in biomedical research. Well designed and conducted RCTs provide highest evidence level on the efficacy of healthcare interventions ( Moher et al., 2010 ; Thiese, 2014 ). Trials with inappropriate methods are associated with a high risk of bias ( Moher et al., 2010 ). Thereby, proper and transparent reporting of all relevant methodological issues of an RCT is crucial ( Altman et al., 2012 ). With regard to adequate reporting of RCTs the CONsolidated Standards Of Reporting Trials (CONSORT) Statement has been developed ( Schulz et al., 2010 ). CONSORT is a 25-item checklist to standardize the reporting of key elements of randomized trials ( Vohra et al., 2015 ). Exercise training interventions are complex interventions which are not appropriately and completely addressed by the CONSORT checklist. Therefore, in extension of CONSORT, the Consensus on Exercise Reporting Template (CERT) has been recently developed and may provide a valuable supplement to report and document randomized exercise trials ( Slade et al., 2016 ).

As reflected in the acronym, RCTs are characterized by two key elements: a control group and randomized allocation of participants to two or more study arms. Additional elements such as predetermined outcome measures and blinding are considered crucial for the quality of an RCT and the internal validity of inferences.

The control group provides a proxy for what would have happened to the participants in an experimental group if they had not received the intervention. Pre–post changes in the experimental group may then be directly compared to changes in the control (comparator) arm to gauge the effects of the intervention ( Senn, 2009 ). By contrast, with an uncontrolled design observed changes are generally attributed entirely to the intervention (assuming that nothing would have changed without it). Different types of time- and learning-effects are alternative explanations for observed changes which may be unmasked by comparison to a control group ( Hecksteden et al., 2013 ). Obviously, the value of the control group in leaving the intervention as the only plausible explanation critically depends on its similarity to the experimental group. This concerns baseline characteristics of participants that should not relevantly differ in both group as well as adequate flow through the study (except for the intervention). Other design features such as randomization and blinding aim at ensuring this similarity between groups.

The proper assignment of participants to the study arms is an important aspect of the trial design. Basically, group allocation should be based on chance, thereby, minimizing the risk of selection bias due to differences in group characteristics (e.g., in the primary endpoint and/or anthropometric or demographic data) ( Senn, 1995 , 2013 ; Moher et al., 2010 ). Two main aspects are particularly important in this regard to prevent correct anticipation of future assignments by anybody involved in the trial: (i) an unpredictable allocation sequence must be generated and (ii) this sequence must be concealed until assignment ( Moher et al., 2010 ). It is important to note that randomization should be done by an independent investigator, who is not directly involved in the testing and intervention. Ideally, the researcher, who is running the randomization procedure, works with the fewest information necessary and is only delivered with coded data.

Simple or pure randomization (i.e., group allocation with a 1:1 ratio based on coin toss) works well in large samples as there is a high probability that potential confounders (i.e., age, gender or current performance or physical activity level) are evenly distributed in all study arms. Random allocation can also be done in several more sophisticated ways in order to ensure a balanced distribution of participants’ characteristics which are known to be potential confounders regarding the main study outcomes ( Moher et al., 2010 ). The most prevalent amendment to simple randomization is stratification.

Stratification can be used to ensure that groups are balanced with regard to particular characteristics of participants (strata), which likely affect intervention outcomes. When appropriate stratification according to pre-defined strata (in training studies, for instance, age, gender, baseline physical activity level or study center in case of multi-center studies) is conducted, the number of participants for each study arm is closely balanced within each stratum. A further method for group allocation, which is not actually a random approach, is minimization ( Pocock and Simon, 1975 ; Treasure and MacRae, 1998 ; Altman and Bland, 2005 ; Moher et al., 2010 ). Applying this approach, the first participant is truly randomized. Each subsequent participant is allocated to a treatment or control group in order to minimize the imbalance on selected pre-defined factors, which are assumed to be potential confounders. The number of prognostic factors which can be incorporated is larger in minimization as compared to stratified allocation ( Scott et al., 2002 ). Minimization is advantageous when small groups should be closely matched with regard to relevant participant characteristics. Minimization may not eliminate bias on all known and, particularly, unknown confounders, but is an acceptable alternative to randomization and by some authors considered superior ( Treasure and MacRae, 1998 ; Scott et al., 2002 ; Altman and Bland, 2005 ; Moher et al., 2010 ).

Another important aspect is the adaptivity of randomization to drop outs. In other words: Does the slot of subjects dropping out of the study re-enter the randomization procedure? If the proportion of drop outs differs between study arms, this offers obvious benefits regarding balance in the final sample. An advantage which is particularly relevant if subject number is limited. Moreover, while such a feedback loop seems counterintuitive bearing the idea of “sealed envelopes” in mind, adaptive randomization is deemed admissible if properly conducted by an external scientist ( Food and Drug Administration, 2010 ; Rosenberger et al., 2012 ).

Alternative Study Designs

Conducting a robust RCT is not always possible and in some instances not even the appropriate approach. Particularly in exercise science, studies are frequently conducted in club, school, hospital, or community settings, respectively, and large trials may require a multicenter approach. In all these cases, participants are packed into clusters, in which observations are not necessarily independent and tend to be correlated. In order to avoid contamination, i.e., the unintentional transfer of intervention (elements) to other members of the cluster which are actually assigned to the control group, the clusters should serve as the units of randomization ( Campbell et al., 2012 ; van Breukelen and Candel, 2012 ). Cluster-randomization affects the power and, consequently, increases the necessary sample size of a trial and clustering has to be considered as a covariate when analyzing the data ( van Breukelen and Candel, 2012 ).

In some instances, it might be necessary to evaluate areas of uncertainty prior to conduct a definitive RCT. In such cases, a pilot randomized trial is the means of choice ( Eldridge et al., 2016 ). Meanwhile, a variety of competitive funding bodies are claiming for those pilot data. The primary aim of a pilot study is commonly feasibility, which affects the methodology used in a pilot trial. Assessments and measurement procedures should be chosen according to the aims of the pilot trial, not necessarily the definitive RCT. The sample size in pilot trials is usually lower compared to the final RCT, but should also be rationalized. Hypothesis testing regarding intervention efficacy is generally not indicated as the pilot trial is likely underpowered for this purpose. However, the standards for conducting and reporting of definitive RCTs also apply to pilot studies ( Eldridge et al., 2016 ).

Beyond the typical parallel group RCT, a randomized crossover trial is another option to implement a control condition and random allocation of participants. Therefore, randomized crossover studies may be categorized in the RCT design family ( Hopewell et al., 2010 ). In contrast to the parallel group RCT, each study participant performs both study conditions in a randomized order, i.e., each person serves as her or his own control ( Thiese, 2014 ). A crossover RCT begins similar to a traditional RCT, but after the first intervention period the participants cross over to the other study arm. Between both periods usually a wash-out period is interposed in order to ensure that baseline data are comparable. Particularly in training studies, this can be a challenge as training effects usually need considerable time to diminish or the intervention under investigation has to be incorporated in practical routines and/or periodized training schedules ( Faude et al., 2013 , 2014 ). The reversibility of a treatment effect is a necessary prerequisite for applying crossover designs and determines the length of the wash-out period. This is a particular challenge if valid data on detraining effects are not available. Potential carry-over effects have to be considered during data analysis. When using a crossover design, no participant is excluded from the promising treatment under investigation and sufficient statistical power can be achieved with fewer participants. The effort needed by each participant, however, is greater.

Robust RCTs are able to reliably predict intervention efficacy on a group level. Prediction of benefits and harms of a particular intervention on the individual level, however, is not possible without a high amount of uncertainty. In such cases, N -of-1 trials might be the appropriate means of choice ( Hecksteden et al., 2015 ; Senn, 2015 ; Vohra et al., 2015 ). When the recruitment of a large enough population is limited, N -of-1 trials might be the ideal methodological alternative. In exercise science and sports medicine, such scenarios are frequent in high performance sports, with the available elite population being naturally limited, in patients with rare diseases or when strong inclusion and exclusion criteria must be applied to arrive at homogeneous samples, limiting the number of eligible participants. A prerequisite for N -of-1 trials is a relatively quick onset of action and termination after discontinuation of the intervention which can make such scenarios difficult with regard to specific training adaptations. N -of-1 trials can be conducted as single or multiple crossovers, comparing a treatment against no or another treatment within one individual serving as her or his own control ( Vohra et al., 2015 ). Thus, potential confounding is eliminated given an appropriate wash-out period is applied in order to ensure similar baseline conditions for the treatment arms. A typical design is treatment – withdrawal – treatment – withdrawal (ABAB design). Multiple crossovers increase the confidence of the obtained results.

In some instances, uncontrolled or non-randomized study designs may be justified. Uncontrolled trials are less expensive, more convenient and faster to conduct than RCTs ( White and Ernst, 2001 ). When applying a single arm pre–post design without a control arm, however, there is a considerable risk that other factors than the intervention (e.g., familiarization, changes in lifestyle or activity behavior in addition to the exercise intervention, social desirability, regression to the mean) may also account for at least a part of the observed changes. Uncontrolled studies may be justified for pilot studies in order to get insight into associations between variables or expectable effect sizes of the intended intervention or regarding the feasibility of an intervention or specific treatment components ( White and Ernst, 2001 ). Uncontrolled trials, however, should be always interpreted very carefully as it is impossible to exclude that any changes, which occurred during the intervention period, would not have occurred without the intervention. Similarly, non-randomized trials include a control group or condition, but the allocation to the group is not due to chance but likely to the preferences of the participant which may affect the efficacy of the program and, thus, leading to a biased interpretation of the intervention. For instance, in injury prevention research, allocation to an intervention group performing an injury prevention program or to a control group doing their normal training routine based on the willingness of the team coaches, is likely biased as coaches who are willing to do the program are more aware of the injury problem and, hence, may also have implemented other measures to reduce injuries. The efficacy of the program may, therefore, be overestimated in non-randomized designs ( Mandelbaum et al., 2005 ; Gilchrist et al., 2008 ). In summary, whenever possible an appropriate comparison group should be included in any interventional study and group allocation should be done randomly.

Whereas RCTs are considered the gold-standard for establishing cause-effect relationships and clinical decision making, there are also some disadvantages regarding the transferability of the results to sports and clinical practice ( Wilkerson and Denegar, 2014 ). For instance, in RCTs frequently strong inclusion and exclusion criteria are used to increase statistical power and the precision of the intervention effect estimate. Furthermore, RCTs provide an estimate of the efficacy of an intervention under ideal, strictly controlled conditions, particularly with regard to the administration of the intervention. The effectiveness, i.e., when administering the intervention under real, more natural circumstances, of a particular treatment cannot be reliably evaluated by an RCT.

Prospective cohort studies may provide a feasible, well justified and useful alternative or complement to traditional RCTs and can result in improved decision making when guiding individual exercise training scenarios ( Thiese, 2014 ; Wilkerson and Denegar, 2014 ). A large cohort is initially recruited and desired baseline parameters, e.g., physical activity or fitness and/or health-related physiological and laboratory markers, are assessed. The cohort will be followed for a pre-defined period of time and individual exposure to a specific training mode will be documented in order to analyze changes in the outcomes of interest in the group which was exposed to training compared to the group which was not exposed. It is possible to analyze a large amount of possible moderators, mediators and confounders allowing for heavily multivariate analyses, given that the sample is large enough. In addition, generalizability of the results can be better as studies usually are conducted in more naturalistic settings.

A summary of design types in exercise training research is presented in Table 1 .

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TABLE 1. Summary on relevant design types in exercise training research.

Critical Specifications

Study aim and hypothesis.

When a general design type has been selected for a particular study question, critical design specifications have to be defined. Initially, a specific study aim together with the study hypothesis must be clearly formulated prior to the start of the trial. In this regard, it is very important to distinctly state whether it is hypothesized that a new intervention (treatment under evaluation) is superior to a reference treatment or control condition (superiority hypothesis) or that a new intervention is similarly efficacious or not worse compared to the reference or current gold standard treatment (equivalence or non-inferiority hypothesis). Whereas studies usually aim to evaluate the superiority of one treatment over another one, equivalence or non-inferiority trials are also justified under particular circumstances ( Piaggio et al., 2012 ). This is, for instance, the case if the new intervention has some advantage other than increased efficacy, such as better availability, better cost-effectiveness, less invasiveness, fewer adverse effects (harms) or easier administration. Equivalence or non-inferiority trials differ from superiority trials with regard to methodological and statistical considerations, for instance, by a priori defining an equivalence region ( Piaggio et al., 2012 ; Lakens, 2017 ; Dixon et al., 2018 ; Lakens et al., 2018 ). For a deeper insight into this issue we refer to the cited literature.

Study Outcomes

Once the specific study question(s) and hypotheses have been formulated, primary and secondary study outcomes have to be defined. The chosen outcomes should be closely related to the study aims and hypothesis with a minimal amount of primary outcome(s) to answer the main study question. Secondary and tertiary outcomes should be defined in order to support the main findings regarding training efficacy or to give insights into potential mechanisms of training adaptations. Particular emphasis has to be put on this aspect when participants are to be classified as responders and non-responders according to observed changes in one or more outcome measures because an individual’s “response” may be outcome specific ( Scharhag-Rosenberger et al., 2012 ). Moreover, if several parameters are to be jointly considered in the response classification, decision rules have to be explicitly fixed during the planning stage of the trial ( Hecksteden et al., 2015 ).

Outcomes should be chosen according to clarify theoretical or evidence-based rationales. Strong outcomes in exercise and health sciences are, for instance, mortality, morbidity or injuries as these are “hard” endpoints with obvious clinical and practical relevance. However, to analyze such outcomes, usually large samples are needed. “Hard” endpoints such as mortality or hospital admission are commonly used in epidemiological research. Conducting an RCT with such outcomes is a challenging endeavor ( Shiroma and Lee, 2018 ). Frequently, surrogate parameters such as maximal oxygen uptake (VO 2 max; as a surrogate of cardiovascular fitness or health) or maximal voluntary contraction strength (as an indicator of muscular fitness or health) are used in RCTs due to their better feasibility and relevance for sports and clinical practice. In such instances, it is important to rationalize the clinical or practical relevance of the chosen parameter. For example, in a homogeneous sample of high-level athletes VO 2 max is a poor indicator of endurance capacity ( Meyer et al., 2005 ). Lactate threshold and running economy might be more suitable under specific circumstances ( Coyle, 1995 ; Faude et al., 2009 ), but simulated sport-specific time trial performance is probably the best choice ( Abbiss and Laursen, 2008 ; Currell and Jeukendrup, 2008 ). Particular value when selecting appropriate study outcomes should be placed on the reliability and validity of the chosen measures ( Atkinson and Nevill, 1998 ; Hopkins, 2000 ; Currell and Jeukendrup, 2008 ). Knowledge of the inter- and intra-individual variability, i.e., the reliability of a particular assessment tool, is necessary to correctly interpret and detect intervention effects based on changes in performance or physiological parameters. Being aware of the test reliability enables a researcher to determine the boundary between true changes and differences which may result from random variability only, i.e., to determine the “minimal detectable change (MDC)” within a specific test ( Atkinson and Nevill, 1998 ; Hopkins, 2000 ; Haley and Fragala-Pinkham, 2006 ; Hecksteden et al., 2015 ).

Study Population and Sample Size

A further prerequisite for a robust exercise training study is the appropriate choice of the population under investigation. Many exercise training studies are conducted using physical education students as participants, simply as this is the population which is directly and most easily available for sport science researchers. Whereas this choice might be justified in some instances, in most cases it is not. For instance, resistance training adaptations in sports students are unlikely the same as in high level resistance athletes with many years of specific training experience and an already extraordinary performance level which does not leave much room for further improvements (ceiling effect). Moreover, interventions which are effective in a healthy, young and active population do not necessarily apply to old and frail people or people with specific diseases. Consequently, the population under investigations should be closely matched to the study aims and hypotheses and the researchers should a priori determine the eligible population and define corresponding inclusion and exclusion criteria. Thereby, all inclusion criteria have to be fulfilled by a participant to be eligible. If at least one exclusion (or non-inclusion) criterion is fulfilled the person must be excluded from study participation.

An important issue regarding the participating population refers to the appropriate sample size. From an ethical perspective, it is important to study a sample which is large enough to detect an effect with an acceptable accuracy ( Hopkins, 2006 ). Is the sample size too large, people are unnecessarily exposed to and waste resources with an intervention which potentially can be risky, harmful, or painful. In this case, small, but clinically or practically irrelevant effects might be detected as significant. Is the sample size too low, resources will be wasted with a high risk of failing to detect a relevant effect. Sample size estimation is usually required before the study protocol is submitted to a funding institution or an ethics committee. In order to estimate an appropriate sample size, several issues have to be considered a priori, i.e., during the process of study planning. Justification and reporting of the required sample size should be done carefully and honestly. Besides the study design, several parameters have to be considered, for example, the number of main outcomes, the smallest clinically or practically worthwhile effect, types I and II error rates, the baseline variability of the main outcome parameter as well as the statistical approach for data analysis ( Batterham and Atkinson, 2005 ; Hopkins, 2006 ; van Breukelen and Candel, 2012 ). Consulting with a statistician is recommended at this stage, particularly when projecting a complex study design. Failure to correctly consider one or more aspects may irremediably preclude study success while, on the other hand, fine tuning the methodology and design “package” reduces the burden imposed on researchers and participants and increases the likelihood of meaningful results (Figure 1 ).

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FIGURE 1. Conceptual aspects and their interrelations in the design of exercise training studies.

Choice of Control “Treatment”

When it comes to the decision on the appropriate control arm, there are several options, which have to be considered. The final decision depends on the complete design “package” of the particular study. From the perspective of internal validity, a control group receiving the recommendation to maintain their usual (e.g., inactive) habits might be considered the best choice as the main purpose of the control group is to control for what might have happened to the intervention participants if they had not received the treatment. Such a choice, however, can have several limitations. Completely inactive participants are not necessarily reflecting the “real world” setting. Most people are aware – or become aware within the study setting – of the beneficial effects of physical activity or exercise training and, therefore, complete inactivity does not reflect externally valid conditions and can be regarded ethically doubtful in some populations (e.g., patients or seniors) when a potentially beneficial intervention is denied. It might be considered to inform or educate the control participants on the beneficial health effects of physical activity and encourage them to follow general physical activity recommendations ( Chodzko-Zajko et al., 2009 ; Garber et al., 2011 ). Another issue with inactive controls is the missing attention, social contact or study-directed activities which may raise expectations and could have a beneficial effect by itself ( Lindquist et al., 2007 ). This should be considered, particularly, in elderly populations where social separation is a relevant problem and being active together with other seniors or the researchers might improve the individual abilities and quality of life by itself. For instance, arranging regular meetings where the control group members meet to play cards and chat, allowing for social contacts, while being physically inactive, might be an option ( Donath et al., 2014 ). Furthermore, participants might refrain from study participation or they drop out during the study period, because they were assigned to the control group and do not receive the anticipated intervention. Such dissatisfaction with group assignment can also lead to unintended and uncontrolled lifestyle changes in control group members potentially leading to considerable confounding and a decrease in power ( Hertogh et al., 2010 ). To overcome such scenarios there are mainly two options. First, a wait-list control group, which receives the intervention after having served as an inactive control, might be applied. This option has the advantage that there is potentially no change in the lifestyle habits of the control group members as they are provided with the intervention at a later time. However, with long intervention periods this approach becomes challenging and expensive and might be regarded unethical (e.g., in patients or seniors). Second, an active control group, which engages in activities related to the research setting and, thus, accounting for potential treatment effects, can be an appropriate choice. Such an active control group can receive health education, social visits or an alternative (but likely ineffective) exercise regimen ( Lindquist et al., 2007 ).

Placebo and Blinding

A double blind placebo controlled parallel group design is considered the “gold standard” in biomedical research ( Beedie et al., 2015 ). The placebo is a negative control as it is pharmacologically inert. It is expected that participants in the placebo control arm show a change or response to the investigation, including the response to a therapeutic ritual, to observation and assessment and to the patient-researcher interaction ( Sedgwick and Hoope, 2014 ). Ideally, placebos should be indistinguishable from the actual intervention and, therefore, participants should be unaware whether they receive the placebo or the treatment, allowing the researchers to estimate the “real” treatment effect ( Beedie et al., 2015 ).

In exercise training studies, it is obviously difficult to blind the participant to the intervention as they usually are aware whether they are training or not. In exercise science and sports medicine, placebo controls can be easily applied when the efficacy of ergogenic aids is assessed ( Beedie and Foad, 2009 ; Berdi et al., 2011 ). Berdi et al. (2011) found that an average overall effect size indicating a placebo effect of 0.4 (95% confidence interval 0.24–0.56) was present in placebo controlled studies analyzing the efficacy of different ergogenic substances. An interesting study was conducted by Broatch et al. (2014) showing that a placebo thermoneutral water immersion resulted in better recovery after high-intensity training compared to pure thermoneutral water immersion. Recovery efficacy was similar for the placebo condition compared to cold water immersion. The placebo effect was facilitated by adding a “recovery lotion” (customary skin cleanser) to the water.

Incorporating an adequate placebo condition is a serious challenge in training studies, is frequently not feasible and it might even be not appropriate ( Beedie and Foad, 2009 ). For instance, applying the placebo effect might be ethically problematic as it may exploit the bond of trust between practitioner and client or scientist and study participant. Furthermore, a nocebo effect may occur, i.e., the (inert) “placebo” treatment induces harmful side effects. Finally, according to the newest version of the Declaration of Helsinki ( World Medical Association, 2013 ), placebo is only acceptable (i) when no proven intervention exists, (ii) based on sound methodological reasons the use of placebo is necessary to determine the efficacy or safety of an intervention or (iii) when the participants are not exposed to additional risks of serious or irreversible harm, because they do not receive the currently best proven intervention. Therefore, an active control arm is usually preferable and the use of placebo has to be seriously justified ( Beedie and Foad, 2009 ; Sedgwick and Hoope, 2014 ). An active control arm, for instance, can be the current best-practice or best-evidence approach. Alternatively, the clinical standard treatment (irrespective of being best-evidence) or treatment as usual can be used as control arm.

Ideally, randomized trials are conducted with appropriate blinding or masking, i.e., withholding information about the intervention which may affect the study outcomes from people involved in the trial ( Moher et al., 2010 ). Blinding is an adequate means against several forms of bias. Risk of bias is highest in parameters which can be affected by subjective expectations. Blinding can be introduced on different levels of the trial design and is sometimes referred to single-, double- or triple-blinding, although this denotation is subject to variability, misinterpretation, and confusion ( Schulz and Grimes, 2002 ; Moher et al., 2010 ). Researchers should honestly report the blinding of all people who may be affected by the knowledge of the intervention assignment. These people can be, the participants of the trial, the providers of the intervention (e.g., physicians, therapists, coaches, teachers, etc.), data collectors (i.e., the testing staff) and those who assess the data ( Schulz and Grimes, 2002 ). Moreover, it has been debated that also those people who manage and analyze the data as well as manuscript writers can be blinded, but this is a matter of debate ( Moher et al., 2010 ). Obviously, masking the assignment of the participants to an exercise intervention group or an inactive control group is nearly impossible. In case of parallel group designs with treatment as usual as a control, it is advisable to withheld information on the hypothesized intervention efficacy from the study participants. Similarly, it is often difficult to blind the intervention providers as they have some expertise in the field of research and corresponding expectations regarding intervention efficacy. Exercise training cannot be masked in a way as, for instance, pharmacological or ergogenic substances.

An important level of blinding in exercise science research remains with the data collectors. Testing staff can affect study outcomes in different ways by their (unconscious) behavior and expectations, when knowing the group allocation of an individual. For instance, encouraging study participants during VO 2 max tests or maximal strength assessment can be realized differently depending on whether the participant was in the intervention or the control group. Therefore, blinding of the testing staff to group allocation and uniform and standardized procedures are advisable in order to minimize the risk of bias on the data assessment level.

Transferability and Implementation Considerations

If the efficacy of a particular exercise intervention has been established in an RCT, the question arises, whether the intervention can be easily transferred to the real-world setting. In most instances, program efficacy is strongly related to compliance and compliance is likely better in a standardized, controlled setting like in RCTs. When constructing an RCT, researchers should at least consider relevant questions on the transfer and uptake of a particular intervention in practical settings and on how the treatment can be maintained in the long-term after efficacy has been established. Researchers should be aware of and describe possible arrangements to give people access to an intervention after a study has identified a treatment as being beneficial for a particular population. In this regard, it is sometimes recommended to involve participants in the design of research and potentially also in the dissemination of an intervention ( Harriss and Atkinson, 2015 ).

A central framework regarding health program implementation is the RE-AIM (Reach, Efficacy, Adoption, Implementation, and Maintenance) framework and its adaptation to the sports setting, the RE-AIM Sports Setting Matrix (RE-AIM SSM) ( Finch and Donaldson, 2010 ). This concept is specific to the implementation context in community sports and may guide the promotion of prevention programs. O’Brien and Finch (2014) systematically reviewed the scientific literature on the reporting of specific implementation components in team ball sport injury prevention programs using the RE-AIM framework. The authors concluded that there are major gaps in adoption and maintenance in injury prevention research and, consequently, that reporting of the implementation context was insufficient. Thus, it is recommended to consider the implementation context already when designing an exercise intervention trial.

Ethical and Legal Obligations

Finally, ethical issues and legal obligations have to be considered. Like all biomedical research, exercise training trials must be in line with the Declaration of Helsinki in its latest version from 2013 ( World Medical Association, 2013 ) as well as with current data protection and data security regulations ( Harriss and Atkinson, 2015 ). Importantly, compliance with ethical and legal constraints cannot be decided by the researchers themselves, instead the final protocol has to be reviewed and approved by an appropriate ethics committee ( Harriss and Atkinson, 2015 ). It is highly recommended to publish the study protocol and several scientific journals require an a priori registration in an appropriate trial registry (for a summary of primary trial registries 1 ) ( World Medical Association, 2013 ). Harms and serious adverse events must be appropriately and honestly reported. This is a particularly underrepresented issue in exercise science research as studies commonly target the undoubtedly beneficial effects of physical activity and exercise training for fitness and health, but frequently ignore potential side effects and harms of being active (e.g., injuries or cardiovascular events) ( Verhagen et al., 2015 ).

Exercise Training Interventions

An important issue of the study “package” in exercise training studies is the design of the exercise intervention. In exercise science there are specific considerations related to the applied intervention, which usually differ considerably from interventions in other biomedical areas and are closely linked to the choice of the other “package” components. Particular considerations regarding the design and documentation of exercise interventions are presented in this chapter.

Comparability of Training Study Arms

When comparing two training modes in terms of efficacy, training characteristics such as setting, mode and load should be thoroughly considered and documented (Table 2 ). For example, if the effects of interval training on improving maximal oxygen uptake are compared to continuous aerobic endurance exercise equicaloric exercise loads are required ( Helgerud et al., 2007 ). Otherwise, it is hardly possible to elucidate whether the interval pattern or the difference in energy expenditure accounts for potential differences in VO 2 max adaptations. Estimating the caloric expenditure in highly intense exercise bouts, however, is a tremendous challenge, as excess carbon dioxide during intense exercise notably affects the calculation of the caloric expenditure. An alternative perspective might be to induce comparable effects with lower volumes. For instance, this has been shown with sprint interval training and its effects on cardiovascular function ( Gibala et al., 2006 ; Burgomaster et al., 2008 ).

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TABLE 2. Relevant training characteristics that should be taken into account prior to the start of a training intervention in order to determine the external load of exercise training studies.

Neuromuscular training is much easier to match properly as the cardiorespiratory or caloric demands are comparatively low. Repetitions, times and loads can be feasibly objectified. The total volume and frequency should be monitored and reported. For example, comparing whether explosive strength training (high movement velocity at lower loads) is favorable than traditional strength training (moderate movement velocity at higher loads) in terms of reducing fall risk factors requires similar training loads. Thus, the load should be precisely calculated for each training session. Thereby, mood states, perceived exertion, heart rate response are complementary means to monitor internal response compared the externally equivalent load. If multimodal training approaches such as agility training that integratively triggers cardiovascular and neuromuscular pathways are considered in preventive exercise training, workload matching is more challenging and should be further investigated compared to separated strength, endurance and balance training ( Donath et al., 2016b ).

Exercise Training Characteristics

The duration of exercise training interventions with a neuromuscular or cardio-circulatory focus can vary between a couple of weeks and years ( Donath et al., 2016a ; Rodrigues et al., 2016 ; Faude et al., 2017 ). Studies lasting one year or longer are rare. The majority of available exercise training studies in clinical and non-clinical populations typically range between 4 and 12 weeks, which is frequently justified by feasibility and economic reasons. Consequently, training concepts generally rely on studies with relatively short interventional periods. Longitudinal studies with follow-up periods of several months or years are urgently required. For instance, the lack of transfer effects in balance training studies, which has been recently reported ( Giboin et al., 2015 ; Kummel et al., 2016 ; Donath et al., 2017 ), is based on training studies lasting up to 12 weeks. It might be possible that transfer effects will occur later during the long-term training process. Furthermore, it seems that the overall training volume constitutes the main trigger for training adaptations, especially resulting from endurance training (e.g., regarding mitochondrial function) ( Granata et al., 2016 ). Therefore, it is reasonable to assume that higher total training volume lead to larger training effects. A variety of training studies suggest that 40–50 sessions over several months induce robust exercise training effects in neuromuscular domains ( Lesinski et al., 2015 ; Sherrington et al., 2017 ). Even hard endpoints (e.g., falls, death) benefit more when intervention duration is long and challenge is high. Ultimately, intervention durations should rely on general and individual responsiveness of the respective functional system of interest (e.g., vagal tone, maximal strength, physical activity or fitness, co-contraction) and the annual/seasonal time course of those parameters. For example, if bone mineral content would have an undulating pattern through the year depending on seasonal variations in physical activity patterns and sun exposure, it might be reasonable to spend more interventional efforts during this time and benefit in the more inactive autumn and winter period. Such considerations are mandatorily required prior to the conceptualization of exercise training studies.

Training characteristics such as periodization of training and exercise intensity distribution become likely more important in athletic populations ( Seiler, 2010 ; Stöggl and Sperlich, 2015 ) and should be particularly considered in training studies in elite populations. Training frequency and intensity are further relevant training characteristics. Few studies investigated differences in training effects depending on frequency and intensity with adjusted total volume in the long term. Interestingly, “weekend warrior” studies indicate that training frequency seems to be a secondary training characteristic in the general population ( Meyer et al., 2006 ; O’Donovan et al., 2017 ). Exercise training effects do not immediately decrease (reversibility) after exercise cessation depending on age and training state and can maintain for 2–6 weeks after the intervention ( Toraman, 2005 ; Toraman and Ayceman, 2005 ). Thus, dosage of exercise training need to be justified based on sustainability of effects and potential side effects of exercise training. It is important to consider the potential benefit-risk relation when applying exercise as medicine or preventive means ( Verhagen et al., 2015 ).

Further important issues regarding intervention studies and, particularly, implementation strategies are individual responsiveness, training specificity and overload (Figure 2 ). In this regard a personalized training schedule, based on individual needs, goals, barriers and background can be regarded essential. As a consequence, researchers should also focus on follow up effects and implementation strategies using behavioral change techniques and face-to-face or remote coaching ( Foster et al., 2013 ). Such individualized and tailored exercise training approaches likely lead to sustainable behavioral change. Furthermore, interference effects (e.g., strength training prior to endurance or vice versa) should be carefully considered when conceptualizing exercise intervention studies. In this regard, numerous research has been undertaken during recent years to elucidate interference effects between strength and endurance training on molecular level ( Hawley, 2009 ; Fyfe et al., 2014 ). These findings do also have impact in the light of general health-related physical activity guidelines that focus on strength, endurance and balance either ways. Thus, the mix of different training stimuli can relevantly affect training adaptations. Another issue in the context of “training variables” is the concept of training progression. To date, only few studies specifically investigated the effects of different progression models on performance or the time course of performance adaptations. Generally, the above mentioned training characteristics (e.g., frequency, intensity, including different work-relief ratios, time, type, volume, Table 3 ) have to be reliably and honestly documented and reported.

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FIGURE 2. Integrative view on exercise training principles.

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TABLE 3. Exercise training characteristics (FITT; Frequency, Intensity, Type, Time).

Training Monitoring and Documentation

Monitoring of exercise training includes external (e.g., distance, power, and velocity) and internal (e.g., ratings of perceived exertion, heart rate, blood lactate concentrations) loads. Training volume is mainly linked to external loads and adaptability is also related to internal loads and responsiveness, respectively. Both components should adequately be taken into account and recorded. A variety of subjective and objective instruments to monitor exercise training loads are available. Those systems are feasible to assess individual acute and chronic response to exercise training and the course of recovery ( Hopkins, 2015 ; Bartlett et al., 2017 ). Since no “one-size-fits-all” gold standard for training load monitoring exists ( Lambert and Borresen, 2010 ), validated (e.g., population, discipline, type of exercise) and reliable methods should be rationalized with regard to the specific study background and appropriately selected. Internal and external training loads can be feasibly documented using web-based and paper-pencil diaries. This is important in order to check training compliance. Furthermore, detailed training documentations potentially enable subgroup analyses in larger cohorts or considerations on responsiveness to the training protocol and constitutes important quality criteria of exercise training trials.

Within recent years, particularly subjective response to training has been emphasized to play an important role in training adaptations and performance enhancement. Also studies on affective valance or enjoyment have been increasingly applied in health-related exercise training research. It seems reasonable to assume that particularly subjective perceived efforts or valance notably affect compliance and adherence to exercise training in the long run. Besides independent assessment of internal and external load, Banister and Calvert (1980) combined exercise duration with a weighted heart rate response. This concept has been proven for continuous endurance exercise and has its limitations within intermittent sports (e.g., soccer and basketball). Promising future concepts can be the monitoring of the response of perceived exertion and the integration of wearables. Those methods should be carefully applied in the intersection between athletes and coaches based on best available evidence in order to predict performance development ( Foster et al., 2017 ).

Compliance and Adherence

From a methodological and dose-response perspective, compliance and adherence are important aspects of exercise training studies. There is a distinct difference between compliance and adherence. Compliance describes the degree to which an individual conforms to the prescribed dosage of an intervention and is necessary in efficacy trials. Adherence refers to a process which is affected by the environment and social contexts and, therefore, relevant in effectiveness studies ( McKay and Verhagen, 2016 ). Generally, adherence is considered a patients’ or a person’s agreement to the recommended exercise training regimen and treatment, respectively ( World Health Organisation, 2003 ). This is particularly important in non-intrinsically motivated participants. In these individuals, the likelihood to comply with or adhere to the exercise regimen is comparatively low. Participants should be seen as active partners. Thus, knowledge and beliefs on the importance of a respective exercise training for, e.g., disease prevention or performance enhancement approach should be educated and communicated. It is well known that poor adherence is a striking issue in the treatment of chronic conditions. Especially persons with the poorest physical, cognitive and psychological functional abilities representing the part of the population at highest burden of disease do not seem reachable in multifactorial risk-based interventions ( Sjosten et al., 2007 ). Resulting consequences are poor health outcomes and increasing health care costs. Adherence is a powerful modulator of health care system effectiveness and, thus, more balanced efforts should be made to improve adherence instead of only developing specific treatment strategies ( Haynes et al., 2008 ). This is particularly important since a lack of adherence or exercise training cessation may lead to detraining effects ( Sherrington et al., 2011 ).

Only few studies are mainly conceptualized to investigate the interrelation between compliance and adherence and the intervention effect. However, such studies are urgently needed. The majority of available studies merely run sub-analyses on the interrelation between intervention effects and compliance and, as a consequence, does not find associations between compliance and intervention efficacy ( Simek et al., 2012 ; McPhate et al., 2013 ). As a consequence, RCTs should at least aim at providing “intention-to-treat” and “as treated” comparisons. Intention-to-treat analyses can be merely conducted when participants agree to posting-testing, although dropping out of the intervention. Reasons for drop-outs and a decline from post-testing should be provided. Drop outs can be a systematic response to the intervention regimen and should be necessarily followed up (e.g., too intense, discomfort, inadequate coaching, logistical efforts, etc.). In large cohorts, also the relationship between attendance rates and training adaptations can be computed. Available drop out-rates in interventions studies should also be considered during the sample size estimation. Depending on the population, training mode, volume and intensity, drop outs normally vary between 20 and 50%. Future research should consider compliance and adherence (study design and data analysis), particularly with regard to the implementation of efficacious exercise-based health care interventions. A multidisciplinary approach toward adherence is needed (coordinated action from health professionals, researchers and policy-makers) ( World Health Organisation, 2003 ).

Data Analysis and Interpretation

Subsequent to the experimental phase, the last steps toward (and hopefully leading to) answering the initial research question consist in analyzing the obtained dataset and interpreting the results. On the bottom line this means trying to make inferences on real-world circumstances based on the data collected. There is considerable controversy regarding appropriate inferential frameworks and statistical techniques and some of the key points in this debate will be addressed later in this paragraph. However, there are also some unequivocal aspects which are dictated by the logic of the trial design. These basic rules will be presented in the following paragraph in (roughly) descending order or indispensability.

The standard case is that (i) the research question concerns the mean efficacy of an intervention (e.g., training induced increase in VO 2 max or maximal strength) and that (ii) a RCT (the current criterion standard for this kind of research question) has been conducted. We shall now harness the specific characteristics of our design to maximize validity of our inferences – that means to zoom in on intervention efficacy by ruling out pertinent alternative explanations.

Basic Rules for Analyzing an RCT

The analytical techniques mentioned in this paragraph represent the mainstream hypothesis-testing approach to inferential statistics and are applicable for outcome measures which are ratio scaled and normally distributed. Please compare later paragraphs for a brief discussion of alternatives.

The absolutely essential rule for the analysis of an RCT is that assessment of intervention efficacy should be based on direct between-group comparison ( Senn, 2009 ). Comparison to changes in the control group lends support to causally ascribing observed changes in the experimental group to the intervention. Consequently, the question to be answered is whether the change in an outcome (e.g., aerobic capacity, maximal power, and balance ability) in the intervention group differs from the change in the control group. Importantly, the between-group comparison has to be made directly and not indirectly by assessing pre–post changes separately within each group. This basic rule is dictated by the rational of the trial design and applies regardless of the inferential framework and statistical approach. Within the mainstream approach of hypothesis testing, the basic option to test the difference in change-scores between groups is to conduct a t -test for independent samples. Alternatively, repeated measures analysis of variance (ANOVA) may be employed with at least the repeat factor (pre- vs. post-test), a group factor (intervention vs. control) and the interaction between the two (which is the main effect of interest).

Moreover, potential baseline imbalances between groups should be considered ( Senn, 1995 , 2009 ). In a controlled trial, randomization (following more or less complex rules as explained above) is primarily employed to ensure equal distribution of covariates between groups in order to rule out alternative explanations and support causal inferences. However, perfect baseline balance is unlikely. Therefore, relevant moderators of intervention efficacy should be considered during data analysis. Importantly, whether or not to consider additional influencing factors, and if so, which ones, should be decided in advance (based on their presumed relevance as moderators of intervention efficacy) and not during analysis (based on observed differences at baseline) ( Senn, 1995 ). Generally, following the “rule of the initial value,” at least the baseline value of the outcome measure itself should be included. These recommendations are supported by many strong arguments, which are exposed comprehensively by Senn (1995) . For the non-statistician, the most compelling one in favor of doing so may be the surprisingly low “cost” in terms of subject number ( Senn, 2013 ). The consequent type of (standard inferential) analysis is analysis of covariance (ANCOVA). Alternatively, covariates and categorical predictors may be included in a mixed model which otherwise comprises at least subject identity as random effect as well as time and group as fixed effects. The mixed model approach is particularly useful if the outcome measure has been determined on more than two time points.

As mentioned above, one of the initial steps in constructing a training trial consists in formulating the hypothesis as specifically as possible. The control group in exercise training trials does either not receive any intervention or the current best-practice treatment. In most instances, it is expected that the average change over the intervention period is superior in the intervention group as compared to the control group (e.g., larger increase in VO 2 max). Therefore, we generally have a directed hypothesis as to the difference of change scores between the intervention and control groups, respectively. This should be matched by the use of one-sided tests ( Fisher, 1991 ; Koch and Gillings, 2006 ), which offer higher power and hence require lower participant numbers. Surprisingly, this simple opportunity for increasing the efficiency of a trial is frequently not taken.

Alternative Approaches

Individual response.

Even for undoubtedly effective interventions (such as exercise training in previously untrained persons) large variability in observed pre–post changes is common ( Bouchard et al., 1999 ; Hecksteden et al., 2015 , 2018 ). From the perspective of verifying (mean) efficacy this variation in observed effects is rather annoying, because it decreases standardized effect sizes and increases the required number of participants. However, when recommending the intervention to individual subjects, the variation in its efficacy between persons is of obvious interest. Importantly, variation in observed pre–post changes does not necessarily reflect interindividual differences in the efficacy of the intervention (“individual response”) but is at least in part due to random variation (e.g., from measurement error and biological day-by-day variability) ( Senn, 2004 , 2015 ; Atkinson and Batterham, 2015 ; Hecksteden et al., 2015 ). Different methods have been proposed for the quantification of individual response, which in part require specific study designs ( Atkinson and Batterham, 2015 ; Hecksteden et al., 2015 , 2018 ; Senn, 2015 ). For an RCT, calculating the surplus in variance in the experimental group (random variation plus individual response) as compared to the control group (random variation only) seems to be the most adequate approach ( Hecksteden et al., 2018 ).

Beyond quantifying individual response in terms of a variance or standard deviations, it is appealing to classify individual subjects into responders and non-responders, respectively. However, this classification is beset with theoretical as well as practical difficulties and limitations which are due to two main factors: (i) the unavoidable inaccuracy of the individual response estimate (due to random variation) and (ii) the challenges of identifying a meaningful response threshold ( Senn, 2004 , 2015 ; Hecksteden et al., 2018 ). Therefore, this intuitively tempting approach should be used and interpreted only with great restraint. Moreover, it is important to keep in mind that (despite the simple concept) a considerable number of definitions and operationalizations have been proposed which may result in inconsistent classifications for many individuals ( Hecksteden et al., 2018 ). If the main aim is to have balanced subgroups with marked differences in observed training effects (e.g., for an exploratory search into moderators of training efficacy) a predefined proportion (e.g., 1/3) of individuals with the highest pre–post difference may be labeled as “responders” and a similar proportion of subjects with the lowest pre–post differences as “non-responders” ( Timmons et al., 2005 ). However, such a classification is obviously dependent on the distribution of training effects within the respective trial and may not be interpreted as a general characteristic of the respective subject ( Hecksteden et al., 2018 ). By contrast, if the aim is to characterize individual participants in a meaningful way, the size and uncertainty of an individual’s response (e.g., as confidence interval or effect size) has to be interpreted in relation to prefixed limits of meaningful benefit or harm. In the context of an RCT, uncertainty in the individual pre–post difference may be roughly estimated from the variability of pre–post changes in the control group ( Hecksteden et al., 2018 ).

Alternative Approaches to Statistical Inference

While describing the data collected during a specific trial (e.g., participant characteristics, distributions of measured values and calculated indicators such as pre–post differences) is clearly important, the ultimate aim of a scientific study generally is to gain insight into real-world circumstances. Therefore, we need to make inferences from (and therefore beyond) our data. A task which is obviously associated with an unavoidable risk of error.

In recent years, the formerly unrivaled p -value based hypothesis-testing approach to inferential statistics has been increasingly criticized ( Sterne and Davey Smith, 2001 ; Wilkinson, 2014 ; Wasserstein and Lazar, 2016 ; McShane and Gal, 2017 ), up to the point of recommending its complete abolishment ( Buchheit, 2016 ). Providing a comprehensive overview of this ongoing controversy including the proposed alternatives (let alone a final judgement) is far beyond the scope of this manuscript. However, a few points should be addressed.

What’s the problem with p -value based hypothesis testing?

When investigating a specific intervention, we generally want to know whether it is beneficial and can be recommended. Of note, there are two questions involved in this: first, we have to know if the intervention is effective and causes detectable changes in the expected direction at all. If this seems to be the case, we will be interested if the magnitude of the effect is relevant and worth the effort or rather trivially small. In fact, p -value based hypothesis testing (at least as it is generally performed in our field) does not provide a direct answer to either of these two questions. Most fundamentally, the p -value does not indicate the probability of the intervention being ineffective based on the data analyzed, but the probability of those (or more extreme) data assuming that the intervention is ineffective. While one might intuitively think that the difference between these two conditional probabilities is marginal, there are situations in which the “error of the transposed conditional” becomes most relevant ( Wasserstein and Lazar, 2016 ; Yaddanapudi, 2016 ).

Moreover, the p -value is an amalgamation of effect size (central tendency and variation of the effect) and degrees of freedom (which in most situations are mainly determined by number of subjects). Following the full hypothesis testing logic, the appropriate number of participants for specified type-1 and type-2 error rates has to be calculated in advance and exactly this number has to be studied ( Fisher, 1991 ). If the sample size is larger, even marginal differences will yield “significant” p -values. When viewed uncritically, this may lead to the application of interventions which in fact do not cause relevant benefit. There are several other limitations and shortcomings, which have been recently summarized ( Wasserstein and Lazar, 2016 ; Yaddanapudi, 2016 ).

Several alternative approaches to statistical inference have been devised in response to the shortcomings of p -value based hypothesis testing. The most fundamental alternative is Bayesian statistics, a framework in which the concept of probability as a long run relative frequency is replaced by probability as a subjective believe (which has to be refined over and over based on empirical data). There are two important strengths of Bayesian statistics. Most importantly, it allows answering the question we really want to know “How likely is the hypothesis true in consideration of given data?” Secondly, it offers a gradual judgment of the hypothesis being true instead of a dichotomous decision yes or no/significant or non-significant. However, despite the fact that the fundamental shortcomings of p -value based hypothesis testing are addressed, truly Bayesian statistics are not yet in mainstream use. One reason may be the subjective notion of probability itself which is counterintuitive for many empirical researchers. More importantly, practical implementation of fully Bayesian analyses is a complex task, which requires learning a new framework of data analysis. There is an increasing number of understandable introductory texts ( Kruschke and Liddell, 2018 ; Quintana and Williams, 2018 ) but only few scientists seem to master fully Bayesian statistics ( Senn, 2011 ).

As an intermediate solution, approaches based on effect sizes and/or confidence intervals have been proposed. In sport science, magnitude based inferences (MBI) as developed by Hopkins, 2004 , Batterham and Hopkins (2006) , Hopkins and Batterham (2016) is increasingly popular ( Buchheit, 2016 ). MBI gauges the magnitude of the effect (which is decisive for its practical relevance) and offers a gradual judgment of it being above or below predefined thresholds of practical relevance. However, so far MBI has only been published in sports science journals and personal websites and comprehensive evaluation within the statistical community is still lacking. Moreover, several issues with respect to mathematical as well as conceptual aspects remain controversial ( Wilkinson, 2014 ; Welsh and Knight, 2015 ; Hopkins and Batterham, 2016 ; Mengersen et al., 2016 ; Sainani, 2018 ; Wilkinson and Winter, 2018 ). It is beyond the scope of this article to give a final appraisal of MBI. However, publication of a new statistical technique in a recognized statistical journal enabling discussion of its strengths and weaknesses by the expert community should be a matter of course before putting it forth for general use – just as effectiveness of any new exercise training intervention has to be established before transfer to sports practice.

Exercise training studies should be carefully constructed focusing on the consistency of the whole “package” from an explicit hypothesis or research question over study design and methodology and on the data analysis and interpretation. In doing so, all available information that might affect study power, ideally derived from pilot studies or previously published research should be considered. A clear study question with hypothesis on the primary and secondary endpoints is recommended and should be generated prior to the start of the study. Explorative or uncontrolled trials are only reasonably indicated in pilot or feasibility studies and to state on baseline variability of the primary endpoint, respectively. Validity and reliability of the included methods should be provided for the respective population and age-group. All relevant training characteristics should be thoroughly considered and recorded. Information on training characteristics as detailed as possible should be given within the manuscript.

Author Contributions

AH, OF, and LD designed the present review. TM contributed to the design. AH, OF, and LD drafted the first manuscript. TM provided notable intellectual input throughout drafting. All authors revised the draft, read and approved the final version of the manuscript.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords : RCT, longitudinal, exercise trial, intervention, statistics, study design, analyzing, reporting

Citation: Hecksteden A, Faude O, Meyer T and Donath L (2018) How to Construct, Conduct and Analyze an Exercise Training Study? Front. Physiol. 9:1007. doi: 10.3389/fphys.2018.01007

Received: 25 April 2018; Accepted: 09 July 2018; Published: 26 July 2018.

Reviewed by:

Copyright © 2018 Hecksteden, Faude, Meyer and Donath. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Oliver Faude, [email protected]

† These authors have contributed equally to this work and shared first authorship.

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Women may realize health benefits of regular exercise more than men

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An NIH-supported observational study finds that even when women and men get the same amount of physical activity, the risk of premature death is lower for women

Women who exercise regularly have a significantly lower risk of an early death or fatal cardiovascular event than men who exercise regularly, even when women put in less effort, according to a National Institutes of Health-supported study. The findings, published in the  Journal of the American College of Cardiology , are based on a prospective analysis of data from more than 400,000 U.S. adults ages 27-61 which showed that over two decades, women were 24% less likely than those who do not exercise to experience death from any cause, while men were 15% less likely. Women also had a 36% reduced risk for a fatal heart attack, stroke, or other cardiovascular event, while men had a 14% reduced risk.   

“We hope this study will help everyone, especially women, understand they are poised to gain tremendous benefits from exercise,” said Susan Cheng, M.D., a cardiologist and the Erika J. Glazer Chair in Women’s Cardiovascular Health and Population Science in the Smidt Heart Institute at Cedars-Sinai, Los Angeles. “It is an incredibly powerful way to live healthier and longer. Women on average tend to exercise less than men and hopefully these findings inspire more women to add extra movement to their lives.”       

The researchers found a link between women experiencing greater reduced risks for death compared to men among all types of exercise . This included moderate aerobic activity, such as brisk walking; vigorous exercise, such as taking a spinning class or jumping rope; and strength training, which could include body-weight exercises.

Scientists found that for moderate aerobic physical activity, the reduced risk for death plateaued for both men and women at 300 minutes, or five hours, per week. At this level of activity, women and men reduced their risk of premature death by 24% and 18% respectively. Similar trends were seen with 110 minutes of weekly vigorous aerobic exercise, which correlated with a 24% reduced risk of death for women and a 19% reduced risk for men.   

Women also achieved the same benefits as men but in shorter amounts of time. For moderate aerobic exercise, they met the 18% reduced risk mark in half the time needed for men: 140 minutes, or under 2.5 hours, per week, compared to 300 minutes for men. With vigorous aerobic exercise, women met the 19% reduced risk mark with just 57 minutes a week, compared to 110 minutes needed by men. 

This benefit applied to weekly strength training exercises, too. Women and men who participated in strength-based exercises had a 19% and 11% reduced risk for death, respectively, compared to those who did not participate in these exercises. Women who did strength training saw an even greater reduced risk of cardiovascular-related deaths – a 30% reduced risk, compared to 11% for men.   

For all the health benefits of exercise for both groups, however, only 33% of women and 43% of men in the study met the standard for weekly aerobic exercise, while 20% of women and 28% of men completed a weekly strength training session.  

“Even a limited amount of regular exercise can provide a major benefit, and it turns out this is especially true for women,” said Cheng. “Taking some regular time out for exercise, even if it’s just 20-30 minutes of vigorous exercise a few times each week, can offer a lot more gain than they may realize.”  

“This study emphasizes that there is no singular  approach for exercise,” said Eric J. Shiroma, Sc.D., a program director in the  Clinical Applications and Prevention branch at the National Heart, Lung, and Blood Institute (NHLBI). “A person’s physical activity needs and goals may change based on their age, health status, and schedule – but the value of any type of exercise is irrefutable.”  

The authors said multiple factors, including variations in anatomy and physiology, may account for the differences in outcomes between the sexes. For example, men often have increased lung capacity, larger hearts, more lean-body mass, and a greater proportion of fast-twitch muscle fibers compared to women. As a result, women may use added respiratory, metabolic, and strength demands to conduct the same movement and in turn reap greater health rewards.  

The Physical Activity Guidelines for Americans  recommend adults get at least 2.5-5 hours of moderate-intensity exercise or 1.25-2.5 hours of vigorous exercise each week, or a combination of both, and participate in two or more days a week of strength-based activities.  

The research was partially supported by NHLBI grants  K23HL153888 ,  R21HL156132 ,  R01HL142983 ,  R01HL151828 ,  R01HL131532 , and R01HL143227 . 

Ji H, Gulati M, Huang TY, et al. Sex differences in association of physical activity with all-cause and cardiovascular mortality. J Am Coll Cardiol. 2024; doi: 10.1016/j.jacc.2023.12.019.  

About the National Heart, Lung, and Blood Institute (NHLBI):  NHLBI is the global leader in conducting and supporting research in heart, lung, and blood diseases and sleep disorders that advances scientific knowledge, improves public health, and saves lives. For more information, visit  www.nhlbi.nih.gov .

About the National Institutes of Health (NIH):  NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit  www.nih.gov .

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Tuesday, February 20, 2024

Women may realize health benefits of regular exercise more than men

An NIH-supported observational study finds that even when women and men get the same amount of physical activity, the risk of premature death is lower for women.

For all the health benefits of exercise for both groups, however, only 33% of women and 43% of men in the study met the standard for weekly aerobic exercise, while 20% of women and 28% of men completed a weekly strength training session.

“Even a limited amount of regular exercise can provide a major benefit, and it turns out this is especially true for women,” said Cheng. “Taking some regular time out for exercise, even if it’s just 20-30 minutes of vigorous exercise a few times each week, can offer a lot more gain than they may realize.”

“This study emphasizes that there is no singular approach for exercise,” said Eric J. Shiroma, Sc.D., a program director in the Clinical Applications and Prevention branch at the National Heart, Lung, and Blood Institute (NHLBI). “A person’s physical activity needs and goals may change based on their age, health status, and schedule – but the value of any type of exercise is irrefutable.”

The authors said multiple factors, including variations in anatomy and physiology, may account for the differences in outcomes between the sexes. For example, men often have increased lung capacity, larger hearts, more lean-body mass, and a greater proportion of fast-twitch muscle fibers compared to women. As a result, women may use added respiratory, metabolic, and strength demands to conduct the same movement and in turn reap greater health rewards.

The Physical Activity Guidelines for Americans recommend adults get at least 2.5-5 hours of moderate-intensity exercise or 1.25-2.5 hours of vigorous exercise each week, or a combination of both, and participate in two or more days a week of strength-based activities.

The research was partially supported by NHLBI grants K23HL153888 , R21HL156132 , R01HL142983 , R01HL151828 , R01HL131532 , and R01HL143227 .

About the National Heart, Lung, and Blood Institute (NHLBI): NHLBI is the global leader in conducting and supporting research in heart, lung, and blood diseases and sleep disorders that advances scientific knowledge, improves public health, and saves lives. For more information, visit https://www.nhlbi.nih.gov . 

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov .

NIH…Turning Discovery Into Health ®

Ji H, Gulati M, Huang TY, et al. Sex differences in association of physical activity with all-cause and cardiovascular mortality. J Am Coll Cardiol . 2024; doi: 10.1016/j.jacc.2023.12.019.

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Women get the same exercise benefits as men, but with less effort

A new study from the Smidt Heart Institute at Cedars-Sinai shows there is a gender gap between women and men when it comes to exercise.

The findings, published in the Journal of the American College of Cardiology ( JACC ), show that women can exercise less often than men, yet receive greater cardiovascular gains.

"Women have historically and statistically lagged behind men in engaging in meaningful exercise," said Martha Gulati, MD, director of Preventive Cardiology in the Department of Cardiology in the Smidt Heart Institute at Cedars-Sinai, the Anita Dann Friedman Chair in Women's Cardiovascular Medicine and Research and co-lead author of the study. "The beauty of this study is learning that women can get more out of each minute of moderate to vigorous activity than men do. It's an incentivizing notion that we hope women will take to heart."

Investigators analyzed data from 412,413 U.S. adults utilizing the National Health Interview Survey database. Participants between the time frame of 1997 to 2019 -- 55% of whom were female -- provided survey data on leisure-time physical activity. Investigators examined gender-specific outcomes in relation to frequency, duration, intensity and type of physical activity.

"For all adults engaging in any regular physical activity, compared to being inactive, mortality risk was expectedly lower," said Susan Cheng, MD, MPH, the Erika J. Glazer Chair in Women's Cardiovascular Health and Population Science, director of the Institute for Research on Healthy Aging in the Department of Cardiology in the Smidt Heart Institute, and senior author of the study. "Intriguingly, though, mortality risk was reduced by 24% in women and 15% in men."

The research team then studied moderate to vigorous aerobic physical activity, such as brisk walking or cycling, and found that men reached their maximal survival benefit from doing this level of exercise for about five hours per week, whereas women achieved the same degree of survival benefit from exercising just under about 2 ½ hours per week.

Similarly, when it came to muscle-strengthening activity, such as weightlifting or core body exercises, men reached their peak benefit from doing three sessions per week and women gained the same amount of benefit from about one session per week.

Cheng said that women had even greater gains if they engaged in more than 2 ½ hours per week of moderate to vigorous aerobic activity, or in two or more sessions per week of muscle-strengthening activities. The investigators note their findings help to translate a longstanding recognition of sex-specific physiology seen in the exercise lab to a now-expanded view of sex differences in exercise-related clinical outcomes.

With all types of exercise and variables accounted for, Gulati says there's power in recommendations based on the study's findings. "Men get a maximal survival benefit when performing 300 minutes of moderate to vigorous activity per week, whereas women get the same benefit from 140 minutes per week," Gulati said. "Nonetheless, women continue to get further benefit for up to 300 minutes a week."

Christine M. Albert, MD, MPH, chair of the Department of Cardiology in the Smidt Heart Institute and the Lee and Harold Kapelovitz Distinguished Chair in Cardiology, says concrete, novel studies like this don't happen often.

"I am hopeful that this pioneering research will motivate women who are not currently engaged in regular physical activity to understand that they are in a position to gain tremendous benefit for each increment of regular exercise they are able to invest in their longer-term health," said Albert, professor of Cardiology.

Other Cedars-Sinai authors include Tzu Yu Huang, MSc; Alan Kwan, MD; David Ouyang, MD; and Joseph Ebinger, MD. Other authors include Hongwei Ji, MD; Kaitlin Casaletto, PhD; Kerrie L. Moreau, PhD; and Hicham Skali, MD, MSc.

Funding: This work was supported in part by NIH grants K23HL153888, K23AG058752, R21HL156132, R01HL142983, R01HL151828, R01HL131532, R01HL143227, R01AG072475, U54AG062319, and U54AG065141, and the Erika J Glazer Family Foundation, National Key R&D Program of China (2022YFC2502800), National Natural Science Foundation of China (82103908), Shandong Provincial Natural Science Foundation (ZR2021QH014), Shuimu Scholar Program of Tsinghua University, and National Postdoctoral Innovative Talent Support Program (BX20230189).

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Story Source:

Materials provided by Cedars-Sinai Medical Center . Note: Content may be edited for style and length.

Journal Reference :

  • Hongwei Ji, Martha Gulati, Tzu Yu Huang, Alan C. Kwan, David Ouyang, Joseph E. Ebinger, Kaitlin Casaletto, Kerrie L. Moreau, Hicham Skali, Susan Cheng. Sex Differences in Association of Physical Activity With All-Cause and Cardiovascular Mortality . Journal of the American College of Cardiology , 2024; 83 (8): 783 DOI: 10.1016/j.jacc.2023.12.019

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Can Exercise Help Prevent Prostate Cancer?

A new study adds to growing evidence that exercise is an important part of preventing one of America’s deadliest cancers.

research articles on exercise science

By Talya Minsberg

In recent years, one of the most provocative questions in cancer research has been whether a regular exercise habit can prevent certain cancers from taking hold.

The answer, as with any question related to cancer, is complicated. But a recent study published in The British Journal of Sports Medicine offered a glimpse of how regular physical activity affects the risk of prostate cancer, the second most common and second most fatal cancer in the United States for men.

In one of the largest such efforts to date, researchers collected data between 1982 and 2019 from 57,652 Swedish men who had participated in at least two fitness tests to see if those who were more active were less likely to develop cancer. Around 1 percent were later diagnosed with prostate cancer. The team found that those who had improved in fitness over the years were 35 percent less likely to have been diagnosed with the disease.

The finding is in line with much of the latest research on the relationship between fitness and cancer diagnosis. According to a 2021 study , for instance, if all adults in the United States were to meet the physical activity guidelines, cancer diagnoses could drop by 3 percent, or 46,000 cases, every year.

But while there has been extensive research on the relationship between exercise and conditions like breast cancer, there has been less research specifically on prostate cancer. The chance of having prostate cancer rises for all men after 50; risk appears to run in families. About one in eight men will be diagnosed with prostate cancer during their lifetime, according to the American Cancer Society.

Some previous studies looking at the connection between physical activity and prostate cancer have been contradictory, according to Dr. Kate Bolam, a co-author of the study. While some showed increased risk of prostate cancer for those who were physically active, others found a decreased risk.

But many of those studies had small sample sizes or were biased toward healthier people, said Dr. Bolam, a researcher at the Swedish School of Sport and Health Sciences.

“Men who are generally more health-conscious,” she said, “are also good at going to the doctor when they are called for their prostate cancer screening tests.”

More testing means more diagnoses, including in men whose cancers will never progress. Sometimes cancer cells can exist in the prostate for one’s entire life and not be dangerous, so many men who are not tested and do not experience symptoms may never know they have prostate cancer.

The Swedish team was able to create a more nuanced picture by using a national database with hundreds of thousands of in-lab results, including fitness tests measuring how well the heart and the lungs supply oxygen to muscles.

Unlike with studies that rely on patients to report their exercise habits, this gave experts objective measurements. The results clearly showed a link between physical activity and a reduced prostate cancer risk. It also showed that greater improvements in fitness were associated with a greater reduction in risk.

This adds to a growing understanding of how important exercise is for prevention of cancer more generally. In 2019, a review by the American College of Sports Medicine found that regular physical activity significantly reduced the risk of bladder, breast, colon, endometrial, esophageal adenocarcinoma, kidney and stomach cancers. The same analysis also found that having a regular exercise habit was tied to improved treatment outcomes and extended the life expectancies of those already living with cancer.

While it’s not clear exactly how this happens, experts said that one explanation may be that exercise helps fight cancer by enhancing how the immune system targets and eradicates cancer cells.

“We know even a single bout of exercise helps our body release immune cells in our circulation,” said Neil M. Iyengar, a medical oncologist and physician scientist at Memorial Sloan Kettering Cancer Center in New York City who was not involved in the study. “It also helps to improve the population of immune cells in our tissues that fight cancer cells.”

He added: “In somebody who exercises, you see more immune cells that are really able to kill cancer cells. Whereas for someone more sedentary, especially someone who is obese, you see the opposite.”

Researchers do not yet know exactly the right dose and type of exercise that might be most effective, but both the American Cancer Society and the American Society of Clinical Oncology recommend 150 minutes per week, or 20 minutes per day, of aerobic exercise. That could be light walking, jogging or weight-bearing exercises.

Both Dr. Iyengar and Dr. Bolam recommended starting simply: Find an activity that is enjoyable, and get moving. That could be playing with children or grandchildren, going for a walk or joining a recreational sports league. Consistency is key, they said, which is why it’s important to find activity that doesn’t feel like a chore.

“Everyone has a chance to do something that’s really cost efficient here to decrease their risk of prostate cancer,” Dr. Bolam said. “And that’s something that is wholly in our control.”

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Here’s three ways to safely exercise  and prevent workout injuries.                    * Whether you loved the presidential physical fitness test or loathed it as a kid, it can still be a revealing measure of health — now that no one’s forcing you to do it.

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ME patients have often been dismissed or stigmatised in the past.

Scientists find link between brain imbalance and chronic fatigue syndrome

Scientists describe small study as long overdue deep dive into biology of condition

Scientists have uncovered compelling evidence for abnormalities in the brain and immune systems of patients with chronic fatigue syndrome (CFS), also known as myalgic encephalomyelitis (ME).

The findings, in one of the most rigorous investigations to date, begin to illuminate the biological basis for the illness that can cause disabling fatigue. The study is the first to demonstrate a link between imbalances in brain activity and feelings of fatigue, and suggests that these changes could be triggered by abnormalities in the immune system.

“People with ME/CFS have very real and disabling symptoms, but uncovering their biological basis has been extremely difficult,” said Walter Koroshetz, director of NIH’s National Institute of Neurological Disorders and Stroke (NINDS) in the US. “This in-depth study of a small group of people found a number of factors that likely contribute to their ME/CFS.”

The study involved only 17 patients and the findings need to be confirmed in a larger group before they can be claimed as a roadmap towards new treatments. It is also not clear to what extent the findings apply to long Covid as the patients were recruited and assessed before the pandemic. But scientists have described the work as a long overdue deep dive into the biology of the condition.

“This is such an important paper and one I am so pleased to see come out,” said Prof Karl Morten, who researches ME/CFS at the John Radcliffe hospital, University of Oxford, and was not involved in the latest work. “We’ve had lots of little studies showing there might be a problem with this cell or that cell, but no one has really looked at everything in one patient before.”

Patients in the study, carefully selected from an initial pool of 300, had all experienced an infection prior to becoming ill. During the study, they stayed at an NIH clinic for a week and were given a wide range of physiological assessments.

Results from functional magnetic resonance imaging (fMRI) brain scans showed that people with ME/CFS had lower activity in a brain region called the temporal-parietal junction (TPJ), which may cause fatigue by disrupting the way the brain decides how to exert effort. The motor cortex, a brain region that directs the body’s movements, also remained abnormally active during fatiguing tasks. However, there were no signs of muscle fatigue.

This suggests that fatigue in ME/CFS could be caused by a dysfunction of brain regions that drive the motor cortex and that changes in the brain may alter patients’ tolerance for exertion and their perception of fatigue.

“We may have identified a physiological focal point for fatigue in this population,” said Brian Walitt, associate research physician at NINDS and first author of the study, published in Nature Communications . “Rather than physical exhaustion or a lack of motivation, fatigue may arise from a mismatch between what someone thinks they can achieve and what their bodies perform.”

Morten said that the discovery of abnormalities in brain function does not suggest that patients are psychologically driving their own illness or have any control over it. “The brain can respond to stimuli and impact on the body,” he said. “The brain is physically, biochemically not functioning properly and it’s the illness that’s doing that, not the patient.”

The patients also had elevated heart rates and their blood pressure took longer to normalise after exertion. There were also changes to patients’ T cells, sampled from cerebrospinal fluid, suggesting these immune cells were trying to fight something off. This could indicate the immune system has failed to stand down after an infection has cleared or that a chronic infection is present, undetected, in the body.

The authors trace out a possible cascade of events, starting with a persistent immune response, which could cause changes in the central nervous system, leading to alterations in brain chemistry and ultimately affect the function of specific brain structures that control motor function and the perception of fatigue.

“We think that the immune activation is affecting the brain in various ways, causing biochemical changes and downstream effects like motor, autonomic, and cardiorespiratory dysfunction,” said Avindra Nath, clinical director at NINDS and senior author of the study.

The findings have been welcomed by scientists as an important step towards uncovering the underlying biological causes of the illness. Until now, the lack of any clear biological basis for the illness has led to patients being dismissed, stigmatised and having to navigate ineffective treatment options.

  • ME / Chronic fatigue syndrome
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Good Scientific Practice and Ethics in Sports and Exercise Science: A Brief and Comprehensive Hands-on Appraisal for Sports Research

Nitin kumar arora.

1 Department of Intervention Research in Exercise Training, German Sport University Cologne, 50933 Cologne, Germany

2 Department of Physiotherapy, University of Applied Sciences, 44801 Bochum, Germany

Golo Roehrken

Sarah crumbach.

3 Institute of Sport Economics and Sport Management, German Sport University Cologne, 50933 Cologne, Germany

Ashwin Phatak

4 Institute of Exercise Training and Sport Informatics, German Sport University Cologne, 50933 Cologne, Germany

Berit K. Labott

5 Institute of Sport Sciences, Otto-von-Guericke University, 39106 Magdeburg, Germany

André Nicklas

Pamela wicker.

6 Department of Sports Science, Bielefeld University, 33615 Bielefeld, Germany

Lars Donath

Associated data.

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Sports and exercise training research is constantly evolving to maintain, improve, or regain psychophysical, social, and emotional performance. Exercise training research requires a balance between the benefits and the potential risks. There is an inherent risk of scientific misconduct and adverse events in most sports; however, there is a need to minimize it. We aim to provide a comprehensive overview of the clinical and ethical challenges in sports and exercise research. We also enlist solutions to improve method design in clinical trials and provide checklists to minimize the chances of scientific misconduct. At the outset, historical milestones of exercise science literature are summarized. It is followed by details about the currently available regulations that help to reduce the risk of violating good scientific practices. We also outline the unique characteristics of sports-related research with a narrative of the major differences between sports and drug-based trials. An emphasis is then placed on the importance of well-designed studies to improve the interpretability of results and generalizability of the findings. This review finally suggests that sports researchers should comply with the available guidelines to improve the planning and conduct of future research thereby reducing the risk of harm to research participants. The authors suggest creating an oath to prevent malpractice, thereby improving the knowledge standards in sports research. This will also aid in deriving more meaningful implications for future research based on high-quality, ethically sound evidence.

1. Introduction

Historical milestones of ethical and scientific misconduct in research.

Until the early 19th century, ‘truth’ was fundamentally influenced by cults, religion, and monarchism [ 1 ]. With the ‘enlightenment’ of academicians, clinicians and researchers in the 19th century [ 2 ], scientific research started to impact the lives of people by providing balanced facts, figures and uncertainties, thereby leading to a better explanation for reality (i.e., evidence vs. eminence). However, dualistic thinking was still interfering with the newer rationalized approach as the estimation of reality by scientific estimation was still being challenged by the dogmatic view of real truth [ 3 ].

Over the last decades, researchers underestimated the importance of good ethical conduct [ 4 ] in human research by misinterpreting the probabilistic nature of scientific reasoning. Scientific research had constantly been exploited for personal reputations, political power, and terror [ 3 ]. The ‘Eugenics program’ originating from the Nazi ideology is an unsettling example of ethical failure and scientific collapse. As part of this program, scientific research was being exploited to justify unwanted sterilization (0.5 million) [ 5 ] and mass-killing (0.25 million) [ 6 ] for the sake of selection and elimination of ‘unfit genetic material’. In 1955, more than 200,000 children were infected with a Polio vaccine that was not appropriately handled as per the recommended routines [ 7 ]. Likewise, the thalidomide disaster of 1962 led to limb deformities and teratogenesis in more than ten thousand newborn children [ 8 ]. Considering the aforementioned unethical practices and misconduct, there is a strong need to comply with and re-emphasize the importance of ethics and good scientific practice in humans and other species alike.

In the process of evolution of scientific research, the Nuremberg code laid the foundation for developing ethical biomedical research principles (e.g., the importance of ‘voluntary and informed consent’) [ 9 ]. Based on the Nuremberg code and the previously available medical literature, the first ethical principles (i.e., Declaration of Helsinki) were put into practice for safe human experimentation by the World Medical Association in 1964. This declaration proved to be a cornerstone of medical research involving humans and emphasized on considering the health of the patients as the topmost priority [ 10 ]. The year 1979 could also be seen as an important milestone, as the ‘Belmont report’ was introduced that supported the idea: ‘the interventions and drugs have to eventually show beneficial effects’. The Belmont report suggests that the recruitment, selection and treatment of participants needs to be equitable. It also highlights the importance of providing a valid rationale for testing procedures to prevent and minimize the risks or harms to the included participants [ 11 ].

As a result of the introduction of ethical principles, it became evident that research designs and results should be independent of political influence and reputational gains. There should also be no undeclared conflicts of interest [ 12 ]. Interestingly, sports and exercise science emerged as politically meaningful instruments for showing power during the Cold War (i.e., Eastern socialism versus Western capitalism) [ 13 ]. Researchers were either being manipulated or sometimes not even published to reduce awareness about the negative effects of performance-enhancing substances [ 14 , 15 ]. Even though these malpractices were strictly against the principles of the Declaration of Helsinki [ 14 ], these were prevalent globally, thereby contributing to several incidents of doping in sports [ 16 ]. To further minimize unethical research practices, the Good Clinical Practice (GCP) Standards were presented in 1997 to guide the design of clinical trials and formulation of valid research questions [ 17 ]. However, some authors criticise the Good Clinical Practice standards as not being morally sufficient to rule out personal conflicts of interest when compared to the ethical standards of the Declaration of Helsinki [ 18 ].

Nowadays, professional development and scientific reputation in the research community are related to an increase in the number of publications in high-ranked journals. However, the increasing number of publications gives very little information about the scientific quality of the employed methods, as some of the published papers either contain manipulated results [ 19 ] or methods that could not be replicated [ 12 ]. Moral and ethical standards are widely followed by sports researchers as evidenced by the applied methods that are mostly safe, justified, valuable, reliable and ethically approved. However, the ethical approval procedures, the dose and the application of exercise training vary greatly between studies and institutions. The review by Kruk et al., 2013 [ 20 ] provides a balanced summary of the various principles based on the Nuremberg Code and the Declaration of Helsinki. GCP standards of blinding (subjects and outcome assessors), randomization, and selection are not consistently considered and are sometimes difficult to follow due to limited financial and organizational resources. There is a prevalent trend in the publication of positive results in the scientific community, as negative results often fail to pass editorial review [ 21 ]. Additionally, certain unethical research practices have been observed, such as the multiple publication of data from a single trial (referred to as “data slicing”), the submission of duplicate findings to multiple journals, and instances of plagiarism [ 22 ]. These limitations negatively affect the power, validity, interpretability and applicability of the available evidence for future research in sports and exercise science. Previous research showed that, if used systematically, lifestyle change and exercise interventions can prove to be one of the most efficient strategies for obtaining positive health outcomes [ 23 ] and longevity [ 24 ]. Hence, the present article recommends avoiding malpractices and using the underlying ethical standards to balance risks and benefits along with preventing data manipulation and portrayal of false-positive results.

2. Codes of Conduct in Sport Research

All the available codes, declarations, statements, and guidelines aim at providing frameworks for conducting ethical research across disciplines. These frameworks generally cover the regulative, punitive, and educational aspects of research. Codes of ethical conduct not only outline the rules and recommendations for conducting research but also outline punishments in case of non-compliance or misconduct. Hence, these ethical codes and guidelines should be considered the most important educational keystones for researchers as these frameworks allow scientists to design and conduct their studies in a better way. Declarations and guidelines are regularly updated to accommodate newer information and corrections. Thus, one also needs to be flexible when using these guidelines as these reflect ongoing scientific and societal development.

Codes and declarations in sport and exercise science regulate both quantitative and qualitative research and include information about human and animal rights, research design and integrity, authorship and plagiarism. We will categorize these guidelines based on the individuals whom guidelines aim to protect (e.g., participants or researchers).

Legal codes and norms of a country are inherently binding to the researchers and institutions who are conducting the research and do not require ratification from the researching individual or organization. These laws can include data storage, child protection, intellectual property rights, or medical regulations applicable to a specific study. However, ethics codes not only cater to the questions of legality but also include moral parameters of research like conducting ‘true’ research. Likewise, if the codes are drafted by a research organization, everyone conducting research for this particular organization is supposed to follow these codes.

Researchers have the responsibility to assess which codes, and standards are relevant to their field of research depending on the country, participants, and research institution ( Table 1 ). This can be confirmed by the academic supervisors or the scientific ethics board of the research institution. While there is a growing number of codes and guidelines for different research fields, it is important to consider that none of these can cater to the needs of every single research design alone. For example, the Code of Ethics of the American Sociological Association (ASA) states: “Most of the Ethical Standards are written broadly in order to apply to sociologists in varied roles, and the application of an Ethical Standard may vary depending on the context” [ 25 ]. Hence, as ethical standards are not exhaustive, scientific conduct that is not specifically addressed by this Code of Ethics is not necessarily ethical or unethical [ 25 ].

Detailed overview of Codes, Declarations, Statements and Guidelines relevant for sports and exercise science research.

It is crucial to recognize the purpose of an ethics code rather than just following it for ticking boxes. Understanding the aims and limitations of an ethics code will allow for a more meaningful application of the underlying principles to the specific context without ignoring the potential limitations of a study. Unintentional transgressions can occur through subconscious bias, fallacies, or human errors. However, the unintentional errors can be mitigated by following the streamlined process of research conception, method development and study conduct following approval from the Institutional Review Boards (IRBs), Ethical Research Commissions (ERCs), supervisors, and peers. In case of intentional errors, the punitive aspect needs to come into action and the transgressors might need to be investigated and sanctioned, either by the research organizations or by law.

3. Differences between Drug and Exercise Trials

Randomized controlled trials (RCTs) are regarded as the highest level of evidence [ 26 , 27 ]. For both the cases (exercise vs. drug studies), RCTs primarily aim at investigating the dose-response relationships and obtaining causal relationships [ 28 ]. Drug trials compare one drug to other alternatives (e.g., another drug, a placebo, or a treatment as usual). Likewise, exercise trials often compare one mode of exercise to another exercise or no exercise interventions (e.g., usual care, waitlist control, true control, etc.), ideally under caloric, workload or time-matched conditions. However, placebo or sham trials are still rare in sports and exercise research due to their challenging nature [ 29 ]. The following quality requirements should be fulfilled for conducting high-quality exercise trials: (a) ensure blinding of assessors, participants and researchers; (b) placebo/sham intervention (if possible), and (c) adequate randomization and concealed allocation.

3.1. Blinding

The term ‘blinding’ (or ‘masking’) involves keeping several involved key persons unaware of the group allocation, the treatment, or the hypothesis of a clinical trial [ 30 , 31 , 32 , 33 , 34 ]. The term blinding and also the types of blinding (single, double, or triple blind) are being increasingly used and accepted by researchers but there is a lack of clarity and consistency in the interpretation of those terms [ 33 , 35 , 36 ]. Blinding should be conducted for participants, health care providers, coaches, outcome assessors, data analysts, etc. [ 31 , 33 , 34 , 37 ]. The blinding process helps in preventing bias due to differential treatment perceptions and expectations of the involved groups [ 28 , 30 , 31 , 32 , 38 , 39 , 40 ].

Previous research has shown that trials with inadequately reported methods [ 41 ] and non-blinded assessors [ 42 ] or participants [ 43 ] tend to overestimate the effects of intervention. Hence, blinding serves as an important prerequisite for controlling the methodological quality of a clinical trial, thereby reducing bias in assessed outcomes. Owing to this reason, most of the current methodological quality assessment tools and reporting checklists have dedicated sections for ‘blinding’. For example, three out of eleven items are meant for assessing ‘blinding’ in the PEDro scale [ 44 ]; the CONSORT checklist for improving the reporting of RCTs also includes a section on ‘blinding’ [ 45 ]. In an ideal trial, all participants involved in the study should be ‘blinded’ [ 30 ]. However, choosing whom to ‘blind’ also depends on and varies with the research question, study design and the research field under consideration. In the case of exercise trials, blinding is either not adequately done or poorly reported [ 36 , 46 ]. The lack of reporting might be the result of a lack of awareness of the blinding procedures rather than the poor methodological conduct of the trial itself [ 34 ]. Hence, blinding is not sufficiently addressed in exercise, medicine and psychology trials [ 47 , 48 ] due to lacking knowledge, awareness and guidance in these scientific fields leading to an increased risk of bias [ 48 ].

Blinding of participants is difficult to achieve and maintain [ 34 , 39 , 40 , 49 ] in exercise trials as the participants would usually be aware of whether they are in the exercise group or the control (inactive) group [ 31 , 39 , 50 ]. Likewise, the therapists are also generally aware of the interventions they are delivering [ 51 ], and the assessors are aware of the group allocation because it is common in sports sciences that researchers are involved in different parts of research (recruiting, assessment, allocation, training, data handling analysis) due to limited financial resources. Thus, the adequacy of blinding is usually not assessed as it is often seen as ’impossible’ in exercise trials.

Consequently, we strongly recommend using independent staff for testing, training, control and supervision to improve possibilities of blinding of the individuals involved in the study [ 39 ]. Researcher also need to decide if it is methodologically feasible and ethically acceptable to withhold the information about the hypothesis and the study aims [ 52 ] from assessors and participants. This needs to be considered, addressed and justified before the trial commences (i.e., a priori). While reporting methods of exercise trials, it is important not only to describe who was blinded but also to elaborate the methods used for blinding [ 33 , 48 ]. This helps the readers and research community to effectively evaluate the level of blinding in the trial under consideration [ 33 , 53 ]. Furthermore, if blinding was carried out, the authors can also include the assessment of success of the blinding procedure [ 33 , 54 ]. Readers can access more information about the various possibilities for blinding using the following link ( http://links.lww.com/PHM/A246 accessed on 10 October 2022) [ 36 ].

3.2. “Placebo” (or Sham Intervention)

‘Placebo’ is an important research instrument used in pharmacology trials to demonstrate the true efficacy of a drug by minimizing therapy expectations of the participants [ 55 ]. As the term placebo is generally used in a broad manner, precise definitions are difficult. Placebo is used as a control therapy in clinical trials owing to their comparable appearance to the ‘real’ treatment without the specific therapeutic activity [ 56 ]. In an ideal research experiment, it would not be possible to differentiate between a placebo and an intervention treatment [ 57 , 58 ]. The participants should not be aware of the treatment group either, because it can lead to the knowledge of whether they received a placebo or the investigated drug [ 57 ]. A review of clinical trials comparing ‘no treatment’ to a ‘placebo treatment’ concluded that the placebo treatment had no significant additional effects overall but may produce relevant clinical effects on an individual level [ 59 ]. As outlined previously, the placebo effect is rarely investigated in sports and exercise studies. It is generally investigated using nutritional supplements, ergonomic aids, or various forms of therapy in the few existing studies [ 60 ]. Placebos have been shown to have a favorable effect on sports performance research [ 61 ], implying that these could be used for improving performance without using any additional performance-enhancing drugs [ 62 ].

However, it is quite difficult to have an adequate placebo in exercise intervention studies, as there is currently no standard placebo for structured exercise training [ 28 ]. For exercise training interventions, a placebo condition is defined as “an intervention that was not generally recognized as efficacious, that lacked adequate evidence for efficacy, and that has no direct pharmacological, biochemical, or physical mechanism of action according to the current standard of knowledge” [ 63 ]. As a result, using a placebo in exercise interventions is often seen as impractical and inefficient [ 57 , 58 ]. As the concept of blinding is also linked to the use of a placebo, it is usually difficult to implement in exercise trials.

When it comes to exercise experiments, an active control group is considered to be more effective than a placebo group [ 10 , 28 ]. In other cases, usual care or standard care can also be used as the control intervention [ 28 ]. In exercise trials, instead of using the term ‘placebo treatment’, the terms “placebo-like treatment” or “sham interventions” should be applied [ 64 , 65 ]. Previous recommendations by other researchers [ 61 ] also underpin our rationale.

3.3. Randomization and Allocation Concealment

Group allocation in a research study should be randomized and concealed by an independent researcher to minimize selection bias [ 66 ]. Randomization procedures ensure that the differences in treatment outcomes solely occur by chance [ 28 , 67 ]. Several methods for randomization are available; however, methods such as stratified randomization are being increasingly popular as they ensure equal distribution of participants to the different groups based on several important characteristics [ 66 ]. Other types of randomization, such as cluster randomization, may be appropriate when investigating larger groups, for example, in multicenter trials [ 28 ].

Since researchers are frequently involved in all phases of a trial (recruitment, allocation, assessment and data processing), randomization should usually be conducted by someone who is not familiar with the project’s aims and hypotheses. In studies with a large number of participants, the interaction between subjects and assessors can significantly impact the results [ 68 ]. The randomization procedure used in the clinical trial should be presented in scientific articles and project reports so that readers can understand and replicate the process if needed [ 66 ]. Based on the aforementioned aspects, exercise trials are not easily comparable to drug trials and the differences lead to difficulties in conducting scientifically conceptualized exercise trials. However, researchers should strive for quality research by using robust methods and providing detailed information on blinding, randomization, choice of control groups, or sham therapies, as appropriate. Researchers should critically evaluate the risk-benefit ratio of exercise so that the positive impacts of exercise on health can be derived and the cardiovascular risks associated with exercise could be minimized [ 69 ].

4. Key Elements of an Ethical Approval in Exercise Science

As previously described, ethical guidelines are needed to protect study participants from potential study risks and increase the chances of attaining results that ease interpretation. Therefore, a prospective ethical approval process is required prior to the recruitment of the participants [ 70 ]. This practice equally benefits the participants by safeguarding them against potential risks and the practitioners who base their clinical decisions on research results. Research results from a study with a strong methodology will enable informed and evidence-based decision making. If the methodology of a research project contains some major flaws, it will negatively affect the practical applicability of the observed results [ 71 ]. Various journal reviewers provide suggestions to reject manuscripts without any option to resubmit if no ethical approval information is provided. This demonstrates the importance of ethical approval and proper scientific conduct in research [ 70 ].

The following key elements need to be addressed in an ethical review proposal: Introduction, method, participant protection, and appendix. These key elements should be detailed in a proposal with at least three crucial characteristics addressed in each section ( Figure 1 ). This hands-on framework would help to expedite the process of decision-making for members of the ethics committee [ 72 ].

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Overview 4 × 3 short list for outlining ethical approval in sport and exercise science.

The ‘introduction’ section should start with a general overview of the current state of research [ 4 ]. Researchers need to describe the rationale of the proposed study in an easy and comprehensible language considering the current state of knowledge on that topic [ 4 ]. The description helps to provide a balanced summary of the risks and benefits associated with the interventions in the proposed study. The novelty of the stated research question and the underlying hypothesis must be justified. If the proposed study fails to expand the current literature on the topic under consideration, conducting the study would be a ‘waste’ of time and financial resources for researchers, participants, and funding agencies [ 73 ]. Hence, ethical approvals should not be given for research projects that fail to provide novelty in the approach to the respective research area. The introduction should also include information on funding sources including the name of the funding partner, duration of monetary/resource support, and any potential conflicts of interest. If no funding is available, authors should declare that ‘This study received no funding’ [ 70 ].

The subsequent ‘methods’ section should include detailed information about the temporal and structural aspects of the study design. Researchers should justify the used study design in a detailed manner [ 4 , 28 ]. Multiple research designs can be utilized for addressing a specific research question, including experimental, quasi-experimental, and single-case trial designs [ 74 , 75 ]. However, a valid rationale should be provided for choosing a randomized cross-over trial design when the gold standard of randomized control trials is also feasible. Readers are advised to refer to the framework laid down by Hecksteden et al., 2018, for extensive information on this section [ 28 ]. Researchers should also provide a broad, global and up-to-date literature-based justification for their interventions or methods employed in the study. For instance, if the participants are asked to consume supplements, the recommendations for the dose needs to be explained based on prior high-quality studies and reviews for that supplement [ 4 ]. The criteria for subject selection (inclusion and exclusion criteria) and sample size estimation need to be explained in detail to allow replication of the study in the future [ 76 ]. Lacking sample size estimations is only acceptable in rare cases and requires detailed explanations (e.g., pilot trials, exploratory trials to formulate a hypothesis, acceptability trials). Moreover, sufficient details should be provided for the measuring devices used in the study and a sound rationale should be provided for the choice of that particular measuring device and the measured parameters [ 4 ].

The section on ‘participant protection’ deals with potential risks (physical and psychological adverse outcomes) and benefits to the participants. The focus should be adjusted to the study population under consideration. For example, while conducting a study on a novel weight training protocol with elite athletes, all information and possible effects on the athletes’ performance need to be considered, as their performance level is their ‘human capital’ [ 4 ]. The investigators also need to provide information on the individuals responsible for different parts of the study, i.e., treatment provider, outcome assessor, statistician, etc. In some cases, externally qualified personnel are needed during the examination process. For example, a physician might be needed for blood sampling or biopsies and this person should also be familiar with the regulations and procedures to avoid risk to the participants due to a lack of experience in this area. Prior experience and qualifications are required for conducting research with vulnerable groups, such as children, the elderly and pregnant females. Williams et al. (2011) summarized essential aspects of conducting research studies with younger participants [ 77 ]. Overall, the personnel should be blinded to the details of the group allocation and participants, if possible [ 30 ]. The study applicants also need to provide information about the planned compensation and the follow-up interventions. Harriss and colleagues suggested that the investigators are not expected to offer the treatments in case of injury to the participants during the study (except first aid) [ 70 ]. However, this recommendation is not usually documented and translated into research practice.

The ‘appendix’ section should contain relevant details about the following: consent, information to the participants and a declaration of pre-registration. The information to the participant and the consent forms need to be documented in an easy to understand language. A brief summary of the purpose of the study and the tasks to be performed by the participants should also be added. Then, a concise but comprehensive overview of the potential risks and benefits is needed. The next section should include information for participants: the participants’ right to decline participation without any consequence and the right to withdraw their consent at any time without any explanation. The regulations for the storage, sharing and retention of study data need to be detailed [ 70 ]. The names and institutional affiliations of all the researchers along with the contact information of the project manager should be listed. A brief overview of the study’s aim, tasks, methods and data acquisition strategies should be described. Finally, consent is needed for processing the recorded personal data [ 70 ]. The last section of the ‘appendix’ must include a declaration of pre-registration (e.g., registration in the Open Science Framework or trial registries) to avoid alterations in the procedure afterward and facilitate replication of study methods [ 78 ].

5. Study Design and Analysis Models

The process of conceptualizing an exercise trial might involve various pitfalls at every stage (hypothesis formulation, study design, methodology, data acquisition, data processing, statistical analysis, presentation and interpretation of results, etc.). Thus, the entire ‘design package’ needs to be considered when constructing an exercise (training) trial [ 28 ]. Formulation of an adequate and justified research question is the essential aspect before starting any research study. Formulating a good research question is pivotal to achieve adequate study quality [ 79 ]. According to Banerjee et al., 2009 [ 80 ], “a strong hypothesis serves the purpose of answering major part of the research question even before the study starts”. As outlined in previous sections, ethical research aspects must be taken into account while framing the research question to protect the privacy and reduce risks to the participants. The confidentiality of data should be ascertained and the participants should be free to withdraw from the study at any time. The authors should also avoid deceptive research practices [ 79 ].

Hecksteden et al., 2018, suggested that RCTs can be regarded as the gold standard for investigating the causal relationships in exercise trials [ 28 ]. However, it is sometimes not feasible to conduct RCTs in the field of sports science due to logistical issues, such as smaller sample sizes and blinding the location of the study (e.g., schools, colleges, clinics, etc.). In this case, alternative study designs such as cluster-RCTs, randomized crossover trials, N-of-1 trials, uncontrolled/non-randomized trials, and prospective cohort studies can be considered [ 81 ]. Considering the complex nature of exercise interventions, the Consensus on Exercise Reporting Template (CERT) has been developed to supplement the reporting and documentation of randomized exercise trials [ 81 ]. Adherence to these templates might help to improve the ethical proposal reporting standards when designing new RCTs.

A recent comment, in the journal ‘Nature’, highlights the importance of using the right statistical test and properly interpreting the results. According to the paper, the results of 51 percent of articles published in five peer-reviewed journals were misinterpreted [ 82 ]. Frequentist statistics and p -values are popular summaries of experimental results but there is a scope for misinterpretation due to the lack of supplementary information with these statistics. For instance, authors tend to draw inferences about the results of a study based on certain ‘ threshold p-values ’ (generally p < 0.05) [ 83 ]. However, with an increase in sample size, the p -value tends to come closer to zero regardless of the effect size of the intervention [ 83 ]. With the rise of larger datasets and thus potentially higher sample sizes, the p -value threshold becomes questionable. A call for action has recently been raised by more than 800 signatories to retire statistical significance and to stop categorizing results as being statistically significant or non-significant. Recently, researchers suggested using confidence intervals for improving the interpretation of study results [ 82 ]. Although alternative methods such as magnitude-based inference (MBI) exists, there is scarce evidence that MBI has checked the use of p -value and hypothesis testing by sports researchers [ 84 ]. MBI tends to reduce the type II error rate but it increases the type I error rate by about two to six times the rate of standard hypothesis testing [ 85 ]. In the next paragraphs, we focus on the commonly used practices within the frequentist statistics domain.

Frequentist statistical tests are categorized into parametric and non-parametric tests. Non-parametric tests do not require the data to be normally distributed, whereas parametric tests do [ 86 ]. The following factors help in deciding the appropriate statistical test: (a) type of dependent and independent variables (continuous, discrete, or ordinal); (b) type of distribution, if the groups are independent or matched; (c) levels of observations; and (d) time dependence. Readers can choose the right statistical tests based on the type of research data they are planning to use [ 87 , 88 ]. A recent publication outlined 25 common misinterpretations concerning p -values, confidence intervals, power calculations and key considerations while interpreting frequentist statistics [ 89 ]. We recommend sports researchers consider the listed warnings while interpreting the results of statistical tests.

Out of the various frequentist statistical methods, analysis of variances (ANOVA) is one of the most widely used tests to analyze the results of RCTs. It does not, however, provide an estimate of the difference between groups, which is usually the most important aspect of an RCT [ 90 ]. Linear models (e.g., t -tests) suffer from similar issues when analyzing categorical variables, which are a wider part of RCT analysis [ 91 ]. Type I errors (false positive, rejecting a null hypothesis that is correct) and Type II errors (false negative, failure to reject a false null hypothesis) are often discussed while interpreting RCT results [ 80 ]. Though it is not possible to completely eliminate these errors, there are ways to minimize their likelihood and report the statistics appropriately. The most commonly used methods for minimizing error rates include the following: (a) increasing the sample size; (b) adjusting for covariates and baseline differences [ 92 ]; (c) eliminating significance testing; and (d) reporting a confusion matrix [ 80 , 86 , 93 ].

Mixed logit models are potential solutions for some of the challenges listed above. They combine the advantages of random effects logistic regression analysis with the benefits of regression models [ 94 ]. In addition, mixed logit models, as part of the larger framework of generalized linear mixed models, provide a viable alternative for analyzing a wide range of outcomes. For increasing the transparency and interpretability of the observed results, mixed logit classification algorithms and evaluation matrices such as cross validation and presentation of a confusion matrix (type I and type II error rates) can be utilized [ 86 ]. Mixed logit models can also be utilized as predictive models rather just ‘inference testing’ models.

6. Limitations

Despite extensive efforts to incorporate empirical and current evidence regarding good scientific practice and ethics into this paper, it is possible that some literature may have been omitted. Nonetheless, the paper comprehensively covers key aspects of prevalent ethical misconducts and the standards that should be upheld to prevent such practices. As a result, readers can have confidence in the literature presented, which is based on a substantial body of existing evidence. Readers are also encouraged to engage in critical evaluation and to consider new approaches that could improve the overall scientific literature.

7. Conclusions

We highlighted the various pitfalls and misconduct that can take place in sports and exercise research. Individual researchers associated with a research organization need to comply with the highest available standards. They need to maintain an intact ‘moral compass’ that is unaffected by expectations and environmental constraints thereby reducing the likelihood of unethical behavior for the sake of publication quantity, interpretability, applicability and societal trust in evidence-based decision-making. To achieve these objectives, a Health and Exercise Research Oath (HERO) could be developed that minimizes the allurement to cheat and could be used by PhD candidates, senior researchers, and professors. Such an oath would prevent intentional or unintentional malpractices in sport and exercise research, thereby strengthening the knowledge standards based on ethical exercise science research. Overall, this will also improve the applicability and interpretability of research outcomes.

Acknowledgments

We acknowledge the financial support of the German Research Foundation (DFG) and the Open Access Publication Fund of Bielefeld University for the article processing charge.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, N.K.A., G.R., S.C., A.P., B.K.L., A.N., P.W. and L.D.; methodology, N.K.A., G.R., S.C., A.P., B.K.L., A.N., P.W. and L.D.; resources, P.W. and L.D.; data curation, N.K.A., G.R., S.C., A.P., B.K.L., A.N., P.W. and L.D.; writing—original draft preparation, N.K.A., G.R., S.C., A.P., B.K.L., A.N., P.W. and L.D.; writing—review and editing, N.K.A., G.R., S.C., A.P., B.K.L., A.N., P.W. and L.D.; visualization, N.K.A., G.R., S.C., A.P., B.K.L., A.N., P.W. and L.D.; supervision, P.W. and L.D.; project administration, L.D. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

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