Comment on: “A Method to Stop Analyzing Random Error and Start Analyzing Differential Responders to Exercise”

N of 1: Optimizing Methodology for the Detection of Individual Response Variation in Resistance Training

Most resistance training research focuses on inference from average intervention effects from observed group-level change scores (i.e., mean change of group A vs group B). However, many practitioners are more interested in training responses (i.e., causal effects of an intervention) on the individual level (i.e., causal effect of intervention A vs intervention B for individual X). To properly examine individual response variation, multiple confounding sources of variation (e.g., random sampling variability, measurement error, biological variability) must be addressed. Novel study designs where participants complete both interventions and at least one intervention twice can be leveraged to account for these sources of variation (i.e., n of 1 trials). Specifically, the appropriate statistical methods can separate variability into the signal (i.e., participant-by-training interaction) versus the noise (i.e., within-participant variance). This distinction can allow researchers to detect evidence of individual response variation. If evidence of individual response variation exists, researchers can explore predictors of the more favorable intervention, potentially improving exercise prescription. This review outlines the methodology necessary to explore individual response variation to resistance training, predict favorable interventions, and the limitations thereof.

Meta-analysis of variation in sport and exercise science: Examples of application within resistance training research

Meta-analysis has become commonplace within sport and exercise science for synthesising and summarising empirical studies. However, most research in the field focuses upon mean effects, particularly the effects of interventions to improve outcomes such as fitness or performance. It is thought that individual responses to interventions vary considerably. Hence, interest has increased in exploring precision or personalised exercise approaches. Not only is the mean often affected by interventions, but variation may also be impacted. Exploration of variation in studies such as randomised controlled trials (RCTs) can yield insight into interindividual heterogeneity in response to interventions and help determine generalisability of effects. Yet, larger samples sizes than those used for typical mean effects are required when probing variation. Thus, in a field with small samples such as sport and exercise science, exploration of variation through a meta-analytic framework is appealing. Despite the value of embracing and exploring variation alongside mean effects in sport and exercise science, it is rarely applied to research synthesis through meta-analysis. We introduce and evaluate different effect size calculations along with models for meta-analysis of variation using relatable examples from resistance training RCTs.

Causes and Consequences of Interindividual Response Variability: A Call to Apply a More Rigorous Research Design in Acute Exercise-Cognition Studies

The different responses of humans to an apparently equivalent stimulus are called interindividual response variability. This phenomenon has gained more and more attention in research in recent years. The research field of exercise-cognition has also taken up this topic, as shown by a growing number of studies published in the past decade. In this perspective article, we aim to prompt the progress of this research field by (i) discussing the causes and consequences of interindividual variability, (ii) critically examining published studies that have investigated interindividual variability of neurocognitive outcome parameters in response to acute physical exercises, and (iii) providing recommendations for future studies, based on our critical examination. The provided recommendations, which advocate for a more rigorous study design, are intended to help researchers in the field to design studies allowing them to draw robust conclusions. This, in turn, is very likely to foster the development of this research field and the practical application of the findings.

Statistical Considerations for Drawing Conclusions About Recovery

BACKGROUND

Numerous studies have found associations when change scores are regressed onto initial impairments in people with stroke (slopes ≈ 0.7). However, there are important statistical considerations that limit the conclusions we can draw about recovery from these studies.

OBJECTIVE

To provide an accessible checklist of conceptual and analytical issues on longitudinal measures of stroke recovery. Proportional recovery is an illustrative example, but these considerations apply broadly to studies of change over time.

METHODS

Using a pooled data set of n = 373 Fugl-Meyer Assessment upper extremity scores, we ran simulations to illustrate 3 considerations: (1) how change scores can be problematic in this context; (2) how "nil" and nonzero null-hypothesis significance tests can be used; and (3) how scale boundaries can create the illusion of proportionality, whereas other analytical procedures (eg, post hoc classifications) can augment this problem.

RESULTS

Our simulations highlight several limitations of common methods for analyzing recovery. We find that uniform recovery leads to similar group-level statistics (regression slopes) and individual-level classifications (into fitters and nonfitters) that have been claimed as evidence for the proportional recovery rule. New analyses, however, also speak to the complexities in variance about the regression slope.

CONCLUSIONS

Our results highlight that one cannot identify whether proportional recovery is true or not based on commonly used methods. We illustrate how these techniques, measurement tools, and post hoc classifications (eg, nonfitters) can create spurious results. Going forward, the field needs to carefully consider the influence of these factors on how we measure, analyze, and conceptualize recovery.

Response heterogeneity in a randomized controlled trial of telerehabilitation interventions among adults with multiple sclerosis

INTRODUCTION

Telerehabilitation may be effective on average but is not equally effective among all people with multiple sclerosis (MS). Thus, the purpose of this secondary analysis of a randomized controlled trial was to explore whether baseline characteristics of participants with MS influence fatigue and physical activity outcomes of three telerehabilitation interventions.

METHODS

Participants were randomized to contact-control intervention (CC), physical activity-only intervention (PA-only), and physical activity plus fatigue self-management intervention (FM+). The 12-week interventions were delivered over the phone. Sociodemographic (age and income), clinical (comorbidities, mental function and physical function), psychosocial (self-efficacy, outcome expectations and goal-setting), and behavioural baseline characteristics (step count and fatigue self-management behaviors) were used in a moderated regression analysis and a responder analysis to examine their influence on the Fatigue Impact Scale (FIS) and Godin Leisure-Time Exercise Questionnaire (GLTEQ) at post-test (i.e. immediately post-interventions).

RESULTS

No interactions terms were statistically significant in the moderation analysis. However, the responder analysis showed that baseline psychosocial characteristics and mental function were significantly different (p < 0.05) between responders and non-responders. Specifically, non-responders on the FIS at post-test in the PA-only intervention had significantly lower baseline scores in goal setting for engaging in fatigue self-management behaviours. Also, non-responders on the GLTEQ at post-test in the FM+ intervention had significantly worse baseline scores in mental function.

DISCUSSION

Further research is needed to understand the complex relationship among baseline characteristics, telerehabilitation and response heterogeneity. We discuss how research on examining response heterogeneity may be advanced by conducting mega-clinical trials, secondary analyses of big data, meta-analyses and employing non-traditional research designs.

TRIAL REGISTRATION

Clinicaltrials.gov (NCT01572714).