Inter‐individual differences in weight change following exercise interventions: a systematic review and meta‐analysis of randomized controlled trials

Abstract

Previous reports of substantial inter-individual differences in weight change following an exercise intervention are often based solely on the observed responses in the intervention group. Therefore, we aimed to quantify the magnitude of inter-individual differences in exercise-mediated weight change. We synthesized randomized controlled trials (RCTs) of structured, supervised exercise interventions. Fourteen electronic databases were searched for relevant studies published up to March 2017. Search terms focused on structured training, RCTs and body weight. We then sifted these results for those RCTs (n = 12, 1500 participants) that included relevant comparator group data. Standard deviations (SDs) of weight change were extracted, thereby allowing the SD for true inter-individual differences in weight loss to be calculated for each study. Using a random effects meta-analysis, the pooled SD (95% CI) for true individual responses was 0.8 (-0.9 to 1.4) kg. The 95% prediction interval (based on 2SDs) for true inter-individual responses was -2.8 to 3.6 kg. The probability (% chance) that the true individual response variability would be clinically meaningful (>2.5 kg) in a future study in similar settings was 23% ('unlikely'). Therefore, we conclude that evidence is limited for the notion that there are clinically important individual differences in exercise-mediated weight change.

An appraisal of the SD IR as an estimate of true individual differences in training responsiveness in parallel‐arm exercise randomized controlled trials

“…In this section, we discuss the different sources of variability that influence an individual’s observed value at a single time point (2.1) and observed pre–post change following an intervention (2.2). The terminology used in this section is a synthesis of terms derived from a series of previously published papers (Hopkins, ; Hopkins, ; Senn, ; Hopkins, ; Senn et al, ; Scharhag‐Rosenberger et al, ; Bouchard et al, ; Astorino and Schubert, ; Leifer et al, ; Bentley et al, ; Atkinson and Batterham, ; Hopkins, ; Hecksteden et al, ; Arnold et al, ; Ross et al, ; Raleigh et al, ; Gurd et al, ; Bonafiglia et al, ; Astorino et al, ; Senn, ; Montero and Lundby, ; deLannoy et al, ; Williamson et al, ; Cadore et al, ; Clarke et al, ; Alvarez et al, ; Williamson et al, ; Swinton et al, ; Hecksteden et al, ). We attempt to use the most common term(s) for each source of variability and provide a list of relevant terms with definitions and alternative names in Table .…”

“…Figure presents variability in changes in behavioral factors in an EX group from a large RCT (Ross et al, ; Ross et al, ), which potentially demonstrates the existence of V ΔWS EX and raises the possibility that variability in these behavioral factors contributed to the SD EX presented in Figure . Importantly, the component of variability within SD EX attributed to V ΔWS EX and V ΔTE EX is purported to occur randomly (Atkinson and Batterham, ; Williamson et al, ; Williamson et al, ). This purported random nature of V ΔWS EX has led it to be called “random within‐subjects variability” (Atkinson and Batterham, ; Williamson et al, ; Williamson et al, ).…”

“…Importantly, there are potential limitations associated with parallel‐arm exercise RCTs that merit consideration when interpreting the SD IR . However, despite several exercise training studies utilizing the SD IR (Stock et al, ; Williamson et al, ; Phillips et al, ; Williamson et al, ; McLaren et al, ; Hammond et al, ; Walsh et al, ), the potential impact of these limitations have yet to be discussed in detail in the individual response literature.…”

Research in nutritional supplements and nutraceuticals for health, physical activity, and performance: moving forward

“…Rather, there should be a priori considerations for defining a nonresponse, with a link to a measure of variability of the outcome measurement. A conceptual framework for quantifying true inter-individual differences in response to an intervention has been discussed extensively in the literature Batterham 2015, 2017;Atkinson et al 2018;Williamson et al 2017Williamson et al , 2018 with specific examples from studies investigating weight change or maximal oxygen uptake in response to exercise interventions (Williamson et al 2017. This framework includes a comparison of the standard deviation of the change in the intervention group with the same change in the comparator (control or untreated) group to make an informed decision regarding whether an observed inter-individual difference is of clinical relevance.…”

Detecting heterogeneity of intervention effects using analysis and meta-analysis of differences in variance between arms of a trial

“…However, just as meta-analysis of mean effects gives greater power to detect an average intervention effect, meta-analysis of differences in variance should give increased power to detect effect modification. A small number of meta-analyses in epidemiology and ecology have reported on differences in variance [8,11,[13][14][15][16][17][18][19][20][21], with applications to RCTs and other types of comparative study. Most of them found evidence of a difference in variance between arms, with varying strength of evidence (Table A1).…”