Abstract
urpose: To conduct a systematic review and multilevel meta-analysis of the current literature as to the effects of interval training (IT) vs moderate intensity continuous training (MICT) on measures of body composition, both on a whole-body and regional level. Methods: We searched English-language papers on PubMed/MEDLINE, Scopus, CINAHL, and sportrxiv for the follow- ing inclusion criteria: (a) randomized controlled trials that directly compared IT vs MICT body composition using a validated measure in healthy children and adults; (b) training was carried out a minimum of once per week for at least four weeks; (c) published in a peer-reviewed English language journal or on a pre-print server. Results: The main model for fat mass effects revealed a trivial
randomized controlled trials that directly compared IT vs MICT body composition using a validated measure in healthy children and adults; (b) training was carried out a minimum of once per week for at least four weeks; (c) published in a peer-reviewed English language journal or on a pre-print server. Results: The main model for fat mass effects revealed a trivial standardized point estimate with high precision for the interval estimate, with moderate heterogeneity ( 0.016 (95%CI 0.07 to 0.04);I 2 = 36%). The main model for fat-free mass (FFM) effects revealed a trivial standardized point estimate with high precision for the interval estimate, with negligible heterogeneity ( 0.0004 (95%CI 0.05 to 0.05);I 2 = 16%). The GRADE summary of ndings suggested high certainty for both main model effects. Conclusions: Our ndings provide compelling evidence that the pattern of intensity of effort and volume during endurance exercise (i.e., IT vs MICT) has minimal in uence on longitudinal changes in fat mass and FFM, which are likely to minimal anyway. Trial registration number: This study was preregistered on the Open Science Framework. Keywords:intensity of effort; fat loss; fat mass; body fat; lean mass 1. Introduction The relative components of fat mass and fat-free mass in the body, collectively termed body composition, has important implications for human health. Excessive levels of body fat show a high correlation with a panoply of disease states, including cardiovascular diseases, metabolic disorders, certain cancers, osteoarthritis, and respiratory conditions [1]. Alternatively, low levels of fat-free mass are associated with a loss of strength, functional capacity, and reduced bone mineral density [24], impairing both the quality and quantity of life [1]. There is an interaction between these two components, whereby the combination of low levels of fat-free mass (FFM) and high levels of body fat potentiate each other, maximizing their impact on disability, morbidity, and mortality [5]. Exercise is commonly recommended as an intervention to improve bodycomposition [ Interventional strategies often employed for this purpose include the following patterns. Sports2021,9, 155.
and high levels of body fat potentiate each other, maximizing their impact on disability, morbidity, and mortality [5]. Exercise is commonly recommended as an intervention to improve bodycomposition [ Interventional strategies often employed for this purpose include the following patterns. Sports2021,9, 155.
Sports2021,9, 155 2 of 28 Moderate intensity continuous training (MICT), herein operationally de ned as mod- erate intensity of effort exercise (<80% peak heart rate or aerobic capacity) performed over a longer (relative to interval training bouts) single bout. Interval training (IT), herein operationally de ned as exercise performed in multiple shorter (relative to continuous training) bouts interspersed with recovery periods either at lower intensities of effort, or as complete rest. IT is often subclassi ed into high intensity interval training (HIIT), herein operationally de ned as high intensity of effort exercise (approximately >80% peak heart rate or aerobic capacity) performed in multiple shorter bouts interspersed with recovery periods either at lower intensities of effort or as complete rest, and sprint interval training (SIT), herein operationally de ned as maximal intensity of effort exercise (`all out' sprint) performed in multiple shorter bouts interspersed with recovery periods either at lower intensities of effort or as complete rest. Although both MICT and IT show ef cacy in improving body composition, contro- versy exists as to whether one strategy is superior to the other for this purpose. For example, an earlier meta-analysis by Keating et al. [8] reported little difference between MICT and IT for body fat reduction, highlighting that, over the short term, neither intervention produced clinically meaningful changes. Following this, Viana et al. [9] conducted a meta-analysis with results showing that IT produced a 28.5% greater reduction in fat mass than MICT. However, the paper was criticized for various methodological issues [10], ultimately lead- ing to its retraction. More recently, Sultana et al. [11] carried out a meta-analysis that included a comparison of IT vs MICT. The analysis did not nd a bene t to low-volume IT on measures of body composition when compared with MICT. However, they limited their analysis to only single measures per study of the constructs of interest (i.e., total body fat mass, body fat percentage, and lean body mass), whereas many studies often report several measures (e.g., regional measures). Furthermore, although several studies have also compared the effects of IT and MICT in younger populations, they
composition when compared with MICT. However, they limited their analysis to only single measures per study of the constructs of interest (i.e., total body fat mass, body fat percentage, and lean body mass), whereas many studies often report several measures (e.g., regional measures). Furthermore, although several studies have also compared the effects of IT and MICT in younger populations, they limited the analysis to adults. Additionally, it is not clear from their analysis which pre-post test correlations were imputed and used for effect size calculations. The magnitude of pre-post test correlations used in calculations of pre-post control group design effect sizes using pooled baseline standard deviations can impact the heterogeneity determined in the meta-analysis [12]. Thus, although the standardized point estimates of Sultana et al. [11] models generally suggested little difference between conditions, the accompanying interval estimates for most outcomes included small effects in favor of either IT or MICT. Furthermore, their models had essentially no heterogeneity, although this may be the result of imputation of pre-post correlations that were relatively low. Application of multilevel meta-analytic models with robust variance estimation to handle multiple effects per study might yield a greater precision of estimates [13], and thus help to con rm whether small differences do in fact exist, and if so, in which direction. Additionally, extraction of information to permit calculation of pre-post test correlations within groups (i.e., see Higgins et al. [14]) would allow for a better estimate of the population pre-post test correlations and may reveal heterogeneity not identi ed in previous analyses. Lastly, although Sultana et al. [11] explored `within-condition' effects for IT in studies that included a non-exercising control condition, they did not similarly explore this outcome for MICT training. It also has been speculated that speci c exercise-induced effects might occur for hy- pertrophy and regional fat mass. Endurance exercise may have bene cial effects on muscle hypertrophy, similar to that of resistance training [15], and some researchers highlight that IT, in particular, may produce a potent anabolic stimulus [16]. Furthermore, it has been suggested that IT may be more effective than MICT for abdominal
speci c exercise-induced effects might occur for hy- pertrophy and regional fat mass. Endurance exercise may have bene cial effects on muscle hypertrophy, similar to that of resistance training [15], and some researchers highlight that IT, in particular, may produce a potent anabolic stimulus [16]. Furthermore, it has been suggested that IT may be more effective than MICT for abdominal fat mass reduction [17]. However, to our knowledge, no previous review has pooled data from research that directly compares changes in FFM between IT and MICT, nor speci cally examined regional effects on changes in fat mass. Lastly, although prior meta-analyses have considered between-conditions comparison of mean intervention effects [11], whether or not differences in the variance of treatment
Sports2021,9, 155 3 of 28 responses are present has been relatively less explored. A recent meta-analysis of aerobic exercise in overweight and obese children and adolescents found no evidence of `true' inter- individual response variation in fat loss [18]. However, numerous studies have purported that there may be inter-individual response variation to IT and MICT for a range of outcomes [1921], and indeed it has been argued that such variation may mask differences between IT and MICT for fat loss [22]. Thus, we also sought to examine whether there is evidence of `true' inter-individual response variation for body composition outcomes for both IT and MICT [23,24]. Given the gaps in the current literature, the purpose of this paper was to conduct a systematic review and multilevel meta-analysis of the current literature as to the effects of IT vs MICT on measures of body composition, both on a whole-body and regional level. Secondarily, we sought to determine if intensity of effort in uences exercise adherence and/or adverse events, as well as whether inter-individual response to IT and MICT in uences changes in body composition. 2. Material and Methods This systematic review was conducted in accordance with the guidelines of the Pre- ferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [25]. The study was preregistered on the Open Science Framework (https://osf.io/dq784), where the detailed prespeci ed methodological protocol can be viewed. 2.1. Inclusion/Exclusion Criteria We included studies that met the following criteria: (a) randomized controlled tri- als (both within- and between-group designs) that directly compared IT vs MICT (both with and without adjuvant dietary interventions) for body composition using a validated measure (DXA, BodPod, hydrostatic weighing, BIA, skinfolds, ultrasound, magnetic reso- nance imaging, and computerized tomography) in healthy children and adults; (b) training was carried out a minimum of once per week for at least four weeks; (c) published in a peer-reviewed English language journal or on a pre-print server. We excluded studies that employed: (a) participants with co-morbidities that might impair aerobic capacity (respiratory conditions, musculoskeletal injury); and (b) an unbalanced resistance train- ing component (e.g., one group performs resistance
(b) training was carried out a minimum of once per week for at least four weeks; (c) published in a peer-reviewed English language journal or on a pre-print server. We excluded studies that employed: (a) participants with co-morbidities that might impair aerobic capacity (respiratory conditions, musculoskeletal injury); and (b) an unbalanced resistance train- ing component (e.g., one group performs resistance training whereas the other does not). Note, our original pre-registration failed to specify the particular intensity of effort and operationalization of this variable for determination of whether an IT intervention could be considered `HIIT'. However, a small number of studies identi ed employed intensities of >75% of peak heart rate or aerobic capacity for their IT conditions. Given our omission of speci city in pre-registration, we felt that these studies should be included, as there was still a reasonable difference in intensity of effort compared with the MICT conditions (typically <60%). 2.2. Search Strategy We carried out a comprehensive search of the PubMed/MEDLINE, Scopus, CINAHL, and sportrxiv databases using the following Boolean string: (interval training OR intermit- tent training OR high intensity OR sprint interval training OR aerobic interval training OR HIIT OR HIIE OR high intensity interval training OR high-intensity interval training OR high intensity interval exercise OR high intensity intermittent exercise OR high-intensity intermittent exercise OR high intensity intermittent training OR high-intensity intermittent training) AND (continuous training OR moderate-intensity continuous exercise OR moder- ate intensity continuous exercise OR moderate-intensity continuous training OR moderate intensity continuous training OR endurance training) AND (body fat OR adiposity OR body composition OR abdominal fat OR visceral fat OR adipose tissue OR fat mass OR fat-free mass OR lean body mass OR lean mass OR muscle mass). Moreover, we screened the reference lists of articles retrieved to uncover any additional studies that might meet
Sports2021,9, 155 4 of 28 inclusion criteria, as described by Greenhalgh and Peacock [26]. The search was nalized on 6 March 2021; FigureSports 2021, 9, x FOR PEER REVIEW 4 of 25 OR moderate intensity continuous training OR endurance training) AND (body fat OR adiposity OR body composition OR abdominal fat OR visceral fat OR adipose tissue OR fat mass OR fat-free mass OR lean body mass OR lean mass OR muscle mass). Moreover, we screened the reference lists of articles retrieved to uncover any additional studies that might meet inclusion criteria, as described by Greenhalgh and Peacock [26]. The search was finalized on 6 March 2021; Figure 1 illustrates a flow chart of the search process. Figure 1. Flow chart of PRISMA search. 2.3. Screening/Coding of Studies Search/screening was carried out separately by two researchers (DP and AR). These researchers read all titles and abstracts and then reviewed full texts for papers deemed relevant based on their title and abstract. Decisions then were made as to whether a study warranted inclusion based on the stated criteria. Any disputes on the inclusion of a given study were settled by a third researcher (MCM). After determining which studies met inclusion, two researchers (DV and HZ) sepa- rately coded the following variables for each study: authors, title and year of publication, sample size, sex, body mass index (BMI), training status, age, description of the training intervention (duration, intensity, frequency, modality), work matched (yes/no), nutrition controlled (yes/no), method for body comp assessment (e.g., DXA, BodPod, BIA, hydro- static weighing, skinfolds, MRI, CT, ultrasound), number of adverse effects associated with the training intervention, adherence to the given training program, mean pre- and post-study body composition values in addition to pre-post change scores with the corre- sponding standard deviation or standard error, and where change score standard devia- tions were not reported we extracted information to allow their calculation, including con- fidence intervals for change scores or within-group pre-post t statistics or p values (where p values were reported only to the studies' level of alpha (e.g., p < 0.05) we took this as a
corre- sponding standard deviation or standard error, and where change score standard devia- tions were not reported we extracted information to allow their calculation, including con- fidence intervals for change scores or within-group pre-post t statistics or p values (where p values were reported only to the studies' level of alpha (e.g., p < 0.05) we took this as a conservative value). In cases where body composition data were not reported numeri- cally, we either extracted the data from graphs when available via online software, or at- tempted to contact the study’s authors. Coding was cross-checked between reviewers, Figure 1.Flow chart of PRISMA search. 2.3. Screening/Coding of Studies Search/screening was carried out separately by two researchers (DP and AR). These researchers read all titles and abstracts and then reviewed full texts for papers deemed relevant based on their title and abstract. Decisions then were made as to whether a study warranted inclusion based on the stated criteria. Any disputes on the inclusion of a given study were settled by a third researcher (MCM). After determining which studies met inclusion, two researchers (DV and HZ) sepa- rately coded the following variables for each study: authors, title and year of publication, sample size, sex, body mass index (BMI), training status, age, description of the training intervention (duration, intensity, frequency, modality), work matched (yes/no), nutri- tion controlled (yes/no), method for body comp assessment (e.g., DXA, BodPod, BIA, hydrostatic weighing, skinfolds, MRI, CT, ultrasound), number of adverse effects associ- ated with the training intervention, adherence to the given training program, mean pre- and post-study body composition values in addition to pre-post change scores with the corresponding standard deviation or standard error, and where change score standard deviations were not reported we extracted information to allow their calculation, including con dence intervals for change scores or within-group pre-post t statistics orpvalues (wherepvalues were reported only to the studies' level of alpha (e.g.,p< 0.05) we took this as a conservative value). In cases where body composition data were not reported numerically, we either extracted the data from graphs when available via online software, or attempted to
allow their calculation, including con dence intervals for change scores or within-group pre-post t statistics orpvalues (wherepvalues were reported only to the studies' level of alpha (e.g.,p< 0.05) we took this as a conservative value). In cases where body composition data were not reported numerically, we either extracted the data from graphs when available via online software, or attempted to contact the study's authors. Coding was cross-checked between reviewers, with any discrepancies resolved by mutual consensus. Consistent with the guidelines of Cooper et al. [27], 30% of the included studies were randomly selected for re-coding to assess for potential coder drift by a third researcher (BM). Agreement was calculated by dividing the number of variables coded the same by the researchers by the total number of variables; acceptance required a mean agreement of 0.90 to avoid re-extraction entirely, and
Sports2021,9, 155 5 of 28 after this was met, only those with differing codes were checked and updated. Extracted data was also double-checked after this process by the lead author (JS) prior to analysis. 2.4. Methodological Quality and Certainty of Evidence Two of the authors independently evaluated each study (JG and BJS) using the 11-point Physiotherapy Evidence Database (PEDro) scale, which has been validated to assess the methodologic quality of randomized trials [28] with acceptable inter-rater reliability [29]. Any discrepancies in agreement on a given scale item were settled by mutual agreement between the researchers. Given that it is infeasible to blind participants and investigators in supervised exercise interventions, we opted to remove assessment items speci c to blinding (numbers 5, 6, and 7 in the scale). After eliminating these items, this created a modi ed 8-point PEDro scale with a maximum value of 7 (the rst item is excluded from the total score). The qualitative methodological ratings were amended, similar to those used in previous exercise-related systematic reviews [30], as follows: excellent (67 points); good (5 points); moderate (4 points); and poor (03 points). We also followed the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) framework [31] for evaluating the certainty of evidence with respect to our primary pre-registered outcomes (absolute fat mass, and absolute lean/fat free mass). We used the GRADEpro online tool [32] for this assessment and generation of the summary of ndings table. It should be noted though that we did not pre-register the use of the GRADE approach to evaluate the evidence presented but decided a posteriori that the assessment would enhance the ability to draw practical inferences from the data. 2.5. Statistical Analyses Quantitative synthesis of data was performed with the `metafor' [33] package in R (v 4.0.2;R Core Team,). All analysis code and data are openly available in the Supplementary Materials (https://osf.io/6karz/). Studies were grouped by design (i.e., within- or between-group), and depending on reporting in individual studies, either post or delta comparisons, or pre-post comparison designs [12] for the purposes of appropriate calculation of standardized effects (Hedges' g) using the escalc
Description
This study compares body composition changes between interval training and moderate intensity continuous training.