Aerobic, resistance, and mind-body exercise are equivalent to mitigate symptoms of depression in older adults: A systematic review and network meta-analysis of randomised controlled trials [version 1; peer review: 1 approved, 2 approved with reservations]

Background: Exercise has been identified as an allied health strategy that can support the management of depression in older adults, yet the relative effectiveness for different exercise modalities is unknown. To meet this gap in knowledge, we present a systematic review and network meta-analysis of randomised controlled trials (RCTs) to examine the head-to-head effectiveness of aerobic, resistance, and mind-body exercise to mitigate depressive symptoms in adults aged ≥ 65 years. Methods: A PRISMA-NMA compliant review was undertaken on RCTs from inception to September 12th, 2019. PubMed, Web of Science, CINAHL, Health Source: Nursing/Academic Edition, PsycARTICLES, PsycINFO, and SPORTDiscus were systematically searched for eligible RCTs enrolling adults with a mean age ≥ 65 years, comparing one or more exercise intervention arms, and which used valid measures of depressive symptomology. Comparative effectiveness was evaluated using network meta-analysis to combine direct and indirect evidence, controlling for inherent variation in trial control groups. Results: The systematic review included 81 RCTs, with 69 meeting eligibility for the network meta-analysis (n = 5,379 participants). Pooled analysis found each exercise type to be effective compared with controls (Hedges’ g = -0.27 to -0.51). Relative head-to-head comparisons were statistically comparable between exercise types: resistance versus aerobic (Hedges’ g = -0.06, PrI = -0.91, 0.79), mindbody versus aerobic (Hedges’ g = -0.12, PrI = -0.95, 0.72), mind-body Open Peer Review Reviewer Status Invited Reviewers 1 2 3 version 2 (revision) 14 Jul 2021 report report version 1 13 Nov 2020 report report report Walid Kamal Abdelbasset , Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia Kasr Al-Aini Hospital, Cairo University, Cairo, Egypt 1. Andrea Camaz Deslandes, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil 2. Christian Imboden , Private Clinic Wyss 3. Page 1 of 49 F1000Research 2020, 9:1325 Last updated: 17 AUG 2021


Introduction
At the close of this decade, the last remaining 'baby boomers' will transition to an expanding peer demographic aged ≥ 65 years projected to constitute more than one billion older adults, worldwide 1 . Physical exercise is proposed as a low-risk adjunctive mitigant of age-associated functional deterioration in mental health, including for dementia 2 and depression 3,4 . In light of impending demographic shifts, and with the burden of age-associated depression estimated to affect ~20% of older adults 5-7 , 2030 may confer a burden of 200 million adults aged ≥ 65 years presenting with clinical depression. In preparation for inevitable future demands on primary care systems 8 , there is prevailing opportunity for concerted efforts to support primary and allied health personnel with informed preventative strategies.
International public health consortia are in concert with the antidepressant effects of exercise as a low-risk adjunct for optimal mental health 9-11 . While we do not yet have the answers for low uptake of exercise in older adults 12 , it may in some way be due to nuanced regimen design, which in turn, may similarly impact compliance in exercise prescription by primary and stakeholders in aged care. By exemplar, 'Exercise is Medicine' is a global initiative promulgated through 40 member countries by the American College of Sports Medicine with a platform to promote and encourage routine physical exercise for general health and a broad range of medical conditions. This manifesto encourages primary care physicians and health care providers to refer patients to qualified exercise personnel when prescribing treatment plans. However, despite conventional agreement for exercise as a prophylactic for geriatric depression, contemporary literature is yet to quantify the antidepressant treatment effectiveness for individual exercise types.
Perhaps a lesser appreciated obstacle to optimising exercise prescription for mental health in older adults lies with the 'catch all' characterisation of exercise. During the past four decades, widely different metabolic, social, and environmental demands between exercise modalities (i.e., running vs. weightlifting vs. Tai Chi) have been well-characterised. Given that there is variation between exercise regimens, and these variations are not merely semantics, one may be surprised to discover that only a few randomised controlled trials (RCTs) have deliberately compared the antidepressant effects of different exercise regimens in older adults 13,14 . This begs a meaningful question, 'are all exercise types equal?'.
In attempting to quantify the magnitude of potential antidepressant effects of exercise, researchers have deployed conventional pairwise meta-analysis 4,15,16 during recent years. In departure from the value offered by pooling conventional treatments during pairwise meta-analysis, this amalgamation is inherently prone to overgeneralisation and concomitant overestimation of treatment effectiveness 17 . More specifically in mental health literature, pooling individual trial effect comparisons during the pairwise meta-analytical process precludes any opportunity for head-to-head comparison between different exercise types. In the same vein, a further nuance of pairwise meta-analysis is that effect-sensitivity is compromised by foregoing potentially relevant characterisation of control arms (i.e., wait-list, usual care, attention-control) of included trials 18 . In this respect, one must acknowledge that pairwise meta-analysis has intrinsic restrictive boundaries in circumstances where head-to-head comparison of different exercise treatments are required.
In circumstances where relative effectiveness of multiple treatment comparisons are required, network meta-analysis offers a methodological solution to provide more precise pooled head-to-head effect estimates than may otherwise be achieved 19,20 . Performed correctly, network meta-analysis can allow comparative effectiveness of exercise treatment regimens (aerobic, resistance, and mind-body), avoiding the assumption of treatment homogeneity, controlling for bias from small study effects, and likewise, controlling for characteristically different control arms (wait-list, usual care, and attention-control).
Exercise regimens are broadly categorized into either aerobic, resistance, or mind-body exercise types 21,22 , and any individual exercise program may consist of multiple combinations of these activities. Extended description of these three exercise types may be found as Extended data. It is well established that independent regimens of either aerobic or resistance exercise elicit uniquely different metabolic and phenotypic adaptations, whereas mind-body exercise is unique as a low impact form of exercise. Therefore, it is reasonable to theorise comparative differences in the capacity to elicit any antidepressant effect. Until recently, investigation of head-to-head treatment effects of aerobic, resistance, and mind-body exercise had not been undertaken and their comparative ability to moderate symptoms of depression in older adults without pre-existing clinical depression is unknown.
Recently, Miller and colleagues 23 quantitatively compared headto-head effectiveness of aerobic, resistance, and mind-body exercise in older adults with pre-existing clinical depression. In realisation of their study hypothesis, Miller et al. 23 excluded studies of older adults which lacked participant-level diagnosis of clinical depression. In doing so, there is an assumption that older adults with diagnosis of clinical depression should not be pooled with similarly aged counterparts without diagnosis. These differences are not merely semantics, and while beyond the broad scope of the present study, are worthy of summary.
Present day phenotypic characterisation of clinically depression in older adults is supported by the coalition of more than six decades of cross-disciplinary research into major depressive disorders. Limbic brain regions, monoamine neurotransmitters, and the hypothalamic-pituitary-adrenal (HPA) axis contribute to the pathophysiology of depression 24 . In addition, hypothalamic oversight of pulsatile stress hormone perception from brain regions is central for homeostatic regulation of human stress response and maintenance of systemic feedback loop physiology in concert with the pituitary and adrenal cortex. Similarly, it is wellestablished that stress hormones have broad infiltration through large areas of the brain via neuronal supply originating from midbrain and brainstem regions. Indeed, these monoaminergic systems have been widely recognised by neuroscientists, pharmacologists, and clinicians alike as key determinants (thus targets) of a person's mood, cognition, sleep quality, appetite, and reward systems; each of which is known to be affected by physical exercise and the cornerstone of many pharmacological treatments for major depressive disorders.
Long-term prospective data 25 has demonstrated qualitatively distinct populations within patients with major depressive disorder, and a more recent systematic review 26 of 67,318 participants enrolled to longitudinal cohort studies identified that people with subthreshold depression had an elevated risk of developing major depressive disorder. However, the existence for a continuum of depressive symptomology is inconclusive 27 and subsyndromal symptoms are not yet classified as a continuum 28 . Taken together, it remains prudent to respect the binary threshold separating clinical diagnosis with subclinical depressive symptomology, and it stands to reason that clinical and mentally healthy categories should not be 'merged' into the same network in order to compare head-to-head effectiveness of different exercise treatments.
With these aspects in mind, the purpose of this systematic review and network meta-analysis was to quantitively assess the best evidence from RCTs to establish relative (head-to-head) effectiveness of resistance, aerobic, and mind-body exercise in adults aged ≥ 65 years below the clinical threshold for diagnosed depression. More specifically, we investigated whether (i) resistance, aerobic, and mind-body exercise training can induce substantive treatment effect on depressive symptoms in older adults, (ii) while considering relative treatment compliance to each exercise regimen, and further, (iii) to juxtapose the optimal exercise treatment for all adults aged ≥ 65 years irrespective of depressive symptomology.

Methods
This review was prospectively registered with PROSPERO (registration number: CRD42018115866, 23/11/2018). The network meta-analysis extension for the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-NMA) guidelines 29 and the Cochrane Intervention Review that Compares Multiple Interventions 30 each provided further support in guiding this review. Guidelines specific for geriatric meta-analyses 31 were consulted to identify baseline characteristics, equity considerations, inclusion/exclusion criteria, known confounders, and potentially important effect modifiers. Extended data for this review can be found online 32 .

Eligibility criteria
Studies were eligible for inclusion if they (i) followed an RCT protocol, (ii) used a wait-list, usual care, or attention-control group, (iii) included one or more aerobic, resistance, or mindbody exercise intervention arms, (iv) reported depressive symptoms as an outcome at baseline and during follow-up, (v) used one or more psychometrically validated depression questionnaires, (vi) recruited participants with a minimum mean sample age of 65 years. Studies were excluded when (i) the intervention condition used a multicomponent treatment including non-exercise components with the exercise condition, or (ii) the participants were diagnosed with clinical depression, defined by DSM or ICD criteria, or a clinical threshold on a questionnaire validated against a structured diagnostic interview prior to study enrollment. Eligibility was not restricted to specific years, languages, or publication status.

Literature search
Studies were identified from computerised searches of the following databases: PubMed, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Health Source: Nursing/Academic Edition, PsycARTICLES, PsycINFO, and SPORTDiscus. Databases were searched for key terms pertaining to four main concepts: older adults, exercise, depressive symptoms, and RCTs. Search terms were identified using text mining procedures and an example of a search strategy is presented in detail as Extended data. Published studies, systematic reviews, and meta-analyses 4,15,16,[33][34][35][36] were also screened for additional articles. Databases were searched up to and including September 12 th , 2019.

Study selection
All articles collated from the systematic search initially went through a title and abstract screening, followed by a full-text screening. Eligibility criteria were used to determine whether each article should be included or excluded. The screening process was performed concurrently for both the current review and a parallel review 23 . Once the screening process was complete, the remaining studies were included in the systematic review and descriptively reported. Studies were only included in the quantitative analysis if they contained sufficient outcome data.

Data extraction and coding
Detailed data extraction was undertaken independently by a minimum of two researchers (KJM, PA, and/or DH) in compliance with a data extraction form (see Extended data), with any inconsistencies being arbitrated by another researcher (CM). Study characteristics are presented in Table 1. Methodological characteristics of each study were used to evaluate the quality of evidence, which included publication status, intention-totreat principle, use of a cluster design, and validated measure(s) of depressive symptoms used.
Control groups within each individual study were further categorised as either wait-list, usual care, or attention-control. Participants undergoing wait-list conditions received the exercise intervention following trial completion. Participants randomised to usual care were those in sole receipt of conventional treatment during the trial. Participants randomised to attentioncontrol conditions (also known as attention placebo control or active control) received activities completely unrelated to physical activity and/or exercise during their respective trials (e.g., social activities, educational programs, etc.). Important participant and intervention characteristics were used to verify whether potential effect modifiers were similarly distributed across comparisons within the network. Participant characteristics were coded according to sample size, age (mean and standard deviation), percentage of females, and place of  residence (community-dwelling, residential care, clinical setting). Relevant inclusion criteria (e.g., sedentary, dementia, etc.) were further used to assess the risk of bias from equity considerations 116 .
Intervention characteristics were appropriately coded. Exercise types were categorised as aerobic, resistance, or mindbody exercise. Length of interventions were coded in weeks or months. Exercise intensities were coded according to ratings of perceived exertion 117 , heart rate maximum (HR max ; low = <50%, moderate = 50-70%, high = >70%), maximal oxygen uptake (VO 2max ; low = <40%, moderate = 40-60%, high = >60%) or one-repetition maximum (1R max ; low = <50%, moderate = 50-74%, high = >74%) 118 , or where unavailable, this was estimated according to the assessment of the original author(s). Frequency was coded as total sessions per week. Duration was coded as the average number of minutes engaging in exercise per session. Format of program was dichotomously coded as exercise in a group or individual setting. Format of supervision was dichotomously coded as supervised or unsupervised exercise. Agreement between the three researchers was 91.3%.

Risk of bias and quality assessment
Risk of bias of the included RCTs was assessed using the Cochrane Collaboration's Tool for Assessing Risk of Bias 119 , which were independently conducted by a minimum of two researchers (KJM, PA, and/or DH). Discrepancies were arbitrated by another co-author (CM). Appraisal for 'other sources of bias' were evaluated with consideration for small sample size (n < 15), low adherence (less than 80%), cluster randomisation, and inequity in the selection of the sample.

Summary of outcomes
Outcome statistics including means (M), standard deviations (SD), and sample sizes (n) for depressive symptoms were used to calculate the mean change in the primary outcome. Test statistics (i.e., t-, F-, and p-values) were used to estimate effect sizes when descriptive statistics were unavailable. Pairwise relative (head-to-head) treatment effects for depressive symptoms were estimated using Hedges' g 120 , which corrects for overestimation biases due to small sample sizes 121 . Hedges' g coefficients were interpreted according to Cohen's conversions 122 , whereby effects were considered small (0.2), medium (0.5), and large (0.8). Independent subgroups (e.g., males vs. females) within studies were treated as independent effect size estimates 123 . When individual studies reported more than one post-treatment depression score for the same group of participants, only the initial post-treatment time-point was used. When studies reported depression scores on multiple outcome measures, the included depression measure was selected in compliance with clinical applicability 124,125 .
Secondary outcome data were also extracted for study attrition, treatment adherence, and adverse events. Pairwise relative (headto-head) treatment effects for study attrition were reported as odds ratios based on the pre-treatment sample size versus posttreatment dropout in the treatment and comparison conditions. Treatment adherence were reported as a percentage of total attendance in, or compliance to, the treatment condition.
Adverse events were qualitatively reported according to the descriptive information provided in the transcript.

Data synthesis
One notable assumption of network meta-analysis relates to comparison group estimates, which are derived from pooling studies with homogenous between-study effect modifiers 126 . If the distribution of an effect modifier is heterogeneous across studies within a specific comparison group, the assumption of transitivity can be violated. In the context of this network metaanalysis, we separate exercise conditions (i.e., aerobic, resistance, mind-body) and control conditions (i.e., wait-list, usual care, attention-control) to account for the between-study variation of these effect modifiers. Given the intricacies of depression severity, a forest plot was generated to depict the juxtaposition of treatment effects between (i) participants in the present network meta-analysis with depressive symptomology but not clinically diagnosed and (ii) participants in a previous network meta-analysis with clinical depression 23 .
Risk of bias assessment identified the potential for unit-ofanalysis error within studies using a cluster design for treatment allocation. Thus, sample sizes were recalculated to account for error in cluster RCTs by determining a design effect 127 with a conservative intracluster correlation coefficient of 0.05, in accordance with past studies 45,128 . The certainty of evidence contributing to network estimates was assessed according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach 129 .
Data were analysed and figures were generated using STATA/SE 15.1 130 . Comparison-adjusted funnel plots were used to evaluate publication bias and small-study effects. Random-effects meta-regression was used to investigate modifying effects of age, gender, source of participants, length of intervention, exercise intensity, frequency, duration, and format of program, which have been identified as potential risks to transitivity in geriatric meta-analyses 4,35,131 .
A multivariate random-effects meta-analysis was conducted using the 'mvmeta' command 132 . A random-effects model assumes variance both within and between studies, explaining the heterogeneity of treatment effects 133 . A common heterogeneity parameter was assumed across comparisons. Heterogeneity was evaluated using tau-squared (τ 2 ), which estimates the deviation in effect sizes across the population of studies 123 . The 95% prediction interval (PrI) was also used to estimate the true dispersion of effect within two standard deviations of the mean effect size 123 . The significance level was p < 0.05 for all analyses.

Study selection
The initial systematic search yielded a total of 3,704 citations. When duplicate studies were removed (n = 1,395), 2,309 eligible records were identified. Titles and abstracts were screened by two independent researchers (KJM and DCGB) with an agreement of 91.9%. Subsequently, 356 full-text articles were assessed for compliance with inclusion criteria and outcome data. Full-text screening was performed independently by two researchers (KJM and PA) with an agreement of 81.8%, where discrepancies were arbitrated and resolved by consensus with a third researcher (CM). Studies using duplicate sample sets (n = 12) were identified, where the most informative publication with complete data being included in the quantitative analysis. Finally, 81 studies fulfilled all inclusion criteria to be included in the systematic review. In cases of missing data, authors were emailed by the first author (KJM). If authors failed to respond following two contact efforts, and there were insufficient data to calculate effect size estimates, the study was excluded from the quantitative analysis (n = 9). Additionally, three studies 60-62 fulfilled all criteria but necessitated exclusion from the quantitative analysis due to control conditions being insufficiently defined, and the authors being unobtainable. Figure 1 outlines study selection and additional exclusion criteria.
Characteristics of the studies Data from a total of 5,379 participants (2,815 treatment and 2,564 control) across 69 studies were pooled in the quantitative analysis. Each study reported pre-and post-treatment measures of depression symptoms, and data from 60 of the 69 studies were obtained for post-treatment attrition. Figure 2 illustrates the network of pairwise comparisons across all studies. Aerobic, resistance, and mind-body conditions had one or more direct comparison with each of the five comparison conditions. Direct comparisons for depressive symptoms were robustly characterised between intervention and corresponding control conditions (aerobic = 28, resistance = 22, mind-body = 32), with proportions being similar for study attrition (aerobic = 24, resistance = 21, mind-body = 25).

Risk of bias and quality assessment
Risk of bias for each study is presented comprehensively as Extended data (see Figure S1). Both blinding of participants and personnel to treatment was implausible due to the implicit nature of exercise training interventions, and thus, the remaining six criteria were used to assess the overall risk of bias within each study.
Methodological quality of included studies can be considered low-to-moderate (M = 4.08/6, where low = 1, unclear, = 0.5, high = 0). Assessment of random sequence generation (selection bias), incomplete outcome data (attrition bias), and selective outcome reporting (reporting bias) may be considered adequate for most studies, whereas allocation concealment (selection bias) and blinding of outcome assessment (detection bias) were diverse. High 'other sources of bias' was due to low study adherence (n = 14), small sample sizes (n = 8), or both (n = 1). See Figure 3 for a summary of the risk of bias in the included studies.

Assessment of inconsistency
Inconsistency network models were used to test the global consistency of direct and indirect effects of pairwise and multiarm comparisons. Assumption of consistency was satisfied for each treatment (p > 0.05). Tests for inconsistency between direct and indirect estimates were not significant (p > 0.05), thus indirect and direct estimates were not different to direct evidence. Inconsistency tables can be found as Extended data (see Tables S1 and S2).
Loop-specific heterogeneity was explored using inconsistency plots (see Extended data, Figure S7 and S8). Within the depressive symptoms network, inconsistency factors (IF) did not indicate high inconsistency (IF = 0.00 to 0.65) or loop-specific heterogeneity (τ 2 = 0.05 to 0.28). The AER-MB-UC loop departed from the minimum lower-bound confidence interval (CI), yet fell short of presenting risk for heterogeneity (IF = 0.54, τ 2 = 0.05, CI = 0.04, 1.03). Within the attrition network, the ratio of two odds ratios (RoR) indicated a high degree of inconsistency for the AER-RES-UC (RoR = 1.87) and MB-RES-UC (RoR = 1.77) loops. Each were interpreted as presenting elevated risk for heterogeneity, which were subsequently downgraded during GRADE assessment. All remaining loops satisfied assumption of consistency.

Publication bias and sensitivity analyses
Comparison-adjusted funnel plots were used to detect publication bias and small-study effects. Funnel plots were roughly symmetrical for both depressive symptoms and attrition, indicative of low risk of publication bias and no presence of small-study effects. See Figure 4 for the depressive symptoms network and Extended data for the attrition network (see Figure S9).
In order to test transitivity across networks, potential effect modifiers were tested with meta-regression sensitivity analyses for the entire pool of studies and separately for each exercise comparison (aerobic vs. resistance vs. mind-body). No significant modifying effects were observed for the pool of studies or any separate exercise comparison for age, gender, source of participants, length of intervention, format of program, exercise intensity, frequency, duration, adherence, year of study, risk of bias, publication status, intention-to-treat analysis, nor cluster design, indicating that the assumption of transitivity was upheld. Full analyses are presented as Extended data (see Tables S6-S8).

Results of the network meta-analysis
Data pooled from 69 eligible studies provided a total of 88 individual comparisons for depressive symptoms and 76 individual comparisons for study attrition. Table 3 presents the network meta-analysis of depressive symptoms and attrition. Network estimates were calculated to establish relative effectiveness between pairs of comparisons.
Each exercise type effectively reduced depressive symptoms compared with control conditions (see Figure 5). Ranking of treatments for depressive symptoms are presented on SUCRA plots of ranked mean values, which can be found as Extended data (see Figure S11 and Table S3). Ranked order of quantitative values determined mind-body exercise to be the most effective type of exercise to mitigate depressive symptoms, followed closely by resistance and aerobic exercise, respectively. The magnitude of study effect did not reach statistical threshold to favour any individual exercise treatment.
Resistance exercise demonstrated the highest study compliance compared with each of the other comparison groups, but the dispersion of effect estimates presented a level of heterogeneity that confounded any substantive differences. Comprehensive reporting of study attrition can be found as Extended data (see Figure S10) in addition to SUCRA plots and ranked order (see Figure S12 and Table S4).

Effectiveness vs. attrition
A two-dimensional clustered ranking plot was employed to illustrate the average reduction in depressive symptoms for each comparison, relative to average attrition rate. Figure 6 presents the ranking of the exercise conditions with respective control conditions in conjunction with SUCRA values for depressive symptoms (effectiveness) and attrition. The three exercise conditions amalgamated a single cluster and were more effective than control conditions.

GRADE assessment
The certainty of evidence was assessed with the GRADE approach 129 . All control comparisons in the depressive symptoms network were rated as high or moderate certainty, which were downgraded due to small sample size or potential risks of bias (i.e., attrition bias, detection bias, or bias resulting from low adherence). Comparisons between exercise interventions were moderate-to-low certainty, resulting from imprecision in confidence intervals and small sample sizes. Detailed summary of the depressive symptoms network is presented in Table 4. Estimates of the attrition network were downgraded due to inconsistency and imprecision, which reflect moderate to very low confidence this outcome (see Extended data, Table S5).
Generalisation for all adults aged ≥ 65 years Figure 7 presents collective representation for all adults aged ≥ 65 years. This comparison employs scaled distribution (Hedges' g) for the present data and that of clinically depressed older adults 23 .

Discussion
The present review offers new information for general exercise prescription to support mental health outcomes for adults aged ≥ 65 years. Specifically, (i) aerobic, resistance, and mindbody each demonstrate equivalent benefit to mitigate symptoms of depression in adults aged ≥ 65 years, (ii) compliance to exercise treatment is notably encouraging for each exercise types, and (iii) when combined with the pool of data from clinically depressed older adults 23 , the effectiveness of aerobic, resistance, and mind-body exercise is comparably consistent for all adults aged ≥ 65 years, irrespective of depression severity. These findings should provide reassurance for personnel and stakeholders in healthy ageing to encourage exercise prescription from a point of pragmatism and in collaboration with patient preference.
Theoretical implications for the current findings Behavioural and physiological research 24,25,27,28 coalesce to provide ample reasoning to separate participant cohorts with existing clinical depression from those without diagnosis. This study is no different. In agreement with findings from previous research 13,14,35 , aerobic and resistance exercise demonstrated similar treatment effectiveness in older samples aged ≥ 65 years (Hedges' g = -0.06, PrI = -0.91, 0.79). Exercise characteristics (i.e., intensity, frequency, duration, etc.) are often similar between aerobic and resistance exercise, representing two sides of the same coin. Meta-analytical data 15 on older adults with existing clinical depression observed that exercise programs incorporating a combination of aerobic and resistance training were most beneficial. Although the synergistic effects of combined exercise types were beyond the scope of the current review, it is conceivable that aerobic and resistance exercise may complement one another in an exercise intervention.
Pooled direct and indirect estimates marginally favoured treatment with mind-body exercise over either aerobic  (Hedges' g = -0.12, PrI = -0.95, 0.72) or resistance exercise (Hedges' g = -0.06, PrI = -0.90, 0.79). However, it must be noted that the magnitude of effect falls short of being statistically different between groups and should be considered equivalent. Certainty of evidence is moderate, due to the dispersion in effect size estimates resulting in imprecision. Direct comparisons from multi-arm RCTs have offered mind-body exercise to be more effective than aerobic 91 or resistance 44 exercise. Moreover, subgroup analyses 16 have indicated that clinically depressed older adults respond more favourably to mind-body exercise, but this hypothesis has not be substantiated 4 .
Since mind-body exercise engages low intensity muscular activity (i.e., yoga, Tai Chi, qigong), the novel evidence in the current systematic analysis challenge the idea that intensity is the primary mechanism for the antidepressive effect of exercise. Rather, mind-body exercise combines the mental and physical aspects of exercise, which may result in similar antidepressive effects to higher intensity exercise 134,135 . Critical to these mental aspects is interoceptive sensations such as an internally directed focus on breathing and proprioception, which have previously been linked to the resilience of depressive states 136,137 . Thus, it is plausible that mind-body exercise allows older adults to regulate negative mood states, which is not normally possible during aerobic and resistance activities.
Other important determinants of successful programming include study attrition, adherence rate, and adverse events. Here, we hypothesised that each exercise condition would demonstrate lower compliance to treatment than wait-list, usual care, and attention-control comparisons. Contrary to expectations, pooled direct and indirect estimates indicated that study attrition was comparable for all comparisons apart from resistance exercise, which offered a higher degree of compliance. However, on deeper scrutiny of absolute sample size, any differences observed in attrition become abrogated due to relatively smaller participant numbers within the resistance exercise studies (n = 705) compared with aerobic (n = 1,143) or mind-body (n = 1,005). Thus, in consideration of the wide dispersion in effect size estimates and a moderate risk of bias in individual studies, there are limitations in the certainty to confidently conclude substantive difference in study attrition between any comparisons.
Each of the three types of exercise had similar adherence rates and time spent per week (calculated as frequency multiplied by duration). Interestingly, mind-body exercise had relatively shorter intervention length than either aerobic or resistance exercise (M diff = 6.09 and 4.33 weeks, respectively) despite having a greater reduction in depressive symptoms. This could be explained by (i) mind-body exercise having the same antidepressive effects in a shorter time than aerobic and resistance exercise, or (ii) a potential plateau effect whereby the antidepressive effects reach a maximum threshold during the first 10-15 weeks of an exercise program and are then maintained during the remaining weeks. Either way, it seems plausible that mind-body exercise provides a slightly more effective intervention against depressive symptoms in older populations without clinical depression, but that these treatment effects are not substantive enough to constitute statistical difference.

Practical considerations
With consideration to projected population estimates over the next decade and the consequential demand on healthcare services 1,8 , the findings from this network meta-analysis offer a message of support for exercise prescription to promote mentally healthy ageing. When considering the collective findings of the present review in conjunction with the recent network meta-analysis in clinically depressed adults aged ≥ 65 years 23 , stakeholders in healthy ageing and exercise prescription have encouraging pooled RCT evidence for the antidepressant effects of either aerobic, resistance, or mind-body exercise for older adults across the mental health continuum.
Treatment safety is a matter of ongoing importance in gerontological health, and exercise treatment programs are no different. Systematic scrutiny of the included RCTs found that study participants reported no major adverse events and only a few minor somatic complaints (n = 28). Taken together, this provides encouraging support for personnel wanting to safely prescribe exercise-based intervention programs in older populations. Of course, there is always a possibility of underreporting adverse events in clinical trials, and the present review was no exception. Therefore, the importance of reporting event outcomes, adverse or otherwise, cannot be understated. In fact, there is a known phenomenon in geriatric exercise research whereby adverse events are often underreported because authors do not consider minor adverse events to be noteworthy and/or essential to the primary purpose of the trial 138 , giving rise to an ongoing issue that will not be corrected until all studies routinely report event outcomes.
Nevertheless, participants engaging in aerobic exercise reported the least adverse events (n = 3), including minor medical attention and hip pain. Amongst studies included in this metaanalysis, aerobic exercise predominantly involved walking and stationary cycling, which may reflect a safe and natural form of exercise for older adults. Resistance exercise was typically associated with participants experiencing mild muscular pain and falls (n = 16), which may be explained by the progressive overloading of resistance-based training. Notably, incidents of falling were reported in an unsupervised exercise program. Finally, mind-body exercise was typically associated with different types of muscular pain and body strain (n = 9). It is speculated that the higher rates of injury in mind-body exercise are predominantly because it incorporates flexibility, balance, and stability movements, which may be unique to older bodies. In general, exercise seems to be a relatively safe intervention for older adults living in both the local community and residential aged care, although intensity and supervision, particularly for resistance training, should be monitored to ensure falls and injury do not occur.
The present review has some notable advantages above a traditional pairwise meta-analysis. RCTs with considerable nonexercising components, such as those using a multicomponent exercise intervention, were excluded because they may have overestimated the magnitude of the true effect in past reviews. Specifically, it is likely that multicomponent exercise interventions such as laughter therapy 139 , depression awareness training 128 , or self-efficacy training 140 may have introduced a risk of bias by inflating the observed effectiveness of the exercise program on depressive symptoms through a secondary, complementary treatment effect. Pairwise meta-analysis also assumes that all control groups are the same, which is not always the case 18 . To manage heterogeneity from this assumption, control groups were separated into individual network comparisons. Taken together, the current findings provide a more accurate estimate of the true effects of exercise on depressive symptoms in adults aged ≥ 65 years.

Limitations and future directions
The present network meta-analysis is not without limitations.
As study participants and personnel cannot be successfully blinded, there is an inherent risk of performance bias. It is also believed that many exercise-based interventions have a small number of participants, shorter follow-up, and do not adequately conceal randomisation 141 , which are all likely to reduce the quality of RCTs and increase the risk of bias. However, we mitigated the impact of this by comparing relative effects with multiple control groups in order to increase reliability and specificity.
This, combined with the relatively low risk of bias in individual RCTs, were extremely important in minimising overall risk of bias and achieving accurate effect comparisons in the present review.
Since most RCTs did not explicitly describe the exclusion of participants with ongoing diagnosis of clinical depression, there was potential contamination with data from participants with existing clinical diagnosis and medical treatment that went unreported. This was primarily managed by separating (i) participants with depressive symptomology but not clinically diagnosed in the present review from (ii) participants with clinical depression in a previous network meta-analysis 23 . Within this review, we further mitigated this effect modifier by only including RCTs, where this risk would be balanced by control participants. We recommended that ageing researchers encourage the reporting of all ongoing pharmacological regimens in trials recruiting older participants.
Although modifying effects were explored using meta-regression, potential compounding effects from exercise modifiers (e.g., fitness improvements, length of program, session frequency and duration, exercise intensity, supervision, group format) were outside the scope of our network meta-analysis. There has been a modicum of such exploration in subgroup and meta-regression analyses of previous reviews 4,16,35 , providing researchers with an encouraging opportunity in their planning of future similar work. Future meta-analyses with extensive subgroup analyses should explain the heterogeneity of effect sizes between similar exercise intervention studies in older persons.

Conclusions
Pooled RCT evidence highlights that each individual exercise mode (aerobic, resistance, and mind-body) demonstrate equivalence to mitigate symptoms of depression in older adults, irrespective of depression severity. As each exercise treatment demonstrated encouraging levels of treatment compliance, we endorse personnel and stakeholders in healthy ageing to encourage individual/patient preference when prescribing exercise to older adults ≥ 65 years presenting with depressive symptomology. This project contains the following extended data: -Supplementary File S1 (PDF file containing additional information, tables, and figures not in the main manuscript)

Reporting guidelines
Figshare: PRISMA-NMA checklist for "Aerobic, resistance, and mind-body exercise are equivalent to mitigate symptoms of depression in older adults: A systematic review and network meta-analysis of randomised controlled trials (extended data

General
The review and network meta-analysis are very well written and conclusive. I especially appreciate the aim of the authors to analyze studies of different modalities of physical activity (PA) on depressive symptoms in older adults without clinical depression and comparing them to their already published network-MA on the effects of PA in clinically depressed older adults. The methods are described thoroughly. The authors analyze RCTs evaluating the effects of aerobic, resistance or mind-body exercise on depressive symptoms separately. In addition, they also group control groups by waitlist, attention control and care as usual which yields a very detailed analysis. They found significant positive effects on depressive symptoms for all three modalities of PA versus control interventions with no specific modality yielding significantly stronger effects than the other. They also analyzed attrition to the study protocol, an important variable that adds additional weight to the analysis. Considering attrition, the authors found comparable values in all comparisons. The results are displayed in appropriate ways by graphical and numerical means.
And thus, provides a good overview of the various results. The statistical methods and interpretation seem absolutely appropriate to me, but since I am not a specialist for network meta-analysis I can not provide an in-depth evaluation.

I have some minor comments to the authors:
P 24, Results of the network-MA: Please spell out "SUCRA" at its first mentioning

Figure 7:
In the notes, the confidence interval is not specified. Is it 95% CI? Please specify. I also don't understand why the horizontal lines (95%? CI) are different from Figure 5: i.e., the comparison AER vs. WL has a 95% CI of -0.63 to -0.14. So why does the horizontal line depicting the CI cross the vertical zero-line in Figure 7? Please explain.

Discussion:
p. 27: you described that mind-body exercise has additional mental effects such as interoceptive sensations. Often mind-body exercise also incorporates aspects of mindfulness. Therefore, I believe it would be appropriate to insert a short paragraph discussing the effects of mindfulness (such as meditation and MBSR) on depressive symptoms. A suggested reference might be: Li et al. On the same page, you discuss compliance to treatment. You state, that you hypothesized that compliance would be lower in exercise treatments. However, the meta-analysis on dropout rates of Stubbs et al. (2016), DOI: 10.1016/j.jad.2015.10.019 2 found similar dropout rates for exercise and control conditions but analyzed samples of adult patients (not specifically elderly). Nonetheless, you might discuss this in a sentence.

Conclusions:
You state that exercise mitigates symptoms of depression regardless of depression severity. If interpreted strictly, according to the current paper this only applies to subclinical severity of symptoms. If you incorporate your earlier paper (network MA of exercise in clinically depressed elderly) into the conclusion, I believe this should be mentioned accordingly. Author

The authors analyze RCTs evaluating the effects of aerobic, resistance or mind-body exercise on depressive symptoms separately. In addition, they also group control groups by waitlist, attention control and care as usual which yields a very detailed analysis. They found significant positive effects on depressive symptoms for all three modalities of PA versus control interventions with no specific modality yielding significantly stronger effects than the other. They also analyzed attrition to the study protocol, an important variable that adds additional weight to the analysis. Considering attrition, the authors found comparable values in all comparisons. The results are displayed in appropriate ways by graphical and numerical means. And thus, provides a good overview of the various results. The statistical methods and interpretation seem absolutely appropriate to me, but since I am not a specialist for network meta-analysis I can not provide an in-depth evaluation.
We wish to firstly thank the Reviewer for their thoughtful commentary. We have taken time to consider all feedback and make the necessary revisions to the article.

I have some minor comments to the authors: P 24, Results of the network-MA: Please spell out "SUCRA" at its first mentioning
We have included "surface under the cumulative ranking curve" before the first mention of the acronym.

Figure 7: In the notes, the confidence interval is not specified. Is it 95% CI? Please specify.
We thankful to the Reviewer for picking up this clerical error, on our behalf. This has now been rectified to state the "95% CI" in notes for Figures 5 and 7. Figure 7? Please explain.

I also don't understand why the horizontal lines (95%? CI) are different from Figure 5: i.e., the comparison AER vs. WL has a 95% CI of -0.63 to -0.14. So why does the horizontal line depicting the CI cross the vertical zero-line in
We are grateful to the Reviewer for highlighting a lack of consistency in the article, specifically for intervals between Figure 5 and 7. Version 1 of the article as read by Reviewers contained both confidence and prediction intervals for Figures 5 and 7, respectively. In light of this, Figure 7 is now converted to 95% confidence intervals in order to maintain observational consistency with Figure 5. We have also proofed consistency of data (and presentation thereof) within article figures.

Discussion: p. 27: you described that mind-body exercise has additional mental effects such as interoceptive sensations. Often mind-body exercise also incorporates aspects of mindfulness. Therefore, I believe it would be appropriate to insert a short paragraph discussing the effects of mindfulness (such as meditation and MBSR) on depressive symptoms. A suggested reference might be: Li et al. (2019) DOI: 10.1111/inm.125681
This is a very reasonable query relating to the potential for 'mindfulness' being an adjunct to potential improvement from mind-body exercise. The authors have taken the opportunity to record these findings within the article, with enhanced detail within the online supplementary material. This supplementary material extends upon the current article discussion to include potential discrete mechanisms that may underpin the psycho-adaptive response to exercise in older adults. This can be found within the online repository which accompanies this article: 'Data Availability' > 'Extended data' > 'Supplementary File S1'

for exercise and control conditions but analyzed samples of adult patients (not specifically elderly). Nonetheless, you might discuss this in a sentence.
This is an interesting point and one which we credited with consideration during the planning of this review. As the Reviewer will be aware, every epidemiological study conducted to date identify older adults to be the least active and least compliant with exercise compared with younger demographics. For reasons of being conservative and containing the potential for observer bias, this was handled in the negative unless data demonstrated otherwise. However, on reflection we deem the use of 'hypothesised' as potentially inappropriate compared with alternatives such as 'anticipated'. This has been amended in the Discussion section to read: "Here, we anticipated that each exercise condition would demonstrate lower compliance to treatment than wait-list, usual care, and attention-control comparisons."

Conclusions: You state that exercise mitigates symptoms of depression regardless of depression severity. If interpreted strictly, according to the current paper this only applies to subclinical severity of symptoms. If you incorporate your earlier paper (network MA of exercise in clinically depressed elderly) into the conclusion, I believe this should be mentioned accordingly.
We extend our gratitude to this Reviewer, who in coalition with Reviewer 2, identified this very same point. On reflection, this was clearly an overreach by the authors. We fully accept the requirement for amendment, which resulted in amendments to the Abstract, Discussion, and Conclusion sections.

© 2021 Camaz Deslandes A.
This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Andrea Camaz Deslandes
Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

Objective:
The study aimed to investigate the head-to-head effectiveness of aerobic, resistance, and mindbody exercise in ameliorating depressive symptoms in older adults. The study is relevant and original, considering the increased prevalence of depression in older adults and the need for treatment through complementary and alternative practices, such as exercise. This topic has indeed received little attention in literature until now. The method used is adequate to the purpose of the study, and the results and the discussion were developed appropriately. However, some minor adjustments are necessary for a better understanding of the manuscript. Please find below some of the specific items regarding the issues.

Introduction:
I suggest a presentation of the concepts of physical activity and exercise, as well as a discussion about divergences in the classification of Yoga and other mind-body practices as physical exercise.

Methods:
The inclusion of older adults with Dementia (n=3), Cognitive Decline (n=3), Parkinson's Disease (n=1), and Cognitive frailty (n=1) can be a possible limitation of the present study, considering the differences related to comorbidity. I suggest the author exclude these studies. Neuropsychiatric symptoms in MCI and Dementia are related to several different mechanisms, as well as the psychological and physiological effects of physical exercise in these populations. The characteristics of Depression in these cases can be very specific, with causes, neurophysiological changes and treatments that differ from depressive symptoms in healthy older adults. The inclusion of these studies can contribute of shrinking the effect size of exercise. For example, the effectiveness of pharmacological approaches to managing depressive symptoms in Dementia and MCI is less effective and the antidepressant effect of exercise in these populations is still controversial. If the authors decide to include these population (MCI, Parkinson, Dementia), several other studies that investigated the effect of exercise on neuropsychiatric symptoms (such as depression) in patients with MCI and Dementia should be included in the present metaanalysis, For more details, see Liang 2018 and Leng et al., 2018. 1 Moreover, this issue should be included in the limitation section. Factors related to the prescription of exercise (frequency, intensity, duration, length, supervision, cognitive engagement) should be more explored in analyses and results.

Discussion:
The author should discuss the possible different mechanisms related to the antidepressant effect of mind-body practices, aerobic and resistance training in older adults, considering both psychological and physiological hypotheses. Please consider more recent neurobiological mechanisms related to the effect of exercise on depressive symptoms (e.g., myokines, neurotrophins, inflammatory cytokines, etc.). opportunity to improve our article. The author team have taken time to deliberate on each point and have reached consensus within our responses below. Thank you.

Introduction: I suggest a presentation of the concepts of physical activity and exercise, as well as a discussion about divergences in the classification of Yoga and other mind-body practices as physical exercise.
We acknowledge this as an important point, and one which would require detailed expansion and integration of broad knowledge of the different forms of exercise classification and their nuanced differences. In order to preserve the flow of rationale within the Introduction section, while similarly being complete with exercise characteristics of aerobic, resistance, and mind-body regimens, we have provided supplementary reading to satisfy the broader readership of exercise specialists, including background material for readers out-with this specialist topic.
We have taken this opportunity to include further statement within the Introduction section to highlight the differences between types of exercise: "We direct the reader to the Extended data of this article for detailed description of the exercise classifications included in this review." 'Extended data' for publication in F1000 is repository for non-essential information or data.
To diligently tackle important divergences in classifications in exercise type required 750 words, which the authors deemed as an important point with an appropriate home within the online repository, which can be found here: 'Data Availability' > 'Extended data' > 'Supplementary File S1'

Methods: The inclusion of older adults with Dementia (n=3), Cognitive Decline (n=3), Parkinson's Disease (n=1), and Cognitive frailty (n=1) can be a possible limitation of the present study, considering the differences related to comorbidity. I suggest the author exclude these studies. Neuropsychiatric symptoms in MCI and Dementia are related to several different mechanisms, as well as the psychological and physiological effects of physical exercise in these populations. The characteristics of Depression in these cases can be very specific, with causes, neurophysiological changes and treatments that differ from depressive symptoms in healthy older adults. The inclusion of these studies can contribute of shrinking the effect size of exercise. For example, the effectiveness of pharmacological approaches to managing depressive symptoms in Dementia and MCI is less effective and the antidepressant effect of exercise in these populations is still controversial.
We agree with the Reviewers comments relating to the antidepressant effects of exercise, and in particular that evidence in these populations remains controversial. Before undertaking this review, specific inclusion criteria for this work were considered greatly, wherein, the authors remained agnostic to any particular effect size resulting from exercise participation on depressive symptoms in older adults. The purpose of the present review was to provide point-specific information that is generalisable to all older adults aged >65 years without diagnosis of clinical depression. The binary approach taken within this review was (a) without diagnosis of clinical depression, thus excluding (b) those diagnosed with clinical depression. This review included RCTs limited to the former, which became the defining characteristic. Specifically, we cannot bias the inclusion criteria based on ubiquitous conditions, and as such, any given adult aged >65 years is by definition a primary candidate for each of the suggested limiting covariates.
We are genuinely grateful to this Reviewer for generating some important points relating to exercise gerontology which remain unresolved. Until such time as epidemiological controversy such as nuanced and unresolved characteristics amongst ageing cohorts is evidenced, it is our belief that we must accept perceived imperfections within the literature, which is a notable limitation of meta-analytical investigation. This Reviewer does imply an important fact, and we do not offer this article as being definitive as such potential limitations are unresolved. However, we do believe that it is best practice to present in the article's current form, but importantly, accept and acknowledge the value of the Reviewer's observation by noting this as a generalised limitation within the limitations section.

If the authors decide to include these population (MCI, Parkinson, Dementia), several other studies that investigated the effect of exercise on neuropsychiatric symptoms (such as depression) in patients with MCI and Dementia should be included in the present metaanalysis, For more details, see Liang 2018 and Leng et al., 2018. 1 Moreover, this issue should be included in the limitation section.
We are further grateful to this Reviewer for providing a list of potential references and have applied article inclusion criteria in review of each. From this list, one study (i.e., Abd El-Kader & Al-Jiffri, 2016) met the inclusion criteria for the systematic review but was not eligible for inclusion in network meta-analytics. Thus, the Systematic Review component of this article has been updated, yet the network meta-analysis remains unchanged. In response to the Reviewer's recommendation, we have carefully examined the review conducted by Leng et al. (2018). We have applied the present article inclusion criteria in review of each study and further included detailed reasoning for inclusion/exclusion for each which may be in the online repository, each of which may be observed at:

Factors related to the prescription of exercise (frequency, intensity, duration, length, supervision, cognitive engagement) should be more explored in analyses and results.
This is an important point to consider (and partially addressed in the previous comment). One primary difference between network meta-analysis and its more widely used counterpart, the 'meta-analysis', relates to the limited ability for network meta-analysis to address significant covariates by sub-group analytics and meta-regression of known moderators of the primary outcome. However, such important factors have a place to address this within network meta-analysis. Although not overtly observable, we have done so within the present article. More specifically, "in order to test transitivity across networks, potential effect modifiers were tested with meta-regression sensitivity analyses for the entire pool of studies and separately for each exercise comparison (aerobic vs. resistance vs. mind-body). No significant modifying effects were observed for the pool of studies or any separate exercise comparison for age, gender, source of participants, length of intervention, format of program, exercise intensity, frequency, duration, adherence, year of study, risk of bias, publication status, intention-to-treat analysis, nor cluster design, indicating that the assumption of transitivity was upheld." Further information can also be found in the online repository, found here: 'Data Availability' > 'Extended data' > 'Supplementary File S1' Possible different mechanisms, and unique characteristics relating to the antidepressant potential of different forms of exercise, remain to be fully defined. The main purpose of meta-analytical review of randomised controlled trials (RCTs) is to leverage the work of other researchers, and by doing so improve statistical power and point estimation for a specific question. In this respect, the present review cannot offer psychological or physiological hypotheses to a degree greater than any one specific RCT. However, we do accept this Reviewer's guidance and further accept that it is important to contextualise contemporary mechanisms relating to the antidepressant effect of exercise.
In light of this, we have included a separate extended piece of 784 words within the online repository to provide an extension of this discussion, found here: 'Data Availability' > 'Extended data' > 'Supplementary File S1' ○ https://doi.org/10.6084/m9.figshare.12998549.v3 ○ Additionally, the authors have added the following text "we direct the reader to the Extended data for further discussion relating to potential mechanisms for the antidepressant effects of exercise in older adults", to add value to this overall body of work and contemporary mechanistic information for the interested reader.
Main findings of the present study Comparison with other studies Implication and explanation of findings Strengths and limitations Conclusion, recommendation, and future direction.
Are the rationale for, and objectives of, the Systematic Review clearly stated? Partly

Is the statistical analysis and its interpretation appropriate? Yes
Are the conclusions drawn adequately supported by the results presented in the review? Partly Should we be accurate in the interpretation of this comment, the Reviewer considers this article to be (i) a systematic review. This is correct. However, it is a systematic review combined with (ii) network meta-analysis, which requires compliance with a specific set of reporting guidelines that are an extension of the noted PRISMA checklist. This article is presented in acknowledgement of a complimentary extension to PRISMA guidelines where we have adhered to the 'PRISMA-NMA guidelines', where 'NMA' accounts for a quantitative addition within this 'network meta-analysis'. To support our response of confirmatory compliance, we direct the Reviewer to the Methodology section within this article which specifies a formal statement of compliance with PRISMA-NMA guidelines. To further offer confirmatory compliance with PRISMA-NMA guidelines, we direct this Reviewer to supplementary materials within the online repository, found here: 'Data Availability' à 'Reporting Guidelines' à 'PRISMA-NMA Checklist' We thank the Reviewer for identifying potentially important rationale for conducting this systematic review and network meta-analysis. In order to provide a comprehensive response to the query, it is necessary to offer two components of 'depression': Definition of depression -This is an interesting request, and the authors believe it to be adequately rationalised within the Introduction section. In compliance PRISMA-NMA criteria, we have specified the generalised term 'depression' as having a characteristic binary format for this work where explicitly (i) 'clinically defined depression' is divergent with (ii) 'depressive symptoms' which fall short of the threshold for the former. The present article focuses on the latter, where we offer contextual dialogue of literature within the domain of 'clinical depression' and articulate within the Discussion section of this article.

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Operational definition for this article -The discrete definition for depressive symptoms used in this review are further detailed within the methodology, where RCTs were deemed eligible where valid determination of depressive symptoms was achieved using accepted psychometrically valid tools.

Determine the relationship between exercise and depression status in older adults. I suggest the authors expose the following points in the introduction: What is known about depression and exercise training in older adults? What is not known?
○ We direct the Reviewer to Paragraphs 2 of the Introduction section: "International public health consortia are in concert with the antidepressant effects of exercise as a low-risk adjunct for optimal mental health (American Psychiatric Association, 2010; Stubbs et al., 2018;World Health Organization, 2010). While we do not yet have the answers for low uptake of exercise in older adults (Knowles et al., 2015), it may in some way be due to nuanced regimen design, which in turn, may similarly impact compliance in exercise prescription by primary and stakeholders in aged care." Similarly, Paragraph 3 of the same Introduction section: "During the past four decades, widely different metabolic, social, and environmental demands between exercise modalities (i.e., running vs. weightlifting vs. Tai Chi) have been well-characterised. Given that there is variation between exercise regimens, and these variations are not merely semantics, one may be surprised to discover that only a few randomised controlled trials (RCTs) have deliberately compared the antidepressant effects of different exercise regimens in older adults (Martins et al., 2011;Penninx et al., 2002)." Reviewer's suggestion in the prior response. In terms of addressing the substantive part of this Reviewer's query, we offer the main findings from this review to be presented at the beginning of the Discussion section, and specifically, in the inaugural paragraph, which reads: "The present review offers new information for general exercise prescription to support mental health outcomes for adults aged ≥ 65 years. Specifically, (i) aerobic, resistance, and mind-body each demonstrate equivalent benefit to mitigate symptoms of depression in adults aged ≥ 65 years, (ii) compliance to exercise treatment is notably encouraging for each exercise types, and (iii) when combined with the pool of data from clinically depressed older adults the effectiveness of aerobic, resistance, and mind-body exercise is comparably consistent for all adults aged ≥ 65 years, irrespective of depression severity. These findings should provide reassurance for personnel and stakeholders in healthy ageing to encourage exercise prescription from a point of pragmatism and in collaboration with patient preference."

Inconsistency models need more details in the discussion section.
○ Within the present article, inconsistency models are detailed within the Results section (see 'Assessment of Inconsistency'). Our generalised ambition in delivering coherence within the Discussion section was to integrate our findings from inconsistency modelling and to communicate in a more active voice in appreciative context with other available literature.
As it turned out, our tests of inconsistency became largely satisfied with generalised assumption of consistency for (i) global and (ii) loop-specific heterogeneity for our main (depressive symptom) network which limited an expanded discussion of inconsistency. To support this Reviewer's query and in our compliance with Cochrane Review guidelines (Higgins et al., 2021), network inconsistency was further interrogated using the GRADE assessment tool (Salanti et al., 2014). In doing this, any important inconsistencies are dialogued in context to the primary objective, which in turn, are dialogued within the Discussion section and benchmarked against our primary outcomes. For instance, Paragraph 5 of the Discussion section reads: "Certainty of evidence is moderate, due to the dispersion in effect size estimates resulting in imprecision."