Sage Journals: Discover world-class research

Abstract

Objective

We sought to evaluate the differential effects of wearable device-based interventions on weight-related and metabolic health outcomes among adults and youth with overweight or obesity.

Methods

We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) published up to November 2025, identified through PubMed, Embase, the Cochrane Library, and ClinicalTrials.gov. Eligible studies compared wearable device interventions to control conditions and reported outcomes such as weight, body mass index (BMI), waist circumference, blood pressure, lipid levels, and hemoglobin A1c (HbA1c). Data were pooled using random-effects models, and subgroup analysis was performed by age group.

Results

Eighteen RCTs were included. Wearable device interventions significantly reduced BMI overall (mean difference [MD], −0.63 kg/m²; 95% confidence interval [CI], −1.01 to −0.25; P = 0.001), with a greater effect in children and adolescents (MD, −0.91 kg/m²; 95% CI, −1.26 to −0.56; P < 0.00001). In adults, waist circumference decreased significantly (MD, −5.22 cm; 95% CI, −9.03 to −1.40; P = 0.007), and HbA1c also improved (MD, −0.24%; 95% CI, −0.30 to −0.18%; P < 0.00001). No significant differences were observed for overall weight change, blood pressure, or lipid profiles in adults. Pediatric participants showed more consistent improvements across multiple indicators.

Conclusion

Wearable device-based interventions led to modest but significant improvements in metabolic health, particularly in children and adolescents. These findings underscore the potential of wearable technologies as supportive tools for obesity management and highlight the importance of age-specific strategies in intervention design.

Keywords

Wearable devices overweight obesity lifestyle intervention meta-analysis

Introduction

The global prevalence of overweight and obesity is rising rapidly and remains a major public health concern. It is projected that, by 2030, approximately 60% of the world's adult population will be overweight or obese.^1–3 Obesity is a well-established precursor to numerous chronic diseases,⁴ triggering various inflammatory responses and significantly increasing the risk of cardiovascular disease, certain cancers, and type 2 diabetes.^5–7 In addition, overweight and obesity contribute to elevated blood pressure and impaired glucose metabolism, leading to conditions such as hypertension and type 2 diabetes.⁸ Beyond the individual health burden, the rising prevalence of obesity and its related comorbidities places a considerable socioeconomic strain on global healthcare systems.⁹

Lifestyle modification, particularly structured exercise, is central to overweight and obesity management. While traditional aerobic exercise improves cardiovascular and lipid-related outcomes, growing evidence indicates that exercise modality and intensity are critical for optimizing cardiometabolic benefits. A network meta-analysis of 81 randomized controlled trials (RCTs) showed that multicomponent approaches, including combined and hybrid-type training, were most effective in improving a broad range of cardiometabolic risk factors.¹⁰ Similarly, high-intensity interval training (HIIT) has emerged as a time-efficient strategy, particularly for individuals with “diabesity,” demonstrating superior improvements in glycemic control and cardiorespiratory fitness compared with moderate-intensity continuous exercise, even without substantial weight loss.¹¹ Taken together, these findings are consistent with accumulating evidence supporting the effectiveness of concurrent, aerobic, interval, and resistance-based exercise programs in improving body composition, cardiometabolic health, and physical fitness among individuals with excess body weight. However, maintaining appropriate exercise intensity and adherence in real-world settings remains challenging.

In response to this growing public health challenge, various interventions targeting the prevention and management of overweight and obesity have emerged. Digital health technologies – particularly smartphone applications and wearable devices – have shown promise as tools for encouraging healthier lifestyles and managing associated health risks.¹² These technologies enable users to track physical activity, caloric intake, and other physiological parameters, thereby promoting a better understanding of energy balance and overall health.^13,14

Wearable devices have evolved beyond basic step-counting to include features such as heart rate monitoring, sleep tracking, and real-time feedback when integrated with smartphone apps. Given that lifestyle behaviors influence not only body weight but also blood pressure and glycemic control, wearable devices have the potential to improve multiple health outcomes in overweight and obese individuals.^15,16

Despite this promise, evidence regarding the effectiveness of wearable device-based interventions remain inconsistent. While many studies have focused on weight loss as a primary outcome, others have evaluated secondary cardiometabolic indicators such as blood pressure and glycemic control, with mixed results.^17–19 Notably, intervention effects appear to vary by age and outcome domain. For example, a recent study reported significant reductions in metabolic syndrome risk among younger adults, whereas benefits were attenuated in older populations.¹⁷ Similarly, a meta-analysis found meaningful reductions in systolic blood pressure but heterogeneous effects on body mass index (BMI), depending on the inclusion of goal-setting or behavioral components.¹⁸

Although emerging evidence suggests differential responses to wearable-based interventions across age groups, existing reviews have largely focused on specific populations or isolated outcomes. To address this gap, the present systematic review and meta-analysis evaluates the effects of wearable device-based interventions on weight-related and metabolic outcomes across diverse populations, incorporating evidence published through November 2025. By examining multiple cardiometabolic indicators across age groups, this study aims to provide evidence to inform more targeted and effective strategies for obesity management.

Methods

This study was conducted as a systematic review and meta-analysis following the Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 guidelines (Supplemental file 1).²⁰

Research question (PICO)

The research question was structured using the PICOS framework:

Population (P): Overweight or obese individuals (adults and children/adolescents)

Intervention (I): Lifestyle interventions incorporating wearable devices

Comparison (C): Control group without wearable device intervention

Outcomes (O): Body weight, BMI, waist and hip circumference, body fat percentage, blood pressure, blood lipid levels, blood glucose, hemoglobin A1c (HbA1c), physical activity, medication use, and quality of life

Study design (S): RCTs

Data sources

A comprehensive search was conducted in four databases (PubMed/Medline, Embase, the Cochrane Library, and ClinicalTrials.gov), up to December 2023. Search date was updated on 30 November 2025. Gray literature was not considered for inclusion in this study. Search terms included combinations of keywords such as “obese,” “overweight,” “wearable device,” “eHealth,” and “smartphone” using Boolean operators (AND, OR). Comprehensive details regarding the overall search strategy employed for each database are documented in a separate source (eTable 1 in the Supplemental file 2).

Controlled vocabularies including MeSH and Emtree terms, as well as natural language terms, were used. The reference lists of relevant reviews were manually searched for additional eligible studies.

Inclusion and exclusion criteria

The inclusion criteria were as follows:

RCTs involving overweight or obese individuals

Studies comparing wearable device-based interventions with control groups

Studies reporting relevant outcomes

The exclusion criteria were as follows:

Studies involving pregnant women

Conference abstracts

Survey-based studies

Protocol papers

Studies unrelated to obesity or wearable devices

Device development or validation studies

Study selection and inter-rater reliability

The literature screening and selection process were conducted in two stages. First, two reviewers independently screened all identified titles and abstracts based on the predefined inclusion and exclusion criteria. In the second stage, the full texts of the remaining articles were thoroughly reviewed for final eligibility. To ensure the objectivity and reliability of this process, any disagreements between the two reviewers were resolved through intensive discussion or, if necessary, by consulting a third reviewer. The inter-rater agreement between the reviewers during the screening process was substantial, as evidenced by a Cohen's kappa statistic of 0.688 (P < 0.001). Data extraction was then performed independently using a standardized data collection form to capture study characteristics, participant demographics, and clinical outcomes.

Data extraction

Data were extracted using a predefined form. The following data were collected:

Study characteristics: Authors, country, study design, intervention details, comparator, duration, and inclusion/exclusion criteria

Participant characteristics: Number of participants, gender distribution, and age

Outcomes: Body weight, BMI, waist circumference, body fat percentage, blood pressure, blood glucose, HbA1c, physical activity, medication use, and quality of life

Criteria for assessing data

As all included studies were RCTs, the risk of bias in the randomization process, deviations from intended intervention, missing outcome data, measurements of the outcome, and selection of the reported result was assessed using the Cochrane risk-of-bias (RoB) 2.0 tool.²¹

Data synthesis and statistical analysis

When studies reported medians and interquartile ranges (IQRs), means and standard deviations (SDs) were estimated using methods proposed by Luo et al. and Wan et al.^22,23 If only 95% confidence intervals (CIs) were provided, SDs were calculated using Review Manager's internal calculator. Meta-analysis was conducted using the pre–post change scores (Δ values) for each outcome. Subgroup analysis was conducted based on age groups, dividing participants into adults and children/adolescents. This study applied the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach to assess certainty for effect of wearable device on overweight or obese.²⁴ The GRADE approach was applied only to outcomes with at least two studies included in the meta-analysis, and for the outcomes of weight and BMI, it was conducted based on age-specific subgroup analysis.

A random-effects model was applied to account for expected heterogeneity across studies in terms of intervention characteristics and populations. All meta-analyses were performed using Review Manager version 5.4 (The Cochrane Collaboration, Copenhagen, Denmark).

Statistical heterogeneity was assessed using Cochran's Q statistic and the I² index. I² values of 25%, 50%, and 75% were considered indicative of low, moderate, and high heterogeneity, respectively. Galbraith (radial) plots were constructed using the metafor package in R (version 5.4.2) to visually assess heterogeneity and identify potential outlier studies. Publication bias was assessed visually by inspecting funnel plots. Formal tests (e.g. Egger's regression) were not performed due to the limited number of included studies.

Ethical considerations

There were no human participants in this study, and informed consent was not required.

Results

Literature search results

Initially, 2272 records were identified through database searches, and an additional 351 records were identified following an updated search. After removing 534 duplicates, titles and abstracts of the remaining articles were screened according to predefined exclusion criteria. The following records were excluded: 405 nonoriginal articles, 522 studies unrelated to wearable devices, 8 were not RCTs, 133 studies focused on the development or validation of applications or wearable devices, 154 studies with unclear intervention or control groups, 222 study protocols, 74 ongoing or discontinued trials, 14 studies involving pregnant women, 107 survey-based studies, 6 studies not written in English or Korean, 281 studies unrelated to overweight or obesity, and 153 articles irrelevant to the study topic. As a result, 10 articles were included at this stage. Eight more studies were subsequently identified through manual searches of the references lists of relevant reviews, leading to the inclusion of 18 studies in the final analysis (eFigure 1 in the Supplemental file 2).^25–42

Characteristics of included studies

Among the 18 included studies, 13 (72%) were conducted in the United States, followed by 2 (11%) in South Korea. One study each was conducted in Australia, Italy, and Spain (Table 1). All studies were RCTs, with intervention durations ranging from 4 weeks to 2 years. Six studies included ≤50 participants, 6 studies included 51 to 100 participants, and 6 studies included >100 participants. Four studies (22%) targeted children/adolescent, while the remaining 14 (78%) targeted adults (Table 1 ). RoB assessment for the 18 studies rated 7 studies as low risk, 7 as having some concerns, and 4 as high risk (eFigure 2 in the Supplemental file 2). In studies involving adults, a total of 944 participants were allocated to the wearable device group and 820 to the control group. Of these, 719 participants in the wearable device group and 595 in the control group were included in the meta-analysis. In the studies involving children/adolescents, a total 108 participants for wearable device group and 81 for control group. Of these, 90 participants for wearable device group and 51 for control group were included in meta-analysis. The range of mean age for studies on adults was 26.5 to 61.7 years, and on children/adolescents was 11.9 to 15 years. The range of mean weight (BMI) for studies on adults was 79.3 kg (28.6 kg/m²) to 114.1 kg (37.0 kg/m²), and on children/adolescents was 62.3 kg (26.8 kg/m²) to 104.28 kg (38.3 kg/m²). Regarding the control conditions used for comparison, the most common was usual care, applied in 9 studies (50%) (Table 1). The number of studies reporting outcome measures according to the type of control intervention is presented in eFigure 3 in the Supplemental file 2.

Table 1.

Characteristics of the included studies.

Study	Country	Followup period	Intervention	Comparison	N (M/F)		Age, year		Results for RoB 2.0
Study	Country	Followup period	Intervention	Comparison	Wearable device	Control	Wearable device	Control	Results for RoB 2.0
Turner-McGrievy et al.	USA	6 months	Bite Counter device	Application	39 (7/32)	42 (7/35)	47.5 ± 12.3	48.6 ± 11.7	Low
Kim et al.	Korea	4 weeks	Wristband-type activity tracker (Samsung Charm)	Application Usual care	15	G 1: 15 G 2: 13	45.3 ± 9.5	G 1: 41.5 ± 11.8 G 2: 40.5 ± 7.4	Some concerns
Gómez-Sánchez et al.	Spain	12 months	Evident 3 app and a smart band (Mi Band 2, Xiaomi)	Meeting, education	127 (42/85)	126 (35/91)	47.96 ± 9.95	47.78 ± 9.62	Low
Cadmus-Bertram et al.	USA	16 weeks	Fitbit One, an accelerometre-based physical activity tracker	Self-monitoring	25	26	58.6 ± 6.5	61.3 ± 7.5	Some concerns
Archer et al.	USA	9 months	Multisensor armband (SWA)	Usual care Meeting, education	G 1: 49 (9/40) G 2: 49 (9/40)	G 3: 50 (8/42) G 4: 49 (10/39)	G 1: 45.7 ± 10.4 G 2: 47.7 ± 11.6	G 3: 47.2 ± 8.9 G 4: 46.8 ± 12.4	Low
Hartman et al.	USA	6 months	Fitbit combined with MyFitnessPal app for physical activity and diet tracking	Usual care	36	18	59.4 ± 5.6	59.8 ± 5.9	Low
Jakicic et al.	USA	24 months	BodyMedia FIT Core, a multisensor device worn on the upper arm for physical activity monitoring	Self-monitoring	237 (69/168)	233 (67/166)	Median [IQR] 31.0 [27.4, 33.3]	Median [IQR] 30.9 [28.0, 33.9]	Low
Chen et al.	USA	3 months	A culturally appropriate smartphone-based intervention	Self-monitoring	23 (14/9)	17 (9/8)	15 ± 1.69	14.77 ± 1.60	Low
Mameli et al.	Italy	3 months	Combined use of wristbands and smartphones	Usual care	16 (11/5)	14 (8/6)	12.6 ± 1.7	12.4 ± 2.2	Some concerns
Pellegrini et al.	USA	6 months	BodyMedia FIT System	Usual care	G 1: 17 (4/13) G 2: 17 (3/14)	17 (0/17)	G 1: 43.3 ± 9.1 G 2: 44.1 ± 8.1	45.1 ± 9.4	High
Shuger et al.	USA	9 months	SenseWear Armband	Usual care, Meeting, education	G 1: 49 (9/40) G 2: 49 (9/40)	G 3: 50 (8/42) G 4: 49 (10/39)	G 1: 45.7 ± 10.4 G 2: 47.7 ± 11.6	G 3: 47.2 ± 8.9 G 4: 46.8 ± 12.4	High
Duncan et al.	Australia	12 months	Not specified in the search results	Usual care	G 1: 41 (11/30) G 2: 39 (12/27)	36 (11/25)	G 1: 45.4 ± 10.2 G 2: 47.2 ± 9.4	40.5 ± 10.7	Some concerns
Vaz et al.	USA	6 months	Fitbit Charge HR and Fitbit Aria smart scale for activity and weight monitoring	Usual care	13 (2/11)	15 (2/13)	40.15 ± 3.72	45.93 ± 3.29	Some concerns
Bowen-Jallow et al.	USA	18 weeks	Wearable activity tracking devices in adolescents, specifics not provided in search results.	Not mentioned	18 (9/9)	30 (15/15)	14.5 ± 1.58	14.47 ± 1.8	High
Lyons et al.	USA	12 weeks	Wearable technology intervention with unspecified devices	Usual care	20 (3/17)	20 (3/17)	61.25 ± 5.00	61.70 ± 6.26	Low
Shin et al.	Korea	12 weeks	Smartcare system with unspecified wearable devices	Meeting, education	G 1: 35 G 2: 35	35	G 1: 28.7 ± 4.6 G 2: 28.1 ± 6.0	26.5 ± 4.0	Some concerns
Peyer et al.	USA	8 weeks	Guided weight loss using unspecified wearables	Meeting, education	G 1: 26 (13/13) G 2: 26 (15/11)	26 (3/23)	G 1: 38.6 ± 14.7 G 2: 37.9 ± 13.1	41.0 ± 14.6	High
Garza et al.	USA	8 weeks 8 weeks + 10 months	Telephone aftercare intervention without specific wearable details	Compensation	G 1: 25 G 2: 26	20	G 1: 11.9 ± 0.36 G 2: 12.0 ± 0.34	12.1 ± 0.33	Some concerns

Data presented as mean ± standard deviation; RoB: risk of bias; G 1: group 1; G 2: group 2; G 3: group 3; G 4: group 4.

Table 2.

Summary of body weight and BMI changes in intervention and control groups.

Study	Measurement type	Weight, kg		BMI, kg/m²
Study	Measurement type	Wearable device	Control	Wearable device	Control
Turner-McGrievy et al.	Mean (SE)	−3.0 (0.8)^†	−6.8 (0.8)^†	-	-
Kim et al.	Mean ± SD Baseline Postintervention	84.3 ± 5.6* 82.9 ± 5.6*	G 1: 93.7 ± 22.3* 91.7 ± 22.9* G 2: 84.3 ± 12.1 83.9 ± 12.6	29.1 ± 2.8* 28.7 ± 3.0*	G 1: 30.8 ± 6.0* 30.1 ± 6.2* G 2: 28.7 ± 2.8 28.6 ± 3.0
Cadmus-Bertram et al.	Mean ± SD	−0.3 ± 2.4	0.01 ± 2.3	-	-
Archer et al.	kg lost/participant	G 1: 6.59 G 2: 1.86	G 3: 0.90 G 4: 3.55	-	-
Hartman et al.	Mean ± SD	−4.4 ± 4.3^†	−0.5 ± 3.8^†	-	-
Jakicic et al.	Mean (95% CI)	−3.5 (−4.5, −2.6) ^†	−5.9 (−6.8, −5) ^†	−1.1 (−1.9, −0.3)	−1.8 (−2.6, −1.0)
Chen et al.	Mean ± SD Baseline Postintervention	-	-	27.37 ± 3.26† 26.91 ± 3.25†	28.35 ± 4.36† 28.81 ± 4.43†
Mameli et al.	Mean (95% CI) Standard deviation score	0.07 (−2.81, 2.96)		0.01 (−0.15, 0.18)
Pellegrini et al.	Mean ± SD Baseline Postintervention	G 1: 102.1 ± 17.5^† 93.3 ± 17.4^† G 2: 92.3 ± 12.1^† 86.4 ± 13.7^†	88.6 ± 12.5† 84.9 ± 13.1†	G 1: 34.7 ± 3.4 31.6 ± 3.4 G 2: 33.4 ± 3.6 31.3 ± 4.5	33.1 ± 3.8 31.7 ± 3.6
Shuger et al.	Mean (SE) Baseline Postintervention	G 1: 101.15 (2.95)* 97.60 (2.99)* G 2: 100.32 (2.97)* 93.73 (2.99)*	G 3: 102.22 (2.97) 101.32 (3.05) G 4: 101.84 (2.95) 99.98 (3.00)	G 1: 34.73 (0.90)* 33.56 (0.92)* G 2: 34.39 (0.91)* 32.11 (0.92)*	G 3: 34.52 (0.91) 34.16 (0.94) G 4: 34.54 (0.90)* 33.84 (0.92)*
Duncan et al.	Mean ± SD Mean (95% CI) Baseline Postintervention	89.8 ± 13.4 87.21 (85.74, 88.69)	92.5 ± 16.1 87.21 (85.00, 89.43)	-	-
Vaz et al.	Mean ± SD	−7.16 ± 1.78*^,†	−3.00 ± 1.05*^,†	-	-
Bowen-Jallow et al.	Mean ± SD Baseline Postintervention	98.53 ± 21.02 (n = 18) 102.7 ± 27.01 (n = 10)	104.28 ± 32.48 (n = 30) 99.15 ± 33.15 (n = 13)	-	-
Lyons et al.	Mean ± SD Baseline Postintervention	82.58 ± 11.96 81.72 ± 11.71	82.14 ± 9.82 82.85 ± 9.77	-	-
Shin et al.	Mean ± SD	G 1: −1.1 ± 2.9 G 2: −3.1 ± 3.7	−0.4 ± 2.5	G 1: −0.5 ± 0.8 G 2: −1.0 ± 1.2	−0.2 ± 0.7
Peyer et al.	Mean (95% CI) Baseline Postintervention	G 1: −5.2 (−2.3, −8.1) G 2: −5.57 (−2.9, −8.2)	−3.94 (−2.0, −5.9)	-	-
Garza et al.	Mean ± SD Baseline Postintervention	G 1: 62.9 ± 2.5 64.2 ± 3.6 G 2: 62.3 ± 2.5* 65.9 ± 3.6*	62.7 ± 2.3* 67.2 ± 3.3*	G 1: 27.6 ± 0.5* 26.8 ± 0.7* G 2: 27.5 ± 0.5 27.7 ± 0.7	27.8 ± 0.6 28.4 ± 0.7

*Significantly difference within group; ^†significantly difference the changes between two group; G 1: group 1; G 2: group 2; G 3: group 3; G 4: group 4; SD: standard deviation; CI: confidence interval; SE: standard error.

Effects on weight, BMI, and other obesity-related indicators

Of the 18 included studies, 17 reported weight and/or BMI as primary outcomes (Table 2). Among these studies, those by Kim et al., Shuger et al., and Garza et al. found statistically significant reductions in both weight and BMI in the intervention group compared to baseline.^26,35,42 Most other studies also demonstrated a general trend of reduction in the intervention groups. Some studies additionally reported secondary obesity-related indicators, such as waist circumference, hip circumference, and body fat percentage (Table 3).

Table 3.

Summary of waist circumference, hip circumference, and body fat changes in intervention and control groups.

Study	Measurement type	Waist circumference, cm		Hip circumference, cm		Body fat, %
Study	Measurement type	Wearable device	Control	Wearable device	Control	Wearable device	Control
Jakicic et al.	Mean (95% CI)	-	-	-	-	−3.4 (−4.3, −2.6)	−5.1 (−6, −4.3)
Pellegrini et al.	Mean ± SD Baseline Postintervention	G 1: 114.3 ± 14.1 106.3 ± 13.0 G 2: 111.7 ± 6.9 104.4 ± 9.9	112.7 ± 12.0 104.3 ± 8.9	G 1: 122.2 ± 6.8 115.2 ± 6.0 G 2: 119.0 ± 5.6 110.9 ± 8.2	120.3 ± 7.3 112.6 ± 6.3	G 1: 45.8 ± 4.6 42.5 ± 5.0 G 2: 46.5 ± 4.4 42.4 ± 4.8	48.9 ± 5.4 46.6 ± 4.7
Shuger et al.	Mean (SE) Baseline Postintervention	G 1: 105.91 (2.18)* 102.32 (2.23)* G 2: 106.04 (2.19)* 99.27 (2.22)*	G 3: 106.26 (2.19)* 102.77 (2.28)* G 4: 108.29 (2.18)* 105.87 (2.24)*	-	-	G 1: 36.57 (0.75)* 32.38 (0.85)* G 2: 36.46 (0.75)* 31.42 (0.81)*	G 3: 36.37 (0.76)* 33.12 (0.92)* G 4: 36.69 (0.75)* 32.37 (0.86)*
Duncan et al.	Mean ± SD Mean (95% CI) Baseline Postintervention	99.6 ± 10.8 96.04 (94.49, 97.59)	99.7 ± 11.7 95.45 (93.10, 97.80)	-	-	-	-
Vaz et al.	Mean ± SD	−7.57 ± 1.65*^,†	−1.86 ± 1.25^†	-	-	-	-
Bowen-Jallow et al.	Mean ± SD	110.89 ± 9.94 (n = 18) 101.3 ± 3.05 (n = 10)	114.1 ± 16.42 (n = 30) 114.2 ± 22.8 (n = 13)	-	-	-	-
Lyons et al.	Mean ± SD Baseline Postintervention	-	-	-	-	44.98 ± 5.28 44.73 ± 5.73	45.17 ± 5.39 45.38 ± 6.06
Shin et al.	Mean ± SD	G 1: −2.4 ± 2.3 G 2: −4.4 ± 3.3	−1.2 ± 3.0	-	-	G 1: −0.6 ± 1.3 G 2: −2.2 ± 2.7	−0.2 ± 1.9

*Significantly difference within group; ^†significantly difference the changes between two group; BMI: body mass index; CI: confidence interval; G 1: group 1; G 2: group 2; G 3: group 3; G 4: group 4; SD: standard deviation; SE: standard error.

In the meta-analysis of weight outcomes, the intervention group showed an average reduction of −1.21 kg compared to controls, though this did not reach statistical significance (P = 0.13; Figure 1). Notably, the BMI meta-analysis revealed a statistically significant overall reduction of −0.63 kg/m² (P = 0.001). This effect was particularly pronounced in the pediatric and adolescent subgroup, which showed a significant decrease of −0.91 kg/m² (P < 0.00001) with moderate certainty of evidence, whereas the adult subgroup did not show a significant change (Figure 2 and eTable 5 in the Supplemental file 2).

Figure 1.

Forest plot for the effect of changes on weight in the overweight or obese between wearable device group versus control group.

Figure 2.

Forest plot for the effect of changes on BMI in the overweight or obese between wearable device group versus control group. BMI: body mass index.

Beyond weight and BMI, other obesity-related indicators were reported across eight studies (Table 3). Among adult participants, waist circumference significantly decreased in the wearable device group compared to controls (MD, −5.22 cm; P = 0.007). Although this result was accompanied by very low certainty of evidence (eTable 5 in the Supplemental file 2), the magnitude of reduction remains clinically noteworthy (eFigure 4 in the Supplemental file 2). However, no significant differences were observed for other indicators, including body fat percentage (P = 0.71), which also showed very low certainty of evidence in the GRADE evaluation (eTable/5 and eFigure 5 in the Supplemental file 2).

Effects on blood pressure, lipid profiles, and glycemic indicators

Outcomes related to systolic and diastolic blood pressures were reported in three studies involving adult participants (eTable 2 in the Supplemental file 2). Meta-analysis showed no statistically significant differences between the groups for either systolic blood pressure (MD, 0.24 mmHg; P = 0.93; eFigure 6 in the Supplemental file 2) or diastolic blood pressure (MD, −2.02 mmHg; P = 0.06; eFigure 7 in the Supplemental file 2).

Regarding lipid profiles, the pediatric and adolescent population demonstrated significant improvements in the wearable device group, with reductions in total cholesterol (−9.18 mg/dL; eFigure 8a in the Supplemental file 2), triglycerides (−15.58 mg/dL; eFigure 8b in the Supplemental file 2), and low-density lipoprotein cholesterol (LDL-C) (−11 mg/dL; eFigure 8c in the Supplemental file 2), alongside an increase in high-density lipoprotein cholesterol (HDL-C) (5.04 mg/dL; eFigure 8d in the Supplemental file 2) (all P < 0.00001). In contrast, adult studies showed no significant changes in any lipid indicators (eFigure 8a-d in the Supplemental file 2).

For glycemic control, a meta-analysis of three adult studies indicated a statistically significant reduction in HbA1c levels in the intervention group compared to controls (MD, −0.24%; P < 0.00001; eFigure 9 in the Supplemental file 2). This result was supported by low certainty of evidence (eTable 5 in the Supplemental file 2).

Publication bias

The funnel plot for the weight and BMI outcomes (eFigure 10 in the Supplemental file 2) appeared symmetric, indicating a low risk of publication bias.

Heterogeneity

Although the I² value indicated substantial heterogeneity, the Galbraith plot for weight and BMI outcomes showed that no individual study fell outside the expected bounds (eFigure 11 in the Supplemental file 2).

Discussion

Summary

This meta-analysis comprehensively evaluated the effects of wearable device-based interventions on health indicators among individuals with overweight or obesity, including weight, BMI, waist circumference, blood glucose, and lipid levels. Overall, users of wearable devices showed significant improvements in some health outcomes compared to controls. Notably, BMI was significantly reduced, with a more pronounced effect observed in the pediatric and adolescent subgroup. Among adults, a reduction in waist circumference was found, and improvements in HbA1c levels were noted. In contrast, no significant effects were observed for body weight, blood pressure, or certain lipid outcomes.

Interpretation of weight and BMI outcomes

Although the overall analysis revealed a trend toward greater weight reduction in the intervention group compared to controls, the difference was not statistically significant. This may be attributed to a combination of factors such as the intensity of the intervention, variations in intervention duration, participant adherence, and the nature of the control group intervention. In particular, many studies included control groups that received some form of education or self-management support, which may have minimized the contrast in effectiveness compared to interventions involving wearable devices alone.

In contrast, the meta-analysis on BMI showed a statistically significant overall reduction, with the pediatric and adolescent subgroup exhibiting a notable decrease of −0.91 kg/m². This suggests that children and adolescents may be more responsive to behavioral interventions during growth periods^43–45 and that early interventions may yield preventative benefits. The notable BMI reduction in younger populations can be understood through the Technology Acceptance Model. Digital natives exhibit higher perceived ease of use and engagement with wearable interfaces. This interaction fosters intrinsic motivation, a core component of Self-Determination Theory, which leads to superior behavioral adherence compared to adults.^46,47 These findings are consistent with previous systematic reviews focusing on younger populations.^48,49

Conversely, the adult subgroup did not show a significant change in BMI. This may reflect the greater difficulty of inducing sustained behavioral changes in adults, or the relatively short average intervention duration among the included studies (approximately 6.6 months, ranging from 1 to 24 months), which may have been insufficient to produce meaningful weight-related changes. A previous meta-analysis on the impact of wearable devices on weight management reported greater reductions in weight and BMI when wearable technology was used for ≥12 weeks.⁵⁰ However, another study suggested that BMI reduction was more prominent in short-term interventions (≤4 months; MD, −0.62) and diminished with longer-term use, potentially reflecting a decline in novelty and engagement over time.⁵¹

Although wearable device-based interventions did not consistently demonstrate significantly greater weight reduction compared with control groups in both adults and children/adolescents, decreases in weight and BMI from baseline were observed among participants using wearable devices. This finding suggests that wearable devices might contribute to weight management in individuals with overweight or obesity by promoting improvements in dietary and physical activity behaviors. Furthermore, based on the GRADE evaluation, the certainty of evidence for BMI reduction in children and adolescents was rated as moderate, supporting the conclusion that wearable device-supported interventions may be more effective than self-monitoring alone in children/adolescents. These findings suggest that wearable devices serve as a practical tool to facilitate the development of healthier behavioral habits during critical growth periods, potentially preventing the progression of obesity and improving long-term metabolic health outcomes.

Interpretation of waist circumference, body fat percentage, and other obesity indicators

Waist circumference was reported in nine studies, with significant reductions observed in the wearable device intervention groups compared to controls, particularly among adult participants. The average decrease of −5.22 cm is clinically meaningful, as waist circumference is considered a more accurate predictor of cardiovascular and metabolic risk than body weight or BMI.^52–54 A previous meta-analysis reported that each 1 cm increase in waist circumference was associated with a significantly higher risk of cardiovascular disease, even after adjusting for age, cohort year, or treatment (relative risk, 1.02; 95% CI, 1.01 to 1.03, P < 0.05).⁵⁵ In addition, a recent study reported that a 1 cm increase in waist circumference was associated with a significantly higher risk of stroke (odds ratio, 1.01; 95% CI, 1.001 to 1.02, P = 0.026).⁵⁶ Based on these findings, the additional 5.22 cm reduction in waist circumference observed in the wearable device group suggests that wearable device-based interventions could contribute to meaningful reductions in the risks of cardiovascular disease and stroke among individuals with overweight or obesity.

Waist circumference reduction observed in the present study is consistent with prior evidence from exercise-based interventions in metabolically dysregulated populations. Meta-analysis have shown that concurrent aerobic and resistance training leads to significant improvements in body composition, including reductions in body fat percentage and abdominal adiposity, as well as favorable changes in lipid and glycemic profiles.⁵⁷ In addition, aerobic exercise-based interventions in patients with diabesity have been associated with significant reductions in waist circumference and other cardiometabolic risk markers, supporting the clinical relevance of central adiposity reduction.⁵⁸ However, this study did not evaluate cardiovascular or cerebrovascular outcomes; therefore, further research is needed to determine whether wearable device–based interventions could reduce major cardiovascular and stroke events.

In contrast, no statistically significant change was observed in body fat percentage, and high heterogeneity across studies suggests that these results should be interpreted with caution. Although hip circumference was reported in some studies, insufficient data prevented its inclusion in the meta-analysis.

Interpretation of blood pressure, lipid, and glycemic outcomes

The effects of wearable devices on blood pressure and metabolic indicators were limited. No statistically significant differences were found between intervention and control groups in either systolic or diastolic blood pressure. Although diastolic pressure tended to decrease in the intervention group, the difference did not reach statistical significance. This may be because changes in blood pressure are difficult to achieve through short-term lifestyle interventions alone^59,60 and are highly influenced by baseline conditions such as a history of hypertension or antihypertensive medication use. Several studies have emphasized the importance of long-term behavioral approaches (≥1 year) for sustained improvements in blood pressure and related outcomes.^59–61

Lipid profiles improved significantly in children/adolescents (total cholesterol, triglycerides, LDL-C, and HDL-C), supporting early preventive intervention. Adult studies showed no significant group differences in lipid indicators, suggesting age-related or baseline metabolic impacts on responsiveness.

Regarding glycemic control, HbA1c levels significantly decreased in the intervention group, suggesting that lifestyle modification had a favorable effect on glucose regulation. HbA1c is known to respond more sensitively and earlier than body weight to behavioral interventions,⁶² supporting the potential utility of wearable device-based strategies for glycemic management. The observed −0.24% reduction in HbA1c is directionally consistent with evidence from diabesity-focused meta-analysis showing that HIIT, when compared with nonexercise or standard care, leads to clinically meaningful improvements in glycemic control and insulin resistance, even in the absence of substantial body composition changes.¹¹ Furthermore, comparative evidence indicates that HIIT achieves glycemic and cardiometabolic outcomes that are at least comparable to those of moderate-intensity continuous training, while conferring greater improvements in insulin sensitivity and cardiorespiratory fitness in patients with diabesity.⁶³ Prior studies further suggest that HIIT induces early metabolic adaptations through mechanisms such as enhanced insulin sensitivity and increased translocation of glucose transporter type 4 (GLUT4), which can occur before substantial weight loss is achieved. In this context, wearable devices may play a key role by supporting adherence to the target exercise intensity required to elicit these early metabolic benefits.^64,65

Age-specific differences in intervention effectiveness

The effectiveness of wearable device-based interventions varied by age group. In particular, the pediatric and adolescent subgroup showed significant improvements in BMI and lipid outcomes compared to controls, with low heterogeneity indicating consistency across studies. This may be explained by greater receptiveness and adherence to behavior change interventions among children and adolescents. Conversely, the adult subgroup exhibited fewer significant effects and higher heterogeneity, limiting the interpretability of results. This discrepancy may be due to slower behavioral changes in adults, shorter intervention durations, or confounding factors such as chronic disease or medication use.

Strengths and limitations

This study provides a comprehensive meta-analysis of wearable device-based interventions across multiple metabolic health markers, including body weight, BMI, waist circumference, lipid profiles, and blood glucose. Our systematic search covered literature through 2025, ensuring up-to-date coverage. Subgroup analysis by age group further elucidates differential effects between adults and children/adolescents. By standardizing reported values (e.g. interquartile ranges and mean differences) and focusing on pre–post change measures, data comparability was enhanced.

However, several limitations exist. First, the study protocol was not pre-registered in PROSPERO, which might raise concerns regarding potential bias in this review process. To mitigate this, predefined eligibility criteria were strictly applied, PRISMA guidelines were followed, and analysis was conducted according to a finalized protocol established prior to data extraction. Second, the number of studies for some outcomes was limited, potentially reducing statistical power. To address this, random-effects models were used, age-specific subgroup analysis was conducted, and the certainty of evidence was evaluated using the GRADE approach. Third, heterogeneity in intervention characteristics, including device types, intervention duration, and control conditions, may limit generalizability. This heterogeneity was explored through subgroup analysis, Galbraith plots, with heterogeneity measures reported transparently. Fourth, some studies were rated as having a high risk of bias. As previously noted, complete blinding of participants is inherently difficult in studies involving wearable device interventions. One method to mitigate this bias is the use of wait-list control designs.^66,67 Among the included studies, three adopted this approach,^36,37,39 and these were assessed as having a low or moderate risk of bias. Finally, most included studies had relatively short followup periods, limiting the assessment of long-term health outcomes and highlighting the need for future longitudinal research.

Public health implications

Wearable devices offer practical tools for promoting health behaviors and supporting lifestyle modification, particularly among children and adolescents who readily adopt digital technologies. Our findings of consistent BMI and metabolic improvements in the pediatric/adolescent subgroup suggest these devices could be integrated into school-based or community health programs. The observed reduction in HbA1c underscores their potential role in chronic disease prevention. Furthermore, the results of this meta-analysis suggest that wearable devices might be a useful adjunct tools for weight management in real-world settings among individuals with overweight or obesity by enabling real-time self-monitoring and feedback on lifestyle behaviors. Health policymakers and clinicians may consider incorporating wearable technologies into preventive health strategies. Implementation strategies should prioritize linking wearable data with electronic health records to develop sustainable, scalable, and personalized interventions. Cost-effectiveness analysis is also warranted.

Conclusion

In summary, wearable device-based interventions were associated with significant improvements in BMI and HbA1c, especially in younger populations, whereas effects on absolute body weight and blood pressure were not statistically significant. These results indicate that wearable devices may be effective for selected outcomes and user groups. Future research should adopt precision intervention designs, address long-term followup, and explore strategies for seamless integration of wearable technologies into routine healthcare to optimize efficacy, engagement, and user adherence.

Supplemental Material

sj-docx-1-dhj-10.1177_20552076261431505 - Supplemental material for Differential effects of wearable device-based interventions on weight and health outcomes in adults and youth with overweight or obesity: A systematic review and meta-analysis

Supplemental material, sj-docx-1-dhj-10.1177_20552076261431505 for Differential effects of wearable device-based interventions on weight and health outcomes in adults and youth with overweight or obesity: A systematic review and meta-analysis by So Yeon Lee, Kyung-In Joung, Kwang Joon Kim and Sook Hee An in DIGITAL HEALTH

Supplemental Material

sj-docx-2-dhj-10.1177_20552076261431505 - Supplemental material for Differential effects of wearable device-based interventions on weight and health outcomes in adults and youth with overweight or obesity: A systematic review and meta-analysis

Supplemental material, sj-docx-2-dhj-10.1177_20552076261431505 for Differential effects of wearable device-based interventions on weight and health outcomes in adults and youth with overweight or obesity: A systematic review and meta-analysis by So Yeon Lee, Kyung-In Joung, Kwang Joon Kim and Sook Hee An in DIGITAL HEALTH

Footnotes

ORCID iDs

Kwang Joon Kim

Sook Hee An

Ethical considerations

There were no human participants in this study, and informed consent was not required.

Author contributions

SHA, KJK, SYL, and KIJ conceptualized and designed the study. SHA, SYL, and KJK collected the data. SHA, KJK, SYL, and KIJ performed the data analysis. SHA, KJK, SYL, and KIJ contributed to manuscription preparation, including drafting, discussion, and revisions.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was conducted with support from the 2023 contract research and development project (20230813B0AA-00) of the Korea Health Promotion Institute (KHPI).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

All datasets analyzed in the current study are available from the corresponding author upon reasonable request.

Guarantor

The guarantors of this work are SHA and KJK, who jointly accept responsibility for the integrity of the data and the accuracy of the analysis.

Supplemental material

Supplemental material for this article is available online.

References

Smith

. Obesity statistics. Prim Care 2016; 43: 121–135.

Hyde

. Europe battles with obesity. Lancet 2008; 371: 2160–2161.

Finucane

Stevens

Cowan

, et al. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants. Lancet 2011; 377: 557–567.

Conway

Rene

. Obesity as a disease: no lightweight matter. Obes Rev 2004; 5: 145–151.

Iyengar

Gucalp

Dannenberg

, et al. Obesity and cancer mechanisms: tumor microenvironment and inflammation. J Clin Oncol 2016; 34: 4270–4276.

Gadde

Martin

Berthoud

, et al. Obesity: pathophysiology and management. J Am Coll Cardiol 2018; 71: 69–84.

Welsh

Hammad

Piña

, et al. Obesity and cardiovascular health. Eur J Prev Cardiol 2024; 31: 1026–1035.

Hall

Hildebrandt

Kuo

. Obesity hypertension: role of leptin and sympathetic nervous system. Am J Hypertens 2001; 14: 103S–115S.

Tremmel

Gerdtham

Nilsson

, et al. Economic burden of obesity: a systematic literature review. Int J Environ Res Public Health 2017; 14: 435.

10.

Batrakoulis

Jamurtas

Metsios

, et al. Comparative efficacy of 5 exercise types on cardiometabolic health in overweight and obese adults: a systematic review and network meta-analysis of 81 randomized controlled trials. Circ Cardiovasc Qual Outcomes 2022; 15: e008243.

11.

Al-Mhanna

Franklin

Tarnopolsky

, et al. Impact of high-intensity interval training on cardiometabolic health in patients with diabesity: a systematic review and meta-analysis of randomized controlled trials. Diabetol Metab Syndr 2025; 17: 406.

12.

Flores Mateo

Granado-Font

Ferré-Grau

, et al. Mobile phone apps to promote weight loss and increase physical activity: a systematic review and meta-analysis. J Med Internet Res 2015; 17: e253.

13.

Jafleh

Alnaqbi

Almaeeni

, et al. The role of wearable devices in chronic disease monitoring and patient care: a comprehensive review. Cureus 2024; 16: e68921.

14.

Ferguson

Olds

Curtis

, et al. Effectiveness of wearable activity trackers to increase physical activity and improve health: a systematic review of systematic reviews and meta-analyses. Lancet Digit Health 2022; 4: e615–e26.

15.

Gagnon

Ouellet

Attisso

, et al. Wearable devices for supporting chronic disease self-management: scoping review. Interact J Med Res 2024; 9: e55925.

16.

Liu

Sun

, et al. Mapping the landscape of digital health intervention strategies: 25-year synthesis. J Med Internet Res 2025; 13: e59027.

17.

Joung

Bang

, et al. Comparative effectiveness of wearable devices and built-in step counters in reducing metabolic syndrome risk in South Korea: population-based cohort study. JMIR Mhealth Uhealth 2025; 25: e64527.

18.

Mattison

Canfell

Forrester

, et al. The influence of wearables on health care outcomes in chronic disease: systematic review. J Med Internet Res 2022; 24: e36690.

19.

Chua

REC

Lau

Ang

, et al. Effectiveness of wearable technology-based physical activity interventions for adults with type 2 diabetes mellitus: a systematic review and meta-regression. J Diabetes 2024; 16: e70002.

20.

Page

McKenzie

Bossuyt

, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Br Med J 2021; 372: n71.

21.

Sterne

JAC

Savović

Page

, et al. Rob 2: a revised tool for assessing risk of bias in randomised trials. Br Med J 2019; 366: l4898.

22.

Luo

Wan

Liu

, et al. Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range. Stat Methods Med Res 2018; 27: 1785–1805.

23.

Wan

Wang

Liu

, et al. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol 2014; 14: 1135.

24.

Balshem

Helfand

Schünemann

, et al. GRADE Guidelines: 3. Rating the quality of evidence. J Clin Epidemiol 2011; 64: 401–406.

25.

Turner-McGrievy

Wilcox

Boutté

, et al. The dietary intervention to enhance tracking with mobile devices (DIET mobile) study: a 6-month randomized weight loss trial. Obesity (Silver Spring) 2017; 25: 1336–1342.

26.

Kim

Ryu

Cho

, et al. Impact of personal health records and wearables on health outcomes and patient response: three-arm randomized controlled trial. JMIR Mhealth Uhealth 2019; 7: e12070.

27.

Gómez-Sánchez

Lugones-Sánchez

, et al. Long-term effectiveness of a smartphone app and a smart band on arterial stiffness and central hemodynamic parameters in a population with overweight and obesity (evident 3 study): randomised controlled trial. Nutrients 2022; 14: 4758.

28.

Cadmus-Bertram

Marcus

Patterson

, et al. Randomized trial of a fitbit-based physical activity intervention for women. Am J Prev Med 2015; 49: 414–418.

29.

Archer

Groessl

Sui

, et al. An economic analysis of traditional and technology-based approaches to weight loss. Am J Prev Med 2012; 43: 176–182.

30.

Hartman

Nelson

Cadmus-Bertram

, et al. Technology- and phone-based weight loss intervention: pilot RCT in women at elevated breast cancer risk. Am J Prev Med 2016; 51: 714–721.

31.

Jakicic

Davis

Rogers

, et al. Effect of wearable technology combined with a lifestyle intervention on long-term weight loss: the IDEA randomized clinical trial. JAMA 2016; 316: 1161–1171.

32.

Chen

Guedes

Lung

. Smartphone-based healthy weight management intervention for Chinese American adolescents: short-term efficacy and factors associated with decreased weight. J Adolesc Health 2019; 64: 443–449.

33.

Mameli

Brunetti

Colombo

, et al. Combined use of a wristband and a smartphone to reduce body weight in obese children: randomized controlled trial. Pediatr Obes 2018; 13: 81–87.

34.

Pellegrini

Verba

Otto

, et al. The comparison of a technology-based system and an in-person behavioral weight loss intervention. Obesity (Silver Spring) 2012; 20: 356–363.

35.

Shuger

Barry

Sui

, et al. Electronic feedback in a diet- and physical activity-based lifestyle intervention for weight loss: a randomized controlled trial. Int J Behav Nutr Phys Act 2011; 8: 41.

36.

Duncan

Fenton

Brown

, et al. Efficacy of a multi-component m-health weight-loss intervention in overweight and obese adults: a randomised controlled trial. Int J Environ Res Public Health 2020; 17: 6200.

37.

Vaz

Carnes

Pousti

, et al. A randomized controlled trial of an innovative, user-friendly, interactive smartphone app-based lifestyle intervention for weight loss. Obes Sci Pract 2021; 7: 555–568.

38.

Bowen-Jallow

Nunez-Lopez

Wright

, et al. Wearable activity tracking device use in an adolescent weight management clinic: a randomized controlled pilot trial. J Obes 2021; 2021: 7625034.

39.

Lyons

Swartz

Lewis

, et al. Feasibility and acceptability of a wearable technology physical activity intervention with telephone counseling for mid-aged and older adults: a randomized controlled pilot trial. JMIR Mhealth Uhealth 2017; 5: 28.

40.

Shin

Yun

Shin

, et al. Enhancing physical activity and reducing obesity through smartcare and financial incentives: a pilot randomized trial. Obesity (Silver Spring) 2017; 25: 302–310.

41.

Peyer

Ellingson

Bus

, et al. Comparative effectiveness of guided weight loss and physical activity monitoring for weight loss and metabolic risks: a pilot study. Prev Med Rep 2017; 6: 271–277.

42.

Garza

Martinez

Yoon

, et al. Effects of telephone aftercare intervention for obese Hispanic children on body fat percentage, physical fitness, and blood lipid profiles. Int J Environ Res Public Health 2019; 16: 5133.

43.

Ridgers

McNarry

Mackintosh

. Feasibility and effectiveness of using wearable activity trackers in youth: a systematic review. JMIR Mhealth Uhealth 2016; 4: e129.

44.

Mackintosh

Chappel

Salmon

, et al. Parental perspectives of a wearable activity tracker for children younger than 13 years: acceptability and usability study. JMIR Mhealth Uhealth 2019; 7: e13858.

45.

Ridgers

Timperio

Brown

, et al. Wearable activity tracker use among Australian adolescents: usability and acceptability study. JMIR Mhealth Uhealth 2018; 6: 86.

46.

Drehlich

Naraine

Rowe

, et al. Using the technology acceptance model to explore adolescents’ perspectives on combining technologies for physical activity promotion within an intervention: usability study. J Med Internet Res 2020; 22: e15552.

47.

Jung

Kang

. Self-determination in wearable fitness technology: the moderating effect of age. Int J Hum–Comput Interact 2022; 38: 1399–1409.

48.

Zhang

Xiong

Zhu

, et al. Promoting child and adolescent health through wearable technology: a systematic review. Digit Health 2024; 10: 20552076241260507.

49.

Goodyear

Armour

. Young people’s perspectives on and experiences of health-related social media, apps, and wearable health devices. Soc Sci 2018; 7: 137.

50.

Yen

Chiu

. The effectiveness of wearable technologies as physical activity interventions in weight control: a systematic review and meta-analysis of randomized controlled trials. Obes Rev 2019; 20: 1485–1493.

51.

Wang

Cheng

Song

, et al. The effectiveness of wearable devices as physical activity interventions for preventing and treating obesity in children and adolescents: systematic review and meta-analysis. JMIR Mhealth Uhealth 2022; 10: e32435.

52.

Ross

Neeland

Yamashita

, et al. Waist circumference as a vital sign in clinical practice: a consensus statement from the IAS and ICCR working group on visceral obesity. Nat Rev Endocrinol 2020; 16: 177–189.

53.

Pinho

CPS

Diniz

ADS

Arruda

IKG

, et al. Waist circumference measurement sites and their association with visceral and subcutaneous fat and cardiometabolic abnormalities. Arch Endocrinol Metab 2018; 62: 416–423.

54.

Dutheil

Gordon

Naughton

, et al. Cardiovascular risk of adipokines: a review. J Int Med Res 2018; 46: 2082–2095.

55.

de Koning

Merchant

Pogue

, et al. Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: meta-regression analysis of prospective studies. Eur Heart J 2007; 28: 850–856.

56.

Liu

Wang

Zhou

, et al. Dynamic waist circumference change: a superior predictor of incident stroke compared to novel and traditional obesity indices. J Stroke Cerebrovasc Dis 2025; 34: 108496.

57.

Al-Mhanna

Alghannam

Alkhamees

, et al. Impact of concurrent aerobic and resistance training on body composition, lipid metabolism and physical function in patients with type 2 diabetes and overweight/obesity: a systematic review and meta-analysis. PeerJ 2025; 13: e19537.

58.

Al-Mhanna

Franklin

Jakicic

, et al. Impact of aerobic exercise on cardiometabolic health in patients with diabesity: a systematic review and meta-analysis of randomized controlled trials. J Diabetes Complications 2025; 39: 109203.

59.

Hinderliter

Sherwood

Craighead

, et al. The long-term effects of lifestyle change on blood pressure: one-year follow-up of the ENCORE study. Am J Hypertens 2014; 27: 734–741.

60.

Moser

. Are lifestyle interventions in the management of hypertension effective? How long should you wait before starting specific medical therapy? An ongoing debate. J Clin Hypertens (Greenwich) 2005; 7: 324–326.

61.

Blumenthal

Hinderliter

Smith

, et al. Effects of lifestyle modification on patients with resistant hypertension: results of the TRIUMPH randomized clinical trial. Circulation 2021; 144: 1212–1226.

62.

Giorgino

Lingvay

Van Gaal

, et al. Early fasting serum glucose or weight reduction with tirzepatide and metabolic outcomes in people with type 2 diabetes: a post hoc analysis of the SURPASS trials. Diabetes Care 2025; 48: 790–798.

63.

Al-Mhanna

Poon

Franklin

, et al. Comparative effectiveness of high-intensity interval training and moderate-intensity continuous training on cardiometabolic health in patients with diabesity: a systematic review and meta-analysis of randomized controlled trials. Diabetol Metab Syndr 2025; 17: 31.

64.

Jelleyman

Yates

O'Donovan

, et al. The effects of high-intensity interval training on glucose regulation and insulin resistance: a meta-analysis. Obes Rev 2015; 16: 942–961.

65.

Bird

Hawley

. Update on the effects of physical activity on insulin sensitivity in humans. BMJ Open Sport Exerc Med 2016; 2: e000143.

66.

Faltinsen

Todorovac

Staxen Bruun

, et al. Control interventions in randomised trials among people with mental health disorders. Cochrane Database Syst Rev 2022; 4: MR000050.

67.

Cunningham

Kypri

McCambridge

. Exploratory randomized controlled trial evaluating the impact of a waiting list control design. BMC Med Res Methodol 2013; 13: 150.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.27 MB

15.45 MB