Dataset Information

Fitbit-Based Interventions for Healthy Lifestyle Outcomes: Systematic Review and Meta-Analysis.

ABSTRACT: BACKGROUND:Unhealthy behaviors, such as physical inactivity, sedentary lifestyle, and unhealthful eating, remain highly prevalent, posing formidable challenges in efforts to improve cardiovascular health. While traditional interventions to promote healthy lifestyles are both costly and effective, wearable trackers, especially Fitbit devices, can provide a low-cost alternative that may effectively help large numbers of individuals become more physically fit and thereby maintain a good health status. OBJECTIVE:The objectives of this meta-analysis are (1) to assess the effectiveness of interventions that incorporate a Fitbit device for healthy lifestyle outcomes (eg, steps, moderate-to-vigorous physical activity, and weight) and (2) to identify which additional intervention components or study characteristics are the most effective at improving healthy lifestyle outcomes. METHODS:A systematic review was conducted, searching the following databases from 2007 to 2019: MEDLINE, EMBASE, CINAHL, and CENTRAL (Cochrane). Studies were included if (1) they were randomized controlled trials, (2) the intervention involved the use of a Fitbit device, and (3) the reported outcomes were related to healthy lifestyles. The main outcome measures were related to physical activity, sedentary behavior, and weight. All the studies were assessed for risk of bias using Cochrane criteria. A random-effects meta-analysis was conducted to estimate the treatment effect of interventions that included a Fitbit device compared with a control group. We also conducted subgroup analysis and fuzzy-set qualitative comparative analysis (fsQCA) to further disentangle the effects of intervention components. RESULTS:Our final sample comprised 41 articles reporting the results of 37 studies. For Fitbit-based interventions, we found a statistically significant increase in daily step count (mean difference [MD] 950.54, 95% CI 475.89-1425.18; P<.001) and moderate-to-vigorous physical activity (MD 6.16, 95% CI 2.80-9.51; P<.001), a significant decrease in weight (MD -1.48, 95% CI -2.81 to -0.14; P=.03), and a nonsignificant decrease in objectively assessed and self-reported sedentary behavior (MD -10.62, 95% CI -35.50 to 14.27; P=.40 and standardized MD -0.11, 95% CI -0.48 to 0.26; P=.56, respectively). In general, the included studies were at low risk for bias, except for performance bias. Subgroup analysis and fsQCA demonstrated that, in addition to the effects of the Fitbit devices, setting activity goals was the most important intervention component. CONCLUSIONS:The use of Fitbit devices in interventions has the potential to promote healthy lifestyles in terms of physical activity and weight. Fitbit devices may be useful to health professionals for patient monitoring and support. TRIAL REGISTRATION:PROSPERO International Prospective Register of Systematic Reviews CRD42019145450; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019145450.

SUBMITTER: Ringeval M

PROVIDER: S-EPMC7589007 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Fitbit-Based Interventions for Healthy Lifestyle Outcomes: Systematic Review and Meta-Analysis.

Ringeval Mickael M Wagner Gerit G Denford James J Paré Guy G Kitsiou Spyros S

Journal of medical Internet research 20201012 10

<h4>Background</h4>Unhealthy behaviors, such as physical inactivity, sedentary lifestyle, and unhealthful eating, remain highly prevalent, posing formidable challenges in efforts to improve cardiovascular health. While traditional interventions to promote healthy lifestyles are both costly and effective, wearable trackers, especially Fitbit devices, can provide a low-cost alternative that may effectively help large numbers of individuals become more physically fit and thereby maintain a good hea ...[more]

PMID: 33044175

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Project description:The objective was to describe Diabetes Prevention Program (DPP)-based lifestyle interventions delivered via electronic, mobile, and certain types of telehealth (eHealth) and estimate the magnitude of the effect on weight loss. A systematic review was conducted. PubMed and EMBASE were searched for studies published between January 2003 and February 2016 that met inclusion and exclusion criteria. An overall estimate of the effect on mean percentage weight loss across all the interventions was initially conducted. A stratified meta-analysis was also conducted to determine estimates of the effect across the interventions classified according to whether behavioral support by counselors post-baseline was not provided, provided remotely with communication technology, or face-to-face. Twenty-two studies met the inclusion/exclusion criteria, in which 26 interventions were evaluated. Samples were primarily white and college educated. Interventions included Web-based applications, mobile phone applications, text messages, DVDs, interactive voice response telephone calls, telehealth video conferencing, and video on-demand programing. Nine interventions were stand-alone, delivered post-baseline exclusively via eHealth. Seventeen interventions included additional behavioral support provided by counselors post-baseline remotely with communication technology or face-to-face. The estimated overall effect on mean percentage weight loss from baseline to up to 15months of follow-up across all the interventions was -3.98%. The subtotal estimate across the stand-alone eHealth interventions (-3.34%) was less than the estimate across interventions with behavioral support given by a counselor remotely (-4.31%), and the estimate across interventions with behavioral support given by a counselor in-person (-4.65%). There is promising evidence of the efficacy of DPP-based eHealth interventions on weight loss. Further studies are needed particularly in racially and ethnically diverse populations with limited levels of educational attainment. Future research should also focus on ways to optimize behavioral support.

Project description:ImportanceProsocial interventions encourage voluntary actions that benefit others. Community solidarity in response to the COVID-19 pandemic, expanding mutual aid programs, and health workforce issues have accelerated prosocial health interventions.ObjectiveTo investigate the association of prosocial interventions with health outcomes in clinical trials and observational studies.Data sourcesIn this systematic review and meta-analysis informed by the Cochrane Handbook for Systematic Reviews of Interventions, 5 databases (MEDLINE [via PubMed], Embase, CINAHL, PsycInfo, and Scopus) were searched from database inception through February 23, 2023. The search included terms for altruism and prosocial behaviors, health outcomes, and study type.Study selectionIncluded studies, determined by multiple reviewers, compared health outcomes in a prosocial intervention group with a nonintervention group.Data extraction and synthesisFollowing the Preferred Reporting Items for Systematic Reviews and Meta-analyses guideline, data extraction and synthesis captured quantitative and qualitative data. To pool data from quantitative studies, random-effects meta-analyses were used to estimate the impact of prosocial interventions. To combine data from quantitative and qualitive studies, data were transformed into qualitative narratives using meta-aggregation.Main outcomes and measuresThe main outcome was whether prosocial interventions were associated with improved health outcomes. Barriers to and facilitators of implementation of these interventions were assessed.ResultsThe search identified 5229 citations; 30 studies were included in the synthesis. Studies indicated that prosocial interventions were associated with positive health outcomes for givers (17 studies [56.7]) and recipients (8 [26.7%]). Prosocial interventions included acts of kindness (12 studies [40.0%]), cash gifts (7 [23.3%]), pay-it-forward approaches (6 [20.0%]), and expressions of kindness (5 [16.7%]). Improvements were reported in depression, testing for sexually transmitted diseases, vaccine uptake, physical activity, and individual biomarkers. Data from 6 studies (20.0%) demonstrated that pay-it-forward approaches were associated with increased uptake of diagnostic tests or vaccines among vulnerable groups (moderate certainty of evidence). Data from 14 studies (46.7%) suggested that community connectedness facilitated prosocial interventions. Shared vulnerabilities among groups (eg, sexual minority individuals, older adults) may provide a context for collective mobilization to improve health in local communities.Conclusions and relevanceThis systematic review and meta-analysis found that prosocial interventions were associated with improved health outcomes among vulnerable groups and have been useful for addressing health disparities. Further research is needed to develop and evaluate prosocial interventions.

Project description:BACKGROUND:Wearable sleep monitors are of high interest to consumers and researchers because of their ability to provide estimation of sleep patterns in free-living conditions in a cost-efficient way. OBJECTIVE:We conducted a systematic review of publications reporting on the performance of wristband Fitbit models in assessing sleep parameters and stages. METHODS:In adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we comprehensively searched the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane, Embase, MEDLINE, PubMed, PsycINFO, and Web of Science databases using the keyword Fitbit to identify relevant publications meeting predefined inclusion and exclusion criteria. RESULTS:The search yielded 3085 candidate articles. After eliminating duplicates and in compliance with inclusion and exclusion criteria, 22 articles qualified for systematic review, with 8 providing quantitative data for meta-analysis. In reference to polysomnography (PSG), nonsleep-staging Fitbit models tended to overestimate total sleep time (TST; range from approximately 7 to 67 mins; effect size=-0.51, P<.001; heterogenicity: I2=8.8%, P=.36) and sleep efficiency (SE; range from approximately 2% to 15%; effect size=-0.74, P<.001; heterogenicity: I2=24.0%, P=.25), and underestimate wake after sleep onset (WASO; range from approximately 6 to 44 mins; effect size=0.60, P<.001; heterogenicity: I2=0%, P=.92) and there was no significant difference in sleep onset latency (SOL; P=.37; heterogenicity: I2=0%, P=.92). In reference to PSG, nonsleep-staging Fitbit models correctly identified sleep epochs with accuracy values between 0.81 and 0.91, sensitivity values between 0.87 and 0.99, and specificity values between 0.10 and 0.52. Recent-generation Fitbit models that collectively utilize heart rate variability and body movement to assess sleep stages performed better than early-generation nonsleep-staging ones that utilize only body movement. Sleep-staging Fitbit models, in comparison to PSG, showed no significant difference in measured values of WASO (P=.25; heterogenicity: I2=0%, P=.92), TST (P=.29; heterogenicity: I2=0%, P=.98), and SE (P=.19) but they underestimated SOL (P=.03; heterogenicity: I2=0%, P=.66). Sleep-staging Fitbit models showed higher sensitivity (0.95-0.96) and specificity (0.58-0.69) values in detecting sleep epochs than nonsleep-staging models and those reported in the literature for regular wrist actigraphy. CONCLUSIONS:Sleep-staging Fitbit models showed promising performance, especially in differentiating wake from sleep. However, although these models are a convenient and economical means for consumers to obtain gross estimates of sleep parameters and time spent in sleep stages, they are of limited specificity and are not a substitute for PSG.

Project description:BackgroundPasifika populations experience high incidence and prevalence of obesity and T2DM. However, no international review of lifestyle intervention studies amongst Pasifika communities exists. This study seeks to identify the effect and translatability of lifestyle strategies on weight amongst Pasifika populations.MethodsLifestyle studies involving ≥90% adult Pasifika participants measuring weight change were eligible for inclusion. Database searching was carried out up to December 2021. Databases searched were MEDLINE (Ovid), EMBASE (Ovid), CINAHL (EBSCOhost) and ProQuest Central. Risk of bias was assessed using RoB2 (RCTs) and the National Heart, Lung and Blood Institute (NHLBI) quality assessment tool. Meta-analysis and meta-regression used a bivariate random-effects model. Strategies were coded against pre-identified components of the newly proposed Cultural and Sustainability Assessment of Intervention (CSAI) framework.FindingsTwenty-three studies (n = 4258 participants) met inclusion and exclusion criteria. Thirty-two lifestyle strategies targeting weight loss (WL) and 7 targeting weight maintenance (WM) were extracted. Meta-analysis estimates small but significant effect of -0.26 standard deviations (95% CI -0.51 to -0.02), with RCTs demonstrating a non-significant effect of -0.23 standard deviations (95% CI -0.49 to 0.035). Culturally relevant strategies included community and peer support facilitators and team-based activities. The CSAI identified 14 out of 23 studies with low cultural competency and sustainability scores (<60%).InterpretationQualitative and quantitative analysis show tailored lifestyle interventions has had an estimated small but beneficial effect on WL amongst Pasifika communities. Potential for tailored interventions design to incorporate psychosocial and behavioural considerations. The CSAI has the potential for systematically identifying cultural and sustainability components of efficacy in interventions.FundingThis review was funded under Western Sydney University's Postgraduate Research Scholarship.

Dataset Information

Fitbit-Based Interventions for Healthy Lifestyle Outcomes: Systematic Review and Meta-Analysis.

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Fitbit-Based Interventions for Healthy Lifestyle Outcomes: Systematic Review and Meta-Analysis.

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