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Meta-Analysis
. 2023 Jun 1;6(6):e2318478.
doi: 10.1001/jamanetworkopen.2023.18478.

Interventions Using Wearable Activity Trackers to Improve Patient Physical Activity and Other Outcomes in Adults Who Are Hospitalized: A Systematic Review and Meta-analysis

Kimberley Szeto  1 , John Arnold  1 , Ben Singh  1 , Bethany Gower  1 , Catherine E M Simpson  1 , Carol Maher  1
Affiliations
Meta-Analysis

Interventions Using Wearable Activity Trackers to Improve Patient Physical Activity and Other Outcomes in Adults Who Are Hospitalized: A Systematic Review and Meta-analysis

Kimberley Szeto et al. JAMA Netw Open. .

Abstract

Importance: Low levels of physical activity during hospitalization are thought to contribute to a range of poor outcomes for patients. Using wearable activity trackers during hospitalization may help improve patient activity, sedentary behavior, and other outcomes.

Objective: To evaluate the association of interventions that use wearable activity trackers during hospitalization with patient physical activity, sedentary behavior, clinical outcomes, and hospital efficiency outcomes.

Data sources: OVID MEDLINE, CINAHL, Embase, EmCare, PEDro, SportDiscuss, and Scopus databases were searched from inception to March 2022. The Cochrane Central Register for Controlled trials, ClinicalTrials.gov, and World Health Organization Clinical Trials Registry were also searched for registered protocols. No language restrictions were imposed.

Study selection: Randomized clinical trials and nonrandomized clinical trials of interventions that used wearable activity trackers to increase physical activity or reduce sedentary behavior in adults (aged 18 years or older) who were hospitalized were included.

Data extraction and synthesis: Study selection, data extraction, and critical appraisal were conducted in duplicate. Data were pooled for meta-analysis using random-effects models. The Preferred Reporting Items for Systematic Reviews and Meta-analyses guideline was followed.

Main outcomes and measures: The primary outcomes were objectively measured physical activity or sedentary behavior. Secondary outcomes included clinical outcomes (eg, physical function, pain, mental health), and hospital efficiency outcomes (eg, length of stay, readmission).

Results: Fifteen studies with a total of 1911 participants were included, representing various surgical cohorts (4 studies), stroke rehabilitation (3 studies), orthopedic rehabilitation (3 studies), mixed rehabilitation (3 studies), and mixed medical (2 studies). All studies were included in meta-analyses. There was a significant association between wearable activity tracker interventions with higher overall physical activity (standardized mean difference, 0.35; 95% CI, 0.15 to 0.54; I2 = 72%; P < .002) and less sedentary behavior (mean difference, -35.46 min/d; 95% CI, -57.43 to -13.48 min/d; I2 = 0; P = .002), and a significant association between wearable activity tracker interventions with improvements in physical function (standardized mean difference, 0.27; 95% CI, 0.08 to 0.46; I2 = 0; P = .006) compared with usual care. There was no significant association between wearable activity tracker interventions with pain, mental health, length of stay, or readmission risk.

Conclusions and relevance: In this systematic review and meta-analysis, interventions that used wearable activity trackers with patients who are hospitalized were associated with higher physical activity levels, less sedentary behavior, and better physical functioning compared with usual care.

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Conflict of interest statement

Conflict of Interest Disclosures: Ms Szeto reported receiving grants from the Australian Government Research Training Program domestic stipend during the conduct of the study. Ms Gower reported receiving personal fees from the Australian Government Research Training Program Scholarship outside the submitted work. Professor Maher reported receiving grants from the National Health and Medical Research Council, Medical Research Future Fund, National Heart Foundation, South Australia Department for Education, Healthway (Western Australia), Hunter New England Local Health District, and LeapForward outside the submitted work. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Flow Diagram of Study Selection
Figure 2.
Figure 2.. Forest Plot of WAT Intervention Association With Overall Physical Activity and Sedentary Behavior
Boxes indicate standardized mean differences (SMDs), with larger boxes reflecting greater weight; horizontal lines indicate 95% CIs; diamonds indicate pooled means, with right and left points indicating 95% CI.
Figure 3.
Figure 3.. Forest Plot of WAT Intervention Association With Clinical Outcomes
Boxes indicate standardized mean differences (SMDs), with larger boxes reflecting greater weight; horizontal lines indicate 95% CIs; diamonds indicate pooled means, with right and left points indicating 95% CI.
Figure 4.
Figure 4.. Forest Plot of WAT Intervention Association With Efficiency Outcomes
Boxes indicate standardized mean differences (SMDs) and risk ratios, with larger boxes reflecting greater weight; horizontal lines indicate 95% CIs; diamonds indicate pooled means or risk ratios, with right and left points indicating 95% CI.
See this image and copyright information in PMC

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