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TRUVACE RECORD VERSION
record: TRV-2026-2742
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:17:45.136579Z
status: published
lens: g_space
sector: health
headline: The impact of machine learning on physical activity–related health outcomes: A systematic review and meta‐analysis
dek: AIM: To analyze randomized controlled trials evaluating the effectiveness of machine learning (ML)-based interventions in promoting physical activity. BACKGROUND: Evidence on the effectiveness of ML-based interventions to increase physical activity from randomized controlled trials is limited. Synthesizing existing evidence is crucial for nurses to integrate such advancements into their care and implement health-promoting interventions. METHODS: Randomized controlled trials from 2013 to 2024 have been accessed b…
gain_title: A meta-analysis of five randomized trials found that machine-learning-based physical activity interventions raised adults' daily step counts more than non-ML comparison programs (Hedge's g = 0.402, 95% CI 0.231-0.573).
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis of five randomized trials found that machine-learning-based physical activity interventions raised adults' daily step counts more than non-ML comparison programs (Hedge's g = 0.402, 95% CI 0.231-0.573).
gain_evidence: ML interventions significantly increased daily step count (Hedge's g = 0.402, 95% CI: 0.231-0.573, p<0.000) | ML-based interventions increased the mean daily step count compared with control groups
problem_reading: (none)
problem_evidence: (none)
quick_read: A systematic review and meta-analysis published in International Nursing Review in May 2025 pooled randomized trials from 2013 to 2024 that tested machine-learning-based programs for getting adults to move more. Ten trials with 2,269 participants (1,349 in intervention groups, 920 in control groups) were included. Five of them fed the meta-analysis of daily steps, which found a small-to-moderate gain for the machine-learning groups (Hedge's g = 0.402, 95% CI 0.231-0.573).

The trials used machine learning in different ways: six to deliver the intervention, two for both delivery and data collection, one for data collection and one to predict frailty after an exercise program. Most delivered the programs through mobile apps. Results on moderate-to-vigorous activity came from three trials and were not pooled.

The review compared ML-based programs with standard or non-ML ones, and the gain was found for steps rather than for broader health outcomes. One of the six step-count trials, from Brazil, found no significant difference between groups, and the review reports little work led by nurses.
limitation: Some included trials had small samples and limited diversity, few were led by nurses, and the review included only English and Turkish publications, so the effects apply to specific groups.
tag: Evidence-backed gain
key_points: The search found 2,761 records; 163 were assessed in full, ten randomized trials were included and five went into the step-count meta-analysis. | Seven trials were in the United States, and one each in China, Australia and Brazil; participants were adults, including one trial of people over 65 and one of college students. | The five trials pooled for steps showed no heterogeneity (I2 = 0.000%), and Egger's and Kendall's tests showed no evidence of publication bias. | One trial found that algorithm-driven personalization raised step count more than random text messages or no intervention. | A Brazilian trial found no significant difference in average daily steps between intervention and control groups. | In one trial, the intervention group's moderate-to-vigorous activity minutes per day rose while the control group's fell. | In one trial of older adults, a stacking model outperformed other algorithms at predicting whether people would reverse frailty after an exercise program.
rundown: The authors searched PubMed, EBSCO, Cochrane and Turkish national databases from October 2023 to December 2024 under the PRISMA statement, with a protocol registered on PROSPERO. Eligible studies were randomized trials in adults that compared an ML-based intervention with a standard or non-ML one and reported outcomes such as steps, moderate-to-vigorous activity or goal adherence.

Risk of bias was low in most studies, though some lacked allocation concealment or blinding of participants and outcome assessors. The authors rated the overall evidence quality high on the GRADE method. Six trials covering 499 participants reported on daily steps, and five of them were pooled.

Beyond steps, the trials reported mixed outcomes: moderate-to-vigorous activity rose in two trials, and in a third the findings concerned who met their activity goal. A website trial found similar usage and satisfaction for video-tailored and text-tailored versions. Self-reported activity rose in groups using the mPED app.

The authors note that some trials had small samples and limited diversity, so the effects apply to specific groups, and that access to these tools can be harder for older people, people with limited digital literacy and those in low-income areas.
sources:
- peer_reviewed | International Nursing Review | https://doi.org/10.1111/inr.70019 | 2025-05-12
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