SynthesisInternational nursing review2025
The impact of machine learning on physical activity-related health outcomes: A systematic review and meta-analysis.
Synthesis in International nursing review, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Effects of virtual reality interventions on motor skills in children and adolescents with intellectual disabilities: a systematic review and meta-analysis.Frontiers in psychiatry · 2026Pooled it
- The impact of machine learning on physical activity-related health outcomes: A systematic review and meta-analysis.International nursing review · 2025Pooled it
- Machine Learning Applications for In-School Physical Activity Data Using IMUs in Children and Adolescents: A Systematic Review for Health Promotion.Journal of primary care & community healthPooled it
- Unveiling the Digital Phenotype of Physical Activity Behavior in Community-Dwelling Older Adults Using Machine Learning.Bioengineering (Basel, Switzerland) · 2026Article
- Digital health solutions for chronic disease physical activity management: wearable devices, artificial intelligence, and public health implementation.Frontiers in public health · 2026Review
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo analyze randomized controlled trials evaluating the effectiveness of machine learning (ML)-based interventions in promoting physical activity.
backgroundEvidence 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.
methodsRandomized controlled trials from 2013 to 2024 have been accessed by PubMed, EBSCO, Cochrane, and Turkish national databases. The study was conducted and reported in accordance with the PRISMA statement. The methodological quality was assessed using the Cochrane Risk of Bias 1 (RoB 1) tool. Ten studies with a total sample size of 2269 individuals were included.
resultsAnalysis of studies showed that ML-based lifestyle interventions are effective in detecting physical activity levels, increasing daily step count and moderate to vigorous physical activity, predicting adherence to physical activity levels goals, and tailoring recommendations and feedback. Meta-analysis revealed that ML interventions significantly increased daily step count (Hedge's g = 0.402, 95% CI: 0.231-0.573, p<0.000). DISCUSSION: The studies involving ML-based physical activity promotion initiatives led by nurses were limited. The inclusion of studies published only in English and Turkish may have excluded potentially valuable data.
conclusionML can effectively support public health initiatives by enabling self-monitoring, personalized recommendations, adaptive interventions, and predicting future physical activity behavior. IMPLICATIONS FOR NURSING PRACTICE AND POLICY: Nurses can leverage ML algorithms to provide timely, tailored, and cost-effective care to promote physical activity. To integrate ML into public health initiatives, and develop programs aligned with care models, it is essential to create opportunities and policies that support collaboration between nurses and software developers with nurses leading the process.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.