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Published on: March 7, 2019
The impact of machine learning on physical activity-related health outcomes: A systematic review and meta-analysis
Ezgi Hasret Kozan Cikirikci1, Melek Nihal Esin1
1Florence Nightingale Faculty of Nursing, Department of Public Health Nursing, Istanbul University-Cerrahpasa, Istanbul, Turkey.
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 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.
Results:
Analysis 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.
Conclusion:
ML 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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