Kozan Cikirikci Ezgi Hasret, Esin Melek Nihal
Florence Nightingale Faculty of Nursing, Department of Public Health Nursing, Istanbul University-Cerrahpasa, Istanbul, Turkey.
Int Nurs Rev. 2025 Jun;72(2):e70019. doi: 10.1111/inr.70019.
To analyze randomized controlled trials evaluating the effectiveness of machine learning (ML)-based interventions in promoting physical activity.
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.
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.
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).
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.
ML can effectively support public health initiatives by enabling self-monitoring, personalized recommendations, adaptive interventions, and predicting future physical activity behavior.
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.
分析评估基于机器学习（ML）的干预措施在促进身体活动方面有效性的随机对照试验。
来自随机对照试验的关于基于ML的干预措施增加身体活动有效性的证据有限。综合现有证据对于护士将此类进展融入其护理工作并实施促进健康的干预措施至关重要。
通过PubMed、EBSCO、Cochrane和土耳其国家数据库检索了2013年至2024年的随机对照试验。该研究按照PRISMA声明进行并报告。使用Cochrane偏倚风险1（RoB 1）工具评估方法学质量。纳入了10项研究，总样本量为2269人。
研究分析表明，基于ML的生活方式干预措施在检测身体活动水平、增加每日步数以及中度至剧烈身体活动、预测对身体活动水平目标的依从性以及定制建议和反馈方面是有效的。荟萃分析显示，ML干预措施显著增加了每日步数（Hedge's g = 0.402，95% CI：0.231 - 0.573，p < 0.000）。
由护士主导的涉及基于ML的身体活动促进倡议的研究有限。仅纳入以英文和土耳其文发表的研究可能排除了潜在有价值的数据。
ML可以通过实现自我监测、个性化建议、适应性干预以及预测未来身体活动行为，有效地支持公共卫生倡议。
护士可以利用ML算法提供及时、定制且具有成本效益的护理，以促进身体活动。为了将ML纳入公共卫生倡议，并制定与护理模式相一致的项目，创建支持护士与软件开发人员合作且由护士引领这一过程的机会和政策至关重要。
