Institute of Applied Biosciences, Centre for Research and Technology Hellas, Greece; Lab of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Greece.
Institute of Applied Biosciences, Centre for Research and Technology Hellas, Greece; Lab of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Greece.
Int J Med Inform. 2018 Mar;111:7-16. doi: 10.1016/j.ijmedinf.2017.12.012. Epub 2017 Dec 17.
The benefits of regular physical activity for health and quality of life are unarguable. New information, sensing and communication technologies have the potential to play a critical role in computerised decision support and coaching for physical activity.
We provide a literature review of recent research in the development of physical activity interventions employing computerised decision support, their feasibility and effectiveness in healthy and diseased individuals, and map out challenges and future research directions.
We searched the bibliographic databases of PubMed and Scopus to identify physical activity interventions with computerised decision support utilised in a real-life context. Studies were synthesized according to the target user group, the technological format (e.g., web-based or mobile-based) and decision-support features of the intervention, the theoretical model for decision support in health behaviour change, the study design, the primary outcome, the number of participants and their engagement with the intervention, as well as the total follow-up duration.
From the 24 studies included in the review, the highest percentage (n = 7, 29%) targeted sedentary healthy individuals followed by patients with prediabetes/diabetes (n = 4, 17%) or overweight individuals (n = 4, 17%). Most randomized controlled trials reported significantly positive effects of the interventions, i.e., increase in physical activity (n = 7, 100%) for 7 studies assessing physical activity measures, weight loss (n = 3, 75%) for 4 studies assessing diet, and reductions in glycosylated hemoglobin (n = 2, 66%) for 3 studies assessing glycose concentration. Accelerometers/pedometers were used in almost half of the studies (n = 11, 46%). Most adopted decision support features included personalised goal-setting (n = 16, 67%) and motivational feedback sent to the users (n = 15, 63%). Fewer adopted features were integration with electronic health records (n = 3, 13%) and alerts sent to caregivers (n = 4, 17%). Theoretical models of decision support in health behaviour to drive the development of the intervention were not reported in most studies (n = 14, 58%).
Interventions employing computerised decision support have the potential to promote physical activity and result in health benefits for both diseased and healthy individuals, and help healthcare providers to monitor patients more closely. Objectively measured activity through sensing devices, integration with clinical systems used by healthcare providers and theoretical frameworks for health behaviour change need to be employed in a larger scale in future studies in order to realise the development of evidence-based computerised systems for physical activity monitoring and coaching.
定期进行身体活动对健康和生活质量的益处是毋庸置疑的。新的信息、传感和通信技术有可能在计算机化的决策支持和身体活动指导方面发挥关键作用。
我们对最近使用计算机化决策支持开发身体活动干预措施的研究进行了文献回顾，评估了这些干预措施在健康和患病个体中的可行性和有效性，并探讨了挑战和未来的研究方向。
我们在 PubMed 和 Scopus 文献数据库中检索了使用计算机化决策支持的身体活动干预措施的研究，这些研究是在真实环境中进行的。根据目标用户群体、技术形式（如基于网络或基于移动的）和干预措施的决策支持功能、健康行为改变决策支持的理论模型、研究设计、主要结果、参与者数量及其对干预措施的参与程度以及总随访时间对研究进行了综合分析。
在综述中纳入的 24 项研究中，有最高百分比（n=7，29%）的研究针对久坐的健康个体，其次是患有前驱糖尿病/糖尿病的患者（n=4，17%）或超重个体（n=4，17%）。大多数随机对照试验报告了干预措施的显著积极效果，即增加身体活动（n=7，100%），7 项研究评估了身体活动测量，体重减轻（n=3，75%），4 项研究评估了饮食，糖化血红蛋白降低（n=2，66%），3 项研究评估了血糖浓度。在近一半的研究中使用了加速度计/计步器（n=11，46%）。大多数采用的决策支持功能包括个性化目标设定（n=16，67%）和向用户发送的激励反馈（n=15，63%）。采用较少的功能包括与电子健康记录的集成（n=3，13%）和向护理人员发送警报（n=4，17%）。在大多数研究中（n=14，58%），没有报告健康行为决策支持的理论模型来驱动干预措施的发展。
使用计算机化决策支持的干预措施有可能促进身体活动，为患病和健康个体带来健康益处，并帮助医疗保健提供者更密切地监测患者。未来的研究需要在更大范围内使用传感器设备进行客观的活动测量、与医疗保健提供者使用的临床系统集成以及健康行为改变的理论框架，以实现基于证据的计算机化身体活动监测和指导系统的发展。
