Singh Ben, Olds Timothy, Brinsley Jacinta, Dumuid Dot, Virgara Rosa, Matricciani Lisa, Watson Amanda, Szeto Kimberley, Eglitis Emily, Miatke Aaron, Simpson Catherine E M, Vandelanotte Corneel, Maher Carol
Alliance for Research in Exercise Nutrition and Activity (ARENA), University of South Australia, Adelaide, SA, Australia.
School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, QLD, Australia.
NPJ Digit Med. 2023 Jun 23;6(1):118. doi: 10.1038/s41746-023-00856-1.
Chatbots (also known as conversational agents and virtual assistants) offer the potential to deliver healthcare in an efficient, appealing and personalised manner. The purpose of this systematic review and meta-analysis was to evaluate the efficacy of chatbot interventions designed to improve physical activity, diet and sleep. Electronic databases were searched for randomised and non-randomised controlled trials, and pre-post trials that evaluated chatbot interventions targeting physical activity, diet and/or sleep, published before 1 September 2022. Outcomes were total physical activity, steps, moderate-to-vigorous physical activity (MVPA), fruit and vegetable consumption, sleep quality and sleep duration. Standardised mean differences (SMD) were calculated to compare intervention effects. Subgroup analyses were conducted to assess chatbot type, intervention type, duration, output and use of artificial intelligence. Risk of bias was assessed using the Effective Public Health Practice Project Quality Assessment tool. Nineteen trials were included. Sample sizes ranged between 25-958, and mean participant age ranged between 9-71 years. Most interventions (n = 15, 79%) targeted physical activity, and most trials had a low-quality rating (n = 14, 74%). Meta-analysis results showed significant effects (all p < 0.05) of chatbots for increasing total physical activity (SMD = 0.28 [95% CI = 0.16, 0.40]), daily steps (SMD = 0.28 [95% CI = 0.17, 0.39]), MVPA (SMD = 0.53 [95% CI = 0.24, 0.83]), fruit and vegetable consumption (SMD = 0.59 [95% CI = 0.25, 0.93]), sleep duration (SMD = 0.44 [95% CI = 0.32, 0.55]) and sleep quality (SMD = 0.50 [95% CI = 0.09, 0.90]). Subgroup analyses showed that text-based, and artificial intelligence chatbots were more efficacious than speech/voice chatbots for fruit and vegetable consumption, and multicomponent interventions were more efficacious than chatbot-only interventions for sleep duration and sleep quality (all p < 0.05). Findings from this systematic review and meta-analysis indicate that chatbot interventions are efficacious for increasing physical activity, fruit and vegetable consumption, sleep duration and sleep quality. Chatbot interventions were efficacious across a range of populations and age groups, with both short- and longer-term interventions, and chatbot only and multicomponent interventions being efficacious.
聊天机器人（也称为对话代理和虚拟助手）有望以高效、吸引人且个性化的方式提供医疗保健服务。本系统评价和荟萃分析的目的是评估旨在改善身体活动、饮食和睡眠的聊天机器人干预措施的效果。检索电子数据库，查找2022年9月1日前发表的评估针对身体活动、饮食和/或睡眠的聊天机器人干预措施的随机和非随机对照试验以及前后对照试验。结局指标包括总身体活动量、步数、中度至剧烈身体活动（MVPA）、水果和蔬菜摄入量、睡眠质量和睡眠时间。计算标准化均值差（SMD）以比较干预效果。进行亚组分析以评估聊天机器人类型、干预类型、持续时间、输出以及人工智能的使用情况。使用有效公共卫生实践项目质量评估工具评估偏倚风险。纳入了19项试验。样本量在25至958之间，参与者的平均年龄在9至71岁之间。大多数干预措施（n = 15，79%）针对身体活动，且大多数试验质量评级较低（n = 14，74%）。荟萃分析结果显示，聊天机器人在增加总身体活动量（SMD = 0.28 [95% CI = 0.16, 0.40]）、每日步数（SMD = 0.28 [95% CI = 0.17, 0.39]）、MVPA（SMD = 0.53 [95% CI = 0.24, 0.83]）、水果和蔬菜摄入量（SMD = 0.59 [95% CI = 0.25, 0.93]）、睡眠时间（SMD = 0.44 [95% CI = 0.32, 0.55]）和睡眠质量（SMD = 0.50 [95% CI = 0.09, 0.90]）方面有显著效果（所有p < 0.05）。亚组分析表明，对于水果和蔬菜摄入量，基于文本的和人工智能聊天机器人比语音聊天机器人更有效，对于睡眠时间和睡眠质量，多成分干预比仅聊天机器人干预更有效（所有p < 0.05）。本系统评价和荟萃分析的结果表明，聊天机器人干预措施在增加身体活动、水果和蔬菜摄入量、睡眠时间和睡眠质量方面是有效的。聊天机器人干预措施在一系列人群和年龄组中均有效，短期和长期干预措施均有效，且仅聊天机器人干预和多成分干预均有效。
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