Stein Natalie, Brooks Kevin
Division of Public Health, College of Human Medicine, Michigan State University, Flint Campus, MI, United States.
Division of Public Health, College of Human Medicine, Michigan State University, East Lansing, MI, United States.
JMIR Diabetes. 2017 Nov 1;2(2):e28. doi: 10.2196/diabetes.8590.
Type 2 diabetes is the most expensive chronic disease in the United States. Two-thirds of US adults have prediabetes or are overweight and at risk for type 2 diabetes. Intensive in-person behavioral counseling can help patients lose weight and make healthy behavior changes to improve their health outcomes. However, with the shortage of health care providers and associated costs, such programs do not adequately service all patients who could benefit. The health care system needs effective and cost-effective interventions that can lead to positive health outcomes as scale. This study investigated the ability of conversational artificial intelligence (AI), in the form of a standalone, fully automated text-based mobile coaching service, to promote weight loss and other health behaviors related to diabetes prevention. This study also measured user acceptability of AI coaches as alternatives to live health care professionals.
The objective of this study was to evaluate weight loss, changes in meal quality, and app acceptability among users of the Lark Weight Loss Health Coach AI (HCAI), with the overarching goal of increasing access to compassionate health care via mobile health. Lessons learned in this study can be applied when planning future clinical trials to evaluate HCAI and when designing AI to promote weight loss, healthy behavior change, and prevention and self-management of chronic diseases.
This was a longitudinal observational study among overweight and obese (body mass index ≥25) participants who used HCAI, which encourages weight loss and healthy diet choices through elements of cognitive behavioral therapy. Weight loss, meal quality, physical activity, and sleep data were collected through user input and, for sleep and physical activity, partly through automatic detection by the user's mobile phone. User engagement was assessed by duration and amount of app use. A 4-question in-app user trust survey assessed app usability and acceptability.
Data were analyzed for participants (N=70) who met engagement standards set forth by the Centers for Disease Control and Prevention criteria for Diabetes Prevention Program, a clinically proven weight loss program focused on preventing diabetes. Weight loss (standard error of the mean) was 2.38% (0.69%) of baseline weight. The average duration of app use was 15 (SD 1.0) weeks, and users averaged 103 sessions each. Predictors of weight loss included duration of AI use, number of counseling sessions, and number of meals logged. Percentage of healthy meals increased by 31%. The in-app user trust survey had a 100% response rate and positive results, with a satisfaction score of 87 out of 100 and net promoter score of 47.
This study showed that use of an AI health coach is associated with weight loss comparable to in-person lifestyle interventions. It can also encourage behavior changes and have high user acceptability. Research into AI and its application in telemedicine should be pursued, with clinical trials investigating effects on weight, health behaviors, and user engagement and acceptability.
2型糖尿病是美国最昂贵的慢性病。三分之二的美国成年人患有糖尿病前期或超重,有患2型糖尿病的风险。强化的面对面行为咨询可以帮助患者减肥,并做出健康的行为改变以改善健康状况。然而,由于医疗保健提供者短缺及相关成本,此类项目无法为所有可能受益的患者提供充分服务。医疗保健系统需要有效且具有成本效益的干预措施,以便在扩大规模的情况下带来积极的健康成果。本研究调查了以独立的、完全自动化的基于文本的移动辅导服务形式存在的对话式人工智能(AI)促进减肥及其他与糖尿病预防相关健康行为的能力。本研究还衡量了人工智能教练作为现场医疗保健专业人员替代方案的用户接受度。
本研究的目的是评估Lark减肥健康教练人工智能(HCAI)用户的体重减轻情况、饮食质量变化及应用程序可接受性,总体目标是通过移动健康增加获得富有同情心的医疗保健的机会。本研究中吸取的经验教训可应用于规划未来评估HCAI的临床试验,以及设计促进减肥、健康行为改变以及慢性病预防和自我管理的人工智能。
这是一项针对使用HCAI的超重和肥胖(体重指数≥25)参与者的纵向观察性研究,HCAI通过认知行为疗法的要素鼓励减肥和选择健康饮食。通过用户输入收集体重减轻、饮食质量、身体活动和睡眠数据,对于睡眠和身体活动,部分数据通过用户手机自动检测收集。通过应用程序使用的持续时间和数量评估用户参与度。一项包含4个问题的应用程序内用户信任调查评估了应用程序的可用性和可接受性。
对符合疾病控制与预防中心糖尿病预防计划标准(一项经临床验证的专注于预防糖尿病的减肥计划)所规定参与标准的参与者(N = 70)的数据进行了分析。体重减轻(平均标准误差)为基线体重的2.38%(0.69%)。应用程序的平均使用时长为15(标准差1.0)周,用户平均每人进行了103次会话。体重减轻的预测因素包括人工智能使用时长、咨询会话次数和记录的用餐次数。健康餐的比例增加了31%。应用程序内用户信任调查的回复率为100%,结果积极,满意度评分为87分(满分100分),净推荐值为47分。
本研究表明,使用人工智能健康教练与减肥相关,效果与面对面的生活方式干预相当。它还可以鼓励行为改变,并具有较高的用户接受度。应开展对人工智能及其在远程医疗中的应用的研究,进行临床试验以调查其对体重、健康行为以及用户参与度和接受度的影响。