%0 Journal Article
%@ 2371-4379
%I JMIR Publications
%V 2
%N 2
%P e28
%T A Fully Automated Conversational Artificial Intelligence for Weight Loss: Longitudinal Observational Study Among Overweight and Obese Adults
%A Stein,Natalie
%A Brooks,Kevin
%+ Division of Public Health, College of Human Medicine, Michigan State University, 200 East 1st Street, Flint Campus, MI, 48502, United States, 1 810 600 5601, steinna1@msu.edu
%K obesity
%K artificial intelligence
%K self efficacy
%K weight loss
%K prediabetes
%K smartphone
%K diabetes
%K compassion
%K mobile health
%K text messaging
%D 2017
%7 01.11.2017
%9 Original Paper
%J JMIR Diabetes
%G English
%X Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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. 
%M 30291087
%R 10.2196/diabetes.8590
%U http://diabetes.jmir.org/2017/2/e28/
%U https://doi.org/10.2196/diabetes.8590
%U http://www.ncbi.nlm.nih.gov/pubmed/30291087