Can the artificial intelligence technique of reinforcement learning use continuously-monitored digital data to optimize treatment for weight loss?
Behavioral weight loss (WL) trials show that, on average, participants regain lost weight unless provided long-term, intensive—and thus costly—intervention. Optimization solutions have shown mixed success. The artificial intelligence principle of "reinforcement learning" (RL) offers a new and more s...
| Publicado en: | Journal of Behavioral Medicine Vol. 42; no. 2; pp. 276 - 291 |
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| Autores principales: | , , , , , , , , |
| Formato: | research tables/charts randomized controlled trial Journal Article |
| Publicado: |
Springer Nature
Apr2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135694332&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135694332 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01607715 JBM jtl: Journal of Behavioral Medicine issn: 01607715 maglogo: N pubinfo: dt: Apr2019 vid: 42 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135694332 135694332 135694332 10.1007/s10865-018-9964-1 135694332 ppf: 276 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Can the artificial intelligence technique of reinforcement learning use continuously-monitored digital data to optimize treatment for weight loss? aug: au: Forman, Evan M. Kerrigan, Stephanie G. Butryn, Meghan L. Juarascio, Adrienne S. Manasse, Stephanie M. Ontañón, Santiago Dallal, Diane H. Crochiere, Rebecca J. Moskow, Danielle affil: Department of Psychology, WELL Center, Drexel University, Stratton Hall, 3141 Chestnut Street, 19104, Philadelphia, PA, USA sug: subj: Weight Loss Therapy Reinforcement (Psychology) Learning Artificial Intelligence Utilization Monitoring, Physiologic Behavior Therapy Methods Treatment Outcomes Economics Human Pilot Studies Randomized Controlled Trials Random Assignment Remote Consultation Methods Algorithms Descriptive Statistics ab: Behavioral weight loss (WL) trials show that, on average, participants regain lost weight unless provided long-term, intensive—and thus costly—intervention. Optimization solutions have shown mixed success. The artificial intelligence principle of "reinforcement learning" (RL) offers a new and more sophisticated form of optimization in which the intensity of each individual's intervention is continuously adjusted depending on patterns of response. In this pilot, we evaluated the feasibility and acceptability of a RL-based WL intervention, and whether optimization would achieve equivalent benefit at a reduced cost compared to a non-optimized intensive intervention. Participants (n = 52) completed a 1-month, group-based in-person behavioral WL intervention and then (in Phase II) were randomly assigned to receive 3 months of twice-weekly remote interventions that were non-optimized (NO; 10-min phone calls) or optimized (a combination of phone calls, text exchanges, and automated messages selected by an algorithm). The Individually-Optimized (IO) and Group-Optimized (GO) algorithms selected interventions based on past performance of each intervention for each participant, and for each group member that fit into a fixed amount of time (e.g., 1 h), respectively. Results indicated that the system was feasible to deploy and acceptable to participants and coaches. As hypothesized, we were able to achieve equivalent Phase II weight losses (NO = 4.42%, IO = 4.56%, GO = 4.39%) at roughly one-third the cost (1.73 and 1.77 coaching hours/participant for IO and GO, versus 4.38 for NO), indicating strong promise for a RL system approach to weight loss and maintenance. pubtype: Academic Journal doctype: research tables/charts randomized controlled trial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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