Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions.
Objective: Providing behavioral health interventions via smartphones allows these interventions to be adapted to the changing behavior, preferences, and needs of individuals. This can be achieved through reinforcement learning (RL), a sub-area of machine learning. However, many challenges could affe...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 28; no. 6; pp. 1225 - 1235 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Jun2021
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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=150938284&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150938284 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Jun2021 vid: 28 iid: 6 pid: 622 pub: Oxford University Press / USA artinfo: ui: 150938284 150938284 NLM33657217 150938284 10.1093/jamia/ocab001 NLM33657217 150938284 ppf: 1225 ppct: 10 formats: tig: atl: Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions. aug: au: Figueroa, Caroline A Aguilera, Adrian Chakraborty, Bibhas Modiri, Arghavan Aggarwal, Jai Deliu, Nina Sarkar, Urmimala Williams, Joseph Jay Lyles, Courtney R Jay Williams, Joseph affil: School of Social Welfare, University of California Berkeley , Berkeley, California, USA sug: subj: Mobile Applications Telemedicine Algorithms Reproducibility of Results Human Comparative Studies Multicenter Studies Evaluation Research Validation Studies Funding Source ab: Objective: Providing behavioral health interventions via smartphones allows these interventions to be adapted to the changing behavior, preferences, and needs of individuals. This can be achieved through reinforcement learning (RL), a sub-area of machine learning. However, many challenges could affect the effectiveness of these algorithms in the real world. We provide guidelines for decision-making.Materials and Methods: Using thematic analysis, we describe challenges, considerations, and solutions for algorithm design decisions in a collaboration between health services researchers, clinicians, and data scientists. We use the design process of an RL algorithm for a mobile health study "DIAMANTE" for increasing physical activity in underserved patients with diabetes and depression. Over the 1.5-year project, we kept track of the research process using collaborative cloud Google Documents, Whatsapp messenger, and video teleconferencing. We discussed, categorized, and coded critical challenges. We grouped challenges to create thematic topic process domains.Results: Nine challenges emerged, which we divided into 3 major themes: 1. Choosing the model for decision-making, including appropriate contextual and reward variables; 2. Data handling/collection, such as how to deal with missing or incorrect data in real-time; 3. Weighing the algorithm performance vs effectiveness/implementation in real-world settings.Conclusion: The creation of effective behavioral health interventions does not depend only on final algorithm performance. Many decisions in the real world are necessary to formulate the design of problem parameters to which an algorithm is applied. Researchers must document and evaulate these considerations and decisions before and during the intervention period, to increase transparency, accountability, and reproducibility.Trial Registration: clinicaltrials.gov, NCT03490253. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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