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Lifestyle Recommendations for Hypertension through Rasch-based Feasibility Modeling

Published: 13 July 2016 Publication History

Abstract

In this work we investigate the use of behavior feasibility to adapt and personalize lifestyle-targeting recommender systems for the prevention and treatment of hypertension. Based on survey data (N=300) we model the feasibiliy of 63 behaviors through a Rasch model, describing the engagement in a behavior as a function of the behavior's difficulty and the person's ability. We formulate two feasibility-tailored recommendation strategies that utilize the Rasch model. The engagement maximization strategy aims at maximizing the probability of engagement by proposing very feasible behaviors while the motivation maximization strategy aims to challenge users by matching the difficulty of the advice with the ability of the user, thereby maximizing motivation. In an online study (N=150) we assessed user preference for either strategies (embodied as virtual coaches) in comparison with a random control strategy. Our results show that coaches selecting feasible health advice resonate better with the patient than control. In general patients significantly preferred the engagement maximization strategy over random advice on most factors, while patients with a medium level of ability significantly preferred the motivation maximization strategy on all factors.

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cover image ACM Conferences
UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization
July 2016
366 pages
ISBN:9781450343688
DOI:10.1145/2930238
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Published: 13 July 2016

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Author Tags

  1. Rasch model
  2. behavior change theory
  3. feasibility
  4. lifestyle interventions
  5. patient-centered design
  6. recommender systems
  7. user modeling

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UMAP '16: User Modeling, Adaptation and Personalization Conference
July 13 - 17, 2016
Nova Scotia, Halifax, Canada

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UMAP '16 Paper Acceptance Rate 21 of 123 submissions, 17%;
Overall Acceptance Rate 162 of 633 submissions, 26%

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  • (2024)Evaluation of health recommender systems: a scoping review protocolBMJ Open10.1136/bmjopen-2023-08335914:10(e083359)Online publication date: 7-Oct-2024
  • (2024)Health Recommender SystemsInternational Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2023)10.1007/978-3-031-52388-5_25(261-272)Online publication date: 9-Feb-2024
  • (2023)Development and Evaluation of Health Recommender Systems: Systematic Scoping Review and Evidence MappingJournal of Medical Internet Research10.2196/3818425(e38184)Online publication date: 19-Jan-2023
  • (2022)Coaching Agent: Making Recommendations for Behavior Change. A Case Study on Improving Eating HabitsProceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems10.5555/3535850.3535994(1292-1300)Online publication date: 9-May-2022
  • (2022)Physical Activity Recommendation System Based on Deep Learning to Prevent Respiratory DiseasesComputers10.3390/computers1110015011:10(150)Online publication date: 11-Oct-2022
  • (2022)Recommendations as Challenges: Estimating Required Effort and User Ability for Health Behavior Change RecommendationsProceedings of the 27th International Conference on Intelligent User Interfaces10.1145/3490099.3511118(106-119)Online publication date: 22-Mar-2022
  • (2021)Health Recommender Systems: Systematic ReviewJournal of Medical Internet Research10.2196/1803523:6(e18035)Online publication date: 29-Jun-2021
  • (2021)Theory Integration for Lifestyle Behavior Change in the Digital Age: An Adaptive Decision-Making FrameworkJournal of Medical Internet Research10.2196/1712723:4(e17127)Online publication date: 9-Apr-2021
  • (2021)A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospectsInformation Fusion10.1016/j.inffus.2021.02.00272(1-21)Online publication date: Aug-2021
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