English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Obesity is a critical healthcare issue affecting the United States. The leastrisky treatments available for obesity are behavioral interventions meant topromote diet and exercise. Often these interventions contain a mobile componentthat allows interventionists to collect participants level data and provideparticipants with incentives and goals to promote long term behavioral change.Recently, there has been interest in using direct financial incentives topromote behavior change. However, adherence is challenging in theseinterventions, as each participant will react differently to differentincentive structure and amounts, leading researchers to consider personalizedinterventions. The key challenge for personalization, is that the clinicians donot know a priori how best to administer incentives to participants, and givenfinite intervention budgets how to disburse costly resources efficiently. Inthis paper, we consider this challenge of designing personalized weight lossinterventions that use direct financial incentives to motivate weight losswhile remaining within a budget. We create a machine learning approach that isable to predict how individuals may react to different incentive scheduleswithin the context of a behavioral intervention. We use this predictive modelin an adaptive framework that over the course of the intervention computes whatincentives to disburse to participants and remain within the study budget. Weprovide both theoretical guarantees for our modeling and optimizationapproaches as well as demonstrate their performance in a simulated weight lossstudy. Our results highlight the cost efficiency and effectiveness of ourpersonalized intervention design for weight loss.

An Adaptive Optimization Approach to Personalized Financial Incentives in Mobile Behavioral Weight Loss Interventions