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

arXiv: https://arxiv.org/abs/2307.00444 · HTML: https://mlsanity.com/paper/2307.00444/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 78 references that are in the map.
- First posted on arXiv 1 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [Model-based Reinforcement Learning and the Eluder Dimension](https://mlsanity.com/paper/1406.1853/) (2014)
- [On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method](https://mlsanity.com/paper/2102.08607/) (2021)
- [Inverse Optimization with Noisy Data](https://mlsanity.com/paper/1507.03266/) (2015)
- [Data-Driven Estimation in Equilibrium Using Inverse Optimization](https://mlsanity.com/paper/1308.3397/) (2013)
- [Non-Stationary Bandits with Habituation and Recovery Dynamics](https://mlsanity.com/paper/1707.08423/) (2017)
- [Behavioral Analytics for Myopic Agents](https://mlsanity.com/paper/1702.05496/) (2017)
- [Model Based Reinforcement Learning for Personalized Heparin Dosing](https://mlsanity.com/paper/2304.10000/) (2023)
- [Model-Free Trajectory-based Policy Optimization with Monotonic Improvement](https://mlsanity.com/paper/1606.09197/) (2016)
- [Planning a Community Approach to Diabetes Care in Low- and Middle-Income Countries Using Optimization](https://mlsanity.com/paper/2305.06426/) (2023)
- [A Minimax Surrogate Loss Approach to Conditional Difference Estimation](https://mlsanity.com/paper/1803.03769/) (2018)

## Abstract (arXiv)

Obesity is a critical healthcare issue affecting the United States. The least risky treatments available for obesity are behavioral interventions meant to promote diet and exercise. Often these interventions contain a mobile component that allows interventionists to collect participants level data and provide participants with incentives and goals to promote long term behavioral change. Recently, there has been interest in using direct financial incentives to promote behavior change. However, adherence is challenging in these interventions, as each participant will react differently to different incentive structure and amounts, leading researchers to consider personalized interventions. The key challenge for personalization, is that the clinicians do not know a priori how best to administer incentives to participants, and given finite intervention budgets how to disburse costly resources efficiently. In this paper, we consider this challenge of designing personalized weight loss interventions that use direct financial incentives to motivate weight loss while remaining within a budget. We create a machine learning approach that is able to predict how individuals may react to different incentive schedules within the context of a behavioral intervention. We use this predictive model in an adaptive framework that over the course of the intervention computes what incentives to disburse to participants and remain within the study budget. We provide both theoretical guarantees for our modeling and optimization approaches as well as demonstrate their performance in a simulated weight loss study. Our results highlight the cost efficiency and effectiveness of our personalized intervention design for weight loss.