ArticleCEUR workshop proceedings2018
Personalizing Mobile Fitness Apps using Reinforcement Learning.
Article in CEUR workshop proceedings, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The impact of machine learning on physical activity-related health outcomes: A systematic review and meta-analysis.International nursing review · 2025Pooled it
- Digital Behavior Change Intervention Designs for Habit Formation: Systematic Review.Journal of medical Internet research · 2024Pooled it
- An Evaluation of the Effect of App-Based Exercise Prescription Using Reinforcement Learning on Satisfaction and Exercise Intensity: Randomized Crossover Trial.JMIR mHealth and uHealth · 2024Trial
- Smart Start - Designing Powerful Clinical Trials Using Pilot Study Data.NEJM evidence · 2024Trial
- Replicable Bandits for Digital Health Interventions.Statistical science : a review journal of the Institute of Mathematical Statistics · 2025Article
- When and Why Hyperbolic Discounting Matters for Reinforcement Learning Interventions.Reinforcement learning journal · 2025Article
- User Experience With a Personalized mHealth Service for Physical Activity Promotion in University Students: Mixed Methods Study.JMIR formative research · 2025Article
- A Deployed Online Reinforcement Learning Algorithm In An Oral Health Clinical Trial.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2025Article
- How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review.Mayo Clinic proceedings. Digital health · 2024Review
- ReBandit: Random Effects Based Online RL Algorithm for Reducing Cannabis Use.IJCAI : proceedings of the conference · 2024Article
- Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling.Machine learning · 2024Article
- Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation.Proceedings of machine learning research · 2024Article
- A scoping review of methodologies for applying artificial intelligence to physical activity interventions.Journal of sport and health science · 2024Article
- Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks.Proceedings of the ... International Joint Conference on Autonomous Agents and Multiagent Systems : AAMAS. International Joint Conference on Autonomous Agents and Multiagent Systems · 2024Article
- Infusing behavior science into large language models for activity coaching.PLOS digital health · 2024Article
- PEAK Mood, Mind, and Marks: a pilot study of an intervention to support university students' mental and cognitive health through physical exercise.Frontiers in psychiatry · 2024Article
- Behavioral Analytics for Myopic Agents.European journal of operational research · 2023Article
- Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care.Proceedings of the ... Innovative Applications of Artificial Intelligence Conference. Innovative Applications of Artificial Intelligence Conference · 2023Article
- Machine Learning in Nutrition Research.Advances in nutrition (Bethesda, Md.) · 2022Article
- Data-driven Interpretable Policy Construction for Personalized Mobile Health.IEEE International Conference on Digital Health. IEEE International Conference on Digital Health · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Despite the vast number of mobile fitness applications (apps) and their potential advantages in promoting physical activity, many existing apps lack behavior-change features and are not able to maintain behavior change motivation. This paper describes a novel fitness app called CalFit, which implements important behavior-change features like dynamic goal setting and self-monitoring. CalFit uses a reinforcement learning algorithm to generate personalized daily step goals that are challenging but attainable. We conducted the Mobile Student Activity Reinforcement (mSTAR) study with 13 college students to evaluate the efficacy of the CalFit app. The control group (receiving goals of 10,000 steps/day) had a decrease in daily step count of 1,520 (SD ± 740) between baseline and 10-weeks, compared to an increase of 700 (SD ± 830) in the intervention group (receiving personalized step goals). The difference in daily steps between the two groups was 2,220, with a statistically significant
Indexed as
Identifiers
32405286PMC7220419What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.