The Last JITAI? The Unreasonable Effectiveness of Large Language Models in Issuing Just-in-Time Adaptive Interventions: Fostering Physical Activity in a Prospective Cardiac Rehabilitation Setting
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| Publicado en: | arXiv.org (Apr 15, 2024), p. n/a |
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| Autor principal: | |
| Otros Autores: | , , , , , , |
| Publicado: |
Cornell University Library, arXiv.org
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 2926343486 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 2926343486 | ||
| 045 | 0 | |b d20240415 | |
| 100 | 1 | |a Haag, David | |
| 245 | 1 | |a The Last JITAI? The Unreasonable Effectiveness of Large Language Models in Issuing Just-in-Time Adaptive Interventions: Fostering Physical Activity in a Prospective Cardiac Rehabilitation Setting | |
| 260 | |b Cornell University Library, arXiv.org |c Apr 15, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a We investigated the viability of using Large Language Models (LLMs) for triggering and personalizing content for Just-in-Time Adaptive Interventions (JITAIs) in digital health. JITAIs are being explored as a key mechanism for sustainable behavior change, adapting interventions to an individual's current context and needs. However, traditional rule-based and machine learning models for JITAI implementation face scalability and flexibility limitations, such as lack of personalization, difficulty in managing multi-parametric systems, and issues with data sparsity. To investigate JITAI implementation via LLMs, we tested the contemporary overall performance-leading model 'GPT-4' with examples grounded in the use case of fostering heart-healthy physical activity in outpatient cardiac rehabilitation. Three personas and five sets of context information per persona were used as a basis of triggering and personalizing JITAIs. Subsequently, we generated a total of 450 proposed JITAI decisions and message content, divided equally into JITAIs generated by 10 iterations with GPT-4, a baseline provided by 10 laypersons (LayPs), and a gold standard set by 10 healthcare professionals (HCPs). Ratings from 27 LayPs and 11 HCPs indicated that JITAIs generated by GPT-4 were superior to those by HCPs and LayPs over all assessed scales: i.e., appropriateness, engagement, effectiveness, and professionality. This study indicates that LLMs have significant potential for implementing JITAIs as a building block of personalized or "precision" health, offering scalability, effective personalization based on opportunistically sampled information, and good acceptability. | |
| 653 | |a Exercise | ||
| 653 | |a Just in time | ||
| 653 | |a Large language models | ||
| 653 | |a Machine learning | ||
| 653 | |a Rehabilitation | ||
| 653 | |a Context | ||
| 653 | |a Customization | ||
| 653 | |a Effectiveness | ||
| 700 | 1 | |a Kumar, Devender | |
| 700 | 1 | |a Gruber, Sebastian | |
| 700 | 1 | |a Sareban, Mahdi | |
| 700 | 1 | |a Treff, Gunnar | |
| 700 | 1 | |a Niebauer, Josef | |
| 700 | 1 | |a Bull, Christopher | |
| 700 | 1 | |a Smeddinck, Jan David | |
| 773 | 0 | |t arXiv.org |g (Apr 15, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2926343486/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2402.08658 |