Papers
Topics
Authors
Recent
Search
2000 character limit reached

PlanFitting: Personalized Exercise Planning with Large Language Model-driven Conversational Agent

Published 22 Sep 2023 in cs.HC, cs.AI, and cs.CL | (2309.12555v2)

Abstract: Creating personalized and actionable exercise plans often requires iteration with experts, which can be costly and inaccessible to many individuals. This work explores the capabilities of LLMs in addressing these challenges. We present PlanFitting, an LLM-driven conversational agent that assists users in creating and refining personalized weekly exercise plans. By engaging users in free-form conversations, PlanFitting helps elicit users' goals, availabilities, and potential obstacles, and enables individuals to generate personalized exercise plans aligned with established exercise guidelines. Our study -- involving a user study, intrinsic evaluation, and expert evaluation -- demonstrated PlanFitting's ability to guide users to create tailored, actionable, and evidence-based plans. We discuss future design opportunities for LLM-driven conversational agents to create plans that better comply with exercise principles and accommodate personal constraints.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (56)
  1. Implementation Intentions and Shielding Goal Striving From Unwanted Thoughts and Feelings. Personality and Social Psychology Bulletin 34, 3 (2008), 381–393. https://doi.org/10.1177/0146167207311201
  2. ACSM. 2023. The American College of Sports Medicine. Retrieved Aug 25, 2023 from https://www.acsm.org/
  3. Do Implementation Intentions Help to Eat a Healthy Diet? A Systematic Review and Meta-analysis of the Empirical Evidence. Appetite 56, 1 (2011), 183–193. https://doi.org/10.1016/j.appet.2010.10.012
  4. Crowdsourcing Exercise Plans Aligned with Expert Guidelines and Everyday Constraints. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1–13. https://doi.org/10.1145/3173574.3173898
  5. PlanSourcing: Generating Behavior Change Plans with Friends and Crowds. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing. 119–133. https://doi.org/10.1145/2818048.2819943
  6. Shakra: Tracking and Sharing Daily Activity Levels with Unaugmented Mobile Phones. Mobile Networks and Applications 12 (2007), 185–199. https://doi.org/10.1007/s11036-007-0011-7
  7. Keep Me Updated! Memory Management in Long-term Conversations. In Findings of the Association for Computational Linguistics: EMNLP 2022. Association for Computational Linguistics, 3769–3787. https://doi.org/10.18653/v1/2022.findings-emnlp.276
  8. Virginia Braun and Victoria Clarke. 2006. Using Thematic Analysis in Psychology. Qualitative Research in Psychology 3, 2 (2006), 77–101. https://doi.org/10.1191/1478088706QP063OA
  9. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems. 1877–1901. https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
  10. Are People in the Bush Really Physically Active? A Systematic Review and Meta-analysis of Physical Activity and Sedentary Behaviour in Rural Australians Populations. Journal of Global Health 10, 1 (2020). https://doi.org/10.7189/jogh.10.010410
  11. Zheng Chen. 2023. PALR: Personalization Aware LLMs for Recommendation. arXiv preprint arXiv:2305.07622 (2023). https://arxiv.org/abs/2305.07622
  12. PaLM: Scaling Language Modeling with Pathways. https://doi.org/10.48550/ARXIV.2204.02311
  13. TaleBrush: Sketching Stories with Generative Pretrained Language Models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3491102.3501819
  14. Naver Cloud. 2023. HyperCLOVA X. Retrieved Aug 25, 2023 from https://www.ncloud.com/solution/featured/hyperclovax
  15. Anna-Lisa Cohen and Peter M Gollwitzer. 2008. The Cost of Remembering to Remember: Cognitive Load and Implementation Intentions Influence Ongoing Task Performance. http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-61225
  16. Mark Conner and Andrea R Higgins. 2010. Long-term Effects of Implementation Intentions on Prevention of Smoking Uptake among Adolescents: a Cluster Randomized Controlled Trial. Health Psychology 29, 5 (2010), 529. https://doi.org/10.1037/a0020317
  17. Design Requirements for Technologies that Encourage Physical Activity. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 457–466. https://doi.org/10.1145/1124772.1124840
  18. Flowers or a Robot Army? Encouraging Awareness & Activity with Personal, Mobile Displays. In Proceedings of the 10th International Conference on Ubiquitous Computing. 54–63. https://doi.org/10.1145/1409635.1409644
  19. More or better: Do the number and specificity of implementation intentions matter in increasing physical activity? Psychology of Sport and Exercise 12, 4 (2011), 471–477. https://doi.org/10.1016/j.psychsport.2011.02.008
  20. Grace T DeSimone. 2019. The Tortoise Factor — Get FITT. ACSM’s Health & Fitness Journal 23, 2 (2019), 3–4.
  21. Chronic disease and the link to physical activity. Journal of Sport and Health Science 2, 1 (2013), 3–11. https://doi.org/10.1016/j.jshs.2012.07.009
  22. Leveraging Large Language Models in Conversational Recommender Systems. arXiv preprint arXiv:2305.07961 (2023). https://arxiv.org/abs/2305.07961
  23. PaTAT: Human-AI Collaborative Qualitative Coding with Explainable Interactive Rule Synthesis. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3544548.3581352
  24. Sparks: Inspiration for Science Writing Using Language Models. In Proceedings of the 2022 ACM Designing Interactive Systems Conference. 1002–1019. https://doi.org/10.1145/3532106.3533533
  25. Peter M Gollwitzer. 1999. Implementation Intentions: Strong Effects of Simple Plans. American Psychologist 54, 7 (1999), 493. https://doi.org/10.1037/0003-066X.54.7.493
  26. Peter M Gollwitzer and Paschal Sheeran. 2006. Implementation Intentions and Goal Achievement: A Meta‐analysis of Effects and Processes. Advances in Experimental Social Psychology 38 (2006), 69–119. https://doi.org/10.1016/S0065-2601(06)38002-1
  27. Google, Inc. 2023. Bard - Chat Based AI Tool from Google, Powered by PaLM 2. Retrieved Aug 25, 2023 from https://bard.google.com/
  28. Promoting children’s fruit and vegetable consumption: Interventions using the Theory of Planned Behaviour as a framework. British Journal of Health Psychology 12, 4 (2007), 639–650. https://doi.org/10.1348/135910706X171504
  29. Martin S. Hagger and Aleksandra Luszczynska. 2013. Implementation Intention and Action Planning Interventions in Health Contexts: State of the Research and Proposals for the Way Forward. Applied Psychology: Health and Well-Being 6, 1 (oct 2013), 1–47. https://doi.org/10.1111/aphw.12017
  30. How to Evaluate Technologies for Health Behavior Change in HCI Research. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 3063–3072. https://doi.org/10.1145/1978942.1979396
  31. Personalization Revisited: A Reflective Approach Helps People Better Personalize Health Services and Motivates Them To Increase Physical Activity. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 743–754. https://doi.org/10.1145/2750858.2807552
  32. Fish’n’Steps: Encouraging Physical Activity with an Interactive Computer Game. In UbiComp 2006: Ubiquitous Computing. Springer, 261–278. https://doi.org/10.1007/11853565_16
  33. Initiation and Maintenance of Physical Exercise: Stage-Specific Effects of a Planning Intervention. Research in Sports Medicine 12, 3 (2004), 221–240. https://doi.org/10.1080/15438620490497567
  34. Self-efficacy as a Moderator of the Planning–-behaviour Relationship in Interventions Designed to Promote Physical Activity. Psychology and Health 26, 2 (2011), 151–166. https://doi.org/10.1080/08870446.2011.531571
  35. Beyond ‘planning’: A meta-analysis of implementation intentions to support smoking cessation. Health Psychology 38, 12 (2019), 1059. https://doi.org/10.1037/hea0000768
  36. The Current State of Personal Training: an Industry Perspective of Personal Trainers in a Small Southeast Community. Journal of Strength and Conditioning Research 22, 3 (2008), 883. https://doi.org/10.1519/JSC.0b013e3181660dab
  37. Meta. 2023. Introducing LLaMA: A foundational, 65-billion-parameter large language model. https://ai.meta.com/blog/large-language-model-llama-meta-ai/
  38. National Academy of Sports Medicine. 2023. How Much Does a Personal Trainer Cost & Should You Hire One? https://blog.nasm.org/how-much-does-a-personal-trainer-cost
  39. OpenAI. 2023a. ChatGPT: Optimizing Language Models for Dialogue. Retrieved Aug 25, 2023 from https://openai.com/blog/chatgpt/
  40. OpenAI. 2023b. Function Calling and Other API Updates. Retrieved Aug 25, 2023 from https://openai.com/blog/function-calling-and-other-api-updates
  41. OpenAI. 2023c. GPT-4. Retrieved Aug 25, 2023 from https://openai.com/gpt-4
  42. OpenAI. 2023d. OpenAI API. Retrieved Aug 25, 2023 from https://openai.com/api/
  43. Implementation of Physical Activity Interventions in Rural, Remote, and Northern Communities: A Scoping Review. INQUIRY: The Journal of Health Care Organization, Provision, and Financing 57 (2020). https://doi.org/10.1177/0046958020935662
  44. The Physical Activity Guidelines for Americans. JAMA 320, 19 (2018), 2020–2028. https://doi.org/10.1001/jama.2018.14854
  45. Dietary Planning as a Mediator of the Intention–Behavior Relation: An Experimental-Causal-Chain Design. Applied Psychology 57 (2008), 194–207. https://doi.org/10.1111/j.1464-0597.2008.00364.x
  46. Physical Activity in U.S. Adults: Compliance with the Physical Activity Guidelines for Americans. American Journal of Preventive Medicine 40, 4 (2011), 454–461. https://doi.org/10.1016/j.amepre.2010.12.016
  47. Using Mental Contrasting with Implementation Intentions to Reduce Bedtime Procrastination: Two Randomised Trials. Psychology & Health 35, 3 (2020), 275–301. https://doi.org/10.1080/08870446.2019.1652753
  48. Nicole A VanKim and Toben F Nelson. 2013. Vigorous Physical Activity, Mental Health, Perceived Stress, and Socializing among College Students. American Journal of Health Promotion 28, 1 (2013), 7–15. https://doi.org/10.4278/ajhp.111101-QUAN-395
  49. Viswanath Venkatesh and Hillol Bala. 2008. Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences 39, 2 (2008), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
  50. Henning Wackerhage and Brad J Schoenfeld. 2021. Personalized, Evidence-Informed Training Plans and Exercise Prescriptions for Performance, Fitness and Health. Sports Medicine 51, 9 (2021), 1805–1813. https://doi.org/10.1007/s40279-021-01495-w
  51. Enabling Conversational Interaction with Mobile UI using Large Language Models. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–17. https://doi.org/10.1145/3544548.3580895
  52. Leveraging Large Language Models to Power Chatbots for Collecting User Self-Reported Data. arXiv preprint arXiv:2301.05843 (2023). https://arxiv.org/abs/2301.05843
  53. AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3491102.3517582
  54. Understanding People’s Experience for Physical Activity Planning and Exploring the Impact of Historical Records on Plan Creation and Execution. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 1–15. https://doi.org/10.1145/3491102.3501997
  55. DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation. arXiv preprint arXiv:1911.00536 (2019). https://arxiv.org/abs/1911.00536
  56. VISAR: A Human-AI Argumentative Writing Assistant with Visual Programming and Rapid Draft Prototyping. arXiv preprint arXiv:2304.07810 (2023). https://arxiv.org/abs/2304.07810
Citations (3)
View on Semantic Scholar

Summary

No one has generated a summary of this paper yet.

Sign Up to Summarize

Paper to Video (Beta)

No one has generated a video about this paper yet.

Sign Up to Generate All Videos Subscribe on YouTube

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Generate Now

Collections

Sign up for free to add this paper to one or more collections.

Sign Up