Efficient and Personalized Mobile Health Event Prediction via Small Language Models
Open accessAbstract
Healthcare monitoring is crucial for early detection, timely intervention, and the ongoing management of health conditions, ultimately improving individuals' quality of life. Recent research shows that Large Language Models (LLMs) have demonstrated impressive performance in supporting healthcare tasks. However, existing LLM-based healthcare solutions typically rely on cloud-based systems, which raise privacy concerns and increase the risk of personal information leakage. As a result, there is growing interest in running these models locally on devices like mobile phones and wearables to protect users' privacy. Small Language Models (SLMs) are potential candidates to solve privacy and computational issues, as they are more efficient and better suited for local deployment. However, the performance of SLMs in healthcare domains has not yet been investigated. This paper examines the capability of SLMs to accurately analyze health data, such as steps, calories, sleep minutes, and other vital statistics, to assess an individual's health status. Our results show that, TinyLlama, which has 1.1 billion parameters, utilizes 4.31 GB memory, and has 0.48s latency, showing the best performance compared other four state-of-the-art (SOTA) SLMs on various healthcare applications. Our results indicate that SLMs could potentially be deployed on wearable or mobile devices for real-time health monitoring, providing a practical solution for efficient and privacy-preserving healthcare.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In domains like industrial automation, healthcare, and consumer electronics, TLMs allow NLP functionalities to be embedded directly within devices, enabling intelligent interactions without constant reliance on the cloud.
“In domains like industrial automation [42], healthcare [43], and consumer electronics [44], TLMs allow NLP functionalities to be embedded directly within devices, enabling intelligent interactions without constant reliance on the cloud.”
From Tiny Language Models for Automation and Control: Overview, Potential Applications, and Future Research Directions (2025) · cites this paper as [43]
Cites
Large language models encode clinical knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06291-2 · Cited by 4,083 · see citing works
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W., Salakhutdinov, R., & Manning, C. D. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1259 · Cited by 1,804 · see citing works
Dinh-Le, C., Chuang, R., Chokshi, S. K., & Mann, D. (2019) · JMIR mhealth and uhealth · doi:10.2196/12861 · Cited by 416 · see citing works
MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use CasesPreprint
Murthy, R., Yang, L., Tan, J.-T., Awalgaonkar, T., Zhou, Y., Heinecke, S., Desai, S., Wu, J., Xu, R., Tan, S., et al. (2024) · arXiv.org · doi:10.48550/arxiv.2406.10290 · Cited by 21 · see citing works
PMData: A sports logging datasetPreprint
Thambawita, V. L., Hicks, S. A., Borgli, H., Pettersen, S. A., Stensland, H. K., Jha, D., Johansen, D., Johansen, H. D., Kupka, T., Grønli, T., et al. (2020) · doi:10.31219/osf.io/k2apb · Cited by 14 · see citing works
Pham, N., Jia, H., Tran, M., Dinh, T., Bui, N., Kwon, Y., Ma, D., Nguyen, P., Mascolo, C., & Vu, T. (2022) · Proceedings of the 28th Annual International Conference on Mobile Computing And Networking · doi:10.1145/3495243.3560533 · Cited by 12 · see citing works
Self-citationUR2M: Uncertainty and Resource-Aware Event Detection on Microcontrollers
Jia, H. G., Kwon, Y. D., Ma, D., Pham, N., Qendro, L., Vu, T. N., & Cecilia Mascolo, C. (2024) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom59722.2024.10494467 · Cited by 3 · see citing works
Self-citationWu, Y. C., Spathis, D., Jia, H. G., Perez-Pozuelo, I., Gonzales, T. I., Brage, S., Wareham, N. J., & Cecilia Mascolo, C. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2307.16651 · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Lamaakal, I., Maleh, Y., El Makkaoui, K., Ouahbi, I., Pławiak, P., Alfarraj, O., Almousa, M., & Abd El-Latif, A. A. (2025) · Sensors · doi:10.3390/s25051318 · Cited by 60 · see citing works
Using Large Language Models for Chronic Disease Management Tasks: Scoping Review
Mukalazi, S. H., Ouyang, J., Namatovu, H. K., Ssemaluulu, P. M., Kimbugwe, N., Orach, C. G., & Waiswa, P. (2025) · JMIR Medical Informatics · doi:10.2196/66905 · Cited by 22 · see citing works
AI-Augmented Literature Reviews: Efficient Clustering and Summarization for Researchers
Yuan, S., & Lipizzi, C. (2025) · IEEE Access · doi:10.1109/access.2025.3603162 · Cited by 5 · see citing works
Language Modelling Techniques for Analysing the Impact of Human Genetic Variation
Hegde, M., Nebel, J., & Rahman, F. (2025) · Bioinformatics and Biology Insights · doi:10.1177/11779322251358314 · Cited by 5 · see citing works
On the robustness of medical term representations in locally deployable language modelsPreprint
Auger, S. D., Graham, N., & Scott, G. (2026) · medRxiv · doi:10.64898/2026.02.24.26346972 · Cited by 2 · see citing works
A Fine-Tuning Pipeline with Small Conversational Data for Healthcare Chatbot
Aguzzi, G., Magnini, M., Pengo, M. F., Mirko Viroli, D., & Montagna, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-95841-0_1 · Cited by 1 · see citing works
Dikici, S., & Bilgin, T. T. (2026) · Expert Systems · doi:10.1111/exsy.70331 · Cited by 1 · see citing works
Smoothing Grounding and Reasoning for MLLM-Powered GUI Agents with Query-Oriented Pivot TaskPreprint
Wu, Z., Cheng, P., Wu, Z., Ju, T., Zhang, Z., & Liu, G. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5770750 · Cited by 1 · see citing works
Enhancing Small Language Models for Graph Tasks Through Graph Encoder Integration
Oh, D., Kang, S., Kim, H.-J., & Oh, D. (2025) · Applied Sciences · doi:10.3390/app15052418
Tıbbi Akıl Yürütme için Gemma 3 Modellerinin CoT Destekli SFT ve GRPO ile İncelenmesi
İşeri, İ., Yıldırım, A., Öztorun, A., Tuna, T., & Turan, A. (2025) · Çukurova Üniversitesi Mühendislik Fakültesi Dergisi · doi:10.21605/cukurovaumfd.1755684
SLM-Arch: Trade-Off Analysis and Optimized Hardware Design for Small Language Models
Li, L., & Aly, M. M. S. (2025) · ACM Transactions on Design Automation of Electronic Systems · doi:10.1109/icm66518.2025.11322455
Trujillo, R., & Poellabauer, C. (2026) · doi:10.2196/preprints.93827
Fukazawa, Y., & Kodaka, M. (2026) · New Generation Computing · doi:10.1007/s00354-026-00319-7
ECG-Agent: On-Device Tool-Calling Agent for ECG Multi-Turn Dialogue
Chung, H., Oh, J. H., Kyung, D., Kim, J., Kwon, Y., Kim, M., & Choi, E. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464123
Lee, E. J., Jung, J. Y., Kim, D. K., Park, J. W., & Kwak, Y. H. (2026) · PLoS ONE · doi:10.1371/journal.pone.0350770
LLM-Based Applications for Healthcare: A Systematic Mapping Study
Malaquias, R. S., Filho, I. D. M. B., de Lima, J. M. M., Gurgel, A. M., & de Brito, B. A. O. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-30488-9_1
Performant and Small: Can We Have Both? SLMs on Mobile Devices for Healthcare Chatbots
Farahmand, A., Montagna, S., Bogliolo, A., Ferretti, S., & Magnini, M. (2026) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops68308.2026.11585382
SpeechLLM Meets Federated Learning for End-to-End ASR: English and Italian Case Studies
Nabih, M., Falavigna, D., & Brutti, A. (2026) · International Conference on Federated Learning and Intelligent Computing Systems (FLICS) · doi:10.1109/flics70075.2026.11621898
A Comprehensive Evaluation of the Phi Family of Small Language Models in Healthcare
Awasthi, R., Shree, S., Bhattad, A., Mishra, S., Singh, N., Khare, A., Edara, S. R., Ramachandran, S. P., Mahapatra, D., Cywinski, J. B., et al. (2026) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi69079.2026.00108
Soyebo, I., Donawa, A., Barrios, R. A., Patchou, B., & Baker, C. E. (2026) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase69719.2026.00055
Sakib, S., Shinde, G., & Roy, N. (2026) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase69719.2026.00033
Curriculum-Trained Vertical Agents for Italian Medical Question Answering
Acunzo, A., Barone, M., Dioguardi, G., Marino, R. D., Serio, F. D., Iozzino, G., Postiglione, M., Riccio, G. E., Romano, A., & Vitiello, L. (2026) · International Conference on Information Technology for Social Good (GoodIT) · doi:10.1145/3794786.3830763
- Authors
- Wang, X., Dang, T., Kostakos, V., & Jia, H.
- Affiliations
- The University of Melbourne (AU)
- Venue
- Proceedings of the 30th Annual International Conference on Mobile Computing and Networking
- Publisher
- ACM
- Published
- 2024-12-04
- Pages
- 2353-2358
- ISSN
- 1543-5679 (ISSN-L)
- OpenAlex
- W4405014857
- Cited by
- 22
- References
- 8
- Citations by year
- 2026: 13 · 2025: 9
- FWCI
- 10.42
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Annual International Conference on Mobile Computing and Networking (MobiCom) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- small language models, mobile health, wearable devices, privacy-preserving healthcare, TinyLlama, medical data analysis, real-time health monitoring
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
