Weather-Based Arthritis Tracking: A Mobile Mechanism for Preventive Strategies

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Jin-Lun Goh
Sin-Ban Ho
Chuie-Hong Tan


Arthritis is a common joint disorder characterised by symptoms such as swelling, pain, stiffness, and limited joint movement. It primarily affects older individuals, women, and athletes. The advent of information technology has created opportunities for patients to manage their health conditions more effectively. Research indicates that weather can affect arthritis symptoms, with many patients experiencing severe discomfort during rainy weather due to the expansion of already inflamed tissues. However, there is currently no mobile application mechanism available that combines weather forecasting with health recommendations for arthritis patients, which means that patients may not have access to important information that could help them manage their symptoms. Furthermore, few research workflows have focused on weather conditions in online arthritis treatment systems. This research aims to develop a weather-based mobile system for arthritis tracking that provides health advice and alerts based on current and forecast weather conditions, as well as features to help patients track how weather affects their arthritis. This system utilises several tools for its development. The Flutter Framework is used for creating mobile apps, while Firebase is chosen as the cloud-hosted database. Visual Studio Code and Android Studio are utilised as the code editor and Android emulator, respectively. Information about weather forecasts is retrieved via the OpenWeather API. The application mechanism will feature a user-friendly interface to help users stay updated on weather forecasts, and it will collect data in a reliable and user-centric manner for generating robust evidence on health outcomes.

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Cleveland clinic, “Arthritis: Symptoms, Causes, Treatment & Prevention,” Cleveland Clinic, 2023.

Mayo clinic, “Arthritis: Symptoms, and Causes,” Mayo Clinic, 2023.

I. Kuznetsov, “What is a weather forecast and how does it work. Meaning, duration, accuracy, more,” Windy App, 2019.

M. A. Dunkin, “Weather-Arthritis Connection | Arthritis Foundation,”, 2021.

Airthings, “What is humidity? Why measure & what your levels mean,” Airthings, 2023.

Fondriest, “Wind speed and direction,” Fondriest Environmental Monitor, 2010.

T. Lish, “What is Barometric Pressure?” Setra, 2017.

Chester, “Grandma's Joints Hurts ... Rain Is Coming: How Weather and Arthritis Are Connected,” Chester Country Hospital, 2019.

D. Blumberg, “Does Weather Affect Joint Pain?” WebMD, 2018., “Best Climate for Arthritis Patients: Humidity's Impact on Your Joints,” Arthritis Foundation, 2023.

N. Akgun, E. A. Demirel, M. Acikgoz, U. Celebi, F. Kokturk, and H. T. Atasoy, “The effect of weather variables on the severity, duration, and frequency of headache attacks in the cases of episodic migraine and episodic tension-type headache,” Turkish Journal of Medical Sciences, vol. 51, no. 3, pp. 1406–1412, 2021, doi:

Ampersand Health, “My Arthritis - Apps on Google Play,” Google Play Store, 2023.

RPM Healthcare, “RA Monitor - Apps on Google Play,” Google Play Store, 2023.

L. A. Bradley, “Pain measurement in arthritis,” Arthritis Care & Research, vol. 6, no. 4, pp. 178–186, 1993, doi:

A. Pan, and F. Zhao, “User acceptance factors for mHealth,” In: Kurosu, M. (eds), Human-Computer Interaction: Interaction in Context (HCI 2018). Lecture Notes in Computer Science, vol. 10902, pp. 173–184, 2018, doi:

E. Sezgin, and S. Ozkan, “A systematic literature review on Health Recommender Systems,” 2013 E-Health and Bioengineering Conference (EHB), Iasi, Romania, pp. 1-4, 2013, doi:

A. Mustaqeem, S. M. Anwar, and M. Majid, “A modular cluster based collaborative recommender system for cardiac patients,” Artificial Intelligence in Medicine, vol. 102, 101761, 2020, doi:

Z. Fayyaz, M. Ebrahimian, D. Nawara, A. Ibrahim, and R. Kashef, “Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities,” Applied Sciences, vol. 10, no. 21, 7748, 2020, doi:

J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, “Evaluating collaborative filtering recommender systems,” ACM Transactions on Information Systems, vol. 22, no. 1, pp. 5–53, 2004, doi:

M. G. Vozalis, and K. G. Margaritis, “Collaborative filtering enhanced by demographic correlation,” Semantic Scholar.

W. X. Ong, S. B. Ho, and C. H. Tan, “Enhancing Migraine Management System through Weather Forecasting for a Better Daily Life,” Journal of Informatics and Web Engineering (JIWE), vol. 2, no. 2, pp. 201-217, 2023, doi:

C. Y. Seek, S. Y. Ooi, Y. H. Pang, S. L. Lew, and X. Y. Heng, “Elderly and Smartphone Apps: Case Study with Lightweight MySejahtera,” Journal of Informatics and Web Engineering (JIWE), vol. 2, no. 1, pp. 13-24, 2023, doi: