Fibromyalgia & musculoskeletal pain
Web search trends on fibromyalgia: development of a machine learning model
Authors' conclusion
Understanding online search behaviour can enhance FM education.
Study context
- Population
- Online fibromyalgia (FM)-related search behaviour, Google Trends 2020–2024, sixteen countries16 countries
- Outcome
- Online search interest in FM: temporal trends and machine learning prediction of search interest
Methodological detailsGoogle Trends data, sixteen countries, Mann-Kendall trend test, seasonal decomposition, Auto-Regressive Integrated Moving Average (ARIMA) forecasting, Random Forest and XGBoost, SHAP feature importance
- Google Trends data
- Sixteen countries
- Mann-Kendall trend test
- Seasonal decomposition
- ARIMA forecasting
- Random Forest and XGBoost
- SHAP feature importance
Main finding
As reported in the abstract
FM search interest across countries · 2020-2024
- Highest engagement: China, the UK, the USA and Canada; lowest: Peru, Spain and Turkey
- Rising search trends: Brazil, Italy and the UK; declines: Argentina, Canada, Greece and the USA
- Seasonal mid-year peaks in Brazil and Italy; late autumn increases in Turkey
- ARIMA forecast for 2025: stable or increasing in Brazil, Canada and Mexico; slight declines in Germany and Venezuela
- Machine learning: short-term search history (previous day, week, month volumes) the most influential predictor
Original publication
Web search trends on fibromyalgia: development of a machine learning model. Clin Exp Rheumatol. Published 25 April 2025. doi:10.
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