{"id":2234,"date":"2026-06-11T10:43:20","date_gmt":"2026-06-11T09:43:20","guid":{"rendered":"https:\/\/sites.manchester.ac.uk\/health-research-from-home\/?p=2234"},"modified":"2026-06-11T10:43:20","modified_gmt":"2026-06-11T09:43:20","slug":"predicting-multiple-sclerosis-symptoms-using-machine-learning","status":"publish","type":"post","link":"https:\/\/sites.manchester.ac.uk\/health-research-from-home\/2026\/06\/11\/predicting-multiple-sclerosis-symptoms-using-machine-learning\/","title":{"rendered":"Predicting multiple sclerosis symptoms using machine learning"},"content":{"rendered":"

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Multiple sclerosis (MS) affects around 2.8 million people worldwide. People living with MS can experience many symptoms, including neurological symptoms affecting cognitive function and movement, depression, fatigue and poor sleep. Some symptoms can be managed, but because clinic visits are often infrequent, treatment can sometimes lag behind what people need.<\/p>\n

Smartphones and wearables can track various aspects of health passively and continuously. This creates an opportunity to use data from people’s devices to predict when symptoms might worsen. The month’s Tap into Research looks at a study testing whether machine learning models can predict short-term changes in MS symptoms using data from smartphones, wearables, and self-reported questionnaires.<\/p>\n

How were smartphones and wearables used in the study?<\/h2>\n

Participants took part for 12-24 weeks and used their own smartphones. The research team collected call activity, location, and screen use. Participants also wore Fitbits provided by the study, which measured heart rate, step count, and sleep patterns.<\/p>\n

Alongside this passive monitoring, participants completed a short symptom questionnaire on their smartphone three times per day.<\/p>\n

Every 2 weeks, participants completed a validated questionnaire on depressive symptoms. Every 4 weeks, they completed questionnaires on neurological symptoms, fatigue, and sleep quality. These questionnaires were used as the “gold standard” to judge how well the models predicted symptoms.<\/p>\n

What did the study find?<\/h2>\n

Models that included smartphone and wearable data were better at predicting short term worsening in depression, neurological symptoms, fatigue, and sleep quality than models that did not use this data.<\/p>\n

The study also found:<\/p>\n