Machine Learning to Detect Motor Agitation in People With Dementia Using Real-Time Location Data

Topic: Risk, prevention and care in population studies

Our findings indicate that using location-based trajectory metrics with clinical measures significantly improves the predictability of motor agitation beyond individual baselines. Our best model achieves an area under the curve of the receiver operating characteristic of 0.81 in classifying presence of motor agitation. Explainability analysis identified that trajectory-based features were the most important features for correctly classifying motor agitation. Location data can be used to distinguish clinically important motor agitation from normal motor activities on a shift-by-shift basis. Behavioral analytics derived from location data are informative for clinical assessment of neuropsychiatric symptoms in dementia.

@article{hasan2025-40634226,
  author  = {Hasan, Zain and Shum, Leia C and Faruk, Tamim and McGilton, Katherine S. and Chu, Charlene H. and McArthur, Caitlin and Flint, Alastair J. and Mihailidis, Alex and others},
  title   = {{Machine Learning to Detect Motor Agitation in People With Dementia Using Real-Time Location Data}},
  journal = {American Journal of Geriatric Psychiatry},
  year    = {2025},
  doi     = {10.1016/j.jagp.2025.06.001},
  pmid    = {40634226},
  url     = {https://doi.org/10.1016/j.jagp.2025.06.001}
}

Key findings

• Our findings indicate that using location-based trajectory metrics with clinical measures significantly improves the predictability of motor agitation beyond individual baselines.
• Our best model achieves an area under the curve of the receiver operating characteristic of 0.81 in classifying presence of motor agitation.
• Explainability analysis identified that trajectory-based features were the most important features for correctly classifying motor agitation.
• Location data can be used to distinguish clinically important motor agitation from normal motor activities on a shift-by-shift basis.
• Behavioral analytics derived from location data are informative for clinical assessment of neuropsychiatric symptoms in dementia.

Why it matters

A machine learning model using real-time location data classified motor agitation in people with dementia with an area under the curve of 0.81, and location-based features were the most important for correct classification. This is a monitoring tool for agitation, not a treatment, cure, or prevention of Alzheimer's disease.

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Study details

• Study designlongitudinal study
• Evidence strengthModerate

Read the original

• DOI: 10.1016/j.jagp.2025.06.001
• PubMed: 40634226
• Original source →
• Free full text: American Journal of Geriatric Psychiatry →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• The Neuropsychiatric Inventory: comprehensive assessment of psychopathology in dementia Neurology · 1994-12-01 · usefulness score 58/100 · Caregiving
• Autonomous unobtrusive detection of mild cognitive impairment in older adults IEEE transactions on bio-medical engineering · 2015-01-09 · usefulness score 48/100 · Digital health tools
• Measurement of severity in advanced Alzheimer's disease Journal of gerontology · 1994-09-01 · usefulness score 44/100 · Digital health tools

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