Related Experiment Video
Updated: Jul 6, 2026
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
Integrating actigraphy with demographic data enhances cognitive performance prediction: a multimodal UK biobank
Mohammad Mahdi Ghiasi1, Ryan Stanley Falck1, Teresa Liu-Ambrose2
1School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada; Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada; Centre for Aging SMART, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada.
Background:
Wrist-worn actigraphy enables continuous, non-invasive measurement of rest-activity patterns and has growing relevance for cognitive health. However, it remains unclear whether actigraphy provides meaningful predictive value for cognitive function beyond demographic factors. Large-scale evaluations are needed to clarify its utility.
Objective:
This study evaluated the effectiveness of 24-hour actigraphy in improving machine learning prediction of cognitive performance, using the computerized version of Digit Symbol Substitution Test (DSST). Complementary objectives included identifying influential predictors for explainability and assessing performance across sexes to evaluate fairness.
Methods:
UK Biobank data included 24-hour actigraphy, demographics, and DSST scores. Random Forest (RF) and Extra Trees (ET) binary classifiers (low vs high DSST) were first trained using demographic features to establish baseline models. Multimodal models then integrated demographics with (1) raw actigraphy features and (2) sine-transformed actigraphy parameters capturing circadian rhythmicity. After preprocessing, the final sample comprised 42,707 participants. Performance was evaluated on a held-out test set (20%) using precision, recall, F1-score, ROC AUC, and PR AUC.
Results:
The baseline demographic-only ET model demonstrated ROC AUC = 0.66 and PR AUC = 0.75. Adding raw actigraphy features improved ET performance (ROC AUC = 0.76; PR AUC = 0.83), outperforming all other models. In contrast, sine-transformed actigraphy features provided no additional benefit. Age, income, and education were the strongest predictors, while activity levels at 7:00-7:59 and 17:00-21:59 were the most informative actigraphy-derived features. Sex-stratified analyses showed slightly improved identification of lower cognitive performance in males and higher cognitive performance in females.
Conclusions:
This novel, large-scale study demonstrated that raw actigraphy features can enhance predictive performance of ET classifier beyond demographics alone. These results support the integration of wearable-derived activity data into population-level and personalized cognitive assessment frameworks. Observed sex-dependent performance differences further highlight the need for sex-aware modeling strategies.
