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The identification of geriatric depression and anxiety is important because such conditions are the most common comorbid mood problems that occur in older adults. The goal of this study was to build a machine learning framework that identifies geriatric mood disorders of depression and anxiety using low-cost activity trackers and minimal geriatric assessment scales. We collected activity tracking data from 352 mild cognitive impairment patients, from 60 to 90 in age, by having them wear activity trackers on their wrist for more than a month. We then extracted the features of 24-h activity rhythms and sleep patterns from the time-series activity tracking data. To increase the accuracy, we designed a novel method to incorporate additional features from questionnaire-based assessments of the geriatric depression scale and geriatric anxiety inventory into the activity tracking features. In the multi-label classification, we applied the binary relevance method to develop two single-label classifiers for depression and anxiety. The best hyper-parameters of classification algorithms for each label were selected by comparing the classification performance. We finally selected the combination of classifiers for depression and anxiety with the lowest Hamming loss as a multi-label classifier. This study successfully demonstrated the possibility of identifying geriatric depression and anxiety using low-cost activity trackers and minimal geriatric assessment scales for use in the real fields.

Identification of Geriatric Depression and Anxiety Using Activity Tracking Data and Minimal Geriatric Assessment Scales