Objective: This study describes a research protocol for a behavioral marker-based predictive model that examines the functional status of older adults with subjective cognitive decline and mild cognitive impairment. Methods: A total of 130 older adults aged ≥65 years with subjective cognitive decline or mild cognitive impairment will be recruited from the Dementia Relief Centers or the Community Service Centers. Data on behavioral and psychosocial markers (e.g. physical activity, mobility, sleep/wake patterns, social interaction, and mild behavioral impairment) will be collected using passive wearable actigraphy, in-person questionnaires, and smartphone-based ecological momentary assessments. Two follow-up assessments will be performed at 12 and 24 months after baseline. Mixed-effect machine learning models: MErf, MEgbm, MEmod, and MEctree, and standard machine learning models without random effects [random forest, gradient boosting machine] will be employed in our analyses to predict functional status over time. Results: The results of this study will be fundamental for developing tailored digital interventions that apply deep learning techniques to behavioral data to predict, identify, and aid in the management of functional decline in older adults with subjective cognitive decline and mild cognitive impairment. These older adults are considered the optimal target population for preventive interventions and will benefit from such tailored strategies. Conclusions: Our study will contribute to the development of self-care interventions that utilize behavioral data and machine learning techniques to provide automated analyses of the functional decline of older adults who are at risk for dementia.
Background: As the global population ages, the economic burden of dementia continues to rise. Social isolation—which includes limited social interaction and feelings of loneliness—negatively affects cognitive function and is a significant risk factor for dementia. Individuals with subjective cognitive decline and mild cognitive impairment represent predementia stages in which functional decline may still be reversible. Therefore, identifying factors related to social isolation in these at-risk groups is crucial, as early detection and intervention can help mitigate the risk of further cognitive decline. Objective: This study aims to develop and validate machine learning models to identify and explore factors related to social interaction frequency and loneliness levels among older adults in the predementia stage. Methods: The study included 99 community-dwelling older adults aged 65 years and above in the predementia stage. Social interaction frequency and loneliness levels were assessed 4 times daily using mobile ecological momentary assessment over a 2-week period. Actigraphy data were categorized into 4 domains: sleep quantity, sleep quality, physical movement, and sedentary behavior. Demographic and health-related survey data collected at baseline were also included in the analysis. Machine learning models, including logistic regression, random forest, Gradient Boosting Machine, and Extreme Gradient Boosting, were used to explore factors associated with low social interaction frequency and high levels of loneliness. Results: Of the 99 participants, 43 were classified into the low social interaction frequency group, and 37 were classified into the high loneliness level group. The random forest model was the most suitable for exploring factors associated with low social interaction frequency (accuracy 0.849; precision 0.837; specificity 0.857; and area under the receiver operating characteristic curve 0.935). The Gradient Boosting Machine model performed best for identifying factors related to high loneliness levels (accuracy 0.838; precision 0.871; specificity 0.784; and area under the receiver operating characteristic curve 0.887). Conclusions: This study demonstrated the potential of machine learning–based exploratory models, using data collected from mobile ecological momentary assessment and wearable actigraphy, to detect vulnerable groups in terms of social interaction frequency and loneliness levels among older adults with subjective cognitive decline and mild cognitive impairment. Our findings highlight physical movement as a key factor associated with low social interaction frequency, and sleep quality as a key factor related to loneliness. These results suggest that social interaction frequency and loneliness may operate through distinct mechanisms. Ultimately, this approach may contribute to preventing cognitive and physical decline in older adults at high risk of dementia.