Kang Bada, Park Min Kyung, Kim Jennifer Ivy, Yoon Seolah, Heo Seok-Jae, Kang Chaeeun, Lee SungHee, Choi Yeonkyu, Hong Dahye
Mo-Im Kim Nursing Research Institute, Yonsei University College of Nursing, Seoul, Republic of Korea.
Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, Republic of Korea.
J Med Internet Res. 2025 Jun 23;27:e69379. doi: 10.2196/69379.
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.
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.
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.
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).
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.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1177/20552076241269555.
随着全球人口老龄化，痴呆症的经济负担持续上升。社会隔离，包括社交互动有限和孤独感，会对认知功能产生负面影响，是痴呆症的一个重要风险因素。主观认知能力下降和轻度认知障碍的个体处于痴呆前阶段，在此阶段功能衰退可能仍可逆。因此，识别这些高危人群中与社会隔离相关的因素至关重要，因为早期发现和干预有助于降低进一步认知衰退的风险。
本研究旨在开发并验证机器学习模型，以识别和探索痴呆前阶段老年人社交互动频率和孤独程度相关的因素。
该研究纳入了99名年龄在65岁及以上、处于痴呆前阶段的社区居住老年人。在为期2周的时间里，使用移动生态瞬时评估法每天4次评估社交互动频率和孤独程度。活动记录仪数据被分为4个领域：睡眠量、睡眠质量、身体活动和久坐行为。分析还包括在基线时收集的人口统计学和健康相关调查数据。使用机器学习模型，包括逻辑回归、随机森林、梯度提升机和极端梯度提升，来探索与社交互动频率低和孤独程度高相关的因素。
99名参与者中，43人被归类为社交互动频率低的组，37人被归类为孤独程度高的组。随机森林模型最适合探索与社交互动频率低相关的因素（准确率0.849；精确率0.837；特异性0.857；受试者工作特征曲线下面积0.935）。梯度提升机模型在识别与孤独程度高相关的因素方面表现最佳（准确率0.838；精确率0.871；特异性0.784；受试者工作特征曲线下面积0.887）。
本研究证明了基于机器学习的探索性模型的潜力，该模型利用从移动生态瞬时评估和可穿戴活动记录仪收集的数据，在主观认知能力下降和轻度认知障碍的老年人中，检测社交互动频率和孤独程度方面的弱势群体。我们的研究结果突出了身体活动是与社交互动频率低相关的关键因素，睡眠质量是与孤独相关的关键因素。这些结果表明社交互动频率和孤独可能通过不同的机制起作用。最终，这种方法可能有助于预防痴呆症高危老年人的认知和身体衰退。
国际注册报告识别号（IRRID）：RR2-10.1177/20552076241269555。
