Medicine · 2025
Construction of decision tree prediction model and group intervention for the mindfulness level of community pre-frail elderly: A cross-sectional study.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zheng Hao, Xu Junrong, Li Fei, Qin Yi, Zheng Daoxun
Аннотация
Pre-frailty, a reversible state before frailty, is key for intervention. Mindfulness, a protective factor against frailty, is underexplored in community-dwelling pre-frail older adults. This study identified its influences, built predictive models, and proposed stratified interventions. 156 pre-frail older adults meeting inclusion and exclusion criteria were recruited from 5 communities in Nanning City, Guigang City, and Yulin City between August 2024 and February 2025 via convenience sampling. Data were collected using a General Demographic Questionnaire, the Chinese Frailty Scale, and the Mindful Attention Awareness Scale (Chinese version). Univariate analysis and multiple linear stepwise regression were employed to identify influencing factors, and a decision tree algorithm was applied to construct the prediction model. The mean mindfulness score among community-dwelling pre-frail older adults was 70.46 ± 9.80 (95% CI: 68.91-72.01), indicating a moderate level. Multiple linear stepwise regression identified monthly income as the primary influencing factor (β = 0.208, P = .011). Secondary factors significantly associated with mindfulness levels were medical burden, place of residence, and insomnia, with no evidence of multicollinearity (P < .05, VIF < 5). The decision tree model comprised 4 layers, 11 nodes, and 6 terminal nodes. Monthly income emerged as the most significant predictor (root node F = 18.645, P < .001), using healthcare burden, insomnia status, number of children, and educational attainment as additional classification nodes. Both methods consistently verified monthly income as the primary predictor. Establishing a targeted intervention framework integrating "Economic Support-Sleep Management-Intergenerational Support" was recommended. prioritizing support for the low-income group with medical burdens through integrated measures including economic assistance, pain management, bundled medical-psychological services, and community care centers; promoting "sleep-mindfulness" combined interventions and adapting urban community support models to rural settings to bridge urban-rural gaps; These findings provide evidence-based guidance for enhancing mental health and delaying frailty progression in this population.