Chan Lloyd L Y, Espinoza Cerda Maria Teresa, Brodie Matthew A, Lord Stephen R, Taylor Morag E
Falls, Balance and Injury Research Centre, Neuroscience Research Australia, Sydney, Australia; School of Health Sciences, Faculty of Medicine and Health, University of New South Wales, Sydney, Australia; Ageing Futures Institute, UNSW Sydney, Sydney Australia.
Falls, Balance and Injury Research Centre, Neuroscience Research Australia, Sydney, Australia; Getafe University Hospital, Madrid, Spain.
Int Psychogeriatr. 2025 Jun;37(3):100031. doi: 10.1016/j.inpsyc.2024.100031. Epub 2025 Jan 7.
To determine if wrist-worn sensor parameters can predict incident dementia in individuals aged 60 + years and to compare prediction with other tools.
Observational cohort study.
Community PARTICIPANTS: The cohort comprised 47,371 participants without dementia, aged 60 + years, who participated in the UK Biobank study (mean age=67 ± 4 years; 52 % female).
Nineteen digital biomarkers were extracted from up-to-7-day wrist-worn sensor accelerometry data at baseline. Univariable and multivariable Cox proportional hazard models examined associations between sensor parameters and prospectively diagnosed dementia.
Median follow-up was 7.5 years (interquartile range: 7.0 to 9.0 years), during this time 387 participants (0.8 %) were diagnosed with dementia. Among the gait parameters, slower maximal walking speed had the strongest association with incident dementia (32 % decrease in hazard for each standard deviation increase) followed by lower daily step counts (30 % decrease) and increased step-time variability (17 % increase). While adjusting for age and sex, running duration, maximal walking speed and early bedtime were identified as independent and significant predictors of dementia. The multivariable prediction model performed comparably to the ANU-ADRI and UKB-Dementia Risk Score models in the UK Biobank cohort.
The study findings indicate that remotely acquired parameters from wrist-worn sensors can predict incident dementia. Since wrist-worn sensors are highly acceptable for long-term use, wrist-worn sensor parameters have the potential to be incorporated into dementia screening programs.
确定佩戴在手腕上的传感器参数能否预测60岁及以上人群的新发痴呆症，并与其他工具的预测效果进行比较。
观察性队列研究。
社区
该队列由47371名无痴呆症的60岁及以上参与者组成，他们参与了英国生物银行研究（平均年龄=67±4岁；52%为女性）。
在基线时，从长达7天的佩戴在手腕上的传感器加速度计数据中提取了19个数字生物标志物。单变量和多变量Cox比例风险模型检验了传感器参数与前瞻性诊断痴呆症之间的关联。
中位随访时间为7.5年（四分位间距：7.0至9.0年），在此期间，387名参与者（0.8%）被诊断为痴呆症。在步态参数中，最大步行速度较慢与新发痴呆症的关联最强（每增加一个标准差，风险降低32%），其次是每日步数减少（30%）和步时变异性增加（17%）。在调整年龄和性别后，跑步时长、最大步行速度和早睡时间被确定为痴呆症的独立且显著的预测因素。在英国生物银行队列中，多变量预测模型的表现与澳大利亚国立大学-阿德莱德痴呆风险指数（ANU-ADRI）模型和英国生物银行痴呆风险评分模型相当。
研究结果表明，从佩戴在手腕上的传感器远程获取的参数可以预测新发痴呆症。由于佩戴在手腕上的传感器长期使用的接受度很高，因此其参数有可能被纳入痴呆症筛查项目。
