The Department of Industrial Engineering, University of Florence, Florence, Italy.
The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Pisa, Italy.
PLoS One. 2023 Aug 2;18(8):e0287380. doi: 10.1371/journal.pone.0287380. eCollection 2023.
This study investigates the possibility of adopting motor and cognitive dual-task (MCDT) approaches to identify subjects with mild cognitive impairment (MCI) and subjective cognitive impairment (SCI).
The upper and lower motor performances of 44 older adults were assessed using the SensHand and SensFoot wearable system during three MCDTs: forefinger tapping (FTAP), toe-tapping heel pin (TTHP), and walking 10 m (GAIT). We developed five pooled indices (PIs) based on these MCDTs, and we included them, along with demographic data (age) and clinical scores (Frontal Assessment Battery (FAB) scores), in five logistic regression models.
Models which consider cognitively normal adult (CNA) vs MCI subjects have accuracies that range from 67% to 78%. The addition of clinical scores stabilised the accuracies, which ranged from 85% to 89%. For models which consider CNA vs SCI vs MCI subjects, there are great benefits to considering all three regressors (age, FAB score, and PIs); the overall accuracies of the three-class models range between 50% and 59% when just PIs and age are considered, whereas the overall accuracy increases by 18% when all three regressors are utilised.
Logistic regression models that consider MCDT PIs and age have been effective in distinguishing between CNA and MCI subjects. The inclusion of clinical scores increased the models' accuracy. Particularly high performances in distinguishing among CNA, SCI, and MCI subjects were obtained by the TTHP PI. This study suggests that a broader framework for MCDTs, which should encompass a greater selection of motor tasks, could provide clinicians with new appropriate tools.
本研究旨在探讨采用运动和认知双重任务（MCDT）方法来识别轻度认知障碍（MCI）和主观认知障碍（SCI）患者的可能性。
使用 SensHand 和 SensFoot 可穿戴系统评估 44 名老年人的上肢和下肢运动表现，进行三种 MCDT：食指敲击（FTAP）、脚趾敲击脚跟（TTHP）和行走 10 米（GAIT）。我们基于这些 MCDT 开发了五个综合指数（PI），并将其与人口统计学数据（年龄）和临床评分（额叶评估量表（FAB）评分）一起纳入五个逻辑回归模型。
考虑认知正常成人（CNA）与 MCI 受试者的模型的准确性范围为 67%至 78%。添加临床评分可稳定准确性，范围为 85%至 89%。对于考虑 CNA 与 SCI 与 MCI 受试者的模型，考虑所有三个回归量（年龄、FAB 评分和 PI）具有很大的优势；仅考虑 PI 和年龄时，三个类别的模型的总准确性在 50%至 59%之间，而当使用所有三个回归量时，总准确性提高了 18%。
考虑 MCDT PI 和年龄的逻辑回归模型在区分 CNA 和 MCI 受试者方面非常有效。纳入临床评分可提高模型的准确性。TTHP PI 在区分 CNA、SCI 和 MCI 受试者方面表现出特别高的性能。本研究表明，更广泛的 MCDT 框架，应包括更多的运动任务选择，可为临床医生提供新的合适工具。
