Performance‐based IADL evaluation of older adults with cognitive impairment within a smart home: A feasibility study
Year: 2021
Type: article
Abstract: INTRODUCTION: Mild cognitive impairment (MCI) is characterized by subtle deficits that functional assessment via informant-report measures may not detect. Sensors can potentially detect deficits in everyday functioning in MCI. This study aims to establish feasibility and acceptability of using sensors in a smart home for performance-based assessments of two instrumental activities of daily living (IADLs). METHODS: Thirty-five older adults (>65 years) performed two IADL tasks in a smart home laboratory equipped with sensors and a web camera. Participants' cognitive states were determined using published criteria including measures of global cognition and comprehensive neuropsychological test batteries. Selected subtasks of the IADL assessment were autonomously captured by the sensors. Total time taken for each task and subtask were computed. A point scoring system captured accuracy and number of attempts. Acceptability of the smart home setup was assessed. RESULTS: Participants with MCI (n = 21) took longer to complete both tasks than participants with healthy cognition (HC; n = 14), with significant time differences observed only in "Cost calculation." Completion time for IADL tasks and scores correlated in the expected direction with global cognition. Over 95% of the participants found the smart home assessment acceptable and a positive experience. DISCUSSION: We demonstrated the feasibility and acceptability of the use of unobtrusive commercially available sensors in a smart home for facilitating parts of the objective assessment of IADL in older adults. Future studies need to identify more IADLs that are suitable for semi-automated or automated assessments through the use of simple, low-cost sensors.
DOI: 10.1002/trc2.12152
Authors: Iris Rawtaer, Khalid Abdul Jabbar, Xiao Liu, Thit Thit Htat Ying, Anh Thuy Giang, Philip Lin Kiat Yap, Rachael Chin Yee Cheong, Hwee Pink Tan, Pius Lee, Shiou Liang Wee, Tze Pin Ng
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,200
Funder: Strong
OpenAlex ID: W3133762964
