Smart Home Technology: A New Approach for Performance Measurements of Activities of Daily Living and Prediction of Mild Cognitive Impairment in Older Adults.


Journal

Journal of Alzheimer's disease : JAD
ISSN: 1875-8908
Titre abrégé: J Alzheimers Dis
Pays: Netherlands
ID NLM: 9814863

Informations de publication

Date de publication:
2019
Historique:
pubmed: 19 2 2019
medline: 23 6 2020
entrez: 19 2 2019
Statut: ppublish

Résumé

Functional assessment is of paramount importance when mild cognitive impairment is suspected, but common assessment tools such as questionnaires lack sensitivity. An alternative and innovative approach consists in using sensor technology in smart apartments during scenario-based assessments of instrumental activities of daily living (IADL). However, studies that investigate this approach are scarce and the technology used is not always transposable in healthcare settings. To explore whether simple and wireless technology used in two different smart environments could add value to performance and rater-based measures of IADL when it comes to predicting mild cognitive impairment (MCI) in older adults. Twenty-six (26) cognitively healthy older adults (CH) and 22 older adults with MCI were recruited. Functional performance in a set of five scripted tasks was evaluated with sensor-based observations (motion, contact, and electric sensors) and performance-based measures (rated with videotapes). The five tasks could be performed in any order and were detailed on an instruction sheet given to participants. Sensor-based observations showed that participants with MCI spent more time in the kitchen and looking into the fridge and kitchen cabinets than CH participants. Moreover, these measures were negatively associated with memory and executive performances of participants and significantly contributed to the prediction of MCI. Simple, wireless, and sensor-based technology holds potential for the detection of MCI in older adults as they perform daily tasks. However, some limits are discussed and we offer recommendations to improve the usefulness of this innovative approach.

Sections du résumé

BACKGROUND
Functional assessment is of paramount importance when mild cognitive impairment is suspected, but common assessment tools such as questionnaires lack sensitivity. An alternative and innovative approach consists in using sensor technology in smart apartments during scenario-based assessments of instrumental activities of daily living (IADL). However, studies that investigate this approach are scarce and the technology used is not always transposable in healthcare settings.
OBJECTIVE
To explore whether simple and wireless technology used in two different smart environments could add value to performance and rater-based measures of IADL when it comes to predicting mild cognitive impairment (MCI) in older adults.
METHODS
Twenty-six (26) cognitively healthy older adults (CH) and 22 older adults with MCI were recruited. Functional performance in a set of five scripted tasks was evaluated with sensor-based observations (motion, contact, and electric sensors) and performance-based measures (rated with videotapes). The five tasks could be performed in any order and were detailed on an instruction sheet given to participants.
RESULTS
Sensor-based observations showed that participants with MCI spent more time in the kitchen and looking into the fridge and kitchen cabinets than CH participants. Moreover, these measures were negatively associated with memory and executive performances of participants and significantly contributed to the prediction of MCI.
CONCLUSION
Simple, wireless, and sensor-based technology holds potential for the detection of MCI in older adults as they perform daily tasks. However, some limits are discussed and we offer recommendations to improve the usefulness of this innovative approach.

Identifiants

pubmed: 30775978
pii: JAD180652
doi: 10.3233/JAD-180652
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

85-96

Auteurs

Maxime Lussier (M)

Research Center of Institut universitaire de gériatrie de Montréal, Montreal, Canada.
Faculty of Medicine, Université de Montréal, Montreal, Canada.

Stéphane Adam (S)

Faculty of Psychology, Speech therapy and Education Sciences, Université de Liège, Liège, Belgique.

Belkacem Chikhaoui (B)

Department of Science and Technology, Université Téluq, 5800, rue Saint-Denis, Montreal, Canada.

Charles Consel (C)

Bordeaux Institute of Technology & Inria, Université de Bordeaux, Bordeaux, France.

Mathieu Gagnon (M)

Faculty of Sciences and Faculty of Medicine, Université de Sherbrooke, Sherbrooke, Canada.

Brigitte Gilbert (B)

Research Center of Institut universitaire de gériatrie de Montréal, Montreal, Canada.

Sylvain Giroux (S)

Faculty of Sciences and Faculty of Medicine, Université de Sherbrooke, Sherbrooke, Canada.

Manon Guay (M)

Faculty of Sciences and Faculty of Medicine, Université de Sherbrooke, Sherbrooke, Canada.

Carol Hudon (C)

Faculty of Social Sciences and Faculty of Medicine, Université Laval, Québec city, Canada.
CERVO Brain Research Centre, Quebec city, Canada.

Hélène Imbeault (H)

CSSS-Institut universitaire de gériatrie de Sherbrooke, Sherbrooke, Canada.

Francis Langlois (F)

CSSS-Institut universitaire de gériatrie de Sherbrooke, Sherbrooke, Canada.

Joel Macoir (J)

Faculty of Social Sciences and Faculty of Medicine, Université Laval, Québec city, Canada.
CERVO Brain Research Centre, Quebec city, Canada.

Hélène Pigot (H)

Faculty of Sciences and Faculty of Medicine, Université de Sherbrooke, Sherbrooke, Canada.

Lise Talbot (L)

Faculty of Sciences and Faculty of Medicine, Université de Sherbrooke, Sherbrooke, Canada.

Nathalie Bier (N)

Research Center of Institut universitaire de gériatrie de Montréal, Montreal, Canada.
Faculty of Medicine, Université de Montréal, Montreal, Canada.

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