Maxime Lussier1,2, Stéphane Adam3, Belkacem Chikhaoui4, Charles Consel5, Mathieu Gagnon6, Brigitte Gilbert1, Sylvain Giroux6, Manon Guay6, Carol Hudon7,8, Hélène Imbeault9, Francis Langlois9, Joel Macoir7,8, Hélène Pigot6, Lise Talbot6, Nathalie Bier1,2. 1. Research Center of Institut universitaire de gériatrie de Montréal, Montreal, Canada. 2. Faculty of Medicine, Université de Montréal, Montreal, Canada. 3. Faculty of Psychology, Speech therapy and Education Sciences, Université de Liège, Liège, Belgique. 4. Department of Science and Technology, Université Téluq, 5800, rue Saint-Denis, Montreal, Canada. 5. Bordeaux Institute of Technology & Inria, Université de Bordeaux, Bordeaux, France. 6. Faculty of Sciences and Faculty of Medicine, Université de Sherbrooke, Sherbrooke, Canada. 7. Faculty of Social Sciences and Faculty of Medicine, Université Laval, Québec city, Canada. 8. CERVO Brain Research Centre, Quebec city, Canada. 9. CSSS-Institut universitaire de gériatrie de Sherbrooke, Sherbrooke, Canada.
Abstract
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.
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.
Authors: John P K Bernstein; Katherine E Dorociak; Nora Mattek; Mira Leese; Zachary T Beattie; Jeffrey A Kaye; Adriana Hughes Journal: J Alzheimers Dis Date: 2021 Impact factor: 4.472
Authors: John P K Bernstein; Katherine Dorociak; Nora Mattek; Mira Leese; Chelsea Trapp; Zachary Beattie; Jeffrey Kaye; Adriana Hughes Journal: Neuropsychol Dev Cogn B Aging Neuropsychol Cogn Date: 2021-04-18
Authors: Ju Wang; Nicolai Spicher; Joana M Warnecke; Mostafa Haghi; Jonas Schwartze; Thomas M Deserno Journal: Sensors (Basel) Date: 2021-01-28 Impact factor: 3.576