Abstract
Currently, the assessment of autonomy and functional ability involves clinical rating scales. However, scales are often limited in their ability to provide objective and sensitive information. By contrast, information and communication technologies may overcome these limitations by capturing more fully functional as well as cognitive disturbances associated with Alzheimer disease (AD). We investigated the quantitative assessment of autonomy in dementia patients based not only on gait analysis but also on the participant performance on instrumental activities of daily living (IADL) automatically recognized by a video event monitoring system (EMS). Three groups of participants (healthy controls, mild cognitive impairment, and AD patients) had to carry out a standardized scenario consisting of physical tasks (single and dual task) and several IADL such as preparing a pillbox or making a phone call while being recorded. After, video sensor data were processed by an EMS that automatically extracts kinematic parameters of the participants’ gait and recognizes their carried out activities. These parameters were then used for the assessment of the participants’ performance levels, here referred as autonomy. Autonomy assessment was approached as classification task using artificial intelligence methods that takes as input the parameters extracted by the EMS, here referred as behavioral profile. Activities were accurately recognized by the EMS with high precision. The most accurately recognized activities were “prepare medication” with 93% and “using phone” with 89% precision. The diagnostic group classifier obtained a precision of 73.46% when combining the analyses of physical tasks with IADL. In a further analysis, the created autonomy group classifier which obtained a precision of 83.67% when combining physical tasks and IADL. Results suggest that it is possible to quantitatively assess IADL functioning supported by an EMS and that even based on the extracted data the groups could be classified with high accuracy. This means that the use of such technologies may provide clinicians with diagnostic relevant information to improve autonomy assessment in real time decreasing observer biases.
Highlights
Until now, the assessment of instrumental activities of daily living (IADL) has been mostly limited to questionnaires and relies often on informants’ reports, such as the disability assessment for dementia scale (DAD) or the IADL scale of Lawton and Brody (1969), which suffer from biases and inaccuracies in informants’ perceptions as well as the possibility that some older adults do not have an individual who can comment on their impact of cognitive impairment on routine activities
The objective of this study is to investigate the use of Information and communication technology (ICT) and, in particular, video analyses in clinical practice for the assessment of autonomy in IADL in healthy elderly mild cognitive impairment (MCI) and Alzheimer disease (AD) patients by demonstrating an accurate automatized autonomy assessment based on automatically extracted video features from gait and IADL performances
The mean IADL scores did not differ between groups, with a mean IADL score of 7 (±1.2) for the healthy controls (HC) group, 6.33 (±1.7) for the MCI group, and (6 ± 1.8) for the AD group
Summary
Studies show that in dementia patients, loss of functioning in instrumental activities of daily living (IADL) is strongly associated with faster cognitive decline (Arrighi et al, 2013) and, in particular, with poorer performances on executive function tasks (Razani et al, 2007; Karzmark et al, 2012) such as the frontal assessment battery (FAB) (Dubois et al, 2000) or the trail making test (version B) (Tombaugh, 2004) It represents an early predictor of cognitive deterioration and possibly even for conversion from mild cognitive impairment (MCI) to AD (Reppermund et al, 2013). This leads to an urgent need for better measures of functional changes in people with the earliest changes associated with AD in clinical trials (Snyder et al, 2014)
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