Early Detection of Mild Cognitive Impairment With In-Home Sensors to Monitor Behavior Patterns in Community-Dwelling Senior Citizens in Singapore: Cross-Sectional Feasibility Study

Abstract

Background Dementia is a global epidemic and incurs substantial burden on the affected families and the health care system. A window of opportunity for intervention is the predementia stage known as mild cognitive impairment (MCI). Individuals often present to services late in the course of their disease and more needs to be done for early detection; sensor technology is a potential method for detection. Objective The aim of this cross-sectional study was to establish the feasibility and acceptability of utilizing sensors in the homes of senior citizens to detect changes in behaviors unobtrusively. Methods We recruited 59 community-dwelling seniors (aged >65 years who live alone) with and without MCI and observed them over the course of 2 months. The frequency of forgetfulness was monitored by tagging personal items and tracking missed doses of medication. Activities such as step count, time spent away from home, television use, sleep duration, and quality were tracked with passive infrared motion sensors, smart plugs, bed sensors, and a wearable activity band. Measures of cognition, depression, sleep, and social connectedness were also administered. Results Of the 49 participants who completed the study, 28 had MCI and 21 had healthy cognition (HC). Frequencies of various sensor-derived behavior metrics were computed and compared between MCI and HC groups. MCI participants were less active than their HC counterparts and had more sleep interruptions per night. MCI participants had forgotten their medications more times per month compared with HC participants. The sensor system was acceptable to over 80% (40/49) of study participants, with many requesting for permanent installation of the system. Conclusions We demonstrated that it was both feasible and acceptable to set up these sensors in the community and unobtrusively collect data. Further studies evaluating such digital biomarkers in the homes in the community are needed to improve the ecological validity of sensor technology. We need to refine the system to yield more clinically impactful information.

Evaluation, Acceptance, and Qualification of Digital Measures: From Proof of Concept to Endpoint

“…For example, gait speed as a measure of function and independence would be a COA, but as a predictor of later mortality it would be a biomarker. [11] or individuals with mild cognitive impairment [12], smartphone-based eye tracking in autism spectrum disorders [13], movement patterns correlating with mood disorder classification [14] Digital measure Evaluation Measurement from a digital device that has been assessed as verifiable and analytically and clinically validated N/A Gait speed during active minutes [15] Corridor gait speed in older adults at home [16] Clinical outcome assessment…”

Nonwearable Sensor-Based In-Home Assessment of Subtle Daily Behavioral Changes as a Candidate Biomarker for Mild Cognitive Impairment

“…Previous studies that evaluated the alteration of daily behavioral patterns in patients with MCI and AD using traditional performance-and questionnaire-based [6][7][8][9][21][22][23][24] (Section 2.2) and nonwearable sensor-based in-home assessments [10,11,[26][27][28][29][30][31][34][35][36][37] (Sections 3.2 and 3.3; Table 3) were described in this review. From the findings of these studies, all these assessment methods are considered useful in differentiating between patients with MCI and healthy elderly people.…”

“…Specifically, patients with dementia revealed unorganized behavior patterns (Figure 2). Rawtaer et al [31] examined changes in behaviors in patients with MCI using the multiple sensor system (passive infrared motion sensors, proximity beacon tags, a sensor-equipped medication box, a bed sensor, and a wearable sensor) for >2 months. Patients with MCI were less active than healthy subjects and had more sleep interruptions per night.…”

EVIDENCE Publication Checklist for Studies Evaluating Connected Sensor Technologies: Explanation and Elaboration

“…Many evaluation studies will not meet criteria for the V3 framework as predefined protocols and acceptance criteria for many measures from connected sensor technologies have not been established. For example, sensor-based measures of forgetfulness, smartphone-based measures of eye tracking or gaze, and actigraphy to predict mood do not have defined evaluation protocols or acceptance criteria [14][15][16]. When performed to a rigorous standard, proof-of-concept studies can characterize measurement properties to inform power calculations in subsequent V3 evaluations.…”