Differences in Life Space Activity Patterns Between Older Adults With Mild Cognitive Impairment Living Alone or as a Couple: Cohort Study Using Passive Activity Sensing

I-CONECT intervention effects on weekly time spent outside of home and social contacts among socially isolated older adults

Background

Socially isolated individuals tend to have less access to cognitively stimulating activities, which could adversely impact their cognitive health. The Internet-Based Conversational Engagement Clinical Trial (I-CONECT) intervention was designed to deliver online conversation sessions to socially isolated older old adults to prevent cognitive decline. The current study examined the intervention efficacy on participants' weekly time spent out-of-home and their social interaction with family and friends.

Methods

The intervention group engaged in frequent conversations with trained interviewers via the Internet. Both intervention and control group participants received 10-min weekly phone check-in calls over 48 weeks, during which they were asked to self-report their time (in hours) spent out of home and whether they had contacted family or friends during this week (yes/no). Linear mixed-effect models for repeated measures were run for time spent out-of-home, and mixed-effect models with a logistic link for contact with family and friends. The intervention effect was modeled by including an interaction term of time (measured in weeks) and group assignments (intervention vs. control). We ran subgroup analyses for participants with normal cognition (NC) and mild cognitive impairment (MCI). All models controlled for age, sex, race, education, and the historical event of COVID-19.

Results

5,495 weekly records were included in the analysis. The main effect of time was statistically significant (p < 0.001), suggesting participants spent more time out of home over time. Among the participants with NC, the intervention group had a steeper increase in their time spent out-of-home (p = 0.016) compared with the control group. Among the participants with MCI, the intervention group had an increased likelihood of contacting friends over time (p = 0.001) than the control group. The intervention effect on contact with family was not significant for either the NC or MCI group.

Discussion

The I-CONECT intervention enhanced social activities among socially isolated older old participants, which could provide additional cognitive stimulation and prevent cognitive decline.

Validating Room Location within the Home for Functional Measures from Digital Health Technologies

Digital health technologies have the potential to measure how a patient feels and functions with low patient burden e.g.: to provide real-world evidence of the benefit of a novel treatment. Despite this potential, traditional wearable devices, e.g. actigraphy, do not provide location-activity information which may improve the validity of these measures relative to established clinical measures. We describe a system that combines a wearable bracelet with Bluetooth-low-energy environment beacons to help localize and provide environmental context to that wearable data, and therefore provide a more clinically relevant measure of function. We describe an initial validation of the accuracy of the location information provided by this system in a study of 5 different real living environments of different size and layout, each collecting data over multiple days and recording their actual location in their home every 15 minutes in a diary. The results are presented in a confusion matrix. Mean overall accuracy was 94.0% (range 88.8-98.8%), which is sufficient to enable construction of more meaningful outcomes for patients than activity alone. For example, to determine if someone is moving around their home more, if they are getting outside more, if they are spending more time in bed, etc. It may be possible to improve location accuracy further with more sophisticated analysis of the beacon data.

A Semantic Framework to Detect Problems in Activities of Daily Living Monitored through Smart Home Sensors

Activities of daily living (ADLs) are fundamental routine tasks that the majority of physically and mentally healthy people can independently execute. In this paper, we present a semantic framework for detecting problems in ADLs execution, monitored through smart home sensors. In the context of this work, we conducted a pilot study, gathering raw data from various sensors and devices installed in a smart home environment. The proposed framework combines multiple Semantic Web technologies (i.e., ontology, RDF, triplestore) to handle and transform these raw data into meaningful representations, forming a knowledge graph. Subsequently, SPARQL queries are used to define and construct explicit rules to detect problematic behaviors in ADL execution, a procedure that leads to generating new implicit knowledge. Finally, all available results are visualized in a clinician dashboard. The proposed framework can monitor the deterioration of ADLs performance for people across the dementia spectrum by offering a comprehensive way for clinicians to describe problematic behaviors in the everyday life of an individual.