# Insights on Recruitment, Implementation, and Movement Pattern Detection by Exploring the Feasibility of Sensor-Based Insole Technology in Long-Term Care: Mixed Methods Feasibility Study

## TL;DR

This study explores the feasibility of using sensor-equipped insoles to monitor mobility and detect agitation in dementia patients within long-term care settings.

## Contribution

The study provides empirical evidence on the practical challenges and technical feasibility of integrating sensor-based insole technology into dementia care.

## Key findings

- Sensor-based insole systems can technically collect continuous mobility data in long-term care settings.
- Simulated agitation patterns were reliably detected by machine-learning models, but natural agitation events were rare and hard to capture.
- Resident acceptance varied, with some perceiving increased stability from the insoles.

## Abstract

Sensor-based footwear is increasingly discussed as a promising tool for mobility monitoring and fall-risk assessment, yet its applicability in long-term care remains largely unexamined. In particular, little is known about whether such systems and the study procedures needed to evaluate them can be feasibly integrated into dementia care settings. This feasibility study provides practice-based evidence of using a sensor-equipped insole system with cognitively impaired residents.

The study examines recruitment feasibility, integration of the device into daily care routines, and the operationalization of the study design under everyday institutional conditions. Furthermore, it explores whether movement patterns, including agitation-related behaviors, can be detected in situ, acknowledging the exploratory nature of this aim due to the rarity and unpredictability of natural agitation episodes.

An exploratory feasibility study was conducted in 2 long-term care facilities in Eastern Switzerland. Six residents with mild-to-moderate cognitive impairment and increased fall risk were recruited through a multistep, staff-supported process, offering rare documentation of real-world recruitment constraints. The insole system recorded continuous gait and movement data during daily activities and weekly walking tests. Controlled simulations of agitation-related patterns were conducted to generate reference data, as natural agitation events were infrequent and difficult to capture systematically. Feasibility outcomes were informed focusing on recruitment capability, acceptability and adherence, feasibility of continuous data acquisition, and the practicality of integrating the system into daily care routines.

The study identified substantial feasibility constraints, including contextual limitations posed by sedation practices, fluctuating health status, and consent-related barriers. Practical challenges such as inconsistent battery charging, communication gaps across rotating staff, and difficulties achieving adequate shoe fit directly affected protocol adherence and data yield. Continuous sensor-based data collection was technically feasible when the shoes were worn, and machine-learning models consistently classified predefined agitation-related patterns under controlled conditions. Natural agitation episodes were rare, making simulated reference data essential for establishing fundamental detectability. Resident acceptance varied, with some individuals reporting increased perceived stability due to the shoe’s firm construction rather than its vibration feature.

This feasibility study demonstrates that sensor-based footwear is technically feasible and shows situational acceptability in long-term care, while also highlighting key contextual constraints related to recruitment, workflow integration, device handling, and adherence. The findings provide early empirical evidence on what is practically achievable in this population and setting, clarifying the exploratory nature and methodological necessity of using simulated agitation patterns, and underscoring the need for context-sensitive, participatory strategies when introducing sensor-based technologies into dementia care.