Standard Error of the Mean and Minimal Detectable Change of Gait Speed in Older Adults Using Japanese Long-Term Care Insurance System

Decrease in the usual walking speed and body fat percentage associated with a deterioration in long-term care insurance certification levels

Background

In Japan, the number of older adults requiring long-term care insurance (LTCI) is increasing and the cost is becoming a social problem. In these fields, the role of geriatric rehabilitation includes maintaining the physical function and LTCI certification levels. The prevalence of sarcopenia is high among older adults requiring LTCI certification, and there are many opportunities to assess the handgrip strength, walking speed, and muscle mass. This study aimed to identify sarcopenia-related assessments sensitive to transitions in LTCI certification levels and determine cut-off values to predict them.

Methods

This prospective cohort study analyzed 98 daycare users (mean age ± standard error: 78.5 ± 0.8 years) between March 2019 and 2023. The participants received LTCI certification before the study, and their levels were renewed between baseline and follow-up (six months later). The measurements included handgrip strength, usual walking speed, body composition, and SARC-F score. Participants were classified into maintenance, deterioration, and improvement groups according to the changes in their LTCI certification levels. We identified factors contributing to the deterioration of LTCI certification levels using baseline and before and after comparisons, multivariate analyses, and receiver operating characteristic analyses.

Results

No significant differences were observed in the baseline data among the groups. Only the deterioration group showed significant changes in the usual walking speed (baseline: 0.64 ± 0.25 m/s, follow-up: 0.53 ± 0.21 m/s, P = 0.008) and body fat percentage (baseline: 29.2 ± 9.9%, follow-up: 27.7 ± 10.3%, P = 0.047). Binomial logistic regression showed that changes in usual walking speed (P = 0.042) and body fat percentage (P = 0.011) were significantly associated with the deterioration of LTCI certification levels, even after adjustment. The cutoff values of change to discriminate the deterioration of LTCI certification levels were -0.14 m/s at the usual walking speed (P = 0.047) and -1.0% for body fat percentage (P = 0.029).

Conclusions

Decreases in usual walking speed and body fat percentage may predict worse certification levels in older adults requiring LTCI.

Automation of Functional Mobility Assessments at Home Using a Multimodal Sensor System Integrating Inertial Measurement Units and Computer Vision (IMU-Vision)

OBJECTIVE

Functional movement assessments are routinely used to evaluate and track changes in mobility. The objective of this study was to evaluate a multimodal movement monitoring system developed for autonomous, home-based, functional movement assessment.

METHODS

Fifty frail and prefrail adults were recruited from the Brigham and Women's Hospital at Home program to evaluate the feasibility and accuracy of applying the multimodal movement monitoring system to autonomously recognize and score functional activities collected in the home. Study subjects completed sit-to-stand, standing balance (Romberg, semitandem, and tandem), and walking test activities in likeness to the Short Physical Performance Battery. Test activities were identified and scored manually and by the multimodal movement monitoring system's activity recognition and scoring algorithms, which were previously trained on lab-based biomechanical data to integrate wearable inertial measurement unit (IMU) and external red-blue-green-depth vision data. Feasibility was quantified as the proportion of completed tests that were analyzable. Accuracy was quantified as the degree of agreement between the actual and system-identified activities. In an exploratory analysis of a subset of functional activity data, the accuracy of a preliminary activity-scoring algorithm was also evaluated.

RESULTS

Activity recognition by the IMU-vision system had good feasibility and high accuracy. Of 271 test activities collected in the home, 217 (80%) were analyzable by the activity-recognition algorithm, which overall correctly identified 206 (95%) of the analyzable activities: 100% of walking, 97% of balance, and 82% of sit-to-stand activities (χ2(2) = 19.9). In the subset of 152 tests suitable for activity scoring, automatic and manual scores showed substantial agreement (Kw = 0.76 [0.69, 0.83]).

CONCLUSIONS

Autonomous recognition and scoring of home-based functional activities is enabled by a multimodal movement monitoring system that integrates inertial measurement unit and vision data. Further algorithm training with ecologically valid data and a kitted system that is independently usable by patients are needed before fully autonomous, functional movement assessment is realizable.

IMPACT

Functional movement assessments that can be administered in the home without a clinician present have the potential to democratize these evaluations and improve care access.