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Related Experiment Video

Updated: Sep 1, 2025

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

App-Based Evaluation of Older People's Fall Risk Using the mHealth App Lindera Mobility Analysis: Exploratory Study.

Nicole Strutz¹, Hanna Brodowski², Joern Kiselev³

¹Geriatrics Research Group, Charité - Universitätsmedizin Berlin (corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin), Berlin, Germany.

|August 16, 2022

Summary

This summary is machine-generated.

The Lindera Mobility Analysis app shows potential for early fall risk detection in older adults. This digital tool correlates well with established mobility assessments, aiding in proactive fall prevention strategies.

Keywords:

accuracy analysis app fall risk mobility mobility restriction older people smartphone

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Last Updated: Sep 1, 2025

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights

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Published on: October 25, 2024

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Published on: March 14, 2017

Area of Science:

Gerontology
Digital Health
Biomedical Engineering

Background:

Falls in older adults significantly increase the risk of losing independence.
Current fall risk detection methods are often delayed, leading to falls before intervention.
Mobile health applications offer a feasible solution for early and continuous fall risk screening.

Purpose of the Study:

To evaluate the Lindera Mobility Analysis (LIN) app for fall risk assessment.
To compare the LIN app's digital mobility measures against validated functional assessments.

Main Methods:

The LIN app was used with 42 community-dwelling older adults (age 65+).
Reference standards included the Berg Balance Scale (BBS), Tinetti Test (TIN), and Timed Up & Go (TUG) tests.
Spearman correlation and Bland-Altman analysis were employed to compare digital and analog measures.

Main Results:

Significant correlations were found between the LIN app and BBS (r=-0.587), TUG (r=0.474), and TIN (r=-0.464).
Bland-Altman plots indicated good agreement between the LIN app and functional tests, with few outliers.

Conclusions:

The LIN app demonstrates potential as a tool for detecting fall risk in the elderly population.
Further research into the clinical applicability of the LIN app is warranted to establish its validity.