ArticleJMIR mHealth and uHealth2021
Simple Smartphone-Based Assessment of Gait Characteristics in Parkinson Disease: Validation Study.
Article in JMIR mHealth and uHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 3 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
29 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- eHealth tools to assess the neurological function for research, in absence of the neurologist - a systematic review, part I (software).Journal of neurology · 2024Pooled it
- Technology-Assisted Cognitive Motor Dual-Task Rehabilitation in Chronic Age-Related Conditions: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Gait and Balance Assessments using Smartphone Applications in Parkinson's Disease: A Systematic Review.Journal of medical systems · 2021Pooled it
- Population Pharmacokinetic and Exposure-Response Analysis of the Cognitive Effects of TAK-071 in Participants With Parkinson Disease and Cognitive Impairment.Clinical pharmacology in drug development · 2025Trial
- An Acetylcholine M1 Receptor-Positive Allosteric Modulator (TAK-071) in Parkinson Disease With Cognitive Impairment: A Phase 2 Randomized Clinical Trial.JAMA neurology · 2025Trial
- Clinical utility and methodological implementation of smartphone-based mobility analysis: a scoping review.Journal of neurology · 2026Article
- From phenotype to biology: a multi-modal roadmap of biofluid, tissue, imaging, and digital biomarkers in Parkinson's disease.Translational neurodegeneration · 2026Review
- Time-stratified daily walking speed measurement via smartphone and its predictive utility for mild cognitive impairment.Scientific reports · 2026Article
- National implementation of a digital Parkinson's disease screening programme in Thailand: reach, adoption, and real-world performance of the CheckPD app.BMC public health · 2026Article
- Video-Based Gait Assessment Using Machine Learning to Classify Age and Sex in Low-Resource Settings: Cross-Sectional Study.JMIR formative research · 2026Article
- Clinical utility of digital technologies in Parkinson's disease.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- Article
- Capturing Motor Signs in Psychosis: How the New Technologies Can Improve Assessment and Treatment?Schizophrenia bulletin · 2025Article
- Psychometric Characteristics of Smartphone-Based Gait Analyses in Chronic Health Conditions: A Systematic Review.Journal of functional morphology and kinesiology · 2025Review
- Validity Analysis of Monocular Human Pose Estimation Models Interfaced with a Mobile Application for Assessing Upper Limb Range of Motion.Sensors (Basel, Switzerland) · 2024Article
- Sensor-Derived Measures of Motor and Cognitive Functions in People With Multiple Sclerosis Using Unsupervised Smartphone-Based Assessments: Proof-of-Concept Study.JMIR formative research · 2024Article
- Remote Digital Technologies for the Early Detection and Monitoring of Cognitive Decline in Patients With Type 2 Diabetes: Insights From Studies of Neurodegenerative Diseases.Journal of diabetes science and technology · 2024Review
- Sensor-Based Gait and Balance Assessment in Healthy Adults: Analysis of Short-Term Training and Sensor Placement Effects.Sensors (Basel, Switzerland) · 2024Article
- A Novel Approach for Improving Gait Speed Estimation Using a Single Inertial Measurement Unit Embedded in a Smartphone: Validity and Reliability Study.JMIR mHealth and uHealth · 2024Article
- The Reliability and Validity of the OneStep Smartphone Application for Gait Analysis among Patients Undergoing Rehabilitation for Unilateral Lower Limb Disability.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundParkinson disease (PD) is a common movement disorder. Patients with PD have multiple gait impairments that result in an increased risk of falls and diminished quality of life. Therefore, gait measurement is important for the management of PD.
objectiveWe previously developed a smartphone-based dual-task gait assessment that was validated in healthy adults. The aim of this study was to test the validity of this gait assessment in people with PD, and to examine the association between app-derived gait metrics and the clinical and functional characteristics of PD.
methodsFifty-two participants with clinically diagnosed PD completed assessments of walking, Movement Disorder Society Unified Parkinson Disease Rating Scale III (UPDRS III), Montreal Cognitive Assessment (MoCA), Hamilton Anxiety (HAM-A), and Hamilton Depression (HAM-D) rating scale tests. Participants followed multimedia instructions provided by the app to complete two 20-meter trials each of walking normally (single task) and walking while performing a serial subtraction dual task (dual task). Gait data were simultaneously collected with the app and gold-standard wearable motion sensors. Stride times and stride time variability were derived from the acceleration and angular velocity signal acquired from the internal motion sensor of the phone and from the wearable sensor system.
resultsHigh correlations were observed between the stride time and stride time variability derived from the app and from the gold-standard system (r=0.98-0.99, P<.001), revealing excellent validity of the app-based gait assessment in PD. Compared with those from the single-task condition, the stride time (F
conclusionsA smartphone-based gait assessment can be used to provide meaningful metrics of single- and dual-task gait that are associated with disease severity and functional outcomes in individuals with PD.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.