SynthesisPloS one2015
Wearable sensor use for assessing standing balance and walking stability in people with Parkinson's disease: a systematic review.
Synthesis in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04510922 (Technology-Based Objective Measures for Gait and Postural Assessment in Parkinson Disease Patients With Orthostatic Hypotension), which is not on this map. Cited by 86 papers, 14 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Technology-Based Objective Measures for Gait and Postural Assessment in Parkinson Disease Patients With Orthostatic Hypotension: Feasibility and Effect-Size Finding Study
Who cites it
86 citing papers in PubMed, 14 syntheses or guidelines pooled it, 213 citations in OpenAlex.
- Wearable Sensors in Gait Assessment for Parkinson Disease and Stroke in Real-World Environments: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Real-world gait study of Parkinson's disease using wearable sensors: a systematic review.Journal of neuroengineering and rehabilitation · 2026Pooled it
- Backward Walking as a Rehabilitation Strategy in Parkinson's Disease: A Focused Systematic Review.Medicina (Kaunas, Lithuania) · 2026Pooled it
- Instrumental balance assessment in Parkinson's disease and parkinsonism. A systematic review with critical appraisal of clinical applications and quality of reporting.Frontiers in neurology · 2025Pooled it
- Effects of Oral Levodopa on Balance in People with Idiopathic Parkinson's Disease.Journal of Parkinson's disease · 2023Pooled it
- Are wearable devices effective for preventing and detecting falls: an umbrella review (a review of systematic reviews).BMC public health · 2021Pooled it
- Inertial Sensor Reliability and Validity for Static and Dynamic Balance in Healthy Adults: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Utilization of wearable technology to assess gait and mobility post-stroke: a systematic review.Journal of neuroengineering and rehabilitation · 2021Pooled it
- Wearable Health Technology to Quantify the Functional Impact of Peripheral Neuropathy on Mobility in Parkinson's Disease: A Systematic Review.Sensors (Basel, Switzerland) · 2020Pooled it
- Wearable Inertial Sensors to Assess Gait during the 6-Minute Walk Test: A Systematic Review.Sensors (Basel, Switzerland) · 2020Pooled it
- Usefulness of Mobile Devices in the Diagnosis and Rehabilitation of Patients with Dizziness and Balance Disorders: A State of the Art Review.Clinical interventions in aging · 2020Pooled it
- Wearable Inertial Sensors to Assess Standing Balance: A Systematic Review.Sensors (Basel, Switzerland) · 2019Pooled it
- Reliability, Validity and Utility of Inertial Sensor Systems for Postural Control Assessment in Sport Science and Medicine Applications: A Systematic Review.Sports medicine (Auckland, N.Z.) · 2019Pooled it
- Wearable technology for spine movement assessment: A systematic review.Journal of biomechanics · 2017Pooled it
- Low-frequency STN-DBS provides acute gait improvements in Parkinson's disease: a double-blinded randomised cross-over feasibility trial.Journal of neuroengineering and rehabilitation · 2021Trial
- Auto detection and segmentation of daily living activities during a Timed Up and Go task in people with Parkinson's disease using multiple inertial sensors.Journal of neuroengineering and rehabilitation · 2017Trial
- Using Machine Learning-Based Classification of Postural Stability in Cervicogenic Headache Patients: Predictors and Clinical Implications.Life (Basel, Switzerland) · 2026Article
- Scoping review of AI-driven wearable technologies for rehabilitation and functional assessment in Parkinson's disease: a protocol.BMJ open · 2026Article
- Classification of fall risk in Parkinson's disease using empirical mode decomposition and machine learning.Frontiers in bioengineering and biotechnology · 2026Article
- Joint Kinematics and Gait Pattern in Multiple Sclerosis: A 3D Analysis Comparative Approach.Bioengineering (Basel, Switzerland) · 2025Article
26 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPostural instability and gait disability threaten the independence and well-being of people with Parkinson's disease and increase the risk of falls and fall-related injuries. Prospective research has shown that commonly-used clinical assessments of balance and walking lack the sensitivity to accurately and consistently identify those people with Parkinson's disease who are at a higher risk of falling. Wearable sensors provide a portable and affordable alternative for researchers and clinicians who are seeking to objectively assess movements and falls risk in the clinical setting. However, no consensus currently exists on the optimal placements for sensors and the best outcome measures to use for assessing standing balance and walking stability in Parkinson's disease patients. Hence, this systematic review aimed to examine the available literature to establish the best sensor types, locations and outcomes to assess standing balance and walking stability in this population.
methodsPapers listed in three electronic databases were searched by title and abstract to identify articles measuring standing balance or walking stability with any kind of wearable sensor among adults diagnosed with PD. To be eligible for inclusion, papers were required to be full-text articles published in English between January 1994 and December 2014 that assessed measures of standing balance or walking stability with wearable sensors in people with PD. Articles were excluded if they; i) did not use any form of wearable sensor to measure variables associated with standing balance or walking stability; ii) did not include a control group or control condition; iii) were an abstract and/or included in the proceedings of a conference; or iv) were a review article or case study. The targeted search of the three electronic databases identified 340 articles that were potentially eligible for inclusion, but following title, abstract and full-text review only 26 articles were deemed to meet the inclusion criteria. Included articles were assessed for methodological quality and relevant data from the papers were extracted and synthesized.
resultsQuality assessment of these included articles indicated that 31% were of low methodological quality, while 58% were of moderate methodological quality and 11% were of high methodological quality. All studies adopted a cross-sectional design and used a variety of sensor types and outcome measures to assess standing balance or walking stability in people with Parkinson's disease. Despite the typically low to moderate methodological quality, 81% of the studies reported differences in sensor-based measures of standing balance or walking stability between different groups of Parkinson's disease patients and/or healthy controls.
conclusionThese data support the use of wearable sensors for detecting differences in standing balance and walking stability between people with PD and controls. Further high-quality research is needed to better understand the utility of wearable sensors for the early identification of Parkinson's disease symptoms and for assessing falls risk in this population. PROSPERO REGISTRATION: CRD42014010838.
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Registered trials
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.