ArticleNPJ digital medicine2020
A data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05819047 (Assessment of Upper Limb Motor Performance Using an Interface With Haptic Feedback), which is not on this map. Cited by 31 papers.
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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.
Assessment of Upper Limb Motor Performance Using an Interface With Haptic Feedback
Who cites it
31 citing papers in PubMed, 51 citations in OpenAlex.
- Multioutput Machine Learning Model for Predicting Postoperative Outcomes After Liposuction: Algorithm Development and Validation Study in a Multicenter Cohort.JMIR medical informatics · 2026Article
- Digital Motor Outcomes Capture Upper Limb and Mild Stage Impairment in Hereditary Spastic Paraplegia.Neurology and therapy · 2026Article
- From Metrics to Meaning in Neurological Rehabilitation: Clinicians' Perspectives on Digital Metrics of Upper Limb Functioning-A Focus Group Study.JMIR rehabilitation and assistive technologies · 2026Article
- Digital Platform to Provide Health Data Feedback for Neurorehabilitation Patients: User-Centered Development and Proof-of-Concept Usability Study.JMIR rehabilitation and assistive technologies · 2026Article
- Digital Outcomes of Upper Limb Ataxia Capture Meaningful Longitudinal Change and Treatment Response.Movement disorders : official journal of the Movement Disorder Society · 2025Article
- 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
- Leverage machine learning to identify key measures in hospital operations management: a retrospective study to explore feasibility and performance of four common algorithms.BMC medical informatics and decision making · 2024Article
- Influence of virtual reality and task complexity on digital health metrics assessing upper limb function.Journal of neuroengineering and rehabilitation · 2024Observational
- Multifeature quantitative motor assessment of upper limb ataxia including drawing and reaching.Annals of clinical and translational neurology · 2024Article
- STASISM: A Versatile Serious Gaming Multi-Sensor Platform for Personalized Telerehabilitation and Telemonitoring.Sensors (Basel, Switzerland) · 2024Article
- Smartphone-Based Assessment of Mobility and Manual Dexterity in Adult People with Spinal Muscular Atrophy.Journal of neuromuscular diseases · 2024Article
- Upper limb movement quality measures: comparing IMUs and optical motion capture in stroke patients performing a drinking task.Frontiers in digital health · 2024Article
- Age-related differences in upper limb motor performance and intrinsic motivation during a virtual reality task.BMC geriatrics · 2023Article
- Upper limb sensorimotor recovery in Asian stroke survivors: a study protocol for the development and implementation of a Technology-Assisted dIgitaL biOmaRker (TAILOR) platform.Frontiers in neurology · 2023Article
- A unified scheme for the benchmarking of upper limb functions in neurological disorders.Journal of neuroengineering and rehabilitation · 2022Article
- Hypothalamic Control of Forelimb Motor Adaptation.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- A low-dimensional representation of arm movements and hand grip forces in post-stroke individuals.Scientific reports · 2022Article
- Reliability and validity of digital health metrics for assessing arm and hand impairments in an ataxic disorder.Annals of clinical and translational neurology · 2022Article
- Reliable and Valid Robotic Assessments of Hand Active and Passive Position Sense in Children With Unilateral Cerebral Palsy.Frontiers in human neuroscience · 2022Article
- Personalized prediction of rehabilitation outcomes in multiple sclerosis: a proof-of-concept using clinical data, digital health metrics, and machine learning.Medical & biological engineering & computing · 2022Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital health metrics promise to advance the understanding of impaired body functions, for example in neurological disorders. However, their clinical integration is challenged by an insufficient validation of the many existing and often abstract metrics. Here, we propose a data-driven framework to select and validate a clinically relevant core set of digital health metrics extracted from a technology-aided assessment. As an exemplary use-case, the framework is applied to the Virtual Peg Insertion Test (VPIT), a technology-aided assessment of upper limb sensorimotor impairments. The framework builds on a use-case-specific pathophysiological motivation of metrics, models demographic confounds, and evaluates the most important clinimetric properties (discriminant validity, structural validity, reliability, measurement error, learning effects). Applied to 77 metrics of the VPIT collected from 120 neurologically intact and 89 affected individuals, the framework allowed selecting 10 clinically relevant core metrics. These assessed the severity of multiple sensorimotor impairments in a valid, reliable, and informative manner. These metrics provided added clinical value by detecting impairments in neurological subjects that did not show any deficits according to conventional scales, and by covering sensorimotor impairments of the arm and hand with a single assessment. The proposed framework provides a transparent, step-by-step selection procedure based on clinically relevant evidence. This creates an interesting alternative to established selection algorithms that optimize mathematical loss functions and are not always intuitive to retrace. This could help addressing the insufficient clinical integration of digital health metrics. For the VPIT, it allowed establishing validated core metrics, paving the way for their integration into neurorehabilitation trials.
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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.