Evidence map›Paper›PMID 38107648›Full record

ArticleFrontiers in neurology2023

Upper limb sensorimotor recovery in Asian stroke survivors: a study protocol for the development and implementation of a Technology-Assisted dIgitaL biOmaRker (TAILOR) platform.

Hsiao-Ju Cheng, Lay Fong Chin, Christoph M Kanzler, Rea Lehner, Christopher W K Kuah, Simone Kager, Eva Josse, Tengiz Samkharadze, Ananda Sidarta, Pablo Cruz Gonzalez and 8 more

Registry-linked trialAbstract read
In one paragraph

Article in Frontiers in neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05322837 (Deep Phenotyping of Upper Limb Sensori-motor Recovery in Asian Stroke Survivors), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

NCT05322837 recruitingnot on this map

Deep Phenotyping of Upper Limb Sensori-motor Recovery in Asian Stroke Survivors: Concept, Development and Implementation of a Rehabilomics-driven Technology-assisted Data Platform

TypeobservationalSponsorTan Tock Seng HospitalRan2023 to 2026Enrolled400ConditionsStrokeArmsClinical and technological-aided assessments and questionnaires
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Hsiao-Ju Cheng *Singapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore.
Lay Fong Chin *Singapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore.
Christoph M KanzlerSingapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore.
Rea LehnerSingapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore.
Christopher W K KuahSingapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore.
Simone KagerSingapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore.
Eva JosseSingapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore.
Tengiz SamkharadzeSingapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore.
Ananda SidartaRehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore, Singapore.
Pablo Cruz GonzalezRehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore, Singapore.
Eloise LieRehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore, Singapore.
Monika Zbytniewska-MégretRehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland.
Seng Kwee WeeSingapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore.
Phyllis LiangSingapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore.
Roger GassertSingapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore.
Karen Chua *Singapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore.
Olivier Lambercy *Singapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore.
Nicole Wenderoth *Singapore-ETH Centre, Future Health Technologies Programme, CREATE Campus, Singapore, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Stroke is a leading cause of lifelong disability worldwide, partially driven by a reduced ability to use the upper limb in daily life causing increased dependence on caregivers. However, post-stroke functional impairments have only been investigated using limited clinical scores, during short-term longitudinal studies in relatively small patient cohorts. With the addition of technology-based assessments, we propose to complement clinical assessments with more sensitive and objective measures that could more holistically inform on upper limb impairment recovery after stroke, its impact on upper limb use in daily life, and on overall quality of life. This paper describes a pragmatic, longitudinal, observational study protocol aiming to gather a uniquely rich multimodal database to comprehensively describe the time course of upper limb recovery in a representative cohort of 400 Asian adults after stroke. Particularly, we will characterize the longitudinal relationship between upper limb recovery, common post-stroke impairments, functional independence and quality of life. Methods: Participants with stroke will be tested at up to eight time points, from within a month to 3 years post-stroke, to capture the influence of transitioning from hospital to community settings. We will perform a battery of established clinical assessments to describe the factors most likely to influence upper limb recovery. Further, we will gather digital health biomarkers from robotic or wearable sensing technology-assisted assessments to sensitively characterize motor and somatosensory impairments and upper limb use in daily life. We will also use both quantitative and qualitative measures to understand health-related quality of life. Lastly, we will describe neurophysiological motor status using transcranial magnetic stimulation. Statistics: Descriptive analyses will be first performed to understand post-stroke upper limb impairments and recovery at various time points. The relationships between digital biomarkers and various domains will be explored to inform key aspects of upper limb recovery and its dynamics using correlation matrices. Multiple statistical models will be constructed to characterize the time course of upper limb recovery post-stroke. Subgroups of stroke survivors exhibiting distinct recovery profiles will be identified. Conclusion: This is the first study complementing clinical assessments with technology-assisted digital biomarkers to investigate upper limb sensorimotor recovery in Asian stroke survivors. Overall, this study will yield a multimodal data set that longitudinally characterizes post-stroke upper limb recovery in functional impairments, daily-life upper limb use, and health-related quality of life in a large cohort of Asian stroke survivors. This data set generates valuable information on post-stroke upper limb recovery and potentially allows researchers to identify different recovery profiles of subgroups of Asian stroke survivors. This enables the comparisons between the characteristics and recovery profiles of stroke survivors in different regions. Thus, this study lays out the basis to identify early predictors for upper limb recovery, inform clinical decision-making in Asian stroke survivors and establish tailored therapy programs. Clinical trial registration: ClinicalTrials.gov, identifier: NCT05322837.

Indexed as

assessmentneurorehabilitationrecoverysensorimotor impairmentsstrokeupper limb

Identifiers

PMID38107648
PMCPMC10722087

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.