Evidence map›Paper›PMID 41190480›Full record

ReviewJMIR rehabilitation and assistive technologies2025

Smart Device-Based Therapy on Hand Motor Function Improvement in Stroke Survivors During Rehabilitation: Scoping Review.

Kristine Krumina, Una Krumina, Agnese Mikelsone, Liva Araka, Klinta Luize Sprudza, Gerda Madara Ziemele, Guna Semjonova

Abstract readReview
In one paragraph

Review in JMIR rehabilitation and assistive technologies, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

7 authors.

Kristine Krumina *Department of Residency, Riga Stradiņš University, Dzirciema Street 16, Riga, LV-1007, Latvia, +371 67409125.ORCID http://orcid.org/0009-0009-4948-6009
Una Krumina *Faculty of Medicine, Riga Stradiņš University, Riga, Latvia.ORCID http://orcid.org/0009-0008-1021-7058
Agnese Mikelsone *Faculty of Medicine, Riga Stradiņš University, Riga, Latvia.ORCID http://orcid.org/0009-0004-3100-2671
Liva Araka *Faculty of Medicine, Riga Stradiņš University, Riga, Latvia.ORCID http://orcid.org/0009-0003-4874-7904
Klinta Luize Sprudza *Faculty of Medicine, Riga Stradiņš University, Riga, Latvia.ORCID http://orcid.org/0009-0001-6686-021X
Gerda Madara Ziemele *Faculty of Medicine, Riga Stradiņš University, Riga, Latvia.ORCID http://orcid.org/0009-0009-0028-2902
Guna Semjonova *Department of Rehabilitation, Faculty of Health and Sports Sciences, Riga Stradiņš University, Riga, Latvia.ORCID http://orcid.org/0000-0002-6554-0716

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The prevalence of upper limb impairment ranges from 40% to 50% in the chronic phase of stroke, presenting a significant public health challenge. Although traditional therapy effectively improves hand motor function, it often faces accessibility challenges. Telerehabilitation, particularly smart device-based therapy, provides a scalable and engaging alternative, although its effectiveness still requires further investigation. Objective: This study aimed to identify smart device-based therapy interventions for improving hand motor function in stroke survivors during rehabilitation and to assess their effectiveness in hand motor function improvement in comparison with traditional therapy methods. Methods: A scoping review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines to identify and evaluate smart device-based therapy interventions aimed at improving hand motor function in stroke survivors. A comprehensive literature search was performed across multiple databases, including Web of Science, PubMed (MEDLINE), EBSCO Complete, Google Scholar, Science Direct, ClinicalKey, and Scopus. Studies were screened based on predefined inclusion criteria, focusing on clinical trials that investigated the effectiveness of smart device-based rehabilitation approaches. Data extraction was carried out systematically, capturing key study characteristics, intervention types, and outcome measures. The effectiveness of these interventions was assessed and synthesized to provide a comprehensive overview of their impact on hand motor function recovery in stroke rehabilitation. Results: A total of 958 studies were identified, of which 17 met the inclusion criteria. The studies encompassed various research designs, including randomized controlled trials (n=9), pilot feasibility studies (n=3), and a comparative nonrandomized trial (n=1). The interventions used diverse digital strategies, including gamified rehabilitation programs (n=14), virtual or mixed reality systems (n=7), Kinect camera or sensor-based approaches (n=6), Jintronix software platforms (n=4), robot-assisted devices (n=3), and tablet- or app-based rehabilitation systems (n=7). Overall, smart device-based therapies were associated with improvements in hand motor function, frequently reaching clinically meaningful thresholds, while also enhancing patient engagement and adherence. Conclusions: The findings of this scoping study highlight the significant potential of smart device therapies for enhancing hand motor functions among stroke survivors. The development of smart devices is an evolving process, highlighting the need for future studies to assess their long-term effectiveness, optimize intervention designs, and explore their broader application in stroke rehabilitation.

Indexed as

hand motor functionsmart device–based therapysmart devicesstroketelerehabilitation

Identifiers

PMID41190480
PMCPMC12587065

What OpenQuestion holds

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Registered trials

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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.