Evidence map›Paper›PMID 42904398›Full record

ReviewJMIR rehabilitation and assistive technologies2026

Digital Technologies in Postsurgical Orthopedic Rehabilitation: Scoping Review Mapping Interventions to the CAMS Framework (2020-2025).

Ngan Kuen Lai, Hai Huang, Tet Khuan Chen, Muhammad Nur Affendy Nor'a, Jeffery Sijore, Wei Yang Chen, YongSheng Luo

Abstract readReview
In one paragraph

Review in JMIR rehabilitation and assistive technologies, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the 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.

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

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

7 authors.

Ngan Kuen LaiSchool of Computing, Asia Pacific University of Technology and Innovation (APU), Jalan Teknologi 5, Taman Teknologi Malaysia, Wilayah Persekutuan Kuala Lumpur, 57000, Malaysia, 60 8996 1000.ORCID http://orcid.org/0000-0001-6806-8034
Hai HuangSchool of Data Science, Zhuhai College of Science and Technology, Zhuhai, China.ORCID http://orcid.org/0009-0009-5121-124X
Tet Khuan ChenSchool of Computing, Asia Pacific University of Technology and Innovation (APU), Jalan Teknologi 5, Taman Teknologi Malaysia, Wilayah Persekutuan Kuala Lumpur, 57000, Malaysia, 60 8996 1000.ORCID http://orcid.org/0009-0007-9703-7043
Muhammad Nur Affendy Nor'aSchool of Computing, Asia Pacific University of Technology and Innovation (APU), Jalan Teknologi 5, Taman Teknologi Malaysia, Wilayah Persekutuan Kuala Lumpur, 57000, Malaysia, 60 8996 1000.ORCID http://orcid.org/0000-0003-3973-3917
Jeffery SijoreSchool of Computing, Asia Pacific University of Technology and Innovation (APU), Jalan Teknologi 5, Taman Teknologi Malaysia, Wilayah Persekutuan Kuala Lumpur, 57000, Malaysia, 60 8996 1000.ORCID http://orcid.org/0009-0008-3643-7518
Wei Yang ChenSchool of Computing, Asia Pacific University of Technology and Innovation (APU), Jalan Teknologi 5, Taman Teknologi Malaysia, Wilayah Persekutuan Kuala Lumpur, 57000, Malaysia, 60 8996 1000.ORCID http://orcid.org/0009-0000-0247-6668
YongSheng LuoSchool of Data Science, Zhuhai College of Science and Technology, Zhuhai, China.ORCID http://orcid.org/0009-0000-1299-8179

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Digital technologies have become common in postoperative orthopedic rehabilitation. The tendency toward this adoption appeared in the past decade. However, no standard method exists to classify their functional capabilities. This absence hinders practical implementation. Clinicians currently lack a structured framework to distinguish between the functional capabilities of different digital rehabilitation technologies effectively. Objective: This scoping review aims to map digital interventions used in postsurgical orthopedic rehabilitation to a proposed Coordination, Alignment, Motivation, and Strength (CAMS) framework and to identify research trends, functional coverage, and gaps in current evidence. Methods: A systematic search was conducted across 5 databases: MEDLINE (via PubMed), Scopus, IEEE Xplore, Cochrane Central Register of Controlled Trials (CENTRAL), and the Physiotherapy Evidence Database (PEDro), following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Studies were mapped to the proposed CAMS framework pillars. Multiple validated appraisal tools were applied descriptively according to study design: version 2 of the Cochrane risk-of-bias tool for randomized trials (RoB 2; n=32), the Newcastle-Ottawa Scale (NOS) for observational studies (n=2), and the Methodological Index for Non-Randomized Studies (MINORS) for nonrandomized designs (n=1). Results: A total of 35 studies met the inclusion criteria. Coordination was the most frequently addressed pillar (29/35, 83%), followed by Motivation (26/35, 74%). Alignment was addressed in 37% (13/35) of studies, while Strength was the least represented pillar (12/35, 34%). Overall, 49% (17/35) of studies were rated "low risk" overall on the primary appraisal tool, 46% (16/35) demonstrated "some concerns," and 6% (2/35) were rated "high risk," with performance bias identified as a consistent methodological characteristic across the corpus. Delivery models were predominantly hybrid (14/35, 40%) or home-based (13/35, 37%), with 8 studies (8/35, 23%) using an exclusively clinical model. Conclusions: Digital health has shown a notable increase in the kinematic monitoring feature, offering effective solutions to movement quality (Coordination and Alignment) and psychological engagement (Motivation). Nevertheless, there is a consistent gap in strength-related studies, as current decentralized tools lack the capability to quantify physiological load. The next phase of innovations should be a shift to integrated 4-pillar solutions that would coordinate resistance-based training with high-precision biomechanical monitoring. The CAMS framework is proposed as an exploratory classification tool to guide future research design and technology evaluation in postsurgical digital rehabilitation.

Indexed as

AICAMS frameworkCoordination, Alignment, Motivation, and Strength frameworkdigital rehabilitationorthopedic surgerytelerehabilitationvirtual reality

Identifiers

PMID42904398
PMCPMC13684352

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.