Health-Enabling Technologies to Assist Patients With Musculoskeletal Shoulder Disorders When Exercising at Home: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Health-enabling technologies (HETs) are information and communication technologies that promote individual health and well-being. An important application of HETs is telerehabilitation for patients with musculoskeletal shoulder disorders. Currently, there is no overview of HETs that assist patients with musculoskeletal shoulder disorders when exercising at home. OBJECTIVE: This scoping review provides a broad overview of HETs that assist patients with musculoskeletal shoulder disorders when exercising at home. It focuses on concepts and components of HETs, exercise program strategies, development phases, and reported outcomes. METHODS: The search strategy used Medical Subject Headings and text words related to the terms upper extremity, exercises, and information and communication technologies. The MEDLINE, Embase, IEEE Xplore, CINAHL, PEDro, and Scopus databases were searched. Two reviewers independently screened titles and abstracts and then full texts against predefined inclusion and exclusion criteria. A systematic narrative synthesis was performed. Overall, 8988 records published between 1997 and 2019 were screened. Finally, 70 articles introducing 56 HETs were included. RESULTS: Identified HETs range from simple videoconferencing systems to mobile apps with video instructions to complex sensor-based technologies. Various software, sensor hardware, and hardware for output are in use. The most common hardware for output are PC displays (in 34 HETs). Microsoft Kinect cameras in connection with related software are frequently used as sensor hardware (in 27 HETs). The identified HETs provide direct or indirect instruction, monitoring, correction, assessment, information, or a reminder to exercise. Common parameters for exercise instructions are a patient's range of motion (in 43 HETs), starting and final position (in 32 HETs), and exercise intensity (in 20 HETs). In total, 48 HETs provide visual instructions for the exercises; 29 HETs report on telerehabilitation aspects; 34 HETs only report on prototypes; and 15 HETs are evaluated for technical feasibility, acceptance, or usability, using different assessment instruments. Efficacy or effectiveness is demonstrated for only 8 HETs. In total, 18 articles report on patients' evaluations. An interdisciplinary contribution to the development of technologies is found in 17 HETs. CONCLUSIONS: There are various HETs, ranging from simple videoconferencing systems to complex sensor-based technologies for telerehabilitation, that assist patients with musculoskeletal shoulder disorders when exercising at home. Most HETs are not ready for practical use. Comparability is complicated by varying prototype status, different measurement instruments, missing telerehabilitation aspects, and few efficacy studies. Consequently, choosing an HET for daily use is difficult for health care professionals and decision makers. Prototype testing, usability, and acceptance tests with the later target group under real-life conditions as well as efficacy or effectiveness studies with patient-relevant core outcomes for every promising HET are required. Furthermore, health care professionals and patients should be more involved in the product design cycle to consider relevant practical aspects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".