Patient-Reported and Technology-Assisted Monitoring in Orthotic Management of Adolescent with Idiopathic Scoliosis: Scoping Review (Preprint)
Bibliographic record
Abstract
Background: Orthotic treatment for adolescents diagnosed with idiopathic scoliosis is long-term and often associated with challenges related to treatment adherence. Traditional patient monitoring strategies include clinical visits combined with patient discussions, clinical examination reports, and the use of standard questionnaires. Sensor-assisted monitoring is explored to improve treatment outcomes, with technology influencing different aspects of the health care system. Objective: The scoping review aimed to map evidence based on 3 research questions (RQs), namely user experience and perception, monitoring tools, and technology-assisted approaches, including smart orthoses with sensors, during orthotic treatment for an adolescent patient (aged 10 to 17.11 y) diagnosed with idiopathic scoliosis. Methods: A systematic search was conducted in MEDLINE, Embase, PsycINFO, Cochrane Library, CINAHL, Web of Science, and Scopus as 2 complementary search components. The primary search targeted studies on smart orthoses, also called braces, and scoliosis, while the secondary search focused on user experiences with orthotic treatment to capture all 3 RQs. PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews) guidelines and checklist supported searching, analyzing, and reporting the results systematically. Predefined inclusion and exclusion criteria were followed for screening. All articles published up to May 2024 were included. Moreover, 2 independent reviewers screened 2088 articles in 2 stages of screening. Data coding was conducted using the 3 RQs by data extraction and its synthesis. Results: A total of 88 articles met the inclusion criteria. Evidence from user experience studies highlighted discomfort, psychosocial burden, and demand for real-time feedback. Monitoring relied primarily on patient-reported outcome measures (PROMs) such as questionnaires and logbooks. Culturally adapting to patients' diverse global needs helps capture richer and more candid user perspectives. Sensor-based systems used temperature, force, or pressure, electromyography, and motion sensors to track orthosis wear time, pressure distribution, activity, and posture. Two overarching monitoring themes emerged: subjective (patient-reported or user experience) and objective (technology-assisted or sensor-based) monitoring. Integrated approaches synthesize both. Conclusions: Subjective clinical tools and objective technologies help monitor orthosis wear of adolescent patients with idiopathic scoliosis. Co-design of a user-centered scoliosis management system for adolescents that integrates PROMs with measured sensor data could provide a multidimensional view of orthosis adherence along with psychosocial and clinical effectiveness. Future research will focus on user-centered care, with real-time integrated monitoring, to provide clinically meaningful feedback that motivates adolescents to achieve their treatment goals.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".