Content and Quality of Mobile Apps for the Monitoring of Musculoskeletal or Neuropathic Pain in Australia: Systematic Evaluation
Bibliographic record
Abstract
Background: Mobile apps offer a potential mechanism for people with persistent pain to monitor pain levels conveniently within their own environment and for clinicians to remotely monitor their patients' pain. However, the quality of currently available apps and the usefulness of included features from a clinical perspective are not known. Objective: The aim of this study was to examine the content and quality of currently available smartphone apps designed for monitoring the intensity or presence of musculoskeletal or neuropathic pain. Methods: A systematic search was performed in the Australian Apple and Google Play stores. Apps were included if they were designed to monitor the intensity or presence of musculoskeletal or neuropathic pain and were available in the English language within the Australian app stores. Data pertaining to the intended use of the app and clinical population were extracted by using a custom-designed data extraction form, and app quality was assessed by using the 23-item Mobile App Rating Scale. Results: Of the 2190 apps screened, 49 met the inclusion criteria. Apps were primarily designed for adult users (36/49, 73%) with nonspecific musculoskeletal or neuropathic pain conditions, arthritis, and joint pain. All apps monitored pain intensity, with almost half (23/49, 47%) also specifying pain location. Overall, the mean quality scores from the Mobile App Rating Scale ranged from 1.5 to 4.4 (out of 5.0). Between 20% (10/49) and 22% (11/49) of apps involved clinicians, consumers, or both in their development, and 20% (10/49) had published literature related to the development or use of the app in clinical scenarios. Although 71% (35/49) had data sharing features, only 5 apps enabled client-clinician communication through the app. Conclusions: The overall quality of mobile apps that are currently available for monitoring pain intensity is acceptable. Presently, mobile apps for remote pain monitoring lack functionality for clinicians to view data between consults. Both users and clinicians should be aware of the limitations of these apps and make informed choices in using or recommending apps that best suit the clinical need.
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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 | 0.034 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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, 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".