Quality of Psychoeducational Apps for Military Members With Mild Traumatic Brain Injury: An Evaluation Utilizing the Mobile Application Rating Scale
Bibliographic record
Abstract
BACKGROUND: Military personnel have an elevated risk of sustaining mild traumatic brain injuries (mTBI) and postconcussion symptoms (PCS). Smartphone apps that provide psychoeducation may assist those with mTBI or PCS to overcome unique barriers that military personnel experience with stigma and access to health care resources. OBJECTIVE: This study aims to (1) use the Mobile Application Rating Scale (MARS) to evaluate smartphone apps purporting to provide psychoeducation for those who have sustained an mTBI or a PCS; (2) explore the relevance, utility, and effectiveness of these apps in facilitating symptom management and overall recovery from mTBI and PCS among military personnel; and (3) discuss considerations pertinent to health care professionals and patients with mTBI when considering the use of mobile health (mHealth), including apps for mTBI psychoeducation. METHODS: A five-step systematic search for smartphone apps for military members with mTBI or PCS was conducted on January 31, 2020. Cost-free apps meeting the inclusion criteria were evaluated using the MARS and compared with evidence-based best practice management protocols for mTBI and PCS. RESULTS: The search yielded a total of 347 smartphone apps. After applying the inclusion and exclusion criteria, 13 apps were subjected to evaluation. Two apps were endorsed by the US Department of Veterans Affairs and the US Department of Defense; all the others (n=11) were developed for civilians. When compared with evidence-based best practice resources, the apps provided various levels of psychoeducational content. There are multiple considerations that health care professionals and those who sustain an mTBI or a PCS have to consider when choosing to use mHealth and selecting a specific app for mTBI psychoeducation. These may include factors such as the app platform, developer, internet requirement, cost, frequency of updates, language, additional features, acknowledgment of mental health, accessibility, military specificity, and privacy and security of data. CONCLUSIONS: Psychoeducational interventions have a good evidence base as a treatment for mTBI and PCS. The use of apps for this purpose may be clinically effective, cost-effective, confidential, user friendly, and accessible. However, more research is needed to explore the effectiveness, usability, safety, security, and accessibility of apps designed for mTBI management.
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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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".