Evaluating the Knowledge Level, Practice, and Behavioral Change Potential of Care Managers in Pressure Injury Prevention Using a Mobile App Prototyping Model in the Home-Care Setting: Single-Arm, Pre-Post Pilot Study
Bibliographic record
Abstract
Background: The use of mobile apps to promote knowledge level, practice, and behavioral change potential has become increasingly common. However, studies on apps targeting social welfare employees working in the home-care setting to prevent pressure injury (PI) are lacking. The care manager (CM) plays a key role in connecting the demand and supply of home-care services. PI is more prevalent in the home-care setting, where resources are limited, than in acute settings. Objective: The research hypothesis was that CMs who use a mobile app will have improved general knowledge and heightened practice for PI prevention, compared to that before using the app. This study aimed to assess the effectiveness of a PI prevention support mobile app prototyping model (Pips-Map) in improving the knowledge level, practice, and behavioral change potential of CMs in PI prevention in the home-care setting. Methods: This was conducted between December 2021 and December 2023 as a single-arm, pre-post pilot study including 27 CMs who worked in a Japanese city. Pips-Map was used for 6 months in daily practice, and a self-administered test questionnaire was used to assess participants' knowledge and practice in PI prevention before or after using Pips-Map. At the end of the posttest, a validated App Behavior Change Scale was used to analyze behavioral change potential. This study followed the Consolidated Standards of Reporting Trials (CONSORT) extension to pilot and feasibility trials. Results: In total, 19 participants were analyzed. Out of 55 points, the total mean knowledge score significantly increased from 30.9 (SD 5.9) in the pretest group to 36.1 (SD 5.9) in the posttest group (P=.0003). The number of participants with a total score of >70% (adequate knowledge level) increased from 2 (11%) to 7 (36.8%), but the difference was not statistically significant (P=.07). For the level of practice, out of 21 points, the total score increased from 15.2 (SD 3.1) in the pretest group to 16.2 (SD 3.0) in the posttest group, but no statistically significant differences were observed (P=.16). The behavior change scale revealed that participants positively evaluated the Pips-Map to provide information on PI prevention guidelines but had concerns regarding inadequate usability and financial incentives of Pips-Map. Conclusions: The use of Pips-Map for 6 months in actual practice increased the knowledge level of Japanese CMs in PI prevention, but it did not change the level of practice. Considering the need for updating apps that aim to promote behavioral change, this study identified some limitations of Pips-Map. Thus, revisions must be made to adapt Pips-Map to home-based care needs.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".