Evaluating the Feasibility of the ABCs of SLEEPING Mobile Application: Exploring Implementation, Acceptability, and Limited Efficacy
Bibliographic record
Abstract
Objective: Behaviourally based sleep problems are common among school-age children. Due to a number of barriers, few children receive evidence-based behavioural interventions for their sleep problems. The ABCs of SLEEPING mobile application was developed to provide parents with individualized and prioritized evidence-based sleep recommendations. Method: The current study assessed the feasibility of the intervention using a cohort pretest posttest design. Acceptability was assessed by collecting parent ratings of satisfaction/suitability and qualitative feedback through open-ended questions. Implementation was assessed weekly for 4 weeks to examine if the intervention was used as intended. Preliminary-efficacy was explored by assessing changes in children’s sleep and bedtime behaviours using parent report. Results: Parents generally reported satisfaction with the each of the three intervention components (questionnaire, report, and tips) and suitability of the tips for their child’s age and sleep problems. Descriptive statistics of the implementation data revealed parents did not use the intervention as much as intended. Content analysis of the qualitative data indicated that some changes to the intervention are needed (e.g., reminders to implement). Statistically significant evidence for preliminary-efficacy was found based on parent-reported questionnaires, but sleep was not normalized. Conclusion: The results indicate that while the ABCs of SLEEPING mobile application was found to have a moderate level of feasibility, it is not yet ready for large-scale testing. Future research should focus on modifying the intervention to imbed features to support implementation of the intervention. Implications for Impact Statement In this study we describe the feasibility testing of a smartphone-application designed to improve access to individualized information about sleep and healthy sleep practices entitled “ The ABCs of SLEEPING .” Our results demonstrated its acceptability and preliminary efficacy; however, the app was not implemented daily as intended. This research also demonstrates the importance of evaluating interventions for the end-user.
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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 | 0.065 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".