The App Behavior Change Scale: Creation of a Scale to Assess the Potential of Apps to Promote Behavior Change
Bibliographic record
Abstract
BACKGROUND: Using mobile phone apps to promote behavior change is becoming increasingly common. However, there is no clear way to rate apps against their behavior change potential. OBJECTIVE: This study aimed to develop a reliable, theory-based scale that can be used to assess the behavior change potential of smartphone apps. METHODS: A systematic review of all studies purporting to investigate app's behavior change potential was conducted. All scales and measures from the identified studies were collected to create an item pool. From this item pool, 3 health promotion exerts created the App Behavior Change Scale (ABACUS). To test the scale, 70 physical activity apps were rated to provide information on reliability. RESULTS: The systematic review returned 593 papers, the abstracts and titles of all were reviewed, with the full text of 77 papers reviewed; 50 papers met the inclusion criteria. From these 50 papers, 1333 questions were identified. Removing duplicates and unnecessary questions left 130 individual questions, which were then refined into the 21-item scale. The ABACUS demonstrates high percentage agreement among reviewers (over 80%), with 3 questions scoring a Krippendorff alpha that would indicate agreement and a further 7 came close with alphas >.5. The scale overall reported high interrater reliability (2-way mixed interclass coefficient=.92, 95% CI 0.81-0.97) and high internal consistency (Cronbach alpha=.93). CONCLUSIONS: The ABACUS is a reliable tool that can be used to determine the behavior change potential of apps. This instrument fills a gap by allowing the evaluation of a large number of apps to be standardized across a range of health categories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".