Russian-Language Mobile Apps for Reducing Alcohol Use: Systematic Search and Evaluation
Bibliographic record
Abstract
BACKGROUND: Personalized prevention tools such as mobile apps designed to reduce alcohol consumption are widespread in mobile app stores accessible in Russia. However, the quality and content of these mobile apps have not been systematically evaluated. OBJECTIVE: This study aimed to identify Russian-language mobile apps for reducing alcohol use and to evaluate their quality and potential to change alcohol-related health behavior. It further aimed to identify apps that could facilitate screening and brief interventions in primary health care in Russia. METHODS: A systematic search for mobile apps available in Russia was carried out between April 1 and 15, 2020, December 1 and 15, 2020, and in March 2021 in the iPhone App Store, Google Play Store, and the 4PDA forum. App quality was assessed using the Mobile App Rating Scale (MARS), and structured searches in electronic libraries and bibliographic databases were used to evaluate the apps' evidence base. The number of features facilitating changes in lifestyle behavior was assessed using the App Behavior Change Scale (ABACUS). RESULTS: We identified 63 mobile apps for reducing alcohol use. The mean MARS quality ratings were high for the subscales of functionality (3.92 out of 5, SD 0.58) and aesthetics (2.96, SD 0.76) and low for engagement (2.42, SD 0.76) and information (1.65, SD 0.60). Additional searches in electronic libraries and bibliographic databases (eLibrary, CyberLeninka, Google Scholar) yielded no studies involving the identified apps. ABACUS scores ranged from 1 to 15 out of 25, with a mean of 5 (SD 3.24). Two of the identified apps might be useful for screening and brief interventions in Russian primary health care after improvements in content and scientific testing. CONCLUSIONS: Russian-language mobile apps for reducing alcohol use are accessible in the app stores. Many of them are aesthetically pleasing, functional, and easy to use. However, information about their scientific trialing or testing is lacking. Most apps contain a low number of features that facilitate changes in lifestyle behavior. Further research should examine the context of Russian-language mobile apps for reducing alcohol use. Our findings underline the need to develop evidence-based apps to mitigate alcohol consumption in Russia and elsewhere. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42020167458; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=167458.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".