Theory-Based Mobile App Intervention to Promote Healthy Salt Intake Among Adults: Randomized Controlled Trial
Bibliographic record
Abstract
Background: Nowadays, mobile health technology has been increasingly used for treatment and prevention at all levels of health care. Associating this technology with the promotion of healthy salt consumption-both cost-effective and cost-saving public health strategies-can reduce this risk factor that contributes significantly to the increase of noncommunicable diseases worldwide. Objective: We aim to assess the usability and the efficacy of a mobile app intervention-"Sal na Medida" app-on the promotion of a healthy salt intake among adults, based on the Behavior Change Wheel framework. Additionally, to investigate if intention, self-efficacy, and habit variables mediated the effect of intervention on salt intake behavior. Methods: An experimental, randomized, and longitudinal study that evaluated the intervention effect with assessments at baseline, postintervention (1 mo), and at follow-up (2 mo). Sociodemographic and clinical data were collected from participants recruited at primary health care centers. The behavior of salt intake and per capita salt consumption were the primary outcomes. Psychosocial variables of intention, self-efficacy, and habit were assessed as possible mediators. Usability was evaluated after 1 month and 2 months of using the app. Results: Eighty-six participants were randomized in the intervention group (IG; n=43) or control group (CG; n=43). Most of the participants were female (IG: n=36, 84% and CG: n=37, 86%). Usability of app intervention scored 77.8 points (on a scale of 0 to 100) among IG participants. There was a significant reduction in salt intake in IG according to the variables of per capita salt and the behavior of salt intake. Furthermore, at the end of the follow-up, individuals in IG were 63% more likely to have a lower salt intake than those in the CG. The regression analysis showed an increase in intention and the perception of self-efficacy, and a more pronounced reduction in the habit of using more than 3 g of salt/day in preparing meals in IG when compared to CG. Habit and self-efficacy were identified as mediators of the intervention's effect. Conclusions: The theory-based mobile app intervention for reducing salt intake has shown promise both in terms of usability and efficacy among adults. Conducting further studies to assess its potential for implementation on a larger scale would be valuable for determining its real-world impact and feasibility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".