Application of Mobile Health Technologies Aimed at Salt Reduction: Systematic Review
Bibliographic record
Abstract
BACKGROUND: High salt consumption has contributed to the rise of noncommunicable diseases around the world. The application of mobile health (mHealth) technologies has witnessed rapid growth in recent years. However, evidence to support mHealth interventions to confront the challenge of salt reduction has not yet been critically reviewed. OBJECTIVE: The aim of this study was to identify, characterize, and evaluate mHealth interventions aimed at salt reduction across the world. METHODS: A systematic search of studies in English or Chinese language published from January 1, 1992 to July 31, 2017 was conducted using 4 English databases (PubMed, MEDLINE, Global Health, and Cochrane) and 3 Chinese databases (Wanfang, China Science and Technology Journal, and China National Knowledge of Infrastructure). All studies directly using mobile technologies in health care with a primary or secondary objective of reducing dietary salt consumption were included. RESULTS: A total of 1609 articles were found using the search strategy, with 11 full articles (8 English and 3 Chinese) being included for data extraction, including 11 interventional studies. Overall, few high-quality interventions were identified. Most interventions were limited by small study population sample sizes, lack of control groups, and short follow-up times, all of which were obstacles in generating long-term scalable approaches. Most interventions employed short message service as a platform for mHealth interventions, whereas some innovative mHealth technologies were also explored. Most interventions had a primary focus of improving awareness of dietary salt consumption. The outcome variables used to measure intervention effectiveness included 24-hour urinary sodium excretion, spot urine sampling, dietary records, and indirect behavior or knowledge indicators targeting salt consumption. Although most interventions displayed positive outcome results, none of them provided reliable evidence to evaluate the effectiveness of salt reduction. CONCLUSIONS: Salt reduction in mHealth initiatives remains relatively unexplored; however, studies that did intervene on salt-reduction show the potential of mHealth as an effective intervention method. We provide 3 recommendations for future mHealth interventions in salt reduction-(1) increased use of new, innovative, and interactive mHealth technologies; (2) development of mHealth interventions with primary prevention measures and goals of salt reduction; and (3) large-scale, rigorously designed, and object-targeted clinical trials of mHealth interventions with appropriate quantitative outcome variables, in particular 24-hour urine sodium.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".