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Record W4317522926 · doi:10.2196/43675

Effectiveness of a Sodium-Reduction Smartphone App and Reduced-Sodium Salt to Lower Sodium Intake in Adults With Hypertension: Findings From the Salt Alternatives Randomized Controlled Trial

2023· article· en· W4317522926 on OpenAlexvenueno aff
Helen Eyles, Jacqueline Grey, Yannan Jiang, Elaine Umali, Rachael McLean, Lisa Te Morenga, Bruce Neal, Anthony Rodgers, Robert N. Doughty, Cliona Ní Mhurchú

Bibliographic record

VenueJMIR mhealth and uhealth · 2023
Typearticle
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsnot available
FundersHealth Research Council of New Zealand
KeywordsMedicineRandomized controlled trialSodiumUrine sodiumBlood pressurePopulationPhysical therapyInternal medicineEnvironmental healthChemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Even modest reductions in blood pressure (BP) can have an important impact on population-level morbidity and mortality from cardiovascular disease. There are 2 promising approaches: the SaltSwitch smartphone app, which enables users to scan the bar code of a packaged food using their smartphone camera and receive an immediate, interpretive traffic light nutrition label on-screen alongside a list of healthier, lower-salt options in the same food category; and reduced-sodium salts (RSSs), which are an alternative to regular table salt that are lower in sodium and higher in potassium but have a similar mouthfeel, taste, and flavor. OBJECTIVE: Our aim was to determine whether a 12-week intervention with a sodium-reduction package comprising the SaltSwitch smartphone app and an RSS could reduce urinary sodium excretion in adults with high BP. METHODS: A 2-arm parallel randomized controlled trial was conducted in New Zealand (target n=326). Following a 2-week baseline period, adults who owned a smartphone and had high BP (≥140/85 mm Hg) were randomized in a 1:1 ratio to the intervention (SaltSwitch smartphone app + RSS) or control (generic heart-healthy eating information from The Heart Foundation of New Zealand). The primary outcome was 24-hour urinary sodium excretion at 12 weeks estimated via spot urine. Secondary outcomes were urinary potassium excretion, BP, sodium content of food purchases, and intervention use and acceptability. Intervention effects were assessed blinded using intention-to-treat analyses with generalized linear regression adjusting for baseline outcome measures, age, and ethnicity. RESULTS: A total of 168 adults were randomized (n=84, 50% per group) between June 2019 and February 2020. Challenges associated with the COVID-19 pandemic and smartphone technology detrimentally affected recruitment. The adjusted mean difference between groups was 547 (95% CI -331 to 1424) mg for estimated 24-hour urinary sodium excretion, 132 (95% CI -1083 to 1347) mg for urinary potassium excretion, -0.66 (95% CI -3.48 to 2.16) mm Hg for systolic BP, and 73 (95% CI -21 to 168) mg per 100 g for the sodium content of food purchases. Most intervention participants reported using the SaltSwitch app (48/64, 75%) and RSS (60/64, 94%). SaltSwitch was used on 6 shopping occasions, and approximately 1/2 tsp per week of RSS was consumed per household during the intervention. CONCLUSIONS: In this randomized controlled trial of a salt-reduction package, we found no evidence that dietary sodium intake was reduced in adults with high BP. These negative findings may be owing to lower-than-anticipated engagement with the trial intervention package. However, implementation and COVID-19-related challenges meant that the trial was underpowered, and it is possible that a real effect may have been missed. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12619000352101; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377044 and Universal Trial U1111-1225-4471.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.025
GPT teacher head0.319
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2023
Admission routes1
Has abstractyes

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