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Record W2908125294 · doi:10.2196/10755

Image-Based Mobile System for Dietary Management in an American Cardiology Population: Pilot Randomized Controlled Trial to Assess the Efficacy of Dietary Coaching Delivered via a Smartphone App Versus Traditional Counseling

2018· article· en· W2908125294 on OpenAlexvenueno aff
Brian G. Choi, Tania Dhawan, Kelli Metzger, Lorraine Marshall, Awdah Akbar, Tushina Jain, Heather A. Young, Richard J. Katz

Bibliographic record

VenueJMIR mhealth and uhealth · 2018
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsnot available
FundersNational Institutes of HealthSmall Business Innovation Research
KeywordsMedicineRandomized controlled trialMediterranean dietPopulationCoachingAdverse effectPhysical therapyDiseaseHealth coachingMobile appsmHealthInternal medicineGerontologyPsychological interventionEnvironmental healthPsychologyComputer scienceNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Randomized controlled trials conducted in Mediterranean countries have shown that the Mediterranean diet lowers adverse cardiovascular events. In the American population, diet remains the biggest uncontrolled risk factor for cardiovascular disease. OBJECTIVE: This study aimed to test the hypothesis that asynchronous dietary counseling supplied through a custom smartphone app results in better adherence to a Mediterranean diet in a non-Mediterranean population than traditional standard-of-care (SOC) counseling. METHODS: In total, 100 patients presenting to the cardiology clinic of an academic medical center were randomized to either the SOC or smartphone app-based experimental (EXP) Mediterranean diet intervention after informed consent and 1 hour of individual face-to-face dietary counseling with a registered dietitian. Participants in EXP received a custom smartphone app that reinforced the Mediterranean diet, whereas participants in SOC received 2 additional sessions of in-person dietary counseling with the registered dietitian-30 min at 1 month and 30 min at 3 months. Preexisting knowledge of a Mediterranean diet was measured by the validated Mediterranean Diet Score (MDS) instrument. Baseline height, weight, blood pressure (BP), and laboratory biomarkers were collected. At 1, 3, and 6 months, participants presented for a follow-up appointment to assess compliance to the Mediterranean diet using the MDS as well as a patient satisfaction survey, BP, and weight. Repeat laboratory biomarkers were performed at 3 and 6 months. RESULTS: , or C-reactive protein (CRP). Participants in EXP achieved a significantly greater weight loss on average of 3.3 pounds versus 3.1 pounds for participants in SOC, P=.04. Adherence to the Mediterranean diet increased significantly over time for both groups (P<.001), but there was no significant difference between groups (P=.69). Similarly, there was no significant difference in diet satisfaction between EXP and SOC, although diet satisfaction increased significantly over time for both groups. The proportion of participants with high Mediterranean diet compliance (defined as the MDS ≥9) increased significantly over time (P<.001)-from 18.4% to 57.1% for SOC and 27.5% to 64.7% for EXP; however, there was no significant difference between the groups. CONCLUSIONS: Both traditional SOC counseling and smartphone-based counseling were effective in getting participants to adhere to a Mediterranean diet, and these dietary changes persisted even after counseling had ended. However, neither method was more effective than the other. This pilot study demonstrates that patients can change to and maintain a Mediterranean diet with either traditional or smartphone app-based nutrition counseling. TRIAL REGISTRATION: ClinicalTrials.gov NCT03897426;https://clinicaltrials.gov/ct2/show/NCT03897426.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.085
GPT teacher head0.383
Teacher spread0.298 · 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

Citations43
Published2018
Admission routes1
Has abstractyes

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