MétaCan
Menu
← Back to cohort
Record W4408559171 · doi:10.2196/69264

Evaluation of a Smartphone-Based Weight Loss Intervention with Telephone Support for Merchant Women With Obesity in Côte d'Ivoire: Protocol for a Randomized Controlled Trial

2025· article· en· W4408559171 on OpenAlexvenueno aff
Rui Usui, Maki Aomori, Shogo Kanamori, Setsuko Watabe, Bi Tra Jamal Sehi, Yuka Kanoya

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionRandomized controlled trialWeight lossIntervention (counseling)MedicinemHealthObesityPhonePhysical therapyGerontologyFamily medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: The obesity rate among women in Côte d'Ivoire is rising, particularly in urban areas. Merchantry is the leading occupation for women in the country, and merchant women face a high risk of obesity owing to their sedentary lifestyle. A previous survey indicated that the obesity rate among merchant women was 30%, double the national average. Furthermore, 82.2% of merchant women with obesity were unaware of their condition, and 40.1% expressed no interest in losing weight. While most weight loss programs target individuals ready to lose weight, community interventions should also address those with minimal readiness. Additionally, low-cost weight-loss interventions that do not require health professionals are needed in countries with limited medical resources. Smartphones could offer a cost-effective solution as they enable self-monitoring and remote communication. OBJECTIVE: This study will evaluate a low-cost smartphone-based intervention that targets individuals who are not ready to lose weight without the involvement of health professionals. METHODS: The intervention will run for 6 months, and its efficacy will be assessed in an unblinded, parallel-group, randomized controlled trial with 108 participants per group. All direct interventions for participants in this study will be carried out by staff without medical qualifications. The intervention group will receive weighing scales and be encouraged to record their weight with a smartphone app. Health education will be provided via weekly group messages and monthly phone calls. The evaluation will be conducted face-to-face. The primary outcome will be the weight change, and the secondary outcome will be differences in body fat percentage, abdominal circumference, and stage of behavioral change in weight loss behaviors from baseline to 3, 6, and 12 months. RESULTS: In accordance with this protocol, the recruitment of participants started on August 26, 2024. A total of 216 participants were allocated, with 108 in the intervention group and 108 in the control group. The baseline survey began on November 15, 2024, and is currently ongoing as of the end of November 2024. CONCLUSIONS: This study will be the first in sub-Saharan African countries to implement a smartphone app-based weight loss program in sub-Saharan Africa that does not require direct intervention by health care professionals but specifically targets communities. Furthermore, if the effectiveness of this program is confirmed, it has the potential to serve as a low-cost sustainable weight loss model at the policy level. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/69264.

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)0.015
metaresearch head score (Gemma)0.015
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.047
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.015
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0110.006
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0470.006

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 head0.200
GPT teacher head0.621
Teacher spread0.421 · 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
GenreProtocol

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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Research Protocols→Same topicMobile Health and mHealth Applications→French-language works237,207→