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Record W2119701512 · doi:10.2196/mhealth.3949

Effectiveness of 6 Months of Tailored Text Message Reminders for Obese Male Participants in a Worksite Weight Loss Program: Randomized Controlled Trial

2015· article· en· W2119701512 on OpenAlexvenueno aff
Ju Young Kim, Sohee Oh, Steven R. Steinhubl, Sohye Kim, Woo Kyung Bae, Jong Soo Han, Keehyuck Lee, Mi Jin Kim

Bibliographic record

VenueJMIR mhealth and uhealth · 2015
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersKorea District Heating CorporationSeoul National University Bundang Hospital
KeywordsWeight lossRandomized controlled trialOverweightMedicineWeight managementBody mass indexPhysical therapyPsychological interventionShort Message ServiceHealth promotionIntervention (counseling)ObesityNursingPublic healthSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Worksite nutrition and physical activity interventions are important to help overweight and obese employees lose weight, but costs and insufficient sustained motivation prevent the majority of these programs from succeeding. Tailored text messaging in aiding weight management has been effective in several studies, but no studies have evaluated the effect of a tailored text message service on weight loss in a worksite health promotion program. OBJECTIVE: We studied the efficacy of a tailored text-messaging intervention for obese male participants in a worksite weight loss program of 6 months duration. METHODS: The study was an unblinded, randomized controlled trial. Men with a body mass index greater than 25 kg/m(2) were recruited from the Korea District Heating Corporation, the Korea Expressway Corporation, and the Korea Gas Corporation. The participants were identified by nurse managers. Participants were randomly allocated to 1 of the following 2 groups for 24 weeks: (1) intervention group, which received tailored text message reminders every other day plus 4 offline education sessions and brief counseling with monthly weight check by nurses for weight control over 6 months and (2) control group, which received the 4 offline education sessions and brief counseling with monthly weight check by nurses about weight control over 6 months. The primary outcome was the difference in weight loss at 6 months. A mixed-model repeated-measures analysis was performed to evaluate the effect of the intervention group's weight loss compared with the control group. RESULTS: A total of 205 obese men were randomized into either the intervention (n=104) or the control group (n=101). At the end of 6 months, the intervention group (n=63) had lost 1.71 kg (95% CI -2.53 to -0.88) and the control group (n=59) had lost 1.56 kg (95% CI -2.45 to -0.66); the difference between the 2 groups was not significant (mean difference -0.15, 95% CI -1.36 to 1.07). At the end of the study, 60% (34/57) of the intervention group rated the message program as helpful for weight control and 46% (26/57) would recommend the text message service to their friends. CONCLUSIONS: Tailored text message reminders did not have a significant effect on weight loss in obese men as part of a worksite weight loss program. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number (ISRCTN): 39629189; http://www.isrctn.com/ISRCTN39629189?q=39629189&filters=&sort=&offset=1&totalResults=1&page=1&pageSize=10&searchType=basic-search (Archived by WebCite at http://www.webcitation.org/6VsFkwJH6).

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)0.002
metaresearch head score (Gemma)0.004
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 score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0120.001

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 head0.084
GPT teacher head0.469
Teacher spread0.385 · 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

Citations49
Published2015
Admission routes1
Has abstractyes

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