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Record W4220916406 · doi:10.2196/33852

Effect of Information and Communication Technology–Based Self-management System DialBeticsLite on Treating Abdominal Obesity in the Specific Health Guidance in Japan: Randomized Controlled Trial

2022· article· en· W4220916406 on OpenAlexvenueno aff
Masahiro Kondo, Teru Okitsu, Kayo Waki, Toshimasa Yamauchi, Masaomi Nangaku, Kazuhiko Ohe

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsmHealthOverweightMedicineCoachingHealth coachingRandomized controlled trialPhysical therapyPsychological interventionObesityIntervention (counseling)Abdominal obesitySelf-managementBehavior changeGerontologyPsychologyMetabolic syndromeNursingInternal medicine

Abstract

fetched live from OpenAlex

Background Mobile health (mHealth) interventions, a more cost-effective approach compared with traditional methods of delivering lifestyle coaching in person, have been shown to improve physical parameters and lifestyle behavior among overweight populations. In Japan, the Specific Health Checkups and Specific Health Guidance (SHG) started in 2008 to treat obesity and abdominal obesity. However, the effectiveness of SHG is limited owing to its in-person counseling. The effect of mHealth on SHG has yet to be demonstrated. Objective This study aims to determine whether a mobile self-management app (DialBeticsLite) could make the SHG more beneficial among patients with abdominal obesity to achieve a reduction in visceral fat area (VFA). Methods This study was an open-label, 2-arm, parallel-design randomized controlled trial. We recruited 122 people in September 2017 and randomly assigned them into either the intervention or control group. All participants attended an educational group session that delivered information regarding diet and exercise. In addition, participants in the intervention group were asked to use DialBeticsLite for 3 months. DialBeticsLite facilitated the daily recording of several physical parameters and lifestyle behavior and provided feedback to encourage an improvement in behavior. The primary outcome was the change in VFA from baseline to the 3-month follow-up. Secondary outcomes included changes in both physical and metabolic parameters from baseline to the 3-month follow-up. The Welch 2-tailed t test was conducted to analyze the effects of DialBeticsLite on both the primary and secondary outcomes. Results Of the 122 participants recruited, 75 (61.5%) were analyzed because 47 (38.5%) were excluded: 37 (30.3%) because of ineligibility and 10 (8.2%) because of withdrawal of consent. The mean age was 49.3 (SD 6.1) years in the intervention group (41/75, 55%) and 48.5 (SD 5.3) years in the control group (34/75, 45%), and all participants were men, although unintentionally. The baseline characteristics did not differ significantly between the intervention and control groups, except for VFA. The average change of VFA was −23.5 (SD 20.6) cm2 in the intervention group and +1.9 (SD 16.2) cm2 in the control group (P<.001). Statistically significant differences were also found for the change of body weight, BMI, and waist circumference. These findings did not change after adjusting for VFA at the baseline. The intervention had no significant effect on any of the metabolic parameters. An exploratory analysis showed significant associations between the change in VFA and steps per day and between the change in VFA and calorie intake per day within the intervention group. Conclusions Our findings indicate that an mHealth intervention facilitating the daily monitoring of several physical parameters and lifestyle behavior can be highly effective in inducing visceral fat loss and weight loss among adults eligible for SHG. Trial Registration UMIN Clinical Trials Registry UMIN000042045; https://tinyurl.com/4vat3v53

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.050
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
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 score0.145
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0500.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.446
Teacher spread0.417 · 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 teacher head, not a consensus.

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

Citations25
Published2022
Admission routes1
Has abstractyes

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