Effectiveness and Feasibility of a Remote Lifestyle Intervention by Dietitians for Overweight and Obese Adults: Pilot Study
Bibliographic record
Abstract
Background: To tackle the problem of obesity and related diseases in Switzerland, cost-efficient, effective, and innovative primary health care interventions for weight management are required. In this context, Oviva has developed a scalable technology for registered dietitians to counsel overweight and obese patients via a mobile phone app. Objective: The aim of this study was to evaluate the effectiveness and feasibility of weight loss counseling by dietitians using a mobile phone app for patients with overweight and obesity. Methods: In this pre- and posttest pilot study, overweight and obese adults participated in a 1-year behavioral intervention to lose weight through remote counseling by dietitians in the German-speaking part of Switzerland. The study started in April 2016 and finished in May 2018. Participants received individual counseling through the app and the exchange with the dietitian focused on regular feedback on photo-based food log, motivation, and education. The contents were tailored to the individual lifestyle goal set. The predefined intensity of remote counseling decreased during the year. Group chat could be used. The outcomes examined were changes in weight (primary outcome), hemoglobin A1c, fasting glucose, fasting insulin, triglyceride, high-density lipoprotein cholesterol, blood pressure (BP), body mass index (BMI), waist circumference, body fat, and responses to a self-administered questionnaire with questions regarding participants’ physical activity, dietary assessment, and health-related quality of life. Changes were tested at baseline, after 3 months, and after 12 months, as well as between the third and the 12th month. Results: In total, 36 women and 7 men, with a mean age of 40.6 years, participated and 36 participants completed the study. Median weight change after the first 12 weeks was −3.8 kg (range: −15 to 2.4 and P<.001), between week 12 and week 52 it was −1.1 kg (range: −9.7 to 7 and P=.08), and the median change during the entire period of intervention was −4.9 kg (range: −21.9 to 7.5 and P<.001). Furthermore, changes in BMI, waist circumference, body fat, and BP between baseline and 12 weeks and between baseline and 52 weeks were also significant. Significant changes in certain eating habits were also demonstrated (higher frequency of vegetable, fruit, and breakfast consumption and lower frequency of alcohol, sweet, and fat consumption). Conclusions: In addition to the professional skills of a dietitian, a profession-specific app such as Oviva can provide effective support that meets the needs of dietitians and clients on the long path of behavioral change and sustainable weight reduction. Trial Registration: ClinicalTrials.gov NCT02694614; https://clinicaltrials.gov/ct2/show/NCT02694614 (Archived by WebCite at http://www.webcitation.org/76gYkGOIc)
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".