Diet Quality of Young Adults Enrolling in TXT2BFiT, a Mobile Phone-Based Healthy Lifestyle Intervention
Bibliographic record
Abstract
BACKGROUND: Young adulthood is associated with poor dietary habits and vulnerability to weight gain. Population studies have revealed that inadequate fruit and vegetable intake, excessive sugar-sweetened beverages, and frequent takeaway food consumption are dietary habits requiring intervention. OBJECTIVE: The aim was to examine the dietary patterns and diet quality of overweight young adults on enrollment into a mobile phone-based healthy lifestyle (mHealth) intervention, TXT2BFiT. METHODS: Baseline diets were analyzed using the online Dietary Questionnaire for Epidemiological Studies version 2. The Healthy Eating Index for Australians (HEIFA) based on the 2013 Dietary Guidelines, was used to rate individual diets according to intake of core foods and deleterious nutrients including sugar, sodium, saturated fat, and alcohol. Findings were compared with the 2011 Australian National Nutrition and Physical Activity Survey (NNPAS). Gender differences were assessed with t tests and chi-square tests. ANOVA models were used to determine linear trends of core and noncore food intake and nutrients across quartiles of HEIFA scores. Associations between HEIFA score, sugar-sweetened beverages, and takeaway food consumption were assessed using linear regression analysis. RESULTS: Diets of 230 participants (females: n=141; males: n=89; body mass index: mean 27.2, SD 2.5 kg/m(2)) were analyzed. The mean diet quality score was 45.4 (SD 8.8, range 21.7-77.0) out of 100 points, with no significant difference between genders. Compared with the NNPAS data for adults aged 19-30 years, this cohort had a lower intake of some core foods and higher intake of alcohol and saturated fat. Better quality diets were associated with higher intakes of fruits, vegetables, and wholegrains (P<.001). Takeaway food (P=.01) and sugar-sweetened beverage consumption (P<.001) were negatively associated with diet quality. CONCLUSIONS: Overweight young adults had poorer diets compared with the reference Australian population within the same age group. This study reinforces that gender-specific interventions are required, as is the current practice in TXT2BFiT, with a need to reduce sodium and alcohol intake in males and sugar intake in females. It also confirms the need to increase fruit and vegetable intake and reduce takeaway food consumption in this population, with additional focus on saturated fat and wholegrain intake.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".