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Record W4283376233 · doi:10.2196/preprints.40431

Evaluating the Effectiveness of a Canadian Family-Based Childhood Obesity Management Virtual Program Delivered During the COVID-19 Pandemic (Preprint)

2022· preprint· en· W4283376233 on OpenAlexaboutno aff
Kayla Nuss, Rebecca Coulter, Bianca DeSilva, Jeann Buenafe, Ronak Sheikhi, Patti‐Jean Naylor, Sam Liu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsScreen timePandemicCoronavirus disease 2019 (COVID-19)MedicineChildhood obesityObesityPhysical activityRepeated measures designPhysical therapyFamily medicineGerontologyOverweightInternal medicineMathematics

Abstract

fetched live from OpenAlex

BACKGROUND Generation Health (GH) was a 10-week family-based lifestyle program designed to promote a healthy lifestyle for families with children who are off the healthy weight trajectory in British Columbia, Canada. GH used a blended delivery format which consisted of 10 weekly in-person sessions and self-guided lessons and activities on a web portal. The blended GH was adapted to be delivered virtually due to the COVID-19 pandemic. Currently, the effectiveness of the virtual GH compared with the blended GH remains unclear. OBJECTIVE 1) to compare the effectiveness of virtual GH delivered during the COVID-19 pandemic with the blended GH delivered prior to the COVID-19 pandemic in changing child physical activity, sedentary, dietary behaviours, screen time behaviours and parental support related behaviours for child physical activity and healthy eating; 2) to explore virtual GH program engagement and satisfaction. METHODS This study used a single-arm design. The blended GH (n=102) was delivered from October 2018 to February 2020, and the virtual GH (n=90) was delivered during the COVID-19 pandemic from April 2020 to March 2021. Families with children between the ages of 8-12 years old and a BMI ≥85th percentile for age and sex were recruited. Participants completed pre-and post-intervention questionnaires to assess the child’s physical activity, dietary, sedentary, screen time and parent support behaviours. Repeated measure ANOVA was used to evaluate the difference between the virtual and blended GH over time. RESULTS Both the virtual and blended GH improved child MVPA (F(1,380)=18.37, p<.00001, ηp2=.07) and reduced screen time (F(1,380)= 9.17, p=.003, ηp2=.06 ). However, participants in the virtual GH reported significantly greater vegetable intake than in blended GH at 10-week follow-up (F(1,380)=15.19, p<.001, ηp2 =.004).. Parents in both virtual and blended GH showed significant improvements in support behaviours for child physical activity (F(1,380)=5.55, p<.02, ηp2 =.002) and healthy eating (F(1,380)=3.91, p<.001, ηp2=.01), as well as self-regulation of parent support for child physical activity (F(1,380)=49.20, p<.0001, ηp2=.16) and healthy eating (F(1,380)=91.13, p<.0001, ηp2) =.28). Families in both the virtual and blended GH were satisfied with the program delivery. There were no significant differences in attendance for the weekly in-person (77%) or group video chat sessions (76%) for the blended and virtual GH, respectively (p>.05). However, webportal usage was significantly greater in the virtual GH (50 [55.82] minutes) compared with blended GH (17 [15.3] minutes) (p<.001). CONCLUSIONS Findings from this study suggested that virtual GH was as effective in improving child lifestyle behaviours and parental support-related behaviours as the blended program. Virtual GH has the potential to improve the flexibility and scalability of family-based childhood obesity management interventions.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.729

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.105
GPT teacher head0.460
Teacher spread0.355 · 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 designObservational
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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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