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Record W2949995246 · doi:10.82308/6726

Identification of successful goal setting strategies for management of childhood obesity: results from a family- centered lifestyle intervention in children 6 to 12 years of age

2015· article· en· W2949995246 on OpenAlexfundaboutno aff
Sarah‐Eve Loiselle

Bibliographic record

VenueeScholarship@McGill (McGill) · 2015
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsnot available
FundersDairy Farmers of CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsMcGill University
KeywordsPsychological interventionTheory of planned behaviorOverweightChildhood obesityObesityBody mass indexGerontologyClinical nutritionGoal settingMedicineBehavior changeIntervention (counseling)PsychologyControl (management)Social psychologyNursingInternal medicine

Abstract

fetched live from OpenAlex

Background: Childhood obesity has grown to epidemic proportions worldwide over the past few decades. Lifestyle interventions in childhood obesity using goal setting have shown promising results. SMART goals are Specific, Measurable, Attainable, Realistic and Timely. Objectives: This study determined if using SMART goals in a family-based lifestyle intervention was linked to reduction in adiposity and defined the nature of SMART goals that were linked to success using the constructs from the Theory of Planned Behavior (TPB) and the Ecological Model (EM). Methodology: Healthy overweight and obese children (n=100) from the McGill Youth Lifestyle Intervention with Food and Exercise Study (clinicaltrials.gov, NCT01290016) were randomized to either intervention group (INT) or control (CTL). INT received 5 SMART-based interventions with a dietitian over 6 mo. Height and weight were measured to compute body mass index-for-age z-scores (BAZ). SMART goals were classified using the TPB (attitude, subjective norm, perceived behavioral control) and the EM (individual, family). Groups were divided into “successful” (SUC) if BAZ maintained/decreased or “unsuccessful” (UNS) if BAZ increased over 6 mo. Differences among groups were tested using mixed model ANOVA and relationships between BAZ and SMART goals using Pearson’s correlations. Results: INT was 75% successful (-0.29 ± 0.22 BAZ) and 25% unsuccessful (0.29 ± 0.27 BAZ) (p<0.001). The SMART goals analysis revealed that the percentage of goals classified as subjective norm from TPB was higher in INT-SUC compared to INT-UNS (66.4 ± 15.1 % vs 57.5 ± 13.6 %, p=0.036). In INT overall, SMART goals classified as subjective norm were inversely correlated with BAZ change (r=-0.39, p=0.007). Additionally, when SMART goals were targeting the family as opposed to the individual (child), a pattern of success was observed through an inverse correlation with BAZ change (r=-0.28, p=0.055). Conclusion: We found that family-centered lifestyle interventions using SMART goals resulted in a greater proportion of successful participants compared to control. SMART goals that addressed the subjective norm concept from the TPB were correlated with favorable changes in BAZ after 6 mo. Subjective norm refers to the social pressure to perform a behavior, transmitted by those that influence an individual’s decisions. In other words, SMART goals targeting recommendations (norms) were more often associated with success. Involvement of the family also seems to have an important role to play in successful lifestyle interventions for childhood obesity.

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)0.003
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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.327
Teacher spread0.287 · 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".

Quick stats

Citations0
Published2015
Admission routes2
Has abstractyes

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