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Record W2979700191 · doi:10.2196/11070

Get Healthy, Stay Healthy: Evaluation of the Maintenance of Lifestyle Changes Six Months After an Extended Contact Intervention

2018· article· en· W2979700191 on OpenAlexvenueno aff
Brianna S. Fjeldsoe, Ana D. Goode, Philayrath Phongsavan, Adrian Bauman, Genevieve Maher, Elisabeth Winkler, Jennifer Job, Elizabeth Eakin

Bibliographic record

VenueJMIR mhealth and uhealth · 2018
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
FundersNSW Ministry of HealthUniversity of Queensland
KeywordsIntervention (counseling)Psychological interventionMedicinePhysical therapyWeight lossGerontologyNursingObesity

Abstract

fetched live from OpenAlex

BACKGROUND: Extended intervention contact after an initial, intensive intervention is becoming accepted as best practice in behavioral weight control interventions. Whether extended contact mitigates weight regain in the longer term or it simply delays weight regain until after the extended intervention contact ceases is not clear. OBJECTIVE: This study aimed to evaluate, in multiple ways, maintenance of weight, diet, and physical activity outcomes following Get Healthy, Stay Healthy (GHSH), a text message-delivered extended contact intervention. METHODS: Clients completing the Get Healthy Service (GHS) lifestyle telephone coaching program were randomized to receive GHSH (n=114) or standard care (no additional contact, n=114) and were assessed at baseline (following completion of GHS), 6 months (following completion of GHSH), and 12 months (noncontact maintenance follow-up). At all 3 assessments, participants self-reported their body weight, waist circumference, physical activity (walking and moderate and vigorous sessions/week), and dietary behaviors (fruit and vegetable serves/day, cups of sweetened drinks per day, takeaway meals per week; fat, fiber, and total indices from the Fat and Fiber Behavior Questionnaire). Moderate-to-vigorous physical activity (MVPA) was also assessed via accelerometry. Maintenance was examined multiple ways: (1) using traditional methods to assess and compare group averages after some period of noncontact (ie, at 12 months), (2) using a novel approach to assess and compare group average changes over the first 6 months of noncontact, and (3) exploring individual participant changes (increase/decrease/no change) over the first 6 months of noncontact. RESULTS: . The between-group differences detected at 6 months were still present and statistically significant at 12 months for bodyweight (-1.33 kg [-2.61 to -0.05]) and accelerometer-assessed MVPA (24.9 min/week [5.8-44.0]). None of the other outcomes were significantly favored compared with the control group at 12 months. Changes over their first 6 months of noncontact for the GHSH group were significantly better than the control group in terms of accelerometer-measured MVPA and self-reported moderate activity (other differences between the groups were all nonsignificant). In addition to the maintenance seen in the group averages, most intervention participants had maintained their behavioral outcomes during the first 6 months of noncontact. CONCLUSIONS: The GHSH participants were better off relative to where they were initially, and relative to their counterparts, not receiving extended contact in terms of MVPA. However, based on the between-group difference in bodyweight over the first 6 months of noncontact, GHSH does appear to simply delay the inevitable weight regain. However, this delay in weight regain, coupled with sustained improvements in MVPA, has public health benefits. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12613000949785; https://www.anzctr.org. au/Trial/Registration/TrialReview.aspx?id=364821&isReview=true.

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)0.004
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.082
GPT teacher head0.428
Teacher spread0.346 · 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

Citations23
Published2018
Admission routes1
Has abstractyes

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