Evidence map›Paper›PMID 29479461›Full record

ArticleObesity science & practice2018

Early predictors of weight loss in a 1-year behavioural weight-loss programme.

B L James, L S Roe, E Loken, B J Rolls

Abstract read
In one paragraph

Article in Obesity science & practice, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

B L JamesDepartment of Nutritional Sciences The Pennsylvania State University University Park PA USA.
L S RoeDepartment of Nutritional Sciences The Pennsylvania State University University Park PA USA.
E LokenDepartment of Human Development and Family Studies The Pennsylvania State University University Park PA USA.
B J RollsDepartment of Nutritional Sciences The Pennsylvania State University University Park PA USA.

Funding

Penn State Clinical and Translational Science InstituteUL1TR000127 · NCATS · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI SINOWAY, LAWRENCE I · 2012 to 2015
$17.1M
Environmental Influences on Intake and ObesityR01DK059853 · NIDDK · PENNSYLVANIA STATE UNIVERSITY-UNIV PARK · PI ROLLS, BARBARA JEAN · 2001 to 2014
$5.0M
NCATS NIH HHS UL1 TR000127NIDDK NIH HHS R01 DK059853
6 · The paper itself

Abstract

Objective: Identifying early predictors of weight loss is key for developing personalized treatment. However, few individual factors have been identified that predict weight loss during intervention, other than early weight loss itself. Methods: Women with overweight or obesity (n = 186, mean ± SD age 50.0 ± 10.6 years, body mass index 34.0 ± 4.2 kg m Results: Although there were no differences in weight loss between intervention groups at the end of the trial, certain individual factors were shown to predict both early weight loss at Month 3 and longer-term weight loss at Month 12. Across all participants, increases in dietary restraint and healthy lifestyle ratings in the first month predicted more rapid weight loss from baseline to Month 3 (P < 0.05) and also predicted more rapid weight loss and slower regain from baseline to Month 12 (both P < 0.01). Early attendance and changes in disinhibition were not associated with subsequent weight loss. Conclusions: Changes in psychological and behavioural measures, such as restraint, in the first month of weight loss intervention predicted longer-term weight loss in women. Early additional support or tailored treatment could promote long-term success by reinforcing these behaviours.

Indexed as

Adultseating behavioursobesity treatmentweight loss

Identifiers

PMID29479461
PMCPMC5818734

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.