Evidence map›Paper›PMID 25760813›Full record

ArticleChildhood obesity (Print)2015

Treatment of child/adolescent obesity using the addiction model: a smartphone app pilot study.

Robert A Pretlow, Carol M Stock, Stephen Allison, Leigh Roeger

3 registry-linked trialsAbstract read
In one paragraph

Article in Childhood obesity (Print), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 3 registered trials, which are not on this map. Cited by 30 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 4 pooled it
–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.

NCT02689154 nacompletednot on this mapstarted 2016, after this paper: background citation

W8Loss2Go: Behavioral Weight Loss Intervention Utilizing Mobile Health Technology in Pediatric Patients Referred to a Tertiary Care Center Weight Management Clinic

TypeinterventionalSponsorChildren's Hospital Los AngelesRan2016 to 2017Enrolled18ConditionsObesityArmsW8Loss2Go
NCT02955017 nacompletednot on this mapstarted 2016, after this paper: background citation

Use of New Technologies for the Follow-up in Adolescent Obesity

TypeinterventionalSponsorAssistance Publique - Hôpitaux de ParisRan2016 to 2019Enrolled91ConditionsObesityArmstraditional follow_up, distance follow-up "new technologies"
NCT03500835 nacompletednot on this mapstarted 2019, after this paper: background citation

An Addiction Model Based Mobile Health Weight Loss Intervention With Coaching in Adolescents With Overweight and Obesity: Multi-Site Randomized Controlled Trial

TypeinterventionalSponsorChildren's Hospital Los AngelesRan2019 to 2023Enrolled161ConditionsPediatric Obesity, Mobile Technology, AddictionArmsApp, Coaching, Clinic: Modified KidsNFitness
3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Trial
  7. Trial
  8. Trial
  9. Article
  10. Article
  11. Article
  12. Article
  13. A Bibliometric Analysis to Identify Research Trends in Intervention Programs for Smartphone Addiction.International journal of environmental research and public health · 2023
    Review
  14. Article
  15. Review
  16. Review
  17. Article
  18. Review
  19. Current Intervention Treatments for Food Addiction: A Systematic Review.Behavioral sciences (Basel, Switzerland) · 2021
    Review
  20. Article
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.

Robert A Pretlow1eHealth International, Inc., Seattle, WA.
Carol M Stock2Buntain School of Nursing, Northwest University, Kirkland, WA.
Stephen Allison3Psychiatry Department, Flinders University, Adelaide, South Australia, Australia.
Leigh Roeger3Psychiatry Department, Flinders University, Adelaide, South Australia, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe aim of this study was to test a weight loss program for young people based on an addiction treatment approach.

methodsA pilot study (n=43) was conducted of a 20-week child/adolescent obesity intervention based on an addiction treatment model (staged, incremental withdrawal from problem foods, snacking/grazing, and excessive amounts at meals) and implemented by a server-integrated smartphone app with health professional support. The primary outcome was standardized %overBMI measured at four time points. Secondary outcomes were participants' self-ratings of self-esteem, control over food, and the degree they turned to food when stressed. User satisfaction data were collected with an online questionnaire. Latent growth modeling techniques were used to identify independent variables and possible mediating treatment process variables associated with weight change.

resultsMean age of participants was 16 years (range, 10-21), 65% girls, and 84% Caucasian. Twenty-seven (63%) completed the program. There was a significant decrease in %overBMI over time of 7.1. There were significant improvements in participant ratings of self-esteem, control over food, and a reduction in turning to food when stressed. Males, younger participants, and participants with higher levels of program compliance achieved better weight loss. Participants who reported that calling obesity an addiction made their guilt worse experienced poorer weight loss. Females were more likely than males to report "addiction guilt," and this partly mediated the overall gender effect.

conclusionsThe staged, incremental food withdrawal approach was feasible to implement and was useful in helping reduce excessive weight, particularly among boys.

Indexed as

Computer-Assisted InstructionHealth BehaviorSmartphoneWeight LossAdolescentAdolescent BehaviorBehavior, AddictiveBody Mass IndexChildChild BehaviorFemaleHumansMalePediatric ObesityPilot ProjectsSelf Concept

Identifiers

PMID25760813
PMCPMC4485371

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.