Adherence and barriers in e-health self-control training for enhancing childhood multidisciplinary obesity treatment.

Adherence and barriers in e-health self-control training for enhancing childhood multidisciplinary obesity treatment.

Naets, Tiffany;Vervoort, Leentje;Tanghe, Ann;Braet, Caroline;
clinical psychology & psychotherapy 2019
283
naets2019adherenceclinical

Abstract

Training self-control as the assumed underlying mechanism for weight loss is a promising pathway for improving long-term outcomes of childhood multidisciplinary obesity treatment (MOT). The present study is the first to analyse adherence to e-health self-control training in paediatric obesity. We hypothesized that low adherence would relate to child characteristics and to contextual treatment barriers. Participants were recruited as a part of a larger randomized controlled trial, evaluating an e-health self-control training during inpatient MOT (intensive phase) and its outpatient aftercare (booster phase). A number of 68 youngsters with severe obesity between 11 to 19 years old were included in the present study. Excellent adherence was observed in the intensive phase during inpatient MOT, but rates decreased in the booster phase. As predicted, the low adherence group had a significantly higher weight status throughout the entire study period. Differences in contextual treatment barriers did not appear. Further in-depth analysis showed that the low adherence group frequently experienced practical obstacles. The end of inpatient MOT and high weight status can be considered important risk factors for low adherence in an additional self-control training aimed at facilitating weight loss.

Access

Citation

ID: 64951
Ref Key: naets2019adherenceclinical
Use this key to autocite in SciMatic or Thesis Manager

References

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
64951
Unique Identifier:
10.1002/cpp.2405
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet

Saymatik Web3.0 Wallet