Literature DB >> 30276505

Determinants for quality of life trajectory patterns in patients with type 2 diabetes.

Ruey-Hsia Wang1,2, Kuan-Chia Lin3,4, Hui-Chun Hsu5, Yau-Jiunn Lee5, Shyi-Jang Shin6.   

Abstract

PURPOSE: The purpose of the study was to identify quality of life (QoL) trajectory patterns and the determinants in patients with Type 2 diabetes (T2DM).
METHODS: A longitudinal design was employed. Totally, 466 patients with T2DM recruited from five diabetic clinics in Taiwan were participants of this study. Demographic and disease characteristics, biomedical factors (HbA1c levels and body mass index), psychosocial factors (self-care behaviors, social support, resilience, diabetes distress), and QoL were collected at baseline. QoL was further measured every 6 months for four waves after baseline. Latent class growth analysis was used to identify QoL trajectory patterns. The multinomial logistic regression was further applied to explore the important determinants of different QoL trajectory patterns.
RESULTS: The "steadily poor" (n = 27, 5.8%), "consistently moderate" (n = 174, 37.3%), and "consistently good" (n = 265, 56.9%) trajectory patterns were identified. The HbA1c levels (OR 2.16) and diabetes distress (OR 1.18) were important for determining participants in the "steadily poor" QoL trajectory pattern. HbA1c levels (OR 1.25) and diabetes distress (OR 1.14) were important for determining participants in the "consistently moderate" QoL trajectory pattern.
CONCLUSIONS: To prevent development of relatively worse QoL trajectory patterns in patients with T2DM in a timelier manner, healthcare providers could regularly assess the QoL and provide intervention, especially for those with high HbA1c levels and high diabetes distress. Meanwhile, early intervention for decreasing HbA1c levels and diabetes distress may improve the trajectory development of QoL in patients with T2DM.

Entities:  

Keywords:  Diabetes distress; Glycemic control; Quality of life; Self-care behaviors; Trajectory pattern; Type 2 diabetes

Mesh:

Year:  2018        PMID: 30276505     DOI: 10.1007/s11136-018-2013-2

Source DB:  PubMed          Journal:  Qual Life Res        ISSN: 0962-9343            Impact factor:   4.147


  3 in total

1.  Predicting trajectories of recovery in prostate cancer patients undergone Robot-Assisted Radical Prostatectomy (RARP).

Authors:  Chiara Marzorati; Dario Monzani; Ketti Mazzocco; Francesca Pavan; Gabriele Cozzi; Ottavio De Cobelli; Massimo Monturano; Gabriella Pravettoni
Journal:  PLoS One       Date:  2019-04-04       Impact factor: 3.240

Review 2.  Population segmentation of type 2 diabetes mellitus patients and its clinical applications - a scoping review.

Authors:  Jun Jie Benjamin Seng; Amelia Yuting Monteiro; Yu Heng Kwan; Sueziani Binte Zainudin; Chuen Seng Tan; Julian Thumboo; Lian Leng Low
Journal:  BMC Med Res Methodol       Date:  2021-03-11       Impact factor: 4.615

Review 3.  Great diversity in the utilization and reporting of latent growth modeling approaches in type 2 diabetes: A literature review.

Authors:  Sarah O'Connor; Claudia Blais; Miceline Mésidor; Denis Talbot; Paul Poirier; Jacinthe Leclerc
Journal:  Heliyon       Date:  2022-09-13
  3 in total

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