Dataset Information

Relationship between depression, sleep quality, and hypoglycemia among persons with type 2 diabetes.

ABSTRACT:

Objective

We analyzed two cohorts of people with type 2 diabetes to evaluate the relationships between depression, sleep quality, and history of hypoglycemia.

Research design and methods

Two adult cohorts from Chicago (n = 193) and Bangkok, Thailand (n = 282) with type 2 diabetes completed questionnaires to assess sleep quality, depressive symptoms, and hypoglycemia frequency. Proportional odds logistic regression models for each cohort adjusted for duration of therapy, insulin and sulfonylurea management, and other factors.

Results

Those with hypoglycemia in both cohorts had a longer duration of diabetes, greater use of insulin, and worse sleep quality. The Chicago cohort used less sulfonylureas but had higher depressive symptom scores. The Thailand cohort had greater sulfonylurea use. In the final Thailand regression model, depressive symptoms were independently associated with hypoglycemia frequency. In both final Chicago and Thailand models, sleep quality was not associated with hypoglycemia frequency.

Conclusions

In the Thailand cohort, depressive symptoms were associated with hypoglycemia frequency.

SUBMITTER: Biggers A

PROVIDER: S-EPMC6352297 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Publications

Relationship between depression, sleep quality, and hypoglycemia among persons with type 2 diabetes.

Biggers Alana A Sharp Lisa K LK Nimitphong Hataikarn H Saetung Sunee S Siwasaranond Nantaporn N Manodpitipong Areesa A Crowley Stephanie J SJ Hood Megan M MM Gerber Ben S BS Reutrakul Sirimon S

Journal of clinical & translational endocrinology 20190102

<h4>Objective</h4>We analyzed two cohorts of people with type 2 diabetes to evaluate the relationships between depression, sleep quality, and history of hypoglycemia.<h4>Research design and methods</h4>Two adult cohorts from Chicago (n = 193) and Bangkok, Thailand (n = 282) with type 2 diabetes completed questionnaires to assess sleep quality, depressive symptoms, and hypoglycemia frequency. Proportional odds logistic regression models for each cohort adjusted for duration of therapy, insulin an ...[more]

PMID: 30723689

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Project description:BackgroundThe fear of hypoglycemia in type 2 diabetes mellitus (T2DM) patients with hypoglycemia has seriously affected their quality of life. They are always afraid of hypoglycemia and often take excessive action to avoid it. Yet, researchers have investigated the relationship between hypoglycemia worries and excessive avoiding hypoglycemia behavior using total scores on self-report measures. However, network analysis studies of hypoglycemia worries and excessive avoiding hypoglycemia behavior in T2DM patients with hypoglycemia are lacking.PurposeThe present study investigated the network structure of hypoglycemia worries and avoiding hypoglycemia behavior in T2DM patients with hypoglycemia and aimed to identify bridge items to help them correctly treat hypoglycemia and properly deal with hypoglycemia fear.MethodsA total of 283 T2DM patients with hypoglycemia were enrolled in our study. Hypoglycemia worries and avoiding hypoglycemia behavior were evaluated with the Hypoglycemia Fear Scale. Network analyses were used for the statistical analysis.ResultsB9 "Had to stay at home for fear of hypoglycemia" and W12 "I am worried that hypoglycemia will affect my judgment" have the highest expected influences in the present network. In the community of hypoglycemia worries, W17 "I worry about hypoglycemia during sleep" has the highest bridge expected influence. And in the community of avoiding hypoglycemia behavior, B9 "Had to stay at home for fear of hypoglycemia" has the highest bridge expected influence.ConclusionComplex patterns of associations existed in the relationship between hypoglycemia worries and avoiding hypoglycemia behavior in T2DM patients with hypoglycemia. From the perspective of network analysis, B9 "Had to stay at home for fear of hypoglycemia" and W12 "I am worried that hypoglycemia will affect my judgment" have the highest expected influence, indicating their highest importance in the network. W17 "I worry about hypoglycemia during sleep" aspect of hypoglycemia worries and B9 "Had to stay at home for fear of hypoglycemia" aspect of avoiding hypoglycemia behavior have the highest bridge expected influence, indicating they have the strongest connections with each community. These results have important implications for clinical practice, which provided potential targets for interventions to reduce hypoglycemia fear and improve the quality of life in T2DM patients with hypoglycemia.

Project description:PurposeCyber victimization is a national mental health concern, especially among adolescents who are digital natives. The current study examined sleep quality as a mediator of the association between cyber victimization and depressive symptoms among adolescents.Design and methodA prospective study design was utilized with a community sample of adolescents (N = 801; 57% female; mean age = 14.45, SD = .85) from the eastern United States. Participants completed (a) the Pittsburgh Sleep Quality Index; (b) the Cyber Victimization Scale; and (c) the Center for Epidemiologic Studies Depression Scale Revised via online surveys at baseline and 6-month follow-up. The inter-relationship between variables was analyzed by Hayes' mediation approach.FindingsCyber victimization was not directly associated with having depressive symptoms 6 months later when controlling for adolescents' poor sleep quality, sex, and age (direct effect [c'] = .012, t(676) = 1.12, p < .05, confidence interval [CI] -.008, .036). The mediation analysis indicated a significant indirect effect of poor sleep quality on the relationship between cyber victimization and depressive symptoms (ab = .005, bootstrapped standard error [SE] = .003, bootstrapped CI .001, .011; a is the effect of cyber victimization on poor sleep quality; b is the effect of poor sleep quality on depressive symptoms). Specifically, adolescents' cyber victimization led to poor sleep quality (a = .039, SE = .041, p < .05), which also led to increased depressive symptoms (b = .116, SE = .019, p < .001), after controlling for depressive symptoms at baseline, sex, and age. The indirect effect of cyber victimization on depressive symptoms was estimated through poor sleep quality (a*b = .039(.116) = .0045).ConclusionsThe findings suggest that poor sleep quality may be a mechanism through which cyber bullying is related prospectively to depressive symptoms. Interventions for cyber-victimized adolescents should include assessment of sleep quality and incorporate sleep hygiene education.Clinical relevanceAdolescents should be screened for cyber victimization and sleep quality. Moreover, promotion of sleep hygiene among cyber-victimized adolescents may help to reduce depression.

Project description:The aim to examine the link between diabetes distress and depression in individuals with diabetes, assess the mediating role of psychological resilience in this relationship, and analyses if these relationships differ between Type 1 and Type 2 diabetes. The study utilized a cross-sectional design. A total of 181 (age 33-72 years, mean = 54.76 years, and SD = 9.05 years) individuals diagnosed with diabetes who were receiving treatment from State Specialist Hospitals in Okitipupa were selected for the study using the convenient sampling technique. The data were analysed using Pearson Multiple correlation and multi-group mediation analysis. The analyses were carried out with Smartpls and IBM/SPSS Version 28.0. The results revealed a significant positive correlation between diabetes distress and depression (r = .80, p < .05), suggesting that higher levels of diabetes distress were associated with increased depression scores. Additionally, psychological resilience partially mediated the relationship between diabetes distress and depression (b = - 0.10, p < .05), signifying that resilience played a crucial role in mitigating the impact of diabetes distress on depression. Furthermore, a multi-group analysis was conducted to explore potential differences between Type 1 and Type 2 diabetes subgroups. The relationship between diabetes distress and depression was found to be more pronounced in the Type 1 subgroup (difference = 0.345, p < .05), while the relationship between psychological resilience and depression was negatively stronger in the Type 2 subgroup (difference = - 0.404, p < .05) compared to the Type 1 subgroup. There is an intricate linkage between diabetes distress, resilience, and depression, emphasizing the differential roles of resilience in Type 1 and Type 2 diabetes. The insights gleaned from this study underscore the importance of considering the type of diabetes when designing interventions and support mechanisms for individuals with diabetes who are also suffering from depression. By advancing our understanding of these dynamics, we can strive for more effective and personalized approaches to improve the overall well-being of those living with diabetes.

Dataset Information

Relationship between depression, sleep quality, and hypoglycemia among persons with type 2 diabetes.

Objective

Research design and methods

Results

Conclusions

Publications

Relationship between depression, sleep quality, and hypoglycemia among persons with type 2 diabetes.

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