PROMIS Sleep Disturbance and Sleep-Related Impairment in Adolescents

Hanish, Alyson; Lin-Dyken, Deborah C.; Han, Joan C.

doi:10.1097/nnr.0000000000000217

Cited by 81 publications

(59 citation statements)

References 21 publications

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“…Then, the original score was converted to a standardized T-score, with higher scores revealing more serious sleep disturbance. 33 The Cronbach's alpha of this scale was 0.91, which indicates that it has a good reliability. And previous study's Cronbach's alpha was 0.819.…”

Section: Sleep Disturbancementioning

confidence: 86%

<p>Recovery Experience as the Mediating Factor in the Relationship Between Sleep Disturbance and Depressive Symptoms Among Female Nurses in Chinese Public Hospitals: A Structural Equation Modeling Analysis</p>

Ding¹,

Gehrman²,

Liu³

et al. 2020

PRBM

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Purpose: Nurses are suffering from considerable work-related stress, which can result in disturbed sleep and depressive symptoms. Whereas, recovery experience can alleviate sleep disturbance and help maintain well-being in the working environment. The aim of this study was to examine whether recovery experiences play a mediating role in the relationship between sleep disturbance and depressive symptoms of female nurses in Chinese public hospitals. Methods: From December 2017 to May 2018, the study with proportional sampling of cross-sectional design was carried out in the public tertiary hospitals from Shenyang of Liaoning Province in China. A total of 1500 female nurses who had been working for one or more years participated in this study. A structural equation model (SEM) was applied to test the assumption that recovery experiences might function as a mediator in the relationship between sleep disturbance and symptoms of depression. Results: Sleep disturbance contributed most to the variance (32.9%) of depressive symptoms and had a significantly positive correlation with depressive symptoms (P<0.01) among nurses. Psychological detachment in recovery experience had a significantly positive correlation with sleep disturbance (P<0.01) and depressive symptoms (P<0.01). Whereas, relaxation, mastery experience, and control in recovery experience were significantly and negatively correlated with sleep disturbance (P<0.01) and depressive symptoms (P<0.01). Recovery experience served as a mediator between sleep disturbance and depressive symptoms (a * b = 0.066, BCa 95% CI: 0.045, 0.095). Conclusion: Sleep disturbance was the predictor of depressive symptoms for female nurses. Moreover, recovery experience could combat the detrimental impacts of sleep disturbances on symptoms of depression. This study suggested that nurses might benefit from interventions on improving sleep quality to relieve depressive symptoms through mediating path of recovery experience.

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Section: Sleep Disturbancementioning

confidence: 86%

<p>Recovery Experience as the Mediating Factor in the Relationship Between Sleep Disturbance and Depressive Symptoms Among Female Nurses in Chinese Public Hospitals: A Structural Equation Modeling Analysis</p>

Ding¹,

Gehrman²,

Liu³

et al. 2020

PRBM

View full text Add to dashboard Cite

show abstract

“…14 In contrast, adolescents and young adults have showed some accuracy in selfreporting sleep behaviors. 15,16 Lab-based polysomnography is the gold standard in objective sleep research and is the only measure of sleep architecture.…”

Section: Introductionmentioning

confidence: 99%

Sleep and type 1 diabetes in children and adolescents: Proposed theoretical model and clinical implications

Monzon

McDonough²,

Meltzer

et al. 2018

Pediatr Diabetes

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Youth with type 1 diabetes mellitus (T1D) experience more sleep disturbances and shorter sleep durations compared to their healthy peers. Researchers have now uncovered the negative mental health and physical health outcomes associated with poor sleep in youth with T1D. The field of T1D sleep research currently operates under the broad notion that sleep behaviors impact treatment adherence, which ultimately lead to worse long‐term health outcomes. This model however does not explain how behavior influences T1D management and sleep outcomes on a day‐to‐day basis, leading to difficulties in providing tailored treatment recommendations. In this review, we present a theoretical framework that describes the recursive cycle between sleep behaviors, T1D outcomes, and symptoms of negative affect/stress over a 24‐hour period. This model is guided by the sleep literature, showing a clear relationship between poor sleep and negative affect, and the T1D literature demonstrating a link between poor sleep and disease management for youth with T1D. Further, emerging literature indicates a need for additional parent sleep assessment considering that T1D management and fear of hypoglycemia negatively impact parent sleep behaviors. Recommendations are provided to move the field toward effective intervention studies and new areas of research to evaluate and modify the proposed model.

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“…PROs were collected using the Patient-Reported Outcomes Measurement Information System (PROMIS) instruments [ 33 , 36 ], which included pediatric asthma impact (PAI; short form 8a), sleep disturbance (SD; short form 6a), and sleep-related impairment (SRI; short form 8a). These instruments were previously validated among adolescents [ 37 , 38 ].…”

Section: Methodsmentioning

confidence: 99%

Exploring the Association Between Self-Reported Asthma Impact and Fitbit-Derived Sleep Quality and Physical Activity Measures in Adolescents

Bian¹,

Guo²,

Xie³

et al. 2017

JMIR Mhealth Uhealth

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BackgroundSmart wearables such as the Fitbit wristband provide the opportunity to monitor patients more comprehensively, to track patients in a fashion that more closely follows the contours of their lives, and to derive a more complete dataset that enables precision medicine. However, the utility and efficacy of using wearable devices to monitor adolescent patients’ asthma outcomes have not been established.ObjectiveThe objective of this study was to explore the association between self‑reported sleep data, Fitbit sleep and physical activity data, and pediatric asthma impact (PAI).MethodsWe conducted an 8‑week pilot study with 22 adolescent asthma patients to collect: (1) weekly or biweekly patient‑reported data using the Patient-Reported Outcomes Measurement Information System (PROMIS) measures of PAI, sleep disturbance (SD), and sleep‑related impairment (SRI) and (2) real-time Fitbit (ie, Fitbit Charge HR) data on physical activity (F-AM) and sleep quality (F‑SQ). To explore the relationship among the self-reported and Fitbit measures, we computed weekly Pearson correlations among these variables of interest.ResultsWe have shown that the Fitbit-derived sleep quality F-SQ measure has a moderate correlation with the PROMIS SD score (average r=−.31, P=.01) and a weak but significant correlation with the PROMIS PAI score (average r=−.18, P=.02). The Fitbit physical activity measure has a negligible correlation with PAI (average r=.04, P=.62).ConclusionsOur findings support the potential of using wrist-worn devices to continuously monitor two important factors—physical activity and sleep—associated with patients’ asthma outcomes and to develop a personalized asthma management platform.

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PROMIS Sleep Disturbance and Sleep-Related Impairment in Adolescents

Cited by 81 publications

References 21 publications

<p>Recovery Experience as the Mediating Factor in the Relationship Between Sleep Disturbance and Depressive Symptoms Among Female Nurses in Chinese Public Hospitals: A Structural Equation Modeling Analysis</p>

<p>Recovery Experience as the Mediating Factor in the Relationship Between Sleep Disturbance and Depressive Symptoms Among Female Nurses in Chinese Public Hospitals: A Structural Equation Modeling Analysis</p>

Sleep and type 1 diabetes in children and adolescents: Proposed theoretical model and clinical implications

Exploring the Association Between Self-Reported Asthma Impact and Fitbit-Derived Sleep Quality and Physical Activity Measures in Adolescents

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