Fatigue Among Adults With Type 1 Diabetes Mellitus and Implications for Self-Management: An Integrative Review

Griggs, Stephanie; Morris, Nancy S.

doi:10.1177/0145721718782148

Cited by 27 publications

(35 citation statements)

References 26 publications

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“…Along with other symptoms, fatigue may herald comorbid psychophysiological changes and acute or chronic complications (Fritschi & Quinn, 2010; Kalra & Sahay, 2018). Additionally, fatigue may reduce an individual’s capacity to perform daily activities essential to optimal glucose control (e.g., glucose monitor, preparing a healthy diet, and regular physical activity; Griggs & Morris, 2018). Empirical evidence also suggests that fatigue could be a barrier to adequate self-care (Fritschi & Quinn, 2010; Kirk et al, 2015; Zhu et al, 2018), which is the cornerstone for effective diabetes management.…”

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confidence: 99%

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Fatigue in Adults with Type 2 Diabetes: A Systematic Review and Meta-analysis

Chen

Liu

2020

West J Nurs Res

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This review aimed to evaluate the level of fatigue in adults with type 2 diabetes measured by the validated Diabetes Symptom Checklist (DSC)and related factors. Ten studies were included. Both DSC (n = 4) and the revised (n = 6) have been used to measure fatigue. When the latter was used (n = 1082), the pooled level of fatigue was 1.77 (on a 0–5 scale). When the former was used (n = 674), the pooled level of fatigue was 1.73 (on a 0–10 scale). Participant demographics (e.g., gender), health information (e.g., BMI), and diabetes-specific factors (e.g., A1C and diabetes distress) were associated with fatigue. Fatigue was negatively related to self-care behaviors. The prevalence of significant fatigue remains unclear due to a lack of a cut-off point of the instrument. Future research is warranted to identify the threshold for clinically significant fatigue and investigate the causal relationship. Nurses are recommended to perform a routine assessment of fatigue.

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confidence: 99%

“…Previous reviews have been focused on fatigue among people with type 1 diabetes (T1D; Griggs & Morris, 2018; Jensen et al, 2017; Pouwer, 2017). Specifically, Jensen et al (2017) reviewed 10 observational studies and found that the prevalence of fatigue in patients with T1D ranged from 23% to 40%.…”

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confidence: 99%

Fatigue in Adults with Type 2 Diabetes: A Systematic Review and Meta-analysis

Chen

Liu

2020

West J Nurs Res

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“…However, health condition does not influence the accuracy performance of the ensemble classifier; the values are between 97.2% and 97.9% for both window sizes. The complex nature of fatigue and the factors affiliated among adults suffering from T1DM is the reason for the limited number of investigations of the influence of fatigue on this group of patients [63] as well as the lack of papers investigating machine-learning classifications based on fatigue-induced tremors in healthy and T1DM participants. MVC amplitude significantly decreases, and fatigue onset occurs sooner in T1DM participants compared to the control group according to [31].…”

Section: Early and Late Fatigue Recognitionmentioning

confidence: 99%

Classification of Fatigue Phases in Healthy and Diabetic Adults Using Wearable Sensor

Aljihmani

Kerdjidj

Zhu

et al. 2020

Sensors

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Fatigue is defined as “a loss of force-generating capacity” in a muscle that can intensify tremor. Tremor quantification can facilitate early detection of fatigue onset so that preventative or corrective controls can be taken to minimize work-related injuries and improve the performance of tasks that require high-levels of accuracy. We focused on developing a system that recognizes and classifies voluntary effort and detects phases of fatigue. The experiment was designed to extract and evaluate hand-tremor data during the performance of both rest and effort tasks. The data were collected from the wrist and finger of the participant’s dominant hand. To investigate tremor, time, frequency domain features were extracted from the accelerometer signal for segments of 45 and 90 samples/window. Analysis using advanced signal processing and machine-learning techniques such as decision tree, k-nearest neighbor, support vector machine, and ensemble classifiers were applied to discover models to classify rest and effort tasks and the phases of fatigue. Evaluation of the classifier’s performance was assessed based on various metrics using 5-fold cross-validation. The recognition of rest and effort tasks using an ensemble classifier based on the random subspace and window length of 45 samples was deemed to be the most accurate (96.1%). The highest accuracy (~98%) that distinguished between early and late fatigue phases was achieved using the same classifier and window length.

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“…Diabetes fatigue has vicious cyclic relationship with numerous factors, including diabetes symptoms, diabetic complications, other endocrine disorders, emotional distress, and lifestyle factors [16] and, in turn, it negatively influences emotions, lifestyle, and blood sugar control, increasing the risks for physical and psychological diabetes complications [17]. While diabetes fatigue has been conceptualized in several ways [16,17,18,19], the existing conceptual frameworks have been limited in their ability to comprehensively encompass the multidimensional characteristics of fatigue, including those related to biological, psychological, social, and environmental contexts. The dynamic biopsychosocial (DBPS) model provides a dynamic, ecological perspective of health, which views health as being determined by the interactions between biological, psychological, and social dynamics [20], and could be a useful conceptual framework for understanding the multidimensional properties of diabetes fatigue.…”

Section: Introductionmentioning

confidence: 99%

Fatigue-Related Factors for Community-Dwelling Older Adults with Diabetes: A Theory-Guided Multi-Dimensional Approach Using the Dynamic Biopsychosocial Model

Kim

Son

2019

IJERPH

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Older adults with diabetes appear more susceptible to fatigue compared to younger adults with diabetes or healthy older adults, since aging and diabetes independently and synergistically influence fatigue. Few studies have investigated fatigue in older adults with diabetes using a multidimensional approach. This study explored the influences of physical, psychological, interpersonal, and contextual factors on diabetes fatigue using a dynamic biopsychosocial model. Face-to-face surveys were administered to community-dwelling older adults with diabetes and included variables across four domains (i.e., physical, psychological, interpersonal, and contextual factors). Univariate analyses and multiple linear regression were used. The mean fatigue score was 3.94 (standard deviation (SD) = 1.81) out of 7, and the prevalence of fatigue was 48.8%. Significant differences in fatigue severity by psychological, interpersonal, and contextual factors were found. Comorbidity and psychological factors were significant predictors of fatigue in the model, explaining 31.9% of the variance. As nearly half the sample experienced moderate or severe fatigue, which was significantly influenced by both comorbidity and psychological factors, including depression, sleep quality, and diet-related psychological characteristics, assessing patients’ psychological status may be important. Awareness of fatigue could be incorporated into dietary interventions for older adults with diabetes.

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Fatigue Among Adults With Type 1 Diabetes Mellitus and Implications for Self-Management: An Integrative Review

Cited by 27 publications

References 26 publications

Fatigue in Adults with Type 2 Diabetes: A Systematic Review and Meta-analysis

Fatigue in Adults with Type 2 Diabetes: A Systematic Review and Meta-analysis

Classification of Fatigue Phases in Healthy and Diabetic Adults Using Wearable Sensor

Fatigue-Related Factors for Community-Dwelling Older Adults with Diabetes: A Theory-Guided Multi-Dimensional Approach Using the Dynamic Biopsychosocial Model

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