Fatigue, insomnia and daytime sleepiness in multiple sclerosis versus narcolepsy

Kalle, Johansson; Wasling, Pontus; Axelsson, Markus

doi:10.1111/ane.13497

Cited by 12 publications

(4 citation statements)

References 43 publications

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“… 62 Some studies concluded that fatigue was unrelated to sleepiness, depression, and obesity. 63 , 64 We speculated that shortened mean sleep latency, coupled with shortened SOL, decreased SE, and prolonged WASO could indicate more serious sleep disturbances and prevented adequate rest and recovery, leading to increased fatigue. This is probably why mean sleep latency was inversely related to FSS scores and brain fatigue.…”

Section: Discussionmentioning

confidence: 99%

The Changed Nocturnal Sleep Structure and Higher Anxiety, Depression, and Fatigue in Patients with Narcolepsy Type 1

Yu,

Zhang,

Cai

et al. 2024

NSS

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Purpose This study aimed to evaluate nocturnal sleep structure and anxiety, depression, and fatigue in patients with narcolepsy type 1 (NT1). Methods Thirty NT1 patients and thirty-five healthy controls were enrolled and evaluated using the Epworth sleepiness scale (ESS), Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, Fatigue Severity Scale (FSS), polysomnography, multiple sleep latency test, and brain function state monitoring. Statistical analyses were performed using SPSS Statistics for Windows, version 23.0. Benjamini-Hochberg correction was performed to control the false discovery rate. Results Apart from typical clinical manifestations, patients with NT1 are prone to comorbidities such as nocturnal sleep disorders, anxiety, depression, and fatigue. Compared with the control group, patients with NT1 exhibited abnormal sleep structure, including increased total sleep time ( P adj =0.007), decreased sleep efficiency ( P adj =0.002), shortening of sleep onset latency ( P adj <0.001), elevated wake after sleep onset ( P adj =0.002), increased N1% ( P adj =0.006), and reduced N2%, N3%, and REM% ( P adj =0.007, P adj <0.001, P adj =0.013). Thirty-seven percent of patients had moderate to severe obstructive sleep apnea-hypopnea syndrome. And sixty percent of patients were complicated with REM sleep without atonia. Patients with NT1 displayed increased anxiety propensity ( P adj <0.001), and increased brain fatigue ( P adj =0.020) in brain function state monitoring. FSS scores were positively correlated with brain fatigue ( P adj <0.001) and mean sleep latency was inversely correlated with FSS scores and brain fatigue ( P adj =0.013, P adj =0.029). Additionally, ESS scores and brain fatigue decreased after 3 months of therapy ( P =0.012, P =0.030). Conclusion NT1 patients had abnormal nocturnal sleep structures, who showed increased anxiety, depression, and fatigue. Excessive daytime sleepiness and fatigue improved after 3 months of treatment with methylphenidate hydrochloride prolonged-release tablets in combination with venlafaxine.

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Section: Discussionmentioning

confidence: 99%

The Changed Nocturnal Sleep Structure and Higher Anxiety, Depression, and Fatigue in Patients with Narcolepsy Type 1

Yu,

Zhang,

Cai

et al. 2024

NSS

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“…The ESS is an eight‐item questionnaire measuring participants’ perceived propensity to doze during common daytime activities. The ESS has commonly been used to measure daytime somnolence in MS (Devos et al, 2021; Johansson et al, 2021; Kołtuniuk et al, 2022). A score of >10 is indicative of excessive daytime sleepiness.…”

Section: Methodsmentioning

confidence: 99%

The relationships between multidimensional sleep health and work productivity in individuals with neurological conditions

Turner,

Laws,

Griffiths

et al. 2023

Journal of Sleep Research

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SummaryNumerous studies have reported the negative impacts of poor sleep on work productivity in the general population. However, despite the known sleep issues that individuals living with neurological conditions experience, no study has explored its impact on their work productivity. Sleep health is a concept that includes multiple domains of sleep, measured with a combination of objective and subjective measures. Therefore, this study aimed to ascertain the associations between sleep health and its domains and work productivity in individuals with neurological conditions. Sleep health domains were determined through actigraphy data collected over 1 week and sleep questionnaires. Work productivity was assessed via the Work Productivity and Activity Impairment Questionnaire. A comparison of sleep health scores between demographic variables was performed using Mann–Whitney U and Kruskal–Wallis tests. Associations between the sleep health domains and work productivity were performed using linear regression models. There were no significant differences in sleep health scores between sex, smoking status, education level, employment status or any work productivity domain. Individuals with non‐optimal sleep timing had greater absenteeism (22.99%) than the optimal group. Individuals with non‐optimal sleep quality had an increase in presenteeism (30.85%), work productivity loss (26.44%) and activity impairment (25.81%) compared to those in the optimal group. The findings from this study highlight that self‐reported sleep quality has the largest impact on work productivity. Improving individuals’ sleep quality through triage for potential sleep disorders or improving their sleep hygiene (sleep behaviour and environment) may positively impact work productivity.

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“…The FSS was originally compiled by Krupp et al [ 49 ] to measure the degree of fatigue in patients with multiple sclerosis. Studies have proven that it has good reliability and validity in other populations, with Cronbach Alpha > 0.89 [ 50 , 51 ].…”

Section: Methodsmentioning

confidence: 99%

Effectiveness of Digital Cognitive Behavior Therapy for the Treatment of Insomnia: Spillover Effects of dCBT

Liu

et al. 2022

IJERPH

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The effects of digital Cognitive Behavior Therapy for insomnia (dCBT-i) on sleep quality have been previously demonstrated but the spillover effects on fatigue, flow (a state of immersion in activities of interest), and cognitive flexibility remain unclear. The current study examined the effectiveness of dCBT-i. A total of 97 college students (20.96 ± 1.87 years, 73.1% female students) were randomly selected from a shortlist and divided into sleep intervention (n = 39), conventional education (n = 37), and healthy control (n = 21) groups. Task switching paradigm, Fatigue Severity Scale (FSS), Flow Experience Scale (FES), and the Chinese version of the Pittsburgh Sleep Quality Index (CPSQI) were measured pre- and post-intervention. Results show that the sleep quality of the intervention group improved, and fatigue was relieved. Participants in the sleep intervention group had increased flow experience scores post-intervention and improved cognitive flexibility. The control group’s sleep quality deteriorated and fatigue level increased. dCBT-i can not only achieve a significant improvement in sleep quality and reduce fatigue, but also improve learning abilities, quality of life, flow, and cognitive flexibility. Future research should pay attention to indicators such as work efficiency, sedative use, and the durability and stability of such effects.

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Fatigue, insomnia and daytime sleepiness in multiple sclerosis versus narcolepsy

Cited by 12 publications

References 43 publications

The Changed Nocturnal Sleep Structure and Higher Anxiety, Depression, and Fatigue in Patients with Narcolepsy Type 1

The Changed Nocturnal Sleep Structure and Higher Anxiety, Depression, and Fatigue in Patients with Narcolepsy Type 1

The relationships between multidimensional sleep health and work productivity in individuals with neurological conditions

Effectiveness of Digital Cognitive Behavior Therapy for the Treatment of Insomnia: Spillover Effects of dCBT

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