The Role of Exercise on Fatigue Among Patients With Multiple Sclerosis in the King Fahad Hospital, Madinah, Saudi Arabia: An Analytical Cross-Sectional Study.

Sleep and fatigue in multiple sclerosis: A questionnaire-based, cross-sectional, cohort study

Poor sleep quality, often resulting from poor sleep hygiene, is common among medical students. Educational interventions aimed at improving sleep knowledge are beneficial for sleep quality in healthy populations. However, sleep education is often given minimal attention in medical school curriculums. The aim of the study was to explore whether a short educational intervention could improve sleep knowledge, and consequently sleep quality, among medical students. We recruited preclinical- and clinical-stage medical students during the 2017-2018 academic year. Students completed a demographic survey, the Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS), and the Assessment of Sleep Knowledge in Medical Education (ASKME) questionnaire. Students then attended a lecture on the physiology and importance of sleep. To assess the efficacy of the intervention, questionnaires were repeated 4 months thereafter. A total of 87 students (31 preclinical) with a mean age of 25.86 years (standard deviation [SD], 3.33), 51 of whom were women, participated in the study. At baseline, students had poor sleep quality with a PSQI mean score of 5.9 (SD, 2.37), without significant sleepiness, and a mean ESS score of 8.86 (SD, 4.32). The mean ASKME scores were consistent with poor sleep knowledge at 11.87 (SD, 4.32). After the intervention, the mean ASKME results improved to 14.15 (SD, 4.5; P < .001), whereas sleep quality did not. The effect was similar in preclinical and clinical medical students. Sleep knowledge was inadequate among medical students, who also experienced poor sleep quality. A short educational intervention improved sleep knowledge but was insufficient at improving sleep quality. Further studies are needed to determine which interventions may provide benefit in both sleep knowledge and sleep quality.

The impact of hospitalization on sleep in late-life is underexplored. The current study examined patterns of sleep quality before, during, and following hospitalization, investigated predictors of sleep quality patterns, and examined predictors of classification discordance between two suggested clinical cutoffs used to demarcate poor/good sleep. This study included older adults (n = 163; mean age 79.7 ± 6.9 years, 31% female) undergoing inpatient post-acute rehabilitation. Upon admission to inpatient post-acute rehabilitation, patients completed the Pittsburgh Sleep Quality Index (PSQI) retrospectively regarding their sleep prior to hospitalization. They subsequently completed the PSQI at discharge, and 3 months, 6 months, 9 months, and 1 year post discharge. Patient demographic and clinical characteristics (pain, depression, cognition, comorbidity) were collected upon admission. Using latent class analysis methods, older adults could be classified into (1) Consistently Good Sleepers and (2) Chronically Poor Sleepers based on patterns of self-reported sleep quality pre-illness, during, and up to 1 year following inpatient rehabilitation. This pattern was maintained regardless of the clinical cutoff employed (> 5 or > 8). Logistic regression analyses indicated that higher pain and depressive symptoms were consistently associated with an increased likelihood of being classified as a chronic poor sleeper. While there was substantial classification discordance based on clinical cutoff employed, no significant predictors of this discordance emerged. Clinicians should exercise caution in assessing sleep quality in inpatient settings. Alterations in the cutoffs employed may result in discordant clinical classifications of older adults. Pain and depression warrant detailed considerations when working with older adults on inpatient units when poor sleep is a concern.

Individuals with mild MS with poor sleep quality have impaired visuospatial memory and lower perceived functional abilities

Background: Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system, which can lead to physical and cognitive disability, fatigue, depression, and sleep disturbance, all of which may impair quality of life (QoL). While the physical disability is widely known to influence the QoL, the relative contributions of cognitive impairment, fatigue, and sleep quality remain incompletely defined. Objectives: To evaluate the relationship between QoL, physical and cognitive disability, sleep quality, fatigue, and depression in people with MS (PwMS), and to explore phenotype-specific differences between relapsing and progressive forms. Methods: In this monocentric cross-sectional study, 112 PwMS underwent physical assessment (EDSS, MSFC), cognitive testing (SDMT, PASAT, MoCA, MMSE), and QoL evaluation (MSIS-29, EQ-5D, EQ-VAS, MSNQ). A subgroup of 29 patients also completed the Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), Modified Fatigue Impact Scale (MFIS), and Beck Depression Inventory (BDI). Correlation and group analyses were performed. Results: Progressive MS patients showed greater physical disability (mean EDSS 5.8 vs. 2.6, p < 0.001), poorer cognitive performance, and lower QoL. Across the cohort, QoL strongly correlated with physical disability (EDSS) and cognitive performance (SDMT), with physical measures showing stronger associations. In relapsing MS, physical and cognitive impairment were linked to reduced QoL, whereas in progressive MS, physical disability predominated. In the sleep subgroup, poorer PSQI scores, longer sleep latency, and daytime sleepiness correlated with higher fatigue (MFIS), depressive symptoms (BDI), and reduced QoL (MSIS-29, EQ-5D). Conclusions: QoL in MS reflects the combined burden of physical disability, cognitive impairment, fatigue, depression, and poor sleep quality, with phenotype-specific patterns. While physical disability is the main QoL determinant in progressive MS, cognitive deficits with slowed processing speed play an important role in relapsing MS. Comprehensive, multidimensional assessment, including sleep and mood screening, may support individualized management strategies in MS.

Assessing the Quality of Sleep in Greek Primary Caregivers of Patients With Secondary Progressive Multiple Sclerosis: A Cross-Sectional Study

ObjectivesMost of the psychological and physical factors associated with poor sleep quality in patients with multiple sclerosis (MS) have a different prevalence in women and men, but whether or not these factors contribute differently to sleep quality in women and men with MS remains unclear. The aim of this study was to identify possible gender differences in factors related to poor sleep quality in MS patients.Material and MethodsWe collected data from 153 patients with MS. Patients filled out the Pittsburgh Sleep Quality Index (PSQI), the Hospital Anxiety and Depression Scale, and one item of the Short Form‐36 regarding pain.ResultsThe best model of predictors of poor sleep quality consisting of gender, depression, anxiety, pain, and the interaction between gender and pain showed that the only variable interacting with gender, which was significantly associated with poor sleep quality was pain (odds ratio [OR] for interaction of pain with male gender was 15.4, 95% CI: 2.4; 39.5). Separate models for men and women consisting of pain, depression, anxiety, after adjustment for age, disease duration, and disability showed that pain was the only variable associated with poor sleep quality in men (OR = 12.7, 95% CI: 1.9; 29.6), whereas depression (OR = 4.1, 95% CI: 1.3; 13.2) and anxiety (OR = 6.8, 95% CI: 2.4; 19.1) were in women.ConclusionsFactors contributing to poor sleep quality in MS patients differ by gender. Depression and anxiety are associated with poor sleep quality in women, whereas pain is in men. This highlights the need to apply gender‐specific approaches to the treatment of sleep disorders.

Sleep problems are a common consequence of multiple sclerosis; however, there is limited evidence regarding the agreement between device-measured and self-reported sleep parameters in adults with multiple sclerosis. The present study examined the agreement between self-reported and device-measured parameters of sleep quality in a sample of adults with multiple sclerosis. Participants (n = 49) completed a 7-day sleep diary and wore a wrist-worn ActiGraph GT3×+ (ActiGraph Corp., Pensecola, FL) for seven consecutive nights to quantify self-reported and device-measured sleep parameters, respectively. There was a significant discrepancy between self-reported and device-measured parameters of total time in bed (mean difference = 19.8 [51.3] min), sleep onset latency (mean difference = 22.2 [19.5] min), and frequency of awakenings during the night (mean difference = 12.8 [6.8]). Intraclass correlation estimates indicated poor agreement between methods on most parameters, except for total time in bed (intraclass correlation = 0.80). Bland-Altman plots suggested that total time in bed and total sleep time had acceptable levels of agreement and linear regression analyses indicated that sleep onset latency (F = 113.91, B = -1.34, P < .001), number of awakenings (F = 543.34, B = 1.85, P < .001), and sleep efficiency (F = 18.39, B = -0.77, P < .001) had significant proportional bias. Our results draw attention to the discrepancies between sleep parameter measurements and highlight the importance of including both self-report and device-measured outcomes for a complete and accurate representation of sleep in adults with multiple sclerosis. Cederberg KLJ, Mathison BG, Schuetz ML, Motl RW. Discrepancies between self-reported and device-measured sleep parameters in adults with multiple sclerosis. J Clin Sleep Med. 2022;18(2):415-421.

The aim of this study was to analyze the clinical and laboratory features of each subtype of multiple sclerosis (MS) (relapsing-remitting, primary progressive, and secondary progressive) in the Tokyo metropolitan area. We retrospectively analyzed the medical records of 104 consecutive patients with a diagnosis of MS, who had been admitted to our university hospital from 1988 to 2002. They all met criteria for definite MS, by clinical or laboratory standards. Eighty-four (80.8%) patients were classified as having relapsing-remitting MS, while 8 patients (7.7%) and 12 patients (11.5%) were classified as having primary progressive MS and secondary progressive MS, respectively. A significant female predominance existed in the relapse-remitting MS (female : male=2.4 : 1) cohort, but this ratio was 1 : 1 in both primary progressive and secondary progressive MS. The age at onset was older in the primary progressive MS (36.6+/-17.1 years of old) population than in either the relapsing-remitting MS (27.9+/-11.1) or the secondary progressive MS (27.8+/-11.5) subjects. Although the duration of illness was similar among the three types of MS, the number of exacerbations in the secondary progressive (5.9+/-4.6) cohort was significantly higher than that in the relapsing-remitting MS subjects (3.2+/-2.6). Patients with primary progressive MS showed a significantly higher rate of gait disturbance (87.5%) as the initial symptom than those with relapsing-remitting MS (23.8%), and this was thought to be due to the higher incidence of brainstem and spinal cord lesions. Visual disturbance as the initial symptom was frequently noted in those with secondary progressive MS (50.0%), while it was noted only in 29.8% and 12.5% in the relapsing-remitting and primary progressive patients, respectively. Primary progressive MS subjects had a higher propensity to be wheelchair-bound (75.0%) than those suffering from relapsing-remitting MS (1.2%). Increased total protein in the cerebrospinal fluid (CSF) of the secondary progressive cohort was statistically significant compared to the relapsing-remitting cohort. The frequency of oligoclonal IgG bands was rather low in each type of MS (17.1-33.3%). Gadolinium enhancement of plaques on MRI was more frequently present in secondary progressive MS (66.7%) than in either relapsing-remitting MS (32.1%) or primary progressive MS (50.0%). Of note, the opticospinal form was found in only 16.3% of the total MS patients, a proportion less than that in previous reports from southern Japan. The present study confirms that while the clinical and laboratory features of the MS patients in the Tokyo metropolitan area are similar to those in Western countries in most regards, features such as proportionally fewer primary and secondary progressive MS patients as well as less oligoclonal IgG bands on CSF analysis are different from those in Western countries.

BackgroundSleep disorders are a major but overlooked symptom in patients with multiple sclerosis (MS).ObjectivesThis article aims to investigate the characteristics of sleep disorders in patients with relapsing–remitting multiple sclerosis (RRMS) and to analyze the correlations between sleep disorders in RRMS and anxiety, depression, fatigue, and cognitive impairment.MethodsA total of 35 patients with RRMS and 35 controls were included, and both groups underwent assessments for sleep, anxiety, depression, fatigue, and cognitive function.ResultsThe RRMS group and the control group showed significant differences in Pittsburgh Sleep Quality Index (PSQI), Athens Insomnia Scale (AIS), Insomnia Severity Index (ISI), Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale (HAMD), Modified Fatigue Impact Scale (MFIS), and Montreal Cognitive Assessment (MoCA). The group with poor sleep quality (PSQI > 5) had significantly higher scores on the AIS, ISI, HAMA, and HAMD Scale compared to the group with good sleep quality (p = 0.036, p < 0.001, p = 0.036, p = 0.054). The PSQI showed a negative correlation with disease duration; the PSQI showed a positive correlation with HAMA, HAMD, and Activities of Daily Living (ADL); AIS, ISI, and Sleep Hygiene Awareness and Practice Scale (SHAPS) all demonstrated significant positive correlations with MFIS, HAMA, and HAMD; Dysfunctional Beliefs and Attitudes about Sleep Scale (DBAS) showed a negative correlation with HAMA and HAMD.ConclusionSleep disorders, fatigue, anxiety, depression, and cognitive impairments are more likely to occur in patients with RRMS; there is a certain correlation between PSQI, AIS, ISI, SHAPS, and DBAS scores in the RRMS group and fatigue, anxiety, and depression.

Characteristics of COVID-19 disease in multiple sclerosis patients

Sleep duration and quality in relation to non-alcoholic fatty liver disease in middle-aged workers and their spouses

PW01-110 - Associations of smoking, exercise, and alcohol drinking with poor sleep quality of Japanese civil servants

Objectives: To assess factors associated with poor sleep quality and excessive daytime sleepiness (EDS) in late pregnancy.Methods: A cross sectional study was carried out on 109 pregnant women in their third trimester admitted to Teaching Hospital Peradeniya using validated Sinhala translations of both Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS). Consecutive sampling was used from February to April 2021.Results: Application of PSQI demonstrated that 59.6% had a poor overall sleep quality (PSQI score&gt;5). Subcomponent analysis showed poor sleep duration (&lt; 06 hours per day) in 27.5%, sleep latency of over 30 minutes in 29.4%, poor sleep efficiency among 33.9% and day-time dysfunction in 30.3%. Poor overall sleep quality was associated with presence of foetal movements (OR=11.8, 95% CI=1.5-93.5) and backache (OR=3.8, 95% CI=1.2-12.3). Poor sleep duration was associated with the presence of one or more pregnancy related complications (OR=3.4, 95% CI=1.4-8.5) and advanced maternal age over 35 years (OR=3.7, 95% CI=1.4-9.7). Increased sleep latency over 30 minutes was seen in mothers over 34 weeks of gestation (OR=9.1, 95% CI=2.9-28.6) and over 10kg of weight gain (OR=5.3, 95% CI=1.2-24.4). Application of ESS demonstrated 26.6% had EDS, which was associated with maternal employment (OR=2.8, 95% CI=1.1-7.1) and higher educational status (OR=4.7, 95% CI=1.5-15.1). EDS did not result in poor sleep quality, however, mothers experiencing insomnia had a higher PSQI score (Mean ± SD 7.2±3.7 vs 6.2±3.4 hours, p=0.044).Conclusion: Majority of pregnant women in third trimester had poor overall sleep quality. EDS was seen among one fourth. Modifiable risk factors were associated with poor sleep quality and EDS.

Background: Approximately 40% of the population reports sleep problems such as poor quality sleep and insufficient sleep duration. Physical activity (PA) can help improve sleep, but data on whether PA intensity or duration is most strongly associated with sleep are lacking. In addition, given that sedentary behaviour (e.g., TV, computer use) is distinct from physical inactivity, the association between sedentary behaviour and sleep in young adults needs to be characterized. Objective: To describe the relationships between sleep quality and sleep duration and (1) frequency and duration of light, moderate, and vigorous PA, and (2) different types of sedentary behaviours (TV, computer, reading) in young adults. Methods: Self-report data for 658 participants were from the 22nd wave of the Nicotine Dependence in Teens (NDIT) cohort study (mean age=24.0 years, 46% male [300 of 658]). PA measures assessed frequency (number of days) and minutes of light, moderate and vigorous PA in the past week. Sedentary measures assessed number of hours spent reading, watching TV, and using the computer per day. Sleep measures included (1) the Pittsburgh Sleep Quality Index (PSQI) which assessed seven dimensions of sleep (daytime dysfunction, disturbances, duration, efficiency, latency, quality, use of sleeping medications), (2) general sleep quality, and (3) sleep duration in the past month. General sleep quality and sleep duration were two separate additional measures distinct from similar PSQI items (r=0.73 between general sleep quality and PSQI score; r=0.69 between sleep duration and PSQI score). Data were analyzed using multiple linear regression. Due to evidence of non-normality the PSQI score was log-transformed. Results: Controlling for age, sex, and maternal education, each additional day of light or vigorous PA was associated with 3 minutes less sleep per night (p&lt;0.05). Each additional 10 minutes of moderate PA was associated with greater general sleep quality (β=0.004, p=0.04). TV was associated with a poorer PSQI score (β=0.01, p&lt;0.05) and each additional hour of reading was associated with 2 minutes less sleep per night (p=0.04). Computer use was associated with a poorer PSQI score (β=0.02, p=0.005) and poorer sleep quality (β=-0.02, p=0.05). Results were similar when sedentary and PA measures were included in the same model. The inclusion of body mass index, self-rated mental and general health, and stress did not affect the results and were omitted from the final models. Conclusion: PA and sedentary behaviours are independently associated with sleep duration and quality. Sedentary behaviours are associated with poorer sleep duration and quality. In contrast, PA frequency may decrease sleep duration while PA duration may improve sleep quality. Clinicians who treat sleep problems in young adults may need to take PA and sedentary behavior into account in treatment plans.