Bright light therapy as a non-pharmacological treatment option for multiple sclerosis-related fatigue: A randomized sham-controlled trial.

Background: Fatigue is a common symptom in people with multiple sclerosis (pwMS) that significantly impairs quality of life. Bright light therapy may be a cheap treatment option with little to no adverse events. Objectives: To evaluate the effectiveness of bright light therapy as a treatment option for MS-related fatigue. Methods: This was randomized sham-controlled trial including 26 pwMS with a Fatigue Severity Scale (FSS) Score ≥36. Participants were assigned to receive either bright white light therapy (n = 13) or dim red light (sham-intervention; n = 13). Participants used the respective intervention for 30 min each morning for two weeks, followed by a two-week washout period. The primary endpoint was the difference in FSS scores following light treatment as calculated by analysis of covariance. Results: There was no significant difference in FSS (F(1,23) = 2.39, p = .136, partial ⴄ2 = .094). However, FSS scores generally improved over the course of the study in a clinically relevant manner. Conclusion: Bright light therapy decreased FSS scores over the course of this study. However, this effect was not significant in comparison to a sham intervention.

The (mis)perception of sleep: factors influencing the discrepancy between self-reported and objective sleep parameters.

STUDY OBJECTIVES: Self-reported perception of sleep often differs from objective sleep study measures, but factors predicting the discrepancy between self-reported and objective sleep parameters are controversial, and a comparison of laboratory vs ambulatory polysomnography (PSG) is lacking. METHODS: We retrospectively analyzed PSGs conducted between 2012 and 2016. Linear regression was applied to predict the discrepancy between self-reported and objective sleep parameters (total sleep time, sleep efficiency, sleep latency, using age, sex, arousal index, type of sleep disorder, and PSG type [laboratory vs ambulatory] as regressors). RESULTS: A total of 303 PSGs were analyzed (49% women, median age 48 years), comprising patients with insomnia (32%), sleep-related breathing disorders (27%), sleep-related movement disorders (15%), hypersomnia/narcolepsy (14%), and parasomnias (12%). Sleep disorder was the best predictor of discrepancy between self-reported and objective total sleep time, and patients with insomnia showed higher discrepancy values compared to all other patient groups (P < .001), independent of age and PSG type (P > .05). Contributory effects for higher discrepancy values were found for lower arousal index. Patients with insomnia underestimated both total sleep time (median discrepancy: 46 minutes, P < .001) and sleep efficiency (median discrepancy: 11%, P < .001). No significant predictor for discrepancy of sleep latency was found. CONCLUSIONS: Misperception of sleep duration and efficiency is common in sleep lab patients, but most prominent in insomnia, independent of age, sex, or laboratory vs ambulatory recording setting. This underlines the role of PSG in patients with a clinical diagnosis of insomnia and its use in cognitive behavioral therapy.

3D Camera and Pulse Oximeter for Respiratory Events Detection.

OBJECTIVE: The purpose of this study was to derive a respiratory movement signal from a 3D time-of-flight camera and to investigate if it can be used in combination with SpO2 to detect respiratory events comparable to polysomnography (PSG) based detection. METHODS: We derived a respiratory signal from a 3D camera and developed a new algorithm that detects reduced respiratory movement and SpO2 desaturation to score respiratory events. The method was tested on 61 patients' synchronized 3D video and PSG recordings. The predicted apnea-hypopnea index (AHI), calculated based on total sleep time, and predicted severity were compared to manual PSG annotations (manualPSG). Predicted AHI evaluation, measured by intraclass correlation (ICC), and severity classification were performed. Furthermore, the results were evaluated by 30-second epoch analysis, labelled either as respiratory event or normal breathing, wherein the accuracy, sensitivity, specificity and Cohen's kappa were calculated. RESULTS: The predicted AHI scored an ICC r = 0.94 (0.90 - 0.96 at 95% confidence interval, p < 0.001) compared to manualPSG. Severity classification scored 80% accuracy, with no misclassification by more than one severity level. Based on 30-second epoch analysis, the method scored a Cohen's kappa = 0.72, accuracy = 0.88, sensitivity = 0.80, and specificity = 0.91. CONCLUSION: Our detection method using SpO2 and 3D camera had excellent reliability and substantial agreement with PSG-based scoring. SIGNIFICANCE: This method showed the potential to reliably detect respiratory events without airflow and respiratory belt sensors, sensors that can be uncomfortable to patients and susceptible to movement artefacts.

Contactless detection of periodic leg movements during sleep: A 3D video pilot study.

In clinical practice, the quality of polysomnographic recordings in children and patients with neurodegenerative diseases may be affected by sensor displacement and diminished total sleep time due to stress during the recording. In the present study, we investigated if contactless three-dimensional (3D) detection of periodic leg movements during sleep was comparable to polysomnography. We prospectively studied a sleep laboratory cohort from two Austrian sleep laboratories. Periodic leg movements during sleep were classified according to the standards of the World Association of Sleep Medicine and served as ground truth. Leg movements including respiratory-related events (A1) and excluding respiratory-related events (A2 and A3) were presented as A1, A2 and A3. Three-dimensional movement analysis was carried out using an algorithm developed by the Austrian Institute of Technology. Fifty-two patients (22 female, mean age 52.2 ± 15.1 years) were included. Periodic leg movement during sleep indexes were significantly higher with 3D detection compared to polysomnography (33.3 [8.1-97.2] vs. 30.7 [2.9-91.9]: +9.1%, p = .0055/27.8 [4.5-86.2] vs. 24.2 [0.00-88.7]: +8.2%, p = .0154/31.8 [8.1-89.5] vs. 29.6 [2.4-91.1]: +8.9%, p = .0129). Contactless automatic 3D analysis has the potential to detect restlessness mirrored by periodic leg movements during sleep reliably and may especially be suited for children and the elderly.

It twitches without kicking - An association between fragmentary myoclonus and arousal?

OBJECTIVE: Fragmentary myoclonus (FM) is a polysomnographic motor phenomenon of unknown clinical relevance. This study investigates FM prevalence, gender differences, sleep stage distribution and association with clinical factors using recently introduced advanced FM scoring criteria. METHODS: We analyzed polysomnographic recordings of 178 patients of a mixed sleep-disorder patient cohort. FM indices (FMI) of newly introduced 25 µV (FMI25) and standard 50 µV (FMI50) amplitude cut-offs were calculated. RESULTS: FMI25 and FMI50 were higher in men compared to women. FMI were higher during wakefulness and lower during S3 compared to all other sleep stages, with stronger effects in men compared to women. FMI25 was correlated with higher age, lower mean oxygen saturation, lower sleep efficiency, higher periodic limb movement (PLM) index, shorter sleep period time and higher arousal index. Linear regression showed that age predicted higher FMI25 in both males and females. Additionally, higher arousal index predicted higher FMI25 in women only. FMI were not associated with the presence of sleep-related breathing disorders. CONCLUSIONS: We suggest FM represents a ubiquitous motor phenomenon occurring spontaneously during relaxed wakefulness and sleep, primarily in men and with advanced age. SIGNIFICANCE: In women, particularly FMI25 may be a surrogate marker for more frequent arousals and sleep fragmentation.

Wanted: a better cut-off value for the Epworth Sleepiness Scale.

BACKGROUND: Excessive daytime sleepiness (EDS) is the main complaint in many neurological sleep disorders, such as idiopathic hypersomnia, narcolepsy, or obstructive sleep apnea/hypopnea syndrome (OSAS). The validity of the Epworth Sleepiness Scale (ESS) as a screening tool for EDS remains controversial. We therefore investigated (1) the interrelation of the ESS total score and the mean sleep latency (MSL) during the multiple sleep latency test (MSLT) and (2) the diagnostic accuracy of the ESS total score to detect EDS in patients with the chief complaint of subjective EDS. METHODS: A total of 94 patients (48 males) with subjective EDS were included in this study. Regression analyses and ROC curve analyses were carried out to assess the predictive value of the ESS score for MSL. RESULTS: The ESS score significantly predicted a shortened MSL (p = 0.01, ß = -0.29). After dichotomizing into two groups, the ESS score predicted MSL only in patients with hypersomnia or narcolepsy (p = 0.01, ß = -0.33), but not in patients with other clinical diagnoses (e. g. OSAS; p = 0.36, ß = -0.15). The ROC curve analyses indicated an optimal ESS cut-off value of 16 with a sensitivity of 70%; however, specificity remained unsatisfactory (55.6%). CONCLUSIONS: Our results suggest that the predictive value of the ESS score in patients with subjective EDS is low and patient subgroup-specific (superior in hypersomnia/narcolepsy vs. other diagnoses) and that the commonly used cut-off of 11 points may be insufficient for clinical practice.