A Cross-Sectional Survey Study of Cannabis Use for Fibromyalgia Symptom Management.

OBJECTIVE: To assess the use of cannabis as a symptom management strategy for patients with fibromyalgia. PATIENTS AND METHODS: An electronic, cross-sectional survey was conducted among patients diagnosed with fibromyalgia and treated in Integrative Medicine & Health at Mayo Clinic, Rochester, Minnesota. The survey was constructed with the Symptom Management Theory tool and was sent anonymously via web-based software to patients with a diagnosis of fibromyalgia. RESULTS: Of 5234 patients with fibromyalgia sent the online survey, 1336 (25.5%) responded and met the inclusion criteria. Survey respondents had a median age of 48 (Q1-Q3: 37.5-58.0) years, and most identified as female. Nearly half of respondents (49.5%, n=661) reported cannabis use since their fibromyalgia diagnosis. The most common symptoms for which respondents reported using cannabis were pain (98.9%, n=654); fatigue (96.2%; n=636); stress, anxiety, or depression (93.9%; n=621); and insomnia (93.6%; n=619). Improvement in pain symptoms with cannabis use was reported by 82.0% (n=536). Most cannabis-using respondents reported that cannabis also improved symptoms of stress, anxiety, and depression and of insomnia. CONCLUSION: Considering that cannabis is a popular choice among patients for managing fibromyalgia symptoms, clinicians should have adequate knowledge of cannabis when discussing therapeutic options for fibromyalgia with their patients.

Effects of cognitive behavioural therapy and bright light therapy for insomnia in youths with eveningness: study protocol for a randomised controlled trial.

BACKGROUND: Insomnia and eveningness are common and often comorbid conditions in youths. While cognitive behavioural therapy for insomnia (CBT-I) has been suggested as a promising intervention, it remains unclear whether it is sufficient to also address circadian issues in youths. In addition, despite that light has been shown to be effective in phase-shifting one's circadian rhythm, there has been limited data on the effects of bright light therapy and its combination with CBT-I on sleep and circadian outcomes in youths. The current protocol outlines a randomised controlled trial that examines the efficacy of CBT-I and CBT-I plus bright light therapy (BLT) in reducing insomnia severity, improving mood symptoms and daytime functioning (e.g. sleepiness, fatigue, cognitive function), and improving subjective and objective sleep and circadian measures compared to a waitlist control group. METHODS: We will carry out a randomised controlled trial (RCT) with 150 youths aged 12-24 who meet the criteria of insomnia and eveningness. Participants will be randomised into one of three groups: CBT-I with bright light therapy, CBT-I with placebo light, and waitlist control. Six sessions of CBT-I will be delivered in a group format, while participants will be currently asked to use a portable light device for 30 min daily immediately after awakening throughout the intervention period for bright light therapy. The CBT-I with light therapy group will receive bright constant green light (506 lx) while the CBT-I with placebo light group will receive the modified light device with the LEDs emitting less than 10 lx. All participants will be assessed at baseline and post-treatment, while the two active treatment groups will be additionally followed up at 1 month and 6 months post-intervention. The primary outcome will be insomnia severity, as measured by the Insomnia Severity Index. Secondary outcomes include self-reported mood, circadian, daytime functioning, and quality of life measures, as well as sleep parameters derived from actigraphy and sleep diary and neurocognitive assessments. Objective measures of the circadian phase using dim-light melatonin onset assessment and sleep parameters using polysomnography will also be included as the secondary outcomes. DISCUSSION: This study will be the first RCT to directly compare the effects of CBT-I and BLT in youths with insomnia and eveningness. Findings from the study will provide evidence to inform the clinical management of insomnia problems and eveningness in youths. TRIAL REGISTRATION: ClinicalTrials.gov NCT04256915. Registered on 5 February 2020.

Translating and establishing the psychometric properties of the Jenkins Sleep Scale for Arabic-speaking individuals.

BACKGROUND: The Jenkins Sleep Scale is a widely used self-report questionnaire that assesses sleep quality and disturbances. This study aimed to translate the scale into Arabic and evaluate its psychometric properties in an Arabic-speaking population. METHODS: The Jenkins Sleep Scale was translated into Arabic using forward and backward translation procedures. The Arabic version was administered to a convenience sample of 420 adults along with the Pittsburgh Sleep Quality Index (PSQI) and Athens Insomnia Scale (AIS) for validation purposes. Reliability was examined using Cronbach's alpha and McDonald's omega coefficients. Confirmatory factor analysis (CFA) was also conducted to test the unidimensional factor structure. Convergent validity was assessed using correlations with PSQI and AIS scores. RESULTS: The Cronbach's alpha and McDonald's omega values for the Arabic Jenkins Sleep Scale were 0.74 and 0.75, respectively, indicating good internal consistency. The 2-week and 4-week test-retest intraclass correlation coefficients were both 0.94 (p < 0.001), indicating excellent test-retest reliability. The CFA results confirmed the unidimensional factor structure (CFI = 0.99, TLI = 0.96, RMSEA = 0.08). The measurement model had an equivalent factor structure, loadings, intercepts, and residuals across sex, age, and marital status. Significant positive correlations were found between the Arabic Jenkins scale score and the PSQI (r = 0.80, p < 0.001) and AIS (r = 0.74, p < 0.001), supporting convergent validity. CONCLUSION: The Arabic version of the Jenkins Sleep Scale demonstrated good psychometric properties. The findings support its use as a valid and reliable measure for evaluating sleep quality and disturbances among Arabic-speaking populations.

[Pathophysiological hypothesis and diagnosis of insomnia disorder]. / Hypothèses physiopathologiques et diagnostic du trouble insomnie.

PATHOPHYSIOLOGICAL HYPOTHESES AND DIAGNOSIS OF INSOMNIA DISORDER. All pathophysiological models place hyperarousal as a central process in the mechanisms of insomnia. These models differ, however, in terms of the importance and role of the variables explaining this hyperarousal. Behavioral and cognitive models describe self-maintenance behaviors and dysfunctional thoughts, such as worries and concerns about sleep and the consequences of insomnia. Alterations in cognitive functions related to hyperarousal in perceptual and memory processes can explain these behaviors and thoughts. Neurobiological models show instability in the sleepwake balance, with orexin possibly involved, but this remains to be confirmed. The diagnosis of insomnia must consider the semiology related to the mechanisms of insomnia, as well as co-morbidities.

HYPOTHÈSES PHYSIOPATHOLOGIQUES ET DIAGNOSTIC DU TROUBLE INSOMNIE. L'ensemble des modèles physiopathologiques place l'hyperéveil comme processus central dans les mécanismes de l'insomnie. Les modèles se différencient cependant au regard de l'importance et du rôle des variables expliquant cet hyperéveil. Les modèles comportementaux et cognitifs décrivent les comportements d'autoentretien et les pensées dysfonctionnelles, de types soucis et inquiétudes concernant le sommeil et les conséquences de l'insomnie. Des altérations des processus cognitifs en lien avec l'hyperéveil dans les domaines perceptuels et mnésiques peuvent expliquer ces comportements et pensées. Les modèles neurobiologiques retrouvent une instabilité de la balance éveil/sommeil avec une possible implication de l'orexine, qui reste à confirmer. Le diagnostic de l'insomnie doit tenir compte de la sémiologie en lien avec ses mécanismes, tout en tenant compte des comorbidités.

[Insomnia and psychiatric disorders]. / Insomnie et troubles psychiatriques.

INSOMNIA AND PSYCHIATRIC DISORDERS. Insomnia is frequent in psychiatric disorders. In particular, insomnia can be a risk factor, as well as a comorbid condition, or a symptom and an early sign of psychiatric disorders. Insomnia may emerge during any stage of illness. It includes prodromal, first episode, acute, recurrence, and even remission stages, thereby being associated with a worse course of illness. Insomnia increased symptom severity, relapses or recurrences, and increased suicidal risk. Thus, insomnia is an important modifiable risk factor to prevent psychiatric disorders and/or achieve and maintain remission. Thereby insomnia evaluation and management should be a priority in psychiatric cares. Indeed, it has been demonstrated that targeting insomnia can not only improve insomnia itself but also have a positive impact on the trajectory of psychiatric disorders.

INSOMNIE ET TROUBLES PSYCHIATRIQUES. L'insomnie est un trouble fréquent au cours des pathologies psychiatriques. En particulier, elle peut constituer un facteur de risque, ainsi qu'une condition comorbide, ou un symptôme et un signe précoce de troubles psychiatriques. Elle correspond au trouble du sommeil le plus courant associé aux pathologies psychiatriques et peut apparaître à n'importe quel stade de la maladie (prodromes, premier épisode, phase aiguë, récidive et même rémission). Elle est associée à une évolution plus défavorable de la maladie, à une sévérité accrue des symptômes, à des rechutes ou des récidives et à un risque suicidaire plus élevé. Ainsi, l'insomnie est un facteur de risque modifiable important pour prévenir les troubles psychiatriques et/ou atteindre et maintenir la rémission. L'évaluation et la prise en charge de l'insomnie devraient donc être une priorité dans les soins psychiatriques. En effet, il a été démontré que le fait de cibler l'insomnie peut non seulement améliorer l'insomnie en elle-même mais également avoir un impact favorable sur la trajectoire des troubles psychiatriques.

Treatment of Chronic Insomnia in Adults.

Insomnia affects 30% of the U.S. population, with 5% to 15% meeting criteria for chronic insomnia. It can negatively impact quality of life, decrease productivity, increase fatigue and drowsiness, and put patients at higher risk of developing other health problems. Initial treatment focuses on nonpharmacologic therapies such as cognitive behavior therapy, which improves negative thought patterns and behaviors through sleep restriction, stimulus control, and relaxation techniques. Other nonpharmacologic treatments include exercise, mindfulness, and acupuncture. If these approaches are ineffective, pharmacologic agents may be considered. Medications such as benzodiazepines and Z-drugs are often prescribed for insomnia but should be avoided, if possible, due to short- and long-term risks associated with their use. Melatonin receptor agonists are safer and well tolerated but have limited effectiveness. Dual orexin receptor antagonists are effective in patients who have sleep maintenance insomnia or difficulty with sleep onset. Evidence for the use of antihistamines to treat insomnia is generally lacking, but doxylamine is effective for up to four weeks.

Validity and reliability of the sleep health index among patients with spinal degenerative diseases.

OBJECTIVE: To test the validity and reliability of the Sleep Health Index (SHI) in a Chinese clinical sample, and thereby provide more evidence for the assessment of sleep health in future research and clinical practice. METHODS: This study used a cross-sectional design. A convenient sample of 265 participants with spinal degenerative diseases was recruited from outpatient clinics. The SHI, Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), Epworth Sleepiness Scale (ESS), Patient Health Questionnaire-9 (PHQ-9), Visual Analogue Scale (VAS), and EuroQoL 5-Dimension 5-Level (EQ-5D-5L) were administered via REDCap. Structural, concurrent, convergent, known-group validity, internal consistency, and test-retest reliability were evaluated. RESULTS: Confirmatory factor analysis confirmed a 3-factor structure (sleep duration, sleep quality, and disordered sleep). The overall SHI score had a high correlation with PSQI and ISI (r = -0.62 and -0.70, respectively) as well as a moderate correlation with PHQ-9 (r = -0.50, p<0.001). The overall SHI was significantly associated with VAS, ESS, and EQ-5D-5L (r = -0.15 to -0.23, p<0.05). Participants with pain had a lower score on the sleep quality sub-index than those without (p<0.001). Those with chronic diseases had a significantly lower score on the sleep duration sub-index than those without (p<0.05). Those with depression, poor sleep quality, and insomnia had lower scores on the overall scale and the three sub-indices than those without (p<0.05). The overall SHI showed acceptable internal consistency (Cronbach's α = 0.74) and test-retest reliability (intraclass correlation coefficient = 0.73). CONCLUSIONS: The Chinese version of SHI showed good validity and acceptable reliability and could be used to assess sleep health among clinical populations.

Evaluating the Effectiveness of the SleepTracker App for Detecting Anxiety- and Depression-Related Sleep Disturbances.

This study emphasises the critical role of quality sleep in physical and mental well-being, exploring its impact on bodily recovery and cognitive function. Investigating poor sleep quality in approximately 40% of individuals with insomnia symptoms, the research delves into its potential diagnostic relevance for depression and anxiety, with a focus on intervention in mental health by understanding sleep patterns, especially in young individuals. This study includes an exploration of phone usage habits among young adults during PPI sessions, providing insights for developing the SleepTracker app. This pivotal tool utilises phone usage and movement data from mobile device sensors to identify indicators of anxiety or depression, with participant information organised comprehensively in a table categorising condition related to phone usage and movement data. The analysis compares this data with survey results, incorporating scores from the Sleep Condition Indicator (SCI), Patient Health Questionnaire-9 (PHQ-9), and Generalised Anxiety Disorder-7 (GAD-7). Generated confusion matrices offer a detailed overview of the relationship between sleep metrics, phone usage, and movement data. In summary, this study reveals the accurate detection of negative sleep disruption instances by the classifier. However, improvements are needed in identifying positive instances, reflected in the F1-score of 0.5 and a precision result of 0.33. While early intervention potential is significant, this study emphasises the need for a larger participant pool to enhance the model's performance.

The natural history of insomnia: evaluating illness severity from acute to chronic insomnia; is the first the worst?

STUDY OBJECTIVES: The 3P and 4P models represent illness severity over the course of insomnia disorder. The 3P model suggests that illness severity is worst during acute onset. The 4P model suggests that illness severity crescendos with chronicity. The present analysis from an archival dataset assesses illness severity with new onset illness (i.e. from good sleep [GS] to acute insomnia [AI] to chronic insomnia [CI]). Illness severity is quantified in terms of total wake time (TWT). METHODS: GSs (N = 934) were followed up to 1 year with digital sleep diaries, and classified as GS, AI, or CI. Data for CIs were anchored to the first of 14 days with insomnia so that day-to-day TWT was represented prior to and following AI onset. A similar graphic (+/-acute onset) was constructed for number of days per week with insomnia. GS data were temporally matched to CI data. Segmented linear mixed regression models were applied to examine the change in slopes in the AI-to-CI period compared to GS-to-AI period. RESULTS: Twenty-three individuals transitioned to AI and then CI. Average TWT rose during the first 2 weeks of AI onset (b = 1.8, SE = 0.57, p = 0.001) and was then stable for 3 months (b = -0.02, SE = 0.04, p = 0.53). Average number of affected days was stable from AI to CI (b = 0.0005, SE = 0.002, p = 0.81). That is, while there was week-to-week variability in the number of days affected, no linear trend was evident. CONCLUSIONS: In our sample of CIs, primarily with middle insomnia, the average severity and number of affected days were worst with the onset of AI (worst is first) and stable thereafter.

Fatigued but not sleepy? An empirical investigation of the differentiation between fatigue and sleepiness in sleep disorder patients in a cross-sectional study.

OBJECTIVE: Sleepiness and fatigue are common complaints among individuals with sleep disorders. The two concepts are often used interchangeably, causing difficulty with differential diagnosis and treatment decisions. The current study investigated sleep disorder patients to determine which factors best differentiated sleepiness from fatigue. METHODS: The study used a subset of participants from a multi-site study (n = 606), using a cross-sectional study design. We selected 60 variables associated with either sleepiness or fatigue, including demographic, mental health, and lifestyle factors, medical history, sleep questionnaires, rest-activity rhythms (actigraphy), polysomnographic (PSG) variables, and sleep diaries. Fatigue was measured with the Fatigue Severity Scale and sleepiness was measured with the Epworth Sleepiness Scale. A Random Forest machine learning approach was utilized for analysis. RESULTS: Participants' average age was 47.5 years (SD 14.0), 54.6% female, and the most common sleep disorder diagnosis was obstructive sleep apnea (67.4%). Sleepiness and fatigue were moderately correlated (r = 0.334). The model for fatigue (explained variance 49.5%) indicated depression was the strongest predictor (relative explained variance 42.7%), followed by insomnia severity (12.3%). The model for sleepiness (explained variance 17.9%), indicated insomnia symptoms was the strongest predictor (relative explained variance 17.6%). A post hoc receiver operating characteristic analysis indicated depression could be used to discriminate fatigue (AUC = 0.856) but not sleepiness (AUC = 0.643). CONCLUSIONS: The moderate correlation between fatigue and sleepiness supports previous literature that the two concepts are overlapping yet distinct. Importantly, depression played a more prominent role in characterizing fatigue than sleepiness, suggesting depression could be used to differentiate the two concepts.

Methodological approach to sleep state misperception in insomnia disorder: Comparison between multiple nights of actigraphy recordings and a single night of polysomnography recording.

STUDY OBJECTIVE: To provide a comprehensive assessment of sleep state misperception in insomnia disorder (INS) and good sleepers (GS) by comparing recordings performed for one night in-lab (PSG and night review) and during several nights at-home (actigraphy and sleep diaries). METHODS: Fifty-seven INS and 29 GS wore an actigraphy device and filled a sleep diary for two weeks at-home. They subsequently completed a PSG recording and filled a night review in-lab. Sleep perception index (subjective/objective × 100) of sleep onset latency (SOL), sleep duration (TST) and wake duration (TST) were computed and compared between methods and groups. RESULTS: GS displayed a tendency to overestimate TST and WASO but correctly perceived SOL. The degree of misperception was similar across methods within the GS group. In contrast, INS underestimated their TST and overestimated their SOL both in-lab and at-home, yet the severity of misperception of SOL was larger at-home than in-lab. Finally, INS overestimated WASO only in-lab while correctly perceiving it at-home. While only the degree of TST misperception was stable across methods in INS, misperception of SOL and WASO were dependent on the method used. CONCLUSIONS: We found that GS and INS exhibit opposite patterns and severity of sleep misperception. While the degree of misperception in GS was similar across methods, only sleep duration misperception was reliably detected by both in-lab and at-home methods in INS. Our results highlight that, when assessing sleep misperception in insomnia disorder, the environment and method of data collection should be carefully considered.

How patients with insomnia interpret and respond to the consensus sleep diary: a cognitive interview study.

OBJECTIVE/BACKGROUND: The Consensus Sleep Diary (CSD) is widely used to assess subjective sleep. Psychometric evaluations and focus-groups support its validity and clinical usefulness, but further research into its validity is needed. The aim of the study was to evaluate a Swedish translation of the CSD regarding test content and response processes in patients with insomnia. PATIENTS/METHODS: In connection with translating the CSD into Swedish, we used cognitive interviewing to evaluate test content and the response process, that is, how people make decisions when responding to survey items. Cognitive interviews were conducted with 13 primary health care patients with insomnia disorder (mean age, 49 years; SD 15.5). Iterative, reparative analysis was used to investigate test content. Descriptive deductive analysis was used to investigate interview transcripts for the themes of the cognitive model: comprehension, retrieval, decision process, and judgement. Together, the themes explain the response process when responding to a patient-reported outcome measure. RESULTS: The overall comprehension of the CSD could be affected by poor adherence to the instructions (comprehension). Patients had difficulty with recall if they did not complete the diary immediately in the morning and just before bedtime (retrieval). They could have problems deciding how to respond to certain items because they imbued sleep-related concepts with extra meaning (decision process), and had trouble finding response alternatives nuanced enough to describe their experience of sleep and tiredness (judgement). CONCLUSIONS: This study contributes knowledge on how the instrument is perceived and used by care-seeking patients with insomnia. In this context, the CSD exhibits known flaws such as memory lapses if the diary is not filled in directly in the morning. To increase the accuracy of patients' responses, therapists should support patients in reading the instructions.

Just Let Me Sleep in: Identifying and Treating Delayed Sleep Phase Disorder in Adolescents.

Individuals with delayed sleep phase disorder (DSPD) are unable to naturally fall asleep and awake at conventional times; for this reason, DSPD is often mistaken for insomnia. However, unlike many patients with insomnia, those with DSPD struggle to get up at appropriate times. DSPD is associated with school refusal, academic difficulties, and lower employment rate. DSPD in youth has prevalence as high as 16%, and is often comorbid with other psychiatric disorders. Treatments include appropriate light exposure during the day, melatonin use, developing an evening routine that minimizes arousal-increasing activities, and gradually shifting sleep-wake times toward more functional ones.

Meaningful Within-Patient Change in Subjective Total Sleep Time in Patients with Insomnia Disorder: An Analysis of the Sleep Diary Questionnaire Using Data from Open-Label and Phase III Clinical Trials.

BACKGROUND: The Sleep Diary Questionnaire (SDQ), a modified version of the Consensus Sleep Diary, is a 17-item sleep diary for assessing subjective total sleep time (sTST: total time spent asleep at night) and other sleep parameters in insomnia trials. sTST is a key parameter of efficacy in insomnia trials; however, the magnitude of improvement in this parameter that people with insomnia disorder consider clinically meaningful is unclear. OBJECTIVE: The aim of this study was to estimate meaningful within-patient change for sTST using clinical trial data. METHODS: Data were from an open-label trial of zolpidem and pooled data from a phase III placebo-controlled trial of daridorexant. In both trials, adults with moderate to severe insomnia completed the SDQ daily. Meaningful change in sTST was estimated in an anchor-based analysis using outcome measures that were correlated with change in weekly average sTST (Spearman correlation coefficient ≥ 0.30): the Insomnia Severity Index, patient global assessments and impressions of severity and change in daytime and night-time symptoms (PGA-S, PGI-S, PGI-C), and clinician global impressions of severity and change in patients' daytime symptoms (CGI-S, CGI-C). Meaningful within-patient change estimates were 'triangulated' to identify a value where they converged. RESULTS: In the open-label trial (N = 114), subjects with a 1-point or 1-step improvement on the anchors had mean increases in sTST of 60.1-83.2 min at day 8 and 55.5-68.2 min at day 15. For subjects with a 2-point or 2-step improvement on the anchors, mean increases in sTST were 79.6-81.4 min at day 8 and 80.1-93.5 min at day 15. In the phase III trial (N = 930), weekly average increases in sTST for subjects with a 1-point or 1-step improvement on the anchors were 39.3-46.7 min at month 1 and 47.3-58.3 min at month 3. For subjects with a 2-point or 2-step improvement on the anchors, mean increases in sTST were 60.7-76.2 min at month 1 and 70.1-87.7 min at month 3. Triangulation of these values supported a meaningful within-patient change threshold starting at 55 min. CONCLUSION: Increasing sTST is an important treatment outcome for people with insomnia. An increase in sleep time of approximately 55 min is meaningful to patients. CLINICAL TRIALS REGISTRATION: NCT03056053 (17 February 2017) and NCT03545191 (4 June 2018).

Evaluating insomnia queries from an artificial intelligence chatbot for patient education.

STUDY OBJECTIVES: We evaluated the accuracy of ChatGPT in addressing insomnia-related queries for patient education and assessed ChatGPT's ability to provide varied responses based on differing prompting scenarios. METHODS: Four identical sets of 20 insomnia-related queries were posed to ChatGPT. Each set differed by the context in which ChatGPT was prompted: no prompt, patient-centered, physician-centered, and with references and statistics. Responses were reviewed by 2 academic sleep surgeons, 1 academic sleep medicine physician, and 2 sleep medicine fellows across 4 domains: clinical accuracy, prompt adherence, referencing, and statistical precision, using a binary grading system. Flesch-Kincaid grade-level scores were calculated to estimate the grade level of the responses, with statistical differences between prompts analyzed via analysis of variance and Tukey's test. Interrater reliability was calculated using Fleiss's kappa. RESULTS: The study revealed significant variations in the Flesch-Kincaid grade-level scores across 4 prompts: unprompted (13.2 ± 2.2), patient-centered (8.1 ± 1.9), physician-centered (15.4 ± 2.8), and with references and statistics (17.3 ± 2.3, P < .001). Despite poor Fleiss kappa scores, indicating low interrater reliability for clinical accuracy and relevance, all evaluators agreed that the majority of ChatGPT's responses were clinically accurate, with the highest variability on Form 4. The responses were also uniformly relevant to the given prompts (100% agreement). Eighty percent of the references ChatGPT cited were verified as both real and relevant, and only 25% of cited statistics were corroborated within referenced articles. CONCLUSIONS: ChatGPT can be used to generate clinically accurate responses to insomnia-related inquiries. CITATION: Alapati R, Campbell D, Molin N, et al. Evaluating insomnia queries from an artificial intelligence chatbot for patient education. J Clin Sleep Med. 2024;20(4):583-594.