Preoccupation with sleep and dysfunctional beliefs about sleep mediate the influence of psychological inflexibility on insomnia in the older adult population

INTRODUCTION

This study aimed to explore whether the Discrepancy between the desired time in Bed and the desired total Sleep Time (DBST) index influences insomnia severity in the older adult population and examined the potential role of psychological inflexibility in this association.

METHODS

An online survey study was conducted for older individuals aged ≥ 65 via a survey company between January and February 2023. A total of 300 responses and data without personally identifiable information were delivered to the researchers. The survey questionnaires include the DBST, Glasgow Sleep Effort Scale (GSES), Insomnia Severity Index (ISI), Dysfunctional Beliefs about Sleep-2 items (DBS-2), and Acceptance and Action Questionnaire-II (AAQ-II).

RESULTS

The analysis included 295 older adult participants. The DBST index was significantly correlated with all questionnaires. Linear regression revealed the DBST index was predicted only by the ISI (β = 0.26, p = 0.003). Mediation analysis showed that the GSES (Z = 2.92, p = 0.003) and DBS-2 (Z = 2.17, p = 0.030) mediated the effect of the DBST index on the ISI, while the AAQ-II did not. Path analysis showed that the DBST could be directly predicted by the ISI (Z = 2.94, p = 0.003), GSES (Z = 2.75, p = 0.006), and DBS2 (Z = 2.71, p = 0.007) but not by the AAQ-II itself. However, the AAQ-II exerted a significant indirect effect on the ISI through the DBS-2 (Z = 2.21, p = 0.027) and GSES (z = 2.24, p = 0.025).

CONCLUSIONS

Our study showed that preoccupation and dysfunctional beliefs about sleep may mediate the relationship between the DBST index and insomnia severity in the older adult population. We opine that psychological inflexibility might play a significant role in insomnia severity via preoccupation with and dysfunctional beliefs about sleep.

Assessing the Accuracy of Generative Conversational Artificial Intelligence in Debunking Sleep Health Myths: Mixed Methods Comparative Study With Expert Analysis

BACKGROUND

Adequate sleep is essential for maintaining individual and public health, positively affecting cognition and well-being, and reducing chronic disease risks. It plays a significant role in driving the economy, public safety, and managing health care costs. Digital tools, including websites, sleep trackers, and apps, are key in promoting sleep health education. Conversational artificial intelligence (AI) such as ChatGPT (OpenAI, Microsoft Corp) offers accessible, personalized advice on sleep health but raises concerns about potential misinformation. This underscores the importance of ensuring that AI-driven sleep health information is accurate, given its significant impact on individual and public health, and the spread of sleep-related myths.

OBJECTIVE

This study aims to examine ChatGPT's capability to debunk sleep-related disbeliefs.

METHODS

A mixed methods design was leveraged. ChatGPT categorized 20 sleep-related myths identified by 10 sleep experts and rated them in terms of falseness and public health significance, on a 5-point Likert scale. Sensitivity, positive predictive value, and interrater agreement were also calculated. A qualitative comparative analysis was also conducted.

RESULTS

ChatGPT labeled a significant portion (n=17, 85%) of the statements as "false" (n=9, 45%) or "generally false" (n=8, 40%), with varying accuracy across different domains. For instance, it correctly identified most myths about "sleep timing," "sleep duration," and "behaviors during sleep," while it had varying degrees of success with other categories such as "pre-sleep behaviors" and "brain function and sleep." ChatGPT's assessment of the degree of falseness and public health significance, on the 5-point Likert scale, revealed an average score of 3.45 (SD 0.87) and 3.15 (SD 0.99), respectively, indicating a good level of accuracy in identifying the falseness of statements and a good understanding of their impact on public health. The AI-based tool showed a sensitivity of 85% and a positive predictive value of 100%. Overall, this indicates that when ChatGPT labels a statement as false, it is highly reliable, but it may miss identifying some false statements. When comparing with expert ratings, high intraclass correlation coefficients (ICCs) between ChatGPT's appraisals and expert opinions could be found, suggesting that the AI's ratings were generally aligned with expert views on falseness (ICC=.83, P<.001) and public health significance (ICC=.79, P=.001) of sleep-related myths. Qualitatively, both ChatGPT and sleep experts refuted sleep-related misconceptions. However, ChatGPT adopted a more accessible style and provided a more generalized view, focusing on broad concepts, while experts sometimes used technical jargon, providing evidence-based explanations.

CONCLUSIONS

ChatGPT-4 can accurately address sleep-related queries and debunk sleep-related myths, with a performance comparable to sleep experts, even if, given its limitations, the AI cannot completely replace expert opinions, especially in nuanced and complex fields such as sleep health, but can be a valuable complement in the dissemination of updated information and promotion of healthy behaviors.

Efficacy and safety of digital therapeutic application of Sleep Index-Based Treatment for Insomnia (dSIBT-I): a pilot study

The aim of this study was to evaluate the safety and efficacy of digital therapeutic application of Sleep Index-Based Treatment for Insomnia (dSIBT-I) and compare them with those of digital application of Cognitive Behavioural Therapy for Insomnia (dCBT-I). This randomised prospective pilot study was conducted at the Asan Medical Center. A total of 50 patients with insomnia were recruited between December 2022 and January 2023 and randomly allocated to the dSIBT-I or dCBT-I group. The study was carried out for one month. The primary outcome was the significant reduction in Insomnia Severity Index score at Week 4 compared to baseline, while the secondary outcome was proportion of participants whose Insomnia Severity Index scores were reduced to <15 at Week 4. We performed linear mixed model and generalised estimating equation analyses. Both dSIBT-I and dCBT-I groups showed significant improvements in Insomnia Severity Index scores at Week 4. There was no significant difference between two groups in terms of Insomnia Severity Index scores at Week 4 (group × time effect, F = 1.07, p = 0.382) and proportion of participants whose Insomnia Severity Index scores were reduced to <15 at Week 4 (group × time effects, F = 1.80, p = 0.615). However, at Week 2, the dSIBT-I group showed better results than the dCBT-I group in terms of both Insomnia Severity Index scores (p = 0.044) and proportion of participants whose Insomnia Severity Index scores were reduced to <15 (82.6% vs. 48.0%, p = 0.017). No treatment-emergent adverse events were reported in either group. The dSIBT-I is a safe and effective therapy for insomnia, with rapid treatment effects.

Changes in Insomnia Severity are Associated With the Changes in Discrepancy Between Desired Time in Bed and Desired Total Sleep Time Among the General Population

OBJECTIVE

The discrepancy between desired time in bed and desired total sleep time (DBST index) is correlated with the severity of insomnia among the general population. This study aimed to explore whether the change in DBST index is associated with changes in insomnia severity.

METHODS

The study was conducted as a single source tracking online survey among the general population. The first survey (T1) was completed by all 399 participants, and the second survey (T2) was completed by 233 participants 5-6 weeks after the T1 survey with a simple instruction of reducing the DBST index. Participants' age, sex, marital status, past psychiatric history, and sleep patterns were collected. In addition to the DBST index, the Glasgow Sleep Effort Scale (GSES), Dysfunctional Beliefs about Sleep-2 items (DBS-2), and Insomnia Severity Index (ISI) were rated.

RESULTS

The change in the ISI (T1-T2) was significantly correlated with the changes in the GSES (r=0.24, p<0.001), DBS-2 (r=0.22, p<0.001), and DBST index (r=0.15, p=0.020). The change in insomnia severity was expected with change in the GSES (β=0.23, p<0.001), DBS-2 (β=0.20, p=0.002), and DBST index (β=0.13, p=0.037). Mediation analysis showed that change in DBST index directly influenced change in insomnia severity and change in GSES or DBS-2 did not mediate the relationship.

CONCLUSION

Changing the DBST index can be a simple way to reduce insomnia severity among the general population.

Efficacy of Blue LED Phototherapy on Sleep Quality and Behavioral and Psychological Symptoms of Dementia: A Double-Blind Randomized Controlled Trial

INTRODUCTION

People with dementia often experience behavioral and psychological symptoms of dementia (BPSD), which are a major cause of caregiver burden and institutionalization. Therefore, we conducted a double-blind, parallel-group randomized controlled trial to examine the efficacy of blue-enriched light therapy for BPSD in institutionalized older adults with dementia.

METHODS

Participants were enrolled and randomly allocated into blue-enriched light therapy (N = 30) or the conventional light group (N = 30) for 60 min in 10 weeks with five sessions per week. The primary outcome was sleep quality measured by actigraphy and Pittsburgh Sleep Quality Index (PSQI). The secondary outcome was overall BPSD severity (Cohen-Mansfield Agitation Inventory [CMAI] and Neuropsychiatric Inventory [NPI-NH]). The outcome indicators were assessed at baseline, mid-test, immediate posttest, 1-month, 3-month, and 6-month follow-up. The effects of the blue-enriched light therapy were examined by the generalized estimating equation model.

RESULTS

Blue-enriched light therapy revealed significant differences in the objective sleep parameters (sleep efficiency: β = 5.81, Waldχ2 = 32.60, CI: 3.82; 7.80; sleep latency: β = -19.82, Waldχ2 = 38.38, CI:-26.09; -13.55), subjective sleep quality (PSQI: β = -2.07, Waldχ2 = 45.94, CI: -2.66; -1.47), and overall BPSD severity (CMAI: β = -0.90, Waldχ2 = 14.38, CI: -1.37; -0.44) (NPI-NH: β = -1.67, Waldχ2 = 30.61, CI: -2.26; -1.08) compared to conventional phototherapy immediate posttest, 1-month, 3-month, and 6-month follow-up. Furthermore, the effects for sleep efficiency and sleep latency lasted for up to 6 months. In the subscale analysis, the differences of the behavioral symptoms changed significantly between the groups in physical/nonaggressive (CI: -1.01; -0.26) and verbal/nonaggressive (CI: -0.97; -0.29).

CONCLUSIONS

Blue-enriched light therapy is a feasible low-cost intervention that could be integrated as a comprehensive therapy program for BPSD among older adults with dementia.

Subjective sleep quality and fatigue assessment in Polish adult patients with primary immunodeficiencies: A pilot study

Introduction

Primary immunodeficiencies (PIDs) are clinically heterogeneous disorders caused by abnormalities in the immune system. However, PIDs are genetically determined and may occur at any age from early childhood to elderly age. Due to chronic patterns, the risk of malignancy and organ damage in patients with PIDs may affect any aspect of life, including sleep patterns. To our knowledge, the prevalence of insomnia and subjective sleep quality have not been investigated in patients with PIDs. Therefore, this pilot study was conducted to investigate sleep quality, the prevalence of sleep disturbances, and fatigue in adult patients with PIDs in Poland.

Methods

All participants were surveyed using the Athens Insomnia Scale, Pittsburgh Sleep Quality Index, Fatigue Severity Scale, and a questionnaire concerning general health and demographic data. We included 92 participants: 48 women (52.2%) and 44 men (47.8%).

Results

Participants' mean age was 41.9 ± 13.9 years. The mean sleep duration was 7.0 ± 1.5 hours, and the mean sleep latency was 41.2 ± 53.1 minutes. Additionally, 44.6% of patients (n=41) had symptoms of insomnia and 44.6% (n=42) had poor sleep quality. Less than one-fourth (n=22; 23.9%) of the patients reported the use of sleeping pills; moreover, clinically significant fatigue was reported in 52.2% (n=48).

Discussion

Our investigation provides insight into the problem of sleep disturbances in patients with PIDs. Data have demonstrated that sleeping disorders with concomitant fatigue are common in patients with PID. Further studies are needed to determine the determinants of poor sleep quality in this specific group of patients.

Four Useful Concepts When Treating Patients With Insomnia: Possibility of Sleep Index-Based Cognitive Behavioral Therapy for Insomnia

Cognitive behavioral therapy for insomnia (CBT-I) is one of the most common treatments for insomnia and is considered as the first-line treatment. People who struggle to fall asleep or stay asleep or those who do not find sleep refreshing can benefit over four to six sessions of CBT-I. Although CBT-I has many benefits, it does have some drawbacks. Therefore, a brief version needs to be developed for use in clinical practice. This study proposes the following concepts that can be readily applied in clinical practice: 1) Concept 1, 17 hours of activity and 7 hours of sleep; 2) Concept 2, discrepancy between desired time in bed and desired total sleep time; 3) Concept 3, time in bed during 24 hours; and 4) Concept 4, taking sleeping pills 7 hours before the waking up time. These concepts based on sleep indices could easily help patients with insomnia and may serve as a foundation for the development of Sleep Index-based CBT-I.

Assessing the Sleep-wake Pattern in Cancer Patients for Predicting a Short Sleep Onset Latency

Objective

We investigated the sleep parameters and clinical factors related to short sleep onset latency (SL) in cancer patients.

Methods

We retrospectively reviewed the medical records of 235 cancer patients. Patient Health Questionnaire-9, State and Trait Anxiety Inventory (State subcategory), Insomnia Severity Index (ISI), Cancer-related Dysfunctional Beliefs about Sleep, and Fear of Progression scale scores and sleep related parameters including sleeping pill ingestion time, bedtime, sleep onset time, and wake-up time were collected. We also calculated the duration from sleeping pill ingestion to bedtime, sleep onset time, and wake-up time; duration from wake-up time to bedtime and sleep onset time; and time spent in bed over a 24 hours period.

Results

Among patients not taking sleeping pills (n = 145), early wake-up time (adjusted odds ratio [OR] 0.39, 95% confidence interval [CI] 0.19−0.78), early sleep onset time (OR 0.50, 95% CI 0.27−0.93), and low ISI score (OR 0.82, 95% CI 0.71−0.93) were identified as expecting variables for SL ≤ 30 minutes. Longer duration from wake-up time to bedtime (OR 2.49, 95% CI 1.48−4.18) predicted SL ≤ 30 minutes. Among those taking sleeping pills (n = 90), early sleep onset time (OR 0.54, 95% CI 0.39−0.76) and short duration from pill ingestion to sleep onset time (OR 0.05, 95% CI 0.02−0.16) predicted SL ≤ 30 minutes.

Conclusion

Cancer patients who fell asleep quickly spent less time in bed during the day. Thus, before cancer patients with insomnia are prescribed sleeping pills, their sleep parameters should be examined to improve their SL.

Discrepancy Between Desired Time in Bed and Desired Total Sleep Time, Insomnia, Depression, and Dysfunctional Beliefs About Sleep Among the General Population

OBJECTIVE

The aim of this study was to explore the factors that can influence the severity of insomnia in the general population. We also aimed to examine whether sleep effort mediates the association between dysfunctional beliefs about sleep or the discrepancy between desired time in bed and desired total sleep time (DBST) and insomnia severity in individuals.

METHODS

A total of 387 participants enrolled in this e-survey study. The symptoms were rated using the insomnia severity index (ISI), Patients Health Questionnaire-9 items, Dysfunctional Beliefs about Sleep-2 items, Glasgow Sleep Effort Scale, and Stress and Anxiety to Viral Epidemics-6 items. In addition, we defined a new sleep index named the DBST index. A linear regression analysis was performed to explore the factors predicting ISI scores, and mediation analysis was implemented to explore whether persistent preoccupation with sleep mediated the influence of dysfunctional beliefs about sleep and DBST on insomnia severity.

RESULTS

A linear regression analysis investigated depression (β=0.17, p<0.001), sleep effort (β=0.50, p<0.001), dysfunctional beliefs about sleep (β=0.13, p=0.001), and DBST (β=0.09, p=0.014) (adjusted R2=0.50, F=65.7, p<0.001). Additionally, we observed that persistent preoccupation with sleep partially mediated the influence of dysfunctional beliefs about sleep and DBST on insomnia severity.

CONCLUSION

Depression, preoccupation with sleep, dysfunctional beliefs about sleep, and DBST influenced the insomnia severity of the general population. We also observed that a persistent preoccupation with sleep partially mediated the influence of dysfunctional beliefs about sleep and the DBST index on insomnia severity.

Subcortical and Cerebellar Neural Correlates of Prodromal Alzheimer’s Disease with Prolonged Sleep Latency

Background: Despite the important associations among sleep, Alzheimer’s disease (AD), subcortical structures, and the cerebellum, structural and functional magnetic resonance imaging (MRI) with regard to these regions and sleep on patients in AD trajectory are scarce. Objective: This study aimed to evaluate the influence of prolonged sleep latency on the structural and functional alterations in the subcortical and cerebellar neural correlates in amyloid-β positive amnestic mild cognitive impairment patients (Aβ+aMCI). Methods: A total of 60 patients with aMCI who were identified as amyloid positive ([18F] flutemetamol+) were recruited in the study, 24 patients with normal sleep latency (aMCI-n) and 36 patients prolonged sleep latency (aMCI-p). Cortical thickness and volumes between the two groups were compared. Volumetric analyses were implemented on the brainstem, thalamus, and hippocampus. Subcortical and cerebellar resting state functional connectivity (FC) differences were measured between the both groups through seed-to-voxel analysis. Additionally, group x Aβ interactive effects on FC values were tested with a general linear model. Result: There was a significantly decreased brainstem volume in aMCI-p subjects. We observed a significant reduction of the locus coeruleus (LC) FC with frontal, temporal, insular cortices, hippocampus, and left thalamic FC with occipital cortex. Moreover, the LC FC with occipital cortex and left hippocampal FC with frontal cortex were increased in aMCI-p subjects. In addition, there was a statistically significant group by regional standardized uptake value ratio interactions discovered in cerebro-cerebellar networks. Conclusion: The aforementioned findings suggest that prolonged sleep latency may be a detrimental factor in compromising structural and functional correlates of subcortical structures and the cerebellum, which may accelerate AD pathophysiology.

Discrepancy between desired time in bed and desired total sleep time in patients with cancer: The DBST index and its relationship with insomnia severity and sleep onset latency

Patients with cancer can often experience insomnia or sleep disturbances. This study aimed to explore whether the discrepancy between a patient's desired time in bed and desired total sleep time (DBST index) can be used as a measurement tool for insomnia severity or sleep onset latency [SOL] in patients with cancer. This retrospective medical records review study gathered clinical information and scores from scales and indices such as the Insomnia Severity Index (ISI), Cancer-related Dysfunctional Beliefs about Sleep (C-DBS) scale, Patient Health Questionnaire-9 items (PHQ-9), State subcategory of State and Trait Anxiety Inventory, and the short form of the Fear of Progression Questionnaire. Sleep indices of time variables (bedtime, sleep onset time, and wake-up time), duration variables [SOL, time in bed (TIB), time in bed over 24 hours (TIB/d), and duration from wake-up time to bedtime (WTB)], and DBST index were calculated. ISI scores were predicted by the PHQ-9 (β = 0.34, P < 0.001), C-DBS scale (β = 0.17, P = 0.034), and DBST indices (β = 0.22, P = 0.004). Long SOL value was predicted by early bedtimes (β = -0.18, P = 0.045), short WTB durations (β = -0.26, P = 0.004), and high DBST index values (β = 0.19, P = 0.013). The DBST index was significantly correlated with both insomnia severity and SOL in patients with cancer.

Time to Take Sleeping Pills and Subjective Satisfaction among Cancer Patients

OBJECTIVE

We investigated the influence of the time to take hypnotics and daytime activity on patient satisfaction with sleeping pills.

METHODS

Ninety-six cancer patients who were currently taking benzodiazepine or z-drug as hypnotics were grouped into satisfied and dissatisfied groups. The subjects' symptoms, time to take sleeping pills, bedtime, sleep onset time, wake up time, and time in bed within 24 hours (TIB/d) were obtained.

RESULTS

The satisfied group had significantly late sleeping pill ingestion time (p=0.04); significantly early wake up time (p=0.01); and significantly shorter sleep latency, TIB/d, duration from the administration of pills to sleep onset, and duration from the administration of pills to wake up time (PTW). Logistic regression analysis revealed that the significant predictors of patient satisfaction to hypnotics were less severity of insomnia [odds ratio (OR)=0.91] and the time variables, including late sleeping pill administration time (OR=1.53) and early wake up time (OR=0.57). Among the duration variables, short PTW (OR=0.30) and short TIB/d (OR=0.64) were significantly related with the satisfaction to hypnotics.

CONCLUSION

Reducing the duration from the administration of hypnotics to wake up time and TIB/d can influence the satisfaction to sleeping pills.