The Veterans Crisis Line: Relations among immediate call outcomes and treatment contact and utilization following the call.

OBJECTIVE: Crisis line callers experience reductions in distress and suicidal ideation and utilize more health care following calls. The purpose of this study was to determine whether changes in distress and suicidal ideation during a call are associated with later healthcare contact and utilization. METHOD: Veterans Crisis Line calls from 599 veterans were extracted with call dates between 12/1/2018 and 11/30/2019. Calls were coded for changes in distress and suicidal ideation and linked with VA medical records to obtain healthcare data. Generalized Linear Mixed Modeling was used to examine the associations of changes in distress and suicidal ideation with healthcare contact (yes/no) and utilization (days of treatment) in the month (30 days) following the call. RESULTS: Reductions in distress were associated with behavioral (i.e., mental and substance use) healthcare utilization, F(1, 596) = 4.52, p = 0.03, and reductions in suicidal ideation were associated with any healthcare utilization, F(1, 596) = 6.45, p = 0.01. Changes in distress and suicidal ideation were not associated with healthcare contact. CONCLUSION: Responders need to help resolve distress and suicidal ideation and link callers with treatment. Unresolved distress and suicidal thoughts may signify later problems with treatment utilization. Research is needed to determine causality.

Veteran Beliefs About the Causes of Gulf War Illness and Expectations for Improvement.

BACKGROUND: Individuals' beliefs about the etiology of persistent physical symptoms (PPS) are linked to differences in coping style. However, it is unclear which attributions are related to greater expectations for improvement. METHOD AND RESULTS: A cross-sectional regression analysis (N = 262) indicated that Veterans with Gulf War Illness (GWI) who attributed their GWI to behavior, (e.g., diet and exercise), had greater expectations for improvement (p = .001) than those who attributed their GWI to deployment, physical, or psychological causes (p values > .05). CONCLUSIONS: Findings support the possible clinical utility of exploring perceived contributing factors of PPS, which may increase perceptions that improvement of PPS is possible. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02161133.

Sleeping with one cerebrum open: patient and staff perceptions of sleep quality and quantity on an inpatient neurology unit.

STUDY OBJECTIVES: This study sought to investigate perceptions of sleep disruptions among patients and staff in the inpatient neurology setting. The objectives were to explore the differences between these groups regarding factors that impact sleep, identify the most significant sleep disruptions, and examine the barriers and opportunities suggested to improve inpatient sleep. METHODS: A survey-based observational study was conducted on a 25-bed inpatient neurology unit at an academic medical center. Staff and patients completed the Potential Hospital Sleep Disruptions and Noises Questionnaire, and focus groups were held to gather qualitative data. Patient-reported sleep measures were collected for additional assessment. Responses were dichotomized for comparison. Regression models were used to assess associations between disruptors and patient-reported sleep measures. Qualitative thematic analyses were performed. RESULTS: Forty-nine inpatient staff and 247 patients completed sleep surveys. Top primary patient diagnoses included stroke, epilepsy, autoimmune diseases, and psychogenic nonepileptic attacks. Medical interventions, environmental factors, patient-related factors, and unit workflows emerged as key themes related to sleep disruptions. Patient-reported sleep efficiency was significantly reduced when pain, anxiety, stress, temperature, and medication administration disrupted sleep. Staff perspectives highlighted medical interventions as most disruptive to sleep, while patients did not find them as disruptive as expected. CONCLUSIONS: Differing perspectives on sleep disruption exist between staff and patients in the inpatient neurology setting. Medical interventions may be overstated in staff perceptions and inpatient sleep research, as pain, anxiety, and stress had the most significant impact on patient-reported sleep efficiency. CITATION: Kadura S, Poulakis A, Roberts DE, et al. Sleeping with one cerebrum open: patient and staff perceptions of sleep quality and quantity on an inpatient neurology unit. J Clin Sleep Med. 2024;20(4):619-629.

Fear of sleep in first responders: associations with trauma types, psychopathology, and sleep disturbances.

Study Objectives: Fear of sleep contributes to insomnia in some individuals with posttraumatic stress disorder (PTSD) but remains uncharacterized in first responders, a population with high rates of insomnia and PTSD. We evaluated the clinical relevance of fear of sleep in first responders by (1) examining its relationship with trauma types and clinical symptoms and (2) assessing differences in fear of sleep severity between those reporting provisional PTSD, insomnia, or both. Methods: A cross-sectional study of 242 first responders across the United States (59.2% male, 86.4% white, 56.2% law enforcement officers, 98.7% active duty, and Myears of service = 17). Participants completed the Fear of Sleep Inventory-Short Form and measures of trauma history, psychopathology (e.g. PTSD), and sleep disturbances (insomnia and trauma-related nightmares). Results: Fear of sleep was associated with trauma types characterized by interpersonal violence and victimization, as well as symptoms of PTSD, depression, anxiety, stress, alcohol use problems, insomnia, and trauma-related nightmares. Fear of sleep was most pronounced among first responders reporting provisional PTSD comorbid with insomnia compared to those with PTSD or insomnia only. Post hoc analyses revealed PTSD hyperarousal symptoms and trauma-related nightmares were independently associated with fear of sleep, even after adjusting for the remaining PTSD clusters, insomnia, sex, and years of service. Conclusions: Fear of sleep is a clinically relevant construct in first responders that is associated with a broad range of psychopathology symptoms and is most severe among those with cooccurring PTSD and insomnia. Fear of sleep may merit targeted treatment in first responders. This paper is part of the Sleep and Circadian Health in the Justice System Collection.

Treatment and life goals among veterans with Gulf War illness.

Medically unexplained syndromes (MUS), also termed persistent physical symptoms, are both prevalent and disabling. Yet treatments for MUS are marked by high rates of patient dissatisfaction, as well as disagreement between patients and providers on the management of persistent physical symptoms. A better understanding of patient-generated goals could increase collaborative goal setting and promote person-centered care, a critical component of MUS treatment; yet research in this area is lacking. This paper aimed to develop a typology of treatment and life goals among Gulf War veterans with a medically unexplained syndrome (Gulf War Illness). We examined participants' responses to open-ended questions about treatment and life goals using Braun and Clarke's thematic analysis methodology. Results showed that treatment goals could be categorized into four overarching themes: 1) Get better/healthier, 2) Improve quality of life, 3) Improve or seek additional treatment, and 4) Don't know/Don't have any. Life goals were categorized into six overarching themes: 1) Live a fulfilling life, 2) Live a happy life, 3) Live a healthy life, 4) Be productive/financially successful, 5) Manage GWI, and 6) Don't know/Don't have any. Treatment goals were largely focused on getting better/healthier (e.g., improving symptoms), whereas life goals focused on living a fulfilling life. Implications for the treatment of Gulf War Illness and patient-provider communication are discussed. ClinicalTrials.gov Identifier: NCT02161133.

Relationship of insomnia to sexual function and sexual satisfaction: Findings from the sleep and sex survey II.

OBJECTIVE: Research suggests that healthy sleep is a key component of sexual function. When evaluating sexual function, however, poor sleep is often overlooked as a contributing factor. This cross-sectional survey expands prior work by exploring relationships among insomnia severity, sexual satisfaction, and sexual function with an updated battery of measures for the Sleep and Sex Survey. METHOD: The sample (N = 1266) consisted of 618 men, 648 women recruited via Prolific, an online survey platform. The Sleep and Sex Survey II included measures addressing insomnia, fear of sleep, nightmares, sexual function, sexual satisfaction, sexual activity, depression, anxiety, posttraumatic stress, and alcohol use. RESULTS: Recent sexual activity was correlated with better sleep, lower anxiety, and lower scores on a posttraumatic stress disorder screener. Insomnia severity was negatively correlated with sexual satisfaction (r = 0.22, p < .001) and the presence of insomnia was associated with higher rates of sexual dysfunction in women (53.8% vs. 31.8%; p < .001) and men (22.7% vs. 12.5%; p = .036). In regression analyses, after accounting for depression and anxiety, insomnia had a statistically significant association with sexual function for women (ß = 0.12; p < .01) but not men (ß = 0.11; p = .60). CONCLUSION: Findings support a relationship of insomnia to sexual function and satisfaction with some gender differences. Future work may be enhanced by assessing these relationships in a gender diverse population including objective sleep measures and addressing contributing mechanisms. Clinically, findings support the assessment of sleep when evaluating patients for sexual dysfunction.

Associations between insomnia medications and risk of death by suicide.

PURPOSE: Insomnia is a modifiable risk factor for suicide often treated with medications. However, little is known about the associations between insomnia medications and risk of death by suicide. The purpose of this study is to model the comparative risk of suicide by each insomnia medication compared to zolpidem, a sedative-hypnotic approved for insomnia. METHODS: First prescription fills of medications commonly used to treat insomnia were identified in electronic medical records. Date and cause of death were identified in death certificates. Cox proportional hazards models were used to analyze time from insomnia prescription to suicide. RESULTS: More than 2 million patients filled a new insomnia prescription between 2005 and 2015, and 518 of them died by suicide within 12 months. Compared to zolpidem, the tricyclic antidepressants (amitriptyline, doxepin) were associated with a 64% lower risk of suicide (HR 0.36 (95% CI 0.22-0.66) and the sedating antihistamines (hydroxyzine, diphenhydramine) a 40% lower risk of suicide (HR 0.60 (0.41-0.89)). In contrast, the tetracyclic antidepressant (mirtazapine) was associated with a 62% higher risk of suicide (HR 1.62 (95% CI 1.10-2.38) compared to zolpidem. CONCLUSION: Insomnia is a modifiable risk factor for suicide, yet many medications used to treat insomnia have never been tested for the indication in clinical trials. To define efficacy in the prevention of suicide, trials are warranted.

Development of a model to predict antidepressant treatment response for depression among Veterans.

BACKGROUND: Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA). METHODS: A 2018-2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample. RESULTS: In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors. CONCLUSIONS: Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

A two-phase, prescriptive comparative effectiveness study to optimize the treatment of co-occurring insomnia and depression with digital interventions.

BACKGROUND: Insomnia and depression frequently co-occur. Significant barriers preclude a majority of patients from receiving first line treatments for both disorders in a sequential treatment episode. Although digital versions of cognitive behavioral therapy for insomnia (CBTI) and for depression (CBTD) hold some promise to meet demand, especially when paired with human support, it is unknown whether heterogeneity of treatment effects exist, such that some patients would be optimally treated with single or sequential interventions. OBJECTIVE: Describe the protocol for a two-phase, prescriptive comparative effectiveness study to develop and evaluate an individualized intervention rule (IIR) for prescribing the optimal digital treament of co-occurring insomnia and depression. METHODS: The proposed sample size is 2300 U.S. military veterans with insomnia and depression recruited nationally (Phase 1 = 1500; Phase 2 = 800). In each phase, the primary endpoint will be remission of both depression and insomnia 3 months following a 12-week intervention period. Phase 1 is a 5-arm randomized trial: two single digital interventions (CBT-I or CBT-D); two sequenced interventions (CBT-I + D or CBT-D + I); and a mood monitoring control condition. A cutting-edge ensemble machine learning method will be used to develop the IIR. Phase 2 will evaluate the IIR by randomizing participants with equal allocation to either the IIR predicted optimal intervention for that individual or by randomization to one the four CBT interventions. RESULTS: Study procedures are ongoing. Results will be reported in future manuscripts. CONCLUSION: The study will generate evidence on the optimal scalable approach to treat co-occurring insomnia and depression.

Cognitive behavioral therapy for insomnia treatment attrition in patients with weekly nightmares.

STUDY OBJECTIVES: This study's objective was to evaluate the effect of nightmares (NMs) on attrition and symptom change following cognitive behavioral therapy for insomnia (CBT-I) treatment using data from a successful CBT-I randomized controlled trial delivered to participants with recent interpersonal violence exposure. METHODS: The study randomized 110 participants (107 women; mean age: 35.5 years) to CBT-I or to an attention-control group. Participants were assessed at 3 time periods: baseline, post-CBT-I (or attention control), and at time 3 (T3) post-cognitive processing therapy received by all participants. NM reports were extracted from the Fear of Sleep Inventory. Participants with weekly NMs were compared with those with fewer than weekly NMs on outcomes including attrition, insomnia, posttraumatic stress disorder, and depression. Change in NM frequency was examined. RESULTS: Participants with weekly NMs (55%) were significantly more likely to be lost to follow-up post-CBT-I (37%) compared with participants with infrequent NMs (15.6%) and were less likely to complete T3 (43%) than patients with less frequent NMs (62.5%). NMs were unrelated to differential treatment response in insomnia, depression, or posttraumatic stress disorder. Treatment with CBT-I was not associated with reduced NM frequency; however, change in sleep-onset latency from post-CBT-I to T3 predicted fewer NMs at T3. CONCLUSIONS: Weekly NMs were associated with attrition but not a reduced change in insomnia symptoms following CBT-I. NM symptoms did not change as a function of CBT-I, but change in sleep-onset latency predicted lower NM frequency. CBT-I trials should screen for NMs and consider augmenting CBT-I to specifically address NMs. CITATION: Hamilton NA, Russell JA, Youngren WA, et al. Cognitive behavioral therapy for insomnia treatment attrition in patients with weekly nightmares. J Clin Sleep Med. 2023;19(11):1913-1921.

Associations between risky alcohol use, disability, and problem-solving impairment among Veterans with Gulf War Illness: Secondary data analysis of a randomized clinical trial.

OBJECTIVE: Gulf War Illness (GWI) and alcohol use are both major sources of disability among Gulf War Veterans. The goal of this secondary data analysis was to examine associations between risky alcohol use, problem-solving impairment, and disability among Veterans in a randomized clinical trial of problem-solving treatment (PST) for GWI. We examined cross-sectional associations and conducted longitudinal analyses to test if alcohol use moderated treatment outcome of PST. METHODS: Participants were 268 United States military Veterans with GWI randomized to PST or a control intervention. Participants were assessed at four timepoints. Measures included the World Health Organization Disability Assessment Schedule 2.0 (WHO-DAS 2.0), Problem Solving Inventory (PSI), and Alcohol Use Disorders Identification Test-Concise (AUDIT-C). We conducted multivariate regression (cross-sectional) and mixed model analyses (longitudinal) with separate models for WHO-DAS 2.0 and PSI. All models included AUDIT-C and household income. This analysis was pre-registered on the Open Science Framework. RESULTS: Cross-sectional analyses revealed a significant negative association with small effect size between AUDIT-C and WHO-DAS 2.0 (p = 0.006; f2 = 0.05); worse disability was associated with less risky alcohol use. There was no evidence that risky alcohol use moderated effects of PST on disability or PSI. CONCLUSION: If replicated, the cross-sectional findings suggest high levels of disability may deter heavy drinking among Veterans with GWI. We did not find evidence that risky alcohol use moderated treatment outcome of PST for GWI. More research is needed to identify moderators of GWI interventions and to understand risky drinking among Veterans with complex health problems.

Beyond maintaining safety: Examining the benefit of emotion-centered problem solving therapy added to safety planning for reducing late life suicide risk.

Few clinical trials have examined brief non-pharmacological treatments for reducing suicide risk in older Veterans, a high-risk group. Problem Solving Therapy (PST) is a promising psychosocial intervention for reducing late life suicide risk by increasing adaptive coping to problems through effective problem solving and related coping skills. The current randomized clinical trial will compare the efficacy of six telephone-delivered sessions of Safety Planning (enhanced usual care; EUC) only or an updated version of PST (emotion-centered PST [EC-PST]) + EUC to determine the added clinical benefit of EC-PST for reducing severity of suicidal ideation and for increasing reasons for living, a critical protective factor. Participants randomized to EC-PST + EUC or EUC only will be 150 Veterans (75 each) with active suicidal ideation who are aged 60 or older; have a current DSM-5 anxiety, depressive, and/or trauma-related disorder; and without significant cognitive impairment. Primary outcomes (Geriatric Suicide Ideation Scale and Reasons for Living-Older Adults scale) will be assessed at 11 timepoints: baseline, after each of 6 treatment sessions, posttreatment, and at follow-up at 1, 3, and 6 months posttreatment, and analyzed using mixed effects modeling. Additionally, moderators and mediators of primary outcomes will be examined-functional disability, executive dysfunction, and problem-solving ability. Qualitative feedback from participants will identify potential Veteran-centric changes to the EC-PST protocol and to EUC. Ultimately, the goal of this study is to inform the evidence-based clinical practice guidelines for treatments to reduce suicide risk in older Veterans and specifically to inform clinical decision-making regarding the merit of adding EC-PST to EUC.

Veterans Crisis Line Call Outcomes: Treatment Contact and Utilization.

INTRODUCTION: Crisis lines are a central component of suicide prevention strategies in the U.S. and for the Department of Veterans Affairs. The purpose of this study is to evaluate the impact of calling the Veterans Crisis Line on treatment contact and utilization. METHODS: Call records from 599 veterans who called in 2019 were linked with medical records and analyzed in 2020. Multilevel generalized linear modeling examined pre-post changes in treatment contact (yes/no) and utilization (number of days of care). RESULTS: In the month after the call, 85% of callers made contact with health care, and 79% made contact with behavioral health care. Callers were more likely to make contact with health care in the month after the call than in the preceding month (AOR=6.27, 95% CI=4.22, 9.32) and more likely to make contact with behavioral health care (AOR=10.21, 95% CI=6.66, 15.67). Days of health care nearly doubled to 4.82, and days of behavioral health care more than doubled to 3.52. CONCLUSIONS: Among veteran callers who are linked to medical records, calling the Veterans Crisis Line may increase contact and utilization of health care and behavioral health care. These findings support crisis lines that are linked with healthcare systems in public health strategies for suicide prevention.

Exploring the acceptability of behavioral interventions for veterans with persistent "medically unexplained" physical symptoms.

OBJECTIVE: This study evaluated the factors that led to enrollment in, and satisfaction with, behavioral interventions for Veterans living with Gulf War Illness (GWI). METHODS: One-on-one interviews were conducted pre- and post-intervention with participants randomized to receive either telephone delivered problem-solving treatment (n = 51) or health education (N = 49). A total of 99 Veterans were interviewed pre-intervention and 60 post-intervention. Qualitative data were thematically coded and similarities in themes across the two interventions were examined. RESULTS: Before the study began, participants reported desiring to learn new information about their GWI, learn symptom-management strategies, and support improvements to care for other patients with GWI. After the intervention, Veterans felt positively about both interventions because they built strong therapeutic relationships with providers, their experiences were validated by providers, and they were provided GWI information and symptom-management strategies. Results also suggested that interventions do not have to be designed to meet all of the needs held by patients to be acceptable. A minority of participants described that they did not benefit from the interventions. CONCLUSION: The results suggest that satisfaction with behavioral interventions for GWI is driven by a strong therapeutic relationship, validating patient's experiences with GWI, and the intervention meeting some of the patient's needs, particularly increasing knowledge of GWI and improving symptom management.

Evaluation of a Model to Target High-risk Psychiatric Inpatients for an Intensive Postdischarge Suicide Prevention Intervention.

Importance: The months after psychiatric hospital discharge are a time of high risk for suicide. Intensive postdischarge case management, although potentially effective in suicide prevention, is likely to be cost-effective only if targeted at high-risk patients. A previously developed machine learning (ML) model showed that postdischarge suicides can be predicted from electronic health records and geospatial data, but it is unknown if prediction could be improved by adding additional information. Objective: To determine whether model prediction could be improved by adding information extracted from clinical notes and public records. Design, Setting, and Participants: Models were trained to predict suicides in the 12 months after Veterans Health Administration (VHA) short-term (less than 365 days) psychiatric hospitalizations between the beginning of 2010 and September 1, 2012 (299 050 hospitalizations, with 916 hospitalizations followed within 12 months by suicides) and tested in the hospitalizations from September 2, 2012, to December 31, 2013 (149 738 hospitalizations, with 393 hospitalizations followed within 12 months by suicides). Validation focused on net benefit across a range of plausible decision thresholds. Predictor importance was assessed with Shapley additive explanations (SHAP) values. Data were analyzed from January to August 2022. Main Outcomes and Measures: Suicides were defined by the National Death Index. Base model predictors included VHA electronic health records and patient residential data. The expanded predictors came from natural language processing (NLP) of clinical notes and a social determinants of health (SDOH) public records database. Results: The model included 448 788 unique hospitalizations. Net benefit over risk horizons between 3 and 12 months was generally highest for the model that included both NLP and SDOH predictors (area under the receiver operating characteristic curve range, 0.747-0.780; area under the precision recall curve relative to the suicide rate range, 3.87-5.75). NLP and SDOH predictors also had the highest predictor class-level SHAP values (proportional SHAP = 64.0% and 49.3%, respectively), although the single highest positive variable-level SHAP value was for a count of medications classified by the US Food and Drug Administration as increasing suicide risk prescribed the year before hospitalization (proportional SHAP = 15.0%). Conclusions and Relevance: In this study, clinical notes and public records were found to improve ML model prediction of suicide after psychiatric hospitalization. The model had positive net benefit over 3-month to 12-month risk horizons for plausible decision thresholds. Although caution is needed in inferring causality based on predictor importance, several key predictors have potential intervention implications that should be investigated in future studies.

Development of a model to predict combined antidepressant medication and psychotherapy treatment response for depression among veterans.

BACKGROUND: Although research shows that more depressed patients respond to combined antidepressants (ADM) and psychotherapy than either alone, many patients do not respond even to combined treatment. A reliable prediction model for this could help treatment decision-making. We attempted to create such a model using machine learning methods among patients in the US Veterans Health Administration (VHA). METHODS: A 2018-2020 national sample of VHA patients beginning combined depression treatment completed self-report assessments at baseline and 3 months (n = 658). A learning model was developed using baseline self-report, administrative, and geospatial data to predict 3-month treatment response defined by reductions in the Quick Inventory of Depression Symptomatology Self-Report and/or in the Sheehan Disability Scale. The model was developed in a 70 % training sample and tested in the remaining 30 % test sample. RESULTS: 30.0 % of patients responded to treatment. The prediction model had a test sample AUC-ROC of 0.657. A strong gradient was found in probability of treatment response from 52.7 % in the highest predicted quintile to 14.4 % in the lowest predicted quintile. The most important predictors were episode characteristics (symptoms, comorbidities, history), personality/psychological resilience, recent stressors, and treatment characteristics. LIMITATIONS: Restrictions in sample definition, a low recruitment rate, and reliance on patient self-report rather than clinician assessments to determine treatment response limited the generalizability of results. CONCLUSIONS: A machine learning model could help depressed patients and providers predict likely response to combined ADM-psychotherapy. Parallel information about potential harms and costs of alternative treatments would be needed, though, to inform optimal treatment selection.

The memory sources of dreams: serial awakenings across sleep stages and time of night.

Memories of waking-life events are incorporated into dreams, but their incorporation is not uniform across a night of sleep. This study aimed to elucidate ways in which such memory sources vary by sleep stage and time of night. Twenty healthy participants (11 F; 24.1 ± 5.7 years) spent a night in the laboratory and were awakened for dream collection approximately 12 times spread across early, middle, and late periods of sleep, while covering all stages of sleep (N1, N2, N3, REM). In the morning, participants identified and dated associated memories of waking-life events for each dream report, when possible. The incorporation of recent memory sources in dreams was more frequent in N1 and REM than in other sleep stages. The incorporation of distant memories from over a week ago, semantic memories not traceable to a single event, and anticipated future events remained stable throughout sleep. In contrast, the relative proportions of recent versus distant memory sources changed across the night, independently of sleep stage, with late-night dreams in all stages having relatively less recent and more remote memory sources than dreams earlier in the night. Qualitatively, dreams tended to repeat similar themes across the night and in different sleep stages. The present findings clarify the temporal course of memory incorporations in dreams, highlighting a specific connection between time of night and the temporal remoteness of memories. We discuss how dream content may, at least in part, reflect the mechanisms of sleep-dependent memory consolidation.

Characterization of sleep among deaf individuals.

OBJECTIVES: Numerous health disparities are documented in deaf population research, but few empirical sleep assessments exist for this under-served population, despite knowledge that sleep contributes to physical and mental health disparities. We sought to document subjective and objective sleep in deaf adults with cross-sectional and prospective measures. METHODS: Twenty deaf participants completed validated sleep and mental health questionnaires, 2-weeks of nightly sleep diaries and continuous wrist-worn actigraphy monitoring, and 1-week of nightly, reduced-montage EEG recordings. RESULTS: Questionnaire data suggest high prevalence of insomnia (70%), poor sleep (75%), daytime sleepiness (25%) and nightmares (20%) among participants. Strong correlations were found between depression and sleep quality, fear of sleep, and insomnia severity (p's < .005). Objective sleep assessments suggest elevated wake after sleep onset and low sleep efficiency and sleep duration. CONCLUSIONS: The prevalence of sleep disturbance recorded from self-report and objective sleep measures provides preliminary evidence of sleep health disparity among deaf adults.

Agreement between actigraphy and sleep diaries: A 28-day real-time monitoring study among suicidal adolescents following acute psychiatric care.

OBJECTIVE: To examine the agreement between, and adherence to, wrist actigraphy and digital sleep diaries as methods for sleep assessment among high-risk adolescents in the 28 days following discharge from acute psychiatric care. Sleep parameters included: number of nighttime awakenings (NWAK), sleep efficiency (SE), sleep onset latency (SOL), total sleep time (TST), and wake after sleep onset (WASO). METHODS: Fifty-three adolescents (12-18 years) were recruited following discharge from acute psychiatric care for suicide risk. Adolescents completed a baseline assessment followed by a 28-day monitoring period with daily sleep diaries and continuous wrist actigraphy. Bland-Altman and multi-level models examined agreement. RESULTS: Adherence to actigraphy was high, but lower for sleep diaries; a similar pattern of adherence emerged on weekdays vs. weekends. Bland-Altman analyses revealed no clinically meaningful bias for sleep parameters (except NWAK), but the limits of agreement make interpretation ambiguous. Our base model indicated strong agreement between actigraphy and sleep diaries for TST (r = 0.850), moderate for SOL (r = 0.325) and SE (r = 0.322), and weak for WASO (r = -0.049) and NWAK (r = 0.114). A similar pattern emerged with the insomnia severity models with baseline insomnia influencing agreement on all parameters. There were significant weekday-weekend differences for WASO and NWAK, but not for SOL, SE, and TST. CONCLUSION: Results suggest that it may be beneficial to find a modeling approach to account for the concordant and discordant information and relevant time-level variables.

Development of a model to predict psychotherapy response for depression among Veterans.

BACKGROUND: Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan. METHODS: This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018-2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample. RESULTS: 32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables. CONCLUSIONS: Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.