Prospective prediction of anxiety onset in the Canadian longitudinal study on aging (CLSA): A machine learning study

BACKGROUND

Anxiety disorders are among the most common mental health disorders in the middle aged and older population. Because older individuals are more likely to have multiple comorbidities or increased frailty, the impact of anxiety disorders on their overall well-being is exacerbated. Early identification of anxiety disorders using machine learning (ML) can potentially mitigate the adverse consequences associated with these disorders.

METHODS

We applied ML to the data from the Canadian Longitudinal Study on Aging (CLSA) to predict the onset of anxiety disorders approximately three years in the future. We used Shapley value-based methods to determine the top factor for prediction. We also investigated whether anxiety onset can be predicted by baseline depression-related predictors alone.

RESULTS

Our model was able to predict anxiety onset accurately (Area under the Receiver Operating Characteristic Curve or AUC = 0.814 ± 0.016 (mean ± standard deviation), balanced accuracy = 0.741 ± 0.016, sensitivity = 0.743 ± 0.033, and specificity = 0.738 ± 0.010). The top predictive factors included prior depression or mood disorder diagnosis, high frailty, anxious personality, and low emotional stability. Depression and mood disorders are well known comorbidity of anxiety; however a prior depression or mood disorder diagnosis could not predict anxiety onset without other factors.

LIMITATION

While our findings underscore the importance of a prior depression diagnosis in predicting anxiety, they also highlight that it alone is inadequate, signifying the necessity to incorporate additional predictors for improved prediction accuracy.

CONCLUSION

Our study showcases promising prospects for using machine learning to develop personalized prediction models for anxiety onset in middle-aged and older adults using easy-to-access survey data.

Interactions between overweight/obesity and alcohol dependence impact human brain white matter microstructure: evidence from DTI

There is inconsistent evidence for an association of obesity with white matter microstructural alterations. Such inconsistent findings may be related to the cumulative effects of obesity and alcohol dependence. This study aimed to investigate the possible interactions between alcohol dependence and overweight/obesity on white matter microstructure in the human brain. A total of 60 inpatients with alcohol dependence during early abstinence (44 normal weight and 16 overweight/obese) and 65 controls (42 normal weight and 23 overweight/obese) were included. The diffusion tensor imaging (DTI) measures [fractional anisotropy (FA) and radial diffusivity (RD)] of the white matter microstructure were compared between groups. We observed significant interactive effects between alcohol dependence and overweight/obesity on DTI measures in several tracts. The DTI measures were not significantly different between the overweight/obese and normal-weight groups (although widespread trends of increased FA and decreased RD were observed) among controls. However, among the alcohol-dependent patients, the overweight/obese group had widespread reductions in FA and widespread increases in RD, most of which significantly differed from the normal-weight group; among those with overweight/obesity, the alcohol-dependent group had widespread reductions in FA and widespread increases in RD, most of which were significantly different from the control group. This study found significant interactive effects between overweight/obesity and alcohol dependence on white matter microstructure, indicating that these two controllable factors may synergistically impact white matter microstructure and disrupt structural connectivity in the human brain.

Randomized controlled trial investigating web-based, therapist delivered eye movement desensitization and reprocessing for adults with suicidal ideation

Introduction

Promising preliminary evidence suggests that EMDR may reduce suicidal ideation (SI) when used to treat Major Depressive Disorder, Posttraumatic Stress Disorder, and trauma symptoms in the context of acute mental health crises. EMDR has never been tested specifically for treating SI, and there is a lack of data regarding the safety and effectiveness of web-based, therapist-delivered EMDR in populations with known SI. The primary objective of this study was to investigate the impact of web-based, therapist-delivered EMDR, targeting experiences associated with suicidal thinking. Secondary objectives included examining the effect of EMDR treatment on symptoms of depression, anxiety, posttraumatic stress, emotional dysregulation, and dissociation, as well as safety and attrition.

Methods

This randomized control trial (ClinicalTrials.gov ID number: NCT04181047) assigned adult outpatients reporting SI to either a web-based EMDR intervention or a treatment as usual (TAU) group. TAU included primary and mental health services available within the Canadian public health system. Participants in the EMDR group received up to 12 web-based EMDR desensitization sessions, delivered twice weekly during the COVID-19 pandemic (2021-2023). The Health Research Ethics Board at the University of Alberta approved the protocol prior to initiation of data collection for this study (protocol ID number: Pro00090989).

Results

Forty-two adult outpatients received either EMDR (n=20) or TAU (n=22). Participants reported a high prevalence of early onset and chronic SI, and there was a high rate of psychiatric comorbidity. In the EMDR group, median SI, depression, anxiety, and posttraumatic symptom scale scores decreased from baseline to the four month follow-up. In the TAU group, only the median SI and posttraumatic symptom scale scores decreased from baseline to four month follow up. Although sample size precludes direct comparison, there were numerically fewer adverse events and fewer dropouts in the EMDR group relative to the TAU group.

Conclusion

Study results provide promising preliminary evidence that web-based EMDR may be a viable delivery approach to address SI. In this complex population, a short treatment course was associated with reductions of SI and other symptoms across multiple diagnostic categories. Further investigation is warranted to verify and extend these results.

Clinical Trial Registration

https://clinicaltrials.gov/study/NCT04181047?id=NCT04181047&rank=1, identifier NCT04181047.

Access to therapy for child sexual abuse survivors: Preliminary dialogue of barriers and facilitators between caregivers

BACKGROUND

Difficulties in access to therapy were highlighted by COVID-19 measures restricting in-person gatherings. Additional challenges arise when focusing on caregivers of child sexual abuse (CSA) survivors in particular, which are a population that has been historically difficult to engage with due to issues of stigma and confidentiality.

OBJECTIVES

To present preliminary qualitative results from caregivers of CSA survivors.

METHODS

This study was conducted with caregivers of CSA survivors. Two hybrid webinar/focus groups were conducted using a video conferencing platform in fall of 2021 with two groups of stakeholders (11 caregivers and 5 moderators/clinical staff at Little Warriors, an intensive episodic treatment facility). Sessions were recorded, transcribed, and thematically-analyzed using standard qualitative methodology.

RESULTS

A total of 11 caregivers contributed to the data. Themes include: (1) Challenges of starting and maintaining treatment (i.e., emotional impact of intake day, challenges of enrolling), (2) Therapeutic benefits of specialized treatment (i.e., feeling safe and supported and the importance of trauma-informed care), and (3) Barriers and facilitators of treatment (i.e., avenues to scale-up and self-care).

CONCLUSION

The importance of a strong therapeutic alliance was highlighted by both caregivers/clinical staff and further support is needed for families post-treatment. The present hybrid webinar/focus group also achieved engagement goals in a population that is typically difficult to reach. Overall, the response rate (12%) was equivalent to reported registrant attendance rates for general business to consumer webinars and the recommended focus group size. This preliminary approach warrants replication in other populations outside our clinical context.

Synchronous Web-Based Psychotherapy for Mental Disorders From a Health Quality Perspective: Scoping Review

BACKGROUND

The COVID-19 pandemic necessitated rapid changes to health care delivery, including a shift from in-person to digitally delivered psychotherapy. While these changes helped ensure timely psychotherapy provision, many concerns exist, including clinical, cultural, practical, privacy, and security issues.

OBJECTIVE

This scoping review systematically mapped existing peer-reviewed research on synchronous, therapist-delivered web-based psychotherapy for individuals with a diagnosed mental illness. Data were analyzed through the lens of the Alberta Quality Matrix for Health (AQMH) to assess to what degree this literature addresses key indicators of health care quality. This analysis aided in the identification and organization of knowledge gaps with regard to web-based psychotherapies, highlighting potential disparities between previously prioritized dimensions of care and those requiring further attention.

METHODS

This review adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We included peer-reviewed primary research studies in the English language investigating synchronous, therapist-delivered remote psychotherapy delivered to adults (aged 18 years and older) with a Diagnostic and Statistical Manual of Mental Disorders or International Statistical Classification of Diseases diagnosed mental illness. All other citations were excluded. Relevant studies were identified through MEDLINE, APA PsycINFO, Embase (OVID), Web of Science: Core Collection (Clarivate), Cochrane Library (Wiley), and Scopus (Elsevier) databases. Databases were searched on March 18, 2021. For every publication that was taken into consideration, the data were charted independently by 2 reviewers, and in the event of a discrepancy, the principal investigator validated the choice of either extractor. Results were thematically described according to the 6 AQMH dimensions: acceptability, accessibility, appropriateness, effectiveness, efficiency, and safety.

RESULTS

From 13,209 publications, 48 articles were included, largely from North American studies. Most studies measured treatment effectiveness (n=48, 100%) and acceptability (n=29, 60%) health quality dimensions. Over 80% (40/48) of studies investigated either a cognitive or exposure intervention for either posttraumatic stress disorder or a mood or anxiety disorder, generally indicating comparable results to in-person therapy. Safety (n=5, 10%) was measured in fewer studies, while treatment accessibility, appropriateness, and efficiency were not explicitly measured in any study, although these dimensions were mentioned as a future direction, hypothesis, or potential outcome.

CONCLUSIONS

In relation to web-based therapist-delivered psychotherapies for those with a diagnosed mental illness, important aspects of health care quality (accessibility, appropriateness, efficiency, and safety) have received little scientific examination, underscoring a need to address these gaps. There are also significant issues related to the generalizability of this literature, including the underrepresentation of many geographic regions, cultures, populations, clinical contexts, and psychotherapy modalities. Qualitative research in underrepresented populations and settings may uncover important patient and contextual factors important for the future implementation of quality web-based psychotherapy.

Multivariate classification based on large-scale brain networks during early abstinence predicted lapse among male detoxified alcohol-dependent patients

Identifying biomarkers to predict lapse of alcohol-dependence (AD) is essential for treatment and prevention strategies, but remains remarkably challenging. With an aim to identify neuroimaging features for predicting AD lapse, 66 male AD patients during early-abstinence (baseline) after hospitalized detoxification underwent resting-state functional magnetic resonance imaging and were then followed-up for 6 months. The relevance-vector-machine (RVM) analysis on baseline large-scale brain networks yielded an elegant model for differentiating relapsing patients (n = 38) from abstainers, with the area under the curve of 0.912 and the accuracy by leave-one-out cross-validation of 0.833. This model captured key information about neuro-connectome biomarkers for predicting AD lapse.

Prediction of depression onset risk among middle-aged and elderly adults using machine learning and Canadian Longitudinal Study on Aging cohort

BACKGROUND

Early identification of the middle-aged and elderly people with high risk of developing depression disorder in the future and the full characterization of the associated risk factors are crucial for early interventions to prevent depression among the aging population.

METHODS

Canadian Longitudinal Study on Aging (CLSA) has collected comprehensive information, including psychological scales and other non-psychological measures, i.e., socioeconomic, environmental, health, lifestyle, cognitive function, personality, about its participants (30,097 subjects aged from 45 to 85) at baseline phase in 2012-2015. We applied machine learning models for the prediction of these participants' risk of depression onset approximately three years later using information collected at baseline phase.

RESULTS

Individual-level risk for future depression onset among CLSA participants can be accurately predicted, with an area under receiver operating characteristic curve (AUC) 0.791 ± 0.016, using all baseline information. We also found the 10-item Center for Epidemiological Studies Depression Scale coupled with age and sex information could achieve similar performance (AUC 0.764 ± 0.016). Furthermore, we identified existing subthreshold depression symptoms, emotional instability, low levels of life satisfaction, perceived health, and social support, and nutrition risk as the most important predictors for depression onset independent from psychological scales.

LIMITATIONS

Depression was based on self-reported doctor diagnosis and depression screening tool.

CONCLUSIONS

The identified risk factors will further improve our understanding of the depression onset among middle-aged and elderly population and the early identification of high-risk subjects is the first step for successful early interventions.

Values and preferences related to workplace mental health programs and interventions: An international survey

INTRODUCTION

This study explores the perspectives of workers and managers on workplace programs and interventions that seek to promote mental wellbeing, and prevent and treat mental health conditions The results contributed supporting evidence for the development of the WHO's first global guidelines for mental health and work, which provide evidence-based recommendations to support the implementation of workplace mental health programs and supports, to improve their acceptability, appropriateness, and uptake.

METHODS

An international online survey was used to examine the values and preferences among workers and managers related to workplace mental health prevention, protection, promotion, and support programs and services. The survey was made available in English, French, and Spanish and recruitment consisted of convenience sampling. Descriptive statistics were used to analyse the survey data. Rapid thematic qualitative analysis was used to analyse the results of open-ended questions.

RESULTS

N = 451 responses representing all WHO regions were included in the analysis. These results provide a unique international perspective on programs and supports for mental health at work, from the standpoint of workers and managers. Results suggest that workers value interventions developed in consultation with workers (including indicated, selective and universal interventions), increased training and capacity building among managers, and targeted interventions to address the pervasive impact of stigma on perceptions about mental health at work and help-seeking.

CONCLUSION

The findings of this study seek to reflect the perspectives of workers and their managers, and therein to promote improved access, availability and uptake of mental health programs and supports at work and-ultimately- to support the potential of workplaces as environments that promote and support mental health.

User Satisfaction With a Daily Supportive Text Message Program (Text4PTSI) for Public Safety Personnel: Longitudinal Cross-Sectional Study

BACKGROUND

Public safety personnel (PSP) are exposed to traumatic events due to their work environments, which increases the risk of mental health challenges. Providing effective and evidence-based interventions, such as SMS text messaging programs, can improve PSP's overall mental well-being with high user satisfaction rates.

OBJECTIVE

This study aims to evaluate users' satisfaction, receptiveness, and perceptions of a cognitive behavioral therapy (CBT)-based supportive SMS text messaging intervention (Text4PTSI).

METHODS

Participants self-subscribed to Text4PTSI and received unidirectional cognitive behavioral-based supportive text messages for 6 months. Participants completed a web-based survey delivered via SMS text message at enrollment, and 6 weeks, 3 months, and 6 months post enrollment. Respondents' perception and receptivity of the program were assessed using a questionnaire measured on a 5-point Likert scale. Data were collected as categorical variables, and overall satisfaction with the Text4PTSI program was measured on a scale from 0 to 100.

RESULTS

There were 131 subscribers to the Text4PTSI program; however, only 81 subscribers responded to the survey, producing 100 survey responses across the 3 follow-up time points. The overall mean score of satisfaction was 85.12 (SD 13.35). More than half of the survey responses agreed or strongly agreed that Text4PTSI helped participants cope with anxiety (79/100 responses, 79%), depressive symptoms (72/100 responses, 72%), and loneliness (54/100 responses, 54%). Similarly, most of the survey responses agreed or strongly agreed that the Text4PTSI program made respondents feel connected to a support system, improved their overall mental well-being (84/100 responses, 84%), felt more hopeful about managing concerns about their mental health or substance use (82 out of responses, 82%), and helped enhance their overall quality of life (77/100 responses, 77%). The available survey responses suggest that the majority always read the supportive text messages (84/100 responses, 84%), took time to reflect on each message (75/100 responses, 75%), and returned to read the text messages more than once (76/100 responses, 76%).

CONCLUSIONS

PSP who responded to the follow-up surveys reported high user satisfaction and appreciation for receiving the Text4PTSI intervention during the 6-month program. The reported satisfaction with the service provided could pave the way to ensuring a better uptake of the service with potential effectiveness to end users.

Systematic Review of Psychiatric Adverse Effects Induced by Chloroquine and Hydroxychloroquine: Case Reports and Population Studies

OBJECTIVE

To perform a systematic review on the psychiatric adverse effects of chloroquine (CQ) and hydroxychloroquine (HCQ); to summarize what is known about psychiatric adverse effects of these drugs; to compare clinical trials, populational studies, and case report studies; and to increase awareness of the potential psychiatric adverse effects of these drugs.

DATA SOURCES

A literature search of PubMed, Scopus, and Web of Science was performed to identify manuscripts published between December 1962 and June 2022. Search terms included CQ, HCQ, psychiatry, psychosis, depression, anxiety, bipolar disorder, delirium, and psychotic disorders.

STUDY SELECTION AND DATA EXTRACTION

Relevant studies included reports of adverse effects after CQ or HCQ ingestion.

DATA SYNTHESIS

The current literature presents evidence for a risk of short-term psychiatric adverse effects induced by either CQ or HCQ. However, the populational-level studies presented some limitations regarding the voluntary response in survey data, self-report adverse effects, and placebo group reporting similar symptoms to the case group. Thus, populational-level studies addressing the discussed limitations and the nature and extent of possible psychiatric adverse effects are needed.

RELEVANCE TO PATIENT CARE AND CLINICAL PRACTICE

Most of the patients who developed such adverse effects did not report a family history of psychiatric disease. The frequency of psychiatric adverse effects depends on the patient's biological sex, age, and body mass index, but not on the drug dosage.

CONCLUSIONS

Based on clinical trials and case reports, the current literature presents evidence for a risk of short-term psychiatric adverse effects induced by either drug.

Functional dysconnectivity of anterior cingulate subregions in schizophrenia and psychotic and nonpsychotic bipolar disorder

Aberrant resting-state functional connectivity (FC) of anterior cingulate cortex (ACC) has been implicated in the pathophysiology of schizophrenia and bipolar disorder (BP). This study investigated the subregional FC of ACC across schizophrenia and psychotic (PBP) and nonpsychotic BP (NPBP) and the relationship between brain functional alterations and clinical manifestations. A total of 174 first-episode medication-naive patients with schizophrenia (FES), 80 patients with PBP, 77 patients with NPBP and 173 demographically matched healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. Brain-wide FC of ACC subregions was computed for each individual, and compared between the groups. General intelligence was evaluated using the short version of the Wechsler Adult Intelligence Scale. Relationships between FC and various clinical and cognitive variables were estimated using the skipped correlation. The FES, PBP and NPBP groups showed differing connectivity patterns in the left caudal, dorsal and perigenual ACC. Transdiagnostic dysconnectivity was found in the subregional ACC associated with cortical, limbic, striatal and cerebellar regions. Disorder-specific dysconnectivity in FES was identified between the left perigenual ACC and bilateral orbitofrontal cortex, and the left caudal ACC coupling with the default mode network (DMN) and visual processing region was correlated with psychotic symptoms. In the PBP group, FC between the left dorsal ACC and the right caudate was correlated with psychotic symptoms, and FC connected with the DMN was associated with affective symptoms. The current findings confirmed that subregional ACC dysconnectivity could be a key transdiagnostic feature and associated with differing clinical symptomology across schizophrenia and PBP.

Congruence couple therapy for alcohol use and gambling disorders with comorbidities (part I): Outcomes from a randomized controlled trial

A nonblinded randomized trial was conducted at two Canadian provincial outpatient addiction clinics that tested the effectiveness of a systemic congruence couple therapy (CCT) versus individual-based treatment-as-usual (TAU) on nine clinical outcomes: (1) primary outcomes-alcohol use and gambling, psychiatric symptoms, and couple adjustment; (2) secondary outcomes-emotion regulation, substance use, depression, post-traumatic stress symptoms, and life stress. Data of primary clients and partners (N = 46) were analyzed longitudinally across baseline, posttreatment (5 months), and follow-up (8 months). Alcohol use disorder (95%) and gambling disorder (5%) were in the severe range at baseline, and co-addiction was 27%. Psychiatric comorbidity was 100%, and 18% of couples were jointly addicted. Between-group comparison favored CCT in primary outcomes with medium-to-large effect sizes (Cohen's h = 0.74-1.44). Secondary outcomes were also significantly stronger for CCT (Cohen's h = 0.27-1.53). Within-group, for all primary outcomes, a significant proportion of symptomatic CCT clients and partners improved, converging with ANOVA results of large effect sizes (0.14-0.29). All secondary outcomes improved significantly in CCT with large effect sizes (0.14-0.50). TAU showed significant within-group improvement in alcohol use, other substance use, and life stress with large effect sizes (0.16-0.40). Primary clients and partners made largely equivalent improvement within CCT and within TAU. Results were triangulated with clients' satisfaction ratings and counselors' reports. Overall, significant within-group effects were detected for CCT both clinically and statistically and between-group difference favored CCT. Future trials are required to validate these promising findings.

Effects of Text4Hope-Addiction Support Program on Cravings and Mental Health Symptoms: Results of a Longitudinal Cross-sectional Study

BACKGROUND

Drug misuse is complex, and various treatment modalities are emerging. Providing supportive text messages to individuals with substance use disorder offers the prospect of managing and improving symptoms of drug misuse and associated comorbidities.

OBJECTIVE

This study evaluated the impact of the daily supportive text message program (Text4Hope-Addiction Support) in mitigating cravings and mental health symptoms in subscribers and quantify user satisfaction with the Text4Hope-Addiction Support program.

METHODS

Subscribers to the Text4Hope-Addiction Support program received daily supportive text messages for 3 months; the messages were crafted based on addiction counseling and cognitive behavioral therapy principles. Participants completed an anonymous web-based questionnaire to assess cravings, anxiety, and depressive symptoms using the Brief Substance Craving Scale (BSCS), Generalized Anxiety Disorder-7 (GAD-7) scale, and Patient Health Questionnaire-9 (PHQ-9) scale at enrollment (baseline), after 6 weeks, and after 3 months. Likert scale satisfaction responses were used to assess various aspects of the Text4Hope-Addiction program.

RESULTS

In total, 408 people subscribed to the program, and 110 of 408 (26.9%) subscribers completed the surveys at least at one time point. There were significant differences between the mean baseline and 3-month BSCS scores P=.01 (-2.17, 95% CI -0.62 to 3.72), PHQ-9 scores, P=.004 (-5.08, 95% CI -1.65 to -8.51), and GAD-7 scores, P=.02 (-3.02, 95% CI -0.48 to -5.56). Participants who received the supportive text messages reported a reduced desire to use drugs and a longer time interval between substance use, which are reflected in 41.1% and 32.5% decrease, respectively, from baseline score. Approximately 89% (23/26) of the participants agreed that Text4Hope-Addiction program helped them cope with addiction-related stress, and 81% (21/25) of the participants reported that the messages assisted them in dealing with anxiety. Overall, 69% (18/26) of the participants agreed that it helped them cope with depression related to addiction; 85% (22/26) of the participants felt connected to a support system; 77% (20/26) of the participants were hopeful of their ability to manage addiction issues; and 73% (19/26) of the participants felt that their overall mental well-being was improved. Most of the participants agreed that the interventions were always positive and affirmative (19/26, 73%), and succinct (17/26, 65%). Furthermore, 88% (21/24) of the participants always read the messages; 83% (20/24) of the participants took positive or beneficial actions after reading; and no participant took a negative action after reading the messages. In addition, most participants agreed to recommend other diverse technology-based services as an adjunctive treatment for their mental and physical health disorders.

CONCLUSIONS

Subscribers of Text4Hope-Addiction Support program experienced improved mental health and addiction symptoms. Addiction care practitioners and policy makers can implement supportive text-based strategies to complement conventional treatments for addiction, given that mobile devices are widely used.

Text4PTSI: A Promising Supportive Text Messaging Program to Mitigate Psychological Symptoms in Public Safety Personnel

BACKGROUND

Public safety personnel experience various mental health conditions due to their work's complex and demanding nature. There are barriers to seeking support and treatment; hence, providing innovative and cost-effective interventions can help improve mental health symptoms in public safety personnel.

OBJECTIVE

The study aimed to evaluate the impact of Text4PTSI on depression, anxiety, trauma, and stress-related symptoms, and the resilience of public safety personnel after six months of providing supportive text message intervention.

METHODS

Public safety personnel subscribed to Text4PTSI and received daily supportive and psychoeducational SMS text messages for six months. Participants were invited to complete standardized self-rated web-based questionnaires to assess depression, anxiety, posttraumatic stress disorder (PTSD), and resilience symptoms measured on the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 scale (GAD-7), Posttraumatic Stress Disorder Checklist-Civilian Version (PCL-C), and the Brief Resilience Scale (BRS), respectively. The assessment of mental health conditions was conducted at baseline (enrolment) and six weeks, three months, and six months after enrollment.

RESULTS

One hundred and thirty-one subscribers participated in the Text4PTSI program, and eighteen completed both the baseline and any follow-up survey. A total of 31 participants completed the baseline survey and 107 total surveys were recorded at all follow-up time points. The baseline prevalence of psychological problems among public safety personnel were as follows: likely major depressive disorder (MDD) was 47.1%, likely generalized anxiety disorder (GAD) was 37.5%, low resilience was 22.2%, and likely PTSD was 13.3%. At six months post-intervention, the prevalence of likely MDD, likely GAD, and likely PTSD among respondents reduced; however, a statistically significant reduction was reported only for likely MDD (-35.3%, X² (1) = 2.55, p = 0.03). There was no significant change in the prevalence of low resilience between baseline and post-intervention. There was a decrease in the mean scores on the PHQ-9, GAD-7, PCL-C, and the BRS from baseline to post-intervention by 25.8%, 24.7%, 9.5%, and 0.3%, respectively. However, the decrease was only statistically significant for the mean change in GAD-7 scores with a low effect size (t (15) = 2.73, p = 0.02).

CONCLUSIONS

The results of this study suggest a significant reduction in the prevalence of likely MDD as well as the severity of anxiety symptoms from baseline to post-intervention for subscribers of the Text4PTSI program. Text4PTSI is a cost-effective, convenient, and easily scalable program that can augment other services for managing the mental health burdens of public safety personnel.

Individualized Prospective Prediction of Opioid Use Disorder

OBJECTIVE

Opioid use disorder (OUD) is a chronic relapsing disorder with a problematic pattern of opioid use, affecting nearly 27 million people worldwide. Machine learning (ML)-based prediction of OUD may lead to early detection and intervention. However, most ML prediction studies were not based on representative data sources and prospective validations, limiting their potential to predict future new cases. In the current study, we aimed to develop and prospectively validate an ML model that could predict individual OUD cases based on representative large-scale health data.

METHOD

We present an ensemble machine-learning model trained on a cross-linked Canadian administrative health data set from 2014 to 2018 (n  =  699,164), with validation of model-predicted OUD cases on a hold-out sample from 2014 to 2018 (n  =  174,791) and prospective prediction of OUD cases on a non-overlapping sample from 2019 (n  =  316,039). We used administrative records of OUD diagnosis for each subject based on International Classification of Diseases (ICD) codes.

RESULTS

With 6409 OUD cases in 2019 (mean [SD], 45.34 [14.28], 3400 males), our model prospectively predicted OUD cases at a high accuracy (balanced accuracy, 86%, sensitivity, 93%; specificity 79%). In accord with prior findings, the top risk factors for OUD in this model were opioid use indicators and a history of other substance use disorders.

CONCLUSION

Our study presents an individualized prospective prediction of OUD cases by applying ML to large administrative health datasets. Such prospective predictions based on ML would be essential for potential future clinical applications in the early detection of OUD.

Digital cognitive behavior therapy for insomnia improving sleep quality: a real-world study

BACKGROUND

Digital cognitive behavior therapy for insomnia (dCBT-I) is an effective treatment in alleviating insomnia. This study examined the effect of dCBT-I for improving sleep quality in patients with insomnia complaints from a clinical population in a real-world setting.

METHODS

The study included 6,002 patients aged 18 years and above with primary complaints of dissatisfying sleep from a sleep clinic in a psychiatric hospital from November 2016 to April 2021. Patients were diagnosed with insomnia, anxiety disorders, or anxiety comorbid with insomnia or depression according to ICD-10. A mobile app was developed for self-reported assessment and delivering dCBT-I interventions and treatment prescriptions to participants. The primary outcome was change in global sleep quality measured by the Pittsburgh Sleep Quality Index (PSQI). At 8- and 12-week follow-up, 509 patients were reassessed. Data were analyzed with non-parametric tests for repeated measures.

RESULTS

Patients treated with dCBT-I monotherapy were younger, with a more frequent family history of insomnia compared to those with medication monotherapy and those with combined dCBT-I and medication therapy. Improvements of sleep quality from baseline to 8-week follow-up were significant in each treatment type. Compared to 8-week follow-up, PSQI scores at 12-week were significantly decreased in the depression group receiving combined therapy and in the anxiety group treated with dCBT-I monotherapy and with combined therapy. A time-by-treatment interaction was detected in anxiety patients indicating differential reduction in PSQI scores over time between different treatment options.

CONCLUSION

The current findings suggest dCBT-I is a practical and effective approach for lessening insomnia symptoms, especially for patients with anxiety symptoms suggesting with a more extended intervention period (i.e., 12 weeks).

TRIAL REGISTRATION

Chinese Clinical Trial Registry (ChiCTR1900022699).

Child sexual abuse survivors: Differential complex multimodal treatment outcomes for pre-COVID and COVID era cohorts

BACKGROUND

Child sexual abuse (CSA) is a form of early-life trauma that affects youth worldwide. In the midst of the current COVID-19 pandemic, it is imperative to investigate the potential impact of added stress on already vulnerable populations.

OBJECTIVE

The aim of this study was to evaluate the effectiveness of a multimodal treatment program on mental health outcomes for youth CSA survivors aged 8-17. Secondary to this, we explored the potential impact of the COVID-19 on treatment outcomes.

PARTICIPANTS AND SETTING

Participants of this study were children and youth aged 8-17 who were engaged in a complex multimodal treatment program specifically designed for youth CSA survivors.

METHODS

Participants were asked to complete self-report surveys at baseline and at the end of two subsequent treatment rounds. Surveys consisted of measures pertaining to: (1) PTSD, (2) depression, (3) anxiety, (4) quality of life, and (5) self-esteem.

RESULTS

Median scores improved for all groups at all timepoints for all five domains. For the pre-Covid participants, the largest improvements in the child program were reported in depression (36.6 %, p = 0.05); in the adolescent program anxiety showed the largest improvement (-35.7 %, p = 0.006). Improvements were generally maintained or increased at the end of round two. In almost every domain, the improvements of the pre-COVID group were greater than those of the COVID-I group.

CONCLUSIONS

A complex multimodal treatment program specifically designed for youth CSA survivors has the capacity to improve a number of relevant determinants of mental health and well-being. The COVID-19 pandemic may have retraumatized participants, resulting in treatment resistance.

Automated Digital Interventions and Smoking Cessation: Systematic Review and Meta-analysis Relating Efficiency to a Psychological Theory of Intervention Perspective

Background

Smoking remains a highly significant preventable global public health problem. In this context, digital interventions offer great advantages in terms of a lack of biological side effects, possibility of automatic delivery, and consequent human resource savings relative to traditional interventions. Such interventions have been studied in randomized controlled trials (RCTs) but have not been systematically reviewed with the inclusion of text-based and multiplatform-based interventions. In addition, this area has not been evaluated from the perspective of the psychological theoretical basis of intervention.

Objective

The aim of this paper is to assess the efficiency of digital interventions in RCT studies of smoking cessation and to evaluate the effectiveness of the strategies used for digital interventions.

Methods

An electronic search of RCTs was conducted using PubMed, Embase, and the Cochrane Library by June 30, 2021. Eligible studies had to compare automated digital intervention (ADI) to the use of a self-help guideline or no intervention. Participants were current smokers (aged 16 years or older). As the main outcome, abstinence after endpoint was extracted from the studies. Systematic review and meta-analysis were conducted to assess the efficiency of ADIs. Metaregressions were conducted to assess the relationship between intervention theory and effectiveness.

Results

A total of 19 trials (15,472 participants) were included in the analysis. The overall abstinence rate (95% CI) at the endpoint was 17.8% (17.0-18.7). The overall risk ratio of the intervention group compared to the controls at the endpoint was 17.8% (17.0-18.7). Cochrane risk-of-bias tool for randomized trials (ROB 2) suggested that most of the studies had a low risk of bias (56.3%). Psychological theory–related constructs or predictors, which refer to other theory-based concepts (rather than only behavioral theory) such as craving or anxiety, are associated with effectiveness.

Conclusions

This study found that ADI had a clear positive effect compared to self-help guidelines or to no intervention, and effectiveness was associated with theory-related constructs or predictors. ADIs should be promoted by policy makers and clinical practitioners to address the huge gap between the need for smoking cessation and availability of traditional treatment resources. Possible increases in ADI efficiency may be achieved by optimally integrating psychotherapeutic theories and techniques.

Trial Registration

PROSPERO CRD42021256593; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=256593

A systematic scoping review of dissociation in borderline personality disorder and implications for research and clinical practice: Exploring the fog

BACKGROUND

Borderline Personality Disorder (BPD) is frequently complicated by the presence of dissociative symptoms. Pathological dissociation is linked with earlier and more severe trauma exposure, emotional dysregulation and worse treatment outcomes in Posttraumatic Stress Disorder and Dissociative Disorders, with implications for BPD.

OBJECTIVE

A systematic scoping review was conducted to assess the extent of current literature regarding the impact of dissociation on BPD and to identify knowledge gaps.

METHODS

Four electronic databases (MEDLINE, APA PsycINFO, EMBASE, CINAHL Plus) were searched, and English peer-reviewed studies with adults with BPD were included, following Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) extension for scoping reviews (PRISMA-ScR) 2018 guidelines.

RESULTS

Most of the 70 included studies were observational (98%) with first authors from Germany (59%). Overall, dissociation was associated with increased BPD symptom severity, self-harm and reduced psychotherapy treatment response; findings regarding suicide risk were mixed. Dissociation was associated with working memory and cognitive deficits, decreased pain perception, altered body ownership, no substance abuse or the abuse of sedative substances, increased fantasy proneness, personality fragmentation, fearful attachment, dream anxiety, perceived stress and altered stress responses, increased cumulative body mass index, decreased water consumption, several neurological correlates and changes in gene expression.

CONCLUSION

BPD with significant dissociative symptoms may constitute a more severe and at-risk subgroup of BPD patients. However, there are significant research gaps and methodological issues in the area, including the possibility of unrecognized Dissociative Disorders in BPD study populations confounding results. Further studies are needed to better understand the impact of dissociation on BPD course and treatment, and to clarify the most appropriate assessment tools for clinical practice. In addition, interventional studies are needed to develop dissociation-specific BPD treatments to determine whether targeting dissociation in BPD can improve treatment outcomes.

Naturalistic randomized controlled trial demonstrating effectiveness of Text4Hope in supporting male population mental health during the COVID-19 pandemic

Background

Mental illness is not uncommon among males. It is estimated that males are more likely to die by suicide, become dependent on alcohol, report frequent drug use, and be dissatisfied with their life, compared to women. In this study, we assessed the potential to offer support to this population using Text4Hope, a texting mental health service.

Methods

The study was a naturalistic randomized controlled trial comparing two populations of Text4Hope male subscribers; an intervention group (IG, Text4Hope subscribers who received once-daily supportive text messages for 6 weeks) and a control group (CG, Text4Hope subscribers who joined the program in the same time frame but were yet to receive text messages). Inferential statistics were used to compare the severity and the prevalence of the likely stress, anxiety, and depression, between the two groups, using the Perceived Stress Scale (PSS-10), the Generalized Anxiety Disorder 7-item (GAD-7), and the Patient Health Questionnaire-9 (PHQ-9), and defined the Composite Mental Health (CMH) score as the sum of these three. T-test, Chi-squared association, and binary logistic regression analyses were applied.

Results

There were 286 male subscribers to Text4Hope. The majority were above 40 years, white, employed, had postsecondary education, were in a relationship, and owned a home. Mean scores of PSS-10, GAD-7, and PHQ-9 scales and the CMH were significantly higher for the CG compared to the IG, 11.4, 28.8, 25.8, and 18.7%, respectively. Similarly, a statistically significantly lower prevalence in IG, compared to the CG, on likely MDD (58.15 vs. 37.4%) and likely GAD (50 vs. 30.8%), with a small effect size. The IG was a significant predictor for lower odds of both likely MDD and likely GAD while controlling for sociodemographic characteristics.

Conclusions

The Text4Hope service is an effective tool for mental health support for male subscribers during the COVID-19 pandemic. Compared to the males who didn't receive the service, those who received it were in better mental health conditions. Further effort is still needed to encourage males to participate in such online services that can help them receive adequate support, particularly during crisis times.

Superior temporal gyrus functional connectivity predicts transcranial direct current stimulation response in Schizophrenia: A machine learning study

Transcranial direct current stimulation (tDCS) is a promising adjuvant treatment for persistent auditory verbal hallucinations (AVH) in Schizophrenia (SZ). Nonetheless, there is considerable inter-patient variability in the treatment response of AVH to tDCS in SZ. Machine-learned models have the potential to predict clinical response to tDCS in SZ. This study aims to examine the feasibility of identifying SZ patients with persistent AVH (SZ-AVH) who will respond to tDCS based on resting-state functional connectivity (rs-FC). Thirty-four SZ-AVH patients underwent resting-state functional MRI at baseline followed by add-on, twice-daily, 20-min sessions with tDCS (conventional/high-definition) for 5 days. A machine learning model was developed to identify tDCS treatment responders based on the rs-FC pattern, using the left superior temporal gyrus (LSTG) as the seed region. Functional connectivity between LSTG and brain regions involved in auditory and sensorimotor processing emerged as the important predictors of the tDCS treatment response. L1-regularized logistic regression model had an overall accuracy of 72.5% in classifying responders vs. non-responders. This model outperformed the state-of-the-art convolutional neural networks (CNN) model-both without (59.41%) and with pre-training (68.82%). It also outperformed the L1-logistic regression model trained with baseline demographic features and clinical scores of SZ patients. This study reports the first evidence that rs-fMRI-derived brain connectivity pattern can predict the clinical response of persistent AVH to add-on tDCS in SZ patients with 72.5% accuracy.

Developing, Implementing, and Evaluating an Artificial Intelligence-Guided Mental Health Resource Navigation Chatbot for Health Care Workers and Their Families During and Following the COVID-19 Pandemic: Protocol for a Cross-sectional Study

Background

Approximately 1 in 3 Canadians will experience an addiction or mental health challenge at some point in their lifetime. Unfortunately, there are multiple barriers to accessing mental health care, including system fragmentation, episodic care, long wait times, and insufficient support for health system navigation. In addition, stigma may further reduce an individual’s likelihood of seeking support. Digital technologies present new and exciting opportunities to bridge significant gaps in mental health care service provision, reduce barriers pertaining to stigma, and improve health outcomes for patients and mental health system integration and efficiency. Chatbots (ie, software systems that use artificial intelligence to carry out conversations with people) may be explored to support those in need of information or access to services and present the opportunity to address gaps in traditional, fragmented, or episodic mental health system structures on demand with personalized attention. The recent COVID-19 pandemic has exacerbated even further the need for mental health support among Canadians and called attention to the inefficiencies of our system. As health care workers and their families are at an even greater risk of mental illness and psychological distress during the COVID-19 pandemic, this technology will be first piloted with the goal of supporting this vulnerable group.

Objective

This pilot study seeks to evaluate the effectiveness of the Mental Health Intelligent Information Resource Assistant in supporting health care workers and their families in the Canadian provinces of Alberta and Nova Scotia with the provision of appropriate information on mental health issues, services, and programs based on personalized needs.

Methods

The effectiveness of the technology will be assessed via voluntary follow-up surveys and an analysis of client interactions and engagement with the chatbot. Client satisfaction with the chatbot will also be assessed.

Results

This project was initiated on April 1, 2021. Ethics approval was granted on August 12, 2021, by the University of Alberta Health Research Board (PRO00109148) and on April 21, 2022, by the Nova Scotia Health Authority Research Ethics Board (1027474). Data collection is anticipated to take place from May 2, 2022, to May 2, 2023. Publication of preliminary results will be sought in spring or summer 2022, with a more comprehensive evaluation completed by spring 2023 following the collection of a larger data set.

Conclusions

Our findings can be incorporated into public policy and planning around mental health system navigation by Canadian mental health care providers—from large public health authorities to small community-based, not-for-profit organizations. This may serve to support the development of an additional touch point, or point of entry, for individuals to access the appropriate services or care when they need them, wherever they are.

International Registered Report Identifier (IRRID)

PRR1-10.2196/33717

Five year cost savings of a multimodal treatment program for child sexual abuse (CSA): a social return on investment study

BACKGROUND

Specialized mental health services for the treatment of Child Sexual Abuse (CSA) are generally expensive and labour intensive. They require a trauma-informed approach that may involve multiple services and therapeutic modalities, provided over the course of several months. That said, given the broad-ranging, long term negative sequelae of CSA, an evaluation of the cost-benefit analysis of treatment is clearly justified.

METHODS

We performed a Social Return on Investment (SROI) analysis of data gathered as part of the treatment program at the Be Brave Ranch in Edmonton, Canada to determine the value-for-money of the services provided. We endeavoured to take a conservative, medium-term (5 year) perspective; this is in contrast to short term (1-2 year) effects, which may rapidly dissipate, or long term (15-20 year) effects, which are likely diffuse and difficult to measure. As such, our analysis was based on an average annual intake of 100 children/adolescents (60:40 split) and their families, followed over a five-year timeframe. Financial proxies were assigned to benefits not easily monetized, and six potential domains of cost savings were identified.

RESULTS

Our analyses suggest that each dollar spent in treatment results in an average cost savings of $11.60 (sensitivity analysis suggests range of 9.20-12.80). The largest value-for-money was identified as the domain of crisis prevention, via the avoidance of rare but costly events associated with the long term impacts of CSA. Somewhat surprisingly, savings related to the area of criminal justice were minimal, compared to other social domains analysed. Implications are discussed.

CONCLUSIONS

Our results support the cost effectiveness of the investment associated with specialized, evidence-based early interventions for CSA. These approaches alleviate severe, negative outcomes associated with CSA, resulting in both economic savings and social benefits. These findings rest upon a number of assumptions, and generalizability of these results is therefore limited to similar programs located in comparable areas. However, the SROI ratio achieved in this analysis, in excess of $11:1, supports the idea that, while costly, these services more than pay for themselves over time.

Sleep clinic service model with closed-loop management for insomnia

Insomnia is a common medical condition associated with other psychological and physiological disorders, and may require long-term treatment and outpatient management. As such, it is critical that effective treatment and management is provided in clinical practice. This study introduces an innovative outpatient service model for patients with insomnia, which includes providing medical care before, during and after diagnosis in public hospital clinics, completing prescribed treatments at home and return visits. It is a digital health-based, patient-centred, collaborative care model with closed-loop management. The proposed management strategy may help achieve a balance between the efficiency and the quality of outpatient medical care for insomnia.

Prediction of Obsessive-Compulsive Disorder: Importance of Neurobiology-Aided Feature Design and Cross-Diagnosis Transfer Learning

BACKGROUND

Machine learning applications using neuroimaging provide a multidimensional, data-driven approach that captures the level of complexity necessary for objectively aiding diagnosis and prognosis in psychiatry. However, models learned from small training samples often have limited generalizability, which continues to be a problem with automated diagnosis of mental illnesses such as obsessive-compulsive disorder (OCD). Earlier studies have shown that features incorporating prior neurobiological knowledge of brain function and combining brain parcellations from various sources can potentially improve the overall prediction. However, it is unknown whether such knowledge-driven methods can provide a performance that is comparable to state-of-the-art approaches based on neural networks.

METHODS

In this study, we apply a transparent and explainable multiparcellation ensemble learning framework EMPaSchiz (Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction) to the task of predicting OCD, based on a resting-state functional magnetic resonance imaging dataset of 350 subjects. Furthermore, we apply transfer learning using the features found effective for schizophrenia to OCD to leverage the commonality in brain alterations across these psychiatric diagnoses.

RESULTS

We show that our knowledge-based approach leads to a prediction performance of 80.3% accuracy for OCD diagnosis that is better than domain-agnostic and automated feature design using neural networks. Furthermore, we show that a selection of reduced feature sets can be transferred from schizophrenia to the OCD prediction model without significant loss in prediction performance.

CONCLUSIONS

This study presents a machine learning framework for OCD prediction with neurobiology-aided feature design using resting-state functional magnetic resonance imaging that is generalizable and reasonably interpretable.

Clinical Outcomes in Routine Evaluation Measures for Patients Discharged from Acute Psychiatric Care: Four-Arm Peer and Text Messaging Support Controlled Observational Study

Background: Peer support workers (PSW) and text messaging services (TxM) are supportive health services that are frequently examined in the field of mental health. Both interventions have positive outcomes, with TxM demonstrating clinical and economic effectiveness and PSW showing its utility within the recovery-oriented model. Objective: To evaluate the effectiveness of PSW and TxM in reducing psychological distress of recently discharged patients receiving psychiatric care. Methods: This is a prospective, rater-blinded, pilot-controlled observational study consisting of 181 patients discharged from acute psychiatric care. Patients were randomized into one of four conditions: daily supportive text messages only, peer support only, peer support plus daily text messages, or treatment as usual. Clinical Outcomes in Routine Evaluation—Outcome Measure (CORE-OM), a standardized measure of mental distress, was administered at four time points: baseline, six weeks, three months, and six months. MANCOVA was used to assess the impact of the interventions on participants’ scores on four CORE-OM subscales across the three follow-up time points. Recovery, clinical change, and reliable change in CORE-OM all-item analysis were examined across the four groups, and the prevalence of risk symptoms was measured. Results: A total of 63 patients completed assessments at each time point. The interaction between PSW and TxM was predictive of differences in scores on the CORE-OM functioning subscale with a medium effect size (F1,63 = 4.19; p = 0.045; ηp2 = 0.07). The PSW + TxM group consistently achieved higher rates of recovery and clinical and reliable improvement compared to the other study groups. Additionally, the text message group and the PSW + TxM group significantly reduced the prevalence of risk of self/other harm symptoms after six months of intervention, with 27.59% (χ2(1) = 4.42, p = 0.04) and 50% (χ2(1) = 9.03, p < 0.01) prevalence reduction, respectively. Conclusions: The combination of peer support and supportive text messaging is an impactful intervention with positive clinical outcomes for acute care patients. Adding the two interventions into routine psychiatric care for patients after discharge is highly recommended.

The Mental Health Impact of the COVID-19 Pandemic Among Physicians, Nurses, and Other Health Care Providers in Alberta: Cross-sectional Survey

Background

During the COVID-19 pandemic, threats to mental health, psychological safety, and well-being are evident, particularly among the first responders and the health care staff.

Objective

This study aims to examine the prevalence and potential predictors of the likely stress, generalized anxiety disorder, and major depressive disorder among health care workers (HCWs).

Methods

A cross-sectional survey was used through a survey link sent to gather demographic information and responses on several self-report scales, including the Perceived Stress Scale, the Generalized Anxiety Disorder 7-item scale, and the Patient Health Questionnaire-9 among HCWs enrolled in the Text4Hope program.

Results

The result from this study suggests that during the COVID-19 pandemic, HCWs reported a high likelihood of moderate-to-high perceived stress (n=840, 81.2%), moderate-to-severe anxiety (n=369, 38.6%), and depression (n=317, 32.7%) symptoms. Nurses and other HCWs were significantly more likely to report depressive symptoms compared to physicians (F(2, 159.47)=15.89, 95% CI –5.05 to –2.04). Younger age groups of HCWs (≤30 years) were more prone to report likely stress, anxiety, and depressive symptoms compared to HCWs 41-50 and >50 years old (odds ratio [OR] 1.82-3.03). Similarly, females and those who reported a lack of social support (separated/divorced and single) among HCWs had a higher likelihood to report likely stress and depressive symptoms, respectively (OR 1.8 and 1.6, respectively).

Conclusions

This cross-sectional study explored a high level of mental health burdens during the COVID-19 pandemic among HCWs in Alberta. Levels of psychological symptoms were more noticeable in the female gender and the nursing profession.

Data-driven study on resting-state functional magnetic resonance imaging during early abstinence of alcohol dependence in male patients and its predictive value for relapse

BACKGROUND

Alcohol dependence is a mental disorder with a high relapse rate. However, specific neuroimaging biomarkers have not been determined for alcohol dependence and its relapse. We conducted data-driven research to investigate resting-state functional magnetic resonance imaging (rs-fMRI) during early abstinence from alcohol dependence and its potential ability to predict relapse.

METHODS

Participants included 68 alcohol-dependent patients and 68 healthy controls (HCs). The regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuations (fALFF) were compared between the alcohol dependence group and the HCs and between the relapse group and the nonrelapse group. The brain regions that presented significantly different ReHo and/or fALFF between the alcohol-dependent patients and HCs and/or between the relapsed and nonrelapsed patients were selected as the seeds to calculate the functional connectivities (FCs).

RESULTS

During a 6-month follow-up period, 52.24% of alcohol-dependent patients relapsed. A regression model for differentiating alcohol-dependent patients and HCs showed that reductions in ReHo in the left postcentral region, fALFF in the right fusiform region, and FC in the right fusiform region to the right middle cingulum were independently associated with alcohol dependence, with an area under the receiver operating characteristic curve (AUC) of 0.841. The baseline FC of the left precentral to the left cerebellum of the relapse group was significantly lower than that of the nonrelapse group. The AUC of this FC to predict relapse was 0.774.

CONCLUSIONS

Our findings contribute to advancing research on the neurobiological etiology and predictive biomarkers for relapse associated with alcohol dependence.

Detecting Presence of PTSD Using Sentiment Analysis From Text Data

Rates of Post-traumatic stress disorder (PTSD) have risen significantly due to the COVID-19 pandemic. Telehealth has emerged as a means to monitor symptoms for such disorders. This is partly due to isolation or inaccessibility of therapeutic intervention caused from the pandemic. Additional screening tools may be needed to augment identification and diagnosis of PTSD through a virtual medium. Sentiment analysis refers to the use of natural language processing (NLP) to extract emotional content from text information. In our study, we train a machine learning (ML) model on text data, which is part of the Audio/Visual Emotion Challenge and Workshop (AVEC-19) corpus, to identify individuals with PTSD using sentiment analysis from semi-structured interviews. Our sample size included 188 individuals without PTSD, and 87 with PTSD. The interview was conducted by an artificial character (Ellie) over a video-conference call. Our model was able to achieve a balanced accuracy of 80.4% on a held out dataset used from the AVEC-19 challenge. Additionally, we implemented various partitioning techniques to determine if our model was generalizable enough. This shows that learned models can use sentiment analysis of speech to identify the presence of PTSD, even through a virtual medium. This can serve as an important, accessible and inexpensive tool to detect mental health abnormalities during the COVID-19 pandemic.

Patients' Expectations and Experiences With a Mental Health-Focused Supportive Text Messaging Program: Mixed Methods Evaluation

BACKGROUND

Web-based services are an economical and easily scalable means of support that uses existing technology. Text4Support is a supportive, complementary text messaging service that supports people with different mental health conditions after they are discharged from inpatient psychiatric care.

OBJECTIVE

In this study, we aim to assess user satisfaction with the Text4Support service to gain a better understanding of subscribers' experiences.

METHODS

This was a mixed methods study using secondary data from a pilot observational controlled trial. The trial included 181 patients discharged from acute psychiatric care and distributed into 4 randomized groups. Out of the 4 study groups in the initial study, 2 groups who received supportive text messages (89/181, 49.2% of patients), either alone or alongside a peer support worker, were included. Thematic and descriptive analyses were also performed. Differences in feedback based on sex at birth and primary diagnosis were determined using univariate analysis. The study was registered with ClinicalTrials.gov (trial registration number: NCT03404882).

RESULTS

Out of 89 participants, 36 (40%) completed the follow-up survey. The principal findings were that Text4Support was well perceived with a high satisfaction rate either regarding the feedback of the messages or their perceived impact. Meanwhile, there was no statistically significant difference between satisfactory items based on the subscriber's sex at birth or primary diagnosis. The patients' initial expectations were either neutral or positive in relation to the expected nature or the impact of the text messages received on their mental well-being. In addition, the subscribers were satisfied with the frequency of the messages, which were received once daily for 6 consecutive months. The participants recommended more personalized messages or mutual interaction with health care personnel.

CONCLUSIONS

Text4Support was generally well perceived by patients after hospital discharge, regardless of their sex at birth or mental health diagnosis. Further personalization and interactive platforms were recommended by participants that may need to be considered when designing similar future services.

Anhedonia in Depression and Schizophrenia: Brain Reward and Aversion Circuits

Anhedonia, which is defined as markedly diminished interest or pleasure, is a prominent symptom of psychiatric disorders, most notably major depressive disorder (MDD) and schizophrenia. Anhedonia is considered a transdiagnostic symptom that is associated with deficits in neural reward and aversion functions. Here, we review the characteristics of anhedonia in depression and schizophrenia as well as shared or disorder-specific anhedonia-related alterations in reward and aversion pathways of the brain. In particular, we highlight that anhedonia is characterized by impairments in anticipatory pleasure and integration of reward-related information in MDD, whereas anhedonia in schizophrenia is associated with neurocognitive deficits in representing the value of rewards. Dysregulation of the frontostriatal circuit and mesocortical and mesolimbic circuit systems may be the transdiagnostic neurobiological basis of reward and aversion impairments underlying anhedonia in these two disorders. Blunted aversion processing in depression and relatively strong aversion in schizophrenia are primarily attributed to the dysfunction of the habenula, insula, amygdala, and anterior cingulate cortex. Furthermore, patients with schizophrenia appear to exhibit greater abnormal activation and extended functional coupling than those with depression. From a transdiagnostic perspective, understanding the neural mechanisms underlying anhedonia in patients with psychiatric disorders may help in the development of more targeted and efficacious treatment and intervention strategies.

Using Machine Learning to Predict Remission in Patients With Major Depressive Disorder Treated With Desvenlafaxine

BACKGROUND

Major depressive disorder (MDD) is a common and burdensome condition that has low rates of treatment success for each individual treatment. This means that many patients require several medication switches to achieve remission; selecting an effective antidepressant is typically a sequential trial-and-error process. Machine learning techniques may be able to learn models that can predict whether a specific patient will respond to a given treatment, before it is administered. This study uses baseline clinical data to create a machine-learned model that accurately predicts remission status for a patient after desvenlafaxine (DVS) treatment.

METHODS

We applied machine learning algorithms to data from 3,399 MDD patients (90% of the 3,776 subjects in 11 phase-III/IV clinical trials, each described using 92 features), to produce a model that uses 26 of these features to predict symptom remission, defined as an 8-week Hamilton Depression Rating Scale score of 7 or below. We evaluated that learned model on the remaining held-out 10% of the data (n = 377).

RESULTS

Our resulting classifier, a trained linear support vector machine, had a holdout set accuracy of 69.0%, significantly greater than the probability of classifying a patient correctly by chance. We demonstrate that this learning process is stable by repeatedly sampling part of the training dataset and running the learner on this sample, then evaluating the learned model on the held-out instances of the training set; these runs had an average accuracy of 67.0% ± 1.8%.

CONCLUSIONS

Our model, based on 26 clinical features, proved sufficient to predict DVS remission significantly better than chance. This may allow more accurate use of DVS without waiting 8 weeks to determine treatment outcome, and may serve as a first step toward changing psychiatric care by incorporating clinical assistive technologies using machine-learned models.

Mental Health Impacts of Wildfire, Flooding and COVID-19 on Fort McMurray School Board Staff and Other Employees: A Comparative Study

Background: Fort McMurray, a city in northern Alberta, Canada, has experienced multiple traumas in the last five years, including the 2016 wildfire, the 2020 floods, and the COVID-19 pandemic. Eighteen months after the wildfire, major depressive disorder (MDD), generalized anxiety disorder (GAD), and Post Traumatic Stress Disorder (PTSD) symptoms were elevated among school board employees in the city. Objective: This study aimed to compare employees of the school board and other employees of Fort McMurray in respect to the impact the 2016 wildfires, the 2019 COVID pandemic, and the 2020 floods had on their mental health. Methodology: A quantitative cross-sectional survey was conducted in Fort McMurray from 24 April to 2 June 2021. Online questionnaires were administered through REDCap and were designed to capture socio-demographic characteristics, clinical as well as wildfire, COVID-19, and flooding-related variables. Mental health outcome variables were captured using self-reported standardized assessment scales. Data were analysed with descriptive statistics, Chi-square/Fisher’s Exact tests, and binary regression analysis. Results: Of the 249 residents who accessed the online survey, 186 completed the survey, giving a response rate of 74.7%. Of these respondents, 93.5% (174) indicated their employment status and were included in the Chi-square analysis. Most of the respondents were female (86.2%, (150)), above 40 years (53.4%, (93)), and were in a relationship (71.3%, (124)). The prevalence values for MDD, GAD and PTSD among respondents were 42.4%, 41.0, and 36.8%, respectively. There was a statistically significant difference between employees of the school board and other employees with respect to likely PTSD prevalence (28% vs. 45%, respectively, p < 0.05), although with other factors controlled for, in a binary logistic regression model, employer type did not significantly predict likely PTSD. Conclusions: The study has established that likely PTSD symptoms were significantly higher in other employees compared to those of school board employees. Greater exposure to the traumatic events and a greater perceived lack of support from other employers might have contributed to the significantly higher prevalence of PTSD in other employees.

Social Media and the Transformation of the Physician-Patient Relationship: Viewpoint

As many as 80% of internet users seek health information online. The social determinants of health (SDoH) are intimately related to who has access to the internet and health care as a whole. Those who face more barriers to care are more likely to benefit from accessing health information online, assuming the information they are retrieving is accurate. Virtual communities on social media platforms are beginning to serve as venues for seeking health information online because peers have been shown to influence health behavior more than almost anything else. As a positive mediator of health, social media can be used as a direct or indirect mode of communication between physicians and patients, a venue for health promotion and health information, and a community support network. However, false or misleading content, social contagion, confirmation bias, and security and privacy concerns must be mitigated to realize the full potential of social media as a positive mediator of health. This paper presents the shifting dynamics of how such communities are affecting physician-patient relationships. With the intersections between the SDoH, social media, and health evolving, physicians must take into consideration these factors when establishing their relationships with patients. We argue a paradigm shift in the physician-patient relationship is warranted, one where physicians acknowledge the impacts of the SDoH on information-seeking behavior, recognize the positive and negative roles of social media as a mediator of health through the lens of the SDoH, and use social media to catalyze positive changes in the physician-patient relationship. We discuss how the physician-patient relationship must evolve to accommodate for the ever-increasing role of social media in health and to best use social media as a tool to improve health outcomes. Finally, we present a fluid and multicomponent diagram that we believe will assist in framing future research in this area. We conclude that it is ineffective and even counterproductive for physicians to ignore the relationship between social media, the SDoH and health, their impact on one another, and the effect it has on designing the medical encounter and the delivery of care under the definition of precision medicine.

Text Messaging Versus Email Messaging to Support Patients With Major Depressive Disorder: Protocol for a Randomized Hybrid Type II Effectiveness-Implementation Trial

BACKGROUND

Major depressive disorder (MDD) accounts for 40.5% of disability-adjusted life years caused by mental and substance use disorders. Barriers such as stigma and financial and physical access to care have been reported, highlighting the need for innovative, accessible, and cost-effective psychological interventions. The effectiveness of supportive SMS text messaging in alleviating depression symptoms has been proven in clinical trials, but this approach can only help those with mobile phones.

OBJECTIVE

This paper presents the protocol for a study that will aim to evaluate the feasibility, comparative effectiveness, and user satisfaction of daily supportive email messaging as an effective strategy compared to daily supportive text messaging as part of the treatment of patients with MDD.

METHODS

This trial will be carried out using a hybrid type II implementation-effectiveness design. This design evaluates the effectiveness of an implementation strategy or intervention, while also evaluating the implementation context associated with the intervention. Patients with MDD receiving usual care will be randomized to receive either daily supportive email messaging or daily supportive text messaging of the same content for 6 months. The Patient Health Questionnaire-9, the Generalized Anxiety Disorder-7, and the 5-item World Health Organization Well-Being Index will be used to evaluate the effectiveness of both strategies. The implementation evaluation will be guided by the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework, as well as the Consolidated Framework for Implementation Research. All outcome measures will be analyzed using descriptive and inferential statistics. Qualitative data will be analyzed using thematic analysis.

RESULTS

Data collection for this trial began in April 2021. We expect the study results to be available within 18 months of study commencement. The results will shed light on the feasibility, acceptability, and effectiveness of using automated emails as a strategy for delivering supportive messages to patients with MDD in comparison to text messaging.

CONCLUSIONS

The outcome of this trial will have translational impact on routine patient care and access to mental health, as well as potentially support mental health policy decision-making for health care resource allocation.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04638231; https://clinicaltrials.gov/ct2/show/NCT04638231.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/29495.

Recovery Following Peer and Text Messaging Support After Discharge From Acute Psychiatric Care in Edmonton, Alberta: Controlled Observational Study

Background

Peer support is an emotional, social, and practical help provided by nonprofessionals to assist others in sustaining health behaviors. Peer support is valued in recovery-oriented models of mental health and is becoming increasingly implemented at the organizational level. Text messaging is a relatively low-cost, high-impact, and easily scalable program that uses existing technology, is devoid of geographic barriers, and is easily accessible to end users.

Objective

This study aims to evaluate the effectiveness of an innovative peer support system plus a supportive text messaging program on the recovery of discharged patients from acute psychiatric care.

Methods

This prospective, rater blinded, controlled observational study included 181 patients who were discharged from acute psychiatric care. Patients were randomized to one of four conditions: treatment as usual (follow-up care), daily supportive text messages only, peer support only, or peer support plus daily supportive text messages. A standardized self-report measure of recovery (Recovery Assessment Scale [RAS]) was completed at baseline, 6 weeks, 3 months, and 6 months. Descriptive analysis, one-way analysis of variance, and repeated measures multivariate analysis of covariance were used to examine the changes in the RAS among the study groups and over the follow-up time points.

Results

A total of 65 patients completed the assessments at each time point. For the overall sample, higher scores were found for the peer support plus text message condition compared with the text message only and treatment as usual condition on several scales (ie, willingness to ask for help and personal confidence and hope) and total score on the RAS, after 6 months of intervention.

Conclusions

Peer support plus supportive text messaging seems to result in improved recovery compared with other interventions. It may be advisable to incorporate the two interventions as part of routine practice for patients with psychiatric disorders upon hospital discharge.

The interactions between childhood adversities and recent stress were associated with early-adulthood depression among Chinese undergraduate students

BACKGROUND

It is widely acknowledged that childhood adversities (CAs) and recent stress are potential risk factors for adult depression. However, the mechanism(s) by which interactions of CAs with recent stress affect adult depression remain unclear.

AIMS

To investigate the predictive association of the interaction among CAs and recent stress with early-adult depression.

METHOD

We conducted an annual survey of all freshmen for the period of 2016-2018 in a Chinese comprehensive university, with a sample size of 23,206. An online questionnaire including standardized self-report instruments was used to assess sociodemographic factors, childhood experiences of left-behind (CELB), and maltreatments (CEMTs) including beating (CEB), neglect (CEN), sexual abuse (CESA), recent stress, and current depression (measured by the 9-item Patient Health Questionnaire).

RESULTS

The correlation of Individual CAs and recent stress was significant. In addition to their significant independent/direct incremental effects, all surveyed CAs were associated with increased severity of early-adult depression, and increased frequency of clinically significant depression (CSD), through significant associations with recent stress (mediation effect). History of CEMTs including CEB, CEN, and CESA significantly increased the effects of recent stress on depression (moderation effect).

CONCLUSIONS

Chinese undergraduate students reported frequent history of exposure to CAs, which increased the likelihood of depression in early adulthood, not only directly but also through the increasing the likelihood (mediation effect) and impact (moderation effect) of recent stress on depression. These novel findings may help to extend our understanding of environmental determinants of depression, and to guide further research, clinical practice, and policy in this context.

Predicting pediatric anxiety from the temporal pole using neural responses to emotional faces

A prominent cognitive aspect of anxiety is dysregulation of emotional interpretation of facial expressions, associated with neural activity from the amygdala and prefrontal cortex. We report machine learning analysis of fMRI results supporting a key role for a third area, the temporal pole (TP) for childhood anxiety in this context. This finding is based on differential fMRI responses to emotional faces (angry versus fearful faces) in children with one or more of generalized anxiety, separation anxiety, and social phobia (n = 22) compared with matched controls (n = 23). In our machine learning (Adaptive Boosting) model, the right TP distinguished anxious from control children (accuracy = 81%). Involvement of the TP as significant for neurocognitive aspects of pediatric anxiety is a novel finding worthy of further investigation.

The Experience of Key Stakeholders During the Implementation and Use of Trauma Therapy via Digital Health for Military, Veteran, and Public Safety Personnel: Qualitative Thematic Analysis

Background

Exposure to occupational stressors and potentially psychologically traumatic events experienced by public safety personnel (eg, paramedics, police, fire, and correctional officers), military members, and veterans can lead to the development of posttraumatic stress injuries and other mental health disorders. Providing emergency services during COVID-19 has intensified the challenges. Owing to COVID-19 restrictions, mental health service providers offering support to these populations have had to rapidly pivot to use digital versus in-person methods of service delivery.

Objective

This paper aims to explore the experience of mental health service providers regarding digital health service delivery, including the current state of digital mental health service delivery, barriers to and facilitators of the use of digital health for mental health service delivery experienced during the pandemic, and recommendations for implementing and integrating digital health into regular mental health service delivery.

Methods

This embedded mixed-methods study included questionnaires and focus groups with key stakeholders (N=31) with knowledge and experience in providing mental health services. Data analysis included descriptive, quantitative, and qualitative thematic analyses.

Results

The following three themes emerged: being forced into change, daring to deliver mental health services using digital health, and future possibilities offered by digital health. In each theme, participants’ responses reflected their perceptions of service providers, organizations, and clients. The findings offer considerations regarding for whom and at what point in treatment digital health delivery is appropriate; recommendations for training, support, resources, and guidelines for digitally delivering trauma therapy; and a better understanding of factors influencing mental health service providers’ perceptions and acceptance of digital health for mental health service delivery.

Conclusions

The results indicate the implementation of digital health for mental health service delivery to military members, public safety personnel, and veterans. As the COVID-19 pandemic continues, remote service delivery methods for trauma therapy are urgently needed to support the well-being of those who have served and continue to serve.

Nurturing Spiritual Resilience to Promote Post-disaster Community Recovery: The 2016 Alberta Wildfire in Canada

The 2016 Alberta wildfire, the largest insured natural disaster in Canada, led to a mass evacuation of residents of Fort McMurray, a small city in northern Alberta. The wildfire resulted in significant damages to housing and community infrastructure. The entire community was displaced for several weeks. Post-disaster, community members experienced individual and collective trauma, and other negative mental health impacts in response to the significant losses and grief they endured. Spirituality has been found to be a major protective factor in facilitating resiliency and recovery following the experience of disaster. Nonetheless, little focus has been directed toward how spirituality can strengthen and empower community capacity and growth during post-disaster recovery. Our study explored various meanings and concerns, along with tools and strategies that helped to nurture spiritual resilience and well-being among residents of Fort McMurray following the Alberta wildfire. Data were collected through interviews and focus group discussions with community influencers working to support long-term recovery efforts in the city. Participants identified a number of spiritual resources such as a strong sense of belonging, a shared positive outlook, faith and hope, compassion, and sense of gratitude, which contributed to increased resilience and positive health and well-being and helped them to support families and communities in the post disaster recovery period. Our findings indicate that spiritual values and beliefs can play a significant role in building resilience and promoting individual and communal healing and recovery post-disaster. These findings have important implications for post-disaster recovery strategies, as they highlight the need to ensure supports for interventions and initiatives that strengthen a collective sense of identity and social cohesion, informed by communal norms and beliefs, including programs and resources which support opportunities for reflexivity to foster shared healing and ongoing recovery processes.

Aberrant triple-network connectivity patterns discriminate biotypes of first-episode medication-naive schizophrenia in two large independent cohorts

Schizophrenia is a complex disorder associated with aberrant brain functional connectivity. This study aims to demonstrate the relation of heterogeneous symptomatology in this disorder to distinct brain connectivity patterns within the triple-network model. The study sample comprised 300 first-episode antipsychotic-naive patients with schizophrenia (FES) and 301 healthy controls (HCs). At baseline, resting-state functional magnetic resonance imaging data were captured for each participant, and concomitant neurocognitive functions were evaluated outside the scanner. Clinical information of 49 FES in the discovery dataset were reevaluated at a 6-week follow-up. Differential features between FES and HCs were selected from triple-network connectivity profiles. Cutting-edge unsupervised machine learning algorithms were used to define patient subtypes. Clinical and cognitive variables were compared between patient subgroups. Two FES subgroups with differing triple-network connectivity profiles were identified in the discovery dataset and confirmed in an independent hold-out cohort. One patient subgroup appearing to have more severe clinical symptoms was distinguished by salience network (SN)-centered hypoconnectivity, which was associated with greater impairments in sustained attention. The other subgroup exhibited hyperconnectivity and manifested greater deficits in cognitive flexibility. The SN-centered hypoconnectivity subgroup had more persistent negative symptoms at the 6-week follow-up than the hyperconnectivity subgroup. The present study illustrates that clinically relevant cognitive subtypes of schizophrenia may be associated with distinct differences in connectivity in the triple-network model. This categorization may foster further analysis of the effects of therapy on these network connectivity patterns, which may help to guide therapeutic choices to effectively reach personalized treatment goals.

Prevalence, Demographic, and Clinical Correlates of Likely PTSD in Subscribers of Text4Hope during the COVID-19 Pandemic

Background: During the COVID-19 pandemic, people may experience increased risk of adverse mental health, particularly post-traumatic stress disorder (PTSD). Methods: A survey measured stress, anxiety, depression, and PTSD symptoms in Text4Hope subscribers using the Perceived Stress Scale, Generalized Anxiety Disorder 7-Item Scale, Patient Health Questionnaire-9, and PTSD Checklist for DSM-5 Part 3, respectively. A Chi-square test and multivariate logistic regression were employed. Results: Most respondents were 41–60 years old (49.5%), Caucasian (83.3%), with post-secondary education (92.1%), employed (70.3%), married/cohabiting/partnered (64.9%), and homeowners (71.7%). Likely PTSD was reported in 46.8% of the respondents. Those who were afraid to contract the coronavirus had a history of depression before the pandemic, and those who received counselling during the pandemic exhibited a high prevalence of likely PTSD (OR (1.7 to 2.2)). Significant lower odds of likely PTSD were observed among subscribers who received absolute support from family/friends. Conclusions: This paper presents findings on the prevalence of likely PTSD and identified vulnerable groups during the COVID-19 pandemic. Our results support the proposal that public health advice should incorporate mental health wellness campaigns aiming to reduce the psychological impact of pandemics.