Jmir Mental Health最新文献

{"title":"Efficacy of eHealth Versus In-Person Cognitive Behavioral Therapy for Insomnia: Systematic Review and Meta-Analysis of Equivalence.","authors":"Sofie Møgelberg Knutzen, Dinne Skjærlund Christensen, Patrick Cairns, Malene Flensborg Damholdt, Ali Amidi, Robert Zachariae","doi":"10.2196/58217","DOIUrl":"10.2196/58217","url":null,"abstract":"<p><strong>Background: </strong>Insomnia is a prevalent condition with significant health, societal, and economic impacts. Cognitive behavioral therapy for insomnia (CBTI) is recommended as the first-line treatment. With limited accessibility to in-person-delivered CBTI (ipCBTI), electronically delivered eHealth CBTI (eCBTI), ranging from telephone- and videoconference-delivered interventions to fully automated web-based programs and mobile apps, has emerged as an alternative. However, the relative efficacy of eCBTI compared to ipCBTI has not been conclusively determined.</p><p><strong>Objective: </strong>This study aims to test the comparability of eCBTI and ipCBTI through a systematic review and meta-analysis of equivalence based on randomized controlled trials directly comparing the 2 delivery formats.</p><p><strong>Methods: </strong>A comprehensive search across multiple databases was conducted, leading to the identification and analysis of 15 unique randomized head-to-head comparisons of ipCBTI and eCBTI. Data on sleep and nonsleep outcomes were extracted and subjected to both conventional meta-analytical methods and equivalence testing based on predetermined equivalence margins derived from previously suggested minimal important differences. Supplementary Bayesian analyses were conducted to determine the strength of the available evidence.</p><p><strong>Results: </strong>The meta-analysis included 15 studies with a total of 1083 participants. Conventional comparisons generally favored ipCBTI. However, the effect sizes were small, and the 2 delivery formats were statistically significantly equivalent (P<.05) for most sleep and nonsleep outcomes. Additional within-group analyses showed that both formats led to statistically significant improvements (P<.05) in insomnia severity; sleep quality; and secondary outcomes such as fatigue, anxiety, and depression. Heterogeneity analyses highlighted the role of treatment duration and dropout rates as potential moderators of the differences in treatment efficacy.</p><p><strong>Conclusions: </strong>eCBTI and ipCBTI were found to be statistically significantly equivalent for treating insomnia for most examined outcomes, indicating eCBTI as a clinically relevant alternative to ipCBTI. This supports the expansion of eCBTI as a viable option to increase accessibility to effective insomnia treatment. Nonetheless, further research is needed to address the limitations noted, including the high risk of bias in some studies and the potential impact of treatment duration and dropout rates on efficacy.</p><p><strong>Trial registration: </strong>PROSPERO CRD42023390811; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=390811.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11384180/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142057001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning, Deep Learning, and Data Preprocessing Techniques for Detecting, Predicting, and Monitoring Stress and Stress-Related Mental Disorders: Scoping Review.","authors":"Moein Razavi, Samira Ziyadidegan, Ahmadreza Mahmoudzadeh, Saber Kazeminasab, Elaheh Baharlouei, Vahid Janfaza, Reza Jahromi, Farzan Sasangohar","doi":"10.2196/53714","DOIUrl":"10.2196/53714","url":null,"abstract":"<p><strong>Background: </strong>Mental stress and its consequent mental health disorders (MDs) constitute a significant public health issue. With the advent of machine learning (ML), there is potential to harness computational techniques for better understanding and addressing mental stress and MDs. This comprehensive review seeks to elucidate the current ML methodologies used in this domain to pave the way for enhanced detection, prediction, and analysis of mental stress and its subsequent MDs.</p><p><strong>Objective: </strong>This review aims to investigate the scope of ML methodologies used in the detection, prediction, and analysis of mental stress and its consequent MDs.</p><p><strong>Methods: </strong>Using a rigorous scoping review process with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, this investigation delves into the latest ML algorithms, preprocessing techniques, and data types used in the context of stress and stress-related MDs.</p><p><strong>Results: </strong>A total of 98 peer-reviewed publications were examined for this review. The findings highlight that support vector machine, neural network, and random forest models consistently exhibited superior accuracy and robustness among all ML algorithms examined. Physiological parameters such as heart rate measurements and skin response are prevalently used as stress predictors due to their rich explanatory information concerning stress and stress-related MDs, as well as the relative ease of data acquisition. The application of dimensionality reduction techniques, including mappings, feature selection, filtering, and noise reduction, is frequently observed as a crucial step preceding the training of ML algorithms.</p><p><strong>Conclusions: </strong>The synthesis of this review identified significant research gaps and outlines future directions for the field. These encompass areas such as model interpretability, model personalization, the incorporation of naturalistic settings, and real-time processing capabilities for the detection and prediction of stress and stress-related MDs.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11375388/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-Administered Interventions Based on Natural Language Processing Models for Reducing Depressive and Anxiety Symptoms: Systematic Review and Meta-Analysis.","authors":"David Villarreal-Zegarra, C Mahony Reategui-Rivera, Jackeline García-Serna, Gleni Quispe-Callo, Gabriel Lázaro-Cruz, Gianfranco Centeno-Terrazas, Ricardo Galvez-Arevalo, Stefan Escobar-Agreda, Alejandro Dominguez-Rodriguez, Joseph Finkelstein","doi":"10.2196/59560","DOIUrl":"10.2196/59560","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The introduction of natural language processing (NLP) technologies has significantly enhanced the potential of self-administered interventions for treating anxiety and depression by improving human-computer interactions. Although these advances, particularly in complex models such as generative artificial intelligence (AI), are highly promising, robust evidence validating the effectiveness of the interventions remains sparse.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The aim of this study was to determine whether self-administered interventions based on NLP models can reduce depressive and anxiety symptoms.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a systematic review and meta-analysis. We searched Web of Science, Scopus, MEDLINE, PsycINFO, IEEE Xplore, Embase, and Cochrane Library from inception to November 3, 2023. We included studies with participants of any age diagnosed with depression or anxiety through professional consultation or validated psychometric instruments. Interventions had to be self-administered and based on NLP models, with passive or active comparators. Outcomes measured included depressive and anxiety symptom scores. We included randomized controlled trials and quasi-experimental studies but excluded narrative, systematic, and scoping reviews. Data extraction was performed independently by pairs of authors using a predefined form. Meta-analysis was conducted using standardized mean differences (SMDs) and random effects models to account for heterogeneity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In all, 21 articles were selected for review, of which 76% (16/21) were included in the meta-analysis for each outcome. Most of the studies (16/21, 76%) were recent (2020-2023), with interventions being mostly AI-based NLP models (11/21, 52%); most (19/21, 90%) delivered some form of therapy (primarily cognitive behavioral therapy: 16/19, 84%). The overall meta-analysis showed that self-administered interventions based on NLP models were significantly more effective in reducing both depressive (SMD 0.819, 95% CI 0.389-1.250; P&lt;.001) and anxiety (SMD 0.272, 95% CI 0.116-0.428; P=.001) symptoms compared to various control conditions. Subgroup analysis indicated that AI-based NLP models were effective in reducing depressive symptoms (SMD 0.821, 95% CI 0.207-1.436; P&lt;.001) compared to pooled control conditions. Rule-based NLP models showed effectiveness in reducing both depressive (SMD 0.854, 95% CI 0.172-1.537; P=.01) and anxiety (SMD 0.347, 95% CI 0.116-0.578; P=.003) symptoms. The meta-regression showed no significant association between participants' mean age and treatment outcomes (all P&gt;.05). Although the findings were positive, the overall certainty of evidence was very low, mainly due to a high risk of bias, heterogeneity, and potential publication bias.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our findings support the effectiveness of self-administered NLP-based interventions in alleviating depressive and anxiety sy","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11375382/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Skill Enactment Among University Students Using a Brief Video-Based Mental Health Intervention: Mixed Methods Study Within a Randomized Controlled Trial.","authors":"Hayley M Jackson, Philip J Batterham, Alison L Calear, Jeneva L Ohan, Louise M Farrer","doi":"10.2196/53794","DOIUrl":"10.2196/53794","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Mental health problems are common among university students, yet many students do not seek professional help. Digital mental health interventions can increase students' access to support and have been shown to be effective in preventing and treating mental health problems. However, little is known about the extent to which students implement therapeutic skills from these programs in everyday life (ie, skill enactment) or about the impact of skill enactment on outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to assess the effects of a low-intensity video-based intervention, Uni Virtual Clinic Lite (UVC-Lite), in improving skill enactment relative to an attention-control program (primary aim) and examine whether skill enactment influences symptoms of depression and anxiety (secondary aim). The study also qualitatively explored participants' experiences of, and motivations for, engaging with the therapeutic techniques.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We analyzed data from a randomized controlled trial testing the effectiveness of UVC-Lite for symptoms of depression and anxiety among university students with mild to moderate levels of psychological distress. Participants were recruited from universities across Australia and randomly assigned to 6 weeks of self-guided use of UVC-Lite (243/487, 49.9%) or an attention-control program (244/487, 50.1%). Quantitative data on skill enactment, depression, and anxiety were collected through baseline, postintervention, and 3- and 6-month follow-up surveys. Qualitative data were obtained from 29 intervention-group participants through open-ended questions during postintervention surveys (n=17, 59%) and semistructured interviews (n=12, 41%) after the intervention period concluded.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Mixed model repeated measures ANOVA demonstrated that the intervention did not significantly improve skill enactment (F&lt;sub&gt;3,215.36&lt;/sub&gt;=0.50; P=.68). Skill enactment was also not found to influence change in symptoms of depression (F&lt;sub&gt;3,241.10&lt;/sub&gt;=1.69; P=.17) or anxiety (F&lt;sub&gt;3,233.71&lt;/sub&gt;=1.11; P=.35). However, higher levels of skill enactment were associated with lower symptom levels among both intervention and control group participants across time points (depression: F&lt;sub&gt;1,541.87&lt;/sub&gt;=134.61; P&lt;.001; anxiety: F&lt;sub&gt;1,535.11&lt;/sub&gt;=73.08; P&lt;.001). Inductive content analysis confirmed low levels of skill enactment among intervention group participants. Participants were motivated to use techniques and skills that were perceived to be personally relevant, easily integrated into daily life, and that were novel or had worked for them in the past.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The intervention did not improve skill enactment or mental health among students with mild to moderate psychological distress. Low adherence impacted our ability to draw robust conclusions regarding the intervention's impact on outcomes. Factors influencing skill enactment","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11375386/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Positive Psychology in Digital Interventions for Children, Adolescents, and Young Adults: Systematic Review and Meta-Analysis of Controlled Trials.","authors":"Sundas Saboor, Adrian Medina, Laura Marciano","doi":"10.2196/56045","DOIUrl":"10.2196/56045","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The rising prevalence of mental health issues in children, adolescents, and young adults has become an escalating public health issue, impacting approximately 10%-20% of young people on a global scale. Positive psychology interventions (PPIs) can act as powerful mental health promotion tools to reach wide-ranging audiences that might otherwise be challenging to access. This increased access would enable prevention of mental disorders and promotion of widespread well-being by enhancing self-efficacy, thereby supporting the achievement of tangible objectives.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We aimed to conduct a comprehensive synthesis of all randomized controlled trials and controlled trials involving children, adolescents, and young adults, encompassing both clinical and nonclinical populations, to comprehensively evaluate the effectiveness of digital PPIs in this age group.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;After a literature search in 9 electronic databases until January 12, 2023, and gray literature until April 2023, we carried out a systematic review of 35 articles, of which 18 (51%) provided data for the meta-analysis. We included randomized controlled trials and controlled trials mainly based on web-based, digital, or smartphone-based interventions using a positive psychology framework as the main component. Studies included participants with a mean age of &lt;35 years. Outcomes of PPIs were classified into indicators of well-being (compassion, life satisfaction, optimism, happiness, resilience, emotion regulation and emotion awareness, hope, mindfulness, purpose, quality of life, gratitude, empathy, forgiveness, motivation, and kindness) and ill-being (depression, anxiety, stress, loneliness, and burnout). PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were used for the selection of studies and data extraction. Quality assessment was performed following the CONSORT (Consolidated Standards of Reporting Trials) guidelines.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;For well-being outcomes, meta-analytic results showed that PPIs augmented the feeling of purpose, gratitude, and hope (Hedges g=0.555), compassion (Hedges g=0.447), positive coping behaviors (Hedges g=0.421), body image-related outcomes (Hedges g=0.238), and positive mindset predisposition (Hedges g=0.304). For ill-being outcomes, PPIs reduced cognitive biases (Hedges g=-0.637), negative emotions and mood (Hedges g=-0.369), and stress levels (Hedges g=-0.342). Of note, larger effect sizes were found when a waiting list control group was considered versus a digital control group. A funnel plot showed no publication bias. Meta-regression analyses showed that PPIs tended to show a larger effect size on well-being outcomes in studies including young adults, whereas no specific effect was found for ill-being outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Revised evidence suggests that PPIs benefit young people's well-being and mi","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11358669/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141983643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Use Rates of Telehealth Services for Substance Use Disorder During and Following COVID-19 Safety Distancing Recommendations: Two Cross-Sectional Surveys.","authors":"Adrijana Pusnik, Bryan Hartzler, Olivia Vjorn, Beth A Rutkowski, Michael Chaple, Sara Becker, Thomas Freese, Maureen Nichols, Todd Molfenter","doi":"10.2196/52363","DOIUrl":"10.2196/52363","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 social distancing guidelines resulted in a dramatic transition to telephone and video technologies to deliver substance use disorder (SUD) treatment. Before COVID-19, the question was \"Will telehealth ever take hold for SUD services?\" Now that social distancing guidelines have been lifted, the question is \"Will telehealth remain a commonly used care modality?\"</p><p><strong>Objective: </strong>The principal purpose of this investigation was to examine the extent to which telehealth use in SUD service settings persisted following the lifting of COVID-19 safety distancing recommendations. Additionally, the study aimed to explore practitioners' perceptions of telehealth convenience and value after its regular implementation during the pandemic. Specifically, the goal of this study was to compare telehealth activity between time intervals: May-August 2020 (during peak COVID-19 safety distancing recommendations) and October-December 2022 (following discontinuation of distancing recommendations). Specifically, we compared (1) telehealth technologies and services, (2) perceived usefulness of telehealth, (3) ease of use of telephone- and video-based telehealth services, and (4) organizational readiness to use telehealth.</p><p><strong>Methods: </strong>An online cross-sectional survey consisting of 108 items was conducted to measure the use of telehealth technologies for delivering a specific set of SUD services in the United States and to explore the perceived readiness for use and satisfaction with telephonic and video services. The survey took approximately 25-35 minutes to complete and used the same 3 sets of questions and 2 theory-driven scales as in a previous cross-sectional survey conducted in 2020. Six of 10 Regional Addiction Technology Transfer Centers funded by the Substance Abuse and Mental Health Services Administration distributed the survey in their respective regions, collectively spanning 37 states. Responses of administrators and clinicians (hereafter referred to as staff) from this 2022 survey were compared to those obtained in the 2020 survey. Responses in 2020 and 2022 were anonymous and comprised two separate samples; therefore, an accurate longitudinal model could not be analyzed.</p><p><strong>Results: </strong>A total of 375 staff responded to the 2022 survey (vs 457 in 2020). Baseline organizational characteristics of the 2022 sample were similar to those of the 2020 sample. Phone and video telehealth utilization rates remained greater than 50% in 2022 for screening and assessment, case management, peer recovery support services, and regular outpatient services. The perceived usefulness of phone-based telehealth was higher in 2022 than in 2020 (mean difference [MD] -0.23; P=.002), but not for video-based telehealth (MD -0.12; P=.13). Ease of use of video-based telehealth was perceived as higher in 2022 than in 2020 (MD-0.35; P<.001), but no difference was found for phone-based telehe","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11331268/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141972124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the Short-Term Efficacy of Digital Cognitive Behavioral Therapy for Insomnia With Different Types of Coaching: Randomized Controlled Comparative Trial.","authors":"Wai Sze Chan, Wing Yee Cheng, Samson Hoi Chun Lok, Amanda Kah Mun Cheah, Anna Kai Win Lee, Albe Sin Ying Ng, Tobias Kowatsch","doi":"10.2196/51716","DOIUrl":"10.2196/51716","url":null,"abstract":"<p><strong>Background: </strong>Digital cognitive behavioral therapy for insomnia (dCBTi) is an effective intervention for treating insomnia. The findings regarding its efficacy compared to face-to-face cognitive behavioral therapy for insomnia are inconclusive but suggest that dCBTi might be inferior. The lack of human support and low treatment adherence are believed to be barriers to dCBTi achieving its optimal efficacy. However, there has yet to be a direct comparative trial of dCBTi with different types of coaching support.</p><p><strong>Objective: </strong>This study examines whether adding chatbot-based and human coaching would improve the treatment efficacy of, and adherence to, dCBTi.</p><p><strong>Methods: </strong>Overall, 129 participants (n=98, 76% women; age: mean 34.09, SD 12.05 y) whose scores on the Insomnia Severity Index [ISI] were greater than 9 were recruited. A randomized controlled comparative trial with 5 arms was conducted: dCBTi with chatbot-based coaching and therapist support (dCBTi-therapist), dCBTi with chatbot-based coaching and research assistant support, dCBTi with chatbot-based coaching only, dCBTi without any coaching, and digital sleep hygiene and self-monitoring control. Participants were blinded to the condition assignment and study hypotheses, and the outcomes were self-assessed using questionnaires administered on the web. The outcomes included measures of insomnia (the ISI and the Sleep Condition Indicator), mood disturbances, fatigue, daytime sleepiness, quality of life, dysfunctional beliefs about sleep, and sleep-related safety behaviors administered at baseline, after treatment, and at 4-week follow-up. Treatment adherence was measured by the completion of video sessions and sleep diaries. An intention-to-treat analysis was conducted.</p><p><strong>Results: </strong>Significant condition-by-time interaction effects showed that dCBTi recipients, regardless of having any coaching, had greater improvements in insomnia measured by the Sleep Condition Indicator (P=.003; d=0.45) but not the ISI (P=.86; d=-0.28), depressive symptoms (P<.001; d=-0.62), anxiety (P=.01; d=-0.40), fatigue (P=.02; d=-0.35), dysfunctional beliefs about sleep (P<.001; d=-0.53), and safety behaviors related to sleep (P=.001; d=-0.50) than those who received digital sleep hygiene and self-monitoring control. The addition of chatbot-based coaching and human support did not improve treatment efficacy. However, adding human support promoted greater reductions in fatigue (P=.03; d=-0.33) and sleep-related safety behaviors (P=.05; d=-0.30) than dCBTi with chatbot-based coaching only at 4-week follow-up. dCBTi-therapist had the highest video and diary completion rates compared to other conditions (video: 16/25, 60% in dCBTi-therapist vs <3/21, <25% in dCBTi without any coaching), indicating greater treatment adherence.</p><p><strong>Conclusions: </strong>Our findings support the efficacy of dCBTi in treating insomnia, reducing thoughts and b","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11339566/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141903304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large Language Models Versus Expert Clinicians in Crisis Prediction Among Telemental Health Patients: Comparative Study.","authors":"Christine Lee, Matthew Mohebbi, Erin O'Callaghan, Mirène Winsberg","doi":"10.2196/58129","DOIUrl":"10.2196/58129","url":null,"abstract":"<p><strong>Background: </strong>Due to recent advances in artificial intelligence, large language models (LLMs) have emerged as a powerful tool for a variety of language-related tasks, including sentiment analysis, and summarization of provider-patient interactions. However, there is limited research on these models in the area of crisis prediction.</p><p><strong>Objective: </strong>This study aimed to evaluate the performance of LLMs, specifically OpenAI's generative pretrained transformer 4 (GPT-4), in predicting current and future mental health crisis episodes using patient-provided information at intake among users of a national telemental health platform.</p><p><strong>Methods: </strong>Deidentified patient-provided data were pulled from specific intake questions of the Brightside telehealth platform, including the chief complaint, for 140 patients who indicated suicidal ideation (SI), and another 120 patients who later indicated SI with a plan during the course of treatment. Similar data were pulled for 200 randomly selected patients, treated during the same time period, who never endorsed SI. In total, 6 senior Brightside clinicians (3 psychologists and 3 psychiatrists) were shown patients' self-reported chief complaint and self-reported suicide attempt history but were blinded to the future course of treatment and other reported symptoms, including SI. They were asked a simple yes or no question regarding their prediction of endorsement of SI with plan, along with their confidence level about the prediction. GPT-4 was provided with similar information and asked to answer the same questions, enabling us to directly compare the performance of artificial intelligence and clinicians.</p><p><strong>Results: </strong>Overall, the clinicians' average precision (0.7) was higher than that of GPT-4 (0.6) in identifying the SI with plan at intake (n=140) versus no SI (n=200) when using the chief complaint alone, while sensitivity was higher for the GPT-4 (0.62) than the clinicians' average (0.53). The addition of suicide attempt history increased the clinicians' average sensitivity (0.59) and precision (0.77) while increasing the GPT-4 sensitivity (0.59) but decreasing the GPT-4 precision (0.54). Performance decreased comparatively when predicting future SI with plan (n=120) versus no SI (n=200) with a chief complaint only for the clinicians (average sensitivity=0.4; average precision=0.59) and the GPT-4 (sensitivity=0.46; precision=0.48). The addition of suicide attempt history increased performance comparatively for the clinicians (average sensitivity=0.46; average precision=0.69) and the GPT-4 (sensitivity=0.74; precision=0.48).</p><p><strong>Conclusions: </strong>GPT-4, with a simple prompt design, produced results on some metrics that approached those of a trained clinician. Additional work must be done before such a model can be piloted in a clinical setting. The model should undergo safety checks for bias, given evidence that LLMs can perpetu","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11329850/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141321831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Behavior Change Techniques Within Digital Interventions for the Treatment of Eating Disorders: Systematic Review and Meta-Analysis.","authors":"Pamela Carien Thomas, Kristina Curtis, Henry W W Potts, Pippa Bark, Rachel Perowne, Tasmin Rookes, Sarah Rowe","doi":"10.2196/57577","DOIUrl":"10.2196/57577","url":null,"abstract":"<p><strong>Background: </strong>Previous systematic reviews of digital eating disorder interventions have demonstrated effectiveness at improving symptoms of eating disorders; however, our understanding of how these interventions work and what contributes to their effectiveness is limited. Understanding the behavior change techniques (BCTs) that are most commonly included within effective interventions may provide valuable information for researchers and developers. Establishing whether these techniques have been informed by theory will identify whether they target those mechanisms of action that have been identified as core to changing eating disorder behaviors. It will also evaluate the importance of a theoretical approach to digital intervention design.</p><p><strong>Objective: </strong>This study aims to define the BCTs within digital self-management interventions or minimally guided self-help interventions for adults with eating disorders that have been evaluated within randomized controlled trials. It also assessed which of the digital interventions were grounded in theory and the range of modes of delivery included.</p><p><strong>Methods: </strong>A literature search identified randomized controlled trials of digital intervention for the treatment of adults with eating disorders with minimal therapist support. Each digital intervention was coded for BCTs using the established BCT Taxonomy v1; for the application of theory using an adapted version of the theory coding scheme (TCS); and for modes of delivery using the Mode of Delivery Ontology. A meta-analysis evaluated the evidence that any individual BCT moderated effect size or that other potential factors such as the application of theory or number of modes of delivery had an effect on eating disorder outcomes.</p><p><strong>Results: </strong>Digital interventions included an average of 14 (SD 2.6; range 9-18) BCTs. Self-monitoring of behavior was included in all effective interventions, with Problem-solving, Information about antecedents, Feedback on behavior, Self-monitoring of outcomes of behavior, and Action planning identified in >75% (13/17) of effective interventions. Social support and Information about health consequences were more evident in effective interventions at follow-up compared with postintervention measurement. The mean number of modes of delivery was 4 (SD 1.6; range 2-7) out of 12 possible modes, with most interventions (15/17, 88%) being web based. Digital interventions that had a higher score on the TCS had a greater effect size than those with a lower TCS score (subgroup differences: χ²₁=9.7; P=.002; I²=89.7%) within the meta-analysis. No other subgroup analyses had statistically significant results.</p><p><strong>Conclusions: </strong>There was a high level of consistency in terms of the most common BCTs within effective interventions; however, there was no evidence that any specific BCT contributed to intervention efficacy. The interventio","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11327638/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141876370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acceptability and Engagement of a Smartphone-Delivered Interpretation Bias Intervention in a Sample of Black and Latinx Adults: Open Trial.","authors":"IreLee Ferguson, Grace George, Kevin O Narine, Amari Turner, Zelda McGhee, Harris Bajwa, Frances G Hart, Sierra Carter, Courtney Beard","doi":"10.2196/56758","DOIUrl":"10.2196/56758","url":null,"abstract":"<p><strong>Background: </strong>Access to evidence-based interventions is urgently required, especially for individuals of minoritized identities who experience unique barriers to mental health care. Digital mental health interventions have the potential to increase accessibility. Previous pilot studies testing HabitWorks, a smartphone app providing an interpretation bias intervention, have found strong engagement and adherence for HabitWorks; however, previous trials' samples consisted of predominantly non-Hispanic, White individuals.</p><p><strong>Objective: </strong>This study conducted an open trial of HabitWorks in a community sample of adults who identified as Black, Hispanic or Latinx, or both. This study aims to test safety, acceptability, and engagement with the HabitWorks app for Black and Latinx adults.</p><p><strong>Methods: </strong>Black, Hispanic or Latinx adults (mean age 32.83, SD 11.06 y; 22/31, 71% women) who endorsed symptoms of anxiety or depression were asked to complete interpretation modification exercises via HabitWorks 3 times per week for 1 month. Interpretation bias and anxiety and depression symptoms were assessed at baseline and posttreatment assessments. Participants completed qualitative interviews to assess overall perceptions of HabitWorks.</p><p><strong>Results: </strong>Of the 31 participants that downloaded the app, 27 (87%) used HabitWorks all 4 weeks. On average, participants completed 15.74 (SD 7.43) exercises out of the 12 prescribed, demonstrating high engagement. Acceptability ratings met all a priori benchmarks except for relevancy. Qualitative interviews also demonstrated high acceptability and few negative experiences. Significant improvements were found in interpretation style (t₃₀=2.29; P<.001), with a large effect size (Cohen d=1.53); anxiety symptoms (t₃₀=2.29; P=.03), with a small effect size (Cohen d=0.41); and depression symptoms (t₃₀=3.065; P=.005), with a medium effect size (Cohen d=0.55).</p><p><strong>Conclusions: </strong>This study adds to the literature evaluating digital mental health interventions in Black and Latinx adults. Preliminary results further support a future controlled trial testing the effectiveness of HabitWorks as an intervention.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11325113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141856870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}