Jmir Mental Health最新文献

{"title":"The Most Effective Interventions for Classification Model Development to Predict Chat Outcomes Based on the Conversation Content in Online Suicide Prevention Chats: Machine Learning Approach.","authors":"Salim Salmi, Saskia Mérelle, Renske Gilissen, Rob van der Mei, Sandjai Bhulai","doi":"10.2196/57362","DOIUrl":"10.2196/57362","url":null,"abstract":"<p><strong>Background: </strong>For the provision of optimal care in a suicide prevention helpline, it is important to know what contributes to positive or negative effects on help seekers. Helplines can often be contacted through text-based chat services, which produce large amounts of text data for use in large-scale analysis.</p><p><strong>Objective: </strong>We trained a machine learning classification model to predict chat outcomes based on the content of the chat conversations in suicide helplines and identified the counsellor utterances that had the most impact on its outputs.</p><p><strong>Methods: </strong>From August 2021 until January 2023, help seekers (N=6903) scored themselves on factors known to be associated with suicidality (eg, hopelessness, feeling entrapped, will to live) before and after a chat conversation with the suicide prevention helpline in the Netherlands (113 Suicide Prevention). Machine learning text analysis was used to predict help seeker scores on these factors. Using 2 approaches for interpreting machine learning models, we identified text messages from helpers in a chat that contributed the most to the prediction of the model.</p><p><strong>Results: </strong>According to the machine learning model, helpers' positive affirmations and expressing involvement contributed to improved scores of the help seekers. Use of macros and ending the chat prematurely due to the help seeker being in an unsafe situation had negative effects on help seekers.</p><p><strong>Conclusions: </strong>This study reveals insights for improving helpline chats, emphasizing the value of an evocative style with questions, positive affirmations, and practical advice. It also underscores the potential of machine learning in helpline chat analysis.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e57362"},"PeriodicalIF":4.8,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11467604/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356376","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":"Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study.","authors":"Jocelyn Shen, Daniella DiPaola, Safinah Ali, Maarten Sap, Hae Won Park, Cynthia Breazeal","doi":"10.2196/62679","DOIUrl":"10.2196/62679","url":null,"abstract":"<p><strong>Background: </strong>Empathy is a driving force in our connection to others, our mental well-being, and resilience to challenges. With the rise of generative artificial intelligence (AI) systems, mental health chatbots, and AI social support companions, it is important to understand how empathy unfolds toward stories from human versus AI narrators and how transparency plays a role in user emotions.</p><p><strong>Objective: </strong>We aim to understand how empathy shifts across human-written versus AI-written stories, and how these findings inform ethical implications and human-centered design of using mental health chatbots as objects of empathy.</p><p><strong>Methods: </strong>We conducted crowd-sourced studies with 985 participants who each wrote a personal story and then rated empathy toward 2 retrieved stories, where one was written by a language model, and another was written by a human. Our studies varied disclosing whether a story was written by a human or an AI system to see how transparent author information affects empathy toward the narrator. We conducted mixed methods analyses: through statistical tests, we compared user's self-reported state empathy toward the stories across different conditions. In addition, we qualitatively coded open-ended feedback about reactions to the stories to understand how and why transparency affects empathy toward human versus AI storytellers.</p><p><strong>Results: </strong>We found that participants significantly empathized with human-written over AI-written stories in almost all conditions, regardless of whether they are aware (t₁₉₆=7.07, P<.001, Cohen d=0.60) or not aware (t₂₉₈=3.46, P<.001, Cohen d=0.24) that an AI system wrote the story. We also found that participants reported greater willingness to empathize with AI-written stories when there was transparency about the story author (t₄₉₄=-5.49, P<.001, Cohen d=0.36).</p><p><strong>Conclusions: </strong>Our work sheds light on how empathy toward AI or human narrators is tied to the way the text is presented, thus informing ethical considerations of empathetic artificial social support or mental health chatbots.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e62679"},"PeriodicalIF":4.8,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11464935/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356372","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":"Long-Term Effects of Internet-Based Cognitive Behavioral Therapy on Depression Prevention Among University Students: Randomized Controlled Factorial Trial.","authors":"Yukako Nakagami, Teruhisa Uwatoko, Tomonari Shimamoto, Masatsugu Sakata, Rie Toyomoto, Kazufumi Yoshida, Yan Luo, Nao Shiraishi, Aran Tajika, Ethan Sahker, Masaru Horikoshi, Hisashi Noma, Taku Iwami, Toshi A Furukawa","doi":"10.2196/56691","DOIUrl":"10.2196/56691","url":null,"abstract":"<p><strong>Background: </strong>Internet-based cognitive behavioral therapy (iCBT) shows promise in the prevention of depression. However, the specific iCBT components that contribute to its effectiveness remain unclear.</p><p><strong>Objective: </strong>We aim to evaluate the effects of iCBT components in preventing depression among university students.</p><p><strong>Methods: </strong>Using a smartphone cognitive behavioral therapy (CBT) app, we randomly allocated university students to the presence or absence of 5 different iCBT components: self-monitoring, behavioral activation, cognitive restructuring, assertiveness training, and problem-solving. The active intervention lasted 8 weeks but the app remained accessible through the follow-up. The primary outcome was the onset of a major depressive episode (MDE) between baseline and the follow-up after 52 weeks, as assessed with the computerized World Health Organization Composite International Diagnostic Interview. Secondary outcomes included changes in the 9-item Patient Health Questionnaire, 7-item General Anxiety Disorder, and CBT Skills Scale.</p><p><strong>Results: </strong>During the 12-month follow-up, 133 of 1301 (10.22%) participants reported the onset of an MDE. There were no significant differences in the incidence of MDEs between the groups with or without each component (hazard ratios ranged from 0.85, 95% CI 0.60-1.20, for assertiveness training to 1.26, 95% CI 0.88-1.79, for self-monitoring). Furthermore, there were no significant differences in the changes on the 9-item Patient Health Questionnaire, 7-item General Anxiety Disorder, or for CBT Skills Scale between component allocation groups. However, significant reductions in depression and anxiety symptoms were observed among all participants at the 52-week follow-up.</p><p><strong>Conclusions: </strong>In this study, we could not identify any specific iCBT components that were effective in preventing depression or the acquisition of CBT skills over the 12-month follow-up period, but all participants with and without intervention of each iCBT component demonstrated significant improvements in depressive and anxiety symptoms. Further research is needed to explore the potential impact of frequency of psychological assessments, nonspecific intervention effects, natural change in the mental state, and the baseline depression level.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e56691"},"PeriodicalIF":4.8,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11445681/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356375","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":"Engagement, Acceptability, and Effectiveness of the Self-Care and Coach-Supported Versions of the Vira Digital Behavior Change Platform Among Young Adults at Risk for Depression and Obesity: Pilot Randomized Controlled Trial.","authors":"Lauren S Weiner, Ryann N Crowley, Lisa B Sheeber, Frank H Koegler, Jon F Davis, Megan Wells, Carter J Funkhouser, Randy P Auerbach, Nicholas B Allen","doi":"10.2196/51366","DOIUrl":"10.2196/51366","url":null,"abstract":"<p><strong>Background: </strong>Adolescence and early adulthood are pivotal stages for the onset of mental health disorders and the development of health behaviors. Digital behavioral activation interventions, with or without coaching support, hold promise for addressing risk factors for both mental and physical health problems by offering scalable approaches to expand access to evidence-based mental health support.</p><p><strong>Objective: </strong>This 2-arm pilot randomized controlled trial evaluated 2 versions of a digital behavioral health product, Vira (Ksana Health Inc), for their feasibility, acceptability, and preliminary effectiveness in improving mental health in young adults with depressive symptoms and obesity risk factors.</p><p><strong>Methods: </strong>A total of 73 participants recruited throughout the United States were randomly assigned to use Vira either as a self-guided product (Vira Self-Care) or with support from a health coach (Vira+Coaching) for 12 weeks. The Vira smartphone app used passive sensing of behavioral data related to mental health and obesity risk factors (ie, activity, sleep, mobility, and language patterns) and offered users personalized insights into patterns of behavior associated with their daily mood. Participants completed self-reported outcome measures at baseline and follow-up (12 weeks). All study procedures were completed via digital communications.</p><p><strong>Results: </strong>Both versions of Vira showed strong user engagement, acceptability, and evidence of effectiveness in improving mental health and stress. However, users receiving coaching exhibited more sustained engagement with the platform and reported greater reductions in depression (Cohen d=0.45, 95% CI 0.10-0.82) and anxiety (Cohen d=0.50, 95% CI 0.13-0.86) compared to self-care users. Both interventions also resulted in reduced stress (Vira+Coaching: Cohen d=-1.05, 95% CI -1.57 to --0.50; Vira Self-Care: Cohen d=-0.78, 95% CI -1.33 to -0.23) and were perceived as useful and easy to use. Coached users also reported reductions in sleep-related impairment (Cohen d=-0.51, 95% CI -1.00 to -0.01). Moreover, participants increased their motivation for and confidence in making behavioral changes, with greater improvements in confidence among coached users.</p><p><strong>Conclusions: </strong>An app-based intervention using passive mobile sensing to track behavior and deliver personalized insights into behavior-mood associations demonstrated feasibility, acceptability, and preliminary effectiveness for reducing depressive symptoms and other mental health problems in young adults. Future directions include (1) optimizing the interventions, (2) conducting a fully powered trial that includes an active control condition, and (3) testing mediators and moderators of outcome effects.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov NCT05638516; https://clinicaltrials.gov/study/NCT05638516.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e51366"},"PeriodicalIF":4.8,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11450360/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298914","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":"Talk Time Differences Between Interregional and Intraregional Calls to a Crisis Helpline: Statistical Analysis","authors":"Robin Turkington, Courtney Potts, Maurice Mulvenna, Raymond Bond, Siobhán O'Neill, Edel Ennis, Katie Hardcastle, Elizabeth Scowcroft, Ciaran Moore, Louise Hamra","doi":"10.2196/58162","DOIUrl":"https://doi.org/10.2196/58162","url":null,"abstract":"Background: National suicide prevention strategies are general population-based approaches to prevent suicide by promoting help-seeking behaviours and implementing interventions. Crisis helplines are one of the suicide prevention resources available for public use where individuals experiencing a crisis can talk to a trained volunteer. Samaritans UK operates on a national scale, with a number of branches located in within each of the UK’s four countries or regions. Objective: The aim of this study is to identify any differences in call duration across the helpline service in order to see if service varied interregionally and /or intraregionally; and to determine the impact of calls answered in the same region as the caller, compared to calls answered in a different region on the duration of calls made from landlines to Samaritans UK. Methods: Calls may be routed in Samaritans, the telephony system sends the call to the next available volunteer, irrespective of location, therefore individuals may be routed to a branch within the same region as the caller’s current region (intra-regional calls) or routed to a branch that is in a different region from that of the caller’s current region (inter-regional calls). The origin of calls by region was identified using the landline prefix of the anonymised caller identifier, along with the region of the destination branch (as branch is recorded in the call details record). Results: Firstly, a Levene’s test of homogeneity of variance was carried out for each condition i.e. England calls, Scotland calls. Again at each condition, a One-way ANOVA/One-way analysis of means was carried out to look at for any significant differences in call duration, which showed that there are significant differences in call durations between intraregional calls and interregional calls (p<0.001). Across all conditions within this study, callers stayed on the phone for a shorter period of time when routed to a branch that is within the same region as the call origin, than if they were put through to a branch within a different region than the call origin. Conclusions: Statistical analyses showed that there were significant differences between interregional and intraregional calls. On average, callers to crisis helplines stayed on the phone for a shorter amount of time if they were routed to a branch within the same region in which the call originated, than if they were routed to a branch in a different region of origin. The findings from this study have practical applications which may allow crisis helplines to manage their resources more effectively and improve caller satisfaction with the service. Clinical Trial: Not applicable","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"118 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regulating AI in Mental Health: Ethics of Care Perspective.","authors":"Tamar Tavory","doi":"10.2196/58493","DOIUrl":"10.2196/58493","url":null,"abstract":"<p><p>This article contends that the responsible artificial intelligence (AI) approach-which is the dominant ethics approach ruling most regulatory and ethical guidance-falls short because it overlooks the impact of AI on human relationships. Focusing only on responsible AI principles reinforces a narrow concept of accountability and responsibility of companies developing AI. This article proposes that applying the ethics of care approach to AI regulation can offer a more comprehensive regulatory and ethical framework that addresses AI's impact on human relationships. This dual approach is essential for the effective regulation of AI in the domain of mental health care. The article delves into the emergence of the new \"therapeutic\" area facilitated by AI-based bots, which operate without a therapist. The article highlights the difficulties involved, mainly the absence of a defined duty of care toward users, and shows how implementing ethics of care can establish clear responsibilities for developers. It also sheds light on the potential for emotional manipulation and the risks involved. In conclusion, the article proposes a series of considerations grounded in the ethics of care for the developmental process of AI-powered therapeutic tools.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e58493"},"PeriodicalIF":4.8,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11450345/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298915","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":"Patient Perspectives on AI for Mental Health Care: Cross-Sectional Survey Study","authors":"Natalie Benda, Pooja Desai, Zayan Reza, Anna Zheng, Shiveen Kumar, Sarah Harkins, Alison Hermann, Yiye Zhang, Rochelle Joly, Jessica Kim, Jyotishman Pathak, Meghan Reading Turchioe","doi":"10.2196/58462","DOIUrl":"https://doi.org/10.2196/58462","url":null,"abstract":"<strong>Background:</strong> The application of artificial intelligence (AI) to health and health care is rapidly increasing. Several studies have assessed the attitudes of health professionals, but far fewer studies have explored the perspectives of patients or the general public. Studies investigating patient perspectives have focused on somatic issues, including those related to radiology, perinatal health, and general applications. Patient feedback has been elicited in the development of specific mental health care solutions, but broader perspectives toward AI for mental health care have been underexplored. <strong>Objective:</strong> This study aims to understand public perceptions regarding potential benefits of AI, concerns about AI, comfort with AI accomplishing various tasks, and values related to AI, all pertaining to mental health care. <strong>Methods:</strong> We conducted a 1-time cross-sectional survey with a nationally representative sample of 500 US-based adults. Participants provided structured responses on their perceived benefits, concerns, comfort, and values regarding AI for mental health care. They could also add free-text responses to elaborate on their concerns and values. <strong>Results:</strong> A plurality of participants (245/497, 49.3%) believed AI may be beneficial for mental health care, but this perspective differed based on sociodemographic variables (all <i>P</i>&lt;.05). Specifically, Black participants (odds ratio [OR] 1.76, 95% CI 1.03-3.05) and those with lower health literacy (OR 2.16, 95% CI 1.29-3.78) perceived AI to be more beneficial, and women (OR 0.68, 95% CI 0.46-0.99) perceived AI to be less beneficial. Participants endorsed concerns about accuracy, possible unintended consequences such as misdiagnosis, the confidentiality of their information, and the loss of connection with their health professional when AI is used for mental health care. A majority of participants (80.4%, 402/500) valued being able to understand individual factors driving their risk, confidentiality, and autonomy as it pertained to the use of AI for their mental health. When asked who was responsible for the misdiagnosis of mental health conditions using AI, 81.6% (408/500) of participants found the health professional to be responsible. Qualitative results revealed similar concerns related to the accuracy of AI and how its use may impact the confidentiality of patients’ information. <strong>Conclusions:</strong> Future work involving the use of AI for mental health care should investigate strategies for conveying the level of AI’s accuracy, factors that drive patients’ mental health risks, and how data are used confidentially so that patients can determine with their health professionals when AI may be beneficial. It will also be important in a mental health care context to ensure the patient–health professional relationship is preserved when AI is used.","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"39 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Integrity Issues With Web-Based Studies: An Institutional Example of a Widespread Challenge","authors":"Blandine French, Camilla Babbage, Katherine Bird, Lauren Marsh, Mirabel Pelton, Shireen Patel, Sarah Cassidy, Stefan Rennick-Egglestone","doi":"10.2196/58432","DOIUrl":"https://doi.org/10.2196/58432","url":null,"abstract":"This paper reports on the growing issues experienced when conducting web-based–based research. Nongenuine participants, repeat responders, and misrepresentation are common issues in health research posing significant challenges to data integrity. A summary of existing data on the topic and the different impacts on studies is presented. Seven case studies experienced by different teams within our institutions are then reported, primarily focused on mental health research. Finally, strategies to combat these challenges are presented, including protocol development, transparent recruitment practices, and continuous data monitoring. These strategies and challenges impact the entire research cycle and need to be considered prior to, during, and post data collection. With a lack of current clear guidelines on this topic, this report attempts to highlight considerations to be taken to minimize the impact of such challenges on researchers, studies, and wider research. Researchers conducting web-based research must put mitigating strategies in place, and reporting on mitigation efforts should be mandatory in grant applications and publications to uphold the credibility of web-based research.","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"25 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Tailoring Just-in-Time Adaptive Intervention Systems for Workplace Stress Reduction: Exploratory Analysis of Intervention Implementation","authors":"Jina Suh, Esther Howe, Robert Lewis, Javier Hernandez, Koustuv Saha, Tim Althoff, Mary Czerwinski","doi":"10.2196/48974","DOIUrl":"https://doi.org/10.2196/48974","url":null,"abstract":"&lt;strong&gt;Background:&lt;/strong&gt; Integrating stress-reduction interventions into the workplace may improve the health and well-being of employees, and there is an opportunity to leverage ubiquitous everyday work technologies to understand dynamic work contexts and facilitate stress reduction wherever work happens. Sensing-powered just-in-time adaptive intervention (JITAI) systems have the potential to adapt and deliver tailored interventions, but such adaptation requires a comprehensive analysis of contextual and individual-level variables that may influence intervention outcomes and be leveraged to drive the system’s decision-making. &lt;strong&gt;Objective:&lt;/strong&gt; This study aims to identify key tailoring variables that influence momentary engagement in digital stress reduction microinterventions to inform the design of similar JITAI systems. &lt;strong&gt;Methods:&lt;/strong&gt; To inform the design of such dynamic adaptation, we analyzed data from the implementation and deployment of a system that incorporates passively sensed data across everyday work devices to send just-in-time stress reduction microinterventions in the workplace to 43 participants during a 4-week deployment. We evaluated 27 trait-based factors (ie, individual characteristics), state-based factors (ie, workplace contextual and behavioral signals and momentary stress), and intervention-related factors (ie, location and function) across 1585 system-initiated interventions. We built logistical regression models to identify the factors contributing to momentary engagement, the choice of interventions, the engagement given an intervention choice, the user rating of interventions engaged, and the stress reduction from the engagement. &lt;strong&gt;Results:&lt;/strong&gt; We found that women (odds ratio [OR] 0.41, 95% CI 0.21-0.77; &lt;i&gt;P&lt;/i&gt;=.03), those with higher neuroticism (OR 0.57, 95% CI 0.39-0.81; &lt;i&gt;P&lt;/i&gt;=.01), those with higher cognitive reappraisal skills (OR 0.69, 95% CI 0.52-0.91; &lt;i&gt;P&lt;/i&gt;=.04), and those that chose calm interventions (OR 0.43, 95% CI 0.23-0.78; &lt;i&gt;P&lt;/i&gt;=.03) were significantly less likely to experience stress reduction, while those with higher agreeableness (OR 1.73, 95% CI 1.10-2.76; &lt;i&gt;P&lt;/i&gt;=.06) and those that chose prompt-based (OR 6.65, 95% CI 1.53-36.45; &lt;i&gt;P&lt;/i&gt;=.06) or video-based (OR 5.62, 95% CI 1.12-34.10; &lt;i&gt;P&lt;/i&gt;=.12) interventions were substantially more likely to experience stress reduction. We also found that work-related contextual signals such as higher meeting counts (OR 0.62, 95% CI 0.49-0.78; &lt;i&gt;P&lt;/i&gt;&lt;.001) and higher engagement skewness (OR 0.64, 95% CI 0.51-0.79; &lt;i&gt;P&lt;/i&gt;&lt;.001) were associated with a lower likelihood of engagement, indicating that state-based contextual factors such as being in a meeting or the time of the day may matter more for engagement than efficacy. In addition, a just-in-time intervention that was explicitly rescheduled to a later time was more likely to be engaged with (OR 1.77, 95% CI 1.32-2.38; &lt;i&gt;P&lt;/i&gt;&lt;.001). &lt;strong&gt;Conclu","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"43 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Mental Health Interventions for Alleviating Depression and Anxiety During Psychotherapy Waiting Lists: Systematic Review","authors":"Sijia Huang, Yiyue Wang, Gen Li, Brian J Hall, Thomas J Nyman","doi":"10.2196/56650","DOIUrl":"https://doi.org/10.2196/56650","url":null,"abstract":"<strong>Background:</strong> Depression and anxiety have become increasingly prevalent across the globe. The rising need for treatment and the lack of clinicians has resulted in prolonged waiting times for patients to receive their first session. Responding to this gap, digital mental health interventions (DMHIs) have been found effective in treating depression and anxiety and are potentially promising pretreatments for patients who are awaiting face-to-face psychotherapy. Nevertheless, whether digital interventions effectively alleviate symptoms for patients on waiting lists for face-to-face psychotherapy remains unclear. <strong>Objective:</strong> This review aimed to synthesize the effectiveness of DMHIs for relieving depression and anxiety symptoms of patients on waiting lists for face-to-face therapy. This review also investigated the features, perceived credibility, and usability of DMHIs during waiting times. <strong>Methods:</strong> In this systematic review, we searched PubMed, PsycINFO, Cochrane, and Web of Science for research studies investigating the effectiveness of DMHIs in reducing either depression or anxiety symptoms among individuals waiting for face-to-face psychotherapy. The search was conducted in June 2024, and we have included the studies that met the inclusion criteria and were published before June 6, 2024. <strong>Results:</strong> Of the 9267 unique records identified, 8 studies met the eligibility criteria and were included in the systematic review. Five studies were randomized controlled trials (RCTs), and 3 studies were not. Among the RCTs, we found that digital interventions reduced depression and anxiety symptoms, but the majority of interventions were not more effective compared to the control groups where participants simply waited or received a self-help book. For the non-RCTs, the interventions also reduced symptoms, but without control groups, the interpretation of the findings is limited. Finally, participants in the included studies perceived the digital interventions to be credible and useful, but high dropout rates raised concerns about treatment adherence. <strong>Conclusions:</strong> Due to the lack of effective interventions among the reviewed studies, especially among the RCTs, our results suggest that waiting list DMHIs are not more effective compared to simply waiting or using a self-help book. However, more high-quality RCTs with larger sample sizes are warranted in order to draw a more robust conclusion. Additionally, as this review revealed concerns regarding the high dropout rate in digital interventions, future studies could perhaps adopt more personalized and human-centered functions in interventions to increase user engagement, with the potential to increase treatment adherence and effectiveness. <strong>Trial Registration:</strong>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"26 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}