Jmir Mental Health最新文献

{"title":"Health Care Professionals' Views on the Use of Passive Sensing, AI, and Machine Learning in Mental Health Care: Systematic Review With Meta-Synthesis.","authors":"Jessica Rogan, Sandra Bucci, Joseph Firth","doi":"10.2196/49577","DOIUrl":"10.2196/49577","url":null,"abstract":"<p><strong>Background: </strong>Mental health difficulties are highly prevalent worldwide. Passive sensing technologies and applied artificial intelligence (AI) methods can provide an innovative means of supporting the management of mental health problems and enhancing the quality of care. However, the views of stakeholders are important in understanding the potential barriers to and facilitators of their implementation.</p><p><strong>Objective: </strong>This study aims to review, critically appraise, and synthesize qualitative findings relating to the views of mental health care professionals on the use of passive sensing and AI in mental health care.</p><p><strong>Methods: </strong>A systematic search of qualitative studies was performed using 4 databases. A meta-synthesis approach was used, whereby studies were analyzed using an inductive thematic analysis approach within a critical realist epistemological framework.</p><p><strong>Results: </strong>Overall, 10 studies met the eligibility criteria. The 3 main themes were uses of passive sensing and AI in clinical practice, barriers to and facilitators of use in practice, and consequences for service users. A total of 5 subthemes were identified: barriers, facilitators, empowerment, risk to well-being, and data privacy and protection issues.</p><p><strong>Conclusions: </strong>Although clinicians are open-minded about the use of passive sensing and AI in mental health care, important factors to consider are service user well-being, clinician workloads, and therapeutic relationships. Service users and clinicians must be involved in the development of digital technologies and systems to ensure ease of use. The development of, and training in, clear policies and guidelines on the use of passive sensing and AI in mental health care, including risk management and data security procedures, will also be key to facilitating clinician engagement. The means for clinicians and service users to provide feedback on how the use of passive sensing and AI in practice is being received should also be considered.</p><p><strong>Trial registration: </strong>PROSPERO International Prospective Register of Systematic Reviews CRD42022331698; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=331698.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10848143/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139522190","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":"Design and Implementation of a Brief Digital Mindfulness and Compassion Training App for Health Care Professionals: Cluster Randomized Controlled Trial.","authors":"Satish Jaiswal, Suzanna R Purpura, James K Manchanda, Jason Nan, Nihal Azeez, Dhakshin Ramanathan, Jyoti Mishra","doi":"10.2196/49467","DOIUrl":"10.2196/49467","url":null,"abstract":"<p><strong>Background: </strong>Several studies show that intense work schedules make health care professionals particularly vulnerable to emotional exhaustion and burnout.</p><p><strong>Objective: </strong>In this scenario, promoting self-compassion and mindfulness may be beneficial for well-being. Notably, scalable, digital app-based methods may have the potential to enhance self-compassion and mindfulness in health care professionals.</p><p><strong>Methods: </strong>In this study, we designed and implemented a scalable, digital app-based, brief mindfulness and compassion training program called \"WellMind\" for health care professionals. A total of 22 adult participants completed up to 60 sessions of WellMind training, 5-10 minutes in duration each, over 3 months. Participants completed behavioral assessments measuring self-compassion and mindfulness at baseline (preintervention), 3 months (postintervention), and 6 months (follow-up). In order to control for practice effects on the repeat assessments and calculate effect sizes, we also studied a no-contact control group of 21 health care professionals who only completed the repeated assessments but were not provided any training. Additionally, we evaluated pre- and postintervention neural activity in core brain networks using electroencephalography source imaging as an objective neurophysiological training outcome.</p><p><strong>Results: </strong>Findings showed a post- versus preintervention increase in self-compassion (Cohen d=0.57; P=.007) and state-mindfulness (d=0.52; P=.02) only in the WellMind training group, with improvements in self-compassion sustained at follow-up (d=0.8; P=.01). Additionally, WellMind training durations correlated with the magnitude of improvement in self-compassion across human participants (ρ=0.52; P=.01). Training-related neurophysiological results revealed plasticity specific to the default mode network (DMN) that is implicated in mind-wandering and rumination, with DMN network suppression selectively observed at the postintervention time point in the WellMind group (d=-0.87; P=.03). We also found that improvement in self-compassion was directly related to the extent of DMN suppression (ρ=-0.368; P=.04).</p><p><strong>Conclusions: </strong>Overall, promising behavioral and neurophysiological findings from this first study demonstrate the benefits of brief digital mindfulness and compassion training for health care professionals and compel the scale-up of the digital intervention.</p><p><strong>Trial registration: </strong>Trial Registration: International Standard Randomized Controlled Trial Number Registry ISRCTN94766568, https://www.isrctn.com/ISRCTN94766568.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10845023/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139513273","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":"Speech Features as Predictors of Momentary Depression Severity in Patients With Depressive Disorder Undergoing Sleep Deprivation Therapy: Ambulatory Assessment Pilot Study.","authors":"Lisa-Marie Wadle, Ulrich W Ebner-Priemer, Jerome C Foo, Yoshiharu Yamamoto, Fabian Streit, Stephanie H Witt, Josef Frank, Lea Zillich, Matthias F Limberger, Ayimnisagul Ablimit, Tanja Schultz, Maria Gilles, Marcella Rietschel, Lea Sirignano","doi":"10.2196/49222","DOIUrl":"10.2196/49222","url":null,"abstract":"<p><strong>Background: </strong>The use of mobile devices to continuously monitor objectively extracted parameters of depressive symptomatology is seen as an important step in the understanding and prevention of upcoming depressive episodes. Speech features such as pitch variability, speech pauses, and speech rate are promising indicators, but empirical evidence is limited, given the variability of study designs.</p><p><strong>Objective: </strong>Previous research studies have found different speech patterns when comparing single speech recordings between patients and healthy controls, but only a few studies have used repeated assessments to compare depressive and nondepressive episodes within the same patient. To our knowledge, no study has used a series of measurements within patients with depression (eg, intensive longitudinal data) to model the dynamic ebb and flow of subjectively reported depression and concomitant speech samples. However, such data are indispensable for detecting and ultimately preventing upcoming episodes.</p><p><strong>Methods: </strong>In this study, we captured voice samples and momentary affect ratings over the course of 3 weeks in a sample of patients (N=30) with an acute depressive episode receiving stationary care. Patients underwent sleep deprivation therapy, a chronotherapeutic intervention that can rapidly improve depression symptomatology. We hypothesized that within-person variability in depressive and affective momentary states would be reflected in the following 3 speech features: pitch variability, speech pauses, and speech rate. We parametrized them using the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS) from open-source Speech and Music Interpretation by Large-Space Extraction (openSMILE; audEERING GmbH) and extracted them from a transcript. We analyzed the speech features along with self-reported momentary affect ratings, using multilevel linear regression analysis. We analyzed an average of 32 (SD 19.83) assessments per patient.</p><p><strong>Results: </strong>Analyses revealed that pitch variability, speech pauses, and speech rate were associated with depression severity, positive affect, valence, and energetic arousal; furthermore, speech pauses and speech rate were associated with negative affect, and speech pauses were additionally associated with calmness. Specifically, pitch variability was negatively associated with improved momentary states (ie, lower pitch variability was linked to lower depression severity as well as higher positive affect, valence, and energetic arousal). Speech pauses were negatively associated with improved momentary states, whereas speech rate was positively associated with improved momentary states.</p><p><strong>Conclusions: </strong>Pitch variability, speech pauses, and speech rate are promising features for the development of clinical prediction technologies to improve patient care as well as timely diagnosis and monitoring of treatment response. Our res","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10835582/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139486295","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":"Developing a Framework to Infer Opioid Use Disorder Severity From Clinical Notes to Inform Natural Language Processing Methods: Characterization Study.","authors":"Melissa N Poulsen, Philip J Freda, Vanessa Troiani, Danielle L Mowery","doi":"10.2196/53366","DOIUrl":"10.2196/53366","url":null,"abstract":"<p><strong>Background: </strong>Information regarding opioid use disorder (OUD) status and severity is important for patient care. Clinical notes provide valuable information for detecting and characterizing problematic opioid use, necessitating development of natural language processing (NLP) tools, which in turn requires reliably labeled OUD-relevant text and understanding of documentation patterns.</p><p><strong>Objective: </strong>To inform automated NLP methods, we aimed to develop and evaluate an annotation schema for characterizing OUD and its severity, and to document patterns of OUD-relevant information within clinical notes of heterogeneous patient cohorts.</p><p><strong>Methods: </strong>We developed an annotation schema to characterize OUD severity based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, 5th edition. In total, 2 annotators reviewed clinical notes from key encounters of 100 adult patients with varied evidence of OUD, including patients with and those without chronic pain, with and without medication treatment for OUD, and a control group. We completed annotations at the sentence level. We calculated severity scores based on annotation of note text with 18 classes aligned with criteria for OUD severity and determined positive predictive values for OUD severity.</p><p><strong>Results: </strong>The annotation schema contained 27 classes. We annotated 1436 sentences from 82 patients; notes of 18 patients (11 of whom were controls) contained no relevant information. Interannotator agreement was above 70% for 11 of 15 batches of reviewed notes. Severity scores for control group patients were all 0. Among noncontrol patients, the mean severity score was 5.1 (SD 3.2), indicating moderate OUD, and the positive predictive value for detecting moderate or severe OUD was 0.71. Progress notes and notes from emergency department and outpatient settings contained the most and greatest diversity of information. Substance misuse and psychiatric classes were most prevalent and highly correlated across note types with high co-occurrence across patients.</p><p><strong>Conclusions: </strong>Implementation of the annotation schema demonstrated strong potential for inferring OUD severity based on key information in a small set of clinical notes and highlighting where such information is documented. These advancements will facilitate NLP tool development to improve OUD prevention, diagnosis, and treatment.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10825772/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139467149","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":"Identification of Predictors of Mood Disorder Misdiagnosis and Subsequent Help-Seeking Behavior in Individuals With Depressive Symptoms: Gradient-Boosted Tree Machine Learning Approach.","authors":"Jiri Benacek, Nimotalai Lawal, Tommy Ong, Jakub Tomasik, Nayra A Martin-Key, Erin L Funnell, Giles Barton-Owen, Tony Olmert, Dan Cowell, Sabine Bahn","doi":"10.2196/50738","DOIUrl":"10.2196/50738","url":null,"abstract":"<p><strong>Background: </strong>Misdiagnosis and delayed help-seeking cause significant burden for individuals with mood disorders such as major depressive disorder and bipolar disorder. Misdiagnosis can lead to inappropriate treatment, while delayed help-seeking can result in more severe symptoms, functional impairment, and poor treatment response. Such challenges are common in individuals with major depressive disorder and bipolar disorder due to the overlap of symptoms with other mental and physical health conditions, as well as, stigma and insufficient understanding of these disorders.</p><p><strong>Objective: </strong>In this study, we aimed to identify factors that may contribute to mood disorder misdiagnosis and delayed help-seeking.</p><p><strong>Methods: </strong>Participants with current depressive symptoms were recruited online and data were collected using an extensive digital mental health questionnaire, with the World Health Organization World Mental Health Composite International Diagnostic Interview delivered via telephone. A series of predictive gradient-boosted tree algorithms were trained and validated to identify the most important predictors of misdiagnosis and subsequent help-seeking in misdiagnosed individuals.</p><p><strong>Results: </strong>The analysis included data from 924 symptomatic individuals for predicting misdiagnosis and from a subset of 379 misdiagnosed participants who provided follow-up information when predicting help-seeking. Models achieved good predictive power, with area under the receiver operating characteristic curve of 0.75 and 0.71 for misdiagnosis and help-seeking, respectively. The most predictive features with respect to misdiagnosis were high severity of depressed mood, instability of self-image, the involvement of a psychiatrist in diagnosing depression, higher age at depression diagnosis, and reckless spending. Regarding help-seeking behavior, the strongest predictors included shorter time elapsed since last speaking to a general practitioner about mental health, sleep problems disrupting daily tasks, taking antidepressant medication, and being diagnosed with depression at younger ages.</p><p><strong>Conclusions: </strong>This study provides a novel, machine learning-based approach to understand the interplay of factors that may contribute to the misdiagnosis and subsequent help-seeking in patients experiencing low mood. The present findings can inform the development of targeted interventions to improve early detection and appropriate treatment of individuals with mood disorders.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10811571/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139418345","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":"Immersive technologies for depression care: A scoping review (Preprint)","authors":"C. M. Reátegui-Rivera, David Villarreal-Zegarra, Kelly De la Cruz-Torralva, Paquita Díaz-Sánchez, Joseph Finkelstein","doi":"10.2196/56056","DOIUrl":"https://doi.org/10.2196/56056","url":null,"abstract":"","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511529","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":"Feasibility, Adherence, and Effectiveness of Blended Psychotherapy for Severe Mental Illnesses: Scoping Review.","authors":"Yamina Ehrt-Schäfer, Milan Rusmir, Johannes Vetter, Erich Seifritz, Mario Müller, Birgit Kleim","doi":"10.2196/43882","DOIUrl":"10.2196/43882","url":null,"abstract":"<p><strong>Background: </strong>Blended psychotherapy (bPT) combines face-to-face psychotherapy with digital interventions to enhance the effectiveness of mental health treatment. The feasibility and effectiveness of bPT have been demonstrated for various mental health issues, although primarily for patients with higher levels of functioning.</p><p><strong>Objective: </strong>This scoping review aims to investigate the feasibility, adherence, and effectiveness of bPT for the treatment of patients with severe mental illnesses (SMIs).</p><p><strong>Methods: </strong>Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, we conducted searches in PubMed, MEDLINE, Embase, PsycINFO, and PsycArticles for studies published until March 23, 2023.</p><p><strong>Results: </strong>Out of 587 screened papers, we incorporated 25 studies encompassing 23 bPT interventions, involving a total of 2554 patients with SMI. The intervention formats and research designs exhibited significant variation. Our findings offer preliminary evidence supporting the feasibility of bPT for SMI, although there is limited research on adherence. Nevertheless, the summarized studies indicated promising attrition rates, spanning from 0% to 37%, implying a potential beneficial impact of bPT on adherence to SMI treatment. The quantity of evidence on the effects of bPT for SMI was limited and challenging to generalize. Among the 15 controlled trials, 4 concluded that bPT interventions were effective compared with controls. However, it is noteworthy that 2 of these studies used the same study population, and the control groups exhibited significant variations.</p><p><strong>Conclusions: </strong>Overall, our review suggests that while bPT appears promising as a treatment method, further research is necessary to establish its effectiveness for SMI. We discuss considerations for clinical implementation, directions, and future research.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10777283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139038131","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":"Efficacy of an Electronic Cognitive Behavioral Therapy Program Delivered via the Online Psychotherapy Tool for Depression and Anxiety Related to the COVID-19 Pandemic: Pre-Post Pilot Study.","authors":"Elnaz Moghimi, Callum Stephenson, Anika Agarwal, Niloofar Nikjoo, Niloufar Malakouti, Gina Layzell, Anne O'Riordan, Jasleen Jagayat, Amirhossein Shirazi, Gilmar Gutierrez, Ferwa Khan, Charmy Patel, Megan Yang, Mohsen Omrani, Nazanin Alavi","doi":"10.2196/51102","DOIUrl":"10.2196/51102","url":null,"abstract":"<p><strong>Background: </strong>Lockdowns and social distancing resulting from the COVID-19 pandemic have worsened the population's mental health and made it more difficult for individuals to receive care. Electronic cognitive behavioral therapy (e-CBT) is a cost-effective and evidence-based treatment for anxiety and depression and can be accessed remotely.</p><p><strong>Objective: </strong>The objective of the study was to investigate the efficacy of online psychotherapy tailored to depression and anxiety symptoms during the pandemic.</p><p><strong>Methods: </strong>The pilot study used a pre-post design to evaluate the efficacy of a 9-week e-CBT program designed for individuals with depression and anxiety affected by the pandemic. Participants were adults (N=59) diagnosed with major depressive disorder and generalized anxiety disorder, whose mental health symptoms initiated or worsened during the COVID-19 pandemic. The online psychotherapy program focused on teaching coping, mindfulness, and problem-solving skills. Symptoms of anxiety and depression, resilience, and quality of life were assessed.</p><p><strong>Results: </strong>Participants demonstrated significant improvements in symptoms of anxiety (P=.02) and depression (P=.03) after the intervention. Similar trends were observed in the intention-to-treat analysis. No significant differences were observed in resilience and quality-of-life measures. The sample comprised mostly females, making it challenging to discern the benefits of the intervention in males. Although a pre-post design is less rigorous than a controlled trial, this design was selected to observe changes in scores during a critical period.</p><p><strong>Conclusions: </strong>e-CBT for COVID-19 is an effective and accessible treatment option. Improvements in clinical symptoms of anxiety and depression can be observed in individuals whose mental health is affected by the COVID-19 pandemic.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov NCT04476667; https://clinicaltrials.gov/study/NCT04476667.</p><p><strong>International registered report identifier (irrid): </strong>RR2-10.2196/24913.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10760511/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138296219","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":"Efficacy of a Smartphone App in Enhancing Medication Adherence and Accuracy in Individuals With Schizophrenia During the COVID-19 Pandemic: Randomized Controlled Trial.","authors":"Huan Hwa Chen, Hsin Tien Hsu, Pei Chao Lin, Chin-Yin Chen, Hsiu Fen Hsieh, Chih Hung Ko","doi":"10.2196/50806","DOIUrl":"https://doi.org/10.2196/50806","url":null,"abstract":"<p><strong>Background: </strong>Poor medication adherence or inaccuracy in taking prescribed medications plays an important role in the recurrence or worsening of psychiatric symptoms in patients with schizophrenia, and the COVID-19 pandemic impacted their medication adherence with exacerbated symptoms or relapse. The use of mobile health services increased during the COVID-19 pandemic, and their role in improving mental health is becoming clearer.</p><p><strong>Objective: </strong>This study aimed to explore the effectiveness of a smartphone app (MedAdhere) on medication adherence and accuracy among patients with schizophrenia and to measure their psychiatric symptoms and cognitive functions.</p><p><strong>Methods: </strong>In this 12-week experimental study, participants were provided interventions with the MedAdhere app, and data were collected between June 2021 and September 2022. A total of 105 participants were randomly assigned to either the experimental or control groups. We used the Positive and Negative Syndrome Scale and Mini-Mental State Examination to measure the participants' psychiatric symptoms and cognitive functions. Generalized estimating equations were used for data analysis.</p><p><strong>Results: </strong>A total of 94 participants met the inclusion criteria and completed the protocol, and the medication adherence rate of the experimental group was 94.72% (2785/2940) during the intervention. Psychotic symptoms (positive, negative, and general psychopathology symptoms) and cognitive functions (memory, language, and executive function) were significantly improved in the experimental group compared to the control group after the intervention.</p><p><strong>Conclusions: </strong>The MedAdhere app effectively and significantly improved medication adherence and, thereby, the psychiatric symptoms of patients with schizophrenia. This artificial intelligence assisted app could be extended to all patients who need to be reminded to take medication on schedule.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov NCT05892120; https://clinicaltrials.gov/study/NCT05892120.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10727482/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138812744","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":"Exposure Versus Cognitive Restructuring Techniques in Brief Internet-Based Cognitive Behavioral Treatment for Arabic-Speaking People With Posttraumatic Stress Disorder: Randomized Clinical Trial.","authors":"Jana Stein, Max Vöhringer, Birgit Wagner, Nadine Stammel, Yuriy Nesterko, Maria Böttche, Christine Knaevelsrud","doi":"10.2196/48689","DOIUrl":"10.2196/48689","url":null,"abstract":"<p><strong>Background: </strong>Cognitive behavioral interventions delivered via the internet are demonstrably efficacious treatment options for posttraumatic stress disorder (PTSD) in underserved, Arabic-speaking populations. However, the role of specific treatment components remains unclear, particularly in conflict-affected areas of the Middle East and North Africa.</p><p><strong>Objective: </strong>This study aims to evaluate 2 brief internet-based treatments in terms of efficacy, including change in PTSD symptom severity during treatment. Both treatments were developed in line with Interapy, an internet-based, therapist-assisted cognitive behavioral therapy protocol for PTSD and adapted to the specific research question. The first treatment comprised self-confrontation and social sharing (exposure treatment; 6 sessions); the second comprised cognitive restructuring and social sharing (cognitive restructuring treatment; 6 sessions). The 2 treatments were compared with each other and with a waitlist control group.</p><p><strong>Methods: </strong>In total, 365 Arabic-speaking participants from the Middle East and North Africa (mean age 25.49, SD 6.68 y) with PTSD were allocated to cognitive restructuring treatment (n=118, 32.3%), exposure treatment (n=122, 33.4%), or a waitlist control group (n=125, 34.2%) between February 2021 and December 2022. PTSD symptom severity, posttraumatic maladaptive cognitions, anxiety, depressive and somatoform symptom severity, and quality of life were assessed via self-report at baseline and after treatment or waiting time. PTSD symptom severity was also measured throughout treatment or waiting time. Treatment satisfaction was assessed after treatment completion. Treatment use and satisfaction were compared between the 2 treatment conditions using appropriate statistical tests (eg, chi-square and Welch tests). Multiple imputation was performed to address missing data and evaluate treatment-associated changes. These changes were analyzed using multigroup change modeling in the completer and intention-to-treat samples.</p><p><strong>Results: </strong>Overall, 200 (N=240, 83.3%) participants started any of the treatments, of whom 123 (61.5%) completed the treatment. Treatment condition was not significantly associated with the proportion of participants who started versus did not start treatment (P=.20) or with treatment completion versus treatment dropout (P=.71). High treatment satisfaction was reported, with no significant differences between the treatment conditions (P=.48). In both treatment conditions, PTSD, anxiety, depressive and somatoform symptom severity, and posttraumatic maladaptive cognitions decreased, and quality of life improved significantly from baseline to the posttreatment time point (P≤.001 in all cases). Compared with the baseline assessment, overall PTSD symptom severity decreased significantly after 4 sessions in both treatment conditions (P<.001). Moreover, both treatment conditions were si","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10753431/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138812750","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}