Frontiers in Digital Health

ObjectiveAifred is an artificial intelligence (AI)-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician-patient interaction. MethodsTwenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-hour study at a clinical interaction simulation centre with standardized patients. Each physicia...

It remains unknown whether de-identifying wearable biometric monitoring data is sufficient to protect the privacy of individuals in the dataset. This systematic review seeks to shed light on this. We searched Web of Science, IEEE Xplore Digital Library, PubMed, Scopus, and the ACM Digital Library on December 6, 2021 (PROSPERO CRD42022312922). We also performed manual searches in journals of interest until April 12, 2022. Though our search strategy had no language restrictions, all retrieved stud...

Wearable devices can collect changes in human behaviors related to mental health including depression and anxiety. Here, we examined whether and how digital metrics from a consumer-grade wearable smart ring (Oura Ring) differed by severity of depression and anxiety symptoms using data from a large-scale population-based sample of young adults (n=1,290, age range: 33-35). Participants wore the ring for two weeks, assessing sleep architecture, nocturnal heart rate (HR), heart rate variability (HRV...

Peripartum depression (PPD) affects 12 to 25% pregnant women worldwide, yet screening often misses real-time symptom changes. Digital phenotyping (DP) offers a promising support, using data like text entries or sleep tracking to detect PPD. This review (PROSPERO: CRD42023461325) evaluated 14 studies, highlighting the substantial potential of personal history and semi-random ecological-momentary data. Future work should focus on improving models and advancing their translation into clinical setti...

Digital phenotyping studies using smartphone-sensed data have identified several behavioral markers associated with depression. However, the generalizability of these markers is constrained by multiple factors, including variability in depressive symptoms and associated behaviors, both between and within individuals over time. This study examines heterogeneity in depression and aims to identify behavioral markers indicative of depression in smartphone-sensed data collected from participants diag...

ObjectiveTo analyze the impact of factors in healthcare delivery on the net benefit of triggering an Advanced Care Planning (ACP) workflow based on predictions of 12-month mortality. Materials and MethodsWe built a predictive model of 12-month mortality using electronic health record data and evaluated the impact of healthcare delivery factors on the net benefit of triggering an ACP workflow based on the models predictions. Factors included non-clinical reasons that make ACP inappropriate, limi...

ObjectiveTo determine whether clinicians will use machine learned clinical order recommender systems for electronic order entry for simulated inpatient cases, and whether such recommendations impact the clinical appropriateness of the orders being placed. Materials and Methods43 physicians used a clinical order entry interface for five simulated medical cases, with each physician-case randomized whether to have access to a previously-developed clinical order recommendation system. A panel of cl...

Online mental health spaces require effective content moderation for safety. Whilst policies acknowledge the need for proactive practices and moderator support, expectations and experiences of internet users engaging with self-harm and suicide content online remain unclear. Therefore, this study aimed to explore participant accounts of moderation, moderators and moderating when engaging online with self-harm/suicide (SH/S) related content. Participants in the DELVE study were interviewed about ...

As digital phenotyping tools become more prevalent in mental health research and care, the question of how to meaningfully return data to participants, clinicians, and caregivers has grown increasingly important. Drawing from the broader DeeP-DD (Deep Phenotyping and Digitalization at the Douglas) project, this work presents the iterative development of individualized feedback reports focused on a psychiatric patient population. This paper also builds on findings from a narrative literature revi...

Mental disorders are a major global cause of morbidity and mortality. The surge in adoption of smartphones and other wearable devices has made it possible to use the data generated by them for clinical purposes. In particular, in psychiatry, detailed and high-resolution information on patients state, mood, and behavior can significantly improve the assessment, diagnosis and the treatment of patients. However, there is long path to turn the raw data created by these sensors, to information and in...

Depression in older adults is both common and frequently underdiagnosed, especially in assisted-living communities, where it often co-occurs with mild cognitive impairment (MCI), creating a complex and vulnerable clinical landscape. Despite this urgency, scalable, interpretable, and easy-to-administer tools for early screening remain scarce. In this study, we introduce a transparent and lightweight AI-driven screening model that uses only four linguistic features extracted from brief conversatio...

BackgroundDepression is a leading cause of global disability. Timely identification of patients at risk for clinical worsening remains a major challenge. Electronic health records (EHRs) facilitate large-scale, real-world analyses of disease trajectories. However, standardized symptom scale data such as the Patient Health Questionnaire-9 are often unavailable or recorded only as unstructured text. In this context, International Classification of Diseases (ICD10) diagnostic-code based severity pr...

ObjectiveDepression is a multifaceted disorder with neurobiological, behavioral, and environmental components. This review aims to explore how artificial intelligence (AI) and computational methods are advancing the understanding and treatment of depression, focusing on neurobiological mechanisms, early detection, and behavioral activation (BA) interventions. MethodsA comprehensive literature review was conducted searching PubMed, Scopus, ACM, and Web of Science databases. From 77654 articles i...

BackgroundClinical diagnostic assessments and outcome monitoring of patients with depression rely predominantly on interviews by professionals and the use of self-report questionnaires. The ubiquity of smartphones and other personal consumer devices has prompted research into the potential of data collected via these devices to serve as digital behavioral markers for indicating presence and monitoring of outcome of depression. ObjectiveThis paper explores the potential of using behavioral data ...

BackgroundCOVID-19 presented significant challenges for occupational therapy (OT) services in Ireland. Public health guidelines necessitated a transition of services from face-to-face delivery to the use of telehealth modalities. Telehealth has yet to be extensively researched within mental health OT, with a particular need for an increased understanding of therapeutic processes when conducted remotely. AimTo explore the experiences of occupational therapists transitioning to telehealth service...

1The therapeutic alliance is widely recognized as a key predictor of psychotherapy outcome, though it is predominantly assessed through subjective self-reports. This proof-of-concept study investigated whether interpersonal movement and heart rate synchrony can serve as objective biomarkers for therapy effectiveness within naturalistic cognitive behavioral therapy (CBT) settings. Twelve patient-therapist dyads were analyzed in a short-term follow-up setting. Physiological signals were continuous...

ObjectiveDepression remains one of the most critical healthcare concerns worldwide. While increasing patient numbers inevitably lead to a greater need for treatment, resources to adequately match this demand are limited. Digital technologies hold promises to complement standard therapeutic approaches such as psychotherapy, thereby alleviating the strain on an increasingly burdened healthcare system. This study aimed at testing a new comprehensive smartwatch-based digital intervention program adm...

ObjectiveWe aimed to examine the characteristics, features, and content of suicide prevention mobile apps available in app stores in Canada and the United Kingdom. DesignSuicide prevention apps were identified from Apple and Android app stores between March-April 2023. Apps were screened against predefined inclusion criteria, and duplicate apps were removed. Data were then extracted based on descriptive (e.g., genre, app developer), security (e.g., password protection), and design features (e.g...

Depression is a critical public health issue among women in rural India, with diagnosis and treatment rates being very low. The paper summarizes the development of MITHRA, a user-centred mobile app for depression screening and treatment among women in self-help groups (SHGs) of rural India. The predevelopment phase involved situation analysis and forming participatory design groups of prospective users. The app development phase used an Agile approach for flexibility and rapid adaptation. The po...

Depression is a common psychiatric disorder with substantial recurrence risk. Accurate prediction from easily collected data would aid in diagnosis, treatment and prevention. We used machine learning in the Generation Scotland cohort to predict lifetime risk of depression and, among cases, recurrent depression. Rank aggregation was used to combine results across ten different algorithms and identify highly predictive variables. The model containing all but the cardiometabolic predictors had the ...