Digital Lifestyle Interventions for Young People With Mental Illness: A Qualitative Study Among Mental Health Care Professionals.

BACKGROUND: Given the physical health disparities associated with mental illness, targeted lifestyle interventions are required to reduce the risk of cardiometabolic disease. Integrating physical health early in mental health treatment among young people is essential for preventing physical comorbidities, reducing health disparities, managing medication side effects, and improving overall health outcomes. Digital technology is increasingly used to promote fitness, lifestyle, and physical health among the general population. However, using these interventions to promote physical health within mental health care requires a nuanced understanding of the factors that affect their adoption and implementation. OBJECTIVE: Using a qualitative design, we explored the attitudes of mental health care professionals (MHCPs) toward digital technologies for physical health with the goal of illuminating the opportunities, development, and implementation of the effective use of digital tools for promoting healthier lifestyles in mental health care. METHODS: Semistructured interviews were conducted with MHCPs (N=13) using reflexive thematic analysis to explore their experiences and perspectives on using digital health to promote physical health in youth mental health care settings. RESULTS: Three overarching themes from the qualitative analysis are reported: (1) motivation will affect implementation, (2) patients' readiness and capability, and (3) reallocation of staff roles and responsibilities. The subthemes within, and supporting quotes, are described. CONCLUSIONS: The use of digital means presents many opportunities for improving the provision of physical health interventions in mental health care settings. However, given the limited experience of many MHCPs with these technologies, formal training and additional support may improve the likelihood of implementation. Factors such as patient symptomatology, safety, and access to technology, as well as the readiness, acceptability, and capability of both MHCPs and patients to engage with digital tools, must also be considered. In addition, the potential benefits of data integration must be carefully weighed against the associated risks.

The Potential for Digital Phenotyping in Understanding Mindfulness App Engagement Patterns: A Pilot Study.

Background: Low app engagement is a central barrier to digital mental health efficacy. With mindfulness-based mental health apps growing in popularity, there is a need for new understanding of factors influencing engagement. This study utilized digital phenotyping to understand real-time patterns of engagement around app-based mindfulness. Different engagement metrics are presented that measure both the total number of app-based activities participants completed each week, as well as the proportion of days that participants engaged with the app each week. Method: Data were derived from two iterations of a four-week study exploring app engagement in college students (n = 169). This secondary analysis investigated the relationships between general and mindfulness-based app engagement with passive data metrics (sleep duration, home time, and screen duration) at a weekly level, as well as the relationship between demographics and engagement. Additional clinically focused analysis was performed on three case studies of participants with high mindfulness activity completion. Results: Demographic variables such as gender, race/ethnicity, and age lacked a significant association with mindfulness app-based engagement. Passive data variables such as sleep and screen duration were significant predictors for different metrics of general and mindfulness-based app engagement at a weekly level. There was a significant interaction effect for screen duration between the number of mindfulness activities completed and whether or not the participant received a mindfulness notification. K-means clusters analyses using passive data features to predict mindfulness activity completion had low performance. Conclusions: While there are no simple solutions to predicting engagement with mindfulness apps, utilizing digital phenotyping approaches at a population and personal level offers new potential. The signal from digital phenotyping warrants more investigation; even small increases in engagement with mindfulness apps may have a tremendous impact given their already high prevalence of engagement, availability, and potential to engage patients across demographics.

From "online brains" to "online lives": understanding the individualized impacts of Internet use across psychological, cognitive and social dimensions.

In response to the mass adoption and extensive usage of Internet-enabled devices across the world, a major review published in this journal in 2019 examined the impact of Internet on human cognition, discussing the concepts and ideas behind the "online brain". Since then, the online world has become further entwined with the fabric of society, and the extent to which we use such technologies has continued to grow. Furthermore, the research evidence on the ways in which Internet usage affects the human mind has advanced considerably. In this paper, we sought to draw upon the latest data from large-scale epidemiological studies and systematic reviews, along with randomized controlled trials and qualitative research recently emerging on this topic, in order to now provide a multi-dimensional overview of the impacts of Internet usage across psychological, cognitive and societal outcomes. Within this, we detail the empirical evidence on how effects differ according to various factors such as age, gender, and usage types. We also draw from new research examining more experiential aspects of individuals' online lives, to understand how the specifics of their interactions with the Internet, and the impact on their lifestyle, determine the benefits or drawbacks of online time. Additionally, we explore how the nascent but intriguing areas of culturomics, artificial intelligence, virtual reality, and augmented reality are changing our understanding of how the Internet can interact with brain and behavior. Overall, the importance of taking an individualized and multi-dimensional approach to how the Internet affects mental health, cognition and social functioning is clear. Furthermore, we emphasize the need for guidelines, policies and initiatives around Internet usage to make full use of the evidence available from neuroscientific, behavioral and societal levels of research presented herein.

Digital Mental Health for Schizophrenia and Other Severe Mental Illnesses: An International Consensus on Current Challenges and Potential Solutions.

BACKGROUND: Digital approaches may be helpful in augmenting care to address unmet mental health needs, particularly for schizophrenia and severe mental illness (SMI). OBJECTIVE: An international multidisciplinary group was convened to reach a consensus on the challenges and potential solutions regarding collecting data, delivering treatment, and the ethical challenges in digital mental health approaches for schizophrenia and SMI. METHODS: The consensus development panel method was used, with an in-person meeting of 2 groups: the expert group and the panel. Membership was multidisciplinary including those with lived experience, with equal participation at all stages and coproduction of the consensus outputs and summary. Relevant literature was shared in advance of the meeting, and a systematic search of the recent literature on digital mental health interventions for schizophrenia and psychosis was completed to ensure that the panel was informed before the meeting with the expert group. RESULTS: Four broad areas of challenge and proposed solutions were identified: (1) user involvement for real coproduction; (2) new approaches to methodology in digital mental health, including agreed standards, data sharing, measuring harms, prevention strategies, and mechanistic research; (3) regulation and funding issues; and (4) implementation in real-world settings (including multidisciplinary collaboration, training, augmenting existing service provision, and social and population-focused approaches). Examples are provided with more detail on human-centered research design, lived experience perspectives, and biomedical ethics in digital mental health approaches for SMI. CONCLUSIONS: The group agreed by consensus on a number of recommendations: (1) a new and improved approach to digital mental health research (with agreed reporting standards, data sharing, and shared protocols), (2) equal emphasis on social and population research as well as biological and psychological approaches, (3) meaningful collaborations across varied disciplines that have previously not worked closely together, (4) increased focus on the business model and product with planning and new funding structures across the whole development pathway, (5) increased focus and reporting on ethical issues and potential harms, and (6) organizational changes to allow for true communication and coproduction with those with lived experience of SMI. This study approach, combining an international expert meeting with patient and public involvement and engagement throughout the process, consensus methodology, discussion, and publication, is a helpful way to identify directions for future research and clinical implementation in rapidly evolving areas and can be combined with measurements of real-world clinical impact over time. Similar initiatives will be helpful in other areas of digital mental health and similarly fast-evolving fields to focus research and organizational change and effect improved real-world clinical implementation.

Digital phenotyping data and anomaly detection methods to assess changes in mood and anxiety symptoms across a transdiagnostic clinical sample.

INTRODUCTION: Clinical assessment of mood and anxiety change often relies on clinical assessment or self-reported scales. Using smartphone digital phenotyping data and resulting markers of behavior (e.g., sleep) to augment clinical symptom scores offers a scalable and potentially more valid method to understand changes in patients' state. This paper explores the potential of using a combination of active and passive sensors in the context of smartphone-based digital phenotyping to assess mood and anxiety changes in two distinct cohorts of patients to assess the preliminary reliability and validity of this digital phenotyping method. METHODS: Participants from two different cohorts, each n = 76, one with diagnoses of depression/anxiety and the other schizophrenia, utilized mindLAMP to collect active data (e.g., surveys on mood/anxiety), along with passive data consisting of smartphone digital phenotyping data (geolocation, accelerometer, and screen state) for at least 1 month. Using anomaly detection algorithms, we assessed if statistical anomalies in the combination of active and passive data could predict changes in mood/anxiety scores as measured via smartphone surveys. RESULTS: The anomaly detection model was reliably able to predict symptom change of 4 points or greater for depression as measured by the PHQ-9 and anxiety as measured for the GAD-8 for both patient populations, with an area under the ROC curve of 0.65 and 0.80 for each respectively. For both PHQ-9 and GAD-7, these AUCs were maintained when predicting significant symptom change at least 7 days in advance. Active data alone predicted around 52% and 75% of the symptom variability for the depression/anxiety and schizophrenia populations respectively. CONCLUSION: These results indicate the feasibility of anomaly detection for predicting symptom change in transdiagnostic cohorts. These results across different patient groups, different countries, and different sites (India and the US) suggest anomaly detection of smartphone digital phenotyping data may offer a reliable and valid approach to predicting symptom change. Future work should emphasize prospective application of these statistical methods.

Digital Tools to Facilitate the Detection and Treatment of Bipolar Disorder: Key Developments and Future Directions.

Bipolar disorder (BD) impacts over 40 million people around the world, often manifesting in early adulthood and substantially impacting the quality of life and functioning of individuals. Although early interventions are associated with a better prognosis, the early detection of BD is challenging given the high degree of similarity with other psychiatric conditions, including major depressive disorder, which corroborates the high rates of misdiagnosis. Further, BD has a chronic, relapsing course, and the majority of patients will go on to experience mood relapses despite pharmacological treatment. Digital technologies present promising results to augment early detection of symptoms and enhance BD treatment. In this editorial, we will discuss current findings on the use of digital technologies in the field of BD, while debating the challenges associated with their implementation in clinical practice and the future directions.

Adverse Events Reporting in Digital Interventions Evaluations for Psychosis: A Systematic Literature Search and Individual Level Content Analysis of Adverse Event Reports.

BACKGROUND: Digital health interventions (DHIs) have significant potential to upscale treatment access to people experiencing psychosis but raise questions around patient safety. Adverse event (AE) monitoring is used to identify, record, and manage safety issues in clinical trials, but little is known about the specific content and context contained within extant AE reports. This study aimed to assess current AE reporting in DHIs. STUDY DESIGN: A systematic literature search was conducted by the iCharts network (representing academic, clinical, and experts by experience) to identify trials of DHIs in psychosis. Authors were invited to share AE reports recorded in their trials. A content analysis was conducted on the shared reports. STUDY RESULTS: We identified 593 AE reports from 18 DHI evaluations, yielding 19 codes. Only 29 AEs (4.9% of total) were preidentified by those who shared AEs as being related to the intervention or trial procedures. While overall results support the safety of DHIs, DHIs were linked to mood problems and psychosis exacerbation in a few cases. Additionally, 27% of studies did not report information on relatedness for all or at least some AEs; 9.6% of AE reports were coded as unclear because it could not be determined what had happened to participants. CONCLUSIONS: The results support the safety of DHIs, but AEs must be routinely monitored and evaluated according to best practice. Individual-level analyses of AEs have merit to understand safety in this emerging field. Recommendations for best practice reporting in future studies are provided.

The Digital Navigator: Standardizing Human Technology Support in App-Integrated Clinical Care.

Background: Mental health apps offer scalable care, yet clinical adoption is hindered by low user engagement and integration challenges into clinic workflows. Human support staff called digital navigators, trained in mental health technology, could enhance care access and patient adherence and remove workflow burdens from clinicians. While the potential of this role is clear, training staff to become digital navigators and assessing their impact are primary challenges. Methods: We present a detailed manual/framework for implementation of the Digital Navigator within a short-term, cognitive-behavioral therapy-focused hybrid clinic. We analyze patient engagement, satisfaction, and digital phenotyping data quality outcomes. Data from 83 patients, for the period spanning September 2022 to September 2023, included Digital Navigator satisfaction, correlated with demographics, mindLAMP app satisfaction, engagement, and passive data quality. Additionally, average passive data across 33 clinic patients from November 2023 to January 2024 were assessed for missingness. Results: Digital Navigator satisfaction averaged 18.8/20. Satisfaction was not influenced by sex, race, gender, or education. Average passive data quality across 33 clinic patients was 0.82 at the time this article was written. Digital Navigator satisfaction scores had significant positive correlation with both clinic app engagement and perception of that app. Conclusions: Results demonstrate preliminary support and patient endorsement for the Digital Navigator role and positive outcomes around digital engagement and digital phenotyping data quality. Through sharing training resources and standardizing the role, we aim to enable clinicians and researchers to adapt and utilize the Digital Navigator for their own needs.

Digital Navigator Training to Increase Access to Mental Health Care in Community-Based Organizations.

Providing human support for users of behavioral health technology can help facilitate the necessary engagement and clinical integration of digital tools in mental health care. A team conducted digital navigator training that taught participants how to promote patrons' digital literacy, evaluate and recommend health apps, and interpret smartphone data. The authors trained 80 participants from 21 organizations, demonstrating this training's feasibility, acceptability, and need. Case studies explore the implementation of this training curriculum. As technology's potential in mental health care expands, training can empower digital navigators to ensure that the use of digital tools is informed, equitable, and clinically relevant.

Digital Mental Health's Unstable Dichotomy-Wellness and Health.

This Viewpoint discusses the unacknowledged risks and harms and unrealized clinical benefits of digital mental wellness and health technologies and offers suggestions for ways to catalyze the next phase of these technologies by focusing on safety, evidence, and engagement.

The impact of mindfulness apps on psychological processes of change: a systematic review.

Mindfulness-based interventions (MBIs) have demonstrated therapeutic efficacy for various psychological conditions, and smartphone apps that facilitate mindfulness practice can enhance the reach and impact of MBIs. The goal of this review was to summarize the published evidence on the impact of mindfulness apps on the psychological processes known to mediate transdiagnostic symptom reduction after mindfulness practice. A literature search from January 1, 1993, to August 7, 2023 was conducted on three databases, and 28 randomized controlled trials involving 5963 adults were included. Across these 28 studies, 67 outcome comparisons were made between a mindfulness app group and a control group. Between-group effects tended to favor the mindfulness app group over the control group in three psychological process domains: repetitive negative thinking, attention regulation, and decentering/defusion. Findings were mixed in other domains (i.e., awareness, nonreactivity, non-judgment, positive affect, and acceptance). The range of populations examined, methodological concerns across studies, and problems with sustained app engagement likely contributed to mixed findings. However, effect sizes tended to be moderate to large when effects were found, and gains tended to persist at follow-up assessments two to six months later. More research is needed to better understand the impact of these apps on psychological processes of change. Clinicians interested in integrating apps into care should consider app-related factors beyond evidence of a clinical foundation and use app databases to identify suitable apps for their patients, as highlighted at the end of this review.

Exploring current smartphone-based cognitive assessments in schizophrenia and bipolar disorder.

Schizophrenia and bipolar disorder are associated with cognitive deficits that contribute significantly to disability. However, traditional in-lab cognitive assessments are time-consuming and not optimized for remote administration. Recent advancements in smartphone technology enable momentary cognitive assessments in a real-world context. This brief report reviews recent research in momentary cognitive assessments in individuals with schizophrenia and bipolar disorder through reviewing mobile platforms and cognitive assessments studied. A total of 14 experimental articles were reviewed, focusing on cognitive domains including visual working memory, processing speed, executive function, verbal fluency, verbal memory, social cognition, and typing patterns. The review highlights the feasibility of remote cognitive assessment with smartphones, and provides a layout of domains studied in this context, but illustrates a low volume of current research, the need for additional studies, and the potential for innovations like digital phenotyping.

Social determinants of mental health in major depressive disorder: Umbrella review of 26 meta-analyses and systematic reviews.

There is a growing recognition of the impact of social determinants of mental health (SDoMHs) on people with, or at risk of, developing serious mental illnesses. Yet it is not known how associations of individual SDoMHs with risk for major depressive disorder (MDD) vary and roughly compare with one another. Following PRISMA guidelines, this umbrella review included 26 meta-analyses and systematic reviews that reported odds ratios, effect sizes, and/or pooled prevalence rates of MDD in samples with versus without specified SDoMHs. Childhood emotional, physical, or sexual abuse and neglect; intimate partner violence in females; and food insecurity were significantly associated with increased risk of MDD, with medium effect sizes. Natural disasters, terrorist acts, and military combat during deployment had small-size adverse effects, and homelessness, incarceration, and migration were associated with significantly elevated prevalence of MDD. Conversely, higher levels of parental care were significantly associated with reduced risk of MDD with medium effect sizes. Evidence supports the use of certain interventions at the individual and community level that can reduce the impact of these factors and promote health, although much more research is warranted in this area along with meaningful healthcare and societal policies to accomplish this goal.

Integrated Digital Mental Health Care: A Vision for Addressing Population Mental Health Needs.

The unmet need for mental health care continues to rise across the world. This article synthesizes the evidence supporting the components of a hypothetical model of integrated digital mental health care to meet population-wide mental health needs. This proposed model integrates two approaches to broadening timely access to effective care: integrated, primary care-based mental health services and digital mental health tools. The model solves for several of the key challenges historically faced by digital health, through promoting digital literacy and access, the curation of evidence-based digital tools, integration into clinical practice, and electronic medical record integration. This model builds upon momentum toward the integration of mental health services within primary care and aligns with the principles of the Collaborative Care Model. Finally, the authors present the major next steps toward implementation of integrated digital mental health care at scale.

Current evidence on the efficacy of mental health smartphone apps for symptoms of depression and anxiety. A meta-analysis of 176 randomized controlled trials.

The mental health care available for depression and anxiety has recently undergone a major technological revolution, with growing interest towards the potential of smartphone apps as a scalable tool to treat these conditions. Since the last comprehensive meta-analysis in 2019 established positive yet variable effects of apps on depressive and anxiety symptoms, more than 100 new randomized controlled trials (RCTs) have been carried out. We conducted an updated meta-analysis with the objectives of providing more precise estimates of effects, quantifying generalizability from this evidence base, and understanding whether major app and trial characteristics moderate effect sizes. We included 176 RCTs that aimed to treat depressive or anxiety symptoms. Apps had overall significant although small effects on symptoms of depression (N=33,567, g=0.28, p<0.001; number needed to treat, NNT=11.5) and generalized anxiety (N=22,394, g=0.26, p<0.001, NNT=12.4) as compared to control groups. These effects were robust at different follow-ups and after removing small sample and higher risk of bias trials. There was less variability in outcome scores at post-test in app compared to control conditions (ratio of variance, RoV=-0.14, 95% CI: -0.24 to -0.05 for depressive symptoms; RoV=-0.21, 95% CI: -0.31 to -0.12 for generalized anxiety symptoms). Effect sizes for depression were significantly larger when apps incorporated cognitive behavioral therapy (CBT) features or included chatbot technology. Effect sizes for anxiety were significantly larger when trials had generalized anxiety as a primary target and administered a CBT app or an app with mood monitoring features. We found evidence of moderate effects of apps on social anxiety (g=0.52) and obsessive-compulsive (g=0.51) symptoms, a small effect on post-traumatic stress symptoms (g=0.12), a large effect on acrophobia symptoms (g=0.90), and a non-significant negative effect on panic symptoms (g=-0.12), although these results should be considered with caution, because most trials had high risk of bias and were based on small sample sizes. We conclude that apps have overall small but significant effects on symptoms of depression and generalized anxiety, and that specific features of apps - such as CBT or mood monitoring features and chatbot technology - are associated with larger effect sizes.

Generative Language Models and Open Notes: Exploring the Promise and Limitations.

Patients' online record access (ORA) is growing worldwide. In some countries, including the United States and Sweden, access is advanced with patients obtaining rapid access to their full records on the web including laboratory and test results, lists of prescribed medications, vaccinations, and even the very narrative reports written by clinicians (the latter, commonly referred to as "open notes"). In the United States, patient's ORA is also available in a downloadable form for use with other apps. While survey studies have shown that some patients report many benefits from ORA, there remain challenges with implementation around writing clinical documentation that patients may now read. With ORA, the functionality of the record is evolving; it is no longer only an aide memoire for doctors but also a communication tool for patients. Studies suggest that clinicians are changing how they write documentation, inviting worries about accuracy and completeness. Other concerns include work burdens; while few objective studies have examined the impact of ORA on workload, some research suggests that clinicians are spending more time writing notes and answering queries related to patients' records. Aimed at addressing some of these concerns, clinician and patient education strategies have been proposed. In this viewpoint paper, we explore these approaches and suggest another longer-term strategy: the use of generative artificial intelligence (AI) to support clinicians in documenting narrative summaries that patients will find easier to understand. Applied to narrative clinical documentation, we suggest that such approaches may significantly help preserve the accuracy of notes, strengthen writing clarity and signals of empathy and patient-centered care, and serve as a buffer against documentation work burdens. However, we also consider the current risks associated with existing generative AI. We emphasize that for this innovation to play a key role in ORA, the cocreation of clinical notes will be imperative. We also caution that clinicians will need to be supported in how to work alongside generative AI to optimize its considerable potential.

Psychiatrists' experiences and opinions of generative artificial intelligence in mental healthcare: An online mixed methods survey.

Following the launch of ChatGPT in November 2022, interest in large language model (LLM)-powered chatbots has surged with increasing focus on the clinical potential of these tools. Missing from this discussion, however, are the perspectives of physicians. The current study aimed to explore psychiatrists' experiences and opinions on this new generation of chatbots in mental health care. An online survey including both quantitative and qualitative responses was distributed to a non-probability sample of psychiatrists affiliated with the American Psychiatric Association. Findings revealed 44 % of psychiatrists had used OpenAI's ChatGPT-3.5 and 33 % had used GPT-4.0 "to assist with answering clinical questions." Administrative tasks were cited as a major benefit of these tools: 70 % somewhat agreed/agreed "documentation will be/is more efficient". Three in four psychiatrists (75 %) somewhat agreed/agreed "the majority of their patients will consult these tools before first seeing a doctor". Nine in ten somewhat agreed/agreed that clinicians need more support/training in understanding these tools. Open-ended responses reflected these opinions but respondents also expressed divergent opinions on the value of generative AI in clinical practice, including its impact on the future of the profession.