Showing papers by "John Torous published in 2019"

Chatbots and Conversational Agents in Mental Health: A Review of the Psychiatric Landscape:

Abstract: Objective:The aim of this review was to explore the current evidence for conversational agents or chatbots in the field of psychiatry and their role in screening, diagnosis, and treatment of mental...

Using science to sell apps: Evaluation of mental health app store quality claims

Abstract: Despite the emergence of curated app libraries for mental health apps, personal searches by consumers remain a common method for discovering apps. App store descriptions therefore represent a key channel to inform consumer choice. This study examined the claims invoked through these app store descriptions, the extent to which scientific language is used to support such claims, and the corresponding evidence in the literature. Google Play and iTunes were searched for apps related to depression, self-harm, substance use, anxiety, and schizophrenia. The descriptions of the top-ranking, consumer-focused apps were coded to identify claims of acceptability and effectiveness, and forms of supporting statement. For apps which invoked ostensibly scientific principles, a literature search was conducted to assess their credibility. Seventy-three apps were coded, and the majority (64%) claimed effectiveness at diagnosing a mental health condition, or improving symptoms, mood or self-management. Scientific language was most frequently used to support these effectiveness claims (44%), although this included techniques not validated by literature searches (8/24 = 33%). Two apps described low-quality, primary evidence to support the use of the app. Only one app included a citation to published literature. A minority of apps (14%) described design or development involving lived experience, and none referenced certification or accreditation processes such as app libraries. Scientific language was the most frequently invoked form of support for use of mental health apps; however, high-quality evidence is not commonly described. Improved knowledge translation strategies may improve the adoption of other strategies, such as certification or lived experience co-design.

Assessment of the Data Sharing and Privacy Practices of Smartphone Apps for Depression and Smoking Cessation

Abstract: Importance Inadequate privacy disclosures have repeatedly been identified by cross-sectional surveys of health applications (apps), including apps for mental health and behavior change. However, few studies have assessed directly the correspondence between privacy disclosures and how apps handle personal data. Understanding the scope of this discrepancy is particularly important in mental health, given enhanced privacy concerns relating to stigma and negative impacts of inadvertent disclosure. Because most health apps fall outside government regulation, up-to-date technical scrutiny is essential for informed decision making by consumers and health care professionals wishing to prescribe health apps. Objective To provide a contemporary assessment of the privacy practices of popular apps for depression and smoking cessation by critically evaluating privacy policy content and, specifically, comparing disclosures regarding third-party data transmission to actual behavior. Design and Setting Cross-sectional assessment of 36 top-ranked (by app store search result ordering in January 2018) apps for depression and smoking cessation for Android and iOS in the United States and Australia. Privacy policy content was evaluated with prespecified criteria. Technical assessment of encrypted and unencrypted data transmission was performed. Analysis took place between April and June 2018. Main Outcomes and Measures Correspondence between policies and transmission behavior observed by intercepting sent data. Results Twenty-five of 36 apps (69%) incorporated a privacy policy. Twenty-two of 25 apps with a policy (88%) provided information about primary uses of collected data, while only 16 (64%) described secondary uses. While 23 of 25 apps with a privacy policy (92%) stated in a policy that data would be transmitted to a third party, transmission was detected in 33 of all 36 apps (92%). Twenty-nine of 36 apps (81%) transmitted data for advertising and marketing purposes or analytics to just 2 commercial entities, Google and Facebook, but only 12 of 28 (43%) transmitting data to Google and 6 of 12 (50%) transmitting data to Facebook disclosed this. Conclusions and Relevance Data sharing with third parties that includes linkable identifiers is prevalent and focused on services provided by Google and Facebook. Despite this, most apps offer users no way to anticipate that data will be shared in this way. As a result, users are denied an informed choice about whether such sharing is acceptable to them. Privacy assessments that rely solely on disclosures made in policies, or are not regularly updated, are unlikely to uncover these evolving issues. This may limit their ability to offer effective guidance to consumers and health care professionals.

User Engagement in Mental Health Apps: A Review of Measurement, Reporting, and Validity

Abstract: Objective:Despite the potential benefits of mobile mental health apps, real-world results indicate engagement issues because of low uptake and sustained use. This review examined how studies have m...

Creating a Digital Health Smartphone App and Digital Phenotyping Platform for Mental Health and Diverse Healthcare Needs: an Interdisciplinary and Collaborative Approach

Abstract: As the potential of smartphone apps and sensors for healthcare and clinical research continues to expand, there is a concomitant need for open, accessible, and scalable digital tools. While many current app platforms offer useful solutions for either clinicians or patients, fewer seek to serve both and support the therapeutic relationship between them. Thus, we aimed to create a novel smartphone platform at the intersection of patient demands for trust, control, and community and clinician demands for transparent, data driven, and translational tools. The resulting LAMP platform has evolved through numerous iterations and with much feedback from patients, designers, sociologists, advocates, clinicians, researchers, app developers, and philanthropists. As an open and free tool, the LAMP platform continues to evolve as reflected in its current diverse use cases across research and clinical care in psychiatry, neurology, anesthesia, and psychology. In this paper, we explore the motivation, features, current progress, and next steps to pair the platform for use in a new digital psychiatry clinic, to advance digital interventions for youth mental health, and to bridge gaps in available mental health care for underserved patient groups. The code for the LAMP platform is freely shared with this paper to encourage others to adapt and improve on our team’s efforts.

Building the case for actionable ethics in digital health research supported by artificial intelligence

Abstract: The digital revolution is disrupting the ways in which health research is conducted, and subsequently, changing healthcare. Direct-to-consumer wellness products and mobile apps, pervasive sensor technologies and access to social network data offer exciting opportunities for researchers to passively observe and/or track patients ‘in the wild’ and 24/7. The volume of granular personal health data gathered using these technologies is unprecedented, and is increasingly leveraged to inform personalized health promotion and disease treatment interventions. The use of artificial intelligence in the health sector is also increasing. Although rich with potential, the digital health ecosystem presents new ethical challenges for those making decisions about the selection, testing, implementation and evaluation of technologies for use in healthcare. As the ‘Wild West’ of digital health research unfolds, it is important to recognize who is involved, and identify how each party can and should take responsibility to advance the ethical practices of this work. While not a comprehensive review, we describe the landscape, identify gaps to be addressed, and offer recommendations as to how stakeholders can and should take responsibility to advance socially responsible digital health research.

Digital mental health apps and the therapeutic alliance: initial review

Abstract: BackgroundAs mental healthcare expands to smartphone apps and other technologies that may offer therapeutic interventions without a therapist involved, it is important to assess the impact of non-traditional therapeutic relationships.AimsTo determine if there were any meaningful data regarding the digital therapeutic alliance in smartphone interventions for serious mental illnesses.MethodA literature search was conducted in four databases (PubMed, PsycINFO, Embase and Web of Science).ResultsThere were five studies that discuss the therapeutic alliance when a mobile application intervention is involved in therapy. However, in none of the studies was the digital therapeutic alliance the primary outcome. The studies looked at different mental health conditions, had different duration of technology use and used different methods for assessing the therapeutic alliance.ConclusionsAssessing and optimising the digital therapeutic alliance holds the potential to make tools such as smartphone apps more effective and improve adherence to their use. However, the heterogeneous nature of the five studies we identified make it challenging to draw conclusions at this time. A measure is required to evaluate the digital therapeutic alliance.

Understanding the quality, effectiveness and attributes of top-rated smartphone health apps

Abstract: Objective This study aimed to understand the attributes of popular apps for mental health and comorbid medical conditions, and how these qualities relate to consumer ratings, app quality and classification by the WHO health app classification framework. Methods We selected the 10 apps from the Apple iTunes store and the US Android Google Play store on 20 July 2018 from six disease states: depression, anxiety, schizophrenia, addiction, diabetes and hypertension. Each app was downloaded by two authors who provided information on the apps’ attributes, functionality, interventions, popularity, scientific backing and WHO app classification rating. Results A total of 120 apps were examined. Although none of these apps had Food and Drug Administration marketing approval, nearly 50% made claims that appeared medical. Most apps offered a similar type of services with 87.5% assigned WHO classification 1.4.2 ‘self-monitoring of health or diagnostic data by a client’ or 1.6.1 ‘client look-up of health information’. The ‘last updated’ attribute was highly correlated with a quality rating of the app although no apps features (eg, uses Global Positioning System, reminders and so on) were. Conclusion Due to the heterogeneity of the apps, we were unable to define a core set of features that would accurately assess app quality. The number of apps making unsupported claims combined with the number of apps offering questionable content warrants a cautious approach by both patients and clinicians in selecting safe and effective ones. Clinical Implications ‘Days since last updated’ offers a useful and easy clinical screening test for health apps, regardless of the condition being examined.

A Social Media Study on the Effects of Psychiatric Medication Use.

Abstract: Understanding the effects of psychiatric medications during mental health treatment constitutes an active area of inquiry. While clinical trials help evaluate the effects of these medications, many trials suffer from a lack of generalizability to broader populations. We leverage social media data to examine psychopathological effects subject to self-reported usage of psychiatric medication. Using a list of common approved and regulated psychiatric drugs and a Twitter dataset of 300M posts from 30K individuals, we develop machine learning models to first assess effects relating to mood, cognition, depression, anxiety, psychosis, and suicidal ideation. Then, based on a stratified propensity score based causal analysis, we observe that use of specific drugs are associated with characteristic changes in an individual’s psychopathology. We situate these observations in the psychiatry literature, with a deeper analysis of pre-treatment cues that predict treatment outcomes. Our work bears potential to inspire novel clinical investigations and to build tools for digital therapeutics.

Smartphone-Based Tracking of Sleep in Depression, Anxiety, and Psychotic Disorders.

Abstract: Sleep is an important feature in mental illness. Smartphones can be used to assess and monitor sleep, yet there is little prior application of this approach in depressive, anxiety, or psychotic disorders. We review uses of smartphones and wearable devices for sleep research in patients with these conditions. To date, most studies consist of pilot evaluations demonstrating feasibility and acceptability of monitoring sleep using smartphones and wearable devices among individuals with psychiatric disorders. Promising findings show early associations between behaviors and sleep parameters and agreement between clinic-based assessments, active smartphone data capture, and passively collected data. Few studies report improvement in sleep or mental health outcomes. Success of smartphone-based sleep assessments and interventions requires emphasis on promoting long-term adherence, exploring possibilities of adaptive and personalized systems to predict risk/relapse, and determining impact of sleep monitoring on improving patients’ quality of life and clinically meaningful outcomes.

Using a Smartphone App to Identify Clinically Relevant Behavior Trends via Symptom Report, Cognition Scores, and Exercise Levels: A Case Series.

Abstract: The use of smartphone apps for research and clinical care in mental health has become increasingly popular, especially within youth mental health. In particular, digital phenotyping, the monitoring of data streams from a smartphone to identify proxies for functional outcomes like steps, sleep, and sociability, is of interest due to the ability to monitor these multiple relevant indications of clinically symptomatic behavior. However, scientific progress in this field has been slow due to high heterogeneity among smartphone apps and lack of reproducibility. In this paper, we discuss how our division utilized a smartphone app to retrospectively identify clinically relevant behaviors in individuals with psychosis by measuring survey scores (symptom report), games (cognition scores), and step count (exercise levels). Further, we present specific cases of individuals and how the relevance of these data streams varied between them. We found that there was high variability between participants and that each individual's relevant behavior patterns relied heavily on unique data streams. This suggests that digital phenotyping has high potential to augment clinical care, as it could provide an efficient and individualized mechanism of identifying relevant clinical implications even if population-level models are not yet possible.

A computational study of mental health awareness campaigns on social media

Abstract: As public discourse continues to progress online, it is important for mental health advocates, public health officials, and other curious parties and stakeholders, ranging from researchers, to those affected by the issue, to be aware of the advancing new mediums in which the public can share content ranging from useful resources and self-help tips to personal struggles with respect to both illness and its stigmatization. A better understanding of this new public discourse on mental health, often framed as social media campaigns, can help perpetuate the allocation of sparse mental health resources, the need for educational awareness, and the usefulness of community, with an opportunity to reach those seeking help at the right moment. The objective of this study was to understand the nature of and engagement around mental health content shared on mental health campaigns, specifically #MyTipsForMentalHealth on Twitter around World Mental Health Awareness Day in 2017. We collected 14,217 Twitter posts from 10,805 unique users between September and October 2017 that contained the hashtag #MyTipsForMentalHealth. With the involvement of domain experts, we hand-labeled 700 posts and categorized them as (a) Fact, (b) Stigmatizing, (c) Inspirational, (d) Medical/Clinical Tip, (e) Resource Related, (f) Lifestyle or Social Tip or Personal View, and (g) Off Topic. After creating a "seed" machine learning classifier, we used both unsupervised and semi supervised methods to classify posts into the various expert identified topical categories. We also performed a content analysis to understand how information on different topics spread through social networks. Our support vector machine classification algorithm achieved a mean cross-validation accuracy of 0.81 and accuracy of 0.64 on unseen data. We found that inspirational Twitter posts were the most spread with a mean of 4.17 retweets, and stigmatizing content was second with a mean of 3.66 retweets. Classification of social media-related mental health interactions offers valuable insights on public sentiment as well as a window into the evolving world of online self-help and the varied resources within. Our results suggest an important role for social media-based peer support to not only guide information seekers to useful content and local resources but also illuminate the socially-insular aspects of stigmatization. However, our results also reflect the challenges of quantifying the heterogeneity of mental health content on social media and the need for novel machine learning methods customized to the challenges of the field.

Smartphone Apps to Support Coordinated Specialty Care for Prodromal and Early Course Schizophrenia Disorders: Systematic Review.

Abstract: Background: Demand for mental health services, especially for clinical high-risk and early psychosis, has increased, creating a need for new solutions to increase access to and quality of care. Smartphones and mobile technology are potential tools to support coordinated specialty care for early psychosis, given their potential to augment the six core roles of care: case management and team leadership, recovery-oriented psychotherapy, medication management, support for employment and education, coordination with primary care services, and family education and support. However, the services smartphones are actually offering specifically for coordinated specialty care and the level of evidence are unknown. Objective: This study aimed to review the published literature on smartphone technology to enhance care for patients with prodromal and early course psychosis and schizophrenia and to analyze studies by type, aligned with coordinated specialty care domains. Methods: A systematic literature search was conducted on August 16 and 17, 2019, using the PubMed, EMBASE, Web of Sciences, and PsycINFO electronic databases. The eligible studies were reviewed and screened based on inclusion and exclusion criteria. Results: The search uncovered 388 unique results, of which 32 articles met the initial inclusion criteria; 21 eligible studies on 16 unique app platforms were identified. Feasibility studies showed a high user engagement and interest among patients, monitoring studies demonstrated a correlation between app assessments and clinical outcomes, and intervention studies indicated that these apps have the potential to advance care. Eighteen studies reported on app use for the case management roles of coordinated specialty care. No app studies focused on employment and education, coordination with primary care services, and family education and support. Conclusions: Although the published literature on smartphone apps for prodromal and first-episode psychosis is small, it is growing exponentially and holds promise to augment both monitoring and interventions. Although the research results and protocols for app studies are not well aligned with all coordinated specialty care roles today, high rates of adoption and feasibility suggest the potential for future efforts. These results will be used to develop coordinated specialty care–specific app evaluation scales and toolkits.

A Telehealth Framework for Mobile Health, Smartphones, and Apps: Competencies, Training, and Faculty Development

Abstract: Technologies like smartphones and apps are reshaping life, health care, and business. Clinicians need skills, knowledge, and attitudes to ensure quality care and to supervise the current generation of trainees, consistent with the Institute of Medicine’s Health Professions Educational Summit. Literature is integrated on patient-, learner-, competency-, and outcome-based themes from the fields of technology, health care, pedagogy, and business. Mobile health, smartphone/device, and app competencies are organized in the Accreditation Council for Graduate Medical Education (ACGME) Milestone domains of patient care, medical knowledge, practice-based learning and improvement, systems-based practice, professionalism, and interpersonal skills and communication. Teaching methods are suggested to align competency outcomes, learning context, and evaluation. Services by mobile health, smartphone/device, and apps have a broader scope than in-person and telehealth and telebehavioral health care. This includes clinical decision support in medicine, hybrid delivery, and integration across health systems’ e-platforms. A curriculum with seminar, case- and problem-based teaching, supervision, evaluation, and quality improvement practices is needed to achieve competency outcomes. Clinicians have to adjust assessment, triage and treatment and attend to ethical, privacy, security, and other challenges. Health systems need to manage change, proactively plan faculty development, and create a positive e-culture for learning. Research is needed on implementing and evaluating mobile health competencies for this significant paradigm shift in health care.

The Opportunity and Obstacles for Smartwatches and Wearable Sensors

Abstract: In September 2017, the U.S. Food and Drug Administration (FDA) made a striking announcement. Transforming its current regulatory practice for approving and certifying medical devices—the FDA announced a bold new plan, the Digital Health Software Precertification (Precert) Program, to offer an entirely new regulatory model to assess smartphone apps, wearables, sensors, and software. This transformation and medicalization of the consumer health market present both opportunities and obstacles, by opening up large markets for health monitoring and diagnosis using inexpensive mass-market, off-the-shelf devices. It also raises challenges, both related to privacy and effective uses of the devices to promote health. The Fitbit and Apple Watch are examples.

Accuracy of Machine Learning Algorithms for the Diagnosis of Autism Spectrum Disorder: Systematic Review and Meta-Analysis of Brain Magnetic Resonance Imaging Studies.

Abstract: Background: In the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, their application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder (ASD). However, given their complexity and potential clinical implications, there is an ongoing need for further research on their accuracy. Objective: This study aimed to perform a systematic review and meta-analysis to summarize the available evidence for the accuracy of machine learning algorithms in diagnosing ASD. Methods: The following databases were searched on November 28, 2018: MEDLINE, EMBASE, CINAHL Complete (with Open Dissertations), PsycINFO, and Institute of Electrical and Electronics Engineers Xplore Digital Library. Studies that used a machine learning algorithm partially or fully for distinguishing individuals with ASD from control subjects and provided accuracy measures were included in our analysis. The bivariate random effects model was applied to the pooled data in a meta-analysis. A subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false-negative, and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw Summary Receiver Operating Characteristics curves, and obtain the area under the curve (AUC) and partial AUC (pAUC). Results: A total of 43 studies were included for the final analysis, of which a meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural magnetic resonance imaging (sMRI) subgroup meta-analysis (12 samples with 1776 participants) showed a sensitivity of 0.83 (95% CI 0.76-0.89), a specificity of 0.84 (95% CI 0.74-0.91), and AUC/pAUC of 0.90/0.83. A functional magnetic resonance imaging/deep neural network subgroup meta-analysis (5 samples with 1345 participants) showed a sensitivity of 0.69 (95% CI 0.62-0.75), specificity of 0.66 (95% CI 0.61-0.70), and AUC/pAUC of 0.71/0.67. Conclusions: The accuracy of machine learning algorithms for diagnosis of ASD was considered acceptable by few accuracy measures only in cases of sMRI use; however, given the many limitations indicated in our study, further well-designed studies are warranted to extend the potential use of machine learning algorithms to clinical settings. Clinical Trial: PROSPERO CRD42018117779; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=117779

Augmenting Mental Health in Primary Care: A 1-Year Study of Deploying Smartphone Apps in a Multi-site Primary Care/Behavioral Health Integration Program.

Abstract: Background: Integrating behavioral health (BH) services into primary care is an evidence-based intervention that can increase access to care, improve patient outcomes, and decrease costs. Digital technology, including smartphone apps, has the potential to augment and extend the reach of these integrated behavioral health services through self-management support impacting lifestyle behaviors. To date, the feasibility and acceptability of using mental health mobile apps within an integrated primary care setting has not yet been explored as part of routine clinical care. Objectives: The objectives of this study were to (a) test the feasibility of using mental health applications to augment integrated primary care services; (b) solicit feedback from patients and providers to guide implementation, and (c) develop a mental health apps toolkit for system-wide dissemination. Methods: Cambridge Health Alliance (CHA) is a safety-net healthcare system that includes three community hospitals and 12 Primary Care (PC) clinics serving nearly 150,000 ethnically and socioeconomically diverse patients around Boston. To select and disseminate mental health apps, a four-phase implementation was undertaken: (1) Evaluation of mental health mobile applications (2) Development of an apps toolkit with stakeholder input, (3) Conducting initial pilot at six primary care locations, and (4) Rolling out the app toolkit across 12 primary care sites and conducting 1-year follow-up survey. Results: Among BH providers, 24 (75%) responded to the follow-up survey and 19 (83%) indicated they use apps as part of their clinical care. Anxiety was the most common condition for which app use was recommended by providers, and 10 (42%) expressed interest in further developing their knowledge of mental health apps. Among patients, 35 (65%) of participants provided feedback; 23 (66%) reported the tools to be helpful, especially for managing stress and anxiety. Conclusions: Our findings indicate mental health apps are applicable and relevant to patients within integrated primary care settings in safety-net health systems. Behavioral health providers perceive the clinical value of using these tools as part of patient care, but require training to increase their comfort-level and confidence applying these tools with patients. To increase provider and patient engagement, mobile apps must be accessible, simple, intuitive and directly relevant to patients' treatment needs.

Smartphone, Social Media, and Mental Health App Use in an Acute Transdiagnostic Psychiatric Sample

Abstract: Background: Despite high rates of smartphone ownership in psychiatric populations, there are very little data available characterizing smartphone use in individuals with mental illness. In particular, few studies have examined the interest and use of smartphones to support mental health. Objective: This study aimed to (1) characterize general smartphone app and social media usage in an acute transdiagnostic psychiatric sample with high smartphone ownership, (2) characterize current engagement and interest in the use of smartphone apps to support mental health, and (3) test demographic and clinical predictors of smartphone use. Methods: The survey was completed by all patients attending an adult partial hospital program, with no exclusion criteria. The primary outcomes were frequency of use of general and mental health smartphone apps (smartphone use survey) and the frequency of social media use and phone-checking behavior (mobile technology engagement scale). Results: Overall, 322 patients (aged mean 33.49, SD 13.87 years; 57% female) reported that their most frequently used app functions were texting, email, and social media. Younger individuals reported more frequent use across most types of apps. Baseline depression and anxiety symptoms were not associated with the frequency of app use. Participants reported health care, calendar, and texting apps as most supportive of their mental health and social media apps as most negatively affecting their mental health. Most patients reported an interest in (73.9% [238/322]) and willingness to use (81.3% [262/322]) a smartphone app to monitor their mental health condition. Less than half (44%) of the patients currently had a mental health app downloaded on their smartphone, with mindfulness and meditation apps being the most common type. Conclusions: The high interest in and willingness to use mental health apps, paired with the only moderate current reported usage, indicate a potential unmet treatment opportunity in psychiatric populations. There is potential to optimize non-mental health–specific apps to better support the needs of those with mental illness and to design a new wave of mental health apps that match the needs of these populations as well as the way they use smartphones in daily life.

Regulating digital health technologies with transparency: the case for dynamic and multi-stakeholder evaluation

Abstract: The prevalence of smartphones today, paired with the increasing precision and therapeutic potential of digital capabilities, offers unprecedented opportunity in the field of digital medicine. Smartphones offer novel accessibility, unique insights into physical and cognitive behavior, and diverse resources designed to aid health. Many of these digital resources, however, are developed and shared at a faster rate than they can be assessed for efficacy, safety, and security—presenting patients and clinicians with the challenge of distinguishing helpful tools from harmful ones. Leading regulators, such as the FDA in the USA and the NHS in the UK, are working to evaluate the influx of mobile health applications entering the market. Efforts to regulate, however, are challenged by the need for more transparency. They require real-world data on the actual use, effects, benefits, and harms of these digital health tools. Given rapid product cycles and frequent updates, even the most thorough evaluation is only as accurate as the data it is based on. In this debate piece, we propose a complementary approach to ongoing efforts via a dynamic self-certification checklist. We outline how simple self-certification, validated or challenged by app users, would enhance transparency, engage diverse stakeholders in meaningful education and learning, and incentivize the design of safe and secure medical apps.

Mobilizing mHealth Data Collection in Older Adults: Challenges and Opportunities.

Abstract: Worldwide, there is an unprecedented and ongoing expansion of both the proportion of older adults in society and innovations in digital technology. This rapidly increasing number of older adults is placing unprecedented demands on health care systems, warranting the development of new solutions. Although advancements in smart devices and wearables present novel methods for monitoring and improving the health of aging populations, older adults are currently the least likely age group to engage with such technologies. In this commentary, we critically examine the potential for technology-driven data collection and analysis mechanisms to improve our capacity to research, understand, and address the implications of an aging population. Alongside unprecedented opportunities to harness these technologies, there are equally unprecedented challenges. Notably, older adults may experience the first-level digital divide, that is, lack of access to technologies, and/or the second-level digital divide, that is, lack of use/skill, alongside issues with data input and analysis. To harness the benefits of these innovative approaches, we must first engage older adults in a meaningful manner and adjust the framework of smart devices to accommodate the unique physiological and psychological characteristics of the aging populace. Through an informed approach to the development of technologies with older adults, the field can leverage innovation to increase the quality and quantity of life for the expanding population of older adults.

Ethics, Transparency, and Public Health at the Intersection of Innovation and Facebook's Suicide Prevention Efforts.

Abstract: Artificial intelligence and machine learning promise to improve health care and health itself, but potential pitfalls exist. A recent example is Facebook's efforts to screen posts to identify users...

Recent Developments in Digital Mental Health Interventions for College and University Students

Abstract: Mental health problems are prevalent among university students. Insufficient resources at student health centers and other barriers to treatment result in low rates of students receiving treatment, potentially impacting academic performance and long-term health. Digital mental health interventions have been proposed as a means of reducing the treatment gap, given their potential for flexibility, cost-effectiveness, and stigma reduction. Randomized controlled trials (RCTs) of short-term online interventions based on cognitive behavioral therapy (CBT), acceptance and commitment therapy (ACT), and mindfulness have had promising short-term effects on measurements of anxiety, depression, and sleep when compared to waitlist controls in small to medium size non-clinical samples of predominantly women university students in high-income countries. Most interventions suffer from low adherence and completion rates, sometimes partially offset by personal support. The impact of these interventions on long-term mental health and academic outcomes remains uncertain. Although studies of Internet-based interventions have shown promising results, the effectiveness of current interventions is limited by low adherence and questionable long-term efficacy in real-world settings.

Actionable digital phenotyping: a framework for the delivery of just-in-time and longitudinal interventions in clinical healthcare.

Abstract: Designed to improve health, today numerous wearables and smartphone apps are used by millions across the world. Yet the wealth of data generated from the many sensors on these wearables and smartwatches has not yet transformed routine clinical care. One central reason for this gap between data and clinical insights is the lack of transparency and standards around data generated from mobile device that hinders interoperability and reproducibility. The clinical informatics community has offered solutions via the Fast Healthcare Interoperability Resources (FHIR) standard which facilities electronic health record interoperability but is less developed towards precision temporal contextually-tagged sensor measurements generated from today’s ubiquitous mobile devices. In this paper we explore the opportunities and challenges of various theoretical approaches towards FHIR compatible digital phenotyping, and offer a concrete example implementing one such framework as an Application Programming Interface (API) for the open-source mindLAMP platform. We aim to build a community with contributions from statisticians, clinicians, patients, family members, researchers, designers, engineers, and more.

A Future Research Agenda for Digital Geriatric Mental Healthcare.

Abstract: The proliferation of mobile, online, and remote monitoring technologies in digital geriatric mental health has the potential to lead to the next major breakthrough in mental health treatments. Unlike traditional mental health services, digital geriatric mental health has the benefit of serving a large number of older adults, and in many instances, does not rely on mental health clinics to offer real-time interventions. As technology increasingly becomes essential in the everyday lives of older adults with mental health conditions, these technologies will provide a fundamental service delivery strategy to support older adults' mental health recovery. Although ample research on digital geriatric mental health is available, fundamental gaps in the scientific literature still exist. To begin to address these gaps, we propose the following recommendations for a future research agenda: 1) additional proof-of-concept studies are needed; 2) integrating engineering principles in methodologically rigorous research may help science keep pace with technology; 3) studies are needed that identify implementation issues; 4) inclusivity of people with a lived experience of a mental health condition can offer valuable perspectives and new insights; and 5) formation of a workgroup specific for digital geriatric mental health to set standards and principles for research and practice. We propose prioritizing the advancement of digital geriatric mental health research in several areas that are of great public health significance, including 1) simultaneous and integrated treatment of physical health and mental health conditions; 2) effectiveness studies that explore diagnostics and treatment of social determinants of health such as "social isolation" and "loneliness;" and 3) tailoring the development and testing of innovative strategies to minority older adult populations.