Social decline in the psychosis prodrome: Predictor potential and heterogeneity of outcome

BACKGROUND

While an established clinical outcome of high importance, social functioning has been emerging as possibly having a broader significance to the evolution of psychosis and long term disability. In the current study we explored the association between social decline, conversion to psychosis, and functional outcome in individuals at clinical high risk (CHR) for psychosis.

METHODS

585 subjects collected in the North American Prodrome Longitudinal Study (NAPLS2) were divided into 236 Healthy Controls (HCs), and CHR subjects that developed psychosis (CHR + C, N = 79), or those that did not (Non-Converters, CHR-NC, N = 270). CHR + C subjects were further divided into those that experienced an atypical decline in social functioning prior to baseline (beyond typical impairment levels) when in min-to-late adolescence (CHR + C-SD, N = 39) or those that did not undergoing a decline (CHR + C-NSD, N = 40).

RESULTS

Patterns of poor functional outcomes varied across the CHR subgroups: CHR-NC (Poor Social 36.3%, Role 42.2%) through CHR + C-NSD (Poor Social 50%, Poor Role 67.5%) to CHR + C-SD (Poor Social 76.9%, Poor Role 89.7%) functioning. The two Converter subgroups had comparable positive symptoms at baseline. At 12 months, the CHR + C-SD group stabilized, but social functioning levels remained significantly lower than the other two subgroups.

CONCLUSIONS

The current study demonstrates that pre-baseline social decline in mid-to-late adolescence predicts psychosis. In addition, we found that this social decline in converters is strongly associated with especially poor functional outcome and overall poorer prognosis. Role functioning, in contrast, has not shown similar predictor potential, and rather appears to be an illness indicator that worsens over time.

The Functionality, Evidence, and Privacy Issues Around Smartphone Apps for the Top Neuropsychiatric Conditions

OBJECTIVE

There are more than 325,000 health-related smartphone applications (apps) on the market. To better understand the apps currently on the market for the five most disabling neuropsychiatric conditions, the authors conducted a study investigating their intended uses (target population and intervention), the data collected, and any privacy policies.

METHODS

This was a cross-sectional study of apps for the five most disabling neuropsychiatric conditions per the World Health Organization: stroke, migraine, depression, Alzheimer's disease and dementia, and anxiety. Up to 15 apps in the U.S. Google Play and Apple app stores were selected based on the following prespecified inclusion criteria: the app appeared in the top 50 search results, offered intervention or tracking capabilities, and listed the condition in the app title or description. Exclusion criteria were <$5.00 to purchase, solely motor versus cognitive-based intervention, or designed for use by caregivers or health care providers. Data abstracted included function, behavior change rewards, and information about intervention, privacy policy, and payment.

RESULTS

Eighty-three apps were reviewed (stroke, N=8; migraine, N=25; Alzheimer's disease and dementia, N=8; depression, N=7; anxiety, N=14; apps targeting depression and anxiety, N=21). Sixty-nine percent of apps had an intervention component, 18% were deemed evidence based, 77% had a privacy policy, 70% required payment for access to all features, and 19% rewarded user behavior changes.

CONCLUSIONS

Most apps on the market targeted migraine, depression, and anxiety and contained interventions, although most of the interventions did not appear to be evidence based. Additionally, although most apps had privacy policies, lay people may have difficulty understanding these policies due to their complexities.

A systematic review of mHealth application interventions for peripartum mood disorders: trends and evidence in academia and industry

In this review, we aim to summarize research findings and marketplace apps for women with perinatal mood disorders with the goal of informing clinicians and patients about current risks and benefits, as well as proposing clinical implementation advice and a harmonized agenda for both academic and industry advancement in this space. Multiple searches were run of academic databases in 2018-2020, examining literature on mobile apps for peripartum mental health. Multiple searches were also run of the iOS and Android app stores in 2019 and 2020, looking at apps for peripartum mental health. Results were compared within the academic dataset as well within the commercial app dataset; the two datasets were also examined for overlap. The academic search results were notable for small sample sizes and heterogeneous endpoints. The app store search results were notable for apps of generally poor quality (as assessed by a modified Silberg scale). Very few of the mHealth interventions studied in the academic literature were available in the app store; very few of the apps from the commercial stores were supported by academic literature. The disconnect between academically developed apps and commercially available apps highlights the need for better collaboration between academia and industry. More collaboration between the two approaches may benefit both app developers and patients in this demographic moving forwards. Additionally, we present a set of practice guidelines for mHealth in perinatal psychiatry based on the trends identified in this review.

Counterpoint. Early intervention for psychosis risk syndromes: Minimizing risk and maximizing benefit

BACKGROUND

Malhi et al. in this issue critique the clinical high risk (CHR) syndrome for psychosis.

METHOD

Response to points of critique.

RESULTS

We agree that inconsistency in CHR nomenclature should be minimized. We respectfully disagree on other points. In our view: a) individuals with CHR and their families need help, using existing interventions, even though we do not yet fully understand disease mechanisms; b) substantial progress has been made in identification of biomarkers; c) symptoms used to identify CHR are specific to psychotic illnesses; d) CHR diagnosis is not "extremely difficult"; e) the pattern of progression, although heterogenous, is discernible; f) "psychosis-like symptoms" are common but are not used to identify CHR; and g) on the point described as 'the real risk,' CHR diagnosis does not frequently cause harmful stigma.

DISCUSSION

Malhi et al.'s arguments do not fairly characterize progress in the CHR field nor efforts to minimize stigma. That said, much work remains in areas of consistent nomenclature, mechanisms of disease, dissecting heterogeneity, and biomarkers. With regard to what the authors term the "real risk" of stigma associated with a CHR "label," however, our view is that avoiding words like "risk" and "psychosis" reinforces the stigma that both they and we mean to oppose. Moreover, patients and their families benefit from being given a term that describes what is happening to them.

Genome-wide association analysis of opioid use disorder: A novel approach using clinical data

BACKGROUND

Opioid use disorder (OUD) represents a large and pervasive global public health challenge. Previous genetic studies have demonstrated the significant heritability of OUD and identified several single-nucleotide polymorphisms (SNPs) associated with its prevalence.

METHODS

In this paper, we conducted a genome-wide association analysis on opioid use disorder that leveraged genetic and clinical data contained in a biobank of 21,310 patients of European ancestry. We identified 1039 cases of opioid use disorder based on diagnostic codes from nearly 16 million encounters in electronic health records (EHRs).

RESULTS

We discovered one novel OUD-associated locus on chromosome 4 that was significant at a genome-wide threshold (p = 2.40 × 10^-8). Heritability analysis suggested that common SNPs explained 0.06 (se 0.02, p = 0.0065) of the phenotypic variation in OUD. When we restricted controls to those with previous opioid prescriptions, we were able to further strengthen the original signal and discovered another significant locus on chromosome 16. Pair-wise genetic correlation analysis yielded strong positive correlations between OUD and two other major substance use disorders, alcohol and nicotine, with the strongest correlation between nicotine and opioid use disorder (genetic correlation 0.65, se = 0.19, p = 0.00048), suggesting a significant shared genetic component across different substance disorders.

CONCLUSIONS

This pragmatic, clinically-focused approach may supplement more traditional methods to facilitate identification of new genetic underpinnings of OUD and related disorders.

Digital health developments and drawbacks: a review and analysis of top-returned apps for bipolar disorder

BACKGROUND

Although a growing body of literature highlights the potential benefit of smartphone-based mobile apps to aid in self-management and treatment of bipolar disorder, it is unclear whether such evidence-based apps are readily available and accessible to a user of the app store.

RESULTS

Using our systematic framework for the evaluation of mental health apps, we analyzed the accessibility, privacy, clinical foundation, features, and interoperability of the top-returned 100 apps for bipolar disorder. Only 56% of the apps mentioned bipolar disorder specifically in their title, description, or content. Only one app's efficacy was supported in a peer-reviewed study, and 32 apps lacked privacy policies. The most common features provided were mood tracking, journaling, and psychoeducation.

CONCLUSIONS

Our analysis reveals substantial limitations in the current digital environment for individuals seeking an evidence-based, clinically usable app for bipolar disorder. Although there have been academic advances in development of digital interventions for bipolar disorder, this work has yet to be translated to the publicly available app marketplace. This unmet need of digital mood management underscores the need for a comprehensive evaluation system of mental health apps, which we have endeavored to provide through our framework and accompanying database (apps.digitalpsych.org).

Digital Phenotyping to Quantify Psychosocial Well-Being Trajectories After Spinal Cord Injury

OBJECTIVE

The aim of the study was to explore feasibility of smartphone-based digital phenotyping methods to examine depression and its relation to psychosocial well-being indicators after spinal cord injury.

DESIGN

Smartphone research platform obtained smartphone sensor and survey data among community-living adult wheelchair users with spinal cord injury. Weekly measurements for 4 mos included Patient Health Questionnaire 8, Spinal Cord Injury-Quality of Life Satisfaction with and ability to participate in social roles and activities, global positioning system-derived community mobility metrics, health conditions, and physical activity.

RESULTS

Forty-three individuals were enrolled. Study retention was higher among individuals offered financial incentives (78%) compared with participants enrolled before incentives (50%). Participants who dropped out more commonly had nontraumatic or acute spinal cord injury, were older, and had less satisfaction and lower participation in social roles and activities. Among 15 individuals with complete data, half had 1 wk or more of mild depression. Those with depression had frequent health issues, less satisfaction, and lower participation in social roles and activities. Those without depression experienced increased social engagement over time. Average community mobility was similar across depression groups. Relationships were typically in-phase but also varied by individual.

CONCLUSIONS

Smartphone-based digital phenotyping of psychosocial well-being after spinal cord injury is feasible but not without attrition challenges. Individual differences in depression patterns highlight clinical utility of scaling these methods.

Making mental health more accessible in light of COVID-19: Scalable digital health with digital navigators in low and middle-income countries

The rapid spread of COVID-19 and the devastating consequences to economies and healthcare systems around the world has highlighted the exigent need for accessible mental health support. Increasing use of mobile devices in Lower Middle-Income Countries (LMIC) such as India offers novel opportunity to expand treatment options and reach underserved populations. Prior efforts have utilized technology to redistribute or supplement clinical care but measurable outcomes of this research are limited. In this paper, we explain the structural barriers that prevent access to care and build on prior research to demonstrate how technology can be utilized to offer treatment if it is aided by education and technical support.

The Role of Digital Navigators in Promoting Clinical Care and Technology Integration into Practice

As the role of technology expands in healthcare, so does the need to support its implementation and integration into the clinic. The concept of a new team member, the digital navigator, able to assume this role is introduced as a solution. With a digital navigator, any clinic today can take advantage of digital health and smartphone tools to augment and expand existing telehealth and face to face care. The role of a digital navigator is suitable as an entry level healthcare role, additional training for an experienced clinician, and well suited to peer specialists. To facilitate the training of digital navigators, we draw upon our experience in creating the role and across health education to introduce a 10-h curriculum designed to train digital navigators across 5 domains: (1) core smartphone skills, (2) basic technology troubleshooting, (3) app evaluation, (4) clinical terminology and data, and (5) engagement techniques. This paper outlines the curricular content, skills, and modules for this training and features a rich online supplementary Appendix with step by step instructions and resources.

Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media

BACKGROUND

The COVID-19 pandemic has caused several disruptions in personal and collective lives worldwide. The uncertainties surrounding the pandemic have also led to multifaceted mental health concerns, which can be exacerbated with precautionary measures such as social distancing and self-quarantining, as well as societal impacts such as economic downturn and job loss. Despite noting this as a "mental health tsunami", the psychological effects of the COVID-19 crisis remain unexplored at scale. Consequently, public health stakeholders are currently limited in identifying ways to provide timely and tailored support during these circumstances.

OBJECTIVE

Our study aims to provide insights regarding people's psychosocial concerns during the COVID-19 pandemic by leveraging social media data. We aim to study the temporal and linguistic changes in symptomatic mental health and support expressions in the pandemic context.

METHODS

We obtained about 60 million Twitter streaming posts originating from the United States from March 24 to May 24, 2020, and compared these with about 40 million posts from a comparable period in 2019 to attribute the effect of COVID-19 on people's social media self-disclosure. Using these data sets, we studied people's self-disclosure on social media in terms of symptomatic mental health concerns and expressions of support. We employed transfer learning classifiers that identified the social media language indicative of mental health outcomes (anxiety, depression, stress, and suicidal ideation) and support (emotional and informational support). We then examined the changes in psychosocial expressions over time and language, comparing the 2020 and 2019 data sets.

RESULTS

We found that all of the examined psychosocial expressions have significantly increased during the COVID-19 crisis-mental health symptomatic expressions have increased by about 14%, and support expressions have increased by about 5%, both thematically related to COVID-19. We also observed a steady decline and eventual plateauing in these expressions during the COVID-19 pandemic, which may have been due to habituation or due to supportive policy measures enacted during this period. Our language analyses highlighted that people express concerns that are specific to and contextually related to the COVID-19 crisis.

CONCLUSIONS

We studied the psychosocial effects of the COVID-19 crisis by using social media data from 2020, finding that people's mental health symptomatic and support expressions significantly increased during the COVID-19 period as compared to similar data from 2019. However, this effect gradually lessened over time, suggesting that people adapted to the circumstances and their "new normal." Our linguistic analyses revealed that people expressed mental health concerns regarding personal and professional challenges, health care and precautionary measures, and pandemic-related awareness. This study shows the potential to provide insights to mental health care and stakeholders and policy makers in planning and implementing measures to mitigate mental health risks amid the health crisis.

ReMindCare App for Early Psychosis: Pragmatic Real World Intervention and Usability Study

Background

eHealth interventions are widely used in clinical trials and increasingly in care settings as well; however, their efficacy in real-world contexts remains unknown. ReMindCare is a smartphone app that has been systematically implemented in a first episode of psychosis program (FEPP) for patients with early psychosis since 2018.

Objective

The objective of this study was to assess the efficacy of ReMindCare after 19 months of use in the clinic and varying use by individual patients.

Methods

The integration of the ReMindCare app into the FEPP started in October 2018. Patients with early psychosis self-selected to the app (ReMindCare group) or treatment as usual (TAU group). The outcome variables considered were adherence to the intervention and number of relapses, hospital admissions, and visits to urgent care units. Data from 90 patients with early psychosis were analyzed: 59 in the ReMindCare group and 31 in the TAU group. The mean age of the sample was 32.8 (SD 9.4) years, 73% (66/90) were males, 91% (83/90) were White, and 81% (74/90) were single.

Results

Significant differences between the ReMindCare and TAU groups were found in the number of relapses, hospitalizations, and visits to urgent care units, with each showing benefits for the app. Only 20% (12/59) of patients from the ReMindCare group had a relapse, while 58% (18/31) of the TAU patients had one or more relapses (χ²=13.7, P=.001). Moreover, ReMindCare patients had fewer visits to urgent care units (χ²=7.4, P=.006) and fewer hospitalizations than TAU patients (χ²=4.6, P=.03). The mean of days using the app was 352.2 (SD 191.2; min/max: 18-594), and the mean of engagement was 84.5 (SD 16.04).

Conclusions

To our knowledge, this is the first eHealth intervention that has preliminarily proven its benefits in the real-world treatment of patients with early psychosis.

International Registered Report Identifier (IRRID)

RR2-10.1111/eip.12960

Deriving symptom networks from digital phenotyping data in serious mental illness

BACKGROUND

Symptoms of serious mental illness are multidimensional and often interact in complex ways. Generative models offer value in elucidating the underlying relationships that characterise these networks of symptoms.

AIMS

In this paper we use generative models to find unique interactions of schizophrenia symptoms as experienced on a moment-by-moment basis.

METHOD

Self-reported mood, anxiety and psychosis symptoms, self-reported measurements of sleep quality and social function, cognitive assessment, and smartphone touch screen data from two assessments modelled after the Trail Making A and B tests were collected with a digital phenotyping app for 47 patients in active treatment for schizophrenia over a 90-day period. Patients were retrospectively divided up into various non-exclusive subgroups based on measurements of depression, anxiety, sleep duration, cognition and psychosis symptoms taken in the clinic. Associated transition probabilities for the patient cohort and for the clinical subgroups were calculated using state transitions between adjacent 3-day timesteps of pairwise survey domains.

RESULTS

The three highest probabilities for associated transitions across all patients were anxiety-inducing mood (0.357, P < 0.001), psychosis-inducing mood (0.276, P < 0.001), and anxiety-inducing poor sleep (0.268, P < 0.001). These transition probabilities were compared against a validation set of 17 patients from a pilot study, and no significant differences were found. Unique symptom networks were found for clinical subgroups.

CONCLUSIONS

Using a generative model using digital phenotyping data, we show that certain symptoms of schizophrenia may play a role in elevating other schizophrenia symptoms in future timesteps. Symptom networks show that it is feasible to create clinically interpretable models that reflect the unique symptom interactions of psychosis-spectrum illness. These results offer a framework for researchers capturing temporal dynamics, for clinicians seeking to move towards preventative care, and for patients to better understand their lived experience.

Smartphone Apps for College Mental Health: A Concern for Privacy and Quality of Current Offerings

OBJECTIVE

The expanding need for mental health services on college campuses has increased interest in smartphone apps for mental health. Although apps can be effective tools, they can also present risks regarding privacy and lack of efficacy, and little is known about the nature or quality of mental health apps that colleges are recommending to students. This study sought to fill that knowledge gap.

METHODS

Sixty college counseling center Web sites were examined for suggested mobile apps offered as resources for students. The features of each app were assessed, including date of last update, privacy policy, and whether any research had been published in peer-reviewed journals.

RESULTS

Twenty-six college counseling centers suggested a total of 218 unique apps. Of these, 28% were no longer available for download. Of the 158 remaining apps, only 44% had been updated in the past 6 months, and 39% had no privacy policy. Of the 97 existing apps with privacy policies, 88% collected user's data and 49% shared users' data with third parties. Efficacy studies had been published in peer-reviewed journals for only 16% of existing apps.

CONCLUSIONS

College counseling centers are not suggesting safe or up-to-date mobile apps. There is an urgent need to help centers identify, curate, and recommend more appropriate apps. The authors suggest that centers use the American Psychiatric Association's app evaluation framework; develop an app review process, with input from clinicians and students; or employ a digital navigator to select apps and help connect students to the appropriate app.

A pilot study using ecological momentary assessment via smartphone application to identify adolescent problematic internet use

For some youth, pathologic Internet use can cause significant distress and dysfunction, a phenomenon known as Problematic Internet Use (PIU). PIU has been associated with poorer health outcomes in adolescents with existing psychiatric illness but understanding PIU has been challenging due to research methodologies using cross-sectional, self-report data. This study assessed the feasibility of using app-based ecological momentary assessment (EMA) to provide more ecologically-valid data to identify and characterize the relationship between mood symptoms and PIU in adolescents in active mental health treatment. 25 youth (aged 12-23) were recruited to use an EMA app for 6 weeks. 96% of participants completed the study and the majority of participants completed surveys at least once weekly. Youth with anxiety disorders endorsed significantly greater benefit from using the app to monitor PIU. While PIU severity was positively correlated with worsened anxiety and depression, analyses of the temporal relationships between PIU and mood symptoms showed that anxiety symptoms were significantly improved after episodes of PIU. Overall results suggest that app-based EMA may be both acceptable and feasible to understand PIU in this population. Follow-up studies should consider personalization of study protocols and use of digital phenotyping methodology to collect more objective measurements of behavior.

Scaling evidence-based treatments through digital mental health

A large number of Internet websites, mobile apps, wearable devices, and video games that are hoped to improve behavioral, cognitive, and affective targets associated with mental health have been developed. Although technology provides the promising potential to help spread evidence-based mental health treatments, that potential is still largely unrealized. The vast majority of technology-based mental health products, deemed digital mental health interventions (DMHIs), have not been tested and show little fidelity to evidence-based treatments. Furthermore, best practices around the use of technologies in clinical services are underdeveloped and few clinicians have been trained to integrate these tools in their practice. We present an overview of DMHIs and discuss key aspects related to their implementation. We organize these aspects according to the Consolidating Framework for Implementation Research, a framework that identifies constructs related to effective implementation. These constructs are categorized into 5 domains including characteristics of DMHIs, the individuals involved, associated processes, the inner setting, and the outer setting. Our goal is to highlight key areas of consideration for leveraging technology to support the implementation of evidence-based treatments and to emphasize challenges and opportunities that come from using technology to scale evidence-based mental health treatments. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Digital phenotyping for mental health of college students: a clinical review

Experiencing continued growth in demand for mental health services among students, colleges are seeking digital solutions to increase access to care as classes shift to remote virtual learning during the COVID-19 pandemic. Using smartphones to capture real-time symptoms and behaviours related to mental illnesses, digital phenotyping offers a practical tool to help colleges remotely monitor and assess mental health and provide more customised and responsive care. This narrative review of 25 digital phenotyping studies with college students explored how this method has been deployed, studied and has impacted mental health outcomes. We found the average duration of studies to be 42 days and the average enrolled to be 81 participants. The most common sensor-based streams collected included location, accelerometer and social information and these were used to inform behaviours such as sleep, exercise and social interactions. 52% of the studies included also collected smartphone survey in some form and these were used to assess mood, anxiety and stress among many other outcomes. The collective focus on data that construct features related to sleep, activity and social interactions indicate that this field is already appropriately attentive to the primary drivers of mental health problems among college students. While the heterogeneity of the methods of these studies presents no reliable target for mobile devices to offer automated help-the feasibility across studies suggests the potential to use these data today towards personalising care. As more unified digital phenotyping research evolves and scales to larger sample sizes, student mental health centres may consider integrating these data into their clinical practice for college students.

A Patient-Centered Framework for Measuring the Economic Value of the Clinical Benefits of Digital Health Apps: Theoretical Modeling

BACKGROUND

As digital health tools such as smartphone apps evolve and enter clinical use, questions regarding their value must be addressed. Although there are scarce generalizable data on the value of health apps given their nascency and diverse use cases, it is possible to estimate the economic value of the clinical improvement they bring to patients using a quality-adjusted life-year (QALY)-based approach and generalized values from existing literature.

OBJECTIVE

This paper aims to provide a patient-centered framework for assessing the economic value of the clinical benefits delivered by digital health apps.

METHODS

We proposed a model based upon 5 levers: country-specific monetary value of a QALY, QALYs lost due to the condition, engagement rate of app users, average effect size of the app's health impact, and duration of the app's impact before remission.

RESULTS

Using 2 digital health apps from the United States and United Kingdom as examples, we explored how this model could generate country-specific estimates of the economic value of the clinical benefits of health apps.

CONCLUSIONS

This new framework can help drive research priorities for digital health by elucidating the factors that influence the economic value.

Assessing the Food and Drug Administration's Risk-Based Framework for Software Precertification With Top Health Apps in the United States: Quality Improvement Study

Background

As the development of mobile health apps continues to accelerate, the need to implement a framework that can standardize the categorization of these apps to allow for efficient yet robust regulation is growing. However, regulators and researchers are faced with numerous challenges, as apps have a wide variety of features, constant updates, and fluid use cases for consumers. As past regulatory efforts have failed to match the rapid innovation of these apps, the US Food and Drug Administration (FDA) has proposed that the Software Precertification (Pre-Cert) Program and a new risk-based framework could be the solution.

Objective

This study aims to determine whether the risk-based framework proposed by the FDA’s Pre-Cert Program could standardize categorization of top health apps in the United States.

Methods

In this quality improvement study during summer 2019, the top 10 apps for 6 disease conditions (addiction, anxiety, depression, diabetes, high blood pressure, and schizophrenia) in Apple iTunes and Android Google Play Store in the United States were classified using the FDA’s risk-based framework. Data on the presence of well-defined app features, user engagement methods, popularity metrics, medical claims, and scientific backing were collected.

Results

The FDA’s risk-based framework classifies an app’s risk by the disease condition it targets and what information that app provides. Of the 120 apps tested, 95 apps were categorized as targeting a nonserious health condition, whereas only 7 were categorized as targeting a serious condition and 18 were categorized as targeting a critical condition. As the majority of apps targeted a nonserious condition, their risk categorization was largely determined by the information they provided. The apps that were assessed as not requiring FDA review were more likely to be associated with the integration of external devices than those assessed as requiring FDA review (15/58, 26% vs 5/62, 8%; P=.03) and health information collection (24/58, 41% vs 9/62, 15%; P=.008). Apps exempt from the review were less likely to offer health information (25/58, 43% vs 45/62, 72%; P<.001), to connect users with professional care (7/58, 12% vs 14/62, 23%; P=.04), and to include an intervention (8/58, 14% vs 35/62, 55%; P<.001).

Conclusions

The FDA’s risk-based framework has the potential to improve the efficiency of the regulatory review process for health apps. However, we were unable to identify a standard measure that differentiated apps requiring regulatory review from those that would not. Apps exempt from the review also carried concerns regarding privacy and data security. Before the framework is used to assess the need for a formal review of digital health tools, further research and regulatory guidance are needed to ensure that the Pre-Cert Program operates in the greatest interest of public health.

Preventive digital mental health interventions for children and young people: a review of the design and reporting of research

Digital health interventions (DHIs) have frequently been highlighted as one way to respond to increasing levels of mental health problems in children and young people. Whilst many are developed to address existing mental health problems, there is also potential for DHIs to address prevention and early intervention. However, there are currently limitations in the design and reporting of the development, evaluation and implementation of preventive DHIs that can limit their adoption into real-world practice. This scoping review aimed to examine existing evidence-based DHI interventions and review how well the research literature described factors that researchers need to include in their study designs and reports to support real-world implementation. A search was conducted for relevant publications published from 2013 onwards. Twenty-one different interventions were identified from 30 publications, which took a universal (n = 12), selective (n = 3) and indicative (n = 15) approach to preventing poor mental health. Most interventions targeted adolescents, with only two studies including children aged ≤10 years. There was limited reporting of user co-design involvement in intervention development. Barriers and facilitators to implementation varied across the delivery settings, and only a minority reported financial costs involved in delivering the intervention. This review found that while there are continued attempts to design and evaluate DHIs for children and young people, there are several points of concern. More research is needed with younger children and those from poorer and underserved backgrounds. Co-design processes with children and young people should be recognised and reported as a necessary component within DHI research as they are an important factor in the design and development of interventions, and underpin successful adoption and implementation. Reporting the type and level of human support provided as part of the intervention is also important in enabling the sustained use and implementation of DHIs.