Youth StepCare: a pilot study of an online screening and recommendations service for depression and anxiety among youth patients in general practice

PRimary carE digital Support ToOl in mental health (PRESTO): Design, development and study protocols

BACKGROUND

About 30-50% of Primary Care (PC) users in Spain suffer mental health problems, mostly mild to moderate anxious and depressive symptoms, which account for 2% of Spain's total Gross domestic product and 50% of the costs associated to all mental disorders. Mobile health tools have demonstrated to cost-effectively reduce anxious and depressive symptoms while machine learning (ML) techniques have shown to accurately detect severe cases. The main aim of this project is to develop a comprehensive ML digital support platform (PRESTO) to cost-effectively screen, assess, triage, and provide personalized treatments for anxious and depressive symptoms in PC.

METHODS

The project will be carried out in 3 complementary phases: First, a ML predictive severity model will be built based on all the cases referred to the PC mental health support programme during the last 5 years in Catalonia. Simultaneously, a smartphone app to monitor and deliver psychological interventions for anxiety and depressive symptoms will be developed and tested in a clinical trial. Finally, the ML models and the app will be integrated in a comprehensive decision-support platform (PRESTO) which will triage and assign to each patient a specific intervention based on individual personal and clinical characteristics. The effectiveness of PRESTO to reduce waiting times in receiving mental healthcare will be tested in a stepped-wedge cluster randomized controlled trial in 5 PC centres.

DISCUSSION

PRESTO will offer timely and personalized cost-effective mental health treatment to people with mild to moderate anxious and depressive symptoms. This will result in a reduction of the burden of mental health problems in PC and on society as a whole.

TRIAL REGISTRATION

The project and their clinical trials were registered in Clinical Trials.gov: NCT04559360 (September 2020).

Examining service utilisation and impact among consumers of a national mental health stepped care programme in Australia: a protocol using linked administrative data

INTRODUCTION

Mental well-being is a global public health priority with increasing mental health conditions having substantial burden on individuals, health systems and society. 'Stepped care', where services are provided at an intensity to meet the changing needs of the consumer, is the chosen approach to mental health service delivery in primary healthcare in Australia for its efficiencies and patient outcomes; yet limited evidence exists on how the programme is being rolled out and its impact in practice. This protocol outlines a data linkage project to characterise and quantify healthcare service utilisation and impacts among a cohort of consumers of a national mental health stepped care programme in one region of Australia.

METHODS AND ANALYSIS

Data linkage will be used to establish a retrospective cohort of consumers of mental health stepped care services between 1 July 2020 and 31 December 2021 in one primary healthcare region in Australia (n=approx. 12 710). These data will be linked with records from other healthcare service data sets (eg, hospitalisations, emergency department presentations, community-based state government-delivered mental healthcare, hospital costs). Four areas for analysis will include: (1) characterising the nature of mental health stepped care service use; (2) describing the cohort's sociodemographic and health characteristics; (3) quantifying broader service utilisation and associated economic costs; and (4) assessing the impact of mental health stepped care service utilisation on health and service outcomes.

ETHICS AND DISSEMINATION

Approval from the Darling Downs Health Human Research Ethics Committee (HREA/2020/QTDD/65518) has been granted. All data will be non-identifiable, and research findings will be disseminated through peer-reviewed journals, conference presentations and industry meetings.

Clinical decision support methods for children and youths with mental health disorders in primary care

BACKGROUND

Mental health disorders among children and youths are common and often have negative consequences for children, youths, and families if unrecognized and untreated. With the goal of early recognition, primary care physicians (PCPs) play a significant role in the detection and referral of mental disorders. However, PCPs report several barriers related to confidence, knowledge, and interdisciplinary collaboration. Therefore, initiatives have been taken to assist PCPs in their clinical decision-making through clinical decision support methods (CDSMs).

OBJECTIVES

This review aimed to identify CDSMs in the literature and describe their functionalities and quality.

METHODS

In this review, a search strategy was performed to access all available studies in PubMed, PsychINFO, Embase, Web of Science, and COCHRANE using keywords. Studies that involved CDSMs for PCP clinical decision-making regarding psychosocial or psychiatric problems among children and youths (0-24 years old) were included. The search was conducted according to PRISMA-Protocols.

RESULTS

Of 1,294 studies identified, 25 were eligible for inclusion and varied in quality. Eighteen CDSMs were described. Fourteen studies described computer-based methods with decision support, focusing on self-help, probable diagnosis, and treatment suggestions. Nine studies described telecommunication methods, which offered support through interdisciplinary (video) calls. Two studies described CDSMs with a combination of components related to the two CDSM categories.

CONCLUSION

Easy-to-use CDSMs of good quality are valuable for advising PCPs on the detection and referral of children and youths with mental health disorders. However, valid multicentre research on a combination of computer-based methods and telecommunication is still needed.

Characteristics of Mobile Health Platforms for Depression and Anxiety: Content Analysis Through a Systematic Review of the Literature and Systematic Search of Two App Stores

Background

Mobile health (mHealth) platforms show promise in the management of mental health conditions such as anxiety and depression. This has resulted in an abundance of mHealth platforms available for research or commercial use.

Objective

The objective of this review is to characterize the current state of mHealth platforms designed for anxiety or depression that are available for research, commercial use, or both.

Methods

A systematic review was conducted using a two-pronged approach: searching relevant literature with prespecified search terms to identify platforms in published research and simultaneously searching 2 major app stores—Google Play Store and Apple App Store—to identify commercially available platforms. Key characteristics of the mHealth platforms were synthesized, such as platform name, targeted condition, targeted group, purpose, technology type, intervention type, commercial availability, and regulatory information.

Results

The literature and app store searches yielded 169 and 179 mHealth platforms, respectively. Most platforms developed for research purposes were designed for depression (116/169, 68.6%), whereas the app store search reported a higher number of platforms developed for anxiety (Android: 58/179, 32.4%; iOS: 27/179, 15.1%). The most common purpose of platforms in both searches was treatment (literature search: 122/169, 72.2%; app store search: 129/179, 72.1%). With regard to the types of intervention, cognitive behavioral therapy and referral to care or counseling emerged as the most popular options offered by the platforms identified in the literature and app store searches, respectively. Most platforms from both searches did not have a specific target age group. In addition, most platforms found in app stores lacked clinical and real-world evidence, and a small number of platforms found in the published research were available commercially.

Conclusions

A considerable number of mHealth platforms designed for anxiety or depression are available for research, commercial use, or both. The characteristics of these mHealth platforms greatly vary. Future efforts should focus on assessing the quality—utility, safety, and effectiveness—of the existing platforms and providing developers, from both commercial and research sectors, a reporting guideline for their platform description and a regulatory framework to facilitate the development, validation, and deployment of effective mHealth platforms.