Effectiveness of Using Mental Health Mobile Apps as Digital Antidepressants for Reducing Anxiety and Depression: Protocol for a Multiple Baseline Across-Individuals Design

Mobile apps to reduce depressive symptoms and alcohol use in youth: A systematic review and meta-analysis: A systematic review

Background

Among youth, symptoms of depression, anxiety, and alcohol use are associated with considerable illness and disability. Youth face many personal and health system barriers in accessing mental health care. Mobile applications (apps) offer youth potentially accessible, scalable, and anonymous therapy and other support. Recent systematic reviews on apps to reduce mental health symptoms among youth have reported uncertain effectiveness, but analyses based on the type of app-delivered therapy are limited.

Objectives

We conducted this systematic review with youth co-researchers to ensure that this review addressed the questions that were most important to them. The objective of this review is to synthesize the best available evidence on the effectiveness of mobile apps for the reduction of depressive symptoms (depression, generalized anxiety, psychological distress) and alcohol use among youth.

Search Methods

We conducted electronic searches of the following bibliographic databases for studies published between January 1, 2008, and July 1, 2022: MEDLINE (via Ovid), Embase (via Ovid), PsycINFO (via Ovid), CINAHL (via EBSCOHost), and CENTRAL (via the Cochrane Library). The search used a combination of indexed terms, free text words, and MeSH headings. We manually screened the references of relevant systematic reviews and included randomized controlled trials (RCTs) for additional eligible studies, and contacted authors for full reports of identified trial registries or protocols.

Selection Criteria

We included RCTs conducted among youth aged 15-24 years from any setting. We did not exclude populations on the basis of gender, socioeconomic status, geographic location or other personal characteristics. We included studies which assessed the effectiveness of app-delivered mental health support or therapy interventions that targeted the management of depressive disorders and/or alcohol use disorders. We excluded apps that targeted general wellness, apps which focused on prevention of psychological disorders and apps that targeted bipolar disorder, psychosis, post-traumatic stress disorder, attention-deficit hyperactivity disorder, substance use disorders (aside from alcohol), and sleep disorders. Eligible comparisons included usual care, no intervention, wait-list control, alternative or controlled mobile applications. We included studies which reported outcomes on depressive symptoms, anxiety symptoms, alcohol use and psychological distress over any follow-up period.

Data Collection and Analysis

We standardized the PICO definitions (population, intervention, comparison, and outcome) of each included study and grouped studies by the type of therapy or support offered by the app. Whenever app design and clinical homogeneity allowed, we meta-analyzed outcomes using a random-effects model. Outcome data measured using categorical scales were synthesized using odds ratios. Outcome data measured using continuous scales were synthesized as the standardized mean difference. We assessed the methodological quality of each included study using the Cochrane Risk of Bias 2.0 tool and we assessed certainty of the evidence using the GRADE approach.

Main Results

From 5280 unique citations, we included 36 RCTs published in 37 reports and conducted in 15 different countries (7984 participants). Among the 36 included trials, we assessed two with an overall low risk of bias, 8 trials with some concern regarding risk of bias, and 26 trials with a high risk of bias. Interventions varied in the type of therapy or supports offered. The most common intervention designs employed mindfulness training, cognitive behavioral therapy (CBT), or a combination of the two (mindfulness + CBT). However, other interventions also included self-monitoring, medication reminders, cognitive bias modification or positive stimulation, dialectical behavioral therapy, gamified health promotion, or social skill building. Mindfulness apps led to short term improvements in depressive symptoms when compared to a withheld control (SMD = -0.36; 95% CI [-0.63, -0.10]; p = 0.007, n = 3 RCTs, GRADE: very low certainty) and when compared to an active control (SMD = -0.27; 95% CI [-0.53, -0.01]; p = 0.04, n = 2 RCTs, GRADE: very low). Apps delivering this type of support also significantly improved symptoms of anxiety when compared to a withheld control (SMD = -0.35; 95% CI [-0.60, -0.09]; p = 0.008, n = 3 RCTs, GRADE: very low) but not when compared to an active control (SMD = -0.24; 95% CI [-0.50, 0.02]; p = 0.07, n = 2 RCTs, GRADE: very low). Mindfulness apps showed improvements in psychological stress that approached statistical significance among participants receiving the mindfulness mobile apps compared to those in the withheld control (SMD = -0.27; 95% CI [-0.56, 0.03]; p = .07, n = 4 RCTs, GRADE: very low). CBT apps also led to short-term improvements in depressive symptoms when compared to a withheld control (SMD = -0.40; 95% CI [-0.80, 0.01]; p = 0.05, n = 2 RCTs, GRADE: very low) and when compared to an active control (SMD = -0.59; 95% CI [-0.98, -0.19]; p = 0.003, n = 2 RCTs, GRADE: very low). CBT-based apps also improved symptoms of anxiety compared to a withheld control (SMD = -0.51; 95% CI [-0.94, -0.09]; p = 0.02, n = 3 RCTs, GRADE: very low) but not when compared to an active control (SMD = -0.26; 95% CI [-1.11, 0.59]; p = 0.55, n = 3 RCTs, GRADE: very low). Apps which combined mindfulness and CBT did not significantly improve symptoms of depression (SMD = -0.20; 95% CI [-0.42, 0.02]; p = 0.07, n = 2 RCTs, GRADE: very low) or anxiety (SMD = -0.21; 95% CI [-0.49, 0.07]; p = 0.14, n = 2 RCTs, GRADE: very low). However, these apps did improve psychological distress (SMD = -0.43; 95% CI [-0.74, -0.12]; p = 0.006, n = 2 RCTs, GRADE: very low). The results of trials on apps to reduce alcohol use were inconsistent. We did not identify any harms associated with the use of apps to manage mental health concerns. All effectiveness results had a very low certainty of evidence rating using the GRADE approach, meaning that apps which deliver therapy or other mental health support may reduce symptoms of depression, anxiety and psychological distress but the evidence is very uncertain.

Authors' Conclusions

We reviewed evidence from 36 trials conducted among youth. According to our meta-analyses, the evidence is very uncertain about the effect of apps on depression, anxiety, psychological distress, and alcohol use. Very few effects were interpreted to be of clinical importance. Most of the RCTs were small studies focusing on efficacy for youth at risk for depressive symptoms. Larger trials are needed to evaluate effectiveness and allow for further analysis of subgroup differences. Longer trials are also needed to better estimate the clinical importance of these apps over the long term.

The Effectiveness of EduMind for Mental Health Promotion Among Youths

Introduction The global surge in mental health issues, particularly among the youth, is evident. Despite the proliferation of digital mental health services, their adoption remains limited, hindered by various barriers. To address this issue, an evidence-based, validated digital mental health intervention is necessary. Although much research has explored the effectiveness of such interventions, there was limited evidence supporting those within the youth population. The objective of this research is to assess the effects of an interventional module on depression, anxiety, and stress symptoms among youths. Methods The EduMind online mental health intervention contents were developed from a needs assessment and a scoping review of effective psychotherapies, achieving a high content validation index (CVI) of 0.96. The contents were integrated into a web application to assess its effectiveness among the target population which consisted of university students of a local institution. A quasi-experimental study compared the intervention group (n=264) to a waitlist-control group (n=200), evaluating changes in mental health status with the Depression, Anxiety and Stress Scale 21 (DASS-21) questionnaire using analysis of covariance (ANCOVA) to analyse mean differences. Results Participants in the intervention group indicated a significant decrease (p < 0.001) in depression, anxiety, and stress. The pre-test and post-test mean scores in the intervention group were 15.81 and 8.97 for depression, 11.46 and 7.02 for anxiety and 14.64 and 6.33 for stress, respectively. In the control group, there was no significant difference between the pre-test and post-test mean scores for depression. However, there was a slight significant reduction for anxiety with pre- and post-test scores of 13.31 and 12.95. Stress mean scores exhibited a slight increase, rising from 13.55 to 14.24.  The final phase revealed significant improvements in mental health status between groups, with significant effect sizes for stress (ƞp2 = 0.57, p < 0.001), depression (ƞp2 = 0.71, p < 0.001), and anxiety (ƞp2 = 0.27, p < 0.001). Conclusion The findings contribute to the advancement of technology-assisted health services, facilitating greater uptake among the population. This study utilized a comprehensive module development framework and demonstrated the effectiveness of the expert-guided mental health intervention module. Furthermore, the study suggests potential integration with the National Strategic Plan for Mental Health 2020-2025 and the National Mental Health Policy, proposing the web application as a potential compulsory student screening tool administered by universities. The information gathered by this application could inform future research directions, propelling technological-assisted mental health services to new heights.

Artificial intelligence: An eye cast towards the mental health nursing horizon

There has been an international surge towards online, digital, and telehealth mental health services, further amplified during COVID-19. Implementation and integration of technological innovations, including artificial intelligence (AI), have increased with the intention to improve clinical, governance, and administrative decision-making. Mental health nurses (MHN) should consider the ramifications of these changes and reflect on their engagement with AI. It is time for mental health nurses to demonstrate leadership in the AI mental health discourse and to meaningfully advocate that safety and inclusion of end users' of mental health service interests are prioritized. To date, very little literature exists about this topic, revealing limited engagement by MHNs overall. The aim of this article is to provide an overview of AI in the mental health context and to stimulate discussion about the rapidity and trustworthiness of AI related to the MHN profession. Despite the pace of progress, and personal life experiences with AI, a lack of MHN leadership about AI exists. MHNs have a professional obligation to advocate for access and equity in health service distribution and provision, and this applies to digital and physical domains. Trustworthiness of AI supports access and equity, and for this reason, it is of concern to MHNs. MHN advocacy and leadership are required to ensure that misogynist, racist, discriminatory biases are not favoured in the development of decisional support systems and training sets that strengthens AI algorithms. The absence of MHNs in designing technological innovation is a risk related to the adequacy of the generation of services that are beneficial for vulnerable people such as tailored, precise, and streamlined mental healthcare provision. AI developers are interested to focus on person-like solutions; however, collaborations with MHNs are required to ensure a person-centred approach for future mental healthcare is not overlooked.

Adoption of a Digital Patient Health Passport as Part of a Primary Healthcare Service Delivery: Systematic Review

The utilization of digital personal health records is considered to be appropriate for present-time usage; it is expected to further enhance primary care's quality-of-service delivery. Despite numerous studies conducted on digital personal health records, efforts in a systematic evaluation of the topic have failed to establish the specific benefits gained by patients, health providers, and healthcare systems. This study aimed to conduct a systematic review regarding the impact of digital personal health records in relation to the delivery of primary care. The review methods included five methodological elements that were directed by the review protocol 2020 (PRISMA). Over a time period of 10 years (2011-2021), 2492 articles were retrieved from various established databases, including Scopus, Web of Science, PubMed, EBSCO-Medline, and Google Scholar, and based on reference mining. The Mixed Method Appraisal Tool (MMAT) was used for quality appraisal. A thematic analysis was performed to develop the themes in this study. The thematic analysis performed on 13 articles resulted in seven main themes, which were empowering the patient, helping with communication, improving relationships, improving the quality of care, maintaining health records, sharing records, and saving time. We concluded the study by expanding the seven themes into 26 sub-themes, of which each served as answers to our main research question that prompted this systematic review.

The Impact of a Mindfulness App on Postnatal Distress

Objectives

The present study investigated the effectiveness of an 8-week mindfulness mobile phone app on women’s depression, anxiety, stress and mindful attention/awareness in the postnatal period.

Methods

The study enrolled 99 mothers of a child under 1 year old, and randomly assigned them to intervention (n = 49, mean age = 31.11, SD = 4.30, years) and control (n = 50, mean age = 31.35, SD = 5.29, years) groups. Multiple regression examined intervention effects on depression, anxiety, stress and mindful attention/awareness measured post-intervention and at 4-week follow-up, controlling for the baseline and post-intervention measurement of the specific outcome, respectively.

Results

The intervention group showed significant decreases in depression, anxiety and stress levels and an increase of mindful attention/awareness post-intervention compared to the control group, with medium to large effect sizes after controlling for effects of corresponding variables at baseline. The intervention group showed further decrease in depression and stress levels and an increase in mindful attention/awareness at 4 weeks post-intervention compared to the control group, with small to medium effect sizes, after controlling for effects of corresponding variables at post-intervention.

Conclusions

The outcomes of the study suggest that delivery of mindfulness via smartphones could be a viable and affordable resource for reducing postnatal depression, anxiety and stress.

Mobile Applications in Mood Disorders and Mental Health: Systematic Search in Apple App Store and Google Play Store and Review of the Literature

OBJECTIVES

The main objective of this work was to explore and characterize the current landscape of mobile applications available to treat mood disorders such as depression, bipolar disorder, and dysthymia.

METHODS

We developed a tool that makes both the Apple App Store and the Google Play Store searchable using keywords and that facilitates the extraction of basic app information of the search results. All app results were filtered using various inclusion and exclusion criteria. We characterized all resultant applications according to their technical details. Furthermore, we searched for scientific publications on each app's website and PubMed, to understand whether any of the apps were supported by any type of scientific evidence on their acceptability, validation, use, effectiveness, etc. Results: Thirty apps were identified that fit the inclusion and exclusion criteria. The literature search yielded 27 publications related to the apps. However, these did not exclusively concern mood disorders. 6 were randomized studies and the rest included a protocol, pilot-, feasibility, case-, or qualitative studies, among others. The majority of studies were conducted on relatively small scales and 9 of the 27 studies did not explicitly study the effects of mobile application use on mental wellbeing.

CONCLUSION

While there exists a wealth of mobile applications aimed at the treatment of mental health disorders, including mood disorders, this study showed that only a handful of these are backed by robust scientific evidence. This result uncovers a need for further clinically oriented and systematic validation and testing of such apps.

Smartphone Psychological Therapy During COVID-19: A Study on the Effectiveness of Five Popular Mental Health Apps for Anxiety and Depression

The aims of this study were to examine the effectiveness of a range of smartphone apps for managing symptoms of anxiety and depression and to assess the utility of a single-case research design for enhancing the evidence base for this mode of treatment delivery. The study was serendipitously impacted by the COVID-19 pandemic, which allowed for effectiveness to be additionally observed in the context of significant community distress. A pilot study was initially conducted using theSuperBetter app to evaluate the proposed methodology, which proved successful with the four finishing participants. In the main study, 39 participants commenced (27 females and 12 males,M_Age = 34.04 years,SD = 12.20), with 29 finishing the intervention phase and completing post-intervention measures. At 6-month follow-up, a further three participants could not be contacted. This study used a digitally enhanced, multiple baseline across-individuals single-case research design. Participants were randomly assigned to the following apps:SuperBetter (n = 8),Smiling Mind (n = 7),MoodMission (n = 8),MindShift (n = 8), andDestressify (n = 8). Symptomatology and life functioning were measured at five different time points: pre-baseline/screening, baseline, intervention, 3-week post-intervention, and 6-month follow-up. Detailed individual perceptions and subjective ratings of the apps were also obtained from participants following the study’s completion. Data were analyzed using visual inspection, time-series analysis, and methods of statistical and clinical significance. Positive results were observed for all apps. Overall, more favorable outcomes were achieved by younger participants, those concurrently undertaking psychotherapy and/or psychotropic medication, those with anxiety and mixed anxiety and depression rather than stand-alone depression, and those with a shorter history of mental illness. Outcomes were generally maintained at 6-month follow-up. It was concluded that a diverse range of evidence-based therapies offered via apps can be effective in managing mental health and improving life functioning even during times of significant global unrest and, like all psychotherapies, are influenced by client features. Additionally, this single-case research design is a low-cost/high value means of assessing the effectiveness of mental health apps.

Clinical Trial Registration: The study is registered with the Australian and New Zealand Clinical Trials Registry (ANZCTR), which is a primary registry in the World Health Organization Registry Network, registration number ACTRN12619001302145p (http://www.ANZCTR.org.au/ACTRN12619001302145p.aspx).

Self-directed Technology-Based Therapeutic Methods for Adult Patients Receiving Mental Health Services: Systematic Review

BACKGROUND

Technological interventions used to treat illnesses and promote health are grouped under the umbrella term of digital therapeutics. The use of digital therapeutics is becoming increasingly common in mental health. Although many technologies are currently being implemented, research supporting their usability, efficacy, and risk requires further examination, especially for those interventions that can be used without support.

OBJECTIVE

This review aims to identify the evidence-based, self-directed, technology-based methods of care that can be used in adult patients after they are discharged from mental health services. The interventions reviewed are automated with no human input required (either at the patient's or at the technology's end), so the patients can implement them without any support.

METHODS

A systematic review was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and PROSPERO (International Prospective Register of Systematic Reviews) guidelines in 3 databases: PubMed, Web of Science, and OVID. The inclusion criteria were self-directed, automated, and technology-based interventions related to mental health, primarily for adults, having a solid evaluation process. The interventions had to be self-directed, in that the participants could use the technology without any external guidance.

RESULTS

We identified 36 papers that met the inclusion criteria: 26 randomized controlled trials, 9 nonrandomized controlled trial quantitative studies, and 1 qualitative study. The technologies used included websites, automated text messaging, phone apps, videos, computer software, and integrated voice response. There were 22 studies focused on internet-based cognitive behavioral therapies as a therapeutic paradigm compared with the waitlist, web-based human-delivered therapy, and other interventions. Among these studies, 14 used paradigms other than the internet-based cognitive behavioral therapy. Of the 8 studies comparing guided and unguided digital care, 3 showed no differences, 3 favored guided interventions, and 2 favored unguided interventions. The research also showed that dropout rates were as high as 80%, citing potential problems with the acceptability of the suggested technologies.

CONCLUSIONS

There is limited research on the efficacy and suitability of self-directed technology-based care options for mental health. Digital technologies have the potential to bridge the gap between ambulatory care and independent living. However, these interventions may need to be developed collaboratively with the users to encourage their acceptability and to avoid high dropout rates.