Mental Health Apps Research Articles

Willing and unwilling digital cyborg assemblages: University students talk about mental health apps.

Explore how students talk about mental health mobile applications (i.e., apps). Data collected in focus group interviews with 51 students (all self-identifying as having mental health problems) and analysed using inductive thematic content analysis. Participants describe mental health apps as an anti-dote to the loss of control, vulnerability, helplessness, impotence, isolation, conspicuousness, and shame which characterise their experience of mental illness. They describe the on-campus clinic as inaccessible and associated with "serious" problems, while configuring psychologists and psychotherapy as out of reach, scarce, formal, structured and anxiety provoking. In contrast, they imagine mental health apps as informal, relaxed, inviting, and accessible. Participants expressed openness and optimism about using apps to improve their mental health. They idealise technology as a means to connect effortlessly, anonymously, and informally, as well as learn skills, assert agency, and act responsibly. They also articulate reluctance to trust technology, show cognisance of participating in a capitalist economy, demonstrate scepticism about the legitimacy of mental health apps, and call for regulation, thereby resisting the position of responsible neoliberal subjects. Participants express ambivalence towards mental health apps without surrendering to either technophobia or technophilia. They express faith in technologies' potential to support mental health while questioning the implicit assumption that people are competent to manage their own mental health. In talking about mental health apps students reproduce broader cultural discourses (including techno-optimism, techno-solutionism, somatopiamism, neo-liberalism, responsibilisationism, technoscepticism, and discourses about neuroplasticity and self-help) thus presenting themselves as both willing and unwilling digital cyborgs.

An exploratory analysis of the effect size of the mobile mental health Application, mindLAMP.

Despite the proliferation of mobile mental health apps, evidence of their efficacy around anxiety or depression is inadequate as most studies lack appropriate control groups. Given that apps are designed to be scalable and reusable tools, insights concerning their efficacy can also be assessed uniquely through comparing different implementations of the same app. This exploratory analysis investigates the potential to report a preliminary effect size of an open-source smartphone mental health app, mindLAMP, on the reduction of anxiety and depression symptoms by comparing a control implementation of the app focused on self-assessment to an intervention implementation of the same app focused on CBT skills. A total of 328 participants were eligible and completed the study under the control implementation and 156 completed the study under the intervention implementation of the mindLAMP app. Both use cases offered access to the same in-app self-assessments and therapeutic interventions. Multiple imputations were utilized to impute the missing Generalized Anxiety Disorder-7 and Patient Health Questionnaire-9 survey scores of the control implementation. Post hoc analysis revealed small effect sizes of Hedge's g = 0.34 for Generalized Anxiety Disorder-7 and Hedge's g = 0.21 for Patient Health Questionnaire-9 between the two groups. mindLAMP shows promising results in improving anxiety and depression outcomes in participants. Though our results mirror the current literature in assessing mental health apps' efficacy, they remain preliminary and will be used to inform a larger, well-powered study to further elucidate the efficacy of mindLAMP.

Journal Digest: IRT; Motivational Therapy; Mental Health Apps; and More

Journal Digest: IRT; Motivational Therapy; Mental Health Apps; and More

Who uses mHealth apps? Identifying user archetypes of mHealth apps.

This study aims to explore the user archetypes of health apps based on average usage and psychometrics. The study utilized a dataset collected through a dedicated smartphone application and contained usage data, i.e. the timestamps of each app session from October 2020 to April 2021. The dataset had 129 participants for mental health apps usage and 224 participants for physical health apps usage. Average daily launches, extraversion, neuroticism, and satisfaction with life were the determinants of the mental health apps clusters, whereas average daily launches, conscientiousness, neuroticism, and satisfaction with life were for physical health apps. Two clusters of mental health apps users were identified using k-prototypes clustering: help-seeking and maintenance users and three clusters of physical health apps users were identified: happy conscious occasional, happy neurotic occasional, and unhappy neurotic frequent users. The findings from this study helped to understand the users of health apps based on the frequency of usage, personality, and satisfaction with life. Further, with these findings, apps can be tailored to optimize user experience and satisfaction which may help to increase user retention. Policymakers may also benefit from these findings since understanding the populations' needs may help to better invest in effective health technology.

Intention to use digital mental health solutions: A cross-sectional survey of university students attitudes and perceptions toward online therapy, mental health apps, and chatbots.

Globally, the high prevalence of mental disorders among university students is a growing public health problem, yet a small minority of students with mental health problems receive treatment. Digital mental health solutions could bridge treatment gaps and overcome many barriers students face accessing treatment. However, there is scant evidence, especially in South Africa (SA), relating to university students' use of and intention to use digital mental health solutions or their attitudes towards these technologies. We aim to explore university 2students attitudes towards and perceptions of digital mental health solutions, and the factors associated with their intention to use them. University students from four SA universities (n = 17 838) completed an online survey to assess experience with, attitudes and perceptions of, and intentions to use, digital mental health solutions. We conducted an exploratory factor analysis to identify factors underlying attitudes and perceptions, and then used multivariate ordinal regression analysis was used to investigate the factors' association with students' intention to use digital mental health solutions. Intention to use digital mental health solutions was high, and attitudes towards and perceptions of digital mental health solutions were largely positive. Importantly, our analysis also shows that 12.6% of users were willing to utilise some form of digital mental health solutions but were unwilling to utilise traditional face-to-face therapies. The greatest proportion of variance was explained by the factor 'Attitudes towards digital technologies' utility to improve student counselling services, provided they are safe'. SA university students are already engaging with digital mental health solutions, and their intention to do so is high. Certain attitudes and perceptions, particularly concerning the utility, effectiveness, and safety, underlie willingness to engage with these solutions, providing potential targets for interventions to increase uptake.

Smartphone apps for mental health and wellbeing: A usage survey and machine learning analysis of psychological and behavioral predictors.

Despite the availability of thousands of mental health applications, the extent to which they are used and the factors associated with their use remain largely unknown. The present study aims to (a) assess in a representative US-based population sample the use of smartphone apps for mental health and wellbeing (SAMHW), (b) determine the variables predicting the use of SAMHW, and (c) explore how a set of variables related to mental health, smartphone use, and smartphone "addiction" may be associated with the use of SAMHW. Data was collected via online questionnaire from 1989 adults. The data gathered included information on smartphone use behavior, mental health, and the use of SAMHW. Latent class analysis was used to categorize participants. Machine learning and logistic regression analyses were used to determine the most important predictors of SAMHW use and associations between predictors and outcome variables. While two-thirds of participants had a statistically high probability for using SAMHW, nearly twice more had high probability for using them to improve wellbeing compared to using them to address mental health problems (43% vs. 18%). In both groups, these participants were more likely to be female and in the younger adult age bracket than male and in the adult or older adult age bracket. According to the machine learning model, the most important predictors for using the relevant smartphone apps were variables associated with smartphone problematic use, COVID-19 impact, and mental health problems. Findings from the present study confirm that the use of SAMHW is growing, particularly among younger adult and female individuals who are negatively impacted by problematic smartphone use, COVID-19, and mental health problems. These individuals tend to bypass traditional care via psychotherapy or psychopharmacology, relying instead on smartphones to address mental health conditions or improve wellbeing. Advising users of these apps to also seek professional help and promoting efforts to prove the efficacy and safety of SAMHW would seem necessary.

Money can't buy happiness: A randomized controlled trial of a digital mental health app with versus without financial incentives.

Mental health disorders are prevalent among college students and increasing in frequency and severity. However, there is a significant gap between those who need treatment and those who engage in treatment. Given the documented efficacy of financial incentives for promoting health behavior change and engagement in treatment, financial incentives may help, along with nonfinancial behavioral incentives such as motivational messaging, gamification, and loss aversion techniques. We compared brief (28-day) use of two versions of a behavioral economics-inspired digital mental health app, NeuroFlow: (1) the full app including financial incentives and nonfinancial behavioral incentives (treatment group) and (2) a version of the app with nonfinancial behavioral incentives only (control group). In our intent-to-treat analyses, in order to examine the primary outcome of app engagement, a one-way analysis of variance (ANOVA) (treatment vs. control) was conducted, and to examine the secondary outcomes (depression, anxiety, emotion dysregulation, and wellbeing), a two-way repeated measures ANOVAs (treatment vs. control × baseline vs. post-trial) were conducted. We found that there were no differences between treatment groups on app engagement or the change in the mental health/wellness outcome measures. There was a main effect of timepoint on symptoms of anxiety and emotion dysregulation, such that there were significantly lower self-reported symptoms at post-trial relative to baseline. Our results suggest that financial incentives in digital mental health apps over and above nonfinancial behavioral incentives do not have an impact on app engagement or mental health/wellness outcomes.

A critical analysis of digital phenotyping and the neuro-digital complex in psychiatry

This article critically examines the emergence and uses of digital phenotyping in contemporary psychiatry. From an analysis of its discourses and practices, we show that digital phenotyping diffusion is directly related to its promise to solve some of the major impasses of the so-called "neuro-turn" in contemporary psychiatry. However, more than a new tool to address old objects of pre-digital psychiatry, we consider digital phenotyping as participating from a new onto-epistemological matrix, the “neuro-digital complex,” which entails the redefinition of psychiatric objects (e.g., brain and mind), diagnostic categories and procedures, subjectivities (e.g., users of mental health apps), and the emergence of a new regime of truth which promises to reveal the neuropsychological core at the individual scale. Despite this techno-utopia, digital phenotyping does not produce neutral mirrors for self-knowledge. We show that it resorts to population statistics, grounded truth data sets built with pre-digital neuropsychological assumptions, and human categorization processes. Nevertheless, we propose not to approach this gap as a misleading ideological fact but to emphasize its productive possibilities. From this perspective, the gap becomes the measure between whom we think we are and who we really are, working as a guide to conduct our lives in neuropsychological terms. Thus, we conclude that, rather than providing personalized diagnoses and treatments, digital phenotyping produces individualized pathways to normalization and neuropsychologization.

ELSI workshop methods for developing needs-based implementation options for mental health apps with AI features

ELSI workshop methods for developing needs-based implementation options for mental health apps with AI features

Information and Communication Technologies in Mental Health: Challenges and Opportunities

The use of Information and Communication Technologies (ICT) in the field of mental health has been growing rapidly in recent years, offering new opportunities for the diagnosis, treatment, and support of individuals with mental health conditions. From teletherapy and online counseling to mental health apps and virtual reality interventions, ICT have the potential to revolutionize the way mental health care is delivered and accessed. However, along with the opportunities, there are also challenges that need to be addressed to ensure the ethical, safe, and effective use of ICT in mental health

ELSI workshop methods for developing needs-based implementation options for mental health apps with AI features

ELSI workshop methods for developing needs-based implementation options for mental health apps with AI features

Assessment of Mental Health Services Available Through Smartphone Apps

As more patients and clinicians are turning to mental health smartphone apps to expand access to services, little is known about the current state of the app marketplaces and what these apps are actually offering in terms of features, privacy, price, and services. To assess the current state of mental health apps, explore the association between app privacy scores and popularity as measured by star ratings and downloads, and to understand opportunities and challenges facing the commercial app landscape. This cross-sectional study had trained raters using the public-facing M-Health Index and Navigation Database (MIND) to assess and review 578 mental health apps. The sample of apps used in this analysis were pulled from MIND and include apps across various conditions including schizophrenia, eating disorders, sleep, and more. Analysis of these apps was conducted in June 2022. There were 578 mental health apps rated across 105 dimensions derived from the American Psychiatric Association's app evaluation framework. App raters assessed each app across 6 categories: (1) app origin and accessibility, (2) privacy and security, (3) clinical foundation, (4) features and engagement, (5) inputs and outputs, and (6) interoperability. Privacy scores were determined by 5 MIND criteria, including (1) having a privacy policy, (2) reporting security measures in place, (3) declaring data use and purpose, (4) allowing for the deletion of data, and (5) allowing users to opt out of data collection. Correlations between privacy scores and popularity metrics (star ratings and number of downloads) were measured. This study included 578 mental health apps that were identified, assessed, and analyzed across 105 MIND dimensions. Psychoeducation, goal setting, and mindfulness were among the top app features. Of the 578 apps analyzed, 443 (77%) had a privacy policy. This analysis of apps with a privacy policy revealed that there was no statistically significant correlation between privacy scores and Apple App Store (r = 0.058, P = .29) or Google Play Store star ratings (r = 0.041; P = .48). The number of app downloads on the Google Play Store, however, was weakly correlated with privacy scores (χ25 = 22.1; P < .001). In this cross-sectional study of mental health apps, findings indicate that the current app marketplaces primarily offered basic features such as psychoeducation, goal tracking, and mindfulness but fewer innovative features such as biofeedback or specialized therapies. Privacy challenges remained common, and app popularity metrics provided little help in identifying apps with more privacy.

Recommendations for design of a mobile application to support management of anxiety and depression among Black American women.

Black American women experience adverse health outcomes due to anxiety and depression. They face systemic barriers to accessing culturally appropriate mental health care leading to the underutilization of mental health services and resources. Mobile technology can be leveraged to increase access to culturally relevant resources, however, the specific needs and preferences that Black women feel are useful in an app to support management of anxiety and depression are rarely reflected in existing digital health tools. This study aims to assess what types of content, features, and important considerations should be included in the design of a mobile app tailored to support management of anxiety and depression among Black women. Focus groups were conducted with 20 women (mean age 36.6 years, SD 17.8 years), with 5 participants per group. Focus groups were led by a moderator, with notetaker present, using an interview guide to discuss topics, such as participants' attitudes and perceptions towards mental health and use of mental health services, and content, features, and concerns for design of a mobile app to support management of anxiety and depression. Descriptive qualitative content analysis was conducted. Recommendations for content were either informational (e.g., information to find a Black woman therapist) or inspirational (e.g., encouraging stories about overcoming adversity). Suggested features allow users to monitor their progress, practice healthy coping techniques, and connect with others. The importance of feeling "a sense of community" was emphasized. Transparency about who created and owns the app, and how users' data will be used and protected was recommended to establish trust. The findings from this study were consistent with previous literature which highlighted the need for educational, psychotherapy, and personal development components for mental health apps. There has been exponential growth in the digital mental health space due to the COVID-19 pandemic; however, a one-size-fits-all approach may lead to more options but continued disparity in receiving mental health care. Designing a mental health app for and with Black women may help to advance digital health equity by providing a tool that addresses their specific needs and preferences, and increase engagement.

Encouraging help-seeking and engagement in a mental health app: What young people want.

Although many young people evince a preference for digital mental health support over face to face, engagement with smartphone apps for mental health remains relatively low, particularly in young people not accessing professional support services. While some of this can be attributed to stigma or embarrassment, there is also a need for developers and researchers to investigate features which promote long-term usage. MoodyTunes is a music-based smartphone app that has been co-designed with young people to help them develop effective self-management strategies for mood through music listening. Four focus groups with young people (N = 24, aged 13-25) were conducted to workshop how MoodyTunes could promote help-seeking behaviours and long-term engagement with the app. Online discussions following a broad topic guide were held in groups of 4-7 in which participants explored topics including the use of social features, promoting help-seeking behaviour, gamification and mood tracking. Participants also viewed visual materials and offered ideas about visual design both verbally and on paper. A general inductive approach was taken to analysis of qualitative data. Three primary themes were identified in the data: privacy; user empowerment; and engagement vs. achievement. Participants voiced a strong desire to remain anonymous and to feel empowered to make their own decisions about accessing professional help. Sensitive use of language was noted as critical, with some participants noting that the use of more directive language could have a negative impact on their mental health and that motivational features should focus on engagement with the primary aim of the app rather than rewarding achievement. Findings highlight the benefits of a co-design approach and provide key insights into how mental health apps may be able to improve long-term user engagement with young people.

Wellbeing and (Mental) Health: A Quantitative Exploration of Portuguese Young Adults’ Uses of M-Apps from a Gender Perspective

Acknowledging the importance of technology in the mental health and wellbeing of individuals during the COVID-19 pandemic, this paper aims to analyse the uses of mobile health applications by young Portuguese adults focusing on the following research question: How do the uses of m-apps by young adults interact with health and wellbeing from a gender perspective? An online survey was conducted to a representative sample of young Portuguese adults (18–30 years), under the objectives of the MyGender project that studies the interconnections of young adults with m-apps, with their technicity and imaginaries. The approach followed in this paper is quantitative-extensive. Results reveal low usage of overall health and wellbeing apps, particularly among single respondents who do not have children. Health and self-tracking apps are among the 10 most frequently used apps. Only 6.20% of the sample considers mental health apps (mHapps) as one of the three most important types of apps. Having children is a factor for a bigger interconnectedness of the respondents with overall health and wellbeing apps and the general quality of care.

Smartphone app engagement and clinical outcomes in a hybrid clinic

Despite the growing prevalence of mental health-related smartphone apps, low real-world engagement has prevented these apps from transforming the mental health landscape. Integrating mental health apps into more traditional therapeutic models appears to support better clinical outcomes, but also raises questions about the relationship between app engagement, the app itself, and the coach or clinician. This study explores patient app engagement patterns and the associated clinical outcomes gathered from piloting a digital clinic. Patients with anxiety or depression completed eight clinical visits and coach visits over a median of 83 days with a standard deviation of 17.25 days. Between clinical visits, patients completed therapeutic activities on the mindLAMP app. Mean PHQ-9 and GAD-7 scores decreased from the intake visit to both visit 4 and visit 8. Patients had high app engagement, but engagement did not correlate with outcomes. From intake visit to visit 4, the interaction effects indicate significant differences in the change of both PHQ-9 and GAD-7 depending on participants’ average app satisfaction and clinician/coach satisfaction (as measured by WAI-SR) with engagement. Overall, results support the feasibility of incorporating an app into a hybrid clinic.

Perceptions of Helpful and Unhelpful Responses to Disclosures of Suicidality in a Sample of Mobile App Users

Objective Assessment and management of suicide risk is dependent on the individual at risk disclosing their suicidal thoughts. This study analyzed self-reported data collected via Youper, a mental-health app, to explore user experiences with suicide-related disclosure. Method 2,952 international users who endorsed suicidal ideation reported on experiences with suicide-related disclosure. Differences in experiences were examined according to age, gender, and symptoms of psychopathology. Results Just over half of users (56.17%) shared their suicidal thoughts. The primary reason for not sharing was thinking others would not understand. Of those who had disclosed their suicidal thoughts, 56.31% perceived the listener’s response to be helpful, with listening and expressing care identified as most helpful. More severe depression and generalized anxiety symptoms were associated with higher disclosure likelihood, whereas higher borderline-personality and social-anxiety symptoms were associated with lower disclosure likelihood. Perceptions of what users found helpful when they disclosed suicidality varied by gender. Lack of understanding was the most frequently reported unhelpful response universally. Conclusion Findings suggest that listening and expressing care should be prioritized to maximize suicidal individuals’ experiences of feeling supported. That said, gender and symptoms impact likelihoods of and experiences with disclosure, which should be considered when receiving suicide-related disclosure. HIGHLIGHTS Nearly half of users shared suicidal thoughts and found listener responses helpful. Users most often endorsed listening as helpful and not understanding as unhelpful. Symptom severity and gender informed suicide-related disclosure experiences.

Public Trust in Artificial Intelligence Applications in Mental Health Care: Topic Modeling Analysis.

Mental disorders (MDs) impose heavy burdens on health care (HC) systems and affect a growing number of people worldwide. The use of mobile health (mHealth) apps empowered by artificial intelligence (AI) is increasingly being resorted to as a possible solution. This study adopted a topic modeling (TM) approach to investigate the public trust in AI apps in mental health care (MHC) by identifying the dominant topics and themes in user reviews of the 8 most relevant mental health (MH) apps with the largest numbers of reviewers. We searched Google Play for the top MH apps with the largest numbers of reviewers, from which we selected the most relevant apps. Subsequently, we extracted data from user reviews posted from January 1, 2020, to April 2, 2022. After cleaning the extracted data using the Python text processing tool spaCy, we ascertained the optimal number of topics, drawing on the coherence scores and used latent Dirichlet allocation (LDA) TM to generate the most salient topics and related terms. We then classified the ascertained topics into different theme categories by plotting them onto a 2D plane via multidimensional scaling using the pyLDAvis visualization tool. Finally, we analyzed these topics and themes qualitatively to better understand the status of public trust in AI apps in MHC. From the top 20 MH apps with the largest numbers of reviewers retrieved, we chose the 8 (40%) most relevant apps: (1) Wysa: Anxiety Therapy Chatbot; (2) Youper Therapy; (3) MindDoc: Your Companion; (4) TalkLife for Anxiety, Depression & Stress; (5) 7 Cups: Online Therapy for Mental Health & Anxiety; (6) BetterHelp-Therapy; (7) Sanvello; and (8) InnerHour. These apps provided 14.2% (n=559), 11.0% (n=431), 13.7% (n=538), 8.8% (n=356), 14.1% (n=554), 11.9% (n=468), 9.2% (n=362), and 16.9% (n=663) of the collected 3931 reviews, respectively. The 4 dominant topics were topic 4 (cheering people up; n=1069, 27%), topic 3 (calming people down; n=1029, 26%), topic 2 (helping figure out the inner world; n=963, 25%), and topic 1 (being an alternative or complement to a therapist; n=870, 22%). Based on topic coherence and intertopic distance, topics 3 and 4 were combined into theme 3 (dispelling negative emotions), while topics 2 and 1 remained 2 separate themes: theme 2 (helping figure out the inner world) and theme 1 (being an alternative or complement to a therapist), respectively. These themes and topics, though involving some dissenting voices, reflected an overall high status of trust in AI apps. This is the first study to investigate the public trust in AI apps in MHC from the perspective of user reviews using the TM technique. The automatic text analysis and complementary manual interpretation of the collected data allowed us to discover the dominant topics hidden in a data set and categorize these topics into different themes to reveal an overall high degree of public trust. The dissenting voices from users, though only a few, can serve as indicators for health providers and app developers to jointly improve these apps, which will ultimately facilitate the treatment of prevalent MDs and alleviate the overburdened HC systems worldwide.

Digital Phenotyping Data to Predict Symptom Improvement and App Personalization: Protocol for a Prospective Study.

Smartphone apps that capture surveys and sensors are increasingly being leveraged to collect data on clinical conditions. In mental health, this data could be used to personalize psychiatric support offered by apps so that they are more effective and engaging. Yet today, few mental health apps offer this type of support, often because of challenges associated with accurately predicting users' actual future mental health. In this protocol, we present a study design to explore engagement with mental health apps in college students, using the Technology Acceptance Model as a theoretical framework, and assess the accuracy of predicting mental health changes using digital phenotyping data. There are two main goals of this study. First, we present a logistic regression model fit on data from a prior study on college students and prospectively test this model on a new student cohort to assess its accuracy. Second, we will provide users with data-driven activity suggestions every 4 days to determine whether this type of personalization will increase engagement or attitudes toward the app compared to those receiving no personalized recommendations. The study was completed in the spring of 2022, and the manuscript is currently in review at JMIR Publications. This is one of the first digital phenotyping algorithms to be prospectively validated. Overall, our results will inform the potential of digital phenotyping data to serve as tailoring data in adaptive interventions and to increase rates of engagement. PRR1-10.2196/37954.

Chatbot-Delivered Cognitive Behavioral Therapy in Adolescents With Depression and Anxiety During the COVID-19 Pandemic: Feasibility and Acceptability Study.

Symptoms of depression and anxiety, suicidal ideation, and self-harm have escalated among adolescents to crisis levels during the COVID-19 pandemic. As a result, primary care providers (PCPs) are often called on to provide first-line care for these youth. Digital health interventions can extend mental health specialty care, but few are evidence based. We evaluated the feasibility of delivering an evidence-based mobile health (mHealth) app with an embedded conversational agent to deliver cognitive behavioral therapy (CBT) to symptomatic adolescents presenting in primary care settings during the pandemic. In this 12-week pilot study, we evaluated the feasibility of delivering the app-based intervention to adolescents aged 13 to 17 years with moderate depressive symptoms who were treated in a practice-based research network (PBRN) of academically affiliated primary care clinics. We also obtained preliminary estimates of app acceptability, effectiveness, and usability. This small, pilot randomized controlled trial (RCT) evaluated depressive symptom severity in adolescents randomized to the app or to a wait list control condition. The primary end point was depression severity at 4-weeks, measured by the 9-item Patient Health Questionnaire (PHQ-9). Data on acceptability, feasibility, and usability were collected from adolescents and their parent or legal guardian. Qualitative interviews were conducted with 13 PCPs from 11 PBRN clinics to identify facilitators and barriers to incorporating mental health apps in treatment planning for adolescents with depression and anxiety. The pilot randomized 18 participants to the app (n=10, 56%) or to a wait list control condition (n=8, 44%); 17 participants were included in the analysis, and 1 became ineligible upon chart review due to lack of eligibility based on documented diagnosis. The overall sample was predominantly female (15/17, 88%), White (15/17, 88%), and privately insured (15/17, 88%). Mean PHQ-9 scores at 4 weeks decreased by 3.3 points in the active treatment group (representing a shift in mean depression score from moderate to mild symptom severity categories) and 2 points in the wait list control group (no shift in symptom severity category). Teen- and parent-reported usability, feasibility, and acceptability of the app was high. PCPs reported preference for introducing mHealth interventions like the one in this study early in the course of care for individuals presenting with mild or moderate symptoms. In this small study, we demonstrated the feasibility, acceptability, usability, and safety of using a CBT-based chatbot for adolescents presenting with moderate depressive symptoms in a network of PBRN-based primary care clinics. This pilot study could not establish effectiveness, but our results suggest that further study in a larger pediatric population is warranted. Future study inclusive of rural, socioeconomically disadvantaged, and underrepresented communities is needed to establish generalizability of effectiveness and identify implementation-related adaptations needed to promote broader uptake in pediatric primary care. ClinicalTrials.gov NCT04603053; https://clinicaltrials.gov/ct2/show/NCT04603053.