Mindcraft, a Mobile Mental Health Monitoring Platform for Children and Young People: Development and Acceptability Pilot Study

Background Children and young people's mental health is a growing public health concern, which is further exacerbated by the COVID-19 pandemic. Mobile health apps, particularly those using passive smartphone sensor data, present an opportunity to address this issue and support mental well-being. Objective This study aimed to develop and evaluate a mobile mental health platform for children and young people, Mindcraft, which integrates passive sensor data monitoring with active self-reported updates through an engaging user interface to monitor their well-being. Methods A user-centered design approach was used to develop Mindcraft, incorporating feedback from potential users. User acceptance testing was conducted with a group of 8 young people aged 15-17 years, followed by a pilot test with 39 secondary school students aged 14-18 years, which was conducted for a 2-week period. Results Mindcraft showed encouraging user engagement and retention. Users reported that they found the app to be a friendly tool helping them to increase their emotional awareness and gain a better understanding of themselves. Over 90% of users (36/39, 92.5%) answered all active data questions on the days they used the app. Passive data collection facilitated the gathering of a broader range of well-being metrics over time, with minimal user intervention. Conclusions The Mindcraft app has shown promising results in monitoring mental health symptoms and promoting user engagement among children and young people during its development and initial testing. The app's user-centered design, the focus on privacy and transparency, and a combination of active and passive data collection strategies have all contributed to its efficacy and receptiveness among the target demographic. By continuing to refine and expand the app, the Mindcraft platform has the potential to contribute meaningfully to the field of mental health care for young people.

[1]  A. Doryab,et al.  Predicting Depression in Adolescents Using Mobile and Wearable Sensors: Multimodal Machine Learning–Based Exploratory Study , 2022, JMIR formative research.

[2]  P. Svedberg,et al.  Mental Health Problems among Young People—A Scoping Review of Help-Seeking , 2022, International journal of environmental research and public health.

[3]  Sana Boudhraâ,et al.  Human-Centered Design Approaches in Digital Mental Health Interventions: Exploratory Mapping Review , 2021, JMIR mental health.

[4]  S. Schueller,et al.  User Experience, Engagement, and Popularity in Mental Health Apps: Secondary Analysis of App Analytics and Expert App Reviews , 2021, JMIR human factors.

[5]  J. Yi-Frazier,et al.  Popular Evidence-Based Commercial Mental Health Apps: Analysis of Engagement, Functionality, Aesthetics, and Information Quality , 2021, JMIR mHealth and uHealth.

[6]  Mohammed Khwaja,et al.  Personality and Engagement with Digital Mental Health Interventions , 2021, UMAP.

[7]  Mohammed Khwaja,et al.  Personalised Recommendations in Mental Health Apps: The Impact of Autonomy and Data Sharing , 2021, CHI.

[8]  Maharjan Sm,et al.  Passive sensing on mobile devices to improve mental health services with adolescent and young mothers in low-resource settings: the role of families in feasibility and acceptability , 2020, BMC Medical Informatics and Decision Making.

[9]  C. Henry,et al.  Toi Même, a Mobile Health Platform for Measuring Bipolar Illness Activity: Protocol for a Feasibility Study , 2020, JMIR research protocols.

[10]  Nicholas C Jacobson,et al.  Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones , 2020, Sensors.

[11]  M. Mattheisen,et al.  A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study. , 2020, JMIR mHealth and uHealth.

[12]  Ezra Golberstein,et al.  Coronavirus Disease 2019 (COVID-19) and Mental Health for Children and Adolescents. , 2020, JAMA pediatrics.

[13]  Sandro Galea,et al.  The Mental Health Consequences of COVID-19 and Physical Distancing: The Need for Prevention and Early Intervention. , 2020, JAMA internal medicine.

[14]  Ashutosh Sabharwal,et al.  Tracking and Predicting Depressive Symptoms of Adolescents Using Smartphone-Based Self-Reports, Parental Evaluations, and Passive Phone Sensor Data: Development and Usability Study , 2020, JMIR mental health.

[15]  J. Bardram,et al.  Smartphone-Based Self-Monitoring, Treatment, and Automatically Generated Data in Children, Adolescents, and Young Adults With Psychiatric Disorders: Systematic Review , 2019, JMIR mental health.

[16]  Mohammed Khwaja,et al.  Aligning daily activities with personality: towards a recommender system for improving wellbeing , 2019, RecSys.

[17]  Aku Visuri,et al.  Towards early detection of depression through smartphone sensing , 2019, UbiComp/ISWC Adjunct.

[18]  Amit Baumel,et al.  Objective User Engagement With Mental Health Apps: Systematic Search and Panel-Based Usage Analysis , 2019, Journal of medical Internet research.

[19]  Mohammed Khwaja,et al.  Modeling Personality vs. Modeling Personalidad , 2019, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[20]  M. Punukollu,et al.  Use of mobile apps and technologies in child and adolescent mental health: a systematic review , 2019, Evidence-Based Mental Health.

[21]  David Ramírez,et al.  Combining Continuous Smartphone Native Sensors Data Capture and Unsupervised Data Mining Techniques for Behavioral Changes Detection: A Case Series of the Evidence-Based Behavior (eB2) Study , 2018, JMIR mHealth and uHealth.

[22]  Bijan Aryana,et al.  Design for mobile mental health: an exploratory review , 2018, Health and Technology.

[23]  L. Price,et al.  The effects of early life stress on reward processing. , 2018, Journal of psychiatric research.

[24]  P. Chandrashekar,et al.  Do mental health mobile apps work: evidence and recommendations for designing high-efficacy mental health mobile apps. , 2018, mHealth.

[25]  T. Insel.  Digital Phenotyping: Technology for a New Science of Behavior. , 2017, JAMA.

[26]  B. Recht,et al.  Well-Being Tracking via Smartphone-Measured Activity and Sleep: Cohort Study , 2017, JMIR mHealth and uHealth.

[27]  Ali Idri,et al.  A systematic review of gamification in e-Health , 2017, J. Biomed. Informatics.

[28]  P. Stallard,et al.  Mental Health Mobile Apps for Preadolescents and Adolescents: A Systematic Review , 2017, Journal of medical Internet research.

[29]  S. Pellis,et al.  Impact of adolescent social experiences on behavior and neural circuits implicated in mental illnesses , 2017, Neuroscience & Biobehavioral Reviews.

[30]  N. Rickard,et al.  Development of a Mobile Phone App to Support Self-Monitoring of Emotional Well-Being: A Mental Health Digital Innovation , 2016, JMIR mental health.

[31]  Syed Ali Hussain,et al.  A qualitative study of user perceptions of mobile health apps , 2016, BMC Public Health.

[32]  F. Wahle,et al.  Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild , 2016, JMIR mHealth and uHealth.

[33]  Rui Wang,et al.  CrossCheck: toward passive sensing and detection of mental health changes in people with schizophrenia , 2016, UbiComp.

[34]  Elizabeth L. Murnane,et al.  Development and Evaluation of a Smartphone-Based Measure of Social Rhythms for Bipolar Disorder , 2016, Assessment.

[35]  Oksana Burford,et al.  Mobile Health Apps to Facilitate Self-Care: A Qualitative Study of User Experiences , 2016, PloS one.

[36]  Emily Mower Provost,et al.  Mood state prediction from speech of varying acoustic quality for individuals with bipolar disorder , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[37]  W. Rössler,et al.  Using Smartphones to Monitor Bipolar Disorder Symptoms: A Pilot Study , 2016, JMIR mental health.

[38]  S. Kosslyn,et al.  Mental Imagery: Functional Mechanisms and Clinical Applications , 2015, Trends in Cognitive Sciences.

[39]  Mi Zhang,et al.  MyBehavior: automatic personalized health feedback from user behaviors and preferences using smartphones , 2015, UbiComp.

[40]  Mirco Musolesi,et al.  Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis , 2015, UbiComp.

[41]  Rachel Kenny,et al.  Feasibility of "CopeSmart": A Telemental Health App for Adolescents , 2015, JMIR mental health.

[42]  Patrick C. Staples,et al.  Realizing the Potential of Mobile Mental Health: New Methods for New Data in Psychiatry , 2015, Current Psychiatry Reports.

[43]  Andrew T. Campbell,et al.  Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health. , 2015, Psychiatric rehabilitation journal.

[44]  Lars Vedel Kessing,et al.  Daily electronic monitoring of subjective and objective measures of illness activity in bipolar disorder using smartphones– the MONARCA II trial protocol: a randomized controlled single-blind parallel-group trial , 2014, BMC Psychiatry.

[45]  Mark Begale,et al.  Purple: A Modular System for Developing and Deploying Behavioral Intervention Technologies , 2014, Journal of medical Internet research.

[46]  H. Christensen,et al.  Smartphones for Smarter Delivery of Mental Health Programs: A Systematic Review , 2013, Journal of medical Internet research.

[47]  Lena Sanci,et al.  Self-monitoring Using Mobile Phones in the Early Stages of Adolescent Depression: Randomized Controlled Trial , 2012, Journal of medical Internet research.

[48]  Mathijs F. G. Lucassen,et al.  The effectiveness of SPARX, a computerised self help intervention for adolescents seeking help for depression: randomised controlled non-inferiority trial , 2012, BMJ : British Medical Journal.

[49]  Tanzeem Choudhury,et al.  Passive and In-Situ assessment of mental and physical well-being using mobile sensors , 2011, UbiComp '11.

[50]  Daniel Gatica-Perez,et al.  Who's Who with Big-Five: Analyzing and Classifying Personality Traits with Smartphones , 2011, 2011 15th Annual International Symposium on Wearable Computers.

[51]  William Nick Street,et al.  Healthcare information systems: data mining methods in the creation of a clinical recommender system , 2011, Enterp. Inf. Syst..

[52]  A. Gloster,et al.  Accuracy of retrospective memory and covariation estimation in patients with obsessive-compulsive disorder. , 2008, Behaviour research and therapy.

[53]  Martin H. Teicher,et al.  Developmental neurobiology of childhood stress and trauma. , 2002, The Psychiatric clinics of North America.

[54]  Hitesh Chhinkaniwala,et al.  Deep Reinforcement Learning Based Personalized Health Recommendations , 2019 .

[55]  S. Livingstone,et al.  Children ' s data and privacy online : growing up in a digital age : an evidence , 2019 .

[56]  R. Pea.  User Centered System Design: New Perspectives on Human-Computer Interaction , 1987 .

[57]  J. L. Gewirtz,et al.  8 – The Attachment Learning Process and Its Relation to Cultural and Biological Evolution: Proximate and Ultimate Considerations , 1985 .

[58]  Tiffany M Field,et al.  The Psychobiology of attachment and separation , 1985 .