Development of a Mobile Phone App to Support Self-Monitoring of Emotional Well-Being: A Mental Health Digital Innovation

Rickard, Nikki; Arjmand, Hussain-Abdulah; Bakker, David; Seabrook, Elizabeth

doi:10.2196/mental.6202

Cited by 118Digital Mental Health InterventionsMobile Health and mHealth ApplicationsMental Health Research Topics

Development of a Mobile Phone App to Support Self-Monitoring of Emotional Well-Being: A Mental Health Digital Innovation

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Rickard, Nikki, Arjmand, Hussain-Abdulah, Bakker, David, Seabrook, Elizabeth·2016·JMIR Mental Health

DOI 10.2196/mental.6202PMC PMC5143469

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Abstract

Background: Emotional well-being is a primary component of mental health and well-being. Monitoring changes in emotional state daily over extended periods is, however, difficult using traditional methodologies. Providing mental health support is also challenging when approximately only 1 in 2 people with mental health issues seek professional help. Mobile phone technology offers a sustainable means of enhancing self-management of emotional well-being. Objective: This paper aims to describe the development of a mobile phone tool designed to monitor emotional changes in a natural everyday context and in real time. Methods: This evidence-informed mobile phone app monitors emotional mental health and well-being, and it provides links to mental health organization websites and resources. The app obtains data via self-report psychological questionnaires, experience sampling methodology (ESM), and automated behavioral data collection. Results: Feedback from 11 individuals (age range 16-52 years; 4 males, 7 females), who tested the app over 30 days, confirmed via survey and focus group methods that the app was functional and usable. Conclusions: Recommendations for future researchers and developers of mental health apps to be used for research are also presented. The methodology described in this paper offers a powerful tool for a range of potential mental health research studies and provides a valuable standard against which development of future mental health apps should be considered.

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In addition, 11 studies did not specify the analysis method used to analyze the data.
“In addition, 11 studies did not specify the analysis method used to analyze the data [33,41-50].”
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“For example, Reid et al [28,38] found that the majority of their adolescent sample using the mobile phone-based mental health app, mobiletype , completed their self-assessments, and that the use of the app increased the practitioners’ understanding of their patients’ mental health.”
Experience Sampling Methodology studies of depression: the state of the art
Telford, C., McCarthy-Jones, S., Corcoran, R., & Rowse, G. (2012) · Psychological Medicine · doi:10.1017/s0033291711002200 · Cited by 64 · cited 1× · see citing works
“ESMs have been used to monitor changes in affective state, and to predict mental health with success to a certain extent [21,22].”
Psychological Precursors of Panic Attacks
Kenardy, J. A., Fried, L., KRAEMER, H. C., & Taylor, C. B. (1992) · The British Journal of Psychiatry · doi:10.1192/bjp.160.5.668 · Cited by 55 · cited 1× · see citing works
“ESM assessments in individuals diagnosed with panic disorder also revealed that the expectation of a panic attack was a significant precursor for the occurrence of a panic attack [24].”
Another Venue for Problematic Interpersonal Behavior: The Effects of Depressive and Anxious Symptoms on Social Networking Experiences
Feinstein, B. A., Bhatia, V., Hershenberg, R., & Davila, J. (2012) · Journal of Social and Clinical Psychology · doi:10.1521/jscp.2012.31.4.356 · Cited by 50 · cited 1× · see citing works
“Positive interactions are associated with better mental health outcomes, and negative interactions may exacerbate mental illness [81-83].”
Using a mobile phone application in youth mental health - an evaluation study.
Reid, S., Kauer, S. D., Khor, A. S., Hearps, S. J. C., Sanci, L. A., Kennedy, A. D. M., & Patton, G. C. (2012) · PubMed · Cited by 49 · see citing works
e‐hub: an online self‐help mental health service in the community
Bennett, K., Reynolds, J., Christensen, H. M., & Griffiths, K. M. (2010) · The Medical Journal of Australia · doi:10.5694/j.1326-5377.2010.tb03694.x · Cited by 37 · cited 1× · see citing works
“The innovative pairing of changes in emotional well-being with rapid delivery of mental health information has the potential to improve a user’s access to relevant resources such as Web-based health portals (eg, eheadspace, eHub), or local GPs when it is needed [75-77].”
Using ecological momentary assessment to determine media use by individuals with and without major depressive disorder
Primack, B. A., Silk, J. S., DeLozier, C. R., Shadel, W. G., Dillman Carpentier, F. R., Dahl, R. E., & Switzer, G. E. (2011) · Archives of Pediatrics & Adolescent Medicine · doi:10.1001/archpediatrics.2011.27 · Cited by 37 · cited 1× · see citing works
“Music listening patterns also appear to predict emotional health [48-50] and given that approximately two thirds of music listening by young people is via mobile devices such as mobile phones [31], it is surprising that relatively few apps have attempted to use music for this purpose [27].”
Promoting Youth Mental Health through Computer-Mediated Communication
Rickwood, D. (2010) · International Journal of Mental Health Promotion · doi:10.1080/14623730.2010.9721817 · Cited by 30 · cited 1× · see citing works
“Mobile phone health technology holds great potential for facilitating the management of emotional health through its ability to deliver flexible, user-oriented intervention and self-management tools; a feature particularly relevant for young people who often report fear of stigma associated with seeking professional s…”
Stressors and well‐being in low socio‐economic status M alaysian adolescents: The role of resilience resources
Noor, N. M., & Alwi, A. (2013) · Asian Journal Of Social Psychology · doi:10.1111/ajsp.12035 · Cited by 28 · cited 1× · see citing works
“Previous research methodologies use cross-sectionally designed studies and typically rely on retrospective reports [88-90].”
Ways to study the nature and frequency of our daily emotions: reply to the commentaries on “Emotions in everyday life”
Scherer, K. R. (2004) · Social Science Information · doi:10.1177/0539018404047713 · Cited by 25 · cited 1× · see citing works
“This triangulation of data collection is considered crucial for advancing the measurement of emotional state [58].”
Is there a gap between recommended and ‘real world’ practice in the management of depression in young people? A medical file audit of practice
Hetrick, S. E., Thompson, A. D., Yuen, K., Finch, S. J., & Parker, A. G. (2012) · BMC Health Services Research · doi:10.1186/1472-6963-12-178 · Cited by 20 · cited 1× · see citing works
“In practice, however, it remains difficult for clinicians or professional mental health service providers to obtain frequent monitoring in real time [13,14].”
Practical CBT: Using Functional Analysis and Standardised Homework in Everyday Therapy
Bakker, G. (2008) · eCite Digital Repository (University of Tasmania) · Cited by 7 · see citing works
In This Issue
(2008) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2008.165.7.a42 · Cited by 2 · see citing works

Flagged first, then by citations

1 of the 77 references couldn’t be matched to a known paper.

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[10]2016-11-02 Goodwin GM. Time in the course of major depressive disorder. Medicographia 2010;32:126-132. http://www.medicographia.com/2010/10/time-in-the-course-of-major-depressive-disorder/
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Sorted by citations

Barriers to and facilitators of user engagement with digital mental health interventions: Systematic review
Borghouts, J., Eikey, E., Mark, G., De Leon, C., Schueller, S. M., Schneider, M., Stadnick, N., Zheng, K., Mukamel, D., & Sorkin, D. H. (2021) · Journal of Medical Internet Research · doi:10.2196/24387 · Cited by 1,069 · see citing works
“The primary barrier to engagement noted in 25 studies was users’ experience of technical issues [44,50,80,92,100,103,118, 129,138,155,172,179,185,195,205,208,212-220], such as a mobile app crashing and shutting down unexpectedly; in 3 studies, participants did not have the resources required to use an intervention [17…”
Background
Do mental health mobile apps work: evidence and recommendations for designing high-efficacy mental health mobile apps
Chandrashekar, P. (2018) · mHealth · doi:10.21037/mhealth.2018.03.02 · Cited by 359 · see citing works
“Increasing ESA, defined as the ability to identify and understand one’s own emotions, has been shown to reduce symptoms of mental illness and improve coping skills (19-22).”
Background
Engagement in mobile phone app for self-monitoring of emotional wellbeing predicts changes in mental health: MoodPrism
Bakker, D., & Rickard, N. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2017.11.016 · Cited by 231 · see citing works
Self-citation
A randomized controlled trial of three smartphone apps for enhancing public mental health
Bakker, D., Kazantzis, N., Rickwood, D., & Rickard, N. (2018) · Behaviour Research and Therapy · doi:10.1016/j.brat.2018.08.003 · Cited by 176 · see citing works
“…the main implementation strengths and population-level utility of MHapps lie in preventative or stepped-care use (Bakker, Kazantzis, Rickwood, & Rickard, 2016; Grist, Porter, & Stallard, 2017; Nicholas, Larsen, Proudfoot, & Christensen, 2015), and the significant effects observed for clinical…”
Self-citation
Evaluation of an mHealth App (DeStressify) on University Students’ Mental Health: Pilot Trial
Lee, R. A., & Jung, M. E. (2018) · JMIR Mental Health · doi:10.2196/mental.8324 · Cited by 138 · see citing works
Predicting Depression From Language-Based Emotion Dynamics: Longitudinal Analysis of Facebook and Twitter Status Updates
Seabrook, E. M., Kern, M. L., Fulcher, B. D., & Rickard, N. S. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9267 · Cited by 144 · see citing works
“MoodPrism is a mood-tracking app that collects data and provides engaging feedback to its users on their mood, mental health, and well-being [42].”
BackgroundSelf-citation
Youth Codesign of a Mobile Phone App to Facilitate Self-Monitoring and Management of Mood Symptoms in Young People With Major Depression, Suicidal Ideation, and Self-Harm
Hetrick, S. E., Robinson, J., Burge, E., Blandon, R., Mobilio, B., Rice, S. M., Simmons, M. B., Alvarez-Jimenez, M., Goodrich, S., & Davey, C. G. (2018) · JMIR Mental Health · doi:10.2196/mental.9041 · Cited by 151 · see citing works
“The functionality of the app is consistent with prototypes developed for adults experiencing depression [9,51,52], with recommendations for suicide and self-harm prevention apps [57,59-62], with what young people in other studies of app”
BackgroundMethod
Mobile Applications to Improve Emotional Intelligence in Autism – A Review
Papoutsi, C., Drigas, A. S., & Skianis, C. A. (2018) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v12i6.9073 · Cited by 131 · see citing works
“They provide the opportunity and have promised to help people change social behavior [38-40], enrich education [41-43] and promote well-being [44-46].”
Background
Human-Centered Design Approaches in Digital Mental Health Interventions: Exploratory Mapping Review
Vial, S., Boudhraâ, S., & Dumont, M. (2022) · JMIR Mental Health · doi:10.2196/35591 · Cited by 114 · see citing works
“Among the studies included in this review, 8 (27%) did not refer to any human-centered design approach, so they are not listed in Table 1 [53-60].”
Background
Using Big Data to study subjective well-being
Luhmann, M. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.07.006 · Cited by 81 · see citing works
Engagement with a cognitive behavioural therapy mobile phone app predicts changes in mental health and wellbeing: MoodMission
Bakker, D., & Rickard, N. S. (2019) · Australian Psychologist · doi:10.1111/ap.12383 · Cited by 72 · see citing works
Self-citation
Development and Pilot Evaluation of Smartphone-Delivered Cognitive Behavior Therapy Strategies for Mood- and Anxiety-Related Problems: MoodMission
Bakker, D., Kazantzis, N., Rickwood, D., & Rickard, N. S. (2018) · Cognitive and Behavioral Practice · doi:10.1016/j.cbpra.2018.07.002 · Cited by 65 · see citing works
Self-citation
From Digital Health to Digital Well-being: Systematic Scoping Review
Smits, M., Kim, C. M., van Goor, H., & Ludden, G. (2022) · Journal of Medical Internet Research · doi:10.2196/33787 · Cited by 53 · see citing works
“The results were presented for each of the 4 problems (see Table 1 and Table 2 for a summary of the general results and Multimedia Appendix 2 [17-133] for more detailed information).”
Background
Extended reality for mental health: Current trends and future challenges
Pons, P., Medrano, S. N., & Soler-Dominguez, J. L. (2022) · Frontiers in Computer Science · doi:10.3389/fcomp.2022.1034307 · Cited by 66 · see citing works
“Mobile apps that require the user to update their status periodically, feelings, or thoughts can be tedious to use on a daily basis, and users frequently stop providing updates to the app over time (Rickard et al., 2016).”
Background
Smartphone screen time reduction improves mental health: a randomized controlled trial
Pieh, C., Humer, E. C., Hoenigl, A., Schwab, J., Mayerhofer, D., Dale, R., & Haider, K. (2025) · BMC Medicine · doi:10.1186/s12916-025-03944-z · Cited by 64 · see citing works
Prevention and early intervention of depression in young people: an integrated narrative review of affective awareness and Ecological Momentary Assessment
Beames, J. R., Kikas, K., & Werner‐Seidler, A. (2021) · BMC Psychology · doi:10.1186/s40359-021-00614-6 · Cited by 50 · see citing works
“A universal uncontrolled pre-post study with ages across the lifespan (13–69 years) evaluated the effectiveness of MoodPrism (a dedicated mood monitoring app) over 30-days [48] and assessed whether awareness mediated the effects on depression [33].”
Method
Patterns of User Engagement With the Mobile App, Manage My Pain: Results of a Data Mining Investigation
Rahman, Q. A., Janmohamed, T., Pirbaglou, M., Ritvo, P., Heffernan, J. M., Clarke, H. A., & Katz, J. D. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.7871 · Cited by 47 · see citing works
“This trend has been documented in the fields of medicine [2], nursing [3], psychology [4], kinesiology [5], nutrition [6], and for multiple health concerns and diseases [1].”
Background
Creating a Theoretically Grounded, Gamified Health App: Lessons From Developing the Cigbreak Smoking Cessation Mobile Phone Game
Edwards, E. A., Caton, H., Lumsden, J., Rivas, C. A., Steed, L., Pirunsarn, Y., Jumbe, S. E., Newby, C., Shenvi, A., Mazumdar, S., et al. (2018) · JMIR Serious Games · doi:10.2196/10252 · Cited by 44 · see citing works
Assessing the Quality of Mobile Phone Apps for Weight Management: User-Centered Study With Employees From a Lebanese University
Bardus, M., Ali, A., Demachkieh, F., & Hamadeh, G. (2019) · JMIR mHealth and uHealth · doi:10.2196/mhealth.9836 · Cited by 44 · see citing works
“…expert reviews of apps for a variety of behaviors such as drink driving [45], sustainable food consumption [46], medication adherence [47], mental health and mindfulness [48], quality of life [49], rheumatoid arthritis [50], weight loss related to smoking cessation [51], and weight management [30].”
Defining and Predicting Pain Volatility in Users of the Manage My Pain App: Analysis Using Data Mining and Machine Learning Methods
Rahman, Q. A., Janmohamed, T., Pirbaglou, M., Clarke, H. A., Ritvo, P., Heffernan, J. M., & Katz, J. D. (2018) · Journal of Medical Internet Research · doi:10.2196/12001 · Cited by 43 · see citing works
“This trend has been documented in medicine [2], nursing [3], psychology [4], kinesiology [5], and nutrition [6], and multiple health concerns and diseases are being addressed [1].”
Background
A Systematic and Comparative Review of Behavior Change Strategies in Stress Management Apps: Opportunities for Improvement
Alhasani, M., Mulchandani, D., Oyebode, O., Baghaei, N., & Orji, R. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.777567 · Cited by 42 · see citing works
“For example, data captured through self-monitoring can be used to track the risk of mild or chronic health conditions [including mental health issues, such as mood disorder (81), depression (82), stress (44), and others (83)] in real-time, and then trigger interventions to mitigate the risk.”
Background
Adapting Evidence-Based Treatments for Digital Technologies: a Critical Review of Functions, Tools, and the Use of Branded Solutions
Tuerk, P. W., Schaeffer, C. M., Mcguire, J. F., Larsen, M. A., Capobianco, N. B., & Piacentini, J. C. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1092-2 · Cited by 34 · see citing works
Socio-technical Imaginaries: Envisioning and Understanding AI Parenting Supports through Design Fiction
Petsolari, M., Ibrahim, S. B., & Slovak, P. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642619 · Cited by 35 · see citing works
“For instance, several self-tracking tools have been developed to self-monitor people’s stress [81], mood [165, 205] and sleep [79] which can help increase self-awareness and motivate health-related behavior change [186, 278, 280].”
Beyond text: ChatGPT as an emotional resilience support tool for Gen Z – A sequential explanatory design exploration
Kavitha, K., Joshith, V. P., & Sharma, S. (2024) · E-Learning and Digital Media · doi:10.1177/20427530241259099 · Cited by 32 · see citing works
“As we delve further into this digital landscape, reshaping our interactions with the world, technology has proven to present exceptional prospects for improving our emotional state (Biloria and Dritsa, 2020; Feijt et al., 2023; Kukkar et al., 2023; Rickard et al., 2016).”
Affective State Prediction from Smartphone Touch and Sensor Data in the Wild
Wampfler, R., Klingler, S., Solenthaler, B., Schinazi, V. R., Groß, M. H., & Holz, C. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501835 · Cited by 33 · see citing works
“The feedback from MoodPrism can help users identify patterns in their feelings, which in turn can improve wellbeing [50].”
Background
Examining the antecedents and health outcomes of health apps and wearables use: an integration of the technology acceptance model and communication inequality
Bao, H., & Lee, E. W. J. (2023) · Behaviour and Information Technology · doi:10.1080/0144929x.2023.2183062 · Cited by 33 · see citing works
“There is also progress in the use of health apps and wearables to promote di ﬀ erent dimensions of mental health, such as emotional, psychological, and social well-being (e.g. Doherty et al. 2018; Rickard et al. 2016; Wetzel et al. 2021).”
Background
“Listen to Music, Listen to Yourself”: Design of a Conversational Agent to Support Self-Awareness While Listening to Music
Cai, W., Jin, Y., Zhao, X., & Chen, L. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581427 · Cited by 31 · see citing works
“, which can help increase self-awareness and motivate health-related behavior change [46, 81, 85].”
Background
The Influence of Three Modes of Human Support on Attrition and Adherence to a Web- and Mobile App–Based Mental Health Promotion Intervention in a Nonclinical Cohort: Randomized Comparative Study
Renfrew, M. E., Morton, D. P., Morton, J. K., Hinze, J. S., Przybylko, G., & Craig, B. A. (2020) · Journal of Medical Internet Research · doi:10.2196/19945 · Cited by 31 · see citing works
“However, despite the many advantages of digital interventions, both high dropout attrition (ie, participants who drop out early or who are lost to follow-up) and nonusage attrition (ie, nonadherence) are persistent problems [5-7].”
Predicting determinants influencing user satisfaction with mental health app: An explainable machine learning approach based on unstructured data
Darko, A. P., Antwi, C. O., Adjei, K., Zhang, B., & Ren, J. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123647 · Cited by 27 · see citing works
Development and evaluation of a mobile-optimized daily self-rating depression screening app: A preliminary study
Chung, K., Jeon, M.-J., Park, J., Lee, S., Kim, C. O., & Park, J. Y. (2018) · PLoS ONE · doi:10.1371/journal.pone.0199118 · Cited by 26 · see citing works
AI for Analyzing Mental Health Disorders Among Social Media Users: Quarter-Century Narrative Review of Progress and Challenges
Owen, D. C., Lynham, A. J., Smart, S. E., Pardiñas, A. F., & Camacho-Collados, J. (2024) · Journal of Medical Internet Research · doi:10.2196/59225 · Cited by 29 · see citing works
“MoodPrism [134] would gauge the emotion of their status updates and the severity of depression (via Patient Health Questionnaire-9) over a 1-year period.”
Artificial intelligence to aid the detection of mood disorders
Cummins, N., Matcham, F., Klapper, J., & Schuller, B. W. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817133-2.00010-0 · Cited by 25 · see citing works
Mental Health Consequences of Adversity in Australia: National Bushfires Associated With Increased Depressive Symptoms, While COVID-19 Pandemic Associated With Increased Symptoms of Anxiety
Arjmand, H., Seabrook, E., Bakker, D., & Rickard, N. S. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.635158 · Cited by 23 · see citing works
“Participants Data were collected from 1,509 participants who downloaded the experience-sampling application (MoodPrism; Rickard et al., 2016).”
BackgroundSelf-citation
Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods
Kim, S., & Lee, K. (2021) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s339412 · Cited by 25 · see citing works
Screen Time and Productivity: An Extension of Goal- setting Theory to Explain Optimum Smartphone Use
Abhari, K., & Vaghefi, I. (2022) · AIS Transactions on Human-Computer Interaction · doi:10.17705/1thci.00169 · Cited by 26 · see citing works
Digital Phenotyping for Adolescent Mental Health: Feasibility Study Using Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data
Kadirvelu, B., Bel, T. B., Freccero, A., Simplicio, M. C. D., Nicholls, D., & Faisal, A. A. (2025) · Journal of Medical Internet Research · doi:10.2196/72501 · Cited by 30 · see citing works
Chatbot Positive Design to Facilitate Referencing Skills and Improve Digital Well-Being
Zaky, Y. A. M. (2023) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v17i09.38395 · Cited by 22 · see citing works
Feasibility of App-Based Postsurgical Assessment of Pain, Pain Impact, and Regional Anesthesia Effects: A Pilot Randomized Controlled Trial
Highland, K. B., Tran, J., Brandon-Edwards, H., Bedöcs, P., Suen, J., & Buckenmaier, C. C. (2018) · Pain Medicine · doi:10.1093/pm/pny288 · Cited by 21 · see citing works
Health Tracking via Mobile Apps for Depression Self-management: Qualitative Content Analysis of User Reviews
Polhemus, A. M., Simblett, S. K., Dawe‐Lane, E., Gilpin, G., Elliott, B., Jilka, S., Novák, J. M., Nica, R. I., Temesi, G., & Wykes, T. (2022) · JMIR Human Factors · doi:10.2196/40133 · Cited by 23 · see citing works
“However, a second complementary effect mechanism has been proposed: by identifying patterns in tracked data, the user learns their own health signals and triggers, enabling proactive health or situation management [4,5].”
Background
Effectiveness of using mental health mobile apps as digital antidepressants for reducing anxiety and depression: protocol for a multiple baseline across-individuals design
Marshall, J. M., Dunstan, D. A., & Bartik, W. (2020) · JMIR Research Protocols · doi:10.2196/17159 · Cited by 24 · see citing works
“Therefore, there may be different aspects of a mental health app that contribute to its effectiveness, such as gamification, aesthetics, usability/interface [76], and evidence-based framework.”
e-Therapy in primary care mental health
Orman, J., & O’Dea, B. (2018) · Australian Journal of General Practice · doi:10.31128/ajgp-11-17-4410 · Cited by 19 · see citing works
Description of the CARE4STROKE programme: A caregiver‐mediated exercises intervention with e‐health support for stroke patients
Vloothuis, J. D. M., de Bruin, J., Mulder, M., Nijland, R. H. M., Kwakkel, G., & van Wegen, E. E. H. (2018) · Physiotherapy Research International · doi:10.1002/pri.1719 · Cited by 19 · see citing works
Conversational User Interfaces on Mobile Devices: Survey
Jaber, R. N., & Mcmillan, D. (2020) · CIU · doi:10.1145/3405755.3406130 · Cited by 19 · see citing works
“report fear of the stigma associated with seeking professional services for sensitive mental health issues [79].”
Background
Effects of Child Sexual Abuse Prevention Education Program Using Hybrid Application (CSAPE-H) on Fifth-Grade Students in South Korea
Kang, S. R., Kim, S., & Kang, K. (2020) · The Journal of School Nursing · doi:10.1177/1059840520940377 · Cited by 18 · see citing works
“The rapid development of smartphone-based applications and advancements in mobile technology has made huge amounts of data accessible and offers an unprecedented opportunity to help people in their daily lives (Morrison et al., 2014; Rickard et al., 2016).”
Background
Tools of Engagement: Practical Considerations for Utilizing Technology-Based Tools in CBT Practice
Muroff, J. R., & Robinson, W. (2020) · Cognitive and Behavioral Practice · doi:10.1016/j.cbpra.2020.01.004 · Cited by 17 · see citing works
Smartphone-Based Platform for Affect Monitoring through Flexibly Managed Experience Sampling Methods
Bailón, C., Damas, M., Pomares, H., Sanabria, D., Perakakis, P., Goicoechea, C., & Baños, O. (2019) · Sensors · doi:10.3390/s19153430 · Cited by 17 · see citing works
“MoodPrism [20] uses mobile ESM questionnaires delivered at the smartphone in order to assess the emotional state.”
Method
Efficacy of Intellect’s self-guided anxiety and worry mobile health programme: A randomized controlled trial with an active control and a 2-week follow-up
Kosasih, F. R., Yee, V. T. S., Toh, S. H. Y., & Sündermann, O. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000095 · Cited by 15 · see citing works
“[37,66] based on the Mobile Application Rating Scale’s (MARS) items and themes, such as engagement, functionality, aesthetics, and information [67].”
Method
Artificial Intelligence in Gamification to Promote Mental Health among University Students: A Scoping Review
Palacios, V. A. F., Jaramillo-Galarza, K. A., Calle, J. V. Q., & Orozco-Cantos, L. (2023) · Salud Ciencia y Tecnología · doi:10.56294/saludcyt2023639 · Cited by 15 · see citing works
"I Don't Know Why I Should Use This App": Holistic Analysis on User Engagement Challenges in Mobile Mental Health
Jin, S., Kim, B., & Han, K. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713732 · Cited by 15 · see citing works
“[13, 23, 165, 170] App usage : Metrics on how and when the app is utilized, including frequency, duration, and feature engagement patterns.”
Young Adults’ Perceptions of 2 Publicly Available Digital Resources for Self-injury: Qualitative Study of a Peer Support App and Web-Based Factsheets
Kruzan, K. P., Whitlock, J. L., Chapman, J., Bhandari, A., & Bazarova, N. N. (2023) · JMIR Formative Research · doi:10.2196/41546 · Cited by 14 · see citing works
“Predictive ads are not doctors”: Mental health tracking and technology companies
Costello, K. L., & Floegel, D. (2020) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.250 · Cited by 13 · see citing works
Digital Self-Monitoring, Bodied Realities: Re-Casting App-Based Technologies in First Episode Psychosis
Berkhout, S., & Zaheer, J. (2021) · Catalyst Feminism Theory Technoscience · doi:10.28968/cftt.v7i1.34101 · Cited by 12 · see citing works
“(Rickard et al. 2016) These viewpoints seem to suggest that EMA/ESM methods, especially when used in young adult populations, may help sensitize young people to their emotional and cognitive states and the need for support, while generating information that is useful to clinicians without solely…”
BackgroundInfluential
Mindcraft, a Mobile Mental Health Monitoring Platform for Children and Young People: Development and Acceptability Pilot Study
Kadirvelu, B., Bel, T. B., Wu, X., Burmester, V., Ananth, S., Branco, B. C. C. C., Serrano, B. G., Gledhill, J. A., Simplicio, M. C. D., Nicholls, D., et al. (2023) · JMIR Formative Research · doi:10.2196/44877 · Cited by 11 · see citing works
Understanding the Social Determinants of Mental Health of Undergraduate Students in Bangladesh: Interview Study
Bhattacharjee, A., Haque, S. M. T., Hady, M. A., Alam, S. M. R., Rabbi, M., Kabir, M. A., & Ahmed, S. I. (2021) · JMIR Formative Research · doi:10.2196/27114 · Cited by 11 · see citing works
Experiences of Using Mental Health Apps to Support Psychological Health and Wellbeing
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Authors: Rickard, N., Arjmand, H.-A., Bakker, D., & Seabrook, E.
Affiliations: The University of Melbourne (AU)
Monash University (AU)
Corresponding author: Rickard, Nikki
ORCID: Rickard, Nikki
Arjmand, Hussain-Abdulah
Bakker, David
Seabrook, Elizabeth
Venue: JMIR Mental Health
Publisher: JMIR Publications Inc.
Published: 2016-11-23
Volume: 3
Issue: 4
Pages: e49
ISSN: 2368-7959 (ISSN-L)
DOI: 10.2196/mental.6202
PMC: PMC5143469
OpenAlex: W2551128326
Cited by: 118
References: 77
Citations by year: 2026: 4 · 2025: 16 · 2024: 15 · 2023: 12 · 2022: 17 · 2021: 13 · 2020: 14 · 2019: 7 · 2018: 16 · 2017: 4
FWCI: 13.87
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
Locations: JMIR Mental Health (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Figshare (submittedVersion, cc-by)
Swinburne figshare (Swinburne University of Technology) (submittedVersion, cc-by)
Swinburne figshare (Swinburne University of Technology) (cc-by)
Topics: Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
Keywords: mobile phone app, emotional well-being, self-monitoring, mental health apps, ecological momentary assessment, self-report questionnaire, app usability, mental health self-management

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Development of a Mobile Phone App to Support Self-Monitoring of Emotional Well-Being: A Mental Health Digital Innovation

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