ArticleJMIR mental health2021
Understanding People's Use of and Perspectives on Mood-Tracking Apps: Interview Study.
Article in JMIR mental health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06455397 (Implementing BEAM), which is not on this map. Cited by 34 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Implementing BEAM: An mHealth Tool to Prevent Mental Health Problems and Improve Developmental Outcomes in Young Children
Who cites it
34 citing papers in PubMed, 3 syntheses or guidelines pooled it, 90 citations in OpenAlex.
- Perinatal Mental Health Detection and Prediction Using Mobile Sensing Data: Systematic Review.JMIR mental health · 2026Pooled it
- Mood Monitoring, Mood Tracking, and Ambulatory Assessment Interventions in Depression and Bipolar Disorder: Systematic Review and Meta-Analysis of Randomized Controlled Trials.JMIR mental health · 2026Pooled it
- Evaluation of Digital Mental Health Technologies in the United States: Systematic Literature Review and Framework Synthesis.JMIR mental health · 2024Pooled it
- Randomized controlled trial of smartphone-based interpretation bias intervention for anxiety and depression.Journal of consulting and clinical psychology · 2026Trial
- Effectiveness of a Mindfulness-Based Mobile Intervention for Improving Perinatal Mental Health and Reducing Depression During Pregnancy: Randomized Controlled Trial.Journal of medical Internet research · 2025Trial
- Effects of a digital self-efficacy training in stressed university students: A randomized controlled trial.PloS one · 2024Trial
- Preliminary Support for a Technology-Enhanced Suicide Prevention Intervention for College Students: Mixed Methods Preimplementation Study.JMIR human factors · 2026Article
- Understanding University Students' Experiences of Engaging With AI and Apps for Their Mental Health and Well-Being: Qualitative Study.Journal of medical Internet research · 2026Article
- Minimally Guided App-Based Intervention for Nontreatment-Seeking Psychologically Distressed Adults: Protocol for a Randomized Controlled Trial.JMIR research protocols · 2026Article
- Advancing family wellbeing through a Massive Online Open Intervention: the LightBEAM program protocol for randomized waitlist control trial {1a}.BMC psychology · 2026Article
- Application of user profiling in mental health management: a scoping review.BMC psychology · 2025Article
- The user experience of ambulatory assessment and mood monitoring in depression: a systematic review & meta-synthesis.NPJ digital medicine · 2025Article
- Effect of Self-Quantification on Caregiver Burden and Depression Among Family Caregivers: A SWING-Japan Study.Psychogeriatrics : the official journal of the Japanese Psychogeriatric Society · 2025Article
- The algorithmic self: how AI is reshaping human identity, introspection, and agency.Frontiers in psychology · 2025Article
- Exploring user experience: A qualitative analysis of the use of a physical activity support app for people with heart failure.PloS one · 2025Article
- Mental Health Monitoring for Young People Through Mood Apps: Protocol for a Scoping Review and Systematic Search in App Stores.JMIR research protocols · 2024Article
- Facilitators of and Barriers to Teachers' Engagement With Consumer Technologies for Stress Management: Qualitative Study.Journal of medical Internet research · 2024Article
- Intention to Use a Mental Health App for Menopause: Health Belief Model Approach.JMIR formative research · 2024Article
- Recruitment and retention of adolescents for an ecological momentary assessment measurement burst mental health study: The MHIM engagement strategy.Health expectations : an international journal of public participation in health care and health policy · 2024Article
- Approaches to Tailoring Between-Session Mental Health Therapy Activities.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSupporting mental health and wellness is of increasing interest due to a growing recognition of the prevalence and burden of mental health issues. Mood is a central aspect of mental health, and several technologies, especially mobile apps, have helped people track and understand it. However, despite formative work on and dissemination of mood-tracking apps, it is not well understood how mood-tracking apps used in real-world contexts might benefit people and what people hope to gain from them.
objectiveTo address this gap, the purpose of this study was to understand motivations for and experiences in using mood-tracking apps from people who used them in real-world contexts.
methodsWe interviewed 22 participants who had used mood-tracking apps using a semistructured interview and card sorting task. The interview focused on their experiences using a mood-tracking app. We then conducted a card sorting task using screenshots of various data entry and data review features from mood-tracking apps. We used thematic analysis to identify themes around why people use mood-tracking apps, what they found useful about them, and where people felt these apps fell short.
resultsUsers of mood-tracking apps were primarily motivated by negative life events or shifts in their own mental health that prompted them to engage in tracking and improve their situation. In general, participants felt that using a mood-tracking app facilitated self-awareness and helped them to look back on a previous emotion or mood experience to understand what was happening. Interestingly, some users reported less inclination to document their negative mood states and preferred to document their positive moods. There was a range of preferences for personalization and simplicity of tracking. Overall, users also liked features in which their previous tracked emotions and moods were visualized in figures or calendar form to understand trends. One gap in available mood-tracking apps was the lack of app-facilitated recommendations or suggestions for how to interpret their own data or improve their mood.
conclusionsAlthough people find various features of mood-tracking apps helpful, the way people use mood-tracking apps, such as avoiding entering negative moods, tracking infrequently, or wanting support to understand or change their moods, demonstrate opportunities for improvement. Understanding why and how people are using current technologies can provide insights to guide future designs and implementations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.