Journal of Medical Internet Research

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Effectiveness of One Videoconference-Based Exposure and Response Prevention Session at Home in Adjunction to Inpatient Treatment in Persons With Obsessive-Compulsive Disorder: Nonrandomized Study

After undergoing videoconference-based ERP, patients perceived depth (ie, potency and value), smoothness of the session (ie, comfort and relaxation), and mood after the session (ie, positivity and arousal) as medium to high. Patients who received videoconference-based ERP rated working alliance (ie, agreement on therapeutic tasks and goals as well as therapeutic bond) with their therapist as high.

Ulrich Voderholzer, Adrian Meule, Stefan Koch, Simone Pfeuffer, Anna-Lena Netter, Dirk Lehr, Eva Maria Zisler

JMIR Ment Health 2024;11:e52790

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Bayesian Networks for Prescreening in Depression: Algorithm Development and Validation

Reference 31: Bayesian networks for mood prediction using unobtrusive ecological momentary assessmentsmood disorders mood disorder moodDepression and Mood Disorders; Suicide Prevention

Eduardo Maekawa, Eoin Martino Grua, Carina Akemi Nakamura, Marcia Scazufca, Ricardo Araya, Tim Peters, Pepijn van de Ven

JMIR Ment Health 2024;11:e52045

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Identification of Predictors of Mood Disorder Misdiagnosis and Subsequent Help-Seeking Behavior in Individuals With Depressive Symptoms: Gradient-Boosted Tree Machine Learning Approach

Mood disorders are debilitating psychiatric conditions that negatively affect a person’s emotional state. They result in impaired ability to function and complete daily tasks, and an increased risk of self-harm and suicide [1]. Two of the most common mood disorders are major depressive disorder (MDD) and bipolar disorder (BD), which affect approximately 3.4% and 0.5% of the global population, respectively, at any given time [2].

Jiri Benacek, Nimotalai Lawal, Tommy Ong, Jakub Tomasik, Nayra A Martin-Key, Erin L Funnell, Giles Barton-Owen, Tony Olmert, Dan Cowell, Sabine Bahn

JMIR Ment Health 2024;11:e50738

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Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments: Hierarchical Bayesian Approach

We found it useful to distinguish between mood detection, ie, predicting the mood based on data from the same day, and mood forecasting, ie, predicting the mood one or more days ahead based on historical data. Smartphone-based mood detection is well studied but remains a difficult problem. Several papers have examined the use of passive smartphone data, such as location, communication logs, and device usage, to detect or classify daily self-reported mood labels [15-21].

Jonas Busk, Maria Faurholt-Jepsen, Mads Frost, Jakob E Bardram, Lars Vedel Kessing, Ole Winther

JMIR Mhealth Uhealth 2020;8(4):e15028

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Mental Health Self-Tracking Preferences of Young Adults With Depression and Anxiety Not Engaged in Treatment: Qualitative Analysis

For example, a recent study of adult users of mood-tracking apps found that one reason people use mood-tracking is to build insight about the connections among events, contextual circumstances, and subsequent mood [11]. When self-tracking experiences are misaligned to a person’s goals, are inflexible, or are aversive or tracked data do not provide a person with actionable information, people who use digital mental health self-tracking tools may stop engaging in self-tracking [12-14].

Miranda L Beltzer, Jonah Meyerhoff, Sarah A Popowski, David C Mohr, Rachel Kornfield

JMIR Form Res 2023;7:e48152

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Immersive Virtual Reality Exergames to Promote the Well-being of Community-Dwelling Older Adults: Protocol for a Mixed Methods Pilot Study

Incorporating physical activity into daily life can improve older adults’ motor function, mood, cognition, quality of life, and independence [10-12]. An extensive body of literature has highlighted the positive effects of physical activity on older adults’ executive functions, including processing speed, attention, inhibition, and working memory [13,14].

Samira Mehrabi, John E Muñoz, Aysha Basharat, Jennifer Boger, Shi Cao, Michael Barnett-Cowan, Laura E Middleton

JMIR Res Protoc 2022;11(6):e32955

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Improving Mood Through Community Connection and Resources Using an Interactive Digital Platform: Development and Usability Study

Second, given that participants received differential text-based follow-up depending on their initial mood rating with the aim to improve their self-reported mood rating by providing continuous support, we hypothesized that participants beginning the intervention with low self-reported mood ratings would experience mood improvement through the 4-week intervention, while those with initial high self-reported mood ratings would retain such a rating.

Robin Ortiz, Lauren Southwick, Rachelle Schneider, Elissa V Klinger, Arthur Pelullo, Sharath Chandra Guntuku, Raina M Merchant, Anish K Agarwal

JMIR Ment Health 2021;8(2):e25834

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Mood and Stress Evaluation of Adult Patients With Moyamoya Disease in Korea: Ecological Momentary Assessment Method Using a Mobile Phone App

Recently, various tools using mobile phone technology have been developed and used to measure mood and stress in diverse patient populations [26]; thus, this study utilized the EMA approach for moyamoya patients’ condition. This study aimed to identify predicting factors associated with momentary mood and stress at both the within-person and between-person levels and to examine individual fluctuation of mood over time in the short term using an EMA method combined with a mobile phone app.

Yong Sook Yang, Gi Wook Ryu, Chang Gi Park, Insun Yeom, Kyu Won Shim, Mona Choi

JMIR Mhealth Uhealth 2020;8(5):e17034

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Assessing Real-Time Moderation for Developing Adaptive Mobile Health Interventions for Medical Interns: Micro-Randomized Trial

Our primary aim focused on discovering how an intern’s previous mood moderates the effect of notifications in general. Specifically, we examined the following: Is the effect of a week of notifications (of any category) on the average daily mood moderated by the previous week’s mood? Here, Outcome=mood; Treatment=any (mood, activity, or sleep); and Moderator=mood.

Timothy NeCamp, Srijan Sen, Elena Frank, Maureen A Walton, Edward L Ionides, Yu Fang, Ambuj Tewari, Zhenke Wu

J Med Internet Res 2020;22(3):e15033

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Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study

Another mobile phone–based study that lasted 12 weeks (N=73) identified mobile phone features that predicted clinically diagnosed depressed mood with 0.74 area under the curve; these features including the total count of outgoing calls, the count of unique numbers texted, absolute distance traveled, dynamic variation of the voice, speaking rate, and voice quality [10]. The combination of wearable sensor and mobile phone data has also been used to study self-reported stress in daily life [11-14].

Akane Sano, Sara Taylor, Andrew W McHill, Andrew JK Phillips, Laura K Barger, Elizabeth Klerman, Rosalind Picard

J Med Internet Res 2018;20(6):e210

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