Background: Supporting 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. Objective: To 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. Methods: We 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. Results: Users 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. Conclusions: Although 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.

The Positivity Resonance Theory of coexperienced positive affect describes moments of interpersonal connection characterized by shared positive affect, caring nonverbal synchrony, and biological synchrony. The construct validity of positivity resonance and its longitudinal associations with health have not been tested. The current longitudinal study examined whether positivity resonance in conflict interactions between 154 married couples predicts health trajectories over 13 years and longevity over 30 years. We used couples' continuous ratings of affect during the interactions to capture coexperienced positive affect and continuous physiological responses to capture biological synchrony between spouses. Video recordings were behaviorally coded for coexpressed positive affect, synchronous nonverbal affiliation cues (SNAC), and behavioral indicators of positivity resonance (BIPR). To evaluate construct validity, we conducted a confirmatory factor analysis to test a latent factor of positivity resonance encompassing coexperienced positive affect, coexpressed positive affect, physiological linkage of interbeat heart intervals, SNAC, and BIPR. The model showed excellent fit. To evaluate associations with health and longevity, we used dyadic latent growth curve modeling and Cox proportional hazards modeling, respectively, and found that greater latent positivity resonance predicted less steep declines in health and increased longevity. Associations were robust when accounting for initial health symptoms, sociodemographic characteristics, health-related behaviors, and individually experienced positive affect. We repeated health and longevity analyses, replacing latent positivity resonance with BIPR, and found consistent results. Findings validate positivity resonance as a multimodal construct, support the utility of the BIPR measure, and provide initial evidence for the characterization of positivity resonance as a positive health behavior. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Background: Affective states are important aspects of healthy functioning; as such, monitoring and understanding affect is necessary for the assessment and treatment of mood-based disorders. Recent advancements in wearable technologies have increased the use of such tools in detecting and accurately estimating mental states (eg, affect, mood, and stress), offering comprehensive and continuous monitoring of individuals over time. Objective: Previous attempts to model an individual’s mental state relied on subjective measurements or the inclusion of only a few objective monitoring modalities (eg, smartphones). This study aims to investigate the capacity of monitoring affect using fully objective measurements. We conducted a comparatively long-term (12-month) study with a holistic sampling of participants’ moods, including 20 affective states. Methods: Longitudinal physiological data (eg, sleep and heart rate), as well as daily assessments of affect, were collected using 3 modalities (ie, smartphone, watch, and ring) from 20 college students over a year. We examined the difference between the distributions of data collected from each modality along with the differences between their rates of missingness. Out of the 20 participants, 7 provided us with 200 or more days’ worth of data, and we used this for our predictive modeling setup. Distributions of positive affect (PA) and negative affect (NA) among the 7 selected participants were observed. For predictive modeling, we assessed the performance of different machine learning models, including random forests (RFs), support vector machines (SVMs), multilayer perceptron (MLP), and K-nearest neighbor (KNN). We also investigated the capability of each modality in predicting mood and the most important features of PA and NA RF models. Results: RF was the best-performing model in our analysis and performed mood and stress (nervousness) prediction with ~81% and ~72% accuracy, respectively. PA models resulted in better performance compared to NA. The order of the most important modalities in predicting PA and NA was the smart ring, phone, and watch, respectively. SHAP (Shapley Additive Explanations) analysis showed that sleep and activity-related features were the most impactful in predicting PA and NA. Conclusions: Generic machine learning–based affect prediction models, trained with population data, outperform existing methods, which use the individual’s historical information. Our findings indicated that our mood prediction method outperformed the existing methods. Additionally, we found that sleep and activity level were the most important features for predicting next-day PA and NA, respectively.

Loneliness is linked to wide ranging physical and mental health problems, including increased rates of mortality. Understanding how loneliness manifests is important for targeted public health treatment and intervention. With advances in mobile sending and wearable technologies, it is possible to collect data on human phenomena in a continuous and uninterrupted way. In doing so, such approaches can be used to monitor physiological and behavioral aspects relevant to an individual’s loneliness. In this study, we proposed a method for continuous detection of loneliness using fully objective data from smart devices and passive mobile sensing. We also investigated whether physiological and behavioral features differed in their importance in predicting loneliness across individuals. Finally, we examined how informative data from each device is for loneliness detection tasks. We assessed subjective feelings of loneliness while monitoring behavioral and physiological patterns in 30 college students over a 2-month period. We used smartphones to monitor behavioral patterns (e.g., location changes, type of notifications, in-coming and out-going calls/text messages) and smart watches and rings to monitor physiology and sleep patterns (e.g., heart-rate, heart-rate variability, sleep duration). Participants reported their loneliness feeling multiple times a day through a questionnaire app on their phone. Using the data collected from their devices, we trained a random forest machine learning based model to detect loneliness levels. We found support for loneliness prediction using a multi-device and fully-objective approach. Furthermore, behavioral data collected by smartphones generally were the most important features across all participants. The study provides promising results for using objective data to monitor mental health indicators, which could provide a continuous and uninterrupted source of information in mental healthcare applications.

Background: Depression is the top contributor to global disability. Early detection of depression and depressive symptoms enables timely intervention and reduces their physical and social consequences. Prevalence estimates of depression approach 30% among college students. Passive, device-based sensing further enables detection of depressive symptoms at a low burden to the individual. Objective: We leveraged an ensemble machine learning method (light gradient boosting machine) to detect depressive symptoms entirely through passive sensing. Methods: A diverse sample of undergraduate students (N=28; mean age 19.96, SD 1.23 y; 15/28, 54% women; 13/28, 46% Latine; 10/28, 36% Asian; 4/28, 14% non-Latine White; 11/28, 4% other) participated in an intensive longitudinal study. Participants wore 2 devices (an Oura ring for sleep and physiology data, and a Samsung smartwatch for physiology and movement data) and installed the AWARE software on their mobile devices, which collects passive sensing data such as screen time. Participants were derived from a randomized controlled trial of a positive psychology mobile health intervention. They completed a self-report measure of depressive symptoms administered weekly over a 19- to 22-week period. Results: The light gradient boosting machine model achieved an F1-score of 0.744 and a Cohen κ coefficient of 0.474, indicating moderate agreement between the predicted labels and the ground truth. The most predictive features of depressive symptoms were sleep quality and missed mobile interactions. Conclusions: Findings suggest that data collected from passive sensing devices may provide real-time, low-cost insight into the detection of depressive symptoms in college students and may present an opportunity for future prevention and perhaps intervention.

Background: Individuals can experience different manifestations of the same psychological disorder. This underscores the need for a personalized model approach in the study of psychopathology. Emerging adulthood is a developmental phase wherein individuals are especially vulnerable to psychopathology. Given their exposure to repeated stressors and disruptions in routine, the emerging adult population is worthy of investigation. Objective: In our prospective study, we aim to conduct multimodal assessments to determine the feasibility of an individualized approach for understanding the contextual factors of changes in daily affect, sleep, physiology, and activity. In other words, we aim to use event mining to predict changes in mental health. Methods: We expect to have a final sample size of 20 participants. Recruited participants will be monitored for a period of time (ie, between 3 and 12 months). Participants will download the Personicle app on their smartphone to track their activities (eg, home events and cycling). They will also be given wearable sensor devices (ie, devices that monitor sleep, physiology, and physical activity), which are to be worn continuously. Participants will be asked to report on their daily moods and provide open-ended text responses on a weekly basis. Participants will be given a battery of questionnaires every 3 months. Results: Our study has been approved by an institutional review board. The study is currently in the data collection phase. Due to the COVID-19 pandemic, the study was adjusted to allow for remote data collection and COVID-19–related stress assessments. Conclusions: Our study will help advance research on individualized approaches to understanding health and well-being through multimodal systems. Our study will also demonstrate the benefit of using individualized approaches to study interrelations among stress, social relationships, technology, and mental health. International Registered Report Identifier (IRRID): DERR1-10.2196/25775