Objective monitoring of loneliness levels using smart devices: A multi-device approach for mental health applications.

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.

Resting heart rate variability and emotion regulation difficulties: Comparing Asian Americans and European Americans.

Asian Americans and European Americans differ in emotion regulation (ER), particularly regarding strategies utilized to adaptively engage in ER. Resting heart rate variability (HRV), a biomarker of ER ability, is suggested to differ between Asian Americans and European Americans, but evidence for such differences has been inconsistent. Yet, research has not considered how Asian Americans and European Americans might differ in the well-established link between resting HRV and ER difficulties, which might lend a better understanding of such inconsistencies. In 374 college-aged individuals (66 Asian Americans; 311 European Americans; 190 women; mean age = 19.3 years [Min. 18, Max 38]), we examined if ethnicity moderated the link between resting HRV and self-reported ER difficulties. Resting HRV was obtained during a 5-min resting-baseline period, and ER difficulties were assessed using the Difficulties in ER Scale, which contained six facets of ER difficulties. Adjusting for gender and body mass index, moderation analyses showed a stronger association between resting HRV and ER difficulties in Asian Americans compared to European Americans. When examining facets of ER, ethnicity moderated only the link between resting HRV and difficulties in accessing ER strategies when facing negative emotions. At lower levels of HRV, Asian Americans reported greater difficulties in ER relative to European Americans. This effect diminished and trended in the opposite direction among those with higher HRV. In sum, these results provide novel evidence that higher resting HRV might be particularly important for adaptive ER among Asian Americans - a marginalized ethnic group - in the U.S.

Objective Prediction of Next-Day's Affect Using Multimodal Physiological and Behavioral Data: Algorithm Development and Validation Study.

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.

Personal mental health navigator: Harnessing the power of data, personal models, and health cybernetics to promote psychological well-being.

Current digital mental healthcare solutions conventionally take on a reactive approach, requiring individuals to self-monitor and document existing symptoms. These solutions are unable to provide comprehensive, wrap-around, customized treatments that capture an individual's holistic mental health model as it unfolds over time. Recognizing that each individual requires personally tailored mental health treatment, we introduce the notion of Personalized Mental Health Navigation (MHN): a cybernetic goal-based system that deploys a continuous loop of monitoring, estimation, and guidance to steer the individual towards mental flourishing. We present the core components of MHN that are premised on the importance of addressing an individual's personal mental health state. Moreover, we provide an overview of the existing physical health navigation systems and highlight the requirements and challenges of deploying the navigational approach to the mental health domain.

Sleep Patterns and Affect Dynamics Among College Students During the COVID-19 Pandemic: Intensive Longitudinal Study.

BACKGROUND: Sleep disturbance is a transdiagnostic risk factor that is so prevalent among young adults that it is considered a public health epidemic, which has been exacerbated by the COVID-19 pandemic. Sleep may contribute to mental health via affect dynamics. Prior literature on the contribution of sleep to affect is largely based on correlational studies or experiments that do not generalize to the daily lives of young adults. Furthermore, the literature examining the associations between sleep variability and affect dynamics remains scant. OBJECTIVE: In an ecologically valid context, using an intensive longitudinal design, we aimed to assess the daily and long-term associations between sleep patterns and affect dynamics among young adults during the COVID-19 pandemic. METHODS: College student participants (N=20; female: 13/20, 65%) wore an Oura ring (Oura Health Ltd) continuously for 3 months to measure sleep patterns, such as average and variability in total sleep time (TST), wake after sleep onset (WASO), sleep efficiency, and sleep onset latency (SOL), resulting in 1173 unique observations. We administered a daily ecological momentary assessment by using a mobile health app to evaluate positive affect (PA), negative affect (NA), and COVID-19 worry once per day. RESULTS: Participants with a higher sleep onset latency (b=-1.09, SE 0.36; P=.006) and TST (b=-0.15, SE 0.05; P=.008) on the prior day had lower PA on the next day. Further, higher average TST across the 3-month period predicted lower average PA (b=-0.36, SE 0.12; P=.009). TST variability predicted higher affect variability across all affect domains. Specifically, higher variability in TST was associated higher PA variability (b=0.09, SE 0.03; P=.007), higher negative affect variability (b=0.12, SE 0.05; P=.03), and higher COVID-19 worry variability (b=0.16, SE 0.07; P=.04). CONCLUSIONS: Fluctuating sleep patterns are associated with affect dynamics at the daily and long-term scales. Low PA and affect variability may be potential pathways through which sleep has implications for mental health.

Shared and unique affective abnormalities in schizotypy dimensions.

Attention to affect is theoretically a precursor to one's ideal affect (i.e., preference for feeling low- and high-arousal positive and negative affect) and emotion regulation (ER). In schizotypy, there have been mixed findings regarding abnormalities in attention to affect. At the same time, little is known about ideal affect in schizotypy or whether differences in ideal affect or ER difficulties in schizotypy are driven by attention to affect. Thus, this study aimed to identify shared and unique abnormalities in attention to affect, ideal affect, and ER difficulties in schizotypy, and to test whether attention to affect underlies differences in ideal affect and ER difficulties. Using groups of individuals with either extreme levels of social anhedonia (SocAnh; n = 181), extreme levels of perceptual aberrations/magical ideation (PerMag; n = 105), or individuals low on both (i.e., controls; n = 531), we tested group differences in attention to affect, ideal affect, and ER difficulties. Our findings suggest both shared and unique affective abnormalities; compared to controls, the SocAnh group paid the least attention to positive affect. Only PerMag had heightened attention to negative affect compared to controls. Additionally, we found unique abnormalities relating to ideal affect but mostly shared difficulties in ER in schizotypy. Abnormalities in ideal affect and ER remain largely consistent after accounting for attention to affect for PerMag, suggesting that attention to affect is not the primary mechanism driving these abnormalities. However, we found evidence that attention to affect underlies some SocAnh-control group differences in ideal affect and ER difficulties. Our work helps to clarify prior work and contributes to the understanding of shared and unique affective abnormalities in schizotypy. Future research may consider longitudinal approaches to test causal mechanisms of affective abnormalities in schizotypy.

Positivity resonance in long-term married couples: Multimodal characteristics and consequences for health and longevity.

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).

Understanding People's Use of and Perspectives on Mood-Tracking Apps: Interview Study.

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.

Using Multimodal Assessments to Capture Personalized Contexts of College Student Well-being in 2020: Case Study.

BACKGROUND: The year 2020 has been challenging for many, particularly for young adults who have been adversely affected by the COVID-19 pandemic. Emerging adulthood is a developmental phase with significant changes in the patterns of daily living; it is a risky phase for the onset of major mental illness. College students during the pandemic face significant risk, potentially losing several protective factors (eg, housing, routine, social support, job, and financial security) that are stabilizing for mental health and physical well-being. Individualized multiple assessments of mental health, referred to as multimodal personal chronicles, present an opportunity to examine indicators of health in an ongoing and personalized way using mobile sensing devices and wearable internet of things. OBJECTIVE: To assess the feasibility and provide an in-depth examination of the impact of the COVID-19 pandemic on college students through multimodal personal chronicles, we present a case study of an individual monitored using a longitudinal subjective and objective assessment approach over a 9-month period throughout 2020, spanning the prepandemic period of January through September. METHODS: The individual, referred to as Lee, completed psychological assessments measuring depression, anxiety, and loneliness across 4 time points in January, April, June, and September. We used the data emerging from the multimodal personal chronicles (ie, heart rate, sleep, physical activity, affect, behaviors) in relation to psychological assessments to understand patterns that help to explicate changes in the individual's psychological well-being across the pandemic. RESULTS: Over the course of the pandemic, Lee's depression severity was highest in April, shortly after shelter-in-place orders were mandated. His depression severity remained mildly severe throughout the rest of the months. Associations in positive and negative affect, physiology, sleep, and physical activity patterns varied across time periods. Lee's positive affect and negative affect were positively correlated in April (r=0.53, P=.04) whereas they were negatively correlated in September (r=-0.57, P=.03). Only in the month of January was sleep negatively associated with negative affect (r=-0.58, P=.03) and diurnal beats per minute (r=-0.54, P=.04), and then positively associated with heart rate variability (resting root mean square of successive differences between normal heartbeats) (r=0.54, P=.04). When looking at his available contextual data, Lee noted certain situations as supportive coping factors and other situations as potential stressors. CONCLUSIONS: We observed more pandemic concerns in April and noticed other contextual events relating to this individual's well-being, reflecting how college students continue to experience life events during the pandemic. The rich monitoring data alongside contextual data may be beneficial for clinicians to understand client experiences and offer personalized treatment plans. We discuss benefits as well as future directions of this system, and the conclusions we can draw regarding the links between the COVID-19 pandemic and college student mental health.

Assessing the Mental Health of Emerging Adults Through a Mental Health App: Protocol for a Prospective Pilot Study.

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.

Higher maternal reflective functioning is associated with toddlers' adaptive emotion regulation.

Parental reflective functioning (RF), the ability to consider the child's behavior as a function of mental states (cognitions, emotions), is theorized to promote emotion regulation in children via its positive impact on parenting sensitivity. Using a sample of mothers and toddlers (N = 151 dyads; 41% Latinx; 54% girls; MAge  = 21 months; SDAge  =  2.5 months), we measured mothers' self-reported RF (high RF = low certainty/high interest-curiosity/low prementalizing), toddlers' distress during a standardized challenging behavioral task (toy removal), and three methods of children's coping with distress. Then, we tested whether RF moderated the association between children's observed distress and coping during the task (mother-directed adaptive coping, task-directed adaptive coping, maladaptive aggression) as an index of emotion regulation. Although RF was not associated with toddlers' distress, indices of RF moderated the associations between distress and coping. As maternal RF increased, the positive association between toddler distress and mother-oriented behavior increased, whereas the association between toddler distress and child aggression decreased. Findings were present only for certainty of mental states, whereas no effects were present for prementalizing or interest/curiosity. We discuss these findings in terms of their contributions to theory regarding parent-child relationships, maternal RF, and child emotion regulation.

La función de reflexión de los padres (RF), la habilidad de considerar el comportamiento del niño como una función de los estados mentales, promueve, según la teoría, la regulación de la emoción en los niños por medio de su impacto en la sensibilidad de los progenitores. Con un grupo muestra de madres y niños pequeñitos (N = 151 díadas; 41% Latinx; 54% niñas; Edad M = 21 meses; Edad SD = 2.5 meses), medimos la auto-reportada RF de las madres (RF alta = baja certeza/alto interés-curiosidad/baja pre-mentalización), la angustia de los niños pequeñitos durante una tarea estándar de reto de comportamiento (apartar un juguete) y tres métodos de cómo los niños se las arreglan con la angustia. Entonces, pusimos a prueba si la RF moderaba la asociación entre la observada angustia de los niños pequeñitos y el arreglárselas con la tarea (el arreglárselas con adaptarse a la directriz de la madre, el arreglárselas con adaptarse a la directriz de la tarea, agresión mal adaptada) como índice de regulación de la emoción. A pesar de que no se asoció la RF con la angustia de los niños pequeñitos, los índices de RF moderaron las asociaciones entre la angustia y el proceso de arreglárselas. A medida que la RF aumentó, también aumentó la positiva asociación entre la angustia del niño pequeñito con ambas, orientado hacia la madre, mientras que la asociación entre la angustia del niño pequeñito y la agresividad del niño disminuyó. Los resultados estuvieron presentes sólo en cuanto a la certeza de los estados mentales, mientras que ningún efecto se presentó en el caso de la pre-mentalización o el interés/curiosidad. Discutimos estos resultados en términos de sus contribuciones a la teoría acerca de las relaciones progenitor-niño, RF materna y la regulación de la emoción del niño.

Parental reflective functioning (RF), the ability to consider the child's behavior as a function of mental states, is theorized to promote emotion regulation in children via its positive impact on parenting sensitivity. Using a sample of mothers and toddlers (N = 151 dyads; 41% Latinx; 54% girls; MAge = 21 months; SDAge = 2.5 months), we measured mothers' self-reported RF (high RF = low certainty/high interest-curiosity/low prementalizing), toddlers' distress during a standardized challenging behavioral task (toy removal), and three methods of children's coping with distress. Then, we tested whether RF moderated the association between children's observed distress and coping during the task (mother-directed adaptive coping, task-directed adaptive coping, maladaptive aggression) as an index of emotion regulation. Although RF was not associated with toddlers' distress, indices of RF moderated the associations between distress and coping. As RF increased, the positive association between toddler distress with both mother-oriented increased, whereas the association between toddler distress and child aggression decreased. Findings were present only for certainty of mental states, whereas no effects were present for prementalizing or interest/curiosity. We discuss these findings in terms of their contributions to theory regarding parent-child relationships, maternal RF, and child emotion regulation.