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
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.
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 (Ōura 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.
Introduction:First impressions can influence interpersonal relationships for extended periods, with negative first impressions leading to more negative judgments and behaviors between individuals months after their initial meeting. Although common factors such as therapeutic alliance (TA) are well studied, less is known of the potential influence of a therapist's first impression of their client's motivation on TA and drinking outcomes. Based on data from a prospective study of the perceptions of the TA among clients receiving cognitive-behavioral treatment (CBT), this study examined how therapists' first impressions may moderate the relationship between client-rated TA and drinking outcomes during treatment.Methods: One hundred fifty-four adults participated in a 12-week course of CBT and completed measures of TA and drinking behaviors following each treatment session.Additionally, therapists completed a measure of their first impression of their client's motivation for treatment following the first session.Results: Time-lagged multilevel modeling revealed a significant within-person TA by therapists' first impression interaction that predicted percent days abstinent (PDA). Specifically, among participants rated as lower on first impressions of treatment motivation, higher within-person TA predicted greater PDA in the interval prior to the next treatment session. Within-person working alliance was not associated with PDA among individuals rated higher on first impressions of treatment motivation who demonstrated higher PDA throughout treatment. Furthermore, significant betweenperson TA by first impressions interactions were found for both PDA and drinks per drinking day (DDD), such that among individuals with lower treatment motivation, TA positively predicted PDA and negatively predicted DDD. Conclusion:Although therapists' first impressions of a client's treatment motivation are positively associated with treatment outcomes, clients' perception of the TA may mitigate the impact of poor first impressions. These findings highlight the need for additional nuanced examinations of the relationship between TA and treatment outcomes, emphasizing the contextual factors that influence this relationship.
In this work, a manganese oxide electrode, containing carbon nanofiber composites (MnO2/CNF), has been made through electrospinning, oxidization, and partial carbonization high-temperature treatment. Scanning electron microscopy (SEM) was used to observe the morphology of the nanofiber and analyze the composition of the fiber. The fiber size range and element distribution were determined. The oxide nanoparticles were modeled as electrorheological suspensions in the polyacrylonitrile polymer solution during electrospinning. The dielectrophoretic behavior of the particles subjected to non-uniform electric fields were analyzed and the motion of the oxide particles under the actions from fluctuating electric fields was investigated to explain the sporadic distribution of nanoparticles within the composite nanofibers. A photoactive anode was made from the composite nanofiber and the decomposition of spilled oil was performed under sunlight illumination. It was observed that the manganese oxide containing carbon nanofiber composite electrode can generate electricity and clean the spilled oil under sunlight. Both energy conversion and environment cleaning concepts were demonstrated.
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