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BackgroundAffective 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.ObjectivePrevious 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.MethodsLongitudinal 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.ResultsRF 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.ConclusionsGeneric 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.

To determine whether the association between perceived social support or strain in close relationships and sleep outcomes varies by gender. Participants were selected from the Biomarker projects of either the MIDUS II or MIDUS Refresher study if they were in a married-or married-like relationship and shared a bed with their partner (N = 989). A subsample also participated in a seven-day sleep study (n = 282). Perceived social support and strain from partner, family, and friends were examined by self-report questionnaires. We used the Pittsburgh Sleep Quality Index, sleep daily diary, and actigraphy to measure both subjective and objective sleep. Social support and strain were both associated with sleep outcomes. Specifically, higher social support was associated with fewer daily reports of light sleep and feeling more rested in the morning, while higher social strain was associated with higher clinical sleep disturbance. For women, but not men, social support was significantly associated with lower daily sleep disturbance while perceived social strain was significantly associated with higher daily sleep disturbance, lighter sleep, feeling less rested in the morning, lower sleep efficiency, and longer sleep onset latency. Mainly among women, social support and strain are associated with an important transdiagnostic health outcome-sleep-which may have implications for a wide range of health disparities. Interpersonal stressors may increase health risks differently for women compared to men and one mechanism that may link social relationships to long-term health outcomes is sleep.

BackgroundSleep 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.ObjectiveIn 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.MethodsCollege 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.ResultsParticipants 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).ConclusionsFluctuating 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.

PurposeThe COVID-19 pandemic and efforts to slow the spread of disease have particularly affected the lives of adolescents. Many studies have recently identified the risks to adolescent mental health posed by the COVID-19 pandemic, yet few have identified the markers of resilience to the events and concerns associated with the pandemic's lived experience. This study examined the moderating role of psychosocial resources in the association between the tangible and emotional experiences of the COVID-19 pandemic and symptoms of common psychiatric problems during adolescence (depression, anxiety, proactive and reactive aggression, and sleep problems).MethodsParticipants were adolescents in the United States who were oversampled for early life adversity before the COVID-19 pandemic. The psychosocial resources assessed were humor styles, emotion regulation, social support, optimism, and purpose in life, which have previously been identified as protective in the acute aftermath of stressful events.ResultsGreater COVID-19 impact was associated with more anxiety, depressive symptoms, sleep disturbance, and proactive aggression. COVID-19 impact and psychiatric symptoms were unrelated among youth reporting high self-enhancing humor and cognitive reappraisal.ConclusionsAdolescents high in humor and cognitive reappraisal may be protected against the mental health correlates of the COVID-19 pandemic and other prolonged stressors. Importantly, these factors are known to be modifiable through behavioral interventions. Attention to their effectiveness in prevention and intervention studies is needed as the pandemic continues to exert its impact on individuals and society.

PurposeThe COVID-19 pandemic and efforts to slow the spread of disease have particularly affected the lives of adolescents. Many studies have recently identified the risks to adolescent mental health posed by the COVID-19 pandemic, yet few have identified the markers of resilience to the events and concerns associated with the pandemic's lived experience. This study examined the moderating role of psychosocial resources in the association between the tangible and emotional experiences of the COVID-19 pandemic and symptoms of common psychiatric problems during adolescence (depression, anxiety, proactive and reactive aggression, and sleep problems).MethodsParticipants were adolescents in the United States who were oversampled for early life adversity before the COVID-19 pandemic. The psychosocial resources assessed were humor styles, emotion regulation, social support, optimism, and purpose in life, which have previously been identified as protective in the acute aftermath of stressful events.ResultsGreater COVID-19 impact was associated with more anxiety, depressive symptoms, sleep disturbance, and proactive aggression. COVID-19 impact and psychiatric symptoms were unrelated among youth reporting high self-enhancing humor and cognitive reappraisal.ConclusionsAdolescents high in humor and cognitive reappraisal may be protected against the mental health correlates of the COVID-19 pandemic and other prolonged stressors. Importantly, these factors are known to be modifiable through behavioral interventions. Attention to their effectiveness in prevention and intervention studies is needed as the pandemic continues to exert its impact on individuals and society.

BACKGROUND:One previous study suggested that the presence of a manic episode before bipolar depression is related to worse response to antidepressants. METHOD:To examine this effect in a larger sample, we used data from the large, multi-site STEP-BD study. We hypothesized that among persons treated with antidepressants for bipolar depression, manic or mixed episodes before depression onset (as compared to euthymia) would predict lower rate of recovery, more sustained depressive symptoms and higher rate of switching into mania/hypomania after antidepressant treatment of bipolar depression. 320 participants were available for analyses (140 male) diagnosed with bipolar I, bipolar II, cyclothymia, bipolar disorder not otherwise specified, or schizoaffective disorder bipolar subtype. Patients were randomly assigned to 3 treatment randomization strata (placebo, bupropion, and paroxetine) as adjuncts to mood stabilizers. RESULTS:Analyses were conducted to examine the effect of episode status before the depressive episode on the degree of change in depressive symptoms at 3 and 6 months, the likelihood of depression recovery and the likelihood of anti-depressant induced switching. Presence of a manic episode before depression in patients with bipolar disorder did not significantly predict response to antidepressants. LIMITATIONS:The study was limited by a high rate of attrition, and consideration of only two antidepressant medications. CONCLUSIONS:Our findings are in agreement with other past studies suggesting that mania and depression may operate separately for those with bipolar disorder, with differential predictors of the onset and offset of mania versus depression. Future directions may consider vulnerability for these episodes separately.