Dynamic fluctuations of emotional states in adolescents with delayed sleep phase—A longitudinal network modeling approach

Abstract

Background: Very late sleep rhythms are risks for social adjustment problems in adolescence.Using ecological momentary assessment data, we quantified and visualized temporal and contemporaneous within-persons dynamical relations of sleepiness and emotions in adolescents with and without late sleep rhythms. Methods:We analyzed a temporal network via multilevel vector autoregression (mlVAR) modeling and a contemporaneous network through the partial associations between the residuals of temporal and the between-subject multilevel models. We tested whether these networks were different between those with a late circadian rhythm [concurrent delayed sleep phase (DSP) N = 172, 37% boys, 63% girls] and those without (N = 143, 22% boys, 78% girls). Results:In adolescents without DSP, the temporal networks showed continuity only for low mood from the previous to the following time point. In adolescents with DSP, there were more predictable patterns of emotions. Feelings of depression led to a decrease of positive emotions and increase of irritation and anxiety. The contemporaneous networks showed clusters of positive and negative emotions in both groups and sleepiness decreased the experience of positive emotions concurrently.Limitations: DSP in our current study was based only on one out of three diagnostic criteria of the full disorder (DSM-5) and it was assessed only once. Conclusions:These findings indicate that the dynamic organization of emotions and sleepiness is different in adolescents with and without DSP. DSP adolescents have more predictable and maladaptive emotional patterns during the day. Results provide new insight about why individuals with DSP are at a heightened risk for decreased emotional adjustment.

Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence

“…Studies used a range of technologies to collect active/subjective, passive/objective, and mixed (i.e., active/subjective and passive/objective) data. Studies employing passively/objectively collected data often produced predictive models with high accuracy in the detection of depression severity involving significant predictors such as geospatial movement, sleep duration, delayed sleep phase, circadian rhythm, audio features, language, accelerometer oscillation, and light exposure during bedtime [ 12 , 29 , 49 , 50 , 52 , 56 ]. Considering the type of technology, reviewed studies employed mobile technology (handheld IT devices such as smartphones, palmtops, tablets, laptops, etc.…”

“…Passive data collection via wearable technology, GPS, accelerometer/actigraph, Wi-Fi location, smartphone usage, and typing metadata Lower levels of physical activity were associated with increased levels of negative affect, depressive feelings, and anhedonia (e.g., reduced ability to enjoy pleasurable activities) Two studies employing non-clinical samples and GPS-derived data found no significant associations between these variables (Chow et al [ 30 ]; Melcher et al) [ 128 ] Social Functioning ( n = 21) Active data collection via self-reported questionnaires, and passive data collection via smartphone embedded audio features, and phone call/SMS frequency Increased levels of depression severity associated with preference for being alone, increased social distance, reduced closeness with other individuals, increased interpersonal stress, reduced speech duration, and reduced phone call and SMS frequency Depression severity showed an association with reliance on social expression such that higher reliance on social expression of feelings (i.e., anger) predicted a decrease in depression severity over time (Chue et al) [ 31 ] Moukaddam et al [ 136 ] used a clinical sample and found no correlations between depression levels and social interaction (SMS and phone call length and frequency) Sleep Quality ( n = 16) Assessment of sleep quality involved self-reported questionnaires, accelerometer inferences (e.g., total steps during bedtime), GPS-derived data, actigraphy, smartphone embedded light sensors (e.g., increased light exposure during bedtime), smartphone use (screen on/off), sound features (e.g., ambient silence), and heart rate (assessed via wearable technology) Most studies detected associations in variability of sleep quality and depression severity. Specifically, studies observed depression scores to be positively correlated with delayed sleep phase, sleep disturbance during weeknights, poor sleep quality, sleep variability, insomnia, and increased exposure to light during bedtime (Ben-Zeev et al [ 12 ]; Di Matteo et al, [ 49 ]; Difrancesco et al, [ 52 ]; Elovainio et al, [ 56 ]; Hung et al, [ 88 ]; Kaufmann et al, [ 100 ]; Kim et al, [ 105 ]; Melcher et al,) [ 128 ] Two studies (1 clinical and 1 non-clinical sample) did not find significant correlations between self-reports of sleep duration and depression (Difrancesco et al, [ 52 ]; Hamilton et al) [ 76 ]. Additionally, 2 studies using non-clinical samples found no significant associations in depression levels and sleep quality a...…”

“…Interestingly, studies made emphasis on positive associations between the variability of one’s sleep, affect and other psycho-motor aspects of depression and self-reported symptom severity [ 13 , 49 , 52 , 56 ]. Thus, variability in depressive signs, such as depressive affect, lack of concentration, lack of motivation/pleasure for social engagement, or suicidal ideation/intention, appear to be accurately captured via digital technology.…”

“… 5 Studies assessing sleep quality include [ 6 , 13 , 21 , 29 , 49 , 52 , 56 , 76 , 88 , 100 , 105 , 128 , 131 , 135 , 138 , 144 ]. …”

Auditing the research practices and statistical analyses of group-level temporal network approach to psychological constructs: A systematic scoping review

“…Both of these examples also illustrate the usefulness of temporal network analyses for clinical applications, by suggesting key variables or symptom inter-relations to target in interventions. Although most of the temporal network articles so far have focused on clinical populations or research questions, others have investigated questions involving general emotion dynamics (Bringmann et al, 2013;Elovainio et al, 2020;Martín-Brufau et al, 2020;Meng et al, 2020) or even personality (Lazarus et al, 2020;Pavani et al, 2017).…”