Twitter as a means to study temporal behaviour

Current Considerations in the Diagnosis and Treatment of Circadian Rhythm Sleep-Wake Disorders in Children

Circadian Rhythm Sleep-Wake Disorders (CRSWDs) are important sleep disorders whose unifying feature is a mismatch between the preferred or required times for sleep and wakefulness and the endogenous circadian drives for these. Their etiology, presentation, and treatment can be different in pediatric patients as compared to adults. Evaluation of these disorders must be performed while viewed through the lens of a patient's comorbid conditions. Newer methods of assessment promise to provide greater diagnostic clarity and critical insights into how circadian physiology affects overall health and disease states. Effective clinical management of CRSWDs is multimodal, requiring an integrated approach across disciplines. Therapeutic success depends upon appropriately timed nonpharmacologic and pharmacologic interventions. A better understanding of the genetic predispositions for and causes of CRSWDs has led to novel clinical opportunities for diagnosis and improved therapeutics.

Smartphone keyboard interaction monitoring as an unobtrusive method to approximate rest-activity patterns: Inter-individual and metric-specific variations (Preprint)

BACKGROUND

Sleep is an important determinant of individuals' health and behavior during the wake phase. Novel research methods for field assessments are required to enable the monitoring of sleep over a prolonged period and across a large number of people. The ubiquity of smartphones offers new avenues for detecting rest-activity patterns in everyday life in a noninvasive an inexpensive manner and on a large scale. Recent studies provided evidence for the potential of smartphone interaction monitoring as a novel tracking method to approximate rest-activity patterns based on the timing of smartphone activity and inactivity throughout the 24-hour day. These findings require further replication and more detailed insights into interindividual variations in the associations and deviations with commonly used metrics for monitoring rest-activity patterns in everyday life.

OBJECTIVE

This study aimed to replicate and expand on earlier findings regarding the associations and deviations between smartphone keyboard-derived and self-reported estimates of the timing of the onset of the rest and active periods and the duration of the rest period. Moreover, we aimed to quantify interindividual variations in the associations and time differences between the 2 assessment modalities and to investigate to what extent general sleep quality, chronotype, and trait self-control moderate these associations and deviations.

METHODS

Students were recruited to participate in a 7-day experience sampling study with parallel smartphone keyboard interaction monitoring. Multilevel modeling was used to analyze the data.

RESULTS

In total, 157 students participated in the study, with an overall response rate of 88.9% for the diaries. The results revealed moderate to strong relationships between the keyboard-derived and self-reported estimates, with stronger associations for the timing-related estimates (β ranging from .61 to .78) than for the duration-related estimates (β=.51 and β=.52). The relational strength between the time-related estimates was lower, but did not substantially differ for the duration-related estimates, among students experiencing more disturbances in their general sleep quality. Time differences between the keyboard-derived and self-reported estimates were, on average, small (<0.5 hours); however, large discrepancies were also registered for quite some nights. The time differences between the 2 assessment modalities were larger for both timing-related and rest duration-related estimates among students who reported more disturbances in their general sleep quality. Chronotype and trait self-control did not significantly moderate the associations and deviations between the 2 assessment modalities.

CONCLUSIONS

We replicated the positive potential of smartphone keyboard interaction monitoring for estimating rest-activity patterns among populations of regular smartphone users. Chronotype and trait self-control did not significantly influence the metrics' accuracy, whereas general sleep quality did: the behavioral proxies obtained from smartphone interactions appeared to be less powerful among students who experienced lower general sleep quality. The generalization and underlying process of these findings require further investigation.

Stylistic variation on the Donald Trump Twitter account: A linguistic analysis of tweets posted between 2009 and 2018

Twitter was an integral part of Donald Trump’s communication platform during his 2016 campaign. Although its topical content has been examined by researchers and the media, we know relatively little about the style of the language used on the account or how this style changed over time. In this study, we present the first detailed description of stylistic variation on the Trump Twitter account based on a multivariate analysis of grammatical co-occurrence patterns in tweets posted between 2009 and 2018. We identify four general patterns of stylistic variation, which we interpret as representing the degree of conversational, campaigning, engaged, and advisory discourse. We then track how the use of these four styles changed over time, focusing on the period around the campaign, showing that the style of tweets shifts systematically depending on the communicative goals of Trump and his team. Based on these results, we propose a series of hypotheses about how the Trump campaign used social media during the 2016 elections.

Capturing sleep-wake cycles by using day-to-day smartphone touchscreen interactions

Body movements drop with sleep, and this behavioural signature is widely exploited to infer sleep duration. However, a reduction in body movements may also occur in periods of intense cognitive activity, and the ubiquitous use of smartphones may capture these wakeful periods otherwise hidden in the standard measures of sleep. Here, we continuously captured the gross body movements using standard wrist-worn accelerometers to quantify sleep (actigraphy) and logged the timing of the day-to-day touchscreen events (‘tappigraphy’). Using these measures, we addressed how the gross body movements overlap with the cognitively engaging digital behaviour (from n = 79 individuals, accumulating ~1400 nights). We find that smartphone use was distributed across a broad spectrum of physical activity levels, but consistently peaked at rest. We estimated the putative sleep onset and wake-up times from the actigraphy data to find that these times were well correlated to the estimates from tappigraphy (R² = 0.9 for sleep-onset time and wake-up time). However, actigraphy overestimated sleep as virtually all of the users used their phones during the putative sleep period. Interestingly, the probability of touches remained greater than zero for ~2 h after the putative sleep onset, and ~2 h before the putative wake-up time. Our findings suggest that touchscreen interactions are widely integrated into modern sleeping habits—surrounding both sleep onset and waking-up periods—yielding a new approach to measuring sleep. Smartphone interactions can be leveraged to update the behavioural signatures of sleep with these peculiarities of modern digital behaviour.

Is Santa Claus an evening owl?

Case studies have a long tradition in biomedical research. Here, I will analyze an important person from a chronobiological aspect, Santa Claus. Although it might be dangerous for researchers to publish analyses about Santa Claus, because, given the possibility that he may be unsatisfied with my analysis, this could lead to an embargo of parcels for my whole family. Nevertheless, some intrepid researches already diagnosed Santa Claus, ending up in some important research results. A search in Web of Science revealed only N = 224 publications about Santa Claus, which is really low. The question, I am addressing here is, whether Santa Claus is an "evening owl" or a night chronotype. In this report, I summarize the facts known about Santa Claus on the one side and about evening chronotypes on the other and sum up these results in a "vote counting" manner as pros and cons. The results are summarized in Table 1. In total, there is 12 times support for the hypothesis, 2 times against and 1 equivocal (Table 1). This result is significant (binomial test, p = 0.013). Therefore, the conclusion is "Santa Claus is an evening type".

Geographically Resolved Rhythms in Twitter Use Reveal Social Pressures on Daily Activity Patterns

Daily rhythms in human physiology and behavior are driven by the interplay of circadian rhythms, environmental cycles, and social schedules. Much research has focused on the mechanism and function of circadian rhythms in constant conditions or in idealized light-dark environments. There have been comparatively few studies into how social pressures, such as work and school schedules, affect human activity rhythms day to day and season to season. To address this issue, we analyzed activity on Twitter in >1,500 US counties throughout the 2012-2013 calendar years in 15-min intervals using geographically tagged tweets representing ≈0.1% of the total population each day. We find that sustained periods of low Twitter activity are correlated with sufficient sleep as measured by conventional surveys. We show that this nighttime lull in Twitter activity is shifted to later times on weekends relative to weekdays, a phenomenon we term "Twitter social jet lag." The magnitude of this social jet lag varies seasonally and geographically-with the West Coast experiencing less Twitter social jet lag compared to the Central and Eastern US-and is correlated with average commuting schedules and disease risk factors such as obesity. Most counties experience the largest amount of Twitter social jet lag in February and the lowest in June or July. We present evidence that these shifts in weekday activity coincide with relaxed social pressures due to local K-12 school holidays and that the direct seasonal effect of altered day length is comparatively weaker.

Rhythms of the Genome: Circadian Dynamics from Chromatin Topology, Tissue-Specific Gene Expression, to Behavior

Circadian rhythms in physiology and behavior evolved to resonate with daily cycles in the external environment. In mammals, organs orchestrate temporal physiology over the 24-h day, which requires extensive gene expression rhythms targeted to the right tissue. Although a core set of gene products oscillates across virtually all cell types, gene expression profiling across tissues over the 24-h day showed that rhythmic gene expression programs are tissue specific. We highlight recent progress in uncovering how the circadian clock interweaves with tissue-specific gene regulatory networks involving functions such as xenobiotic metabolism, glucose homeostasis, and sleep. This progress hinges on not only comprehensive experimental approaches but also computational methods for multivariate analysis of periodic functional genomics data. We emphasize dynamic chromatin interactions as a novel regulatory layer underlying circadian gene transcription, core clock functions, and ultimately behavior. Finally, we discuss perspectives on extending the knowledge of the circadian clock in animals to human chronobiology.