Daily Rhythms in Mobile Telephone Communication

Circadian rhythm analysis using wearable-based accelerometry as a digital biomarker of aging and healthspan

Recognizing the pivotal role of circadian rhythm in the human aging process and its scalability through wearables, we introduce CosinorAge, a digital biomarker of aging developed from wearable-derived circadian rhythmicity from 80,000 midlife and older adults in the UK and US. A one-year increase in CosinorAge corresponded to 8-12% higher all-cause and cause-specific mortality risks and 3-14% increased prospective incidences of age-related diseases. CosinorAge also captured a non-linear decline in resilience and physical functioning, evidenced by an 8-33% reduction in self-rated health and a 3-23% decline in health-related quality of life score, adjusting for covariates and multiple testing. The associations were robust in sensitivity analyses and external validation using an independent cohort from a disparate geographical region using a different wearable device. Moreover, we illustrated a heterogeneous impact of circadian parameters associated with biological aging, with young (<45 years) and fast agers experiencing a substantially delayed acrophase with a 25-minute difference in peak timing compared to slow agers, diminishing to a 7-minute difference in older adults (>65 years). We demonstrated a significant enhancement in the predictive performance when integrating circadian rhythmicity in the estimation of biological aging over physical activity. Our findings underscore CosinorAge's potential as a scalable, economic, and digital solution for promoting healthy longevity, elucidating the critical and multifaceted circadian rhythmicity in aging processes. Consequently, our research contributes to advancing preventive measures in digital medicine.

Examining Human-Smartphone Interaction as a Proxy for Circadian Rhythm in Patients With Insomnia: Cross-Sectional Study

BACKGROUND

The sleep and circadian rhythm patterns associated with smartphone use, which are influenced by mental activities, might be closely linked to sleep quality and depressive symptoms, similar to the conventional actigraphy-based assessments of physical activity.

OBJECTIVE

The primary objective of this study was to develop app-defined circadian rhythm and sleep indicators and compare them with actigraphy-derived measures. Additionally, we aimed to explore the clinical correlations of these indicators in individuals with insomnia and healthy controls.

METHODS

The mobile app "Rhythm" was developed to record smartphone use time stamps and calculate circadian rhythms in 33 patients with insomnia and 33 age- and gender-matched healthy controls, totaling 2097 person-days. Simultaneously, we used standard actigraphy to quantify participants' sleep-wake cycles. Sleep indicators included sleep onset, wake time (WT), wake after sleep onset (WASO), and the number of awakenings (NAWK). Circadian rhythm metrics quantified the relative amplitude, interdaily stability, and intradaily variability based on either smartphone use or physical activity data.

RESULTS

Comparisons between app-defined and actigraphy-defined sleep onsets, WTs, total sleep times, and NAWK did not reveal any significant differences (all P>.05). Both app-defined and actigraphy-defined sleep indicators successfully captured clinical features of insomnia, indicating prolonged WASO, increased NAWK, and delayed sleep onset and WT in patients with insomnia compared with healthy controls. The Pittsburgh Sleep Quality Index scores were positively correlated with WASO and NAWK, regardless of whether they were measured by the app or actigraphy. Depressive symptom scores were positively correlated with app-defined intradaily variability (β=9.786, SD 3.756; P=.01) and negatively correlated with actigraphy-based relative amplitude (β=-21.693, SD 8.214; P=.01), indicating disrupted circadian rhythmicity in individuals with depression. However, depressive symptom scores were negatively correlated with actigraphy-based intradaily variability (β=-7.877, SD 3.110; P=.01) and not significantly correlated with app-defined relative amplitude (β=-3.859, SD 12.352; P=.76).

CONCLUSIONS

This study highlights the potential of smartphone-derived sleep and circadian rhythms as digital biomarkers, complementing standard actigraphy indicators. Although significant correlations with clinical manifestations of insomnia were observed, limitations in the evidence and the need for further research on predictive utility should be considered. Nonetheless, smartphone data hold promise for enhancing sleep monitoring and mental health assessments in digital health research.

The impact of the COVID-19 pandemic on daily rhythms

OBJECTIVE

The COVID-19 pandemic has significantly impacted daily activity rhythms and life routines with people adjusting to new work schedules, exercise routines, and other everyday life activities. This study examines temporal changes in daily activity rhythms and routines during the COVID-19 pandemic, emphasizing disproportionate changes among working adult subgroups.

MATERIALS AND METHODS

In June 2021, we conducted a year-long study to collect high-resolution fitness tracker data and questionnaire responses from 128 working adults. Questionnaire data were analyzed to explore changes in exercise and work routines during the pandemic. We build temporal distributions of daily step counts to quantify their daily movement rhythms, then measure their consistency over time using the inverse of the Earth mover's distance. Linear mixed-effects models were employed to compare movement rhythm variability among subpopulations.

RESULTS

During the pandemic, our cohort exhibited a shift in exercise routines, with a decrease in nonwalking physical exercises, while walking remained unchanged. Migrants and those living alone had less consistent daily movement rhythms compared to others. Those preferring on-site work maintained more consistent daily movement rhythms. Men and migrants returned to work more quickly after pandemic restriction measures were eased.

DISCUSSION

Our findings quantitatively show the pandemic's unequal impact on different subpopulations. This study opens new research avenues to explore why certain groups return to on-site work, exercise levels, or daily movement rhythms more slowly compared to prepandemic times.

CONCLUSIONS

Considering the pandemic's unequal impact on subpopulations, organizations and policymakers should address diverse needs and offer tailored support during future crises.

The rhythms of transient relationships: allocating time between weekdays and weekends

A fundamental question of any new relationship is, will it last? Transient relationships, recently defined by the authors, are an ideal type of social tie to explore this question: these relationships are characterized by distinguishable starting and ending temporal points, linking the question of tie longevity to relationship finite lifetime. In this study, we use mobile phone data sets from the UK and Italy to analyse the weekly allocation of time invested in maintaining transient relationships. We find that more relationships are created during weekdays, with a greater proportion of them receiving more contact during these days of the week in the long term. The smaller group of relationships that receive more phone calls during the weekend tend to remain active for more time. We uncover a sorting process by which some ties are moved from weekdays to weekends and vice versa, mostly in the first half of the relationship. This process also carries more information about the ultimate lifetime of a tie than the part of the week when the relationship started, which suggests an early evaluation period that leads to a decision on how to allocate time to different types of transient ties.

Hearing Aid Benefit and Satisfaction Results from the MarkeTrak 2022 Survey: Importance of Features and Hearing Care Professionals

The hearing aid market is rapidly evolving with advancements in features and potential changes in service delivery models, including the new over-the-counter device category. Data from the MarkeTrak 2022 survey indicate most hearing aid owners report regular quality-of-life benefits from hearing aids, even more than in previous surveys. The increased likelihood of hearing aid benefits might be attributable to modern hearing aid features advancements, such as wireless connectivity and rechargeable batteries. Hearing aid satisfaction rates have been relatively stable over the years, indicating that more than 80% of hearing aid owners are satisfied with their devices. Hearing aid satisfaction rates do not appreciably vary by fitting channel; hearing aid owners fitted in person, fitted remotely, or self-fit are similarly likely to report high satisfaction with their device. However, only respondents in the in-person channel gave establishment ratings (reflecting their willingness to recommend) that resulted and reflected a positive net promoter score. Given the potential for net promoter scores to be related to brand growth and customer loyalty, this finding has implications for the development of over-the-counter hearing aid service-delivery models. Additional work is warranted to explore the factors that negatively affect hearing aid owners' satisfaction with the companies delivering limited services.

Quantifying daily rhythms with non-negative matrix factorization applied to mobile phone data

Human activities follow daily, weekly, and seasonal rhythms. The emergence of these rhythms is related to physiology and natural cycles as well as social constructs. The human body and its biological functions undergo near 24-h rhythms (circadian rhythms). While their frequencies are similar across people, their phases differ. In the chronobiology literature, people are categorized into morning-type, evening-type, and intermediate-type groups called chronotypes based on their tendency to sleep at different times of day. Typically, this typology builds on carefully designed questionnaires or manually crafted features of time series data on people’s activity. Here, we introduce a method where time-stamped data from smartphones are decomposed into components using non-negative matrix factorization. The method does not require any predetermined assumptions about the typical times of sleep or activity: the results are fully context-dependent and determined by the most prominent features of the activity data. We demonstrate our method by applying it to a dataset of mobile phone screen usage logs of 400 university students, collected over a year. We find four emergent temporal components: morning activity, night activity, evening activity and activity at noon. Individual behavior can be reduced to weights on these four components. We do not observe any clear categories of people based on the weights, but individuals are rather placed on a continuous spectrum according to the timings of their phone activities. High weights for the morning and night components strongly correlate with sleep and wake-up times. Our work points towards a data-driven way of characterizing people based on their full daily and weekly rhythms of activity and behavior, instead of only focusing on the timing of their sleeping periods.

Awakening City: Traces of the Circadian Rhythm within the Mobile Phone Network Data

In this study, call detail records (CDR), covering Budapest, Hungary, are processed to analyze the circadian rhythm of the subscribers. An indicator, called wake-up time, is introduced to describe the behavior of a group of subscribers. It is defined as the time when the mobile phone activity of a group rises in the morning. Its counterpart is the time when the activity falls in the evening. Inhabitant and area-based aggregation are also presented. The former is to consider the people who live in an area, while the latter uses the transit activity in an area to describe the behavior of a part of the city. The opening hours of the malls and the nightlife of the party district are used to demonstrate this application as real-life examples. The proposed approach is also used to estimate the working hours of the workplaces. The findings are in a good agreement with the practice in Hungary, and also support the workplace detection method. A negative correlation is found between the wake-up time and mobility indicators (entropy, radius of gyration): on workdays, people wake up earlier and travel more, while on holidays, it is quite the contrary. The wake-up time is evaluated in different socioeconomic classes, using housing prices and mobile phones prices, as well. It is found that lower socioeconomic groups tend to wake up earlier.

Behavioral Consistency in the Digital Age

Efforts to infer personality from digital footprints have focused on behavioral stability at the trait level without considering situational dependency. We repeated a classic study of intraindividual consistency with secondary data (five data sets) containing 28,692 days of smartphone usage from 780 people. Using per-app measures of pickup frequency and usage duration, we found that profiles of daily smartphone usage were significantly more consistent when taken from the same user than from different users (d > 1.46). Random-forest models trained on 6 days of behavior identified each of the 780 users in test data with 35.8% accuracy for pickup frequency and 38.5% accuracy for duration frequency. This increased to 73.5% and 75.3%, respectively, when success was taken as the user appearing in the top 10 predictions (i.e., top 1%). Thus, situation-dependent stability in behavior is present in our digital lives, and its uniqueness provides both opportunities and risks to privacy.

A Multi-Layer Semantic Approach for Digital Forensics Automation for Online Social Networks

Currently, law enforcement and legal consultants are heavily utilizing social media platforms to easily access data associated with the preparators of illegitimate events. However, accessing this publicly available information for legal use is technically challenging and legally intricate due to heterogeneous and unstructured data and privacy laws, thus generating massive workloads of cognitively demanding cases for investigators. Therefore, it is critical to develop solutions and tools that can assist investigators in their work and decision making. Automating digital forensics is not exclusively a technical problem; the technical issues are always coupled with privacy and legal matters. Here, we introduce a multi-layer automation approach that addresses the automation issues from collection to evidence analysis in online social network forensics. Finally, we propose a set of analysis operators based on domain correlations. These operators can be embedded in software tools to help the investigators draw realistic conclusions. These operators are implemented using Twitter ontology and tested through a case study. This study describes a proof-of-concept approach for forensic automation on online social networks.

Rapid indicators of deprivation using grocery shopping data

Measuring socio-economic indicators is a crucial task for policy makers who need to develop and implement policies aimed at reducing inequalities and improving the quality of life. However, traditionally this is a time-consuming and expensive task, which therefore cannot be carried out with high temporal frequency. Here, we investigate whether secondary data generated from our grocery shopping habits can be used to generate rapid estimates of deprivation in the city of London in the UK. We show the existence of a relationship between our grocery shopping data and the deprivation of different areas in London, and how we can use grocery shopping data to generate quick estimates of deprivation, albeit with some limitations. Crucially, our estimates can be generated very rapidly with the data used in our analysis, thus opening up the opportunity of having early access to estimates of deprivation. Our findings provide further evidence that new data streams contain accurate information about our collective behaviour and the current state of our society.

Behavioral gender differences are reinforced during the COVID-19 crisis

Behavioral gender differences have been found for a wide range of human activities including the way people communicate, move, provision themselves, or organize leisure activities. Using mobile phone data from 1.2 million devices in Austria (15% of the population) across the first phase of the COVID-19 crisis, we quantify gender-specific patterns of communication intensity, mobility, and circadian rhythms. We show the resilience of behavioral patterns with respect to the shock imposed by a strict nation-wide lock-down that Austria experienced in the beginning of the crisis with severe implications on public and private life. We find drastic differences in gender-specific responses during the different phases of the pandemic. After the lock-down gender differences in mobility and communication patterns increased massively, while circadian rhythms tended to synchronize. In particular, women had fewer but longer phone calls than men during the lock-down. Mobility declined massively for both genders, however, women tended to restrict their movement stronger than men. Women showed a stronger tendency to avoid shopping centers and more men frequented recreational areas. After the lock-down, males returned back to normal quicker than women; young age-cohorts return much quicker. Differences are driven by the young and adolescent population. An age stratification highlights the role of retirement on behavioral differences. We find that the length of a day of men and women is reduced by 1 h. We interpret and discuss these findings as signals for underlying social, biological and psychological gender differences when coping with crisis and taking risks.

Effects of text supplementation on speech intelligibility for listeners with normal and impaired hearing: a systematic review with implications for telecommunication

OBJECTIVE

Telecommunication can be difficult in the presence of noise or hearing loss. The purpose of this study was to systematically review evidence regarding the effects of text supplementation (e.g. captions, subtitles) of auditory or auditory-visual signals on speech intelligibility for listeners with normal or impaired hearing.

DESIGN

Three databases were searched. Articles were evaluated for inclusion based on the Population Intervention Comparison Outcome framework. The Effective Public Health Practice Project instrument was used to evaluate the quality of the identified articles.

STUDY SAMPLE

After duplicates were removed, the titles and abstracts of 2019 articles were screened. Forty-six full texts were reviewed; ten met inclusion criteria.

RESULTS

The quality of all ten articles was moderate or strong. The articles demonstrated that text added to auditory (or auditory-visual) signals improved speech intelligibility and that the benefits were largest when auditory signal integrity was low, accuracy of the text was high, and the auditory signal and text were synchronous. Age and hearing loss did not affect benefits from the addition of text.

CONCLUSIONS

Although only based on ten studies, these data support the use of text as a supplement during telecommunication, such as while watching television or during telehealth appointments.

Impact of environmental changes on the dynamics of temporal networks

Dynamics of complex social systems has often been described in the framework of temporal networks, where links are considered to exist only at the moment of interaction between nodes. Such interaction patterns are not only driven by internal interaction mechanisms, but also affected by environmental changes. To investigate the impact of the environmental changes on the dynamics of temporal networks, we analyze several face-to-face interaction datasets using the multiscale entropy (MSE) method to find that the observed temporal correlations can be categorized according to the environmental similarity of datasets such as classes and break times in schools. By devising and studying a temporal network model considering a periodically changing environment as well as a preferential activation mechanism, we numerically show that our model could successfully reproduce various empirical results by the MSE method in terms of multiscale temporal correlations. Our results demonstrate that the environmental changes can play an important role in shaping the dynamics of temporal networks when the interactions between nodes are influenced by the environment of the systems.

Reconstructing unseen transmission events to infer dengue dynamics from viral sequences

For most pathogens, transmission is driven by interactions between the behaviours of infectious individuals, the behaviours of the wider population, the local environment, and immunity. Phylogeographic approaches are currently unable to disentangle the relative effects of these competing factors. We develop a spatiotemporally structured phylogenetic framework that addresses these limitations by considering individual transmission events, reconstructed across spatial scales. We apply it to geocoded dengue virus sequences from Thailand (N = 726 over 18 years). We find infected individuals spend 96% of their time in their home community compared to 76% for the susceptible population (mainly children) and 42% for adults. Dynamic pockets of local immunity make transmission more likely in places with high heterotypic immunity and less likely where high homotypic immunity exists. Age-dependent mixing of individuals and vector distributions are not important in determining spread. This approach provides previously unknown insights into one of the most complex disease systems known and will be applicable to other pathogens.

Phylogeographic analyses can provide broad descriptions of the spread of pathogens between populations, but are limited by incomplete sampling. Here, the authors develop an inference framework that reconstructs sequential transmission events and use it to characterise dynamics of dengue in Thailand.

The rhythms of the night: increase in online night activity and emotional resilience during the spring 2020 Covid-19 lockdown

CONTEXT

The lockdown orders established in multiple countries in response to the Covid-19 pandemic are arguably one of the most widespread and deepest shock experienced by societies in recent years. Studying their impact trough the lens of social media offers an unprecedented opportunity to understand the susceptibility and the resilience of human activity patterns to large-scale exogenous shocks. Firstly, we investigate the changes that this upheaval has caused in online activity in terms of time spent online, themes and emotion shared on the platforms, and rhythms of content consumption. Secondly, we examine the resilience of certain platform characteristics, such as the daily rhythms of emotion expression.

DATA

Two independent datasets about the French cyberspace: a fine-grained temporal record of almost 100 thousand YouTube videos and a collection of 8 million Tweets between February 17 and April 14, 2020.

FINDINGS

In both datasets we observe a reshaping of the circadian rhythms with an increase of night activity during the lockdown. The analysis of the videos and tweets published during lockdown shows a general decrease in emotional contents and a shift from themes like work and money to themes like death and safety. However, the daily patterns of emotions remain mostly unchanged, thereby suggesting that emotional cycles are resilient to exogenous shocks.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1140/epjds/s13688-021-00262-1.

Gaining Insights Into the Estimation of the Circadian Rhythms of Social Activity in Older Adults From Their Telephone Call Activity With Statistical Learning: Observational Study

Background

Understanding the social mechanisms of the circadian rhythms of activity represents a major issue in better managing the mechanisms of age-related diseases occurring over time in the elderly population. The automated analysis of call detail records (CDRs) provided by modern phone technologies can help meet such an objective. At this stage, however, whether and how the circadian rhythms of telephone call activity can be automatically and properly modeled in the elderly population remains to be established.

Objective

Our goal for this study is to address whether and how the circadian rhythms of social activity observed through telephone calls could be automatically modeled in older adults.

Methods

We analyzed a 12-month data set of outgoing telephone CDRs of 26 adults older than 65 years of age. We designed a statistical learning modeling approach adapted for exploratory analysis. First, Gaussian mixture models (GMMs) were calculated to automatically model each participant’s circadian rhythm of telephone call activity. Second, k-means clustering was used for grouping participants into distinct groups depending on the characteristics of their personal GMMs.

Results

The results showed the existence of specific structures of telephone call activity in the daily social activity of older adults. At the individual level, GMMs allowed the identification of personal habits, such as morningness-eveningness for making calls. At the population level, k-means clustering allowed the structuring of these individual habits into specific morningness or eveningness clusters.

Conclusions

These findings support the potential of phone technologies and statistical learning approaches to automatically provide personalized and precise information on the social rhythms of telephone call activity of older individuals. Futures studies could integrate such digital insights with other sources of data to complete assessments of the circadian rhythms of activity in elderly populations.

Novel statistical approach for assessing the persistence of the circadian rhythms of social activity from telephone call detail records in older adults

How circadian rhythms of activity manifest themselves in social life of humans remains one of the most intriguing questions in chronobiology and a major issue for personalized medicine. Over the past years, substantial advances have been made in understanding the personal nature and the robustness—i.e. the persistence—of the circadian rhythms of social activity by the analysis of phone use. At this stage however, the consistency of such advances as their statistical validity remains unclear. The present paper has been specifically designed to address this issue. To this end, we propose a novel statistical procedure for the measurement of the circadian rhythms of social activity which is particularly well-suited for the existing framework of persistence analysis. Furthermore, we illustrate how this procedure works concretely by assessing the persistence of the circadian rhythms of telephone call activity from a 12-month call detail records (CDRs) dataset of adults over than 65 years. The results show the ability of our approach for assessing persistence with a statistical significance. In the field of CDRs analysis, this novel statistical approach can be used for completing the existing methods used to analyze the persistence of the circadian rhythms of a social nature. More importantly, it provides an opportunity to open up the analysis of CDRs for various domains of application in personalized medicine requiring access to statistical significance such as health care monitoring.

Exploitation of Outgoing and Incoming Telephone Calls in the Context of Circadian Rhythms of Social Activity Among Elderly People: Observational Descriptive Study

Background

In the elderly population, analysis of the circadian rhythms of social activity may help in supervising homebound disabled and chronically ill populations. Circadian rhythms are monitored over time to determine, for example, the stability of the organization of daily social activity rhythms and the occurrence of particular desynchronizations in the way older adults act and react socially during the day. Recently, analysis of telephone call detail records has led to the possibility of determining circadian rhythms of social activity in an objective unobtrusive way for young patients from their outgoing telephone calls. At this stage, however, the analysis of incoming call rhythms and the comparison of their organization with respect to outgoing calls remains to be performed in underinvestigated populations (in particular, older populations).

Objective

This study investigated the persistence and synchronization of circadian rhythms in telephone communication by older adults.

Methods

The study used a longitudinal 12-month data set combining call detail records and questionnaire data from 26 volunteers aged 70 years or more to determine the existence of persistent and synchronized circadian rhythms in their telephone communications. The study worked with the following four specific telecommunication parameters: (1) recipient of the telephone call (alter), (2) time at which the call began, (3) duration of the call, and (4) direction of the call. We focused on the following two issues: (1) the existence of persistent circadian rhythms of outgoing and incoming telephone calls in the older population and (2) synchronization with circadian rhythms in the way the older population places and responds to telephone calls.

Results

The results showed that older adults have their own specific circadian rhythms for placing telephone calls and receiving telephone calls. These rhythms are partly structured by the way in which older adults allocate their communication time over the day. In addition, despite minor differences between circadian rhythms for outgoing and incoming calls, our analysis suggests the two rhythms could be synchronized.

Conclusions

These results suggest the existence of potential persistent and synchronized circadian rhythms in the outgoing and incoming telephone activities of older adults.

Relationships of Linear and Non-linear Measurements of Post-stroke Walking Activity and Their Relationship to Weather

Background: Stroke survivors are more sedentary than the general public. Previous research on stroke activity focuses on linear quantities. Non-linear measures, such as Jensen-Shannon Divergence and Lempel-Ziv Complexity, may help explain when and how stroke survivors move so that interventions to increase activity may be designed more effectively.

Objectives: Our objective was to understand what factors affect a stroke survivor's physical activity, including weather, by characterizing activity by step counts, structure, and complexity.

Methods: A custom MATLAB code was used to analyze clinical trial (NCT02835313, https://clinicaltrials.gov/ct2/show/NCT02835313) data presented as minute by minute step counts. Six days of data were analyzed for 142 participants to determine the regularity of activity structure across days and complexity patterns of varied cadences. The effect of steps on structure and complexity, the season's effect on steps, structure, and complexity, and the presence of precipitation's effect on steps and complexity were all analyzed.

Results: Step counts and regularity were linearly related (p < 0.001). Steps and complexity were quadratically related (r² = 0.70 for mean values, 0.64 for daily values). Season affected complexity between spring and winter (p = 0. 019). Season had no effect on steps or structure. Precipitation had no effect on steps or complexity.

Conclusions: Stroke survivors with high step counts are active at similar times each day and have higher activity complexities as measured through patterns of movement at different intensity levels. Non-linear measures, such as Jensen-Shannon Divergence and Lempel-Ziv Complexity, are valuable in describing a person's activity. Weather affects our activity parameters in terms of complexity between spring and winter.

Circadian rhythms in temporal-network connectivity

Human activity follows a circadian rhythm. In online activity, this rhythm is visible both at the level of individuals as well as at the population level from Wikipedia edits to mobile telephone calls. However, much less is known about circadian patterns at the level of network structure, that is, beyond the day-night cycle of the frequency of activity. Here, we study how the temporal connectivity of communication networks changes through the day, focusing on sequences of communication events that follow one another within a limited time. Such sequences can be thought to be characteristic of information transfer in the network. We find that temporal connectivity also follows a circadian rhythm, where at night a larger fraction of contacts is associated with such sequences and where contacts appear more independent at daytime. This result points out that temporal networks show richer variation in time than what has been known thus far.

Generalized priority-based model for smartphone screen touches

The distribution of intervals between human actions such as email posts or keyboard strokes demonstrates distinct properties at short versus long timescales. For instance, at long timescales, which are presumably controlled by complex process such as planning and decision making, it has been shown that those interevent intervals follow a scale-invariant (or power-law) distribution. In contrast, at shorter timescales-which are governed by different processes such as sensorimotor skill-they do not follow the same distribution and we know little about how they relate to the scale-invariant pattern. Here, we analyzed 9 million intervals between smartphone screen touches of 84 individuals which span several orders of magnitudes (from milliseconds to hours). To capture these intervals, we extend a priority-based generative model to smartphone touching events. At short timescale, the model is governed by refractory effects, while at longer timescales, the intertouch intervals are governed by the priority difference between smartphone tasks and other tasks. The flexibility of the model allows us to capture interindividual variations at short and long timescales, while its tractability enables efficient model fitting. According to our model, each individual has a specific power-law exponent which is tightly related to the effective refractory time constant suggesting that motor processes which influence the fast actions are related to the higher cognitive processes governing the longer interevent intervals.

Validation of the English version of the Mood Rhythm Instrument

Background

Disruption of biological rhythms has been linked to the pathophysiology of mental disorders. However, little is known regarding the rhythmicity of mood symptoms due to the lack of validated clinical questionnaires. A better understanding of the rhythmicity of mood symptoms can help identifying individuals whose severity of mood symptoms follows an altered circadian rhythm. The objective of this study was to validate the English version of the Mood Rhythm Instrument (MRhI), a self-reported measure of self-perceived rhythmicity of mood symptoms and behaviours, in a sample of the general population from Canada.

Methods

After the translation process, the final English version of the Mood Rhythm Instrument (MRhI-English) was applied on participants recruited at McMaster University and St. Joseph’s Healthcare Hamilton campuses. Individuals were also asked to answer the Reduced Morningness-Eveningness Questionnaire (rMEQ).

Results

Four hundred one individuals completed the English version of the MRhI and the rMEQ. The MRhI-English presented a Cronbach’s alpha of 0.75. The factorial analysis grouped the MRhI-15 items in 3 factors (cognitive, affective and somatic), with affective items having a lower frequency of self-reported 24-h peaks. Comparison between sexes showed that women reported a higher frequency of daily peaks in irritability, anxiety, sadness and talking to friends, while men exhibited peaks more frequently in problem-solving, sexual arousal and motivation to exercise.

Conclusions

Our findings suggest that the English version of the MRhI displayed good internal consistency. Future directions will include the use of the MRhI instrument in individuals with mood disorders, aiming to provide a better understanding of the relationship between daily patterns of mood variability and mental health outcomes.

Text and phone calls: user behaviour and dual-channel communication prediction

The contact list size of modern mobile phone users has increased up to hundreds of contacts, making contact retrieval a relatively difficult task. Various algorithms have been designed to predict the contact that a user will call at a given time. These algorithms use historical call data to make this prediction. However, modern mobile users do not just make calls, but also rely on various communication channels like text messages and calls to maintain their social relations. Despite the prevalence of multiple communication channels, predictive analysis of these channels has not been studied so far. Hence, this study deliberated on proposing a predictive model for dual-channel (text and calls). This study initially investigated the dual-channel communication behaviour of smartphone users by using a mixed approach i.e. subjective and objective data analysis and found many peculiarities. It was observed that the preferred communication channel was different for various contacts, even for a single user. Although the cost-effective texts were found to be more popular over phone calls, a significant proportion of user pairs seemed to prefer calls for most of their communication. A generic predictive framework for the dual-channel environment was proposed based upon these findings. This model predicts the next communication event by modelling temporal information of call and text on a 2D plane. This framework has three variations which not only predict the person who will be contacted at a particular time but also predict the channel of communication (call or text). Finally, the performance of different versions of the algorithm was evaluated using real-world dual-channel data. One version of the predictive model outperformed the other variations with a prediction accuracy over 90 percent, while the other variations also performed well.

Dynamics of calling activity to toll-free numbers in China

The quantitative understanding of human behavior is a key issue in modern science. Recently, inhomogeneous human activities have been described by bursts (consecutive activities separated by long periods of inactivity) and characterized by fat-tailed inter-event time (interval between two activities) distributions. However, the dynamics between number of activities and activity duration are still unclear. In this study, we analyzed 133 million toll-free call records from China to study the dynamics between call frequency and call duration. We confirmed that both call frequency and call duration exhibit circadian cycles and weekly cycles. By analyzing intraday patterns of these two metrics, we found the opposite volatility and clustered distributions. Results of clustering analysis showed that calling activity to toll-free numbers can be clustered into four clusters. In the “Work” cluster, the distribution of call duration was significantly different from that in the other clusters. The corresponding time of “Work” cluster was much shorter than estimates based on common sense. Intraday patterns and clustering results showed that both call frequency and call duration are primarily related to circadian cycles, the nature of human beings, and that work is a secondary factor that affects these variables. Moreover, we found a strong positive correlation between call frequency and call duration, as well as polarization of joint probability. The polarization indicates two extremes in inhomogeneous calling activity to toll-free numbers, i.e., either people are very busy or very idle. The empirical probability of the extreme was approximately four times that of random probability. Our findings may have great usage for studying the dynamics of inhomogeneous human behavior.

Circadian Rhythms in the Telephone Calls of Older Adults: Observational Descriptive Study

Background

Recent studies have thoughtfully and convincingly demonstrated the possibility of estimating the circadian rhythms of young adults’ social activity by analyzing their telephone call-detail records (CDRs). In the field of health monitoring, this development may offer new opportunities for supervising a patient’s health status by collecting objective, unobtrusive data about their daily social interactions. However, before considering this future perspective, whether and how similar results could be observed in other populations, including older ones, should be established.

Objective

This study was designed specifically to address the circadian rhythms in the telephone calls of older adults.

Methods

A longitudinal, 12-month dataset combining CDRs and questionnaire data from 26 volunteers aged 65 years or older was used to examine individual differences in the daily rhythms of telephone call activity. The study used outgoing CDRs only and worked with three specific telecommunication parameters: (1) call recipient (alter), (2) time of day, and (3) call duration. As did the studies involving young adults, we analyzed three issues: (1) the existence of circadian rhythms in the telephone call activity of older adults, (2) their persistence over time, and (3) the alter-specificity of calls by calculating relative entropy.

Results

We discovered that older adults had their own specific circadian rhythms of outgoing telephone call activity whose salient features and preferences varied across individuals, from morning until night. We demonstrated that rhythms were consistent, as reflected by their persistence over time. Finally, results suggested that the circadian rhythms of outgoing telephone call activity were partly structured by how older adults allocated their communication time across their social network.

Conclusions

Overall, these results are the first to have demonstrated the existence, persistence, and alter-specificity of the circadian rhythms of the outgoing telephone call activity of older adults. These findings suggest an opportunity to consider modern telephone technologies as potential sensors of daily activity. From a health care perspective, these sensors could be harnessed for unobtrusive monitoring purposes.

Link-centric analysis of variation by demographics in mobile phone communication patterns

We present a link-centric approach to study variation in the mobile phone communication patterns of individuals. Unlike most previous research on call detail records that focused on the variation of phone usage across individual users, we examine how the calling and texting patterns obtained from call detail records vary among pairs of users and how these patterns are affected by the nature of relationships between users. To demonstrate this link-centric perspective, we extract factors that contribute to the variation in the mobile phone communication patterns and predict demographics-related quantities for pairs of users. The time of day and the channel of communication (calls or texts) are found to explain most of the variance among pairs that frequently call each other. Furthermore, we find that this variation can be used to predict the relationship between the pairs of users, as inferred from their age and gender, as well as the age of the younger user in a pair. From the classifier performance across different age and gender groups as well as the inherent class overlap suggested by the estimate of the bounds of the Bayes error, we gain insights into the similarity and differences of communication patterns across different relationships.

Assessment of e-Social Activity in Psychiatric Patients

This paper introduces a novel method to assess the social activity maintained by psychiatric patients using information and communication technologies. In particular, we model the daily usage patterns of phone calls and social and communication apps using point processes. We propose a novel nonhomogeneous Poisson process model with periodic (circadian) intensity function using a truncated Fourier series expansion, which is inferred using a trust-region algorithm. We also extend the model using a mixture of periodic intensity functions to cope with the different daily patterns of a person. The analysis of the usage of phone calls and social and communication apps of a cohort of 259 patients reveals common patterns shared among patients with relatively high homogeneity and differences among patient pathologies.

Extracting the multi-timescale activity patterns of online financial markets

Online financial markets can be represented as complex systems where trading dynamics can be captured and characterized at different resolutions and time scales. In this work, we develop a methodology based on non-negative tensor factorization (NTF) aimed at extracting and revealing the multi-timescale trading dynamics governing online financial systems. We demonstrate the advantage of our strategy first using synthetic data, and then on real-world data capturing all interbank transactions (over a million) occurred in an Italian online financial market (e-MID) between 2001 and 2015. Our results demonstrate how NTF can uncover hidden activity patterns that characterize groups of banks exhibiting different trading strategies (normal vs. early vs. flash trading, etc.). We further illustrate how our methodology can reveal "crisis modalities" in trading triggered by endogenous and exogenous system shocks: as an example, we reveal and characterize trading anomalies in the midst of the 2008 financial crisis.

Predicting financial trouble using call data-On social capital, phone logs, and financial trouble

An ability to understand and predict financial wellbeing for individuals is of interest to economists, policy designers, financial institutions, and the individuals themselves. According to the Nilson reports, there were more than 3 billion credit cards in use in 2013, accounting for purchases exceeding US$ 2.2 trillion, and according to the Federal Reserve report, 39% of American households were carrying credit card debt from month to month. Prior literature has connected individual financial wellbeing with social capital. However, as yet, there is limited empirical evidence connecting social interaction behavior with financial outcomes. This work reports results from one of the largest known studies connecting financial outcomes and phone-based social behavior (180,000 individuals; 2 years' time frame; 82.2 million monthly bills, and 350 million call logs). Our methodology tackles highly imbalanced dataset, which is a pertinent problem with modelling credit risk behavior, and offers a novel hybrid method that yields improvements over, both, a traditional transaction data only approach, and an approach that uses only call data. The results pave way for better financial modelling of billions of unbanked and underbanked customers using non-traditional metrics like phone-based credit scoring.

The role of gender in social network organization

The digital traces we leave behind when engaging with the modern world offer an interesting lens through which we study behavioral patterns as expression of gender. Although gender differentiation has been observed in a number of settings, the majority of studies focus on a single data stream in isolation. Here we use a dataset of high resolution data collected using mobile phones, as well as detailed questionnaires, to study gender differences in a large cohort. We consider mobility behavior and individual personality traits among a group of more than 800 university students. We also investigate interactions among them expressed via person-to-person contacts, interactions on online social networks, and telecommunication. Thus, we are able to study the differences between male and female behavior captured through a multitude of channels for a single cohort. We find that while the two genders are similar in a number of aspects, there are robust deviations that include multiple facets of social interactions, suggesting the existence of inherent behavioral differences. Finally, we quantify how aspects of an individual's characteristics and social behavior reveals their gender by posing it as a classification problem. We ask: How well can we distinguish between male and female study participants based on behavior alone? Which behavioral features are most predictive?

Tracking urban human activity from mobile phone calling patterns

Timings of human activities are marked by circadian clocks which in turn are entrained to different environmental signals. In an urban environment the presence of artificial lighting and various social cues tend to disrupt the natural entrainment with the sunlight. However, it is not completely understood to what extent this is the case. Here we exploit the large-scale data analysis techniques to study the mobile phone calling activity of people in large cities to infer the dynamics of urban daily rhythms. From the calling patterns of about 1,000,000 users spread over different cities but lying inside the same time-zone, we show that the onset and termination of the calling activity synchronizes with the east-west progression of the sun. We also find that the onset and termination of the calling activity of users follows a yearly dynamics, varying across seasons, and that its timings are entrained to solar midnight. Furthermore, we show that the average mid-sleep time of people living in urban areas depends on the age and gender of each cohort as a result of biological and social factors.

Seasonal and geographical impact on human resting periods

We study the influence of seasonally and geographically related daily dynamics of daylight and ambient temperature on human resting or sleeping patterns using mobile phone data of a large number of individuals. We observe two daily inactivity periods in the people's aggregated mobile phone calling patterns and infer these to represent the resting times of the population. We find that the nocturnal resting period is strongly influenced by the length of daylight, and that its seasonal variation depends on the latitude, such that for people living in two different cities separated by eight latitudinal degrees, the difference in the resting periods of people between the summer and winter in southern cities is almost twice that in the northern cities. We also observe that the duration of the afternoon resting period is influenced by the temperature, and that there is a threshold from which this influence sets in. Finally, we observe that the yearly dynamics of the afternoon and nocturnal resting periods appear to be counterbalancing each other. This also lends support to the notion that the total daily resting time of people is more or less conserved across the year.

Analysis of the communities of an urban mobile phone network

Being able to characterise the patterns of communications between individuals across different time scales is of great importance in understanding people's social interactions. Here, we present a detailed analysis of the community structure of the network of mobile phone calls in the metropolitan area of Milan revealing temporal patterns of communications between people. We show that circadian and weekly patterns can be found in the evolution of communities, presenting evidence that these cycles arise not only at the individual level but also at that of social groups. Our findings suggest that these trends are present across a range of time scales, from hours to days and weeks, and can be used to detect socially relevant events.

Chronotype, gender and general health

BACKGROUND

Light-dark alternation has always been the strongest external circadian "zeitgeber" for humans. Due to its growing technological preference, our society is quickly transforming toward a progressive "eveningness" (E), with consequences on personal circadian preference (chronotype), depending on gender as well. The aim of this study was to review the available evidence of possible relationships between chronotype and gender, with relevance on disturbances that could negatively impact general health, including daily life aspects.

METHODS

Electronic searches of the published literature were performed in the databases MEDLINE and Web of Science, by using the Medical Subject Heading (MeSH), when available, or other specific keywords.

RESULTS

Results were grouped into four general areas, i.e. (a) "General and Cardiovascular Issues", (b) "Psychological and Psychopathological Issues", (c) "Sleep and Sleep-Related Issues" and (d) "School and School-Related Issues". (a) E is associated with unhealthy and dietary habits, smoking and alcohol drinking (in younger subjects) and, in adults, with diabetes and metabolic syndrome; (b) E is associated with impulsivity and anger, depression, anxiety disorders and nightmares (especially in women), risk taking behavior, use of alcohol, coffee and stimulants, psychopathology and personality traits; (c) E has been associated, especially in young subjects, with later bedtime and wake-up time, irregular sleep-wake schedule, subjective poor sleep, school performance and motivation, health-related quality of life; (d) E was associated with lowest mood and lower overall grade point average (especially for women).

CONCLUSIONS

Eveningness may impact general health, either physical or mental, sleep, school results and achievements, especially in younger age and in women. The role of family support is crucial, and parents should be deeply informed that abuse of technological devices during night hours may lead to the immature adjustment function of children's endogenous circadian pacemakers.

Is problematic mobile phone use explained by chronotype and personality?

In this study, the relationships among problematic mobile phone use, age, gender, personality and chronotype of Turkish university students were examined. The study included 902 university students (73% female, 27% male) and their participation in the study was anonymous and voluntary. Data were collected from each participant by assessing a demographic questionnaire, Composite Scale of Morningness (CSM) as a measure of chronotype, the Big Five Inventory (BIG-5) for personality assessment and Mobile Phone Problem Usage Scale (MPPUS). The most important result was that CSM scores were the best predictor for problematic mobile phone usage, and as a consequence, evening-oriented university students scored higher on the MPPUS. This result remained, even when compared with the most influential personality predictor, conscientiousness. In addition, while extraversion positively predicted, emotional stable and chronotype negatively predicted problematic mobile phone use. Lastly, age and gender were not predictors of problematic mobile phone use.