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

Aubourg, Timothée; Demongeot, Jacques; Vuillerme, Nicolas

doi:10.1038/s41598-020-77795-4

Cited by 7 publications

(5 citation statements)

References 50 publications

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“…This process may involve leveraging longitudinal data, or it can also be accomplished with the current dataset comprising one week of wearable data. Existing research provides methodologies for effectively evaluating the consistency of circadian rhythms of activity, even when dealing with limited digital activity datasets 38 , 39 .…”

Section: Discussionmentioning

confidence: 99%

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

Shim,

Fleisch,

Barata

2024

npj Digit. Med.

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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.

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Section: Discussionmentioning

confidence: 99%

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

Shim,

Fleisch,

Barata

2024

npj Digit. Med.

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“…More new relationships are formed during the weekdays, but relationships with more communication on the weekend have a longer lifetime. This is an unexplored side of the large body of literature regarding calling patterns based on allocation of timing of relationships, with the only major example being circadian rhythms over all relationships [16][17][18]34]. Additionally, these results are relevant in the discussion of the 'Seven Pillars of Friendship' [26], a homophily driven theory that explains how friendships are formed.…”

Section: Discussionmentioning

confidence: 99%

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

Vergara Hidd,

Zhang,

Centellegher

et al. 2023

R. Soc. open sci.

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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.

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“…Data collection, volunteer recruitment, and data preprocessing followed the general principles set in previous investigations of this secondary analysis [39][40][41][42][43] and remain unchanged for consistency.…”

Section: Methodsmentioning

confidence: 99%

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 (Preprint)

Aubourg¹,

Demongeot²,

Vuillerme³

2020

Preprint

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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.

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

Cited by 7 publications

References 50 publications

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

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

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

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 (Preprint)

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