JMIR Publications

ABSTRACT

Background:

Understanding the social mechanisms of the circadian rhythms of activity does represent a major issue to better manage controlling the mechanisms of age-related diseases occurring in time in elderly population. The automated analysis of Call Detail Records (CDRs) provided by modern phone technologies can serve such an objective. At this stage, however, whether and how the circadian rhythms of telephone call activity can be automatically and properly modeled in elderly population remains to be established.

Objective:

To address whether and how the circadian rhythms of social activity observed through the telephone calls could be automatically modeled in older adults.

Methods:

We analyzed a 12-month dataset of outgoing telephone call detail records of 26 adults older than 65 years. 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. Then, K-means clustering was used for grouping participants in distinct groups depending on the characteristics of their personal GMM.

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, Gaussian mixture models (GMMs) permit to figure out personal habits such as morningness-eveningness for making calls. At the population level, k-means clustering permit to structure these individual habits into specific morningness or eveningness clusters.

Conclusions:

These present findings support the potential of phone technologies and statistical learning approaches for automatically providing personalized and precise information on the social rhythms of telephone call activity of an older individual. Futures studies could integrate such digital insights with other sources of data to complete the assessment of the circadian rhythms of activity in elderly population.