Publication

Using smartphone behaviour to understand healthy ageing and neurological disease

Leiden University researchers and their collaborators report the development of new research frameworks that use day-to-day smartphone behaviour gathered from a large sample of healthy people to help understand ageing, and how ageing alters with epilepsy and stroke. These reports occupy two back-to-back publications in the journal iScience and involve about 300 million smartphone interactions.

Author: Enea Ceolini, Ruchella Kock, Iris Brunner, Johanna Bunschoten, Marian H.J.M. Majoie, Roland D. Thijs, Guido P.H. Band, Gijsbert Stoet & Arko Ghosh
Date: 05 August 2022
Links: iScience - Temporal clusters of age-related behavioural alterations captured in smartphone touchscreen interactions; iScience - A model of healthy ageing based on smartphone interactions reveals advanced behavioural age in neurological disease

Summary - Temporal clusters of age-related behavioural alterations captured in smartphone touchscreen interactions

Smartphones touchscreen interactions may help resolve if and how real-world behavioral dynamics are shaped by aging. Here, in a sample spanning the adult life span (16 to 86 years, N = 598, accumulating 355 million interactions), we clustered the smartphone interactions according to their next inter-touch interval dynamics. There were age-related behavioral losses at the clusters occupying short intervals (∼100 ms, R² ∼ 0.8) but gains at the long intervals (∼4 s, R² ∼ 0.4). Our approach revealed a sophisticated form of behavioral aging where individuals simultaneously demonstrated accelerated aging in one behavioral cluster versus a deceleration in another. Contrary to the common notion of a simple behavioral decline with age based on conventional cognitive tests, we show that the nature of aging systematically varies according to the underlying dynamics. Of all the imaginable factors determining smartphone interactions, age-sensitive cognitive and behavioral processes may dominatingly shape smartphone dynamics.

Summary - A model of healthy ageing based on smartphone interactions reveals advanced behavioural age in neurological disease

Smartphones offer unique opportunities to trace the convoluted behavioral patterns accompanying healthy aging. Here we captured smartphone touchscreen interactions from a healthy population (N = 684, ∼309 million interactions) spanning 16 to 86 years of age and trained a decision tree regression model to estimate chronological age based on the interactions. The interactions were clustered according to their next interval dynamics to quantify diverse smartphone behaviors. The regression model well-estimated the chronological age in health (mean absolute error = 6 years, R² = 0.8). We next deployed this model on a population of stroke survivors (N = 41) to find larger prediction errors such that the estimated age was advanced by 6 years. A similar pattern was observed in people with epilepsy (N = 51), with prediction errors advanced by 10 years. The smartphone behavioral model trained in health can be used to study altered aging in neurological diseases.