Relating smartphone ‘App-journeys’ to mental health: A novel network analysis approach
Abstract
Despite substantial concern about how technology use relates to mental health, research attempting to identify beneficial or harmful smartphone activities has yielded mixed results. Here, we show that abstracting away from specific activities, their transition patterns across time relate robustly to user mental health. To quantify this transition structure, we develop a novel network analysis method to capture how each phone user journeys across Apps within a smartphone session. Across two independent datasets, including an exploratory UK sample aged 18-22 (n = 82), and a preregistered replication in the USA aged 15-18 (n = 565, ABCD cohort), we show that ‘App-journey’ networks relate consistently to user mental health. For example, users with higher anxiety symptoms transition to a wider variety of Apps within each phone session (higher network density) and are less likely to confine each session to repeated groups of Apps (lower network modularity). In contrast, we find no relationship between ADHD and network modularity or density, suggesting that App-journey networks may distinguish among different mental health profiles. Additionally, in the exploratory dataset, Apps which users describe as more ‘automatic’ are more central in App-journey networks. In summary, we uncover a relationship between the temporal structure of digital activity and specific mental health symptom categories, which generalizes across samples and age groups. The temporal structure of digital activity may thus represent a hitherto neglected, potentially clinically-relevant dimension of digital behaviour for future research and interventions.
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BibTeX
@misc{turner2026relating,
title = {{Relating smartphone ‘App-journeys’ to mental health: A novel network analysis approach}},
author = {Georgia Turner and Maria Concepcion Valdez Gastelum and Ian Anderson and Zelda Brufal and Steven Dillmann and Luisa Fassi and Lukas J. Gunschera and Ivonne Monarca and Oscar Peña Ramirez and Monica Tentori},
howpublished = {PsyArXiv},
year = {2026},
month = oct,
doi = {10.31234/osf.io/uytfr_v5},
url = {https://osf.io/preprints/psyarxiv/uytfr_v5/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Relating smartphone ‘App-journeys’ to mental health: A novel network analysis approach
AU - Georgia Turner
AU - Maria Concepcion Valdez Gastelum
AU - Ian Anderson
AU - Zelda Brufal
AU - Steven Dillmann
AU - Luisa Fassi
AU - Lukas J. Gunschera
AU - Ivonne Monarca
AU - Oscar Peña Ramirez
AU - Monica Tentori
PY - 2026
DA - 2026/10/28
PB - PsyArXiv
DO - 10.31234/osf.io/uytfr_v5
UR - https://osf.io/preprints/psyarxiv/uytfr_v5/
AB - Despite substantial concern about how technology use relates to mental health, research attempting to identify beneficial or harmful smartphone activities has yielded mixed results. Here, we show that abstracting away from specific activities, their transition patterns across time relate robustly to user mental health. To quantify this transition structure, we develop a novel network analysis method to capture how each phone user journeys across Apps within a smartphone session. Across two independent datasets, including an exploratory UK sample aged 18-22 (n = 82), and a preregistered replication in the USA aged 15-18 (n = 565, ABCD cohort), we show that ‘App-journey’ networks relate consistently to user mental health. For example, users with higher anxiety symptoms transition to a wider variety of Apps within each phone session (higher network density) and are less likely to confine each session to repeated groups of Apps (lower network modularity). In contrast, we find no relationship between ADHD and network modularity or density, suggesting that App-journey networks may distinguish among different mental health profiles. Additionally, in the exploratory dataset, Apps which users describe as more ‘automatic’ are more central in App-journey networks. In summary, we uncover a relationship between the temporal structure of digital activity and specific mental health symptom categories, which generalizes across samples and age groups. The temporal structure of digital activity may thus represent a hitherto neglected, potentially clinically-relevant dimension of digital behaviour for future research and interventions.
ER -