Parsing inter-individual variability in the digital phenotype across the menstrual cycle
- 1Donders Institute for Brain, Cognition and Behaviour
Abstract
Digital phenotyping, which is defined as quantifying someones behaviour with digital devices, provides unprecedented opportunities for understanding human mental health but is hampered by high levels of inter-individual variability. Here, we propose a new method to address this, parsing inter-individual variability by decomposing the digital phenotype dynamics into latent trajectories and using each individuals trajectory membership as a moderator when modelling psychopathology over the same timeframe. We applied our method in the context of mood symptom exacerbation across the menstrual cycle, where symptom severity and timing are inconsistent between individuals. Using the BiAffect platform to collect smartphone typing dynamics, we found stable trajectories in smartphone movement rate: one group of participants showed substantial movement rate fluctuations across the menstrual cycle, whilst the others did not. Participants with movement fluctuations displayed increased fluctuations across the cycle in prospective anhedonia and depression ratings, but not in anxiety, irritability, and suicidal ideation.
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BibTeX
@misc{knol2026parsing,
title = {{Parsing inter-individual variability in the digital phenotype across the menstrual cycle}},
author = {L. Knol and A. Nagpal and F. Hussain and C. F. Beckmann and A. Leow and T. A. Eisenlohr-Moul and A. F. Marquand},
howpublished = {medRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.26.26361403},
url = {https://www.medrxiv.org/content/10.64898/2026.08.26.26361403v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Parsing inter-individual variability in the digital phenotype across the menstrual cycle
AU - L. Knol
AU - A. Nagpal
AU - F. Hussain
AU - C. F. Beckmann
AU - A. Leow
AU - T. A. Eisenlohr-Moul
AU - A. F. Marquand
PY - 2026
DA - 2026/08/26
PB - medRxiv
DO - 10.64898/2026.08.26.26361403
UR - https://www.medrxiv.org/content/10.64898/2026.08.26.26361403v1
AB - Digital phenotyping, which is defined as quantifying someones behaviour with digital devices, provides unprecedented opportunities for understanding human mental health but is hampered by high levels of inter-individual variability. Here, we propose a new method to address this, parsing inter-individual variability by decomposing the digital phenotype dynamics into latent trajectories and using each individuals trajectory membership as a moderator when modelling psychopathology over the same timeframe. We applied our method in the context of mood symptom exacerbation across the menstrual cycle, where symptom severity and timing are inconsistent between individuals. Using the BiAffect platform to collect smartphone typing dynamics, we found stable trajectories in smartphone movement rate: one group of participants showed substantial movement rate fluctuations across the menstrual cycle, whilst the others did not. Participants with movement fluctuations displayed increased fluctuations across the cycle in prospective anhedonia and depression ratings, but not in anxiety, irritability, and suicidal ideation.
ER -