Did you miss me? Making the most of digital phenotyping data by imputing missingness with point process models: observational study.

Abstract

Smartphone-based digital phenotyping can provide low-burden behavioural measures for mental disorder monitoring. However, progress in making inferences from these data is challenged by the common occurrence of missing data. We propose a method to impute missingness using non-homogeneous Poisson point process models (PPPMs), where activities (overall phone, social media, communication app usage, outgoing/incoming calls) are modelled as 'points'. We evaluate personalised PPPMs for imputation and investigate their influence on downstream analysis. In a ground truth evaluation (in and out-of-sample), we evaluate time-varying covariates ('hour of the day', 'day of the week'; encoded using one-hot encoding and sine-cosine transformation) to model behavioural patterns in participants from SMARD (depression; n=26). We train a hidden Markov model (HMM) on data simulated by the PPPMs and compare this to a ground truth HMM. We then perform a replication of a prior HMM analysis in PRISM (Alzheimer's disease, schizophrenia, healthy controls; n=65) and Hersenonderzoek studies (Alzheimer's disease, memory complaints, healthy controls; n=283). In the ground truth evaluation, 'hour' was consistently significant in in-sample likelihood ratio tests and 'day' was less commonly significant. PPPMs including one-hot encoded hour generally provided the highest out-of-sample likelihood. Using this PPPM variant, HMM properties were preserved, and prior findings were replicated. Personalised PPPMs provide behavioural simulations that can be used for temporal imputation. These models capture average patterns and could be extended to include further temporal components. Non-homogeneous PPPMs are a promising imputation tool that may contribute to improved utility of digital phenotyping by providing realistic temporal imputations.