Exploring the Digital Footprint of Depression: A PRISMA Systematic Literature Review of the Empirical Evidence.

Abstract

Background: This PRISMA systematic literature review examined the use of digital data collection methods (including ecological momentary assessment [EMA], experience sampling method [ESM], digital biomarkers, passive sensing, mobile sensing, ambulatory assessment, and time-series analysis), emphasizing on digital phenotyping (DP) to study depression. DP is defined as the use of digital data to profile health information objectively. Aims: Four distinct yet interrelated goals underpin this study: (a) to identify empirical research examining the use of DP to study depression; (b) to describe the different methods and technology employed; (c) to integrate the evidence regarding the efficacy of digital data in the examination, diagnosis, and monitoring of depression and (d) to clarify DP definitions and digital mental health records terminology. Results: Overall, 118 studies were assessed as eligible. Considering the terms employed, “EMA”, “ESM”, and “DP” were the most predominant. A variety of DP data sources were reported, including voice, language, keyboard typing kinematics, mobile phone calls and texts, geocoded activity, actigraphy sensor-related recordings (i.e., steps, sleep, circadian rhythm), and self-reported apps’ information. Reviewed studies employed subjectively and objectively recorded digital data in combination with interviews and psychometric scales. Conclusions: Findings suggest links between a person’s digital records and depression. Future research recommendations include (a) deriving consensus regarding the DP definition and (b) expanding the literature to consider a person’s broader contextual and developmental circumstances in relation to their digital data/records.

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“…Other current research focused on general psychopathology using the NIMH Research Domain Criteria (RDoC; Camacho et al, 2021;Thunnissen et al, 2021) to combine different levels of information, from genomics to behaviors, to describe mental health processes, problems, and illnesses. Also, information like sociability, including patterns of interaction and different behaviors like caffeine consumption, mind wandering, drinking alcohol, sleep, and physical activity, cognitive ability, are all information possible to relate to mental health (De Angel et al, 2022;Zarate et al, 2022). In rare cases, studies (de Vries et al, 2020) summarized the literature on mental well-being, including happiness, quality of life, life satisfaction, and positive affect.…”