January 2024
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17 Reads
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January 2024
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17 Reads
April 2023
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7 Reads
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2 Citations
Technology Mind and Behavior
August 2022
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38 Reads
BMC Psychiatry
An amendment to this paper has been published and can be accessed via the original article.
June 2022
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88 Reads
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.
... Taking these into consideration, the present study innovatively examined a recently collected longitudinal dataset using AI/ML classifiers, aiming to translate gamers reported UAB identification, immersion, and compensation/idealization into their present and prospective (i.e., six months later) GD risk, while also taking into consideration their age and years of videogame engagement. In particular the choice of a longitudinal design was chosen over cross-sectional data collection because it allows the examination of the direction of causality between the behaviours examined, while additionally enabling the potential translation of the user-avatar bond into prospective GD risk (Zarate, Dorman, Prokofieva, Morda, & Stavropoulos, 2023). Consequently, the following research questions (RQs) were formulated: RQ1: How can, if at all, ML/AI applications be trained to identify whether a gamer presents with current GD risk, based on their UAB reported identification, immersion, compensation, age, and length of gaming involvement (i.e., concurrent GD phenotype)? ...
April 2023
Technology Mind and Behavior