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 and describe empirical studies examining the use of DP to study depression; (b) to describe the different methods and technology employed; © to integrate the evidence to ascertain the efficacy of digital data in the studying, diagnosis, and monitoring of depression; and (d) to describe 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 suggested 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.