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.

Internet Gaming Disorder (IGD) embodies a persistent and recurrent engagement with video games, to the exclusion of other activities, that cannot be controlled, and with significant impairments in everyday functioning. Previous research suggests that IGD is experienced differently depending on the gamer's profile, while distress symptoms such as depression, anxiety, and stress have been independently associated to the development of IGD. Interestingly, no study to date has aimed to profile gamers based on these three psychopathologies. The present study aimed to (a) profile gamers concerning their depression, anxiety, and stress, and (b) examine the differences in IGD levels between the different profiles of distress. A sample consisting of 968 gamers (18-64 years, M age = 29.54) was assessed with the Depression, Anxiety and Stress Scale (DASS-21) and the Internet Gaming Disorder Scale-Short-Form (IGDS9-SF). Latent class analysis (LCA) identified three distinct distress profiles. These encompassed "High-Distress Comorbidity" (HDC; 25.9%), "Medium-Distress Comorbidity" (MDC; 48.7%) and "Low-Distress Comorbidity" (LDC; 25.4%) gamers. As hypothesised, higher distress comorbidity profiles are linked with higher IGD levels. Findings suggest that there are different distress profiles varying by symptom severity, with more distressed gamers presenting with higher IGD levels independent of the specific distress symptom they reported. Thus, differential diagnosis of depression, anxiety, and stress symptoms may not be necessary in the context of IGD treatment, but rather distress behaviours should be targeted concurrently and independent of their nature. Public Significance StatementBoth the type of gamer a person is and their level of distress play a part in the likelihood of developing Internet Gaming Disorder (IGD). Different profiles of distress were examined in relation to IGD levels, with results showing that higher distress profiles were accompanied by higher IGD levels.

Background As nightmares may be a risk factor for, or symptom of, multiple psychological disorders, some researchers suggest that nightmares should be screened, diagnosed, and treated. Treatments for nightmares include trauma-focused Cognitive Behavioural Therapy and Image Rehearsal Therapy, and pharmacological interventions such as prazosin and nitrazepam. As recent research has put into question our current understanding of treatment efficacy, there is a need to systematically review findings related to the effectiveness of nightmare treatments to inform best practice. The current review assessed the efficacy of psychosocial treatments of nightmare in all cohorts. Methods A systematic search of four databases for peer reviewed journal articles from 2000 onwards produced 69 (35 RCTs, 34 non-RCTs) eligible articles that underwent narrative synthesis. Results The results provide strong evidence for exposure and image rehearsal treatments for the reduction of nightmare frequency, severity, and distress, in civilian, military, idiopathic, and posttraumatic stress disorder (PTSD) cohorts. There is emerging evidence that self-guided and brief treatment modalities offer efficient and effective treatment options. There is an urgent need for clinical trials of treatment effectiveness in children. Conclusions The results suggest that treatments for nightmares are most effective when they facilitate a sense of control or mastery by directly targeting the nightmare content and/or the client’s emotional responses to the nightmare content. Trial registration A review protocol was registered with PROSPERO (CRD42020204861).

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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.