Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence

Utility of Consumer-Grade Wearable Devices for Inferring Physical and Mental Health Outcomes in Severe Mental Illness: Systematic Review

BACKGROUND

Digital wearable devices, worn on or close to the body, have potential for passively detecting mental and physical health symptoms among people with severe mental illness (SMI); however, the roles of consumer-grade devices are not well understood.

OBJECTIVE

This study aims to examine the utility of data from consumer-grade, digital, wearable devices (including smartphones or wrist-worn devices) for remotely monitoring or predicting changes in mental or physical health among adults with schizophrenia or bipolar disorder. Studies were included that passively collected physiological data (including sleep duration, heart rate, sleep and wake patterns, or physical activity) for at least 3 days. Research-grade actigraphy methods and physically obtrusive devices were excluded.

METHODS

We conducted a systematic review of the following databases: Cochrane Central Register of Controlled Trials, Technology Assessment, AMED (Allied and Complementary Medicine), APA PsycINFO, Embase, MEDLINE(R), and IEEE XPlore. Searches were completed in May 2024. Results were synthesized narratively due to study heterogeneity and divided into the following phenotypes: physical activity, sleep and circadian rhythm, and heart rate.

RESULTS

Overall, 23 studies were included that reported data from 12 distinct studies, mostly using smartphones and centered on relapse prevention. Only 1 study explicitly aimed to address physical health outcomes among people with SMI. In total, data were included from over 500 participants with SMI, predominantly from high-income countries. Most commonly, papers presented physical activity data (n=18), followed by sleep and circadian rhythm data (n=14) and heart rate data (n=6). The use of smartwatches to support data collection were reported by 8 papers; the rest used only smartphones. There was some evidence that lower levels of activity, higher heart rates, and later and irregular sleep onset times were associated with psychiatric diagnoses or poorer symptoms. However, heterogeneity in devices, measures, sampling and statistical approaches complicated interpretation.

CONCLUSIONS

Consumer-grade wearables show the ability to passively detect digital markers indicative of psychiatric symptoms or mental health status among people with SMI, but few are currently using these to address physical health inequalities. The digital phenotyping field in psychiatry would benefit from moving toward agreed standards regarding data descriptions and outcome measures and ensuring that valuable temporal data provided by wearables are fully exploited.

TRIAL REGISTRATION

PROSPERO CRD42022382267; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=382267.

Momentary Depression Severity Prediction in Patients With Acute Depression Who Undergo Sleep Deprivation Therapy: Speech-Based Machine Learning Approach

Background

Mobile devices for remote monitoring are inevitable tools to support treatment and patient care, especially in recurrent diseases such as major depressive disorder. The aim of this study was to learn if machine learning (ML) models based on longitudinal speech data are helpful in predicting momentary depression severity. Data analyses were based on a dataset including 30 inpatients during an acute depressive episode receiving sleep deprivation therapy in stationary care, an intervention inducing a rapid change in depressive symptoms in a relatively short period of time. Using an ambulatory assessment approach, we captured speech samples and assessed concomitant depression severity via self-report questionnaire over the course of 3 weeks (before, during, and after therapy). We extracted 89 speech features from the speech samples using the Extended Geneva Minimalistic Acoustic Parameter Set from the Open-Source Speech and Music Interpretation by Large-Space Extraction (audEERING) toolkit and the additional parameter speech rate.

Objective

We aimed to understand if a multiparameter ML approach would significantly improve the prediction compared to previous statistical analyses, and, in addition, which mechanism for splitting training and test data was most successful, especially focusing on the idea of personalized prediction.

Methods

To do so, we trained and evaluated a set of >500 ML pipelines including random forest, linear regression, support vector regression, and Extreme Gradient Boosting regression models and tested them on 5 different train-test split scenarios: a group 5-fold nested cross-validation at the subject level, a leave-one-subject-out approach, a chronological split, an odd-even split, and a random split.

Results

In the 5-fold cross-validation, the leave-one-subject-out, and the chronological split approaches, none of the models were statistically different from random chance. The other two approaches produced significant results for at least one of the models tested, with similar performance. In total, the superior model was an Extreme Gradient Boosting in the odd-even split approach (R²=0.339, mean absolute error=0.38; both P<.001), indicating that 33.9% of the variance in depression severity could be predicted by the speech features.

Conclusions

Overall, our analyses highlight that ML fails to predict depression scores of unseen patients, but prediction performance increased strongly compared to our previous analyses with multilevel models. We conclude that future personalized ML models might improve prediction performance even more, leading to better patient management and care.

Select or adjust? How information from early treatment stages boosts the prediction of non-response in internet-based depression treatment

BACKGROUND

Internet-based interventions produce comparable effectiveness rates as face-to-face therapy in treating depression. Still, more than half of patients do not respond to treatment. Machine learning (ML) methods could help to overcome these low response rates by predicting therapy outcomes on an individual level and tailoring treatment accordingly. Few studies implemented ML algorithms in internet-based depression treatment using baseline self-report data, but differing results hinder inferences on clinical practicability. This work compares algorithms using features gathered at baseline or early in treatment in their capability to predict non-response to a 6-week online program targeting depression.

METHODS

Our training and test sample encompassed 1270 and 318 individuals, respectively. We trained random forest algorithms on self-report and process features gathered at baseline and after 2 weeks of treatment. Non-responders were defined as participants not fulfilling the criteria for reliable and clinically significant change on PHQ-9 post-treatment. Our benchmark models were logistic regressions trained on baseline PHQ-9 sum or PHQ-9 early change, using 100 iterations of randomly sampled 80/20 train-test-splits.

RESULTS

Best performances were reached by our models involving early treatment characteristics (recall: 0.75-0.76; AUC: 0.71-0.77). Therapeutic alliance and early symptom change constituted the most important predictors. Models trained on baseline data were not significantly better than our benchmark.

CONCLUSIONS

Fair accuracies were only attainable by involving information from early treatment stages. In-treatment adaptation, instead of a priori selection, might constitute a more feasible approach for improving response when relying on easily accessible self-report features. Implementation trials are needed to determine clinical usefulness.

Examining how gamers connect with their avatars to assess their anxiety: A novel artificial intelligence approach

Research has supported that a gamer's attachment to their avatar can offer significant insights about their mental health, including anxiety. To assess this hypothesis, longitudinal data from 565 adult and adolescent participants (Mage = 29.3 years, SD = 10.6) was analyzed at two points, six months apart. Respondents were assessed using the User-Avatar Bond (UAB) scale and the Depression Anxiety Stress Scale (DASS) to measure their connection with their avatar and their risk for anxiety. The records were processed using both untuned and tuned artificial intelligence [AI] classifiers to evaluate present and future anxiety. The findings indicated that AI models are capable of accurately and autonomously discerning cases of anxiety risk based on the gamers' self-reported UAB, age, and duration of gaming, both at present and after six months. Notably, random forest algorithms surpassed other AI models in effectiveness, with avatar compensation emerging as the most significant factor in model training for prospective anxiety. The implications for assessment, prevention, and clinical practice are discussed.

Personalized mood prediction from patterns of behavior collected with smartphones

Over the last ten years, there has been considerable progress in using digital behavioral phenotypes, captured passively and continuously from smartphones and wearable devices, to infer depressive mood. However, most digital phenotype studies suffer from poor replicability, often fail to detect clinically relevant events, and use measures of depression that are not validated or suitable for collecting large and longitudinal data. Here, we report high-quality longitudinal validated assessments of depressive mood from computerized adaptive testing paired with continuous digital assessments of behavior from smartphone sensors for up to 40 weeks on 183 individuals experiencing mild to severe symptoms of depression. We apply a combination of cubic spline interpolation and idiographic models to generate individualized predictions of future mood from the digital behavioral phenotypes, achieving high prediction accuracy of depression severity up to three weeks in advance (R2 ≥ 80%) and a 65.7% reduction in the prediction error over a baseline model which predicts future mood based on past depression severity alone. Finally, our study verified the feasibility of obtaining high-quality longitudinal assessments of mood from a clinical population and predicting symptom severity weeks in advance using passively collected digital behavioral data. Our results indicate the possibility of expanding the repertoire of patient-specific behavioral measures to enable future psychiatric research.

Translating the user-avatar bond into depression risk: A preliminary machine learning study

Research has shown a link between depression risk and how gamers form relationships with their in-game figure of representation, called avatar. This is reinforced by literature supporting that a gamer's connection to their avatar may provide broader insight into their mental health. Therefore, it has been argued that if properly examined, the bond between a person and their avatar may reveal information about their current or potential struggles with depression offline. To examine whether the connection with an individuals' avatars may reveal their risk for depression, longitudinal data from 565 adults/adolescents (Mage = 29.3 years, SD = 10.6) were evaluated twice (six months apart). Participants completed the User-Avatar-Bond [UAB] scale and Depression Anxiety Stress Scale to measure avatar bond and depression risk. A series of tuned and untuned artificial intelligence [AI] classifiers analyzed their responses concurrently and prospectively. This allowed the examination of whether user-avatar bond can provide cross-sectional and predictive information about depression risk. Findings revealed that AI models can learn to accurately and automatically identify depression risk cases, based on gamers' reported UAB, age, and length of gaming involvement, both at present and six months later. In particular, random forests outperformed all other AIs, while avatar immersion was shown to be the strongest training predictor. Study outcomes demonstrate that UAB can be translated into accurate, concurrent, and future, depression risk predictions via trained AI classifiers. Assessment, prevention, and practice implications are discussed in the light of these results.

Directional associations among real-time activity, sleep, mood, and daytime symptoms in major depressive disorder using actigraphy and ecological momentary assessment

Previous research has suggested that individuals with major depressive disorder (MDD) experienced alterations in sleep and activity levels. However, the temporal associations among sleep, activity levels, mood, and daytime symptoms in MDD have not been fully investigated. The present study aimed to fill this gap by utilizing real-time data collected across time points and days. 75 individuals with MDD and 75 age- and gender-matched healthy controls were recruited. Ecological momentary assessments (EMA) were adopted to assess real-time mood status for 7 days, and actigraphy was employed to measure day-to-day sleep-activity patterns. Multilevel modeling analyses were performed. Results revealed a bidirectional association between mood/daytime symptoms and activity levels across EMA intervals. Increased activity levels were predictive of higher alert cognition and positive mood, while an increase in positive mood also predicted more increase in activity levels in depressed individuals. A bidirectional association between sleep and daytime symptoms was also found. Alert cognition was found to be predictive of better sleep in the subsequent night. Contrariwise, higher sleep efficiency predicted improved alert cognition and sleepiness/fatigue the next day. A unidirectional association between sleep and activity levels suggested that higher daytime activity levels predicted a larger increase in sleep efficiency among depressed individuals. This study indicated how mood, activity levels, and sleep were temporally and intricately linked to each other in depressed individuals using actigraphy and EMA. It could pave the way for novel and efficacious treatments for depression that target not just mood but sleep and activity levels.

Speech Features as Predictors of Momentary Depression Severity in Patients With Depressive Disorder Undergoing Sleep Deprivation Therapy: Ambulatory Assessment Pilot Study

BACKGROUND

The use of mobile devices to continuously monitor objectively extracted parameters of depressive symptomatology is seen as an important step in the understanding and prevention of upcoming depressive episodes. Speech features such as pitch variability, speech pauses, and speech rate are promising indicators, but empirical evidence is limited, given the variability of study designs.

OBJECTIVE

Previous research studies have found different speech patterns when comparing single speech recordings between patients and healthy controls, but only a few studies have used repeated assessments to compare depressive and nondepressive episodes within the same patient. To our knowledge, no study has used a series of measurements within patients with depression (eg, intensive longitudinal data) to model the dynamic ebb and flow of subjectively reported depression and concomitant speech samples. However, such data are indispensable for detecting and ultimately preventing upcoming episodes.

METHODS

In this study, we captured voice samples and momentary affect ratings over the course of 3 weeks in a sample of patients (N=30) with an acute depressive episode receiving stationary care. Patients underwent sleep deprivation therapy, a chronotherapeutic intervention that can rapidly improve depression symptomatology. We hypothesized that within-person variability in depressive and affective momentary states would be reflected in the following 3 speech features: pitch variability, speech pauses, and speech rate. We parametrized them using the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS) from open-source Speech and Music Interpretation by Large-Space Extraction (openSMILE; audEERING GmbH) and extracted them from a transcript. We analyzed the speech features along with self-reported momentary affect ratings, using multilevel linear regression analysis. We analyzed an average of 32 (SD 19.83) assessments per patient.

RESULTS

Analyses revealed that pitch variability, speech pauses, and speech rate were associated with depression severity, positive affect, valence, and energetic arousal; furthermore, speech pauses and speech rate were associated with negative affect, and speech pauses were additionally associated with calmness. Specifically, pitch variability was negatively associated with improved momentary states (ie, lower pitch variability was linked to lower depression severity as well as higher positive affect, valence, and energetic arousal). Speech pauses were negatively associated with improved momentary states, whereas speech rate was positively associated with improved momentary states.

CONCLUSIONS

Pitch variability, speech pauses, and speech rate are promising features for the development of clinical prediction technologies to improve patient care as well as timely diagnosis and monitoring of treatment response. Our research is a step forward on the path to developing an automated depression monitoring system, facilitating individually tailored treatments and increased patient empowerment.

What digital health technology types are used in mental health prevention and intervention? Review of systematic reviews for systematization of technologies

Digital health technology has been widely applied to mental health interventions worldwide. Using digital phenotyping to identify an individual's mental health status has become particularly important. However, many technologies other than digital phenotyping are expected to become more prevalent in the future. The systematization of these technologies is necessary to accurately identify trends in mental health interventions. However, no consensus on the technical classification of digital health technologies for mental health interventions has emerged. Thus, we conducted a review of systematic review articles on the application of digital health technologies in mental health while attempting to systematize the technology using the Delphi method. To identify technologies used in digital phenotyping and other digital technologies, we included 4 systematic review articles that met the inclusion criteria, and an additional 8 review articles, using a snowballing approach, were incorporated into the comprehensive review. Based on the review results, experts from various disciplines participated in the Delphi process and agreed on the following 11 technical categories for mental health interventions: heart rate estimation, exercise or physical activity, sleep estimation, contactless heart rate/pulse wave estimation, voice and emotion analysis, self-care/cognitive behavioral therapy/mindfulness, dietary management, psychological safety, communication robots, avatar/metaverse devices, and brain wave devices. The categories we defined intentionally included technologies that are expected to become widely used in the future. Therefore, we believe these 11 categories are socially implementable and useful for mental health interventions.

Digital and precision clinical trials: innovations for testing mental health medications, devices, and psychosocial treatments

Mental health treatment advances - including neuropsychiatric medications and devices, psychotherapies, and cognitive treatments - lag behind other fields of clinical medicine such as cardiovascular care. One reason for this gap is the traditional techniques used in mental health clinical trials, which slow the pace of progress, produce inequities in care, and undermine precision medicine goals. Newer techniques and methodologies, which we term digital and precision trials, offer solutions. These techniques consist of (1) decentralized (i.e., fully-remote) trials which improve the speed and quality of clinical trials and increase equity of access to research, (2) precision measurement which improves success rate and is essential for precision medicine, and (3) digital interventions, which offer increased reach of, and equity of access to, evidence-based treatments. These techniques and their rationales are described in detail, along with challenges and solutions for their utilization. We conclude with a vignette of a depression clinical trial using these techniques.

Implementation of case-based learning in science education: A systematic review

Case-Based Learning (CBL) in science education has developed rapidly. This paper reviews the literature on trends in implementing CBL in science education. For this systematic review, we followed the recommendation of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Data were obtained from the ERIC, Scopus, and Google Scholar databases by taking scientific articles from reputable international journals with a Scopus Q1-Q4 index and impact factor ranging from 0.040 to 3.092, which is the main indicator of choosing quality of journal articles. Articles were searched using titles and keywords "Case-Based or Case Method or Science Education" from 2012 to 2022. The search yielded 1183 articles, and the selection results were 52 articles for review. The study found that CBL was represented mostly in three learning approaches, namely CBL-IBL, CBL-PBL, CBL-PjBL, and the rest being CBL-Blended, CBL-Oline, and CBL-Collaborative. Case-based applications in science education were dominated by health (58%), chemistry (35%), physics (1%) and biology (6%). The reviewed studies encountered some difficulties in implementing CBL. One of them is that solving the problem takes a long time. This review revealed case-based approach to be appropriate to be implemented in an active learning activity based on real-life context.

Digital Phenotyping for Mood Disorders: Methodology-Oriented Pilot Feasibility Study

BACKGROUND

In the burgeoning area of clinical digital phenotyping research, there is a dearth of literature that details methodology, including the key challenges and dilemmas in developing and implementing a successful architecture for technological infrastructure, patient engagement, longitudinal study participation, and successful reporting and analysis of diverse passive and active digital data streams.

OBJECTIVE

This article provides a narrative rationale for our study design in the context of the current evidence base and best practices, with an emphasis on our initial lessons learned from the implementation challenges and successes of this digital phenotyping study.

METHODS

We describe the design and implementation approach for a digital phenotyping pilot feasibility study with attention to synthesizing key literature and the reasoning for pragmatic adaptations in implementing a multisite study encompassing distinct geographic and population settings. This methodology was used to recruit patients as study participants with a clinician-validated diagnostic history of unipolar depression, bipolar I disorder, or bipolar II disorder, or healthy controls in 2 geographically distinct health care systems for a longitudinal digital phenotyping study of mood disorders.

RESULTS

We describe the feasibility of a multisite digital phenotyping pilot study for patients with mood disorders in terms of passively and actively collected phenotyping data quality and enrollment of patients. Overall data quality (assessed as the amount of sensor data obtained vs expected) was high compared to that in related studies. Results were reported on the relevant demographic features of study participants, revealing recruitment properties of age (mean subgroup age ranged from 31 years in the healthy control subgroup to 38 years in the bipolar I disorder subgroup), sex (predominance of female participants, with 7/11, 64% females in the bipolar II disorder subgroup), and smartphone operating system (iOS vs Android; iOS ranged from 7/11, 64% in the bipolar II disorder subgroup to 29/32, 91% in the healthy control subgroup). We also described implementation considerations around digital phenotyping research for mood disorders and other psychiatric conditions.

CONCLUSIONS

Digital phenotyping in affective disorders is feasible on both Android and iOS smartphones, and the resulting data quality using an open-source platform is higher than that in comparable studies. While the digital phenotyping data quality was independent of gender and race, the reported demographic features of study participants revealed important information on possible selection biases that may result from naturalistic research in this domain. We believe that the methodology described will be readily reproducible and generalizable to other study settings and patient populations given our data on deployment at 2 unique sites.

Deep learning(s) in gaming disorder through the user-avatar bond: A longitudinal study using machine learning

Background and aims

Gaming disorder [GD] risk has been associated with the way gamers bond with their visual representation (i.e., avatar) in the game-world. More specifically, a gamer's relationship with their avatar has been shown to provide reliable mental health information about the user in their offline life, such as their current and prospective GD risk, if appropriately decoded.

Methods

To contribute to the paucity of knowledge in this area, 565 gamers (Mage = 29.3 years; SD =10.6) were assessed twice, six months apart, using the User-Avatar-Bond Scale (UABS) and the Gaming Disorder Test. A series of tuned and untuned artificial intelligence [AI] classifiers analysed concurrently and prospectively their responses.

Results

Findings showed that AI models learned to accurately and automatically identify GD risk cases, based on gamers' reported UABS score, age, and length of gaming involvement, both concurrently and longitudinally (i.e., six months later). Random forests outperformed all other AIs, while avatar immersion was shown to be the strongest training predictor.

Conclusion

Study outcomes demonstrated that the user-avatar bond can be translated into accurate, concurrent and future GD risk predictions using trained AI classifiers. Assessment, prevention, and practice implications are discussed in the light of these findings.

Classifying and clustering mood disorder patients using smartphone data from a feasibility study

Differentiating between bipolar disorder and major depressive disorder can be challenging for clinicians. The diagnostic process might benefit from new ways of monitoring the phenotypes of these disorders. Smartphone data might offer insight in this regard. Today, smartphones collect dense, multimodal data from which behavioral metrics can be derived. Distinct patterns in these metrics have the potential to differentiate the two conditions. To examine the feasibility of smartphone-based phenotyping, two study sites (Mayo Clinic, Johns Hopkins University) recruited patients with bipolar I disorder (BPI), bipolar II disorder (BPII), major depressive disorder (MDD), and undiagnosed controls for a 12-week observational study. On their smartphones, study participants used a digital phenotyping app (mindLAMP) for data collection. While in use, mindLAMP gathered real-time geolocation, accelerometer, and screen-state (on/off) data. mindLAMP was also used for EMA delivery. MindLAMP data was then used as input variables in binary classification, three-group k-nearest neighbors (KNN) classification, and k-means clustering. The best-performing binary classification model was able to classify patients as control or non-control with an AUC of 0.91 (random forest). The model that performed best at classifying patients as having MDD or bipolar I/II had an AUC of 0.62 (logistic regression). The k-means clustering model had a silhouette score of 0.46 and an ARI of 0.27. Results support the potential for digital phenotyping methods to cluster depression, bipolar disorder, and healthy controls. However, due to inconsistencies in accuracy, more data streams are required before these methods can be applied to clinical practice.

Identifying self-disclosed anxiety on Twitter: A natural language processing approach

BACKGROUND

Text analyses of social media posts are a promising source of mental health information. This study used natural language processing to explore distinct language patterns on Twitter related to self-reported anxiety diagnosis.

METHODS

A total of 233.000 tweets made by 605 users (300 reporting anxiety diagnosis and 305 not) over six months were comparatively analysed, considering user behavior, Linguistic Inquiry Word Count (LIWC), and sentiment analysis. Twitter users with a self-disclosed diagnosis of anxiety were classified as 'anxious' to facilitate group comparisons.

RESULTS

Supervised machine learning models showed a high prediction accuracy (Naïve Bayes 81.1 %, Random Forests 79.8 %, and LASSO-regression 79.4 %) in identifying Twitter users' self-disclosed diagnosis of anxiety. Additionally, a Latent Profile Analysis (LPA) identified four profiles characterized by high sentiment (31 % anxious participants), low sentiment (68 % anxious), self-immersed (80 % anxious), and normative behavior (38 % anxious).

CONCLUSION

The digital footprint of self-disclosed anxiety on Twitter posts presented a high frequency of words conveying either negative sentiment, a low frequency of positive sentiment, a reduced frequency of posting, and lengthier texts. These distinct patterns enabled highly accurate prediction of anxiety diagnosis. On this basis, appropriately resourced, awareness raising, online mental health campaigns are advocated.

Feasibility of Using Research Electronic Data Capture (REDCap) to Collect Daily Experiences of Parent-Child Dyads: Ecological Momentary Assessment Study

BACKGROUND

Intensive longitudinal data collection, including ecological momentary assessment (EMA), has the potential to reduce recall biases, collect more ecologically valid data, and increase our understanding of dynamic associations between variables. EMA is typically administered using an application that is downloaded on participants' devices, which presents cost and privacy concerns that may limit its use. Research Electronic Data Capture (REDCap), a web-based survey application freely available to nonprofit organizations, may allow researchers to overcome these barriers; however, at present, little guidance is available to researchers regarding the setup of EMA in REDCap, especially for those who are new to using REDCap or lack advanced programming expertise.

OBJECTIVE

We provide an example of a simplified EMA setup in REDCap. This study aims to demonstrate the feasibility of this approach. We provide information on survey completion and user behavior in a sample of parents and children recruited across Canada.

METHODS

We recruited 66 parents and their children (aged 9-13 years old) from an existing longitudinal cohort study to participate in a study on risk and protective factors for children's mental health. Parents received survey prompts (morning and evening) by email or SMS text message for 14 days, twice daily. Each survey prompt contained 2 sections, one for parents and one for children to complete.

RESULTS

The completion rates were good (mean 82%, SD 8%) and significantly higher on weekdays than weekends and in dyads with girls than dyads with boys. Children were available to respond to their own survey questions most of the time (in 1134/1498, 75.7% of surveys submitted). The number of assessments submitted was significantly higher, and response times were significantly faster among participants who selected SMS text message survey notifications compared to email survey notifications. The average response time was 47.0 minutes after the initial survey notification, and the use of reminder messages increased survey completion.

CONCLUSIONS

Our results support the feasibility of using REDCap for EMA studies with parents and children. REDCap also has features that can accommodate EMA studies by recruiting participants across multiple time zones and providing different survey delivery methods. Offering the option of SMS text message survey notifications and reminders may be an important way to increase completion rates and the timeliness of responses. REDCap is a potentially useful tool for researchers wishing to implement EMA in settings in which cost or privacy are current barriers. Researchers should weigh these benefits with the potential limitations of REDCap and this design, including staff time to set up, monitor, and clean the data outputs of the project.

Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health

The term "digital phenotype" refers to the digital footprint left by patient-environment interactions. It has potential for both research and clinical applications but challenges our conception of health care by opposing 2 distinct approaches to medicine: one centered on illness with the aim of classifying and curing disease, and the other centered on patients, their personal distress, and their lived experiences. In the context of mental health and psychiatry, the potential benefits of digital phenotyping include creating new avenues for treatment and enabling patients to take control of their own well-being. However, this comes at the cost of sacrificing the fundamental human element of psychotherapy, which is crucial to addressing patients' distress. In this viewpoint paper, we discuss the advances rendered possible by digital phenotyping and highlight the risk that this technology may pose by partially excluding health care professionals from the diagnosis and therapeutic process, thereby foregoing an essential dimension of care. We conclude by setting out concrete recommendations on how to improve current digital phenotyping technology so that it can be harnessed to redefine mental health by empowering patients without alienating them.

The Use of Mobile Assessments for Monitoring Mental Health in Youth: Umbrella Review

BACKGROUND

Improving mental health in youth is a major concern. Future approaches to monitor and intervene in youth mental health problems should rely on mobile tools that allow for the daily monitoring of mental health both actively (eg, using ecological momentary assessments [EMAs]) and passively (eg, digital phenotyping) by capturing individuals' data.

OBJECTIVE

This umbrella review aims to (1) report the main characteristics of existing reviews on mental health and young people, including mobile approaches to mental health; (2) describe EMAs and trace data and the mental health conditions investigated; (3) report the main results; and (4) outline promises, limitations, and directions for future research.

METHODS

A systematic literature search was carried out in 9 scientific databases (Communication & Mass Media Complete, Psychology and Behavioral Sciences Collection, PsycINFO, CINAHL, ERIC, MEDLINE, the ProQuest Sociology Database, Web of Science, and PubMed) on January 30, 2022, coupled with a hand search and updated in July 2022. We included (systematic) reviews of EMAs and trace data in the context of mental health, with a specific focus on young populations, including children, adolescents, and young adults. The quality of the included reviews was evaluated using the AMSTAR (Assessment of Multiple Systematic Reviews) checklist.

RESULTS

After the screening process, 30 reviews (published between 2016 and 2022) were included in this umbrella review, of which 21 (70%) were systematic reviews and 9 (30%) were narrative reviews. The included systematic reviews focused on symptoms of depression (5/21, 24%); bipolar disorders, schizophrenia, or psychosis (6/21, 29%); general ill-being (5/21, 24%); cognitive abilities (2/21, 9.5%); well-being (1/21, 5%); personality (1/21, 5%); and suicidal thoughts (1/21, 5%). Of the 21 systematic reviews, 15 (71%) summarized studies that used mobile apps for tracing, 2 (10%) summarized studies that used them for intervention, and 4 (19%) summarized studies that used them for both intervention and tracing. Mobile tools used in the systematic reviews were smartphones only (8/21, 38%), smartphones and wearable devices (6/21, 29%), and smartphones with other tools (7/21, 33%). In total, 29% (6/21) of the systematic reviews focused on EMAs, including ecological momentary interventions; 33% (7/21) focused on trace data; and 38% (8/21) focused on both. Narrative reviews mainly focused on the discussion of issues related to digital phenotyping, existing theoretical frameworks used, new opportunities, and practical examples.

CONCLUSIONS

EMAs and trace data in the context of mental health assessments and interventions are promising tools. Opportunities (eg, using mobile approaches in low- and middle-income countries, integration of multimodal data, and improving self-efficacy and self-awareness on mental health) and limitations (eg, absence of theoretical frameworks, difficulty in assessing the reliability and effectiveness of such approaches, and need to appropriately assess the quality of the studies) were further discussed.

TRIAL REGISTRATION

PROSPERO CRD42022347717; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=347717.

Conceptualising social media addiction: a longitudinal network analysis of social media addiction symptoms and their relationships with psychological distress in a community sample of adults

BACKGROUND

Problematic social media use has been identified as negatively impacting psychological and everyday functioning and has been identified as a possible behavioural addiction (social media addiction; SMA). Whether SMA can be classified as a distinct behavioural addiction has been debated within the literature, with some regarding SMA as a premature pathologisation of ordinary social media use behaviour and suggesting there is little evidence for its use as a category of clinical concern. This study aimed to understand the relationship between proposed symptoms of SMA and psychological distress and examine these over time in a longitudinal network analysis, in order better understand whether SMA warrants classification as a unique pathology unique from general distress.

METHOD

N = 462 adults (Mage = 30.8, SDage = 9.23, 69.3% males, 29% females, 1.9% other sex or gender) completed measures of social media addiction (Bergen Social Media Addiction Scale), and psychological distress (DASS-21) at two time points, twelve months apart. Data were analysed using network analysis (NA) to explore SMA symptoms and psychological distress. Specifically, NA allows to assess the 'influence' and pathways of influence of each symptom in the network both cross-sectionally at each time point, as well as over time.

RESULTS

SMA symptoms were found to be stable cross-sectionally over time, and were associated with, yet distinct, from, depression, anxiety and stress. The most central symptoms within the network were tolerance and mood-modification in terms of expected influence and closeness respectively. Depression symptoms appeared to have less of a formative effect on SMA symptoms than anxiety and stress.

CONCLUSIONS

Our findings support the conceptualisation of SMA as a distinct construct occurring based on an underpinning network cluster of behaviours and a distinct association between SMA symptoms and distress. Further replications of these findings, however, are needed to strengthen the evidence for SMA as a unique behavioural addiction.

A Systematic Review of Location Data for Depression Prediction

Depression contributes to a wide range of maladjustment problems. With the development of technology, objective measurement for behavior and functional indicators of depression has become possible through the passive sensing technology of digital devices. Focusing on location data, we systematically reviewed the relationship between depression and location data. We searched Scopus, PubMed, and Web of Science databases by combining terms related to passive sensing and location data with depression. Thirty-one studies were included in this review. Location data demonstrated promising predictive power for depression. Studies examining the relationship between individual location data variables and depression, homestay, entropy, and the normalized entropy variable of entropy dimension showed the most consistent and significant correlations. Furthermore, variables of distance, irregularity, and location showed significant associations in some studies. However, semantic location showed inconsistent results. This suggests that the process of geographical movement is more related to mood changes than to semantic location. Future research must converge across studies on location-data measurement methods.

Reinventing mental health care in youth through mobile approaches: Current status and future steps

In this perspective, we aim to bring together research on mobile assessments and interventions in the context of mental health care in youth. After the COVID-19 pandemic, one out of five young people is experiencing mental health problems worldwide. New ways to face this burden are now needed. Young people search for low-burden services in terms of costs and time, paired with high flexibility and easy accessibility. Mobile applications meet these principles by providing new ways to inform, monitor, educate, and enable self-help, thus reinventing mental health care in youth. In this perspective, we explore the existing literature reviews on mobile assessments and interventions in youth through data collected passively (e.g., digital phenotyping) and actively (e.g., using Ecological Momentary Assessments-EMAs). The richness of such approaches relies on assessing mental health dynamically by extending beyond the confines of traditional methods and diagnostic criteria, and the integration of sensor data from multiple channels, thus allowing the cross-validation of symptoms through multiple information. However, we also acknowledge the promises and pitfalls of such approaches, including the problem of interpreting small effects combined with different data sources and the real benefits in terms of outcome prediction when compared to gold-standard methods. We also explore a new promising and complementary approach, using chatbots and conversational agents, that encourages interaction while tracing health and providing interventions. Finally, we suggest that it is important to continue to move beyond the ill-being framework by giving more importance to intervention fostering well-being, e.g., using positive psychology.

Passive Sensing in the Prediction of Suicidal Thoughts and Behaviors: Protocol for a Systematic Review

BACKGROUND

Suicide is a severe public health problem, resulting in a high number of attempts and deaths each year. Early detection of suicidal thoughts and behaviors (STBs) is key to preventing attempts. We discuss passive sensing of digital and behavioral markers to enhance the detection and prediction of STBs.

OBJECTIVE

The paper presents the protocol for a systematic review that aims to summarize existing research on passive sensing of STBs and evaluate whether the STB prediction can be improved using passive sensing compared to prior prediction models.

METHODS

A systematic search will be conducted in the scientific databases MEDLINE, PubMed, Embase, PsycINFO, and Web of Science. Eligible studies need to investigate any passive sensor data from smartphones or wearables to predict STBs. The predictive value of passive sensing will be the primary outcome. The practical implications and feasibility of the studies will be considered as secondary outcomes. Study quality will be assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). If studies are sufficiently homogenous, we will conduct a meta-analysis of the predictive value of passive sensing on STBs.

RESULTS

The review process started in July 2022 with data extraction in September 2022. Results are expected in December 2022.

CONCLUSIONS

Despite intensive research efforts, the ability to predict STBs is little better than chance. This systematic review will contribute to our understanding of the potential of passive sensing to improve STB prediction. Future research will be stimulated since gaps in the current literature will be identified and promising next steps toward clinical implementation will be outlined.

TRIAL REGISTRATION

OSF Registries osf-registrations-hzxua-v1; https://osf.io/hzxua.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/42146.

Correction: Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence

An amendment to this paper has been published and can be accessed via the original article.