Estimating longitudinal depressive symptoms from smartphone data in a transdiagnostic cohort

The integration of geographic methods and ecological momentary assessment in public health research: A systematic review of methods and applications

With the widespread prevalence of mobile devices, ecological momentary assessment (EMA) can be combined with geospatial data acquired through geographic techniques like global positioning system (GPS) and geographic information system. This technique enables the consideration of individuals' health and behavior outcomes of momentary exposures in spatial contexts, mostly referred to as "geographic ecological momentary assessment" or "geographically explicit EMA" (GEMA). However, the definition, scope, methods, and applications of GEMA remain unclear and unconsolidated. To fill this research gap, we conducted a systematic review to synthesize the methodological insights, identify common research interests and applications, and furnish recommendations for future GEMA studies. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines to systematically search peer-reviewed studies from six electronic databases in 2022. Screening and eligibility were conducted following inclusion criteria. The risk of bias assessment was performed, and narrative synthesis was presented for all studies. From the initial search of 957 publications, we identified 47 articles included in the review. In public health, GEMA was utilized to measure various outcomes, such as psychological health, physical and physiological health, substance use, social behavior, and physical activity. GEMA serves multiple research purposes: 1) enabling location-based EMA sampling, 2) quantifying participants' mobility patterns, 3) deriving exposure variables, 4) describing spatial patterns of outcome variables, and 5) performing data linkage or triangulation. GEMA has advanced traditional EMA sampling strategies and enabled location-based sampling by detecting location changes and specified geofences. Furthermore, advances in mobile technology have prompted considerations of additional sensor-based data in GEMA. Our results highlight the efficacy and feasibility of GEMA in public health research. Finally, we discuss sampling strategy, data privacy and confidentiality, measurement validity, mobile applications and technologies, and GPS accuracy and missing data in the context of current and future public health research that uses GEMA.

From smartphone data to clinically relevant predictions: A systematic review of digital phenotyping methods in depression

BACKGROUND

Smartphone-based digital phenotyping enables potentially clinically relevant information to be collected as individuals go about their day. This could improve monitoring and interventions for people with Major Depressive Disorder (MDD). The aim of this systematic review was to investigate current digital phenotyping features and methods used in MDD.

METHODS

We searched PubMed, PsycINFO, Embase, Scopus and Web of Science (10/11/2023) for articles including: (1) MDD population, (2) smartphone-based features, (3) validated ratings. Risk of bias was assessed using several sources. Studies were compared within analysis goals (correlating features with depression, predicting symptom severity, diagnosis, mood state/episode, other). Twenty-four studies (9801 participants) were included.

RESULTS

Studies achieved moderate performance. Common themes included challenges from complex and missing data (leading to a risk of bias), and a lack of external validation.

DISCUSSION

Studies made progress towards relating digital phenotypes to clinical variables, often focusing on time-averaged features. Methods investigating temporal dynamics more directly may be beneficial for patient monitoring. European Research Council consolidator grant: 101001118, Prospero: CRD42022346264, Open Science Framework: https://osf.io/s7ay4.

Recommendations for selection and adaptation of rating scales for clinical studies of rapid-acting antidepressants

The novel mechanisms of action (MOA) derived from some recently introduced molecular targets have led to regulatory approvals for rapid acting antidepressants (RAADs) that can generate responses within hours or days, rather than weeks or months. These novel targets include the N-methyl-D-glutamate receptor antagonist ketamine, along with its enantiomers and various derivatives, and the allosteric modulators of gamma-aminobutyric acid (GABA) receptors. There has also been a strong resurgence in interest in psychedelic compounds that impact a range of receptor sites including D1, 5-HT7, KOR, 5-HT5A, Sigma-1, NMDA, and BDNF. The RAADs developed from these novel targets have enabled successful treatment for difficult to treat depressed individuals and has generated a new wave of innovation in research and treatment. Despite the advances in the neurobiology and clinical treatment of mood disorders, we are still using rating instruments that were created decades ago for drugs from a different era (e.g., The Hamilton and Montgomery-Åsberg depression rating scales, HDRS, and MADRS) continue to be used. These rating instruments were designed to assess mood symptoms over a 7-day time frame. Consequently, the use of these rating instruments often requires modifications to address items that cannot be assessed in short time frames, such as the sleep and appetite items. This review describes the adaptative approaches that have been made with the existing scales to meet this need and examines additional domains such as daily activities, side effects, suicidal ideation and behavior, and role functioning. Recommendations for future studies are described, including the challenges related to implementation of these adapted measures and approaches to mitigation.

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.

Smartphone accelerometer data as a proxy for clinical data in modeling of bipolar disorder symptom trajectory

Being able to track and predict fluctuations in symptoms of mental health disorders such as bipolar disorder outside the clinic walls is critical for expanding access to care for the global population. To that end, we analyze a dataset of 291 individuals from a smartphone app targeted at bipolar disorder, which contains rich details about their smartphone interactions (including typing dynamics and accelerometer motion) collected everyday over several months, along with more traditional clinical features. The aim is to evaluate whether smartphone accelerometer data could serve as a proxy for traditional clinical data, either by itself or in combination with typing dynamics. Results show that accelerometer data improves the predictive performance of machine learning models by nearly 5% over those previously reported in the literature based only on clinical data and typing dynamics. This suggests it is possible to elicit essentially the same "information" about bipolar symptomology using different data sources, in a variety of settings.