Screening accuracy of a 14-day smartphone ambulatory assessment of depression symptoms and mood dynamics in a general population sample: Comparison with the PHQ-9 depression screening

Abstract

Introduction Major depression affects over 300 million people worldwide, but cases are often detected late or remain undetected. This increases the risk of symptom deterioration and chronification. Consequently, there is a high demand for low threshold but clinically sound approaches to depression detection. Recent studies show a great willingness among users of mobile health apps to assess daily depression symptoms. In this pilot study, we present a provisional validation of the depression screening app Moodpath. The app offers a 14-day ambulatory assessment (AA) of depression symptoms based on the ICD-10 criteria as well as ecologically momentary mood ratings that allow the study of short-term mood dynamics. Materials and methods N = 113 Moodpath users were selected through consecutive sampling and filled out the Patient Health Questionnaire (PHQ-9) after completing 14 days of AA with 3 question blocks (morning, midday, and evening) per day. The psychometric properties (sensitivity, specificity, accuracy) of the ambulatory Moodpath screening were assessed based on the retrospective PHQ-9 screening result. In addition, several indicators of mood dynamics (e.g. average, inertia, instability), were calculated and investigated for their individual and incremental predictive value using regression models. Results We found a strong linear relationship between the PHQ-9 score and the AA Moodpath depression score (r = .76, p < .001). The app-based screening demonstrated a high sensitivity (.879) and acceptable specificity (.745). Different indicators of mood dynamics covered substantial amounts of PHQ-9 variance, depending on the number of days with mood data that were included in the analyses. Discussion AA and PHQ-9 shared a large proportion of variance but may not measure exactly the same construct. This may be due to the differences in the underlying diagnostic systems or due to differences in momentary and retrospective assessments. Further validation through structured clinical interviews is indicated. The results suggest that ambulatory assessed mood indicators are a promising addition to multimodal depression screening tools. Improving app-based AA screenings requires adapted screening algorithms and corresponding methods for the analysis of dynamic processes over time.

Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods

“…version, and the diagnostic reliability and effectiveness are identical to offline methods 102,103 and with smartphones. 9,53,104 Similar to the depression diagnostic values of PHQ-9 through the existing traditional technique in this paper, 105 when PHQ-9 data from mobile devices are analyzed with a machine learning algorithm, good results are reported with 80% sensitivity and 85% specificity. This indicates good diagnostic properties.…”

“…In addition, the level of depression indicated by the PHQ-9 score shows a high correlation with the features of depression that can be detected by a smartphone. 12,42,[53][54][55] In addition to the merits of PHQ-9 as a discriminate tool for depression, it has high sensitivity and specificity. In a primary care study PHQ-9 data, the algorithm sensitivity and specificity was 73% and 98%, 56 respectively.…”

“…These have strengths in the identification of depression by using machine learning techniques. 53,54,58,59,70,71 In this study, studies were analyzed using machine learning techniques on PHQ-9 depression screening data collected through mobile devices according to the latest depression screening trends. In addition, we intend to contribute to predicting depression through mobile devices in the future by examining the predictive diagnostic power of PHQ-9 on mobile devices through diagnostic metaanalysis.…”

Reliability and validity of the Brief Emotional Experience Scale (BEES) for mood monitoring

“…We expect the BEES has a lot of potential as a useful mood monitoring measure in both applied clinical settings, and for community-based wellness interventions. For example, to help screen, plan, monitor, evaluate, and provide feedback to participants on psychological and/or pharmaceutical treatment (Burchert et al, 2021;Dogan et al, 2017;Dubad et al, 2018;Hollis et al, 2017;Sequeira et al, 2020;Vinci et al, 2018), and to better understand the experience of clinical conditions at a more personalised level (Bonsall et al, 2012;Carr et al, 2018;Dunkley et al, 2017;Wenzel et al, 2016). It may also be useful for mood monitoring in wellness interventions for the public that involves mood monitoring (Bakker & Rickard, 2018;de Girolamo et al, 2020;Hollis et al, 2017;Hussain-Shamsy et al, 2020).…”

Does app-based unguided self-management improve mental health literacy, patient empowerment and access to care for people with mental health impairments? Study protocol for a randomised controlled trial

“…Research on a previous version of the MindDoc App yielded good diagnostic accuracy compared with a gold-standard measure for depression. 38 In case a user indicates suicidal ideation within the monitoring feature of the application, a crisis chat bot is immediately activated that directs the user to a national crisis helpline (in Germany: Telefonseelsorge) that can be called directly from the app.…”