Sebastian Burchert1, André Kerber1, Johannes Zimmermann2, Christine Knaevelsrud1. 1. Division of Clinical Psychological Intervention, Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany. 2. Institute of Psychology, University of Kassel, Kassel, Germany.
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.
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.
Authors: Joseph Firth; John Torous; Jennifer Nicholas; Rebekah Carney; Abhishek Pratap; Simon Rosenbaum; Jerome Sarris Journal: World Psychiatry Date: 2017-10 Impact factor: 49.548
Authors: John W Ayers; Benjamin M Althouse; Jon-Patrick Allem; J Niels Rosenquist; Daniel E Ford Journal: Am J Prev Med Date: 2013-05 Impact factor: 5.043
Authors: Katie M White; Charlotte Williamson; Nicol Bergou; Carolin Oetzmann; Valeria de Angel; Faith Matcham; Claire Henderson; Matthew Hotopf Journal: NPJ Digit Med Date: 2022-06-29
Authors: Andrian Liem; Karmia A Pakingan; Melissa R Garabiles; Hao Fong Sit; Sebastian Burchert; Agnes I F Lam; Brian J Hall Journal: Front Psychiatry Date: 2022-05-03 Impact factor: 5.435
Authors: Jean P M Mendes; Ivan R Moura; Pepijn Van de Ven; Davi Viana; Francisco J S Silva; Luciano R Coutinho; Silmar Teixeira; Joel J P C Rodrigues; Ariel Soares Teles Journal: J Med Internet Res Date: 2022-02-17 Impact factor: 7.076
Authors: Robert P Cowan; Alan M Rapoport; Jim Blythe; John Rothrock; Kerry Knievel; Addie M Peretz; Elizabeth Ekpo; Bharati M Sanjanwala; Yohannes W Woldeamanuel Journal: Headache Date: 2022-06-03 Impact factor: 5.311