Prospective Follow-Up of Adolescents With and at Risk for Depression: Protocol and Methods of the Identifying Depression Early in Adolescence Risk Stratified Cohort Longitudinal Assessments

Abstract

Objective: To present the protocol and methods for the prospective longitudinal assessments-including clinical and digital phenotyping approaches-of the Identifying Depression Early in Adolescence Risk Stratified Cohort (IDEA-RiSCo) study, which comprises Brazilian adolescents stratified at baseline by risk of developing depression or presence of depression.

Method: Of 7,720 screened adolescents aged 14 to 16 years, we recruited 150 participants (75 boys, 75 girls) based on a composite risk score: 50 with low risk for developing depression (LR), 50 with high risk for developing depression (HR), and 50 with an active untreated major depressive episode (MDD). Three annual follow-up assessments were conducted, involving clinical measures (parent- and adolescent-reported questionnaires and psychiatrist assessments), active and passive data sensing via smartphones, and neurobiological measures (neuroimaging and biological material samples). Retention rates were 96% (Wave 1), 94% (Wave 2), and 88% (Wave 3), with no significant differences by sex or group (p > .05). Participants highlighted their familiarity with the research team and assessment process as a motivator for sustained engagement.

Discussion: This protocol relied on novel aspects, such as the use of a WhatsApp bot, which is particularly pertinent for low- to-middle-income countries, and the collection of information from diverse sources in a longitudinal design, encompassing clinical data, self-reports, parental reports, Global Positioning System (GPS) data, and ecological momentary assessments. The study engaged adolescents over an extensive period and demonstrated the feasibility of conducting a prospective follow-up study with a risk-enriched cohort of adolescents in a middle-income country, integrating mobile technology with traditional methodologies to enhance longitudinal data collection.

Keywords: adolescence; cohort; depression; digital phenotyping; risk score.

Figure 1

Timeline of Each Data Collection Mode Across the 3 Waves Note:EBM = electronic behavioral monitoring; GLH = Google Location History; IDEABot = The Identifying Depression Early in Adolescence Chatbot; K-SADS-PL = Schedule for Affective Disorders and Schizophrenia for School-Age Children—Present and Lifetime version.

Figure 2

Timeline of Active and Passive Digital Phenotyping Acquisition Procedure Note:Self-report questionnaires were completed by participants and caregivers at all data collection waves; clinical assessments were performed in person at W0 and via telemedicine at W3 and included K-SADS-PL and other clinician-administered instruments; biological material collection included blood samples at W0 and W3, saliva samples at W0, and hair samples at W3; neuroimaging was performed at W0 and W3; IDEABot was used to collect active sensing data at W2 and W3; the EBM app was used to collect passive sensing data at W2 and W3; GLH/Google Fit were used to collect passive sensing data at W3; actimetry was used to collect rest–activity data linked to the add-on study CHRONO-IDEA during 14 days at W0 and 23 days at W3; the Brain Explorer app was used to collect remote cognitive data during W3. EBM = Electronic Behavioral Monitoring; GLH = Google Location History; IDEABot = The Identifying Depression Early in Adolescence Chatbot; K-SADS-PL = Schedule for Affective Disorders and Schizophrenia for School-Age Children—Present and Lifetime version; W0 = Wave 0; W1 = Wave 1; W2 = Wave 2; W3 = Wave 3.