Guided internet interventions for depression: impact of sociodemographic factors on treatment outcome in Indonesia. (July 2020)

• Record Type: Journal Article
• Authors: van der Wal, Junus M.; Arjadi, Retha; Nauta, Maaike H.; Burger, Huibert; Bockting, Claudi L.H.
• Is Part Of: Behaviour research and therapy. Volume 130(2020)
• Publication Date: 2020-07
• Subjects: Depression -- Internet intervention -- Treatment effect -- Moderators -- Sociodemographic factors -- Lay counsellor -- Dropout rates -- Lower- and middle income country -- Indonesia; Cognitive therapy -- Periodicals; Psychotherapy -- Periodicals; 616.891
• DOI: 10.1016/j.brat.2020.103589
• Languages: English
• ISSNs: 0005-7967

Abstract

Abstract: Depression is the leading cause of disability worldwide, but an alarming treatment gap exists, especially in lower- and middle income countries (LMIC), where people are exposed to many societal and sociodemographic risk factors. As internet access increases in LMIC, online interventions could decrease this gap, especially when shown suitable for all demographics, including vulnerable groups with low socioeconomic status (SES). We used mixed-model analysis to explore moderating effects of sociodemographic factors (age, sex, education level, SES and urbanicity) on treatment effect in a recent trial in Indonesia, comparing guided online behavioral activation versus online psychoeducation only for depression, in 313 participants from (sub)urban areas. Outcome measures were self-reported Patient Health Questionnaire 9 (PHQ-9) and Inventory of Depressive Symptomatology (IDS-SR). Without correction for multiple testing, we found urbanicity to moderate treatment effect, with stronger treatment effect in suburban relative to urban participants (IDS-SR 24 weeks past baseline, p = 0.04) and a trend towards moderation by SES, with stronger treatment effect in low SES groups (PHQ-9 10 weeks past baseline, p = 0.07). These exploratory results suggest online treatments are a promising mental health intervention for all demographics in a (sub)urban LMIC setting, but hypothesis-testing studies including rural participants are warranted. Highlights: Socioeconomic- and education status did not moderate treatment effect of guided online treatment for depression in Indonesia. Suburban participants showed stronger treatment effect then urban paticipants; the number of rural participants was low. There was no difference in dropout rate between different sociodemographic subgroups. Guided online treatment for depression is a promising tool to narrow a treatment gap in lower- and middle-income countries. Future studies on this topic should increase efforts to improve statistical power and include participants from rural areas.