PROJECT SUMMARY/ABSTRACT
There is a high global burden of untreated postpartum depression. Untreated postpartum depression presents
acute and long-term risks to a mother's mental and physical health, as well as her child's survival, health, and
development. Although there is increasing willingness among funders and health systems to involve non-
specialists, such as community health workers, in the delivery of psychological interventions for postpartum
depression, the outcomes of these non-specialist interventions have been mixed. Digital technology, specifically
the use of passive sensing data collection to learn more about a mother's life and support behavior change, has
the potential to improve the effectiveness of psychological interventions delivered by non-specialists. For the
proposed R21/R33, we will build on preliminary development work of Sensing Technologies for Maternal
Depression Treatment in Low Resource Settings (StandStrong) to demonstrate feasibility, acceptability, and
functionality for postpartum depression treatment in Nepal. StandStrong is a digital platform consisting of two
mobile apps, internet-of-things (IoT) passive sensors, and a data analysis engine built using modern machine
learning approaches. StandStrong incorporates passive sensing data collection from mothers and their infants
that can be monitored by non-specialist counselors and then incorporated to better personalize psychological
treatment. For Aim 1 (R21 phase), we will use human-centered design to iteratively refine the StandStrong
platform and associated implementation material. For Aim 2 (R21 phase), we will evaluate the StandStrong
platform and associated contents according to acceptability, feasibility, usage, benefit, and validity. If milestones
are met for the R21, we will proceed to the R33 stage, in which we will conduct a pilot randomized controlled
trial (RCT) comparing a psychological treatment as usual with the psychological treatment supplemented by the
StandStrong passive sensing platform with 112 depressed mothers (56 in each arm). For Aim 3 (R33 phase),
we will identify parameters for sustainability and scalability through engagement of policy, healthcare, and
telecom stakeholders, as well as costing the intervention and building mHealth research capacity. Successful
completion contributes to the NIMH's 2020 Strategic Plan to tailor existing interventions and develop innovative
service deliver for diverse populations. This project will contribute to NIMH's Opportunities and Challenges of
Developing Information Technologies through improving treatment of mental illness through use of passive
sensing technologies.