Smartphone-based mindfulness training for health following early life adversity

PROJECT SUMMARY Over 25% of the US population reports exposure to early life adversity (ELA)—physical or emotional trauma or neglect in childhood. ELA predicts life-long health disparities, including increased risk for chronic cardiometabolic and autoimmune diseases and early mortality. Exposure to adversity in childhood is thought to program biological adaptations to stress that contribute to disease risk in adulthood. There are currently no evidence-based interventions to offset these risks in adulthood. Mindfulness meditation interventions have been effective for reducing stress and improving markers of physical health. However, it is unclear whether mindfulness interventions are acceptable and effective for improving health among people with a history of ELA. In a sample of emerging and young adults with a history of ELA, the proposed study evaluates feasibility of using smartphone-based mindfulness and control interventions and of measuring stress in daily life using ambulatory assessment and mobile sensor models with the long-term goal of implementing a stress-triggered just-in-time (JIT) mindfulness intervention in future research. Emerging/young adults with a history of ELA (n=80) will provide two weeks of baseline ambulatory stress ratings, continuous mobile sensor data (e.g., heart rate, activity, and location from wearable devices and smartphones), and blood samples to assess inflammatory biomarkers. Participants will then be randomly assigned to a 14-lesson smartphone-based (a) mindfulness training intervention or (b) matched coping control intervention, both boosted with randomly-delivered practice prompts to permit retrospective evaluation of JIT prompts. Ambulatory stress, sensor data, and inflammatory biomarkers will again be assessed at post-intervention and one-month follow-up. The following primary hypotheses will be tested: (1) interventions and assessments will be feasible in an ELA sample and (2) mindfulness training will improve daily life stress and inflammatory processes. Additionally, toward the design of a JIT mindfulness intervention, mobile sensor data will be used to develop machine-learning models predicting daily life stress, and practice prompts delivered at high stress moments will be evaluated for stress reduction benefit. To meet these aims, the candidate has assembled a mentorship team and training plan focused on (1) conceptual and methodological issues in ELA and health disparities, (2) advanced methods for assessing ambulatory and biological outcomes, and (3) new technology for optimizing outcome assessment and intervention delivery. Combined with the candidate’s background in mechanistic mindfulness research, the proposed research and training will launch an independent career dedicated to maximizing the efficacy of mindfulness interventions for addressing health disparities. These goals align with NCCIH’s objectives to advance understanding of complementary health interventions for improving health in at-risk populations using novel assessment technologies.