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Researchers increasingly use passive sensing data and frequent self-report to implement personalized mobile health (mHealth) interventions. Yet, we know that certain populations may find these technical protocols burdensome and intervention uptake as well as treatment efficacy may be affected as a result. In the present study, we predicted feasibility (participant adherence to protocol) and acceptability (participant engagement with intervention content) as a function of baseline sociodemographic, mental health, and well-being characteristics of 99 women randomized in the personalized preventive intervention Wellness-for-Two (W-4-2), a randomized trial evaluating stress-related alterations during pregnancy and their effect on infant neurodevelopmental trajectories. The W-4-2 study used ecological momentary assessment (EMA) and wearable electrocardiograph (ECG) sensors to detect physiological stress and personalize the intervention. Participant adherence to protocols was 67% for EMAs and 52% for ECG bio-sensors. Higher baseline negative affect significantly predicted lower adherence to both protocols. Women assigned to the intervention group engaged on average with 42% of content they received. Women with higher annual household income were more likely to engage with more of the intervention content. Researchers should carefully consider tailoring of the intensity of technical intervention protocols to reduce fatigue, especially among participants with higher baseline negative affect, which may improve intervention uptake and efficacy findings at scale.

affective science

Feasibility of Passive ECG Bio-sensing and EMA Emotion Reporting Technologies and Acceptability of Just-in-Time Content in a Well-being Intervention, Considerations for Scalability and Improved Uptake