This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2226) Text-messaging (SMS, WeChat etc)-Based Interventions (624) Perinatal Depression; Postpartum Depression; PPD (88) Depression and Mood Disorders; Suicide Prevention (1290) Mobile Health (mhealth) (2777) Anxiety and Stress Disorders (1013) Users' and Patients' Needs for Mental Health Services (382) Perinatal Education for Parents (210) mHealth for Wellness, Behavior Change and Prevention (2754)

Published on in Vol 5, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30995, first published .
Integrating SMS Text Messages Into a Preventive Intervention for Postpartum Depression Delivered via In-Home Visitation Programs: Feasibility and Acceptability Study

Integrating SMS Text Messages Into a Preventive Intervention for Postpartum Depression Delivered via In-Home Visitation Programs: Feasibility and Acceptability Study

Integrating SMS Text Messages Into a Preventive Intervention for Postpartum Depression Delivered via In-Home Visitation Programs: Feasibility and Acceptability Study

Authors of this article:

Alinne Z Barrera1 Author Orcid Image ;   Jaime Hamil2 Author Orcid Image ;   Darius Tandon2 Author Orcid Image
Article Authors Cited by (4) Tweetations (3) Metrics

Journals

  1. Cummings P, Petitclerc A, Moskowitz J, Tandon D, Zhang Y, MacNeill L, Alshurafa N, Krogh-Jespersen S, Hamil J, Nili A, Berken J, Grobman W, Rangarajan A, Wakschlag L. 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. Affective Science 2022;3(4):849 View
  2. Rahman T, Nayan N. Text Messages for Depression, Anxiety and Alcohol Abuse Therapy—Are Construction Guidelines Needed?. International Journal of Environmental Research and Public Health 2022;19(23):15701 View
  3. Ng A, Wei B, Jain J, Ward E, Tandon S, Moskowitz J, Krogh-Jespersen S, Wakschlag L, Alshurafa N. Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation. JMIR mHealth and uHealth 2022;10(8):e33850 View
  4. Zingg A, Singh T, Franklin A, Ross A, Selvaraj S, Refuerzo J, Myneni S. Digital health technologies for peripartum depression management among low-socioeconomic populations: perspectives from patients, providers, and social media channels. BMC Pregnancy and Childbirth 2023;23(1) View