IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i18p11634-d915934.html
   My bibliography  Save this article

Study Protocol of an App-Based Prevention Program for Perinatal Depression

Author

Listed:
  • Xiangmin Tan

    (Xiangya School of Nursing, Central South University, Changsha 410013, China
    These authors contributed equally to this work.)

  • Yuqing He

    (Xiangya School of Nursing, Central South University, Changsha 410013, China)

  • Nan Hua

    (Xiangya School of Nursing, Central South University, Changsha 410013, China)

  • James Wiley

    (School of Nursing, University of California, San Francisco, CA 94118, USA)

  • Mei Sun

    (Xiangya School of Nursing, Central South University, Changsha 410013, China
    These authors contributed equally to this work.)

Abstract

The prevalence of perinatal depression (PND) in China is continuously rising, and the suicide rate among pregnant women is remarkably high. Preventing the occurrence of PND based on the management of primary health care is of great significance. Improving adherence to intervention programs is a key concern for PND prevention. Thus, a new intervention strategy based on mobile health could bring a new perspective to prevent the occurrence of PND and reduce the sample dropout rate. A single-blind, cluster randomized controlled trial will be performed to evaluate the effectiveness of a personalized, dynamic, and stratified intervention strategy based on an app. Four health centers will be randomly selected and randomly assigned to an intervention group (two centers) and a control group (two centers). Participants ( n = 426) will be enrolled from the four selected health centers, with 213 in each group. The intervention group will receive the interventions personalized by the feature-matching algorithm of the user profile and be reassigned to the low-risk group (Edinburgh Postnatal Depression Scale [EPDS] < 9) or moderate/high-risk group (9 ≤ EPDS < 13 and EPDS ≥ 13, but not meeting the criteria for PND) for intervention based on each EPDS score until 6 months after delivery. The control group will receive the same intervention components of the app but without the dynamic, personalized, and stratified function. Depression status, negative emotion symptoms, parental competence, and sample dropout rate will be measured at different weeks of pregnancy (12–16 [baseline], 24, 37) and at 42 days, 3 months, and 6 months after delivery. Follow-up evaluation (t 6 : 12 months after delivery) will also be conducted. If the intervention is effective, it will provide a personalized, time-friendly, and dynamic intervention for preventing PND. This phenomenon can effectively reduce the sample dropout rate and provide an empirical basis for promoting maternal mental health.

Suggested Citation

  • Xiangmin Tan & Yuqing He & Nan Hua & James Wiley & Mei Sun, 2022. "Study Protocol of an App-Based Prevention Program for Perinatal Depression," IJERPH, MDPI, vol. 19(18), pages 1-9, September.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:18:p:11634-:d:915934
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/18/11634/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/18/11634/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Aliyah Dosani & Harshmeet Arora & Sahil Mazmudar, 2020. "mHealth and Perinatal Depression in Low-and Middle-Income Countries: A Scoping Review of the Literature," IJERPH, MDPI, vol. 17(20), pages 1-18, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:18:p:11634-:d:915934. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.