| Unique ID issued by UMIN | UMIN000054751 |
|---|---|
| Receipt number | R000062568 |
| Scientific Title | Breastfeeding support during the first month postpartum using a deep learning based nipple damage assessment system effects on the breastfeeding experience. |
| Date of disclosure of the study information | 2024/07/01 |
| Last modified on | 2025/03/27 14:24:01 |
Breastfeeding support during the first month postpartum using a deep learning based nipple damage assessment system
effects on the breastfeeding experience.
Breastfeeding support during the first month postpartum using a deep learning based nipple damage assessment system
effects on the breastfeeding experience.
Breastfeeding support during the first month postpartum using a deep learning based nipple damage assessment system
effects on the breastfeeding experience.
Breastfeeding support during the first month postpartum using a deep learning based nipple damage assessment system
effects on the breastfeeding experience.
| Japan |
nipple injury
| Obstetrics and Gynecology | Nursing |
Others
NO
To examine the effect of breastfeeding support in the first month postpartum on the breastfeeding experience, using a system developed by the applicants, which uses deep learning to determine the severity of nipple damage (see below).
Safety,Efficacy
Confirmatory
Explanatory
Not applicable
Lactation-related information during hospitalisation (method of feeding, frequency of feeding, nipple pain score), Japanese Breastfeeding Self-Efficacy Scale (BSES-SF), Revised Breastfeeding Rating Scale (JMBFES)
Interventional
Parallel
Non-randomized
Open -no one is blinded
No treatment
2
Treatment
| Behavior,custom |
In addition to the usual care received at the facility, such as breastfeeding support and newborn visits, the intervention group submits images of the breast area taken with a research-specific smartphone that is not set up to connect to the internet (hereafter, research smartphone) on one occasion during the delivery hospitalisation. The submitted images are analysed using deep learning and the research subjects receive materials according to the results of the decision.
The control group receives only usual care.
| 18 | years-old | <= |
| 45 | years-old | >= |
Female
Longitudinal study of approximately 200 Japanese women aged 18 years and over, from late pregnancy to one month postpartum.
Not eligible if direct breastfeeding of the newborn is not confirmed (e.g. internal feeding due to mental illness, risk of mother-to-child infection, expected long-term separation of mother and child) or if the general operation of the smartphone is difficult.
200
| 1st name | Yasuhiko |
| Middle name | |
| Last name | Ebina |
Faculty of Health Sciences, Hokkaido University
Department of Midwifery, Maternity Nursing and Women's Health
060-0812
Kita 12,Nishi 5, Kita-ku, Sapporo
011-706-2822
ebiyas@hs.hokudai.ac.jp
| 1st name | Maya |
| Middle name | |
| Last name | Nakamura |
Faculty of Health Sciences, Hokkaido University
Department of Midwifery, Maternity Nursing and Women's Health
060-0812
Kita 12,Nishi 5, Kita-ku, Sapporo
011-706-2822
ebiyas@hs.hokudai.ac.jp
Hokkaido University
Yasuhiko Ebina
Hokkaido University
Self funding
Faculty of Health Sciences, Hokkaido University
Kita 12,Nishi 5, Kita-ku, Sapporo
011-706-3338
shomu@hs.hokudai.ac.jp
NO
| 2024 | Year | 07 | Month | 01 | Day |
Unpublished
23
Completed
| 2024 | Year | 06 | Month | 10 | Day |
| 2024 | Year | 06 | Month | 30 | Day |
| 2024 | Year | 07 | Month | 01 | Day |
| 2024 | Year | 11 | Month | 01 | Day |
| 2024 | Year | 06 | Month | 25 | Day |
| 2025 | Year | 03 | Month | 27 | Day |
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000062568