Background: Limited data exist on health outcomes during pregnancy and childbirth in low- and middle-income countries. This is a pilot of an innovative data collection tool using mobile technology to collect patient-reported outcome measures (PROMs) selected from the International Consortium of Health Outcomes Measurement (ICHOM) Pregnancy and Childbirth Standard Set in Nairobi, Kenya.
Methods: Pregnant women in the third trimester were recruited at three primary care facilities in Nairobi and followed prospectively throughout delivery and until six weeks postpartum. PROMs were collected via mobile surveys at three antenatal and two postnatal time points. Outcomes included incontinence, dyspareunia, mental health, breastfeeding and satisfaction with care. Hospitals reported morbidity and mortality. Descriptive statistics on maternal and child outcomes, survey completion and follow-up rates were calculated.
Results: In six months, 204 women were recruited: 50% of women returned for a second ante-natal care visit, 50% delivered at referral hospitals and 51% completed the postnatal visit. The completion rates for the five PROM surveys were highest at the first antenatal care visit (92%) and lowest in the postnatal care visit (38%). Data on depression, dyspareunia, fecal and urinary incontinence were successfully collected during the antenatal and postnatal period. At six weeks postpartum, 86% of women breastfeed exclusively. Most women that completed the survey were very satisfied with antenatal care (66%), delivery care (51%), and post-natal care (60%).
Conclusion: We have demonstrated that it is feasible to use mobile technology to follow women throughout pregnancy, track their attendance to pre-natal and post-natal care visits and obtain data on PROM. This study demonstrates the potential of mobile technology to collect PROM in a low-resource setting. The data provide insight into the quality of maternal care services provided and will be used to identify and address gaps in access and provision of high quality care to pregnant women.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6795527 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0222978 | PLOS |
Publication Analysis
Top Keywords
Similar Publications
An overview of sound source localization based condition monitoring robots.
ISA Trans
December 2024
Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK. Electronic address:
As artificial intelligence advances and demand for cost-effective equipment maintenance in various fields increases, it is worth insightful research on utilizing robots embedded with sound source localization (SSL) technology for condition monitoring. Combining the two techniques has significant advantages, which are conducive to further classifying and tracking abnormal sources, thereby enhancing system performance at a lower cost. The paper provides an overview of current acoustic-based robotic techniques for condition monitoring, highlights the common SSL methods, and finds that localization performance heavily depends on signal quality.
View Article and Find Full Text PDFA review of effects of electromagnetic fields on ageing and ageing dependent bioeffects of electromagnetic fields.
Sci Total Environ
January 2025
Zhejiang Key Laboratory of Geriatrics and Geriatrics Institute of Zhejiang Province, Zhejiang Hospital, 310030, Hangzhou, China. Electronic address:
Thanks to the progress of science and technology, human life expectancy has dramatically increased in the past few decades, but accompanied by rapid ageing of population, resulting in increased burden on society. At the same time, the living environment, especially the electromagnetic environment, has also greatly changed due to science and technology advances. The effect of artificial electromagnetic fields (EMFs) emitted from power lines, mobile phones, wireless equipment, and other devices on ageing and ageing-related diseases are receiving increasing attention.
View Article and Find Full Text PDFSpiking-PhysFormer: Camera-based remote photoplethysmography with parallel spike-driven transformer.
Neural Netw
January 2025
Tsinghua University, Beijing, China. Electronic address:
Artificial neural networks (ANNs) can help camera-based remote photoplethysmography (rPPG) in measuring cardiac activity and physiological signals from facial videos, such as pulse wave, heart rate and respiration rate with better accuracy. However, most existing ANN-based methods require substantial computing resources, which poses challenges for effective deployment on mobile devices. Spiking neural networks (SNNs), on the other hand, hold immense potential for energy-efficient deep learning owing to their binary and event-driven architecture.
View Article and Find Full Text PDFProbing Native CB2 Receptor Mobility in Plasma Membranes of Living Cells by Fluorescence Recovery After Photobleaching.
Chembiochem
January 2025
University of Teramo: Universita degli Studi di Teramo, Veterinary Medicine, Piano d'Accio snc, 64100, Teramo, ITALY.
In this study, we employed a novel fluorescent probe, RO7304924-which selectively targets cannabinoid 2 receptor (CB2R)-to assess the lateral mobility of CB2R within the plasma membrane of Chinese hamster ovary cells stably expressing a functional, untagged receptor variant. Utilizing confocal fluorescence recovery after photobleaching (FRAP), we quantified the diffusion coefficient and mobile fraction of CB2R, thereby demonstrating the efficacy of RO7304924 as an innovative tool for elucidating the dynamics of this major endocannabinoid-binding G protein-coupled receptor. Our present findings highlight the potential of combining advanced ligand-based fluorescent probes with FRAP for future investigations into the biochemical details of CB2R mobility in living cells, and its impact on receptor-dependent cellular processes.
View Article and Find Full Text PDFIntroductionAsthma attacks are set off by triggers such as pollutants from the environment, respiratory viruses, physical activity and allergens. The aim of this research is to create a machine learning model using data from mobile health technology to predict and appropriately warn a patient to avoid such triggers.MethodsLightweight machine learning models, XGBoost, Random Forest, and LightGBM were trained and tested on cleaned asthma data with a 70-30 train-test split.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!