Introduction: Mobile health (mHealth) has the potential to change how patients make healthcare decisions. We sought to determine the readiness to use mHealth technology in underserved communities.
Methods: We conducted a cross-sectional survey of patients presenting with low-acuity complaints to an urban emergency department (ED) with an underserved population. Patients over the age of two who presented with low-acuity complaints were included. We conducted structured interview with each patient or parent (for minors) about willingness to use mHealth tools for guidance. Analysis included descriptive statistics and univariate analysis based on age and gender.
Results: Of 560 patients included in the survey, 80% were adults, 64% female, and 90% Black. The mean age was 28 ± 9 years for adults and 9 ± 5 years for children. One-third of patients reported no primary care physician, and 55% reported no access to a nurse or clinician for medical advice. Adults were less likely to have access to phone consultation than parents of children (odds ratio [OR] 0.49, 95% confidence interval [CI], 0.32 - 0.74), as were males compared to females (OR 0.52, 95% CI, 0.37-0.74). Most patients (96%) reported cellular internet access. Two-thirds of patients reported using online references. When asked how they would behave if an mHealth tool advised them that their current health problem was low risk, 69% of patients responded that they would seek care in an outpatient clinic instead of the ED (30%), stay home and not seek urgent medical care (28%), or use telehealth (11%).
Conclusion: In this urban community we found a large capacity and willingness to use mHealth technology in medical triage.
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http://dx.doi.org/10.5811/westjem.2019.6.41911 | DOI Listing |
Psychol Health Med
December 2024
School of Public Health, China Medical University, Shenyang, Liaoning, China.
This study assessed the relationships among cognitive risk, phone use behaviors, and sleep quality. We used a questionnaire, which included the Pittsburgh Sleep Quality Index (PSQI), mobile phone use behaviours, and questionnaires on mobile phone use cognitive risk to gather information from 1204 college students. T-test, chi-square test, and Wilcoxon signed rank test were applied to test differences in measurement data.
View Article and Find Full Text PDFIran Biomed J
December 2024
Department of Health Information Technology, Faculty of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.
Iran Biomed J
December 2024
Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
ANZ J Surg
December 2024
Royal Prince Alfred Institute of Academic Surgery, Sydney Local Health District, Sydney, New South Wales, Australia.
Background: Facial prosthetics are an important means to rehabilitate patients with congenital or acquired facial defects. However, with a time-consuming manual workflow and workforce shortage, access to facial prosthetics is limited in Australia and worldwide, especially for rural and remote patients. Optical 3D scanning has been increasingly integrated in digitizing data.
View Article and Find Full Text PDFJ Med Eng Technol
December 2024
Department of Computer Engineering and Information Technology, Razi University, Kermanshah, Iran.
Nowadays, photoplethysmograph (PPG) technology is being used more often in smart devices and mobile phones due to advancements in information and communication technology in the health field, particularly in monitoring cardiac activities. Developing generative models to generate synthetic PPG signals requires overcoming challenges like data diversity and limited data available for training deep learning models. This paper proposes a generative model by adopting a genetic programming (GP) approach to generate increasingly diversified and accurate data using an initial PPG signal sample.
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