BACKGROUND AND OBJECTIVE

First responders must be physically and mentally healthy to ensure effective emergency response. However, literature showed that Filipino first responders continue to have elevated levels of stress and increased risk for post-traumatic stress and other mental health problems months after their deployment. The “Mi Salud” app was created to help Filipino first responders, their team leaders, and their agencies monitor and manage the responders’ real-time stress levels before, during, and after their deployment more effectively.

The “Mi Salud” app was pretested with Filipino first responders (n=30) to establish convergent validity using existing validated scales measuring the same construct. Participants also completed a Likert scale and questionnaire to assess user experience and app recommendations. During the rollout, first responders (n=32) tested the app and completed a survey on user experience and app recommendations. A focus group discussion (n=11; FGD) was conducted to further explore their experiences with the app. Survey data were analyzed using descriptive statistical methods, while FGD data were examined through thematic analysis.

Results from the online survey showed that the app was generally found to be helpful and that the recommendations within the app were useful. The emerging themes from the FGD corroborated many of the themes from the survey, particularly the benefits of using the app and the app’s ease of use. Positive effects were observed both on the responders and on the responders’ team leader and teammates, which further established the value of the “Mi Salud” app.

The findings show that the “Mi Salud” stress check-app may serve as a useful tool for monitoring and managing the stress levels, a critical aspect for Filipino first responders to maintain optimal functioning during deployments and daily activities.

PURPOSE: To construct a decision-making app for pre-hospital damage control resuscitation (PHDCR) for severely injured patients, and to make a preliminary trial test on the effectiveness and usability aspects of the constructed app. METHODS: Decision-making algorithms were first established by a thorough literature review, and were then used to be learned by computer with 3 kinds of text segmentation algorithms, i.e., dictionary-based segmentation, machine learning algorithms based on labeling, and deep learning algorithms based on understanding. B/S architecture mode and Spring Boot were used as a framework to construct the app. A total of 16 Grade-5 medical students were recruited to test the effectiveness and usability aspects of the app by using an animal model-based test on simulated PHDCR. Twelve adult Bama miniature pigs were subjected to penetrating abdominal injuries and were randomly assigned to the 16 students, who were randomly divided into 2 groups (n = 8 each): group A (decided on PHDCR by themselves) and group B (decided on PHDCR with the aid of the app). The students were asked to complete the PHDCR within 1 h, and then blood samples were taken and thromboelastography, routine coagulation test, blood cell count, and blood gas analysis were examined. The lab examination results along with the value of mean arterial pressure were used to compare the resuscitation effects between the 2 groups. Furthermore, a 4-statement-based post-test survey on a 5-point Likert scale was performed in group B students to test the usability aspects of the constructed app. RESULTS: With the above 3 kinds of text segmentation algorithm, B/S architecture mode, and Spring Boot as the development framework, the decision-making app for PHDCR was successfully constructed. The time to decide PHDCR was (28.8 ± 3.41) sec in group B, much shorter than that in group A (87.5 ± 8.53) sec (p < 0.001). The outcomes of animals treated by group B students were much better than that by group A students as indicated by higher mean arterial pressure, oxygen saturation and fibrinogen concentration and maximum amplitude, and lower R values in group B than those in group A. The post-test survey revealed that group B students gave a mean score of no less than 4 for all 4 statements. CONCLUSION A decision-making app for PHDCR was constructed in the present study and the preliminary trial test revealed that it could help to improve the resuscitation effect in animal models of penetrating abdominal injury.
Animals ; Swine ; Resuscitation/methods* ; Mobile Applications ; Humans ; Algorithms ; Emergency Medical Services/methods* ; Male ; Decision Making ; Female

BACKGROUND

Diabetes Mellitus Type 2 is a significant global health issue with a high prevalence in the Philippines. Managing this condition effectively is crucial, and digital technologies, particularly smartphone (mHealth) applications, have emerged as a potential tool in diabetes self-management.

This study evaluated the effectiveness of smartphone (mHealth) application use in achieving glycemic control among adults with Type 2 Diabetes Mellitus, focusing on HbA1c levels and medication adherence.

This systematic review and meta-analysis, adhering to PRISMA guidelines, analyzed randomized controlled trials from databases like PubMed and Embase, comparing interventions using mHealth applications with standard care. The primary measures were HbA1c levels and medication adherence.

Ten studies involving 20,984 participants were included in the meta-analysis. Using mHealth applications led to an average HbA1c reduction of 0.36%, indicating improved glycemic control. There was considerable heterogeneity (I2 = 91%) because of the clinical and methodological diversity of the included studies. Subgroup analysis showed that the younger and older age groups, shorter and longer T2DM duration, and lower and higher HbA1c baseline benefited from its use. Sensitivity analysis still showed high heterogeneity (95%-97%), reflecting clinical diversity. A narrative analysis of two studies highlighted the utility of mHealth applications in tracking diet, physical activity, and vital stats, aiding medication adherence through reminders and data sharing with healthcare providers.

This systematic review and meta-analysis showed the effectiveness of mHealth application use in achieving glycemic control among adults with Type 2 Diabetes Mellitus by improving HbA1c levels and medication adherence. Integrating mHealth applications as adjuncts in family and community medicine as part of personalized care for managing type 2 diabetes in the Philippines can help achieve glycemic control and medication adherence. Future studies should focus on longitudinal assessments, exploring cultural and linguistic factors in the Filipino context to optimize diabetes care within this specialized medical framework.

INTRODUCTION: Due to COVID-19 pandemic, many have shifted into working at home which led to physical inactivity. This may cause musculoskeletal discomfort, chronic disease, muscle atrophy and spinal imbalance due to improper and prolonged sitting posture. Since mobile devices are relatively available for most of the office workers, there were still a lack of evidence-based mobile applications that can counteract the inactivity through exercises, which led to the researchers to create an application called SitMate that consists of evidence-based exercises which aimed to prevent musculoskeletal discomfort among a business process outsourcing company Workforce Management Personnel (BPO-WMP). METHODS: Eleven participants (18-40 years old) full-time, work-from-home BPO-WMP were randomized into Treatment Group(TG)(n=6) and Control Group (CG)(n=5). The TG received one month intervention with the use of SitMate Application containing relaxation exercises, range of motion exercises and stretching exercises, and notifications for postural correction while the CG continued their usual working schedule. RESULTS: There were no significant differences between two groups on all body parts that were measured using the Cornell Musculoskeletal Discomfort Questionnaire, and no significant differences in the intragroup pre-test and post-test scores on all body parts between TG and CG. For the intra-group post-test of the TG, there were noted improvements on the hip/buttock, right shoulder, upper back (median = 0) and right wrist (median = 1.5). There was also a noted increase in discomfort on the neck (median = 1.5) and lower back (median = 3). For the post-test of the CG, there were noted improvements on the right shoulder, right wrist (median = 0) and lower back (median = 1.5). CONCLUSION This study has shown that the SitMate application does not effectively reduce the prolonged sitting-related discomfort among the personnel after 1 month of intervention.
mobile application ; musculoskeletal discomfort ; physical inactivity ; low back pain ; posture

Aged ; Feasibility Studies ; Health Status ; Humans ; Mobile Applications ; Treatment Outcome

BACKGROUND: To combat coronavirus disease 2019 (COVID-19), many countries have used contact tracing apps, including Japan's voluntary-use contact-confirming application (COCOA). The current study aimed to identify industry and workplace characteristics associated with the downloading of this COVID-19 contact tracing app. METHODS: This cross-sectional study of full-time workers used an online survey. Multiple logistic regression analysis was used to evaluate the associations of industry and workplace characteristics with contact tracing app use. RESULTS: Of the 27,036 participants, 25.1% had downloaded the COCOA. Workers in the public service (adjusted odds ratio [aOR] = 1.29, 95% confidence interval [CI] 1.14-1.45) and information technology (aOR = 1.38, 95% CI 1.20-1.58) industries were more likely to use the app than were those in the manufacturing industry. In contrast, app usage was less common among workers in the retail and wholesale (aOR = 0.87, 95% CI 0.76-0.99) and food/beverage (aOR = 0.81, 95% CI 0.70-0.94) industries, but further adjustment for company size attenuated these associations. Workers at larger companies were more likely to use the app. Compared with permanent employees, the odds of using the app were higher for managers and civil servants but lower for those who were self-employed. CONCLUSIONS Downloading of COCOA among Japanese workers was insufficient; thus, the mitigating effect of COCOA on the COVID-19 pandemic is considered to be limited. One possible reason for the under-implementation of the contact tracing app in the retail and wholesale and food/beverage industries is small company size, as suggested by the fully adjusted model results. An awareness campaign should be conducted to promote the widespread use of the contact tracing app in these industries.
Adult ; COVID-19/prevention & control* ; Contact Tracing/methods* ; Cross-Sectional Studies ; Female ; Humans ; Industry/classification* ; Japan/epidemiology* ; Male ; Middle Aged ; Mobile Applications/statistics & numerical data* ; SARS-CoV-2 ; Smartphone ; Workplace/statistics & numerical data*

This paper designs a bluetooth-based low-power multi-parameter monitoring system. The system is mainly composed of ECG signal acquisition, respiratory signal acquisition, body temperature acquisition, bluetooth 4.0 transmission module and Android mobile phone APP display. The system collects the corresponding physiological signals through various collection parts, and can realize the monitoring of three physiological signals of electrocardiogram, respiration and body temperature. The Android mobile APP can display ECG, respiratory waveform and temperature data in real time. The system is small in size and low in power consumption, and has a good application prospect in portable and wearable medical applications.
Body Temperature ; Cell Phone ; Electrocardiography ; Humans ; Mobile Applications ; Monitoring, Physiologic/instrumentation* ; Respiratory Rate

Mobile healthcare services have been applied to manage some diseases, particularly chronic diseases. We aimed to evaluate the utility of a mobile application in nutritional intervention by observing cases of a mobile application in a series of patients with GDM. We provided a mobile-based intervention to GDM patients and collected biochemical and nutritional information. The mobile-based nutritional intervention was effective in controlling carbohydrate intake and improving blood glucose level for patients with GDM.]]>
Blood Glucose ; Child ; Chronic Disease ; Delivery of Health Care ; Diabetes, Gestational ; Diet Therapy ; Diet, Food, and Nutrition ; Female ; Humans ; Mobile Applications ; Mothers ; Pregnancy ; Prevalence

The reference sites of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) were renewed in 2019. The DG Santé good practice Mobile Airways Sentinel networK was reviewed to meet the objectives of the EIP on AHA. It included 1) Management of care process, 2) Blueprint of digital transformation, 3) EIP on AHA, innovation to market, 4) Community for monitoring and assessment framework, 5) Political, organizational, technological and financial readiness, 6) Contributing to European co-operation and transferability, 7) Delivering evidence of impact against the triple win approach, 8) Contribution to the European Digital Transformation of Health and Care and 9) scale of demonstration and deployment of innovation
Asthma ; Masks ; Mobile Applications ; Rhinitis

Recent studies have developed simple techniques for monitoring and assessing sleep. However, several issues remain to be solved for example high-cost sensor and algorithm as a home-use device. In this study, we aimed to develop an inexpensive and simple sleep monitoring system using a camera and video processing. Polysomnography (PSG) recordings were performed in six subjects for four consecutive nights. Subjects' body movements were simultaneously recorded by the web camera. Body movement was extracted by video processing from the video data and fi ve parameters were calculated for machine learning. Four sleep stages (WAKE, LIGHT, DEEP and REM) were estimated by applying these fi ve parameters to a support vector machine. The overall estimation accuracy was 70.3 ± 11.3% with the highest accuracy for DEEP (82.8 ± 4.7%) and the lowest for LIGHT (53.0 ± 4.0%) compared with correct sleep stages manually scored on PSG data by a sleep technician. Estimation accuracy for REM sleep was 68.0 ± 6.8%. The kappa was 0.19 ± 0.04 for all subjects. The present non-contact sleep monitoring system showed suffi cient accuracy in sleep stage estimation with REM sleep detection being accomplished. Low-cost computing power of this system can be advantageous for mobile application and modularization into home-device.
Machine Learning ; Methods ; Mobile Applications ; Polysomnography ; Sleep Stages ; Sleep, REM ; Support Vector Machine