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BackgroundScreening for influenza in primary care is challenging due to the low sensitivity of rapid antigen tests and the lack of proper screening tests. ObjectiveThe aim of this study was to develop a machine learning–based screening tool using patient-generated health data (PGHD) obtained from a mobile health (mHealth) app. MethodsWe trained a deep learning model based on a gated recurrent unit to screen influenza using PGHD, including each patient’s fever pattern and drug administration records. We used meteorological data and app-based surveillance of the weekly number of patients with influenza. We defined a single episode as the set of consecutive days, including the day the user was diagnosed with influenza or another disease. Any record a user entered 24 hours after his or her last record was considered to be the start of a new episode. Each episode contained data on the user’s age, gender, weight, and at least one body temperature record. The total number of episodes was 6657. Of these, there were 3326 episodes within which influenza was diagnosed. We divided these episodes into 80% training sets (2664/3330) and 20% test sets (666/3330). A 5-fold cross-validation was used on the training set. ResultsWe achieved reliable performance with an accuracy of 82%, a sensitivity of 84%, and a specificity of 80% in the test set. After the effect of each input variable was evaluated, app-based surveillance was observed to be the most influential variable. The correlation between the duration of input data and performance was not statistically significant (P=.09). ConclusionsThese findings suggest that PGHD from an mHealth app could be a complementary tool for influenza screening. In addition, PGHD, along with traditional clinical data, could be used to improve health conditions.

BackgroundPostvaccination fever is a mild adverse event that naturally improves without complications, but is highly prevalent and can be accompanied by febrile convulsions in some cases. These adverse effects may cause parents to delay or avoid vaccinating their children. ObjectiveThis study aimed to identify postvaccination fever patterns and the ability of antipyretics to affect changes in these patterns from data collected from a mobile app named Fever Coach. MethodsData provided by parents of feverish children derived from a mobile app, Fever Coach, were used to identify postvaccination fever patterns according to vaccinations and the use of antipyretic drugs. We selected single vaccination records that contained five or more body temperature readings performed within 48 hours of vaccination, and we analyzed postvaccination fever onset, offset, duration, and maximum body temperature. Through observing the postvaccination fever response to vaccination, we identified the effects of antipyretic drugs on postvaccination fever onset, offset, and duration times; the extent of fever; and the rate of decline. We also performed logistic regression analysis to determine demographic variables (age, weight, and sex) involved in relatively high fevers (body temperature ≥39°C). ResultsThe total number of Fever Coach users was 25,037, with 3834 users having entered single vaccination records, including 4448 vaccinations and 55,783 body temperature records. Most records were obtained from children receiving the following vaccinations: pneumococcus (n=2069); Japanese encephalitis (n=911); influenza (n=669); diphtheria, tetanus, and pertussis (n=403); and hepatitis A (n=252). According to the 4448 vaccination records, 3427 (77.05%) children had taken antipyretic drugs, and 3238 (89.15%) children took antibiotics at body temperatures above 38°C. The number of children taking antipyretics at a body temperature of 38°C was more than four times that of those taking antipyretics at 37.9°C (307 vs 67 cases). The number of instances in which this temperature threshold was reached was more than four times greater than the number when the temperature was 37.9°C. A comparative analysis of antipyretic and nonantipyretic cases showed there was no difference in onset time; however, offset and duration times were significantly shorter in nonantipyretic cases than in antipyretic cases (P

This study investigates the effects of technostress perceived by employees on psychological contract violations and resistance to organizational change, as information and communication technology is applied to various tourism industry work sites. This study's sample consisted of employees working in the Korean tourism industry in June 2023, who were surveyed using snowball sampling. Four hypotheses were proposed. First, technostress is associated with psychological contract violations. Second, psychological contract violations are associated with organizational change resistance. Third, technostress is associated with organizational change resistance. Fourth, psychological contract violations may play a mediating role in the relationship between technostress and organizational change resistance. All hypotheses in this study were adopted. Therefore, organizations and management in the tourism industry should identify and improve the factors that cause employee technostress arising from expanding information and communication technology to provide psychological stability. [ABSTRACT FROM AUTHOR]

To achieve net-zero carbon emissions by 2050, it is vital to prioritize climate action and monitor the progress of policies with accurate emission estimates. As CO2 emission estimates can be independently verified using atmospheric CO2 measurements, the need for optimal CO2 monitoring networks has increased. This study proposed an experimental method for designing national-scale atmospheric CO2 monitoring networks. We used gridded data for fossil fuel CO2 emissions, facilitating the selection of emission grids as potential monitoring sites. First, we determined the appropriate number of CO2 monitoring sites, which increased in proportion to the magnitude and variability of CO2 emissions within the region. Subsequently, the emission grids corresponding to the region were arranged in descending order of emissions. Grids were then selected at regular intervals as potential monitoring sites, aligning with the predetermined number of sites. This selection process ensured that monitoring sites were evenly distributed, ranging from areas with high emissions to those with lower emissions. Lastly, as a verification step to assess the suitability of this potential network, a transport model simulating meteorological conditions was employed to evaluate its coverage to detect the influence of CO2 emissions. This method was applied to South Korea, and 96 candidate monitoring sites were created. The optimal CO2 monitoring network distributed evenly across South Korea could evaluate variations in CO2 emissions. The simple monitoring network design method proposed in this study can accelerate the installation of a national CO2 monitoring network, ultimately enabling the verification of CO2 emissions and supporting climate policies. [ABSTRACT FROM AUTHOR]