Objective: To assess the healthcare utilization, economic burden, and long-term neurological complications and mortality of an adult population with Japanese encephalitis (JE). Methods: This study utilized two nationwide datasets in Taiwan: the Notifiable Disease Dataset of confirmed cases from the Centers for Disease Control to identify JE patients, and the National Health Insurance Research Database to obtain patients’ healthcare utilization. Survival analyses were performed to identify prognostic factors associated with the all-cause mortality of patients. Results: This study included 352 adult cases with JE (aged≥20 years). The mean age of JE patients was 45 years. Stroke (event rate: 3.49/100 person-years) was the most common neurological complication, followed by epilepsy/convulsions (3.13/100 person-years), encephalopathy/delirium (2.20/100 person-years), and parkinsonism (1.97/100 person-years). Among the 336 hospitalized patients at JE diagnosis, 58.33% required intensive care. Among 79 patients who died following JE diagnosis, 48.84% of death events occurred within the year of diagnosis. The medical costs increased considerably at JE diagnosis and subsequent-year costs remained significantly higher than the costs before diagnosis (p<0.05). Having a four-dose JE vaccination (i.e., born after 1976) versus no JE vaccination history (i.e., born before 1963) was significantly associated with lower all-cause mortality (hazard ratio: 0.221 [95% confidence interval: 0.067, 0.725]). Comorbid diabetes and incident epilepsy/convulsion events significantly increased the mortality risk by 2.47- and 1.85-fold, respectively (p<0.05). Conclusion: A considerable medical burden associated with JE was observed in affected adults, even in the years following JE diagnosis. Vaccination should be considered to prevent this sporadic, but lethal, viral infection.

Purpose: Sleep disturbance is one of the most prevalent symptoms among persons living with HIV (PLWH). However, the trajectory of sleep patterns in persons newly diagnosed with HIV remains underrecognized. The current study aimed to estimate the trajectory of sleep quality and its associated factors among newly diagnosed PLWH. Patients and Methods: A prospective study was conducted in the outpatient clinic of a medical center in southern Taiwan from January 2015 to December 2017. Our primary outcome was sleep quality using the Chinese version of the Pittsburgh Sleep Quality Index (CPSQI). Participants completed the questionnaire at baseline and at four follow-up interval visits: at 3– 6, 6– 9, 9– 12 and 12– 15 months. A generalized equation estimation (GEE) model was applied to analyze the relationships among poor sleep quality, depression and antiretroviral therapy among persons newly diagnosed with HIV. Results: A total of 217 PLWH were included. The mean age of the sample was 29.3 years, and males (98.6%) were predominant. A total of 56.2% of HIV-infected persons were considered to have poor sleep quality at baseline. After controlling for the confounding effects of demographic characteristics, the following factors increased the risk of poor sleep quality: older age (ß= 0.07, CI: 0.03– 0.11, p=0.001), level of depression (ß= 0.32, CI: 0.27– 0.37, p< 0.001) and detectable viral load (ß= 0.61, CI: 0.04 – 1.18, p= 0.037). However, there was no significant difference in BMI, CD4 counts, HIV viral load, disclosure status, or highly active antiretroviral therapy (HAART) regimen. Conclusion: Our results demonstrate that one in two persons with newly diagnosed HIV had poor sleep quality. Being older, having higher levels of depression, and having detectable HIV viral loads were identified as risk factors for developing poor sleep quality in persons living with HIV.

The advancements of Internet of Things (IoT) technologies have enabled the implementation of smart and wearable sensors, which can be employed to provide older adults with affordable and accessible continuous biophysiological status monitoring. The quality of such monitoring data, however, is unsatisfactory due to excessive noise induced by various disturbances, such as motion artifacts. Existing methods take advantage of summary statistics, such as mean or median values, for denoising, without taking into account the biophysiological patterns embedded in data. In this research, a functional data analysis modeling method was proposed to enhance the data quality by learning individual subjects’ diurnal heart rate (HR) patterns from historical data, which were further improved by fusing newly collected data. This proposed data-fusion approach was developed based on a Bayesian inference framework. Its effectiveness was demonstrated in an HR analysis from a prospective study involving older adults residing in assisted living or home settings. The results indicate that it is imperative to conduct personalized healthcare by estimating individualized HR patterns. Furthermore, the proposed calibration method provides a more accurate (smaller mean errors) and more precise (smaller error standard deviations) HR estimation than raw HR and conventional methods, such as the mean.

Background: Most older adults prefer to age in place. Technology-assisted monitoring can enhance safety while maintaining independence. However, there is limited understanding of older adult end users’ preferences and experiences. Methods: In this interpretive phenomenological study, we interviewed eight older adults, with and without dementia, to understand their lived experiences with monitoring technology and its impact on self-identity, independence, and aging-in-place. Results: We found that older adults use pragmatic strategies to process the meaning of life as “monitored” individuals, reflected in four themes: (1) freedom to age in place, (2) the need for active and integrated intervention, (3) individualized approaches to technology based on temperament, usefulness, and worldview, and (4) a sense of changing situations while remaining unchanged. Adaptive techniques for older adults with dementia successfully elicited complex thoughts and desires when participants were given sufficient time and space. Conclusions: As technology-assisted monitoring becomes more common, it is imperative to understand the perspectives of older adult end users. Focusing on lived experiences offers valuable insights to ensure technology-assisted monitoring interventions are effective and accepted as older adults navigate changes in their capabilities and endeavor to age in place.