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Patient portals have drawn much attention, as they are considered an important tool for health providers in facilitating patient engagement. However, little is known about whether the intensive use of patient portals contributes to improved management of patients' health in terms of their confidence in acquiring health information and exercising self-care. There is a lack of randomized trials with these outcomes measured both pre- and postadoption of patient portals.

The aim of this study was to examine the causal relationship between the usage of patient portals and patients' self-efficacy toward obtaining health information and performing self-care.

This study was a secondary data analysis that used data from a US national survey, the National Cancer Institute's Health Information National Trends Survey 5 Cycle 1. Patient portal usage frequency was used to define the treatment. Survey items measuring self-efficacy on a Likert-type scale were selected as the main outcomes, including patients' confidencesults support the use of patient portals and encourage better support and education to patients. The proposed statistical method can be used to exploit the potential of national survey data for causal inference studies.

The results support the use of patient portals and encourage better support and education to patients. The proposed statistical method can be used to exploit the potential of national survey data for causal inference studies.

Teams working in the community to manage crisis in dementia currently exist, but with widely varying models of practice, it is difficult to determine the effectiveness of such teams.

The aim of this study is to develop a "best practice model" for dementia services managing crisis, as well as a set of resources to help teams implement this model to measure and improve practice delivery. These will be the best practice tool and toolkit to be utilized by teams to improve the effectiveness of crisis teams working with older people with dementia and their caregivers. This paper describes the protocol for a prospective study using qualitative methods to establish an understanding of the current practice to develop a "best practice model."

Participants (people with dementia, caregivers, staff members, and stakeholders) from a variety of geographical areas, with a broad experience of crisis and noncrisis work, will be purposively selected to participate in qualitative approaches including interviews, focus groups, a consensus workshop, and development and field testing of both the best practice tool and toolkit.

Data were collected between October 2016 and August 2018. Thematic analysis will be utilized to establish the current working of teams managing crisis in dementia in order to draw together elements of the best practice.

This is the first study to systematically explore the requirements needed to fulfill effective and appropriate home management for people with dementia and their caregivers at the time of mental health crisis, as delivered by teams managing crisis in dementia. PKI1422amide,myristoylated This systematic approach to development will support greater acceptability and validity of the best practice tool and toolkit and lay the foundation for a large scale trial with teams managing crisis in dementia across England to investigate the effects on practice and impact on service provision, as well as the associated experiences of people with dementia and their caregivers.

RR1-10.2196/14781.

RR1-10.2196/14781.

The internet has emerged as a main venue of health information delivery and health-related activities. However, few studies have examined how health literacy determines online health-related behavior.

The aim of this study was to investigate the current level of health-related information-seeking using the internet and how health literacy, access to technology, and sociodemographic characteristics impact health-related information-seeking behavior.

We conducted a cross-sectional study through a survey with Minnesotan adults (N=614) to examine their health literacy, access to technology, and health-related information-seeking internet use. We used multivariate regression analysis to assess the relationship between health-related information-seeking on the internet and health literacy and access to technology, controlling for sociodemographic characteristics.

Better health literacy (β=.35, SE 0.12) and greater access to technological devices (eg, mobile phone and computer or tablet PC; β=.06, SE 0.19) wractice attention are needed to address the disparities in access to health information as well as to ensure the quality of the information and improve health literacy.

Although most current medication error prevention systems are rule-based, these systems may result in alert fatigue because of poor accuracy. Previously, we had developed a machine learning (ML) model based on Taiwan's local databases (TLD) to address this issue. However, the international transferability of this model is unclear.

This study examines the international transferability of a machine learning model for detecting medication errors and whether the federated learning approach could further improve the accuracy of the model.

The study cohort included 667,572 outpatient prescriptions from 2 large US academic medical centers. Our ML model was applied to build the original model (O model), the local model (L model), and the hybrid model (H model). The O model was built using the data of 1.34 billion outpatient prescriptions from TLD. A validation set with 8.98% (60,000/667,572) of the prescriptions was first randomly sampled, and the remaining 91.02% (607,572/667,572) of the prescriptions served a the model.

Mobile health technology has demonstrated the ability of smartphone apps and sensors to collect data pertaining to patient activity, behavior, and cognition. It also offers the opportunity to understand how everyday passive mobile metrics such as battery life and screen time relate to mental health outcomes through continuous sensing. Impulsivity is an underlying factor in numerous physical and mental health problems. However, few studies have been designed to help us understand how mobile sensors and self-report data can improve our understanding of impulsive behavior.

The objective of this study was to explore the feasibility of using mobile sensor data to detect and monitor self-reported state impulsivity and impulsive behavior passively via a cross-platform mobile sensing application.

We enrolled 26 participants who were part of a larger study of impulsivity to take part in a real-world, continuous mobile sensing study over 21 days on both Apple operating system (iOS) and Android platforms. The mobile sensing system (mPulse) collected data from call logs, battery charging, and screen checking.