Correction: The Effect of an mHealth Self-Monitoring Intervention (MI-BP) on Blood Pressure Among Black Individuals With Uncontrolled Hypertension: Randomized Controlled Trial.

[This corrects the article DOI: 10.2196/57863.].

The Effect of an mHealth Self-Monitoring Intervention (MI-BP) on Blood Pressure Among Black Individuals With Uncontrolled Hypertension: Randomized Controlled Trial.

Background: Hypertension is one of the most important cardiovascular disease risk factors and affects >100 million American adults. Hypertension-related health inequities are abundant in Black communities as Black individuals are more likely to use the emergency department (ED) for chronic disease-related ambulatory care, which is strongly linked to lower blood pressure (BP) control, diminished awareness of hypertension, and adverse cardiovascular events. To reduce hypertension-related health disparities, we developed MI-BP, a culturally tailored multibehavior mobile health intervention that targeted behaviors of BP self-monitoring, physical activity, sodium intake, and medication adherence in Black individuals with uncontrolled hypertension recruited from ED and community-based settings.

The theory of mind and human-robot trust repair.

Nothing is perfect and robots can make as many mistakes as any human, which can lead to a decrease in trust in them. However, it is possible, for robots to repair a human's trust in them after they have made mistakes through various trust repair strategies such as apologies, denials, and promises. Presently, the efficacy of these trust repairs in the human-robot interaction literature has been mixed.

Heterogeneous human-robot task allocation based on artificial trust.

Effective human-robot collaboration requires the appropriate allocation of indivisible tasks between humans and robots. A task allocation method that appropriately makes use of the unique capabilities of each agent (either a human or a robot) can improve team performance. This paper presents a novel task allocation method for heterogeneous human-robot teams based on artificial trust from a robot that can learn agent capabilities over time and allocate both existing and novel tasks.

A Systematic Review of Human and Robot Personality in Health Care Human-Robot Interaction.

Robots have become vital to the delivery of health care and their personalities are often important to understanding their effectiveness as health care providers. Despite this, there is a lack of a systematic overarching understanding of personality in health care human-robot interaction. This makes it difficult to understand what we know and do not know about the impact of personality in health care human-robot interaction (H-HRI).

Predicting driver takeover performance in conditionally automated driving.

In conditionally automated driving, drivers have difficulty taking over control when requested. To address this challenge, we aimed to predict drivers' takeover performance before the issue of a takeover request (TOR) by analyzing drivers' physiological data and external environment data. We used data sets from two human-in-the-loop experiments, wherein drivers engaged in non-driving-related tasks (NDRTs) were requested to take over control from automated driving in various situations.

Pedestrian Trust in Automated Vehicles: Role of Traffic Signal and AV Driving Behavior.

Pedestrians' acceptance of automated vehicles (AVs) depends on their trust in the AVs. We developed a model of pedestrians' trust in AVs based on AV driving behavior and traffic signal presence. To empirically verify this model, we conducted a human-subject study with 30 participants in a virtual reality environment.