Learning motivation and self-assessment in health economics: a survey on overconfidence in healthcare providers.

Introduction: Lifelong learning is the foundation for professionals to maintain competence and proficiency in several aspects of economy and medicine. Until now, there is no evidence of overconfidence (the belief to be better than others or tested) and clinical tribalism (the belief that one's own group outperforms others) in the specialty of health economics. We investigated the hypothesis of overconfidence effects and their relation to learning motivation and motivational patterns in healthcare providers regarding healthcare economics.

Overconfidence effects and learning motivation refreshing BLS: An observational questionnaire study.

Aim Of The Study: Regular refresher skill courses are necessary to maintain competence in basic life support. The utilization of these training programs strongly depends on the motivation to learn. Learning motivation may be affected by overconfidence and clinical tribalism, as they both imply a higher competence compared to others, and therefore, a lower demand for training.

Evaluating machine learning models to classify occupants' perceptions of their indoor environment and sleep quality from indoor air quality.

A variety of factors can affect a person's perception of their environment and health, but one factor that is often overlooked in indoor settings is the air quality. To address this gap, we develop and evaluate four Machine Learning (ML) models on two disparate datasets using Indoor Air Quality (IAQ) parameters as primary features and components of self-reported IAQ satisfaction and sleep quality as target variables. In each case, we compare models to each other as well as to a simple model that always predicts the majority outcome.

circadian rhythms are not captured equal: Exploring Circadian metrics extracted by differentcomputational methods from smartphone accelerometer and GPS sensors in daily life tracking.

Objective: To identify the differences between circadian rhythm (CR) metrics characterized by different mobile sensors and computational methods.

Methods: We used smartphone tracking and daily survey data from 225 college student participants, applied four methods (survey construct automation, cosinor regression, non-parametric method, Fourier analysis) on two types of smartphone sensor data (GPS, accelerometer) to characterize CR. We explored the inter-relations among the extracted circadian metrics as well as between the circadian metrics and participants' self-reported mood and sleep outcomes.

Exploring Post COVID-19 Outbreak Intradaily Mobility Pattern Change in College Students: A GPS-Focused Smartphone Sensing Study.

With the outbreak of the COVID-19 pandemic in 2020, most colleges and universities move to restrict campus activities, reduce indoor gatherings and move instruction online. These changes required that students adapt and alter their daily routines accordingly. To investigate patterns associated with these behavioral changes, we collected smartphone sensing data using the Beiwe platform from two groups of undergraduate students at a major North American university, one from January to March of 2020 (74 participants), the other from May to August (52 participants), to observe the differences in students' daily life patterns before and after the start of the pandemic.

Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts.

Background: As mobile technologies become ever more sensor-rich, portable, and ubiquitous, data captured by smart devices are lending rich insights into users' daily lives with unprecedented comprehensiveness and ecological validity. A number of human-subject studies have been conducted to examine the use of mobile sensing to uncover individual behavioral patterns and health outcomes, yet minimal attention has been placed on measuring living environments together with other human-centered sensing data. Moreover, the participant sample size in most existing studies falls well below a few hundred, leaving questions open about the reliability of findings on the relations between mobile sensing signals and human outcomes.