Did people really drink bleach to prevent COVID-19? A guide for protecting survey data against problematic respondents

Survey respondents who are non-attentive, respond randomly, or misrepresent who they are can impact the outcomes of surveys. Prior findings reported by the CDC have suggested that people engaged in highly dangerous cleaning practices during the COVID-19 pandemic, including ingesting household cleaners such as bleach. In our attempts to replicate the CDC’s results, we found that 100% of reported ingestion of household cleaners are made by problematic respondents. Once inattentive, acquiescent, and careless respondents are removed from the sample, we find no evidence that people ingested cleaning products to prevent a COVID-19 infection. These findings have important implications for public health and medical survey research, as well as for best practices for avoiding problematic respondents in all survey research conducted online

Crowdsourcing the Perception of Machine Teaching

Teachable interfaces can empower end-users to attune machine learning systems to their idiosyncratic characteristics and environment by explicitly providing pertinent training examples. While facilitating control, their effectiveness can be hindered by the lack of expertise or misconceptions. We investigate how users may conceptualize, experience, and reflect on their engagement in machine teaching by deploying a mobile teachable testbed in Amazon Mechanical Turk. Using a performance-based payment scheme, Mechanical Turkers (N = 100) are called to train, test, and re-train a robust recognition model in real-time with a few snapshots taken in their environment. We find that participants incorporate diversity in their examples drawing from parallels to how humans recognize objects independent of size, viewpoint, location, and illumination. Many of their misconceptions relate to consistency and model capabilities for reasoning. With limited variation and edge cases in testing, the majority of them do not change strategies on a second training attempt.Comment: 10 pages, 8 figures, 5 tables, CHI2020 conferenc

Mobile Exercise Apps and Increased Leisure Time Exercise Activity: A Moderated Mediation Analysis of the Role of Self-Efficacy and Barriers

BACKGROUND: There are currently over 1000 exercise apps for mobile devices on the market. These apps employ a range of features, from tracking exercise activity to providing motivational messages. However, virtually nothing is known about whether exercise apps improve exercise levels and health outcomes and, if so, the mechanisms of these effects. OBJECTIVE: Our aim was to examine whether the use of exercise apps is associated with increased levels of exercise and improved health outcomes. We also develop a framework within which to understand how exercise apps may affect health and test multiple models of possible mechanisms of action and boundary conditions of these relationships. Within this framework, app use may increase physical activity by influencing variables such as self-efficacy and may help to overcome exercise barriers, leading to improved health outcomes such as lower body mass index (BMI). METHODS: In this study, 726 participants with one of three backgrounds were surveyed about their use of exercise apps and health: (1) those who never used exercise apps, (2) those who used exercise apps but discontinued use, and (3) those who are currently using exercise apps. Participants were asked about their long-term levels of exercise and about their levels of exercise during the previous week with the International Physical Activity Questionnaire (IPAQ). RESULTS: Nearly three-quarters of current app users reported being more active compared to under half of non-users and past users. The IPAQ showed that current users had higher total leisure time metabolic equivalent of task (MET) expenditures (1169 METs), including walking and vigorous exercise, compared to those who stopped using their apps (612 METs) or who never used apps (577 METs). Importantly, physical activity levels in domains other than leisure time activity were similar across the groups. The results also showed that current users had lower BMI (25.16) than past users (26.8) and non-users (26.9) and that this association was mediated by exercise levels and self-efficacy. That relationship was also moderated by perceived barriers to exercise. Multiple serial mediation models were tested, which revealed that the association between app use and BMI is mediated by increased self-efficacy and increased exercise. CONCLUSIONS: Exercise app users are more likely to exercise during their leisure time, compared to those who do not use exercise apps, essentially fulfilling the role that many of these apps were designed to accomplish. Data also suggest that one way that exercise apps may increase exercise levels and health outcomes such as BMI is by making it easier for users to overcome barriers to exercise, leading to increased self-efficacy. We discuss ways of improving the effectiveness of apps by incorporating theory-driven approaches. We conclude that exercise apps can be viewed as intervention delivery systems consisting of features that help users overcome specific barriers

A look at the first quarantined community in the United States: Response of religious communal organizations and implications for public health during the COVID-19 pandemic

The current study examined anxiety and distress among members of the first community to be quarantined in the United States due to the COVID-19 pandemic. In addition to being historically significant, the current sample was unusual in that those quarantined were all members of a Modern Orthodox Jewish community and were connected via religious institutions at which exposure may have occurred. We sought to explore the community and religious factors unique to this sample, as they relate to the psychological and public health impact of quarantine. Community organizations were trusted more than any other source of COVID 19-related information, including federal, state, and other government agencies, including the CDC, WHO and media news sources. This was supported qualitatively with open-ended responses in which participants described the range of supports organized by community organizations. These included tangible needs (i.e. food delivery), social support, virtual religious services, and dissemination of COVID-19 related information. The overall levels of distress and anxiety were elevated and directly associated with what was reported to be largely inadequate and inconsistent health related information received from local departments of health. In addition, the majority of participants felt that perception of or concern about future stigma related to a COVID-19 diagnosis or association of COVID-19 with the Jewish community was high and also significantly predicted distress and anxiety. The current study demonstrates the ways in which religious institutions can play a vital role in promoting the well-being of their constituents. During this unprecedented pandemic, public health authorities have an opportunity to form partnerships with religious institutions in the common interests of promoting health, relaying accurate information and supporting the psychosocial needs of community members, as well as protecting communities against stigma and discrimination

Tapped out or barely tapped? Recommendations for how to harness the vast and largely unused potential of the Mechanical Turk participant pool.

Mechanical Turk (MTurk) is a common source of research participants within the academic community. Despite MTurk's utility and benefits over traditional subject pools some researchers have questioned whether it is sustainable. Specifically, some have asked whether MTurk workers are too familiar with manipulations and measures common in the social sciences, the result of many researchers relying on the same small participant pool. Here, we show that concerns about non-naivete on MTurk are due less to the MTurk platform itself and more to the way researchers use the platform. Specifically, we find that there are at least 250,000 MTurk workers worldwide and that a large majority of US workers are new to the platform each year and therefore relatively inexperienced as research participants. We describe how inexperienced workers are excluded from studies, in part, because of the worker reputation qualifications researchers commonly use. Then, we propose and evaluate an alternative approach to sampling on MTurk that allows researchers to access inexperienced participants without sacrificing data quality. We recommend that in some cases researchers should limit the number of highly experienced workers allowed in their study by excluding these workers or by stratifying sample recruitment based on worker experience levels. We discuss the trade-offs of different sampling practices on MTurk and describe how the above sampling strategies can help researchers harness the vast and largely untapped potential of the Mechanical Turk participant pool