Male Weight Control: Crowdsourcing and an Intervention to Discover More

Men and women have similar rates of obesity but the combined prevalence of overweight and obesity is higher among men. Men who are overweight are a high-risk group for many obesity-related chronic diseases, as they are more likely to carry excess weight in the abdomen, which is generally more harmful than weight stored in the lower body. Men are also less likely than women to perceive themselves as overweight, and thus are less likely to initiate weight loss through organized weight loss programs. On average, less than 27% of weight loss trial participants have been men. Internet-based research is a low-cost, efficient way to produce novel hypotheses related to weight loss that may have previously escaped weight loss professionals. Additionally, incentives are an effective tool to motivate behavior change, and there is ample evidence to support the use of incentives to encourage many health-promoting behaviors, such as weight loss. The purpose our initial study was to facilitate intervention development by using crowdsourcing to detect unexpected beliefs and unpredicted barriers to male weight loss. The aim of our main study was to evaluate the impact of financial incentives to facilitate weight loss in men, delivered as part of a weight loss intervention. Two separate studies were conducted. In the first project, participants were recruited to a crowdsourcing survey website which was used to generate hypotheses for behaviors related to overweight and obesity in men. Participants provided 21,846 responses to 193 questions. While several common themes seen in prior research were revealed such as previous health diagnoses and physical activity participation, other potential weight determinants such as dietary habits, sexual behaviors and self-perception were reported. Crowdsourcing in this context provides a mechanism to further investigate perceptions of weight and weight loss interventions in the male population that have not previously been documented. These insights will help guide future intervention design. For the main project, a randomized trial compared the Gutbusters weight loss program (based on the REFIT program) alone with Gutbusters with escalating incentives for successful weight loss. The six-month intervention was conducted online with weekly in-person weight collections for the first 12 weeks. Gutbusters encouraged participants to make six 100-calorie changes to their daily diet, utilizing a variety of online lessons targeting specific eating behaviors. Measures included demographic information, height, weight, waist circumference, and body fat percentage. Participants (N=102, 47. 0± 12. 3 yrs old, 32. 5 kg/m2, 80. 4% with at least two years of college) were randomized in a 1:1 ratio to Gutbusters or Gutbusters+Incentive. Significantly more Gutbusters+Incentive participants lost at least 5% of their baseline weight compared to the Gutbusters group at both 12 and 24 weeks. Similar to the aforementioned REFIT program, Gutbusters participants were able to achieve clinically significant weight loss. The Gutbusters+Incentive achieved greater rates of weight loss than the Gutbusters alone group, further supporting the value of incentives in promoting health behaviors

Emerging Opportunities: Monitoring and Evaluation in a Tech-Enabled World

Various trends are impacting on the field of monitoring and evaluation in the area of international development. Resources have become ever more scarce while expectations for what development assistance should achieve are growing. The search for more efficient systems to measure impact is on. Country governments are also working to improve their own capacities for evaluation, and demand is rising from national and community-based organizations for meaningful participation in the evaluation process as well as for greater voice and more accountability from both aid and development agencies and government.These factors, in addition to greater competition for limited resources in the area of international development, are pushing donors, program participants and evaluators themselves to seek more rigorous – and at the same time flexible – systems to monitor and evaluate development and humanitarian interventions.However, many current approaches to M&E are unable to address the changing structure of development assistance and the increasingly complex environment in which it operates. Operational challenges (for example, limited time, insufficient resources and poor data quality) as well as methodological challenges that impact on the quality and timeliness of evaluation exercises have yet to be fully overcome

Diminished Control in Crowdsourcing: An Investigation of Crowdworker Multitasking Behavior

Obtaining high-quality data from crowds can be difficult if contributors do not give tasks sufficient attention. Attention checks are often used to mitigate this problem, but, because the roots of inattention are poorly understood, checks often compel attentive contributors to complete unnecessary work. We investigated a potential source of inattentiveness during crowdwork: multitasking. We found that workers switched to other tasks every five minutes, on average. There were indications that increasing switch frequency negatively affected performance. To address this, we tested an intervention that encouraged workers to stay focused on our task after multitasking was detected. We found that our intervention reduced the frequency of task-switching. It also improves on existing attention checks because it does not place additional demands on workers who are already focused. Our approach shows that crowds can help to overcome some of the limitations of laboratory studies by affording access to naturalistic multitasking behavior

Improving fairness in machine learning systems: What do industry practitioners need?

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019

Principles for Designing Context-Aware Applications for Physical Activity Promotion

Mobile devices with embedded sensors have become commonplace, carried by billions of people worldwide. Their potential to influence positive health behaviors such as physical activity in people is just starting to be realized. Two critical ingredients, an accurate understanding of human behavior and use of that knowledge for building computational models, underpin all emerging behavior change applications. Early research prototypes suggest that such applications would facilitate people to make difficult decisions to manage their complex behaviors. However, the progress towards building real-world systems that support behavior change has been much slower than expected. The extreme diversity in real-world contextual conditions and user characteristics has prevented the conception of systems that scale and support end-users’ goals. We believe that solutions to the many challenges of designing context-aware systems for behavior change exist in three areas: building behavior models amenable to computational reasoning, designing better tools to improve our understanding of human behavior, and developing new applications that scale existing ways of achieving behavior change. With physical activity as its focus, this thesis addresses some crucial challenges that can move the field forward. Specifically, this thesis provides the notion of sweet spots, a phenomenological account of how people make and execute their physical activity plans. The key contribution of this concept is in its potential to improve the predictability of computational models supporting physical activity planning. To further improve our understanding of the dynamic nature of human behavior, we designed and built Heed, a low-cost, distributed and situated self-reporting device. Heed’s single-purpose and situated nature proved its use as the preferred device for self-reporting in many contexts. We finally present a crowdsourcing system that leverages expert knowledge to write personalized behavior change messages for large-scale context-aware applications.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144089/1/gparuthi_1.pd

The role of online and social media in combating sexual harassment in Egypt

Violence against women - particularly sexual harassment is a widespread problem faced by women around the world. In Egypt, research shows that a large number of women have been harassed at least once in their lifetime. The Egyptian Government, international organizations and non-governmental organizations have been working for several years on interventions and activities to combat sexual harassment. With the widespread use of online and social media in Egypt, this new media became a better and easily accessible form of conveying combating sexual harassment messages. This study, thus, aims to identify ways through which online and social media could be used through development communication campaigns to combat sexual harassment in Egypt. The study is based on a theoretical framework built on the Social Ecological Model, and seeks to identify how online and social media could be utilized along its five levels to combat harassment through social change, social mobilization, and advocacy. The study uses the single exploratory case study of HarassMap - an Egyptian NGO working on combating sexual harassment through online and social media. Theoretical propositions were developed for each of the five levels, and based on a content analysis of HarassMap\u27s website and Facebook Page, the theoretical propositions were verified and modified. Findings of the study show that online and social media could be used through functional participatory communication campaigns, following a social change and social mobilization approach to: (1) encourage sexual harassment survivors to respond to harassment through changing beliefs, increasing self-efficacy, and changing behavior through social prompting; (2) encourage bystander intervention through changing beliefs, increasing bystander-efficacy, and changing behavior through social prompting; (3) change the society\u27s attitudes and beliefs as related to assignment of responsibility and attribution of sexual harassment and increase the society\u27s collective-efficacy to fight acceptability of harassment; (4) advocate for organizational change to have sexual harassment-free workplaces/educational institutions through targeting the organization and its surrounding environment; and (5) advocate for more stringent sexual harassment law/law enforcement

Scientific Utopia III: crowdsourcing science

Most scientific research is conducted by small teams of investigators who together formulate hypotheses, collect data, conduct analyses, and report novel findings. These teams operate independently as vertically integrated silos. Here we argue that scientific research that is horizontally distributed can provide substantial complementary value, aiming to maximize available resources, promote inclusiveness and transparency, and increase rigor and reliability. This alternative approach enables researchers to tackle ambitious projects that would not be possible under the standard model. Crowdsourced scientific initiatives vary in the degree of communication between project members from largely independent work curated by a coordination team to crowd collaboration on shared activities. The potential benefits and challenges of large-scale collaboration span the entire research process: ideation, study design, data collection, data analysis, reporting, and peer review. Complementing traditional small science with crowdsourced approaches can accelerate the progress of science and improve the quality of scientific research

To Speak up or Shut up? Revealing the Drivers of Crowdworker Voice Behaviors in Crowdsourcing Work Environments

This study examines worker voice behaviors in the microtask crowdsourcing work environment (CSWE) where voice channels are absent. Informed by employee voice research, this study adopts the revealed causal mapping method to analyze the detailed narratives of 60 workers from Amazon Mechanical Turk. Our data analysis shows that the crowdworkers did engage in voice behaviors, but their voices were not always heard, depending on recipients. The crowdworker voice was directed to three different recipients (worker community, job requester, and platform) and influenced by six antecedents (duty orientation, efficacy judgment, workgroup identification, anger/frustration, futility, and achievement orientation). Based on the findings, we propose a model of worker voice antecedents and moderators in the CSWE. This study extends employee voice research by presenting a moderator perspective in the CSWE. Moreover, our study provides a nuanced understanding of crowdworker voice behaviors from two major aspects – antecedent and recipient – contributing to crowdsourcing research