Harnessing the Fifth Element of Distributional Statistics for Psychological Science: A Practical Primer and Shiny App for Measures of Statistical Inequality and Concentration

Although distributional inequality and concentration are important statistical concepts in many research fields (including economics, political and social science, information theory, and biology and ecology), they rarely are considered in psychological science. This practical primer familiarizes with the concepts of statistical inequality and concentration and presents an overview of more than a dozen useful, popular measures of inequality (including the Gini, Hoover, Rosenbluth, Herfindahl-Hirschman, Simpson, Shannon, generalized entropy, and Atkinson indices, and tail ratios). Additionally, an interactive web application (R Shiny) for calculating and visualizing these measures, with downloadable output, is described. This companion Shiny app provides brief introductory vignettes to this suite of measures, along with easy-to-understand user guidance. The Shiny app can readily be used as an intuitively accessible, interactive learning and demonstration environment for teaching and exploring these methods. We provide various examples for the application of measures of inequality and concentration in psychological science and discuss venues for further development.

Perceptions of factors influencing engagement with health and wellbeing apps: a qualitative study using the COM-B model and Theoretical Domains Framework (Preprint)

BACKGROUND Digital health media, such as health and wellbeing smartphone apps, could offer an accessible and cost-effective way to deliver health and wellbeing interventions. A key component of the effectiveness of these apps is user engagement. However, engagement with health and wellbeing apps is typically sub-optimal. Previous studies have identified multiple factors that influence engagement, however, most of these studies were conducted on specific populations or focused on apps targeting a particular behaviour. Understanding factors that influence engagement with a wide range of health and wellbeing apps can help inform the design and development of more engaging apps. OBJECTIVE The aim of this study was to explore users’ experiences of and reasons for engaging and not engaging with a wide range of health and wellbeing apps. METHODS A sample of adults in the UK (N=17) interested in using a health or wellbeing app took part in a semi-structured interview to explore experiences of engaging and reasons for not engaging with these apps. Participants were recruited via social media platforms. Data were analysed with the framework approach, informed by the Capability, Opportunity, Motivation – Behaviour (COM-B) model and the Theoretical Domains Framework, two widely used frameworks that incorporate a comprehensive set of behavioural influences. RESULTS Factors appearing to influence the capability of participants to engage with health and wellbeing apps included available user guidance, statistical and health information, reduced cognitive load, well-designed reminders, self-monitoring features, features that help to establish a routine, features that allow retaining the app for a potential precipitating event in the future (‘safety netting’) and features that offer a first step in the behaviour change process (‘stepping stone’). Tailoring, peer support and embedded professional support were identified as important factors that appeared to enhance users’ opportunity for engagement with health and wellbeing apps. Feedback, rewards, encouragement, goal setting, action planning, self-confidence and commitment were judged to be motivation factors affecting engagement with health and wellbeing apps. CONCLUSIONS Multiple factors were identified across all components of the COM-B model that may be valuable for enhancing the engagement of health and wellbeing apps. Engagement appears to be influenced primarily by features that provide user guidance, promote minimal cognitive load and support self-monitoring (capability), provide embedded social support (opportunity), and goal setting with action planning (motivation). We provide recommendations for policy makers, industry, health care providers and app developers on how to increase engagement. CLINICALTRIAL Not applicable.

Calibration Venus: An Interactive Camera Calibration Method Based on Search Algorithm and Pose Decomposition

Cameras are widely used in many scenes such as robot positioning and unmanned driving, in which the camera calibration is a major task in this field. The interactive camera calibration method based on a plane board is becoming popular due to its stability and handleability. However, most methods choose suggestions subjectively from a fixed pose dataset, which is error-prone and limited for different camera models. In addition, these methods do not provide clear guidelines on how to place the board in the specified pose. This paper proposes a new interactive calibration method, named ‘Calibration Venus’, including two main parts: pose search and pose decomposition. First, a pose search algorithm based on simulated annealing (SA) algorithm is proposed to select the optimal pose in the entire pose space. Second, an intuitive and easy-to-use user guidance method is designed to decompose the optimal pose into four sub-poses: translation, each rotation along X-, Y-, Z-axes. Thereby the users could follow the guide step by step to accurately complete the placement of the calibration board. Experimental results evaluated on simulated and real datasets show that the proposed method can reduce the difficulty of calibration, and improve the accuracy of calibration, as well as provide better guidance.