A cross-sectional survey assessing the influence of theoretically informed behavioural factors on hand hygiene across seven countries during the COVID-19 pandemic.

BACKGROUND: Human hygiene behaviours influence the transmission of infectious diseases. Changing maladaptive hygiene habits has the potential to improve public health. Parents and teachers can play an important role in disinfecting surface areas and in helping children develop healthful handwashing habits. The current study aims to inform a future intervention that will help parents and teachers take up this role using a theoretically and empirically informed behaviour change model called the Capabilities-Opportunities-Motivations-Behaviour (COM-B) model. METHODS: A cross-sectional online survey was designed to measure participants' capabilities, opportunities, and motivations to [1] increase their children's handwashing with soap and [2] increase their cleaning of surface areas. Additional items captured how often participants believed their children washed their hands. The final survey was administered early in the coronavirus pandemic (May and June 2020) to 3975 participants from Australia, China, India, Indonesia, Saudi Arabia, South Africa, and the United Kingdom. Participants self-identified as mums, dads, or teachers of children 5 to 10 years old. ANOVAs analyses were used to compare participant capabilities, opportunities, and motivations across countries for handwashing and surface disinfecting. Multiple regressions analyses were conducted for each country to assess the predictive relationship between the COM-B components and children's handwashing. RESULTS: The ANOVA analyses revealed that India had the lowest levels of capability, opportunity, and motivation, for both hand hygiene and surface cleaning. The regression analyses revealed that for Australia, Indonesia, and South Africa, the capability component was the only significant predictor of children's handwashing. For India, capability and opportunity were significant. For the United Kingdom, capability and motivation were significant. Lastly, for Saudi Arabia all components were significant. CONCLUSIONS: The discussion explores how the Behaviour Change Wheel methodology could be used to guide further intervention development with community stakeholders in each country. Of the countries assessed, India offers the greatest room for improvement, and behaviour change techniques that influence people's capability and opportunities should be prioritised there.

An exploratory randomised controlled trial evaluating text prompts in Lebanon to encourage health-seeking behaviour for hypertension.

AIMS OF THE STUDY: The current study evaluates the effectiveness of an opportunistic mobile screening on the percentage of people who are aware of whether they may be hypertensive (in an observational study) and the effectiveness of reminder prompts on the percentage of people who seek further medical attention (in a randomised controlled trial). METHODS USED TO CONDUCT THE STUDY: The screening of 1227 participants (529 female) was conducted during the registration period of the 2018 Beirut International Marathon in Lebanon. Next, 266 participants whose screening indicated hypertension (64 Female) were randomly allocated to a treatment group or a control group in a 1:1 fashion. The treatment group received a reminder prompt to seek further medical attention for their potential hypertension and the control group did not. The overt nature of the text message meant that participants in the treatment group could not be blinded to their group allocation. The primary outcome is participants' self-reports of whether they sought further medical attention. RESULTS OF THE STUDY: For the opportunistic screening, a 25% prevalence rate and a 24% awareness rate of hypertension was indicated. A McNemar analysis suggested that the screening increased participant awareness (X2 (N = 1227) = 72.16, P < .001). For the randomised controlled trial, 219 participants provided follow-up data via a phone call (82% retention). A Chi-squared analysis suggested that the reminder prompt successfully encouraged more participants to seek further medical attention, 45.5% treatment group vs 28.0% control group (X2 (1, N = 219) = 7.19, P = .007, φ = 0.18). CONCLUSIONS DRAWN AND CLINICAL IMPLICATIONS: Extra support in the form of a brief reminder message can increase the percentage of people who seek further medical attention after attending an opportunistic screening at a marathon event. The discussion reviews how the results align with previous research, strengths and limitations of the current study, and implications for future research and practice.

Tracking physical activity using smart phone apps: assessing the ability of a current app and systematically collecting patient recommendations for future development.

BACKGROUND: Within the United Kingdom's National Health System (NHS), patients suffering from obesity may be provided with bariatric surgery. After receiving surgery many of these patients require further support to continue to lose more weight or to maintain a healthy weight. Remotely monitoring such patients' physical activity and other health-related variables could provide healthworkers with a more 'ecologically valid' picture of these patients' behaviours to then provide more personalised support. The current study assesses the feasibility of two smartphone apps to do so. In addition, the study looks at the barriers and facilitators patients experience to using these apps effectively. METHODS: Participants with a BMI > 35 kg/m2 being considered for and who had previously undergone bariatric surgery were recruited. Participants were asked to install two mobile phone apps. The 'Moves' app automatically tracked participants' physical activity and the 'WLCompanion' app prompted participants to set goals and input other health-related information. Then, to learn about participants' facilitators and barriers to using the apps, some participants were asked to complete a survey informed by the Theoretical Domains Framework. The data were analysed using regressions and descriptive statistics. RESULTS: Of the 494 participants originally enrolled, 274 participants data were included in the analyses about their activity pre- and/or post-bariatric surgery (ages 18-65, M = 44.02, SD ± 11.29). Further analyses were performed on those 36 participants whose activity was tracked both pre- and post-surgery. Participants' activity levels pre- and post-surgery did not differ. In addition, 54 participants' survey responses suggested that the main facilitator to their continued use of the Moves app was its automatic nature, and the main barrier was its battery drain. CONCLUSIONS: The current study tracked physical activity in patients considered for and who had previously undergone bariatric surgery. The results should be interpreted with caution because of the small number of participants whose data meet the inclusion criteria and the barriers participants encountered to using the apps. Future studies should take note of the barriers to develop more user-friendly apps. TRIAL REGISTRATION: ClinicalTrials.gov- NCT01365416 on the 3rd of June 2011.

The use of control charts by laypeople and hospital decision-makers for guiding decision making.

Graphs presenting healthcare data are increasingly available to support laypeople and hospital staff's decision making. When making these decisions, hospital staff should consider the role of chance-that is, random variation. Given random variation, decision-makers must distinguish signals (sometimes called special-cause data) from noise (common-cause data). Unfortunately, many graphs do not facilitate the statistical reasoning necessary to make such distinctions. Control charts are a less commonly used type of graph that support statistical thinking by including reference lines that separate data more likely to be signals from those more likely to be noise. The current work demonstrates for whom (laypeople and hospital staff) and when (treatment and investigative decisions) control charts strengthen data-driven decision making. We present two experiments that compare people's use of control and non-control charts to make decisions between hospitals (funnel charts vs. league tables) and to monitor changes across time (run charts with control lines vs. run charts without control lines). As expected, participants more accurately identified the outlying data using a control chart than using a non-control chart, but their ability to then apply that information to more complicated questions (e.g., where should I go for treatment?, and should I investigate?) was limited. The discussion highlights some common concerns about using control charts in hospital settings.