Exploring Associations Between Children's Obesogenic Behaviors and the Local Environment Using Big Data: Development and Evaluation of the Obesity Prevention Dashboard

Human factors methods in the design of digital decision support systems for population health: a scoping review

BACKGROUND

While Human Factors (HF) methods have been applied to the design of decision support systems (DSS) to aid clinical decision-making, the role of HF to improve decision-support for population health outcomes is less understood. We sought to comprehensively understand how HF methods have been used in designing digital population health DSS.

MATERIALS AND METHODS

We searched English documents published in health sciences and engineering databases (Medline, Embase, PsychINFO, Scopus, Comendex, Inspec, IEEE Xplore) between January 1990 and September 2023 describing the development, validation or application of HF principles to decision support tools in population health.

RESULTS

We identified 21,581 unique records and included 153 studies for data extraction and synthesis. We included research articles that had a target end-user in population health and that used HF. HF methods were applied throughout the design lifecycle. Users were engaged early in the design lifecycle in the needs assessment and requirements gathering phase and design and prototyping phase with qualitative methods such as interviews. In later stages in the lifecycle, during user testing and evaluation, and post deployment evaluation, quantitative methods were more frequently used. However, only three studies used an experimental framework or conducted A/B testing.

CONCLUSIONS

While HF have been applied in a variety of contexts in the design of data-driven DSSs for population health, few have used Human Factors to its full potential. We offer recommendations for how HF can be leveraged throughout the design lifecycle. Most crucially, system designers should engage with users early on and throughout the design process. Our findings can support stakeholders to further empower public health systems.

Citizen science for monitoring the health and well-being related Sustainable Development Goals and the World Health Organization's Triple Billion Targets

Achieving the health and well-being related Sustainable Development Goals (SDGs) and the World Health Organization's (WHO) Triple Billion Targets depends on informed decisions that are based on concerted data collection and monitoring efforts. Even though data availability has been increasing in recent years, significant gaps still remain for routine surveillance to guide policies and actions. The COVID-19 crisis has shown that more and better data and strengthened health information systems are needed to inform timely decisions that save lives. Traditional sources of data such as nationally representative surveys are not adequate for addressing this challenge alone. Additionally, the funding required to measure all health and well-being related SDG indicators and Triple Billion Targets using only traditional sources of data is a challenge to achieving efficient, timely and reliable monitoring systems. Citizen science, public participation in scientific research and knowledge production, can contribute to addressing some of these data gaps efficiently and sustainably when designed well, and ultimately, could contribute to the achievement of the health and well-being related SDGs and Triple Billion Targets. Through a systematic review of health and well-being related indicators, as well as citizen science initiatives, this paper aims to explore the potential of citizen science for monitoring health and well-being and for mobilizing action toward the achievement of health and well-being related targets as outlined in the SDG framework and Triple Billion Targets. The results demonstrate that out of 58 health and well-being related indicators of the SDGs and Triple Billion Targets covered in this study, citizen science could potentially contribute to monitoring 48 of these indicators and their targets, mostly at a local and community level, which can then be upscaled at a national level with the projection to reach global level monitoring and implementation. To integrate citizen science with official health and well-being statistics, the main recommendation is to build trusted partnerships with key stakeholders including National Statistical Offices, governments, academia and the custodian agencies, which is mostly the WHO for these health and well-being related targets and indicators.

Evaluation of an Emergency Department Visit Data Mental Health Dashboard

CONTEXT

Local health departments (LHDs) need timely county-level and subcounty-level data to monitor health-related trends, identify health disparities, and inform areas of highest need for interventions as part of their ongoing assessment responsibilities; yet, many health departments rely on secondary data that are not timely and cannot provide subcounty insights.

OBJECTIVE

We developed and evaluated a mental health dashboard in Tableau for an LHD audience featuring statewide syndromic surveillance emergency department (ED) data in North Carolina from the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT).

DESIGN

We developed a dashboard that provides counts, crude rates, and ED visit percentages at statewide and county levels, as well as breakdowns by zip code, sex, age group, race, ethnicity, and insurance coverage for 5 mental health conditions. We evaluated the dashboards through semistructured interviews and a Web-based survey that included the standardized usability questions from the System Usability Scale.

PARTICIPANTS

Convenience sample of LHD public health epidemiologists, health educators, evaluators, and public health informaticians.

RESULTS

Six semistructured interview participants successfully navigated the dashboard but identified usability issues when asked to compare county-level trends displayed in different outputs (eg, tables vs graphs). Thirty respondents answered all questions on the System Usability Scale for the dashboard, which received an above average score of 86.

CONCLUSIONS

The dashboards scored well on the System Usability Scale, but more research is needed to identify best practices in disseminating multiyear syndromic surveillance ED visit data on mental health conditions to LHDs.

A Novel Approach to Characterize State-level Food Environment and Predict Obesity Rate Using Social Media Data: Correlational Study

BACKGROUND

Community obesity outcomes can reflect the food environment to which the community belongs. Recent studies have suggested that the local food environment can be measured by the degree of food accessibility, and survey data are normally used to calculate food accessibility. However, compared with survey data, social media data are organic, continuously updated, and cheaper to collect.

OBJECTIVE

The objective of our study was to use publicly available social media data to learn the relationship between food environment and obesity rates at the state level.

METHODS

To characterize the caloric information of the local food environment, we used food categories from Yelp and collected caloric information from MyFitnessPal for each category based on their popular dishes. We then calculated the average calories for each category and created a weighted score for each state. We also calculated 2 other dimensions from the concept of access, acceptability and affordability, to build obesity prediction models.

RESULTS

The local food environment characterized using only publicly available social media data had a statistically significant correlation with the state obesity rate. We achieved a Pearson correlation of 0.796 between the predicted obesity rate and the reported obesity rate from the Behavioral Risk Factor Surveillance System across US states and the District of Columbia. The model with 3 generated feature sets achieved the best performance.

CONCLUSIONS

Our study proposed a method for characterizing state-level food environments only using continuously updated social media data. State-level food environments were accurately described using social media data, and the model also showed a disparity in the available food between states with different obesity rates. The proposed method should elastically apply to local food environments of different sizes and predict obesity rates effectively.

Toward Systems Models for Obesity Prevention: A Big Role for Big Data

The relation among the various causal factors of obesity is not well understood, and there remains a lack of viable data to advance integrated, systems models of its etiology. The collection of big data has begun to allow the exploration of causal associations between behavior, built environment, and obesity-relevant health outcomes. Here, the traditional epidemiologic and emerging big data approaches used in obesity research are compared, describing the research questions, needs, and outcomes of 3 broad research domains: eating behavior, social food environments, and the built environment. Taking tangible steps at the intersection of these domains, the recent European Union project "BigO: Big data against childhood obesity" used a mobile health tool to link objective measurements of health, physical activity, and the built environment. BigO provided learning on the limitations of big data, such as privacy concerns, study sampling, and the balancing of epidemiologic domain expertise with the required technical expertise. Adopting big data approaches will facilitate the exploitation of data concerning obesity-relevant behaviors of a greater variety, which are also processed at speed, facilitated by mobile-based data collection and monitoring systems, citizen science, and artificial intelligence. These approaches will allow the field to expand from causal inference to more complex, systems-level predictive models, stimulating ambitious and effective policy interventions.

Mapping of Outdoor Food and Beverage Advertising around Spanish Schools

Overweight and obesity rates have increased worldwide in the last decades. The marketing strategies of food considered to be unhealthy significantly exacerbate the childhood obesity dilemma. Studies typically analyze the content of advertisement in television, movies, or social media, but there is a gap in the assessment of the real-life promotion of food and beverages around the schools. The primary aim of the study was to assess the products advertised around public and concerted schools in three cities in the north of Spain, and to categorize them as healthy (core) or unhealthy (discretionary). The secondary aim was to describe the types of food and beverages in advertisements, as well as to determine the density of core and discretionary product advertisements. A cross-sectional descriptive study was carried out between September and December 2021. The units of analysis were outdoor food and beverage advertisements that were located around public and concerted schools of three cities in the north of Spain. We found 104 schools that met the criteria. We identified 6469 products advertised, 35.1% core and 61.2% discretionary, observing significant differences (p < 0.001) among the cities. Fruit (core) and alcohol (discretionary) were the most heavily advertised products. In conclusion, children attending schools located in the assessed cities are currently exposed to a significant amount of discretionary product advertisement, a situation that should be regulated.

The impact of COVID-19 on Physical Activity of Czech children

INTRODUCTION

The pandemic of coronavirus disease (COVID-19) and related restrictions (closed schools and sports centers, social isolation, masks) may have a negative impact on children's health. The purpose of this study was to evaluate the level of physical activity (PA) of Czech children during COVID-19 in autumn 2020.

METHODS

Ninety-eight Czech children (mean age = 10.1 ± 1.47 years) completed the standardized Physical Activity Questionnaire for Older Czech Children (PAQ-C/cz) during COVID lockdown. Data were compared with previously published norms. Thirty-five children also reported daily number of steps measured by accelerometers.

RESULTS

Total PAQ-C score was 0.38 lower during COVID compared to Pre-COVID [t(302) = 5.118., p < .001]. The male PAQ-C total score was 0.37 lower [t(146) = 3.21., p = .002)] and the female total score was 0.39 lower [t(154) = 3.97., p < .001] during COVID compared to Pre-COVID. Specifically, responses of PA during spare time, before-school, physical education (PE), and recess were significantly lower during COVID. The average number of steps was 7.767 steps/day (boys = 9.255; girls = 6.982).

CONCLUSION

COVID lockdown resulted in significant reduction of PA in Czech children. Strategies to promote adequate PA of children during the pandemic need to be determined.