An innovative approach to addressing childhood obesity: a knowledge-based infrastructure for supporting multi-stakeholder partnership decision-making in Quebec, Canada

Childhood Obesity as a Global Problem: a Cross-sectional Survey on Global Awareness and National Program Implementation

Objective

The rising global epidemic of childhood obesity is a major public health challenge. Despite the urgency, there is a lack of data on the awareness and implementation of preventative measures. The aim of this study was to identify areas for improvement in the prevention and management of childhood obesity worldwide.

Methods

A cross-sectional electronic survey was distributed to 132 members of national pediatric societies of the International Pediatric Association.

Results

Twenty-eight (21.2%) participants, each from a different country across six World Health Organization (WHO) regions completed the survey. Most participants reported that national prevalence data of childhood obesity is available (78.6%), and the number increased during the Coronavirus disease-2019 pandemic (60.7%). In most countries (78.6%), the amount of sugar and salt in children’s products is provided but only 42.9% enacted regulations on children-targeted advertising. Childhood obesity prevention programs from the government (64.3%) and schools (53.6%) are available with existing support from private or non-profit organizations (71.4%). Participants were aware of WHO’s guidance concerning childhood obesity (78.6%), while fewer were aware of The United Nations International Children’s Emergency Fund’s (UNICEF) guidance (50%). Participants reported that WHO/UNICEF guidance acted as a reference to develop policies, regulations and national programs. However, progress was hindered by poor compliance. Lastly, participants provided suggestions on tackling obesity, with responses ranging from developing and reinforcing policies, involvement of schools, and prevention across all life stages.

Conclusion

There are different practices in implementing prevention measures to counter childhood obesity globally, particularly in statutory regulation on food advertising and national programs. While support and awareness was relatively high, implementation was hindered. This reflects the need for prompt, country-specific evaluation and interventions.

Public Health Surveillance of Behavioral Cancer Risk Factors During the COVID-19 Pandemic: Sentiment and Emotion Analysis of Twitter Data

BACKGROUND

The COVID-19 pandemic and its associated public health mitigation strategies have dramatically changed patterns of daily life activities worldwide, resulting in unintentional consequences on behavioral risk factors, including smoking, alcohol consumption, poor nutrition, and physical inactivity. The infodemic of social media data may provide novel opportunities for evaluating changes related to behavioral risk factors during the pandemic.

OBJECTIVE

We explored the feasibility of conducting a sentiment and emotion analysis using Twitter data to evaluate behavioral cancer risk factors (physical inactivity, poor nutrition, alcohol consumption, and smoking) over time during the first year of the COVID-19 pandemic.

METHODS

Tweets during 2020 relating to the COVID-19 pandemic and the 4 cancer risk factors were extracted from the George Washington University Libraries Dataverse. Tweets were defined and filtered using keywords to create 4 data sets. We trained and tested a machine learning classifier using a prelabeled Twitter data set. This was applied to determine the sentiment (positive, negative, or neutral) of each tweet. A natural language processing package was used to identify the emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, and trust) based on the words contained in the tweets. Sentiments and emotions for each of the risk factors were evaluated over time and analyzed to identify keywords that emerged.

RESULTS

The sentiment analysis revealed that 56.69% (51,479/90,813) of the tweets about physical activity were positive, 16.4% (14,893/90,813) were negative, and 26.91% (24,441/90,813) were neutral. Similar patterns were observed for nutrition, where 55.44% (27,939/50,396), 15.78% (7950/50,396), and 28.79% (14,507/50,396) of the tweets were positive, negative, and neutral, respectively. For alcohol, the proportions of positive, negative, and neutral tweets were 46.85% (34,897/74,484), 22.9% (17,056/74,484), and 30.25% (22,531/74,484), respectively, and for smoking, they were 41.2% (11,628/28,220), 24.23% (6839/28,220), and 34.56% (9753/28,220), respectively. The sentiments were relatively stable over time. The emotion analysis suggests that the most common emotion expressed across physical activity and nutrition tweets was trust (69,495/320,741, 21.67% and 42,324/176,564, 23.97%, respectively); for alcohol, it was joy (49,147/273,128, 17.99%); and for smoking, it was fear (23,066/110,256, 20.92%). The emotions expressed remained relatively constant over the observed period. An analysis of the most frequent words tweeted revealed further insights into common themes expressed in relation to some of the risk factors and possible sources of bias.

CONCLUSIONS

This analysis provided insight into behavioral cancer risk factors as expressed on Twitter during the first year of the COVID-19 pandemic. It was feasible to extract tweets relating to all 4 risk factors, and most tweets had a positive sentiment with varied emotions across the different data sets. Although these results can play a role in promoting public health, a deeper dive via qualitative analysis can be conducted to provide a contextual examination of each tweet.

Using a Personal Health Library-Enabled mHealth Recommender System for Self-Management of Diabetes Among Underserved Populations: Use Case for Knowledge Graphs and Linked Data

Background

Traditionally, digital health data management has been based on electronic health record (EHR) systems and has been handled primarily by centralized health providers. New mechanisms are needed to give patients more control over their digital health data. Personal health libraries (PHLs) provide a single point of secure access to patients' digital health data and enable the integration of knowledge stored in their digital health profiles with other sources of global knowledge. PHLs can help empower caregivers and health care providers to make informed decisions about patients’ health by understanding medical events in the context of their lives.

Objective

This paper reports the implementation of a mobile health digital intervention that incorporates both digital health data stored in patients’ PHLs and other sources of contextual knowledge to deliver tailored recommendations for improving self-care behaviors in diabetic adults.

Methods

We conducted a thematic assessment of patient functional and nonfunctional requirements that are missing from current EHRs based on evidence from the literature. We used the results to identify the technologies needed to address those requirements. We describe the technological infrastructures used to construct, manage, and integrate the types of knowledge stored in the PHL. We leverage the Social Linked Data (Solid) platform to design a fully decentralized and privacy-aware platform that supports interoperability and care integration. We provided an initial prototype design of a PHL and drafted a use case scenario that involves four actors to demonstrate how the proposed prototype can be used to address user requirements, including the construction and management of the PHL and its utilization for developing a mobile app that queries the knowledge stored and integrated into the PHL in a private and fully decentralized manner to provide better recommendations.

Results

To showcase the main features of the mobile health app and the PHL, we mapped those features onto a framework comprising the user requirements identified in a use case scenario that features a preventive intervention from the diabetes self-management domain. Ongoing development of the app requires a formative evaluation study and a clinical trial to assess the impact of the digital intervention on patient-users. We provide synopses of both study protocols.

Conclusions

The proposed PHL helps patients and their caregivers take a central role in making decisions regarding their health and equips their health care providers with informatics tools that support the collection and interpretation of the collected knowledge. By exposing the PHL functionality as an open service, we foster the development of third-party applications or services and provide motivational technological support in several projects crossing different domains of interest.

Process Evaluation and Lessons Learned From Engaging Local Policymakers in the B'More Healthy Communities for Kids Trial

Partnerships linking researchers to the policymaking process can be effective in increasing communication and supporting health policy. However, these policy partnerships rarely conduct process evaluation. The Policy Working Group (Policy WG) was the policy-level intervention of the multilevel B'More Healthy Communities for Kids (BHCK) trial. The group sought to align interests of local policymakers, inform local food and nutrition policy, introduce policymakers to a new simulation modeling, and sustain intervention levels of BHCK. We conducted an evaluation on the Policy WG between July 2013 and May 2016. We evaluated process indicators for reach, dose-delivered, and fidelity and developed a SWOT (strengths, weaknesses, opportunities, and threats) analysis. The policy intervention was implemented with high reach and dose-delivered. Fidelity measures improved from moderate to nearly high over time. The number of health-related issues on policymakers' agenda increased from 50% in the first 2 years to 150% of the high standard in Year 3. SWOT analysis integrated a stakeholder feedback survey to consider areas of strength, weakness, opportunity, and threats. Although the fidelity of the modeling was low at 37% of the high standard, stakeholders indicated that the simulation modeling should be a primary purpose for policy intervention. Results demonstrate that process evaluation and SWOT analysis is useful for tracking the progress of policy interventions in multilevel trials and can be used to monitor the progress of building partnerships with policymakers.

An Interoperable System toward Cardiac Risk Stratification from ECG Monitoring

Many indices have been proposed for cardiovascular risk stratification from electrocardiogram signal processing, still with limited use in clinical practice. We created a system integrating the clinical definition of cardiac risk subdomains from ECGs and the use of diverse signal processing techniques. Three subdomains were defined from the joint analysis of the technical and clinical viewpoints. One subdomain was devoted to demographic and clinical data. The other two subdomains were intended to obtain widely defined risk indices from ECG monitoring: a simple-domain (heart rate turbulence (HRT)), and a complex-domain (heart rate variability (HRV)). Data provided by the three subdomains allowed for the generation of alerts with different intensity and nature, as well as for the grouping and scrutinization of patients according to the established processing and risk-thresholding criteria. The implemented system was tested by connecting data from real-world in-hospital electronic health records and ECG monitoring by considering standards for syntactic (HL7 messages) and semantic interoperability (archetypes based on CEN/ISO EN13606 and SNOMED-CT). The system was able to provide risk indices and to generate alerts in the health records to support decision-making. Overall, the system allows for the agile interaction of research and clinical practice in the Holter-ECG-based cardiac risk domain.

Dynamics of Implementation and Maintenance of Organizational Health Interventions

In this study, we present case studies to explore the dynamics of implementation and maintenance of health interventions. We analyze how specific interventions are built and eroded, how the building and erosion mechanisms are interconnected, and why we can see significantly different erosion rates across otherwise similar organizations. We use multiple comparative obesity prevention case studies to provide empirical information on the mechanisms of interest, and use qualitative systems modeling to integrate our evolving understanding into an internally consistent and transparent theory of the phenomenon. Our preliminary results identify reinforcing feedback mechanisms, including design of organizational processes, motivation of stakeholders, and communication among stakeholders, which influence implementation and maintenance of intervention components. Over time, these feedback mechanisms may drive a wedge between otherwise similar organizations, leading to distinct configurations of implementation and maintenance processes.

Lessons Learned by Community Stakeholders in the Massachusetts Childhood Obesity Research Demonstration (MA-CORD) Project, 2013-2014

Introduction

Childhood obesity is a multifaceted disease that requires sustainable, multidimensional approaches that support change at the individual, community, and systems levels. The Massachusetts Childhood Obesity Research Demonstration project addressed this need by using clinical and public health evidence-based methods to prevent childhood obesity. To date, little information is known about successes and lessons learned from implementing such large-scale interventions. To address this gap, we examined perspectives of community stakeholders from various sectors on successes achieved and lessons learned during the implementation process.

Methods

We conducted 39 semistructured interviews with key stakeholders from 6 community sectors in 2 low-income communities from November 2013 through April 2014, during project implementation. Interviews were audio-recorded, transcribed, and analyzed by using the constant comparative method. Data were analyzed by using QSR NVivo 10.

Results

Successes included increased parental involvement in children’s health and education, increased connections within participating organizations and within the broader community, changes in organizational policies and environments to better support healthy living, and improvements in health behaviors in children, parents, and stakeholders. Lessons learned included the importance of obtaining administrative and leadership support, involving key stakeholders early in the program planning process, creating buffers that allow for unexpected changes, and establishing opportunities for regular communication within and across sectors.

Conclusion

Study findings indicate that multidisciplinary approaches support health behavior change and provide insight into key issues to consider in developing and implementing such approaches in low-income communities.

Community Stakeholders' Perceptions of Major Factors Influencing Childhood Obesity, the Feasibility of Programs Addressing Childhood Obesity, and Persisting Gaps

Prior research has identified numerous factors contributing to increased rates of childhood obesity. However, few studies have focused explicitly on the experience of community stakeholders in low-income communities. This study sought to capture the perspectives of these on-the-ground experts regarding major factors contributing to childhood obesity as well as gaps in current prevention and control efforts. We conducted semi-structured interviews with 39 stakeholders from different community sectors (e.g., healthcare providers, childcare providers, teachers). Data were drawn from the Massachusetts Childhood Obesity Research Demonstration project, a multi-level, multi-sector intervention designed to reduce childhood obesity being implemented in two low-income communities in Massachusetts. Interviews were conducted at baseline, transcribed, coded using grounded theory approach, and analyzed in NVivo 10.0. The vast majority of stakeholders had recently participated in obesity prevention strategies, and nearly all of them identified gaps in prevention efforts either within their organizations or in the broader community. In addition to factors previously identified in the literature, several themes emerged including the need to change policies to increase physical activity during school, offer healthier snacks in schools and afterschool programs, and increase communication and collaboration within the community in prevention efforts. Community stakeholders can impact the success of interventions by bridging the gap between science and lived experience. The results of this study can guide future research by highlighting the importance of including stakeholders' frontline experiences with target populations, and using information on identified gaps to augment intervention planning efforts.

Modularising ontology and designing inference patterns to personalise health condition assessment: the case of obesity

BACKGROUND

The public health initiatives for obesity prevention are increasingly exploiting the advantages of smart technologies that can register various kinds of data related to physical, physiological, and behavioural conditions. Since individual features and habits vary among people, the design of appropriate intervention strategies for motivating changes in behavioural patterns towards a healthy lifestyle requires the interpretation and integration of collected information, while considering individual profiles in a personalised manner. The ontology-based modelling is recognised as a promising approach in facing the interoperability and integration of heterogeneous information related to characterisation of personal profiles.

RESULTS

The presented ontology captures individual profiles across several obesity-related knowledge-domains structured into dedicated modules in order to support inference about health condition, physical features, behavioural habits associated with a person, and relevant changes over time. The modularisation strategy is designed to facilitate ontology development, maintenance, and reuse. The domain-specific modules formalised in the Web Ontology Language (OWL) integrate the domain-specific sets of rules formalised in the Semantic Web Rule Language (SWRL). The inference rules follow a modelling pattern designed to support personalised assessment of health condition as age- and gender-specific. The test cases exemplify a personalised assessment of the obesity-related health conditions for the population of teenagers.

CONCLUSION

The paper addresses several issues concerning the modelling of normative concepts related to obesity and depicts how the public health concern impacts classification of teenagers according to their phenotypes. The modelling choices regarding the ontology-structure are explained in the context of the modelling goal to integrate multiple knowledge-domains and support reasoning about the individual changes over time. The presented modularisation pattern enhances reusability of the domain-specific modules across various health care domains.