Promoting precautionary behavior during the COVID-19 pandemic: Development and validation of a behavior-change messaging campaign

mhealth-based interventions to improving liver cancer screening among high-risk populations: a study protocol for a randomized controlled trial

BACKGROUND

Liver cancer (LC) screening, such as AFP test and abdominal ultrasound, is an effective way to prevent LC, one of the most common cancers worldwide. Despite the proven screening benefits, screening participation among high-risk populations for LC remains low. This suggests that targeted, systematic, and effective interventions should be provided to improve knowledge and awareness related to LC screening, enhance screening intentions, and thereby promote screening behaviors. Telephone is people's main medium of daily communication and mHealth-based programs offer a potential and effective solution for promoting health behaviors. The purpose of this study is to develop and implement a mHealth (WeChat app) based intervention guided by Fogg's Behavior Model (FBM) to augment the knowledge of LC prevention among people at risk of LC and enhance their motivation for screening, and to validate its effectiveness in improving LC screening.

METHODS

We propose a two-arm, single-blind randomized controlled trial with 82 at-risk individuals of LC, delivering a 6-month mHealth-based intervention program with optional health counseling. Recruitment will be through tertiary hospitals and community organizations in 4 districts in Heng Yang. In total, 82 individuals at high risk for HCC will be randomized 1:1 to intervention or control (usual care) groups. The intervention group will receive intervention, whose contents are based on the FBM model, via multiple forms of media including PowerPoint presentation, multimedia video, health information booklet and screening message, which is delivered in the WeChat Applet. Control dyads will be provided with usual health education. Outcomes will be assessed at baseline and post-intervention.

DISCUSSION

The findings of this study will provide evidence of the benefits of utilizing mHealth-based approaches in intervention development to enhance the effectiveness of screening adherence for high-risk people of LC. Further, the findings would provide reference to the potential incorporation of the targeted intervention in local community organizations.

TRIAL REGISTRATION

Chinese Clinical Trial Registry (ChiCTR2400080530) Date registered: 31/1/2024.

Policy instruments facilitate China's COVID-19 work resumption

Governments worldwide have announced stimulus packages to remobilize the labor force after COVID-19 and therefore to cope with the COVID-19-related recession. However, it is still unclear how to facilitate large-scale work resumption. This paper aims to clarify the issue by analyzing the large-scale prefecture-level dataset of human mobility trajectory information for 320 million workers and about 500,000 policy documents in China. We model work resumption as a collective behavioral change due to configurations of capacity, motivation, and policy instruments by using qualitative comparative analysis. We find that the effectiveness of post-COVID-19 recovery stimulus varied across China depending on the fiscal and administrative capacity and the policy motivation of the prefecture. Subnational fiscal and procurement policies were more effective for the wholesale and retail sector and the hotel and catering sector, whereas the manufacturing and business services sectors required more effort regarding employment policies. Due to limited prefectural capacity and wavering policy motivation, the simultaneous adoption of fiscal, employment, and procurement policy interventions endangered post-COVID-19 work resumption. We highlight the necessity of tailored postcrisis recovery strategies based on local fiscal and administrative capacity and the sectoral structure.

Investigating the intention of purchasing private pension scheme based on an integrated FBM-UTAUT model: The case of China

The newly established private pension scheme in China has received great attention as it would be an important supplement to China’s social safety net and corporate annuity amid an aging population. It provides a way of helping to address the challenge of ensuring adequate retirement income, and the scheme is expected to grow significantly in the coming years. This study investigates factors affecting the intention of purchasing the private pension scheme using a conceptual model based on the integration of Fogg Behavioral Model (FBM) and Unified Theory of Acceptance and Use of Technology (UTAUT) model. The questionnaire-based data from a sample of 462 respondents had been analyzed. Both exploratory factor analysis and confirmatory factor analysis were used to assess validity. The hypothesized relationships in the integrated FBM-UTAUT model were tested using structural equation modeling. The research findings indicate that anticipation, social influence, effort expectancy, performance expectancy, side benefits and facilitating conditions have significant positive impacts on intention to purchase. According to the exploratory factor analysis, the integrated FBM-UTAUT model can explain more than 70% of the total variance. Meanwhile, effort expectancy can be affected by time effort, thought effort and physical effort collectively, while performance expectancy can be affected by risk and trust. It is revealed that the integrated FBM-UTAUT model can be effective in explaining purchase intentions in a private pension scheme context, and this study is expected to offer helpful advice on the design of pension products and the reform of pension policies.

Identifying Drivers of COVID-19 Vaccine Uptake among Residents of Yopougon Est, Abidjan, Côte d'Ivoire

This study applied the Fogg Behavioral Model (FBM) to identify and prioritize factors influencing COVID-19 vaccination among residents of Yopougon Est, Abidjan, Côte d'Ivoire. A total of 568 respondents were recruited from among individuals entering eleven participant recruitment and data collection sites located near high pedestrian trafficked areas. Among all respondents, 52% reported being vaccinated versus 48% who reported not being vaccinated. Of those who reported being vaccinated, 42% reported received a single dose, 54% a double dose, and 4% three or more doses. A categorical regression analysis suggested that potential predictors of COVID-19 vaccination included acceptance and rejection factors, which are both aligned with motivation in the FBM and socio-demographic characteristics, proximity to services, and religion. Our findings suggest that demand creation activities should target individuals with less formal education, those who are not formally employed, non-Catholic Christians, and individuals who do not identify as Akan. Results also suggest the need to design programmatic messages and activities that focus on generating family and community support for COVID-19 vaccination.

Utilizing geospatial intelligence and user modeling to allow for a customized health awareness campaign during the pandemic: The case of COVID-19 in Saudi Arabia

BACKGROUND

As of 2022, people are getting better at learning how to coexist with the Covid-19 global pandemic. In Saudi Arabia, many attempts have been made to raise public health awareness. However, most health awareness campaigns are generic and might not influence the desired behavior among individuals.

OBJECTIVES

This study aims to apply geospatial intelligence and user modeling to profile the districts of the city of Jeddah. This customized map can provide a baseline for a customized health awareness campaign that targets the locals of each district individually based on the virus spread level.

METHODOLOGY

It is ongoing research, which has resulted in the creation of a health messages library in the first phase [1]. This paper focuses on a second phase of the research study, which aims to provide a customized baseline for this campaign by applying the geospatial artificial intelligence technique known as space-time cube (STC). STC was applied to create a local map of the Saudi city of Jeddah, representing three different profiles for the city's districts. The model is built using valid COVID-19 clinical data obtained from one of Jeddah's general hospitals.

RESULTS AND IMPLICATIONS

When applied, STC displays three profiles for the districts of Jeddah city: high infection, moderate infection, and low infection. To assess the geo-intelligent map, a new instrument was created and validated. The usability and practicality of this map were quantitatively evaluated in a cross-sectional survey using the goal-question-metric measurement framework, and a total of 43 participants filled out the questionnaire. The results indicate that the geo-intelligent map is suitable for everyday use, as evidenced by the participants' responses. We argue that the developed instrument can also be used to assess any geo-intelligence map. This research provides a legitimate approach to customizing health awareness messages during pandemics.