IntroductionThe recent years have witnessed a continuous increase in lifestyle related health challenges around the world. As a result, researchers and health practitioners have focused on promoting healthy behavior using various behavior change interventions. The designs of most of these interventions are informed by health behavior models and theories adapted from various disciplines. Several health behavior theories have been used to inform health intervention designs, such as the Theory of Planned Behavior, the Transtheoretical Model, and the Health Belief Model (HBM). However, the Health Belief Model (HBM), developed in the 1950s to investigate why people fail to undertake preventive health measures, remains one of the most widely employed theories of health behavior. However, the effectiveness of this model is limited. The first limitation is the low predictive capacity (R2 < 0.21 on average) of existing HBM’s variables coupled with the small effect size of individual variables. The second is lack of clear rules of combination and relationship between the individual variables. In this paper, we propose a solution that aims at addressing these limitations as follows: (1) we extended the Health Belief Model by introducing four new variables: Self-identity, Perceived Importance, Consideration of Future Consequences, and Concern for Appearance as possible determinants of healthy behavior. (2) We exhaustively explored the relationships/interactions between the HBM variables and their effect size. (3) We tested the validity of both our proposed extended model and the original HBM on healthy eating behavior. Finally, we compared the predictive capacity of the original HBM model and our extended model.Methods:To achieve the objective of this paper, we conducted a quantitative study of 576 participants’ eating behavior. Data for this study were collected over a period of one year (from August 2011 to August 2012). The questionnaire consisted of validated scales assessing the HBM determinants – perceived benefit, barrier, susceptibility, severity, cue to action, and self-efficacy – using 7-point Likert scale. We also assessed other health determinants such as consideration of future consequences, self-identity, concern for appearance and perceived importance. To analyses our data, we employed factor analysis and Partial Least Square Structural Equation Model (PLS-SEM) to exhaustively explore the interaction/relationship between the determinants and healthy eating behavior. We tested for the validity of both our proposed extended model and the original HBM on healthy eating behavior. Finally, we compared the predictive capacity of the original HBM model and our extended model and investigated possible mediating effects.Results:The results show that the three newly added determinants are better predictors of healthy behavior. Our extended HBM model lead to approximately 78% increase (from 40 to 71%) in predictive capacity compared to the old model. This shows the suitability of our extended HBM for use in predicting he...
Self-monitoring is the cornerstone of many health and wellness persuasive interventions. However, applications designed to promote health and wellness that use this strategy have recorded varying degrees of success. In this study, we investigated why the self-monitoring strategy might work in some contexts and fail in others. We conducted a series of large-scale studies, with a total of 1768 participants, to explore the strengths and weaknesses of the self-monitoring strategy. Our results uncover important strengths and weaknesses that could facilitate or hinder the effectiveness of self-monitoring to promote the health and wellness of its users. The strengths include its tendency to reveal problem behaviours, provide real and concrete information, foster reflection, make people accept responsibility, create awareness and raise users’ consciousness about their health and wellness. Some of the weaknesses include its tendency to provoke health disorder, be tedious and boring. We contribute to the digital health community by offering design guidelines for operationalising self-monitoring to overcome its weaknesses and amplify its strengths.
Background The COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives. In the absence of vaccines and antivirals, several behavioral change and policy initiatives such as physical distancing have been implemented to control the spread of COVID-19. Social media data can reveal public perceptions toward how governments and health agencies worldwide are handling the pandemic, and the impact of the disease on people regardless of their geographic locations in line with various factors that hinder or facilitate the efforts to control the spread of the pandemic globally. Objective This paper aims to investigate the impact of the COVID-19 pandemic on people worldwide using social media data. Methods We applied natural language processing (NLP) and thematic analysis to understand public opinions, experiences, and issues with respect to the COVID-19 pandemic using social media data. First, we collected over 47 million COVID-19–related comments from Twitter, Facebook, YouTube, and three online discussion forums. Second, we performed data preprocessing, which involved applying NLP techniques to clean and prepare the data for automated key phrase extraction. Third, we applied the NLP approach to extract meaningful key phrases from over 1 million randomly selected comments and computed sentiment score for each key phrase and assigned sentiment polarity (ie, positive, negative, or neutral) based on the score using a lexicon-based technique. Fourth, we grouped related negative and positive key phrases into categories or broad themes. Results A total of 34 negative themes emerged, out of which 15 were health-related issues, psychosocial issues, and social issues related to the COVID-19 pandemic from the public perspective. Some of the health-related issues were increased mortality, health concerns, struggling health systems, and fitness issues; while some of the psychosocial issues were frustrations due to life disruptions, panic shopping, and expression of fear. Social issues were harassment, domestic violence, and wrong societal attitude. In addition, 20 positive themes emerged from our results. Some of the positive themes were public awareness, encouragement, gratitude, cleaner environment, online learning, charity, spiritual support, and innovative research. Conclusions We uncovered various negative and positive themes representing public perceptions toward the COVID-19 pandemic and recommended interventions that can help address the health, psychosocial, and social issues based on the positive themes and other research evidence. These interventions will help governments, health professionals and agencies, institutions, and individuals in their efforts to curb the spread of COVID-19 and minimize its impact, and in reacting to any future pandemics.
Persuasive technologies are tools for motivating behaviour change using persuasive strategies. socially-driven persuasive technologies employ three common socially-oriented persuasive strategies in many health domains: competition, social comparison, and cooperation. Research has shown the possibilities for socially-driven persuasive interventions to backfire by demotivating behaviour, but we lack knowledge about how the interventions could motivate or demotivate behaviours. To close this gap, we studied 1898 participants, specifically Socially-oriented strategies and their comparative effectiveness in socially-driven persuasive health interventions that motivate healthy behaviour change. The results of a thematic analysis of 278 pages of qualitative data reveal important strengths and weaknesses of the individual socially-oriented strategies that could facilitate or hinder their effectiveness at motivating behaviour change. These include their tendency to simplify behaviours and make them fun, challenge people and make them accountable, give a sense of accomplishment and their tendency to jeopardize user's privacy and relationships, creates unnecessary tension, and reduce self-confidence and self-esteem, and provoke a health disorder and body shaming, respectively. We contribute to the health informatics community by developing 15 design guidelines for operationalizing the strategies in persuasive health intervention to amplify their strengths and overcome their weaknesses.
Gameful elements and persuasive strategies can motivate and encourage people to take charge of their health and achieve their ultimate wellness goal.
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