Culture and Health Belief Model: Exploring the Determinants of Physical Activity Among Saudi Adults and the Moderating Effects of Age and Gender

Physical inactivity is a significant risk factor for many non-communicable diseases such as heart disease, diabetes, and evidence shows that physical inactivity is one of the highest risk factors for death globally. Research has shown that theory-driven persuasive interventions are more effective at promoting behaviour change than generic ones. However, research on the determinants of physical activity and the moderating effect of age and gender among non-Western culture is limited. To close this gap, we conduct a large-scale study of the determinants of physical activity among 217 participants from Saudi Arabia using the extended Health Belief Model (HBM), a commonly applied behavioural model in health interventions design. We also assessed for the moderating effect of age and gender. Our findings show that Social influence, Cue to action and Perceived severity are the strongest determinants of physical activity in Saudi adults. We map these determinants to their corresponding persuasive strategies that can be used in operationalizing them in persuasive applications for promoting physical activity. Finally, we discuss the implication of our findings and offer design guidelines for persuasive interventions that appeal to both a broad audience and tailored to a particular group depending on their gender and age group.

[1]  R. Dellavalle,et al.  Mobile medical and health apps: state of the art, concerns, regulatory control and certification , 2014, Online journal of public health informatics.

[2]  A. Aro,et al.  Lack of facilities rather than sociocultural factors as the primary barrier to physical activity among female Saudi university students , 2015, International journal of women's health.

[3]  M. Johnston,et al.  From Theory to Intervention: Mapping Theoretically Derived Behavioural Determinants to Behaviour Change Techniques , 2008 .

[4]  E. Puigdomènech,et al.  Patterns of sedentary behavior in overweight and moderately obese users of the Catalan primary-health care system , 2018, PloS one.

[5]  E. Al-Eisa,et al.  Physical Activity and Health Beliefs among Saudi Women , 2012, Journal of nutrition and metabolism.

[6]  Julita Vassileva,et al.  Towards an Effective Health Interventions Design: An Extension of the Health Belief Model , 2012, Online journal of public health informatics.

[7]  Kiemute Oyibo,et al.  Personalizing health theories in persuasive game interventions to gamer types: an African perspective , 2018, AfriCHI.

[8]  Silvia Lindtner,et al.  Fish'n'Steps: Encouraging Physical Activity with an Interactive Computer Game , 2006, UbiComp.

[9]  Tammy Toscos,et al.  Chick clique: persuasive technology to motivate teenage girls to exercise , 2006, CHI Extended Abstracts.

[10]  Kiemute Oyibo,et al.  Socially-driven persuasive health intervention design: Competition, social comparison, and cooperation , 2019, Health Informatics J..

[11]  H. Al-Hazzaa,et al.  School backpack. How much load do Saudi school boys carry on their shoulders? , 2006, Saudi medical journal.

[12]  Rebecca A. Vidourek,et al.  Vigorous Physical Activity Among College Students: Using the Health Belief Model to Assess Involvement and Social Support , 2014 .

[13]  Harri Oinas-Kukkonen,et al.  Persuasive Technology in Mobile Applications Promoting Physical Activity: a Systematic Review , 2016, Journal of Medical Systems.

[14]  M. Gelfand,et al.  Converging measurement of horizontal and vertical individualism and collectivism , 1998 .

[15]  Dirceu da Silva,et al.  Structural Equation Modeling with the SmartPLS , 2014 .

[16]  Linda L. Carli Gender and Social Influence , 2001 .

[17]  Rita Orji,et al.  How Effective Are Social Influence Strategies in Persuasive Apps for Promoting Physical Activity?: A Systematic Review , 2019, UMAP.

[18]  B. J. Fogg,et al.  Persuasive technology: using computers to change what we think and do , 2002, UBIQ.

[19]  Susan L. Kasser,et al.  Health beliefs and physical activity behavior in adults with multiple sclerosis. , 2012, Disability and health journal.

[20]  Juho Hamari,et al.  Do Persuasive Technologies Persuade? - A Review of Empirical Studies , 2014, PERSUASIVE.

[21]  Jason W. Osborne,et al.  Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. , 2005 .

[22]  Harald Reiterer,et al.  Integrating Taxonomies Into Theory-Based Digital Health Interventions for Behavior Change: A Holistic Framework , 2018, JMIR research protocols.

[23]  M. Moeini,et al.  Investigating the effect of an education plan based on the health belief model on the physical activity of women who are at risk for hypertension , 2014, Iranian journal of nursing and midwifery research.

[24]  Joshua H. West,et al.  Apps of Steel: Are Exercise Apps Providing Consumers With Realistic Expectations? , 2013, Health education & behavior : the official publication of the Society for Public Health Education.

[25]  A. Nevill,et al.  Obesity, Physical Activity and Sedentary Behavior Amongst British and Saudi Youth: A Cross-Cultural Study , 2012, International journal of environmental research and public health.

[26]  P. Kasmaei,et al.  Brushing behavior among young adolescents: does perceived severity matter , 2014, BMC Public Health.

[27]  Bongshin Lee,et al.  Time for Break: Understanding Information Workers' Sedentary Behavior Through a Break Prompting System , 2018, CHI.

[28]  L MandrykRegan,et al.  Developing culturally relevant design guidelines for encouraging healthy eating behavior , 2014 .

[29]  A. Kriska,et al.  Physical activity and cardiovascular risk factors in a developing population. , 2001, Medicine and science in sports and exercise.

[30]  I. Ajzen The theory of planned behavior , 1991 .

[31]  Julita Vassileva,et al.  Towards a Data-Driven Approach to Intervention Design: A Predictive Path Model of Healthy Eating Determinants , 2012, PERSUASIVE.

[32]  J. Manson,et al.  Sedentary behavior and mortality in older women: the Women's Health Initiative. , 2014, American Journal of Preventive Medicine.

[33]  Julita Vassileva,et al.  Tailoring persuasive health games to gamer type , 2013, CHI.

[34]  Rita Orji,et al.  Personalizing Persuasive Strategies in Gameful Systems to Gamification User Types , 2018, CHI.

[35]  J. Prochaska,et al.  In Search of How People Change: Applications to Addictive Behaviors , 1992, The American psychologist.

[36]  S. Michie,et al.  Are interventions theory-based? Development of a theory coding scheme. , 2010, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[37]  Wynne W. Chin The partial least squares approach for structural equation modeling. , 1998 .

[38]  C. Eccleston,et al.  Smartphone applications for pain management , 2011, Journal of telemedicine and telecare.

[39]  James Noble,et al.  Persuasive interaction for collectivist cultures , 2006, AUIC.

[40]  Kiemute Oyibo,et al.  BEN'FIT: Design, Implementation and Evaluation of a Culture-Tailored Fitness App , 2019, UMAP.

[41]  Henrique Luiz Monteiro,et al.  Sedentary behaviour is associated with diabetes mellitus in adults: findings of a cross-sectional analysis from the Brazilian National Health System. , 2018, Journal of public health.

[42]  I. Rosenstock Why people use health services. , 1966, The Milbank Memorial Fund quarterly.

[43]  Marie-France Hivert,et al.  Sedentary Behavior and Cardiovascular Morbidity and Mortality: A Science Advisory From the American Heart Association , 2016, Circulation.

[44]  Chrysanne Di Marco,et al.  Towards Personality-driven Persuasive Health Games and Gamified Systems , 2017, CHI.

[45]  Masami Takahashi,et al.  Mobile walking game and group-walking program to enhance going out for older adults , 2016, UbiComp Adjunct.

[46]  R. Orji,et al.  DESIGN FOR BEHAVIOUR CHANGE: A MODEL-DRIVEN APPROACH FOR TAILORING PERSUASIVE TECHNOLOGIES , 2014 .

[47]  Jennifer Gristwood Applying the Health Belief Model to Physical Activity Engagement Among Older Adults , 2011 .

[48]  G. Hofstede,et al.  Cultures and Organizations: Software of the Mind , 1991 .

[49]  D. Ketchen A Primer on Partial Least Squares Structural Equation Modeling , 2013 .

[50]  Marko Sarstedt,et al.  PLS-SEM: Indeed a Silver Bullet , 2011 .

[51]  Ombretta Gaggi,et al.  Stairstep recognition and counting in a serious Game for increasing users’ physical activity , 2016, Personal and Ubiquitous Computing.

[52]  Hichang Cho,et al.  The influence of self-efficacy, subjective norms, and risk perception on behavioral intentions related to the H1N1 flu pandemic: A comparison between Korea and the US , 2015 .

[53]  Lorien C Abroms,et al.  iPhone apps for smoking cessation: a content analysis. , 2011, American journal of preventive medicine.

[54]  David M Williams,et al.  Social-cognitive determinants of physical activity: the influence of social support, self-efficacy, outcome expectations, and self-regulation among participants in a church-based health promotion study. , 2006, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[55]  Regan L. Mandryk,et al.  Developing culturally relevant design guidelines for encouraging healthy eating behavior , 2014, Int. J. Hum. Comput. Stud..

[56]  Detmar W. Straub,et al.  Structural Equation Modeling and Regression: Guidelines for Research Practice , 2000, Commun. Assoc. Inf. Syst..

[57]  F. Kamoun Communications of the Association for Information Systems , 2017 .

[58]  A. Bandura Social Foundations of Thought and Action: A Social Cognitive Theory , 1985 .