Impact of goal priority and goal conflict on the intention–health-behavior relationship: Tests on physical activity and other health behaviors.

Schools with health-promoting school (HPS) frameworks are actively committed to enhancing healthy lifestyles. This study explored the contribution of school participation in HPS on students’ health behaviors, namely, physical activity (PA), sedentary behavior, and dieting. Data from the 2018/2019 Health Behavior in School-aged Children study on Israeli adolescents aged 11–17 years were used. Schools were selected from a sample of HPSs and non-HPSs. Between-group differences and predictions of health behavior were analyzed. No between-group differences were observed in mean number of days/week with at least 60 min of PA (HPS: 3.84 ± 2.19 days/week, 95% confidence interval of the mean = 3.02–3.34; non-HPS: 3.93 ± 2.17 days/week, 95% confidence interval of the mean = 3.13–3.38). Most children engaged in screen time behavior for >2 h/day (HPS: 60.83%; non-HPS: 63.91%). The odds of being on a diet were higher among more active children (odds ratio [OR] = 1.20), higher socio-economic status (OR = 1.23), and female (OR = 2.29). HPS did not predict any health behavior. These findings suggest that HPSs did not contribute to health behaviors more than non-HPSs. Therefore, health-promoting activities in HPSs need to be improved in order to justify their recognition as members of the HPS network and to fulfill their mission.

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Utilizing Participatory Research to Engage Underserved Populations to Improve Health-Related Outcomes in Delaware

Nutrients ◽

10.3390/nu13072353 ◽

2021 ◽

Vol 13 (7) ◽

pp. 2353

Author(s):

Shannon M. Robson ◽

Samantha M. Rex ◽

Katie Greenawalt ◽

P. Michael Peterson ◽

Elizabeth Orsega-Smith

Keyword(s):

Physical Activity ◽

Health Behavior ◽

Health Behaviors ◽

Participatory Research ◽

Cooperative Extension ◽

Underserved Populations ◽

Community Outreach ◽

Outreach Program ◽

Health Related ◽

Improve Health

Cooperative Extension is a community outreach program. Despite its large reach, there is a need for the evaluation of changes in health-related outcomes for individuals engaged with Cooperative Extension. A team-based challenge was developed using community-engaged participatory research integrated with Cooperative Extension to encourage healthy eating and physical activity behaviors through Cooperative Extension programming. Thus, the primary purpose of this secondary analysis was to (1) evaluate changes in anthropometric outcomes and (2) evaluate changes in health behavior outcomes. Associations of anthropometric changes and health behavior changes with engagement in the three-month team-based challenge were explored. Anthropometrics were measured using standard procedures, and intake of fruits and vegetables and physical activity were self-reported. Of the 145 participants in the community-engaged participatory research portion of the study, 52.4% (n = 76) had complete anthropometrics before and after the team-based challenge and were included in this study. At 3 months, there was a significant reduction in body mass index (−0.3 kg/m2, p = 0.024) and no significant change in waist circumference (p = 0.781). Fruit and vegetable intake significantly increased (+0.44 servings/day, p = 0.018). Physical activity did not significantly change based on (1) the number of days 30 or more minutes of physical activity was conducted (p = 0.765) and (2) Godin Leisure-Time Exercise Questionnaire scores (p = 0.612). Changes in anthropometrics and health behaviors were not associated with engagement in the team-based challenge. Using community-engaged participatory research with community outreach programs, such as Cooperative Extension, can improve health-related outcomes in underserved populations. However, despite a participatory approach, changes in anthropometrics and health behaviors were not associated with engagement in the developed team-based challenge.

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Abstract 04: Racial Disparities in Cardiovascular Health Behaviors are Partially Explained by Socioeconomic, Psychosocial and Environmental Factors: the Coronary Artery Risk Development in Young Adults (CARDIA) Study

Circulation ◽

10.1161/circ.135.suppl_1.04 ◽

2017 ◽

Vol 135 (suppl_1) ◽

Author(s):

Kara M Whitaker ◽

David R Jacobs ◽

Kiarri N Kershaw ◽

John N Booth ◽

David C Goff ◽

...

Keyword(s):

Physical Activity ◽

Environmental Factors ◽

Health Behavior ◽

Health Behaviors ◽

Socioeconomic Factors ◽

Racial Differences ◽

Fast Food ◽

Cardiovascular Health ◽

Race Difference ◽

Explanatory Role

Introduction: There are known racial differences in cardiovascular health behaviors, including smoking, physical activity, and diet quality. A better understanding of factors that explain these differences may suggest novel intervention targets for reducing disparities in cardiovascular disease. Objective: To examine whether socioeconomic, psychosocial and environmental factors mediate racial differences in health behaviors. Methods: We studied 3,028 Black or White CARDIA participants who were enrolled at age 18-30 years in 1985-86 and completed the 30 year follow-up visit in 2015-2016. Health behaviors included smoking (current, former ≤ 12 months, never smoker/quit >12 months), physical activity (inactive, active but not meeting guidelines, meeting guidelines), and a surrogate for healthy eating using fast food and sugar-sweetened beverage consumption (frequency per week ≥ 2, some but < 2, none). Each behavior was assigned a value of 0 for poor, 1 for intermediate or 2 for ideal and summed to calculate an overall health behavior score for each participant (range 0-6). The race difference (β) in health behavior score was estimated using linear regression. Formal mediation analyses computed the proportion of the total effect of race on health behavior score explained by socioeconomic, psychosocial, and environmental factors (see Table footnote). Results: Blacks had a lower health behavior score than Whites in crude analyses (mean difference: -1.04, p<0.001). After adjustment for sex, age and field center, socioeconomic factors mediated 50.5% of the association between race and the health behavior score, psychosocial factors 26.8% and environmental factors 9.0% (p<0.05 for all). Joint associations mediated 58.1% of the race-health behavior score association. Conclusions: Observed racial differences in the health behavior score are predominately mediated by socioeconomic factors, which appear to play a stronger explanatory role than psychosocial and environmental factors.

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Fear of Cancer Recurrence: A Model Examination of Physical Symptoms, Emotional Distress, and Health Behavior Change

Journal of Oncology Practice ◽

10.1200/jop.18.00787 ◽

2019 ◽

Vol 15 (9) ◽

pp. e787-e797 ◽

Cited By ~ 6

Author(s):

Daniel L. Hall ◽

Rachel B. Jimenez ◽

Giselle K. Perez ◽

Julia Rabin ◽

Katharine Quain ◽

...

Keyword(s):

Physical Activity ◽

Health Behavior ◽

Behavior Change ◽

Health Behaviors ◽

Emotional Distress ◽

Cancer Recurrence ◽

Health Behavior Change ◽

Physical Symptoms ◽

Fear Of Cancer Recurrence ◽

Fear Of Cancer

PURPOSE: Fear of cancer recurrence is highly prevalent among adult survivors of cancer. The role of fear of recurrence in the emotional distress of survivors of cancer, as well as health behaviors that may directly affect their health, remains unclear. To advance oncology practice, this study sought to examine the extent to which fear of recurrence stemming from physical symptoms accounts for emotional distress in a large sample of adult survivors of cancer and to extend the model to explain postdiagnosis self-reported health behavior change. METHODS: In 2016, 258 survivors of cancer at an academic hospital completed a survey of psychosocial needs. Items assessed physical symptoms (checklist), fear of cancer recurrence (Assessment of Survivor Concerns), emotional distress (anxiety and depressed mood), and health behaviors (current alcohol use, physical activity, diet, and sunscreen use, as well as changes after cancer diagnosis) informed by National Comprehensive Cancer Network survivorship guidelines. Indirect effects regression models accounting for relevant covariates (age and treatment history) used 5,000-iteration bootstrapping. RESULTS: Higher fear of cancer recurrence was associated with greater number of physical symptoms ( P < .001), greater emotional distress ( P < .05), lower moderate or vigorous physical activity ( P < .05), higher sunscreen use ( P < .05), and postdiagnosis increases in alcohol use ( P < .01) and reductions in physical activity ( P < .01). Fear of cancer recurrence models accounted for almost half of the variance in distress of survivors of cancer ( R2 = 0.44, P < .001) and, to a lesser yet significant extent, changes in alcohol consumption ( R2 = 0.09, P < .001) and physical activity ( R2 = 0.06, P = .003). CONCLUSION: Fear of cancer recurrence plays a central role in the emotional distress and key health behaviors of survivors of cancer. These findings support fear of cancer recurrence as a potential target for emotional health and health behavior change interventions.

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Conscientiousness and Cardiometabolic Risk: A Test of the Health Behavior Model of Personality Using Structural Equation Modeling

Annals of Behavioral Medicine ◽

10.1093/abm/kaab027 ◽

2021 ◽

Author(s):

Mark C Thomas ◽

Katherine A Duggan ◽

Thomas W Kamarck ◽

Aidan G C Wright ◽

Matthew F Muldoon ◽

...

Keyword(s):

Physical Activity ◽

Substance Use ◽

Health Behavior ◽

Health Behaviors ◽

Latent Variable ◽

Structural Equation ◽

Cardiometabolic Risk ◽

Equation Modeling ◽

Inverse Association ◽

Subjective Sleep Quality

Abstract Background High trait conscientiousness is associated with lower cardiometabolic risk, and health behaviors are a putative but relatively untested pathway that may explain this association. Purpose To explore the role of key health behaviors (diet, physical activity, substance use, and sleep) as links between conscientiousness and cardiometabolic risk. Methods In a cross-sectional analysis of 494 healthy, middle-aged working adults (mean age = 42.7 years, 52.6% women, 81.0% White), participants provided self-reports of conscientiousness, physical activity, substance use, diet, and sleep, and wore monitors over a 7-day monitoring period to assess sleep (Actiwatch-16) and physical activity (SenseWear Pro3). Cardiometabolic risk was expressed as a second-order latent variable from a confirmatory factor analysis involving insulin resistance, dyslipidemia, obesity, and blood pressure. Direct, indirect, and specific indirect effect pathways linking conscientiousness to health behaviors and cardiometabolic risk were examined. Unstandardized indirect effects for each health behavior class were computed separately using bootstrapped samples. Results After controlling for demographics (sex, age, race, and education), conscientiousness showed the predicted, inverse association with cardiometabolic risk. Among the examined health behaviors, objectively-assessed sleep midpoint variability (b = −0.003, p = .04), subjective sleep quality (b = −0.003, p = .025), and objectively-assessed physical activity (b = −0.11, p = .04) linked conscientiousness to cardiometabolic risk. Conclusions Physical activity and sleep partially accounted for the relationship between conscientiousness and cardiometabolic risk.

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Parents’ Perspectives on the Theoretical Domains Framework Elements Needed in a Pediatric Health Behavior App: A Crowdsourced Social Validity Study (Preprint)

10.2196/preprints.9808 ◽

2018 ◽

Author(s):

Christopher C Cushing ◽

David A Fedele ◽

Erin E Brannon ◽

Tiffany Kichline

Keyword(s):

Physical Activity ◽

Health Behavior ◽

Health Behaviors ◽

Procedural Knowledge ◽

Social Validity ◽

Theoretical Domains Framework ◽

Mobile App ◽

Amazon Mechanical Turk ◽

Validity Study ◽

Pediatric Health

BACKGROUND Most pediatric studies do not include parent stakeholders in the design of the intervention itself and many pediatric mobile health (mHealth) interventions are not meaningfully disseminated after the trial period ends. Consequently, the consumer desire for mobile apps targeting pediatric health behavior is likely to be met by commercial products that are not based in theory or evidence and may not take stakeholder preferences into account. OBJECTIVE The aim was to assess parent preference for mobile app features that map onto specific Theoretical Domains Framework (TDF) elements. METHODS This study was a crowdsourced social validity study of 183 parents who were asked to rate their preferences for mobile app features that correspond to elements of the TDF. The TDF organizes a large number of theoretical models and constructs into three components: (1) capability, (2) motivation, and (3) opportunity. Parents of children were recruited through Amazon Mechanical Turk. RESULTS The majority of participants were Caucasian and mean age was 36.9 (SD 8.0) years. Results revealed broad acceptability of communication, motivation, and opportunity domains. However, the degree to which each domain was valued varied within behavioral category. Parents demonstrated a preference for increasing procedural knowledge for physical activity and diet behaviors over sleep (F2,545=5.18, P=.006). Similarly, parents valued self-monitoring as more important for physical activity than sleep (F2,546=4.04, P=.02). When asked about the value of features to help children develop skills, parents preferred those features for dietary behavior over sleep (F2,546=3.57, P=.03). Parents perceived that goal-setting features would be most useful for physical activity over sleep and diet (F2,545=5.30, P=.005). Incentive features within the app were seen as most useful for physical activity over sleep (F2,546=4.34, P=.01). CONCLUSIONS This study presents a low-cost strategy for involving a large number of stakeholders in the discussion of how health behavior theory should be applied in a mHealth intervention. Our approach is innovative in that it took a scientific framework (ie, TDF) and made it digestible to parents so that they could then provide their opinions about features that might appear in a future app. Our survey items discriminated between various health behaviors allowing stakeholders to communicate the different health behaviors that they would like a TDF feature to change. Moreover, we were able to develop a set of consumer opinions about features that were directly linked to elements of the TDF.

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Analyze the Impact of Healthy Behavior on Weight Change with a Mathematical Model using the Harris-Benedict Equations

10.21203/rs.3.rs-584141/v1 ◽

2021 ◽

Author(s):

AYAN CHATTERJEE ◽

Ram Bajpai ◽

Martin W. Gerdes

Keyword(s):

Physical Activity ◽

Mathematical Model ◽

Health Behavior ◽

Health Behaviors ◽

Weight Change ◽

Simulated Data ◽

Future Research ◽

Healthy Behavior ◽

Lifestyle Diseases ◽

The Impact

Abstract Background: Lifestyle diseases are the leading cause of death worldwide. The gradual increase of negative behavior in humans because of physical inactivity, unhealthy habit, and improper nutrition expedites the growth of lifestyle diseases. Proper lifestyle management in the obesity context may help to reach personal weight goal or maintain a normal weight range with optimization of health behaviors (physical activity, diet, and habits). Objective: In this study, we develop a mathematical model to analyze the impact of regular physical activity, a proper diet, and healthy habits on weight change, targeting obesity as a study case. Followed by, we design an algorithm to verify our proposed model with simulated data and compare it with related proven models based on the defined constrains. Methods: We proposed a weight-change mathematical model as a function of activity, habit, and nutrition with the first law of thermodynamics, basal metabolic rate ( BMR ), total daily energy expenditure ( TDEE ), and body-mass-index ( BMI ) to establish a relationship between health behavior and weight change. Followed by, we verify the model with simulated data and compared it with related established models. In this study, we have used revised Harris-Benedict equations (HB) for BMR and TDEE calculation. Results: The proposed mathematical model showed a strong relationship between health behavior and weight change. We verified the mathematical model with a proposed algorithm using simulated data with defined constraints. The adoption of BMR and TDEE calculation following revised Harris-Benedict equations has beaten the classical Wishnofsky’s rule (3500 cal. ≈ 1 lb.) , and the models proposed by Toumasis et al., Azzeh et. Al., and Mickens et. al. with a standard deviation of ±1.829, ±2.006, ±1.85, and ±1.80, respectively. Conclusions: This study helped us to understand the impact of healthy behavior on weight change with mathematical implications and the importance of a healthy lifestyle. As a future research scope, we wish to use this model in a health eCoach system to generate personalized lifestyle recommendations to optimize health behaviors to accomplish personal weight goals.

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The Relationship Between Behavior Change Strategies, Physical Activity, and Fruit and Vegetable Intake Following a Cardiac Event

Home Health Care Management & Practice ◽

10.1177/1084822311405455 ◽

2011 ◽

Vol 23 (5) ◽

pp. 386-391 ◽

Cited By ~ 1

Author(s):

Mary A. Nies ◽

Ross A. Dierkhising ◽

Randal J. Thomas ◽

Kristin Vickers ◽

Simone Salandy

Keyword(s):

Physical Activity ◽

Health Behavior ◽

Behavior Change ◽

Health Behaviors ◽

Cardiac Event ◽

Vegetable Intake ◽

Fruit And Vegetable Intake ◽

Change Strategies ◽

Fruit And Vegetable

Objective: To assess expectations for recovery and use of behavior change strategies as predictors of subsequent diet and physical activity among adults recently hospitalized for a cardiac event. Design: Quasi-experimental design in which adults with recent cardiac event-related hospitalization completed surveys assessing health behaviors and attitudes immediately post-discharge and three months later. Results: Among those completing the study (n = 323), positive expectations about recovery and more frequent use of behavior change skills predicted greater physical activity and better nutrition at follow-up. In multivariate models, baseline health behavior was a significant predictor of physical activity and fruit and vegetable intake at follow-up (alpha = 0.05). Conclusions: Individuals participating in physical activity and eating a heart-healthy diet approximately three months after cardiac hospitalization possessed behavior change skills. Early intervention is critical as health behaviors occurring just days after hospitalization predicted future health behavior.

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Is There a Spillover Effect of Targeted Dietary Change on Untargeted Health Behaviors? Evidence From a Dietary Modification Trial

Health Education & Behavior ◽

10.1177/1090198119831756 ◽

2019 ◽

Vol 46 (4) ◽

pp. 569-581 ◽

Cited By ~ 1

Author(s):

Elizabeth A. Sarma ◽

Anne Moyer ◽

Catherine R. Messina ◽

Helena H. Laroche ◽

Linda Snetselaar ◽

...

Keyword(s):

Physical Activity ◽

Alcohol Consumption ◽

Health Behavior ◽

Health Behaviors ◽

Latent Class Analysis ◽

Repeated Measures ◽

Latent Class ◽

Dietary Change ◽

Dietary Modification ◽

First Year

Background. The gateway behavior hypothesis posits that change in a health behavior targeted for modification may promote positive changes in other untargeted health behaviors; however, previous studies have shown inconsistent results. Aims. To examine the patterns and predictors of change in untargeted health behaviors in a large health behavior change trial. Method. Using repeated-measures latent class analysis, this study explored patterns of change in untargeted physical activity, alcohol consumption, and smoking behavior during the first year of the Women’s Health Initiative dietary modification trial that targeted total fat reduction to 20% kcal and targeted increased fruit and vegetable intake. Participants were healthy postmenopausal women who were randomly assigned to either the low-fat dietary change intervention ( n = 8,193) or a control ( n = 12,187) arm. Results. Although there were increases in untargeted physical activity and decreases in alcohol consumption and smoking in the first year, these changes were not consistently associated with study arm. Moreover, although the results of the repeated-measures latent class analysis identified three unique subgroups of participants with similar patterns of untargeted health behaviors, none of the subgroups showed substantial change in the probability of engagement in any of the behaviors over 1 year, and the study arms had nearly identical latent class solutions. Discussion and Conclusion. These findings suggest that the dietary intervention did not act as a gateway behavior for change in the untargeted behaviors and that researchers interested in changing multiple health behaviors may need to deliberately target additional behaviors.

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