scholarly journals Affective Determinants of Physical Activity: A Conceptual Framework and Narrative Review

2020 ◽  
Vol 11 ◽  
Author(s):  
Courtney J. Stevens ◽  
Austin S. Baldwin ◽  
Angela D. Bryan ◽  
Mark Conner ◽  
Ryan E. Rhodes ◽  
...  
Keyword(s):  
Physical Activity ◽  
Health Behavior ◽  
Conceptual Framework ◽  
Affective Response ◽  
Relevant Research ◽  
Future Directions ◽  
Motivational States ◽  
Affective Constructs ◽  
Affective Associations ◽  
Affect Processing

The literature on affective determinants of physical activity (PA) is growing rapidly. The present paper aims to provide greater clarity regarding the definition and distinctions among the various affect-related constructs that have been examined in relation to PA. Affective constructs are organized according to the Affect and Health Behavior Framework (AHBF), including: (1) affective response (e.g., how one feels in response to PA behavior) to PA; (2) incidental affect (e.g., how one feels throughout the day, unrelated to the target behavior); (3) affect processing (e.g., affective associations, implicit attitudes, remembered affect, anticipated affective response, and affective judgments); and (4) affectively charged motivational states (e.g., intrinsic motivation, fear, and hedonic motivation). After defining each category of affective construct, we provide examples of relevant research showing how each construct may relate to PA behavior. We conclude each section with a discussion of future directions for research.

Overview of Affective Determinants of Health Behavior

2018 ◽  
Author(s):  
David M. Williams ◽  
Ryan E. Rhodes ◽  
Mark T. Conner
Keyword(s):  
Health Behavior ◽  
Health Behaviors ◽  
Implicit Attitudes ◽  
Determinants Of Health ◽  
Affective Attitudes ◽  
Anticipated Affect ◽  
Core Affect ◽  
Behavior Theories ◽  
Affective Associations ◽  
Affect Processing

This chapter provides a brief introduction to the topic of affective determinants of health behavior. In doing so it analyzes each aspect of the book’s topic. It begins by outlining what is meant by “health behavior.” It then considers traditional views of the key determinants of such behaviors and the value of and need for integrating affective determinants within health behavior theories. Next, it offers a conceptualization of affective determinants in relation to health behaviors, including distinctions between/among (1) affect proper versus affect processing (the latter also known as affective judgments or cognitively mediated affect); (2) core affect versus moods and emotions; (3) integral versus incidental affect; and (4) anticipated affect, affective attitudes, implicit attitudes, and affective associations. It closes with a brief overview of measurement of affect in the context of health behavior research.

Analyzing the Impact of Healthy Behavior on Weight Change with a Provable Mathematical Model (Preprint)

2020 ◽  
Author(s):  
Ayan Chatterjee ◽  
Ram Bajpai ◽  
Pankaj Khatiwada
Keyword(s):  
Physical Activity ◽  
Mathematical Model ◽  
Health Behavior ◽  
Weight Change ◽  
Simulated Data ◽  
Negative Behavior ◽  
Healthy Behavior ◽  
Healthy Habits ◽  
Lifestyle Diseases ◽  
The Impact

BACKGROUND Lifestyle diseases are the primary cause of death worldwide. The gradual growth of negative behavior in humans due to physical inactivity, unhealthy habit, and improper nutrition expedites lifestyle diseases. In this study, we develop a mathematical model to analyze the impact of regular physical activity, healthy habits, and a proper diet on weight change, targeting obesity as a case study. Followed by, we design an algorithm for the verification of the proposed mathematical model with simulated data of artificial participants. OBJECTIVE This study intends to analyze the effect of healthy behavior (physical activity, healthy habits, and proper dietary pattern) on weight change with a proposed mathematical model and its verification with an algorithm where personalized habits are designed to change dynamically based on the rule. METHODS We developed 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 verified the model with simulated data. RESULTS The proposed provable mathematical model showed a strong relationship between health behavior and weight change. We verified the mathematical model with the proposed algorithm using simulated data following the necessary constraints. The adoption of BMR and TDEE calculation following Harris-Benedict’s equation has increased the model's accuracy under defined settings. CONCLUSIONS This study helped us understand the impact of healthy behavior on obesity and overweight with numeric implications and the importance of adopting a healthy lifestyle abstaining from negative behavior change.

scholarly journals Human-Centered Artificial Intelligence for Designing Accessible Cultural Heritage

2021 ◽  
Vol 11 (2) ◽  
pp. 870
Author(s):  
Galena Pisoni ◽  
Natalia Díaz-Rodríguez ◽  
Hannie Gijlers ◽  
Linda Tonolli
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Cultural Heritage ◽  
Conceptual Framework ◽  
Interaction Design ◽  
Participatory Design ◽  
Future Directions ◽  
Heritage Sites ◽  
Review Analysis

This paper reviews the literature concerning technology used for creating and delivering accessible museum and cultural heritage sites experiences. It highlights the importance of the delivery suited for everyone from different areas of expertise, namely interaction design, pedagogical and participatory design, and it presents how recent and future artificial intelligence (AI) developments can be used for this aim, i.e.,improving and widening online and in situ accessibility. From the literature review analysis, we articulate a conceptual framework that incorporates key elements that constitute museum and cultural heritage online experiences and how these elements are related to each other. Concrete opportunities for future directions empirical research for accessibility of cultural heritage contents are suggested and further discussed.

scholarly journals Can Health-Promoting Schools Contribute to Better Health Behaviors? Physical Activity, Sedentary Behavior, and Dietary Habits among Israeli Adolescents

2021 ◽  
Vol 18 (3) ◽  
pp. 1183
Author(s):  
Hila Beck ◽  
Riki Tesler ◽  
Sharon Barak ◽  
Daniel Sender Moran ◽  
Adilson Marques ◽  
...  
Keyword(s):  
Physical Activity ◽  
Health Behavior ◽  
Confidence Interval ◽  
Health Behaviors ◽  
Sedentary Behavior ◽  
Dietary Habits ◽  
Group Differences ◽  
Time Behavior ◽  
Health Promoting ◽  
The Mean

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.

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

Nutrients ◽  
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.

scholarly journals Verona Coding Definitions of Emotional Sequences (VR-CoDES): Conceptual framework and future directions

2017 ◽  
Vol 100 (12) ◽  
pp. 2303-2311 ◽  
Author(s):  
Lidia Del Piccolo ◽  
Arnstein Finset ◽  
Anneli V. Mellblom ◽  
Margarida Figueiredo-Braga ◽  
Live Korsvold ◽  
...  
Keyword(s):  
Conceptual Framework ◽  
Future Directions

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 ◽  
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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