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Journal articles on the topic 'Health behavior'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 August 2024

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1

Kulbok, Pamela A., Kimberly F. Carter, Joan H. Baldwin, Mattia J. Gilmartin, and Bessie Kirkwood. "The Multidimensional Health Behavior Inventory." Journal of Nursing Measurement 7, no. 2 (January 1999): 177–95. http://dx.doi.org/10.1891/1061-3749.7.2.177.

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Gaps in knowledge about what constitutes healthy and risky behaviors for young people hinder successful health promotion intervention strategies. With the development of appropriate instruments, behaviors can be measured and interventions can be implemented to improve health outcomes. The structure of a new health behavior instrument, the Multidimensional Health Behavior Inventory (MHBI), was explored with data from 1,077 college students, ages 18 to 24 years. Factor analysis of 116 health behavior questions yielded 7 factor-based scales with 57 items: diet (13 items), substance use (10 items), safety (9 items), checkup (9 items), social (6 items), stress (6 items), and exercise (4 items). Evaluation of the 7 behavior scales of the MHBI using subgroups defined by age, gender, and race will contribute to an understanding of health behaviors of older adolescents and young adults and will provide directions for research and clinical interventions.

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Sobota, Kristen Finley, Joshua Blackwell, Brittany Dye, Kanika Kapoor, Elizabeth Roediger, and Micah Jared Sobota. "Mental Health Outreach at Community Behavior Health Centers." Health 06, no. 07 (2014): 531–40. http://dx.doi.org/10.4236/health.2014.67072.

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3

Chin, Jessie, Ece Üreten, and Catherine Burns. "Health Behavior Nudging Through Health Information Exposure and Information Search." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 8, no. 1 (September 2019): 126. http://dx.doi.org/10.1177/2327857919081029.

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As the Internet has become one of the dominant sources of health information, online health information plays an important role for patients to acquire health knowledge and regulate their health behavior (European Commission, 2014). Researchers suggested different ways to nudge public health behavior through environments and policies (Marteau et al., 2011); few studies had explored the potential to use online health information environment to nudge health behavior. While there was established evidence showing the individual differences in online health information search behavior across the lifespan (e.g., Chin et al., 2009; Sharit et al., 2008), the current study was to examine the nudging effects on health behavior through online health information exposure and search. An online mixed-factor-design experiment was conducted on 136 adults across the lifespan (Mean age=49.79, SD=16.00). We examined two kinds of nudging routes, (1) health information exposure (manipulated by the experimenters), and (2) health information search (decided by the participants), on two kinds of health behaviors varying in the costs of taking these health behaviors. Target health behaviors included, (1) self-related health behavior: participants were asked to take a break for doing a stretch (low cost) or a walk (high cost) after long sitting; (2) self-unrelated health behavior: participants were asked to have researchers to donate to the rare disease association through writing down the date (low cost) or a 100-word endorsement article (high cost). In the experiment, each participant was assigned to read four topics (3 articles under each topic) and answer the questions after each health topic. The questions varied in difficulties, which participants could decide to answer the questions based on their own knowledge, their memory from reading, or searching the answers online. To manipulate health information exposure, half of the participants were assigned to read the online articles related to the target health behaviors (such as the harms of long sitting and the target rare disease). Participants were not disclosed about the study goals at the beginning. They were not told that the study goal was to examine whether they took the target health behaviors or not, but to examine how adults learn from online health information. To measure the actions of target health behaviors, for the self-related health behavior, after roughly 40 minutes of the study, participants were requested to take a break for 10 minutes. For the self-unrelated health behavior, at the end of the study, participants were asked whether they would like to show their support to a rare disease association. The manipulations in the costs of health behaviors were assigned in counterbalanced order. Logistic regressions were used to examine the effects of nudging routes and costs of actions on two kinds of target health behaviors. Results suggested that mere information exposure did not affect the likelihood to take the target health behaviors regardless of its relatedness to self-interests or costs of actions. Further, for self-related health behavior, adults were more likely to take actions after a more deliberate engagement with the information - through information search. For health behavior that was unrelated to self-interests, participants were more likely to take actions after they searched the information about this rare disease and only when the costs of actions were low. This study has shown the potentials and limitations of health nudging in different health behaviors, and has its implications on designing effective health nudging strategies on different health behaviors.

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Turunen, Kaisa, Kristiina Helander, Kari J. Mattila, and Markku Sumanen. "Health behavior after intrahepatic cholestasis of pregnancy." Health 05, no. 01 (2013): 96–101. http://dx.doi.org/10.4236/health.2013.51013.

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5

Neumann, Till, Nico Reinsch, Stefan Esser, Peter Krings, Thomas Konorza, Tanja Woiwoid, Michael Miller, Norbert Brockmeyer, and Raimund Erbel. "Smoking behavior of HIV-infected patients." Health 02, no. 08 (2010): 913–18. http://dx.doi.org/10.4236/health.2010.28135.

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6

Graham, Rodger Gary, and Gareth I. Martin. "Health Behavior." American Journal of Preventive Medicine 43, no. 4 (October 2012): 451–55. http://dx.doi.org/10.1016/j.amepre.2012.06.016.

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7

Choi, Seung Hee, and Sonia A. Duffy. "Analysis of Health Behavior Theories for Clustering of Health Behaviors." Journal of Addictions Nursing 28, no. 4 (2017): 203–9. http://dx.doi.org/10.1097/jan.0000000000000195.

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8

Lippke, Sonia, and Jochen P. Ziegelmann. "Health Behavior and Health Behavior ChangeTheories and Evidence." Applied Psychology 57, no. 4 (October 2008): 541–43. http://dx.doi.org/10.1111/j.1464-0597.2008.00338.x.

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9

Stanojević, Dragana, Miljana Pavićević, Tijana Živković, Olivera Radović, and Biljana Jaredić. "Health beliefs and health anxiety as predictors of COVID-19 health behavior: Data from Serbia." Zbornik radova Filozofskog fakulteta u Pristini 52, no. 3 (2022): 301–16. http://dx.doi.org/10.5937/zrffp52-38184.

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The end of 2019 and the beginning of 2020 were marked by the appearance of the virus SARS-CoV-2, which led to a health crisis around the world. Health preventive behavior was highlighted as, at that time, the only form of prevention of the spread of the disease. Factors that will lead people to adhere to the recommended forms of behavior have become the subject of research in various scientific disciplines. The Model of Health Belief is one of the dominant frameworks for studying health behaviors, and thus behaviors related to COVID-19. Health anxiety and beliefs about illness and preventive behavior are the starting point for considering the level at which individuals adhere to the recommended measures. The main goal of this research was to examine a model in which health anxiety and health beliefs are predictors of preventive health behavior in relation to COVID-19. The sample consisted of 420 respondents, 66.3% of whom were women. They completed an online questionnaire comprising the following instruments: Short Health Anxiety Inventory, COVID-19 Health Belief Scale, and COVID-19 Health Behavior Scale with two subscales-Protection in Social Contacts and Hygiene. After controlling for effects of gender and presence of chronic disease, perceived benefit of preventive behavior and the observed barrier can predict protection in social contacts. Hygiene can be predicted by the perceived benefit of preventive behavior and the perceived barrier. Health anxiety has not been shown to be a significant predictor of health behavior. The paper discusses the theoretical and practical implications of the obtained results. The obtained results partially support the Model of Health Beliefs. In order to increase the degree to which individuals adhere to health behaviors, the benefits of preventive behaviors should be emphasized while the barriers should be reduced.

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10

Whitehead, Brenda R. "Health behaviors in older adults: Considering age, affect, and attitudes." Journal of Health Psychology 22, no. 13 (February 29, 2016): 1652–57. http://dx.doi.org/10.1177/1359105316631814.

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Identifying psychological factors associated with engagement in healthy behaviors in later life is a key to effective behavior interventions. In all, 204 adults ( Mage = 80) took a questionnaire assessing objective and perceived health, positive affect and negative affect, aging attitudes, and three classes of health behaviors: eating/nutrition, exercise, and general health behavior. Regression models found better eating behavior was best explained by older age, more exercise was best explained by more positive affect, and better general lifestyle behavior was best explained by worse perceived health. Programs promoting health behaviors in older adults can utilize the findings to tailor interventions to the health behavior of interest.

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11

Wood, Wendy, and David T. Neal. "Healthy through Habit: Interventions for Initiating & Maintaining Health Behavior Change." Behavioral Science & Policy 2, no. 1 (April 2016): 71–83. http://dx.doi.org/10.1177/237946151600200109.

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Interventions to change health behaviors have had limited success to date at establishing enduring healthy lifestyle habits. Despite successfully increasing people's knowledge and favorable intentions to adopt healthy behaviors, interventions typically induce only short-term behavior changes. Thus, most weight loss is temporary, and stepped-up exercise regimens soon fade. Few health behavior change interventions have been successful in the longer term. In this article, we unpack the behavioral science of health-habit interventions. We outline habit-forming approaches to promote the repetition of healthy behaviors, along with habit-breaking approaches to disrupt unhealthy patterns. We show that this two-pronged approach—breaking existing unhealthy habits while simultaneously promoting and establishing healthful ones—is best for long-term beneficial results. Through specific examples, we identify multiple intervention components for health policymakers to use as a framework to bring about lasting behavioral public health benefits.

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Martínez-Lanz, Patricia, Carlos Cuevas-Covarrubias, and Patricia Hernández-Valdez. "Principal Component Analysis of Male Criminal Behavior." Health 13, no. 10 (2021): 1112–28. http://dx.doi.org/10.4236/health.2021.1310083.

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13

Tian, Wei-Hua, and Joseph J. Tien. "Health Behaviors and Health Status among Middle-Aged and Older Adults with Chronic Diseases in Taiwan." International Journal of Environmental Research and Public Health 17, no. 19 (October 1, 2020): 7196. http://dx.doi.org/10.3390/ijerph17197196.

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Changes in lifestyle behaviors may effectively maintain or improve the health status of individuals with chronic diseases. However, such health behaviors adopted by individuals are unlikely to demonstrate similar patterns. This study analyzed the relationship between the heterogeneous latent classes of health behavior and health statuses among middle-aged and older adults with hypertension, diabetes, or hyperlipidemia in Taiwan. After selecting 2103 individuals from the 2005 and 2009 Taiwan National Health Interview Survey (NHIS), we first identified heterogeneous groups of health behaviors through latent class analysis (LCA). We further explored the relationship between each latent class of health behavior and health status through ordered logit regression. We identified the following five distinct health behavior classes: the all-controlled, exercise and relaxation, healthy diet and reduced smoking or drinking, healthy diet, and least-controlled classes. Regression results indicated that individuals in classes other than the all-controlled class all reported poor health statuses. We also found great magnitude of the coefficient estimates for individuals who reported their health status to be poor or very poor for the least-controlled class. Therefore, health authorities and medical providers may develop targeted policies and interventions that address multiple modifiable health behaviors in each distinct latent class of health behavior.

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Oster, Emily. "Health Recommendations and Selection in Health Behaviors." American Economic Review: Insights 2, no. 2 (June 1, 2020): 143–60. http://dx.doi.org/10.1257/aeri.20190355.

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Consider a case in which a new research finding links a health behavior with good health outcomes. A possible consequence is take-up of this behavior among individuals who engage in other positive health behaviors. If this occurs, later analyses of observational data may be biased by the change in selection. This paper evaluates these dynamic biases in empirical settings. Using data from vitamin supplementation and diet, I show that selection responds endogenously to health recommendations. These results highlight how spurious findings on health behaviors can be self-reinforcing. (JEL I12)

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15

Schwarzer, Ralf, Sonia Lippke, and Jochen P. Ziegelmann. "Health action process approach." Zeitschrift für Gesundheitspsychologie 16, no. 3 (July 2008): 157–60. http://dx.doi.org/10.1026/0943-8149.16.3.157.

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Abstract. Health Psychology at the Freie Universität Berlin is devoted to research and teaching in the entire field of health psychology, including stress, coping, social support, self-efficacy, personality, quality of life, and health behavior change. In this article, we briefly describe one theory that represents our line of thinking (the Health Action Process Approach), followed by examples of longitudinal and experimental studies on health behavior change. A major finding is that interventions to improve physical activity, healthy nutrition, and dental hygiene are most effective when matched to three stages of change. Moreover, we address the field of health self-regulation across the life span: We are involved in the consortium Autonomy Despite Multimorbidity in Old Age (AMA), co-investigating the project Health Behaviors and Multiple Illnesses in Old Age (PREFER), and we are the home institution of the project Fostering Lifelong Autonomy and Resources in Europe: Behaviour and Successful Aging (FLARE-BSA).

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16

Goto, Eiko, Hirono Ishikawa, Kazuhiro Nakayama, and Takahiro Kiuchi. "Comprehensive Health Literacy and Health-Related Behaviors Within a General Japanese Population: Differences by Health Domains." Asia Pacific Journal of Public Health 30, no. 8 (November 2018): 717–26. http://dx.doi.org/10.1177/1010539518806806.

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The present study aimed to explore how different health-related domains of health literacy, as measured by the European Health Literacy Survey Questionnaire, were associated with health-related behaviors among a general population in Japan. We conducted a cross-sectional observational study of 1002 Japanese residents. Our questionnaire addressed socioeconomic status, health status, health-related behaviors, and health literacy. Among the 3 health-related domains of health literacy (health care, disease prevention, and health promotion), a multivariate model revealed that the disease prevention domain was associated with exercise behavior and alcohol consumption. The health promotion domain was associated with dietary behavior and exercise behavior. There were strong correlations among all health-related domains of health literacy; however, there were different associations between health literacy and health-related behaviors depending on those domains. Additional research is needed to determine how and to what extent each domain of health literacy is related to what health behaviors and outcomes.

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Radzimińska, Agnieszka, Magdalena Weber-Rajek, Ewelina Lulińska-Kuklik, Urszula Kaźmierczak, and Waldemar Moska. "ACADEMIC YOUTH’S HEALTH BEHAVIOR." Physical education of students 20, no. 6 (December 12, 2016): 55–62. http://dx.doi.org/10.15561/20755279.2016.0607.

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Introduction: A very important role in the protection of human health is their life style, their habits and patterns of conduct. Early adulthood is the best period to achieve long-term benefits from a selection of healthy living. However, the results of studies on health-related behavior of youth in Poland and in the world are not satisfactory. The purpose of the study: The purpose of the research was to assess the health behaviors of students of higher education in Bydgoszcz. Material: The study involved 272 students (124 women and 148 men) Bydgoszcz higher education students in the following fields of study: physiotherapy, nutrition, logistics and national security. The Inventory of Health-Related Behavior by Zygfryd Juczyński has been used in the research. The statistical analysis was performed using the package PQ Stat 1.6.2. Results: Throughout the treatment group an average level of health-related behavior has been shown. The results of the different categories of health-related behavior were lower than the results of the standardization groups. A higher level of health behavior has been shown in a group of medical students compared to non-medical students. The results for women were higher than men's results. Conclusions: The results of personal research and the research findings of other authors demonstrate that there is a need for implementation of programs of health promotion and health education in all fields of study.

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Akulume, Martha, and Suzanne N. Kiwanuka. "Health Care Waste Segregation Behavior among Health Workers in Uganda: An Application of the Theory of Planned Behavior." Journal of Environmental and Public Health 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/8132306.

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Objective. The goal of this study was to assess the appropriateness of the theory of planned behavior in predicting health care waste segregation behaviors and to examine the factors that influence waste segregation behaviors. Methodology. One hundred and sixty-three health workers completed a self-administered questionnaire in a cross-sectional survey that examined the theory of planned behavior constructs (attitudes, subjective norms, perceived behavioral control, and intention) and external variables (sociodemographic factors, personal characteristics, organizational characteristics, professional characteristics, and moral obligation). Results. For their most recent client 21.5% of the health workers reported that they most definitely segregated health care waste while 5.5% did not segregate. All the theory of planned behavior constructs were significant predictors of health workers’ segregation behavior, but intention emerged as the strongest and most significant (r=0.524, P<0.001). The theory of planned behavior model explained 52.5% of the variance in health workers’ segregation behavior. When external variables were added, the new model explained 66.7% of the variance in behavior. Conclusion. Generally, health workers’ health care waste segregation behavior was high. The theory of planned behavior significantly predicted health workers’ health care waste segregation behaviors.

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Santos, Osvaldo, Ana Virgolino, António Vaz Carneiro, and Margarida Gaspar de Matos. "Health Behavior and Planetary Health." European Psychologist 26, no. 3 (July 2021): 212–18. http://dx.doi.org/10.1027/1016-9040/a000437.

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Abstract. Health and environmental psychology have long been walking side by side. These two disciplines of psychology have imported and applied common psychological frameworks and each of them developed specific theories and methodologies. At a time when humankind faces tremendous challenges ahead (climate change, global warming, ocean sickness, the reemergence of infections pandemics), environmental health is more and more a crucial domain of research. Both environmental psychology and health psychology need to be engaged in environmental health issues in order to enhance planetary health. Environmental psychology traditional fields of research provide understanding about how natural or constructed environments impact human identity, attitudes, and behaviors (more recently, environmental psychology is also investing in determinants of pro-environmental behaviors). On the other hand, health psychology has an extensive comprehensive framework about how to promote healthy habits (i.e., automatically activated behaviors). We live in a global and extremely complex and interconnected world, which promotes syndemic phenomena (several interactive epidemics sharing common etiological factors), also resulting in accelerated depletion of natural resources. This current scenario might justify the development of an Environmental Health Psychology discipline, joining together tools from both environmental psychology and health psychology in a synergic and strategic way.

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Adler, Nancy, Christine Bachrach, and Aric A. Prather. "Physical health and health behavior." Journal of Economic and Social Measurement 40, no. 1-4 (August 18, 2015): 357–74. http://dx.doi.org/10.3233/jem-150395.

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21

Best, J. Allan, and Roy Cameron. "Health Behavior and Health Promotion." American Journal of Health Promotion 1, no. 2 (September 1986): 48–57. http://dx.doi.org/10.4278/0890-1171-1.2.48.

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22

Glanz, Karen, Frances Marcus Lewis, and Barbara K. Rimer. "Health Behavior and Health Education." Medicine & Science in Sports & Exercise 23, no. 12 (December 1991): 1404. http://dx.doi.org/10.1249/00005768-199112000-00016.

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&NA;. "Health Behavior and Health Education." Medical Care 29, no. 6 (June 1991): 499. http://dx.doi.org/10.1097/00005650-199106000-00006.

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Hoerster, Katherine D., Keren Lehavot, Tracy Simpson, Miles McFall, Gayle Reiber, and Karin M. Nelson. "Health and Health Behavior Differences." American Journal of Preventive Medicine 43, no. 5 (November 2012): 483–89. http://dx.doi.org/10.1016/j.amepre.2012.07.029.

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Lin, I.-Pei, Ding-Tien Chung, Li-Yun Lee, Hsiang-Ju Hsu, and Shu-Ching Chen. "Health Belief, Behavior Intention, and Health Behaviors Related to Colorectal Cancer Screening in Taiwan." International Journal of Environmental Research and Public Health 17, no. 12 (June 14, 2020): 4246. http://dx.doi.org/10.3390/ijerph17124246.

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Health belief and behavior intention affect subsequent health behaviors. The purpose of this study was to assess the levels of health belief, behavior intention, and health behavior, and to identify the factors related to health behaviors in adults receiving colorectal cancer (CRC) screening in Taiwan. This cross-sectional study recruited patients receiving a CRC screening from the cancer screening outpatient department of a teaching hospital in northern Taiwan. Demographic and health characteristics were recorded, and participants were assessed using Champion’s health belief model scale, cancer screening intention scale, and the health protective behavior scale. Of the 125 subjects (aged 49–75 years), 27.2% reported active screening; the rest passively received screening after doctor referral. Those who were doctor-referred had lower levels of health behavior, including general behavior, self-knowledge, and health care. Positive health behaviors related to CRC screening were associated with not smoking, greater seriousness in health belief, more confidence in health belief, consuming the recommended amount of fruits and vegetables, and motivation for CRC screening; these factors explained 35.0% of the variance in positive health behaviors related to CRC screening. A comprehensive education program encouraging CRC cancer screening should include access to available resources and encourage positive health belief and behavior intention related to this important cancer screening activity.

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Grygiel, Ewa, Elżbieta Cygnar, Katarzyna Zwolińska-Mirek, Janusz Mirek, Monika Bigosińska, and Urszula Halska. "Health behaviours and life satisfaction of health spa patients." Medical Science Pulse 12, no. 2 (June 30, 2018): 22–28. http://dx.doi.org/10.5604/01.3001.0012.1160.

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Background: Determining the relationship between health behaviors and life satisfaction in patients who use health spas as an accurate measure of well-being (subjective well-being reflecting health condition) can be the basis for implementing preventive activities, setting trends for behavior modification, and monitoring changes in health-promoting practices. Aim of the study: To determine an empirical verification of the relationship between health behaviors and life satisfaction. Material and methods: The study included 123 women and men aged 32–80 years – all of whom were health spa patients. The Health Behavior Inventory (HBI) by Juczyński, and the Polish adaptation of the Satisfaction With Life Scale (SWLS) by Diener, Emmons, Larsen, and Griffin were used to measure health behavior and life satisfaction, respectively. Results: There was a significant positive correlation between health behavior indicators and the level of life satisfaction in patients, as well as an increase in the practice of health behaviors with age. Gender appears to differentiate the frequency of the performance of health behaviors in the subjects. A higher overall rate of health behaviors was observed in females compared with males. For instance, females attached greater importance to healthy eating habits compared with males. However, a higher level of life satisfaction is associated with more intensified preventive behaviors in males. Conclusions: A health education program should take into account both the age and gender of health spa patients. It should also aim to not only develop health awareness, but also to include psychological factors, such as a sense of life satisfaction, which can strengthen patients’ beliefs in the importance of health-promoting activities.

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Beest, Ferdy van, Christiaan Lako, and Esther-Mirjam Sent. "Health insurance and switching behavior: Evidence from the Netherlands." Health 04, no. 10 (2012): 811–20. http://dx.doi.org/10.4236/health.2012.410125.

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Joyner, Chelsea, Ryan E. Rhodes, and Paul D. Loprinzi. "The prospective association between the Five Factor personality model with health behaviors and health behavior clusters." Europe’s Journal of Psychology 14, no. 4 (November 30, 2018): 880–96. http://dx.doi.org/10.5964/ejop.v14i4.1450.

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To examine the prospective association of personality with individual behavior, multibehavior and clustered health behavior profiles. A prospective study design was employed. Two hundred young adults provided baseline data and 126 (mean age: 21.6 yrs) provide complete data for a 5-month follow-up assessment (63% response rate). Personality and health behaviors (and covariates) were assessed via validated questionnaires. A multibehavior index variable was created ranging from 0-5; two separate health behavior cluster indices were created, including high (4-5 behaviors) vs. low (2 or fewer) behavior adoption and an energy balance cluster (MVPA and diet). When examining MVPA as a continuous variable, the personality trait conscientiousness was prospectively associated with MVPA and a healthy diet. Extraversion was prospectively associated with high (vs. low) behavioral clustering (OR = 1.18; 95% CI: 1.00-1.40) and conscientiousness was prospectively associated with energy balance clustering (OR = 1.09; 95% CI: 1.01-1.17). Extraversion, conscientiousness, openness to experience, and agreeableness were associated with select health-related behaviors. Further, extraversion and conscientiousness were associated with health behavior clustering.

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Kim, Kyung Hee. "Depression, Self-efficacy and Health Promotion Behaviors of Health University College Students." Journal of Health Informatics and Statistics 46, no. 4 (November 30, 2021): 435–41. http://dx.doi.org/10.21032/jhis.2021.46.4.435.

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Objectives: The Purpose of this study is to find out the relationship among depression, self-efficacy, and health promotion behaviors of health college students and to develop programs and mediate strategies to improve desirable health promotion behaviors.Methods: This descriptive study examined the correlation between the depression, self-efficacy, and health promotion behaviors of health college students located in Daegu Metropolitan City participated in the study from October to December. The collected data were analyzed using IBM SPSS 25.0. The general characteristics of the subjects were presented in frequency and percentage, and depression, self-efficacy and health promotion behaviors were presented in average and standard deviation. The results of this study were as follows: First, the correlation between depression, self-efficacy and health promotion behavior was analyzed by t-test and ANOVA. Multiple regression analysis was conducted to identify the effects of general characteristics, depression, and self-efficacy on health promotion behaviors.Results: The depression of health college students was 14.94 ± 11.51, the total score of self-efficacy was 74.60 ± 12.92, and the health promotion behavior was 74.85 ± 18.62. As a result of analyzing the correlation between depression, self-efficacy and health promotion behavior, depression had a statistically significant negative correlation with health promotion behavior, and self-efficacy and health promotion behavior had a statistically significant positive correlation. The factors that depression and self-efficacy have influence on health promotion behavior were gender, depression (-0.15, p= 0.048), and self-efficacy (0.59, p< 0.001).Conclusions: The depression, self-efficacy, and health promotion behaviors of health college students were found to be correlated; development of health promotion programs to improve desirable health promotion behaviors is required.

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Jang EunYoung and 이경순. "Does others' health behavior influence one's health behavior?: The effects of prevalence and self-improvement motive in healthy diet behavior." Korean Journal of Health Psychology 14, no. 1 (March 2009): 53–72. http://dx.doi.org/10.17315/kjhp.2009.14.1.004.

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Biduchak, Anzhela, Nataliya Hopko, Mohammad Wathek O. Alsalama, Zhanetta Chornenka, and Olha Mazur. "Peculiarities of Medical Personnel Behavior Styles in Conflict Situations." Health 14, no. 12 (2022): 1210–26. http://dx.doi.org/10.4236/health.2022.1412086.

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Hurtado, María, and Gabriela Topa. "Health Behavior and Health and Psychosocial Planning for Retirement among Spanish Health Professionals." Journal of Clinical Medicine 7, no. 12 (November 28, 2018): 495. http://dx.doi.org/10.3390/jcm7120495.

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The aging of the workforce among health professionals demands attention to the study of their health behavior before retirement. The aim of the present study is to analyze the relationships between health planning and psychosocial planning—on the one hand—and health professionals’ health behavior, on the other. In addition, we will study the mediator role of public protection, self-insurance, and self-protection in the relationship between planning and health behavior. The sample includes 169 healthcare professionals from a public hospital in Spain. A serial mediation model estimating all of the parameters simultaneously was tested. The findings have confirmed the relationship between health planning and health behavior, as well as the serial mediation of the behaviors in this relationship. As the main causes of death in the Western world are not transmittable diseases, but cardiovascular diseases, diabetes, and other diseases that are closely linked to lifestyle factors, our findings strongly support that we are responsible for our long-term health status and well-being.

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Callahan, Daniel, and David S. Gochman. "Health Behavior Research." Hastings Center Report 29, no. 1 (January 1999): 47. http://dx.doi.org/10.2307/3528541.

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34

Agras, Stewart W. "Health and behavior." Current Opinion in Psychiatry 3, no. 6 (1990): 795–99. http://dx.doi.org/10.1097/00001504-199012000-00016.

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35

Akman, Mehmet. "Health seeking behavior." Türkiye Aile Hekimliği Dergisi 19, no. 2 (April 1, 2015): 53–54. http://dx.doi.org/10.15511/tahd.15.02053.

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Agras, Stewart W. "Health and behavior." Current Opinion in Pediatrics 3, no. 6 (December 1990): 795–99. http://dx.doi.org/10.1097/00008480-199012000-00016.

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Gill, David. "Predicting health behavior." Journal of Psychosomatic Research 43, no. 4 (October 1997): 437. http://dx.doi.org/10.1016/s0022-3999(97)87486-7.

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Kapor-Stanulovic, Nila. "Health and Behavior." Journal of Nervous and Mental Disease 179, no. 6 (June 1991): 379. http://dx.doi.org/10.1097/00005053-199106000-00025.

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Connell, Cathleen M., and Mary P. Gallant. "Caregiver Health Behavior." Activities, Adaptation & Aging 24, no. 2 (January 19, 2001): 1–16. http://dx.doi.org/10.1300/j016v24n02_01.

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Maddux, James E. "Habit, Health, and Happiness." Journal of Sport and Exercise Psychology 19, no. 4 (December 1997): 331–46. http://dx.doi.org/10.1123/jsep.19.4.331.

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The notion of habit figures prominently in theories of health-related behavior and in efforts to encourage people to develop consistency and regularity in the healthful behavior of daily life. The consensus definition of habit as automatic and mindless behavior, however, presents three logical and philosophical problems. First, this definition of habit is at odds with the way most of our theories of health behavior try to employ the notion. Second, the behaviors of concern to most health, exercise, and sport psychologists are not the kinds of behaviors to which this definition of habit applies easily, if at all. Third, the kind of mindless behavior suggested by this definition may be conducive to enhancing physical health and athletic performance, but it may be inconsistent with the essential elements of happiness or subjective well-being according to Eastern philosophies such as Taoism and Buddhism, and according to the growing research on the psychology of happiness.

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Lee, Sang Mae. "The Effect of Senior's Lifestyle on Health Promotion Behaviors: The Mediating Effect of Health Control Behavior." Korea Gerontological Society 42, no. 6 (December 1, 2022): 1051–71. http://dx.doi.org/10.31888/jkgs.2022.42.6.1051.

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The purpose of this study was to investigate the effects of senior lifestyle on health promotion behavior and to investigate the mediating effects of health control behavior on these relationships. For this purpose, an online survey (under Corona 19 Pendemic Situation) was conducted from July 18 to August 20, 2021 for seniors in their 50s, 60s and 70s, and finally 412 data were used for analysis. The collected data were analyzed by SPSS for Win 27.0 and the mediating effect was analyzed by hierarchical regression analysis. The main results of the study are as follows. First, it was confirmed that senior's lifestyle had a positive effect on health promotion behavior. Second, health control behavior had a positive effect on health promotion behavior. Third, it was confirmed that the mediating effect of internal and other dependent health control behavior was significant in the relationship between lifestyle and health promotion behavior. Fourth, it was confirmed that the mediating effect of accidental health control behavior was not significant in the relationship between lifestyle and health promotion behavior. Based on this, this study suggested the necessity of changing into internal and other dependent health control behaviors in order to maintain health when the health control behaviors of accidental tendencies are high. In this study, the degree of internal and other dependent health control behaviors and the degree of tendency of accidental health control behaviors were diagnosed and educated.

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Malkoc, Nedim, and Serap Çolak. "Investigation of Health Sciences University Students' Healthy Living Behavior Perceptions." World Journal of Education 10, no. 4 (July 24, 2020): 124. http://dx.doi.org/10.5430/wje.v10n4p124.

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All of the behaviors that people exhibit in order to stay healthy both physically and psychologically and maintain their health during their lifetime are included in the definition of "healthy life behavior". In this study, it was aimed to identify and compare the perceptions of healthy life behavior of students of the Department of Exercise and Sport Sciences and Psychology Department Students who study at the University of Health Sciences. A total of 199 students, 97 students from the sports science department (age: 19.84 ± 1.801, weight: 65.30 ± 12.460), and 102 students from the psychology department (age: 20.12 ± 1.916 weight: 60.17 ± 9.881) participated in the study. There was a statistically significant difference between the scale total scores of the participants and the dimensions of health responsibility, physical activity, nutrition, stress management, and the measurements of the students of sports science students and students of psychology sciences in terms of an increase in sport sciences students (p <0.05). There was no significant difference between the participants' interpersonal communication and spiritual development scores, which are the sub-dimensions of the scale (p˃0.05). University education is an education period in which students can decide independently. These results showed that sports students and psychology sciences students' different undergraduate education also affected their healthy lifestyle behaviors. In this context, it is concluded that directing students of other departments of the university to exercise and sports activities will contribute to their perceptions of healthy lifestyle.

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Wood, Arya, and Mohd. Nor Shahizan Ali. "Media Health Literacy on Preventive Health Behavior Among Indonesian Women." International Journal of Media and Communication Research 3, no. 1 (January 21, 2022): 44–52. http://dx.doi.org/10.25299/ijmcr.v3i1.8138.

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The understanding of media health literacy has been increasing for several decades, along with the basic concept of health literacy among women of the reproductive age group in Indonesia. Media health literacy has been advancing and becoming a strong concept based on the use of technology and the level of education in modern society. The perspective of media health literacy among women in Indonesia is distinguished by the depth of one’s educational background, which then influences their health decisions regarding preventive behaviors. Media health literacy has been significantly connected to health behavior. In contrast, this type of interaction creates a new meaning of media within the context of health literacy, which embodies the new definition of media health literacy. Meanwhile, health behaviors are dependent on the health literacy level, which is connected by the media, with a significant influence on public opinion and clinical procedure. As a result, it has a limitless ability to either reinforce or inhibit preventive health behaviors. As many as 12 Indonesian women with various educational backgrounds were involved in this study, which showed that perspective and preventive health behavior on cervical cancer are associated with media health literacy. The study also suggested the media health literacy pyramid, which stated that respondents’ educational background plays a vital role in influencing preventive health behaviors to make informed health decisions.

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Bishop, Lauche, Cramer, Pinto, Leung, Hall, Leach, et al. "Health Behavior Change and Complementary Medicine Use: National Health Interview Survey 2012." Medicina 55, no. 10 (September 24, 2019): 632. http://dx.doi.org/10.3390/medicina55100632.

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Background and objectives: Complementary and alternative medicine (CAM) use has been associated with preventive health behaviors. However, the role of CAM use in patients’ health behaviors remains unclear. This study aimed to determine the extent to which patients report that CAM use motivates them to make changes to their health behaviors. Materials and Methods: This secondary analysis of 2012 National Health Interview Survey data involved 10,201 CAM users living in the United States who identified up to three CAM therapies most important to their health. Analyses assessed the extent to which participants reported that their CAM use motivated positive health behavior changes, specifically: eating healthier, eating more organic foods, cutting back/stopping drinking alcohol, cutting back/quitting smoking cigarettes, and/or exercising more regularly. Results: Overall, 45.4% of CAM users reported being motivated by CAM to make positive health behavior changes, including exercising more regularly (34.9%), eating healthier (31.4%), eating more organic foods (17.2%), reducing/stopping smoking (16.6% of smokers), or reducing/stopping drinking alcohol (8.7% of drinkers). Individual CAM therapies motivated positive health behavior changes in 22% (massage) to 81% (special diets) of users. People were more likely to report being motivated to change health behaviors if they were: aged 18–64 compared to those aged over 65 years; of female gender; not in a relationship; of Hispanic or Black ethnicity, compared to White; reporting at least college education, compared to people with less than high school education; without health insurance. Conclusions: A sizeable proportion of respondents were motivated by their CAM use to undertake health behavior changes. CAM practices and practitioners could help improve patients’ health behavior and have potentially significant implications for public health and preventive medicine initiatives; this warrants further research attention.

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Puciato, Daniel, Paweł F. Nowak, Cezary Kuśnierz, and Andrzej Bunio. "Health behaviors as determinants of the quality of life of the elderly." Physical Activity Review 8, no. 2 (2020): 48–55. http://dx.doi.org/10.16926/par.2020.08.21.

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Background: Quality of life is a subject of research by representatives of various fields of science, including psychology, sociology, economics, and medicine. The results of some previous studies show that the quality of life of elderly people is related to their health behaviors. The aim of the study was to assess the relationships between the quality of life, considered in general and in detail, and health behaviors of healthy and ill elderly people from the Opolskie Voivodeship (Poland). Methods: 105 people (81 women and 24 men) participated in the study, including 61 patients and 44 healthy people, aged from 60 to 89 years. The average age of respondents was 71.2 ± 6.4 years. The main research method was the diagnostic questionnaire survey. The Health Behavior Inventory and R. Cummins’s Comprehensive Quality of Life Scale were used. Results: The study results showed that the average value of the health behavior index in healthy respondents was significantly higher than in patients. The analysis of particular categories of health behaviors revealed that healthy persons declared normal eating habits more often than patients. The mean value of the general quality of life index was significantly higher in healthy respondents than in patients. Health status also significantly differentiated the quality of life in favor of healthy individuals in the domains of health and intimacy. In the analyzed group of respondents, health behaviors significantly determined the quality of life of patients only. The odds that the quality of life of patients are above average were greater in patients with average and higher health behavior indices than in patients with low health behavior indices. Conclusion: Measures of quality of life improvement should also be aimed at increasing the prevalence of health behaviors in the elderly.

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Kim, Bon, Kyungmin Kim, Jeffrey A. Burr, and Gyounghae Han. "Health Behavior Profiles of Korean Baby Boomers." International Journal of Aging and Human Development 90, no. 4 (January 30, 2019): 363–84. http://dx.doi.org/10.1177/0091415018811095.

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This study identified profiles of health behaviors among Korean baby boomers and examined a set of individual characteristics associated with these latent profiles. We analyzed a sample of 4,047 middle-aged adults (aged 53–61) from the Korean Baby Boomer Panel Study (2014). Latent profile analysis was used to uncover distinct health behavior profiles, and multinomial regression was performed to investigate the associations between health behavior profiles and predisposing, enabling, and need factors—following from the behavioral models of health behaviors and health services use. Five profiles of health behaviors were identified: (a) low-risk and high-preventive behaviors (50%), (b) low-risk and low-preventive behaviors (35%), (c) moderate-risk and moderate-preventive behaviors (7%), (d) moderate-risk and high-preventive behaviors (6%), and (e) high-risk and low-preventive behaviors (2%). Further, individuals with more enabling and need characteristics, indicated by higher socioeconomic status and greater health concerns, were more likely to engage in healthier profiles.

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Leonita, Olvie, Ahmad Yamin, and Nur Oktavia Hidayati. "Risk Behaviors of SMP-SMA-SMK Students." Jurnal Keperawatan Jiwa 8, no. 4 (August 27, 2020): 401. http://dx.doi.org/10.26714/jkj.8.4.2020.401-410.

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Maladaptive behavior among teenagers, such as increased sexual behavior, smoking, alcoholism, and drugs abuse in big cities also in other regencies in Indonesia and if there is no real intervention it can conduct a decrease on the quality of the younger generation successor of the nation. This research aim on knowing overview of students risk behaviors uses quantitative descriptive method with proportionate random sampling involving 290 respondents. The measuring instrument used was Adolescent Exploratory Behaviour and Risk Rating Scale (AEERS). This study was used by univariate analysis. Result showed that students risk behavior have a low-risk behaviors (62.1%), it is also split in high health risk behavior (59.7%) and low prosocial risk behavior (80.7%). It conclude, students have a low risk behavior, but also have high health-risk behavior and low risk towards prosocial behaviour.

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Lewczuk, Joanna, and Anna Anyzewska. "Personality correlates of health behaviors among men training recreationally in the interdisciplinary spirit of health pedagogy." Studia z Teorii Wychowania XIII, no. 4 (41) (December 20, 2022): 193–212. http://dx.doi.org/10.5604/01.3001.0016.1644.

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The aim of the study was to identify the personality traits (according to the so-called Big Five personality model) in relation to healthy behaviour among amateur athletes. One hundred physically active men participated in the study. Health Behaviour Inventory questionnaire developed by Juczyński was used to assess health-related behavior, and the NEO-Five Factor Inventory (NEO-FFI) developed by Costa and McCrae, in the Polish adaptation, was used to determine principal personality dimensions. Significant correlations between the level of health-related behavior or some kind of them and personality dimensions were observed. Two clusters (personality profiles) related to prohealthy behaviours were separated. Observed correlations between health behaviours and personality traits of physically active men confirm the validity of the research and a need for healthy behaviour education. Understanding the associations between healthy behaviours and personality traits can be used in health pedagogy, including development of effective methods of education and health prophylaxis in amateur athletes.

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Jang, Ji-Eon. "The Relationship of Health Behavior, Oral Management Behaviors and Oral Health Status." Joural of the Korea Entertainment Industry Association 9, no. 3 (September 30, 2015): 429. http://dx.doi.org/10.21184/jkeia.2015.09.9.3.429.

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Huelsnitz, Chloe O., Rachael E. Jones, Jeffry A. Simpson, Keven Joyal-Desmarais, Erin C. Standen, Lisa A. Auster-Gussman, and Alexander J. Rothman. "The Dyadic Health Influence Model." Personality and Social Psychology Review 26, no. 1 (December 7, 2021): 3–34. http://dx.doi.org/10.1177/10888683211054897.

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Relationship partners affect one another’s health outcomes through their health behaviors, yet how this occurs is not well understood. To fill this gap, we present the Dyadic Health Influence Model (DHIM). The DHIM identifies three routes through which a person (the agent) can impact the health beliefs and behavior of their partner (the target). An agent may (a) model health behaviors and shape the shared environment, (b) enact behaviors that promote their relationship, and/or (c) employ strategies to intentionally influence the target’s health behavior. A central premise of the DHIM is that agents act based on their beliefs about their partner’s health and their relationship. In turn, their actions have consequences not only for targets’ health behavior but also for their relationship. We review theoretical and empirical research that provides initial support for the routes and offer testable predictions at the intersection of health behavior change research and relationship science.

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