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Journal articles on the topic 'Behavior Change'

Author: Grafiati

Published: 4 June 2021

Last updated: 23 November 2024

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1

Bartholomew, John B. "Environments Change Child Behavior, But Who Changes Environments?" Kinesiology Review 4, no. 1 (February 2015): 71–76. http://dx.doi.org/10.1123/kr.2014-0077.

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Numerous interventions have been designed to modify children's physical activity and eating behaviors. While early research centered on the individual as the target of intervention, more recent work targets change in the environment. These studies have consistently supported the importance of environmental contributors to both physical activity and eating behavior, but little research has considered those who are responsible for implementing environmental change. For example, if we expect school environments to support activity and healthy eating, we must consider the motivation of school administrators to affect change. This review will present examples of an ecological approach to behavior change along with recent data to support this approach.

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Duckworth, Angela L., and James J. Gross. "Behavior change." Organizational Behavior and Human Decision Processes 161 (November 2020): 39–49. http://dx.doi.org/10.1016/j.obhdp.2020.09.002.

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Vollmer, Timothy R. "Behavior Change or Behavior Analysis?" Contemporary Psychology 45, no. 1 (February 2000): 58–59. http://dx.doi.org/10.1037/002163.

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Aziz, Aziz Nuri Satriawan. "Modifikasi Perilaku Terhadap Anak." Jurnal Pendidikan Dasar dan Keguruan 5, no. 1 (May 1, 2020): 13–21. http://dx.doi.org/10.47435/jpdk.v5i1.209.

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AbstractBehavior modification as behavior therapy, this technique is often used by educators and parents because its success is easily observed and easily appilied to other bwhaviors, is a soluble use of conditioning techniques in humans to produce changes in the frequency of certain behaviors, namely changing non-adaptive behavior, by leaving it, and adaptive behavior confirmed, behavior change is carried out with a carefully crafted experimental desaign, this behavior modification makes it possible to be realized to the actors to be changed ie change the maladaptive behavior towards adaptive behavior. This qualitative research was conducted on children in Kenteng sub-village, Sumberejo village, Sine Ngawi sub-district. Research subjects are parents, neighbors and children who have maladaprive behavior. At the end of the study showed that self-management techniques and social skills training is able to change the behavior of children both to anticipate so as not to behave negatively and eliminate negative behaviors and foster positive behavior.Keywords : behavior modification, self-management, social skills

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Flores, Alina L., Christine E. Prue, and Katherine Lyon Daniel. "Broadcasting Behavior Change." Health Promotion Practice 8, no. 2 (April 2007): 145–53. http://dx.doi.org/10.1177/1524839906289167.

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Pichora-Fuller, M. Kathleen. "Model Behavior Change." Hearing Journal 68, no. 5 (May 2015): 19. http://dx.doi.org/10.1097/01.hj.0000465740.56146.cf.

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Cunningham, Brian P., Caitlin J. Bakker, Harsh R. Parikh, Herman Johal, and Marc F. Swiontkowski. "Physician Behavior Change." Journal of Orthopaedic Trauma 33 (November 2019): S62—S72. http://dx.doi.org/10.1097/bot.0000000000001616.

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Edwards, Patricia K., Alan C. Acock, and Robert L. Johnston. "Nutrition Behavior Change." Evaluation Review 9, no. 4 (August 1985): 441–59. http://dx.doi.org/10.1177/0193841x8500900404.

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Audrain-McGovern, Janet, Chanita Hughes, and Freda Patterson. "Effecting behavior change." American Journal of Preventive Medicine 24, no. 2 (February 2003): 183–89. http://dx.doi.org/10.1016/s0749-3797(02)00592-5.

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Cassidy, Catherine A. "Facilitating Behavior Change." AAOHN Journal 45, no. 5 (May 1997): 239–46. http://dx.doi.org/10.1177/216507999704500505.

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Sharma, B. P. "Epilepsy-Behavior Change." Journal of Nepal Medical Association 5, no. 2 (January 1, 2003): 121–26. http://dx.doi.org/10.31729/jnma.1007.

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Bishop, F. Michler. "Self-guided Change: The most common form of long-term, maintained health behavior change." Health Psychology Open 5, no. 1 (January 2018): 205510291775157. http://dx.doi.org/10.1177/2055102917751576.

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Millions of people change risky, health-related behaviors and maintain those changes. However, they often take years to change, and their unhealthy behaviors may harm themselves and others and constitute a significant cost to society. A review—similar in nature to a scoping review—was done of the literature related to long-term health behavior change in six areas: alcohol, cocaine and heroin misuse, gambling, smoking, and overeating. Based on the limited research available, reasons for change and strategies for changing and for maintaining change were also reviewed. Fifty years of research clearly indicate that as people age, in the case of alcohol, heroin and cocaine misuse, smoking, and gambling, 80–90 percent moderate or stop their unhealthy behaviors. The one exception is overeating; only 20 percent maintain their weight loss. Most of these changes, when they occur, appear to be the result of self-guided change. More ways to accelerate self-guided, health-related behavior change need to be developed and disseminated.

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Reesor, Layton, Elizabeth M. Vaughan, Daphne C. Hernandez, and Craig A. Johnston. "Addressing Outcomes Expectancies in Behavior Change." American Journal of Lifestyle Medicine 11, no. 6 (August 11, 2017): 430–32. http://dx.doi.org/10.1177/1559827617722504.

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Individuals commonly seek help for problem health behaviors, such as excessive drinking, smoking, and weight gain. Yet there is a high rate of recidivism in these behaviors because outcome expectancies are either too high, negative outcome expectancies are not considered, or outcome expectancies are not properly addressed. Health care providers are recommended to list the outcome expectancy for the problem behavior and corresponding treatment for their patient. Through the process it is important to acknowledge both the positive and negative outcomes of engaging in the problem behavior. Health care providers are then encouraged to have their patient identify the goals and objectives that will assist in achieving the desired outcome. By recognizing and addressing outcome expectancies, it is more likely that the patient will be less resistant to the health care provider’s recommendations to change problematic behavior.

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Paul, Sara, and Nancee V. Sneed. "Strategies for Behavior Change in Patients With Heart Failure." American Journal of Critical Care 13, no. 4 (July 1, 2004): 305–13. http://dx.doi.org/10.4037/ajcc2004.13.4.305.

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Appropriate management of chronic heart failure and its signs and symptoms requires a considerable amount of participation by patients. Behavioral changes that prevent or minimize signs and symptoms and disease progression are just as important as the medications prescribed to treat the heart failure. The most difficult lifestyle changes include smoking cessation, weight loss, and restriction of dietary sodium. The Transtheoretical Model is a framework for assessing and addressing the concept of readiness for behavior change, which occurs in a 6-step process. The model consists of 3 dimensions: the stages of change, the processes of change on which interventions are based, and the action criteria for actual behavior. The stages of change are discussed, and interventions are presented to assist patients with heart failure in progressing through those stages toward maintenance of changed lifestyle behaviors. Methods for measuring the level of readiness for change of patients with heart failure are also presented, because correct staging is required before appropriate interventions matched to a patient’s stage can be delivered.

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Stilwell, Rebecca A., William A. Pasmore, and DaHee Shon. "Change Leader Behavior Inventory." Journal of Applied Behavioral Science 52, no. 4 (August 20, 2016): 373–95. http://dx.doi.org/10.1177/0021886316663406.

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While the pace and complexity of change is increasing, the rate of failed change attempts continues to be unacceptably high. Leaders at every level play a central role in change, yet few receive formal training on how to lead change and to date, a validated assessment to provide feedback on their performance has been lacking. The current study is intended to help close this gap. This article describes an effort to develop and validate an instrument to measure change leadership behaviors, the Change Leader Behavior Inventory.

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Smith, William A., and John Elder. "Applied Behavior Change: A framework for behavior change interventions and research." Drugs: Education, Prevention and Policy 3, no. 1 (January 1996): 91–99. http://dx.doi.org/10.3109/09687639609019314.

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Sarma, Elizabeth A., Anne Moyer, Catherine R. Messina, Helena H. Laroche, Linda Snetselaar, Linda Van Horn, and Dorothy S. Lane. "Is There a Spillover Effect of Targeted Dietary Change on Untargeted Health Behaviors? Evidence From a Dietary Modification Trial." Health Education & Behavior 46, no. 4 (February 27, 2019): 569–81. http://dx.doi.org/10.1177/1090198119831756.

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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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Riebe, Deborah. "It’s Time for a Change…Behavior Change." ACSM's Health & Fitness Journal 16, no. 4 (2012): 33–34. http://dx.doi.org/10.1249/fit.0b013e31825a6f10.

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Elwyn, Glyn, Katy Marrin, Dominick L. Frosch, and James White. "Sustainable Change Sequence: A framework for developing behavior change interventions for patients with long-term conditions." European Journal for Person Centered Healthcare 2, no. 2 (April 8, 2014): 212. http://dx.doi.org/10.5750/ejpch.v2i2.736.

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ObjectiveInteractive interventions are increasingly advocated to support behavior change for patients who have long-term conditions. Such interventions are most likely to achieve behavior change when they are based on appropriate theoretical frameworks. Developers of interventions are faced with a diverse set of behavioral theories that do not specifically address intervention development. The aim of our work was to develop a framework to guide the developers of interactive healthcare interventions that was derived from relevant theory, and which guided developers towards appropriate behavior change techniques.MethodsWe reviewed theories that inform behavior change interventions, where relevant to the management of long-term conditions. Theoretical constructs and behavior change techniques were grouped according to similarity in aims.ResultsWe developed a logic model that operationalizes behavior change theories and techniques into five steps likely to lead to sustained behavior change. The steps are: 1) create awareness of need; 2) facilitate learning; 3) enhance motivation; 4) prompt behaviour change; and 5) ensure sustainability of behaviour change.Conclusion and Practice implicationsA framework that sequences behavioural change techniques along a sustainability model provides a practical template for the developers of interactive healthcare applications and interventions.

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20

Janssen, Xanne, Leanne Fleming, Alison Kirk, Lesley Rollins, David Young, Madeleine Grealy, Bradley MacDonald, Paul Flowers, and Lynn Williams. "Changes in Physical Activity, Sitting and Sleep across the COVID-19 National Lockdown Period in Scotland." International Journal of Environmental Research and Public Health 17, no. 24 (December 14, 2020): 9362. http://dx.doi.org/10.3390/ijerph17249362.

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We examine the impact of the COVID-19 outbreak and concomitant restrictions (i.e., lockdown) on 24-hour movement behaviors (i.e., physical activity, sitting, sleep) in a purposive sample of people (n = 3230) reporting change recruited online. Participants’ self-reported time spent in moderate-to-vigorous physical activity (MVPA), walking, sitting and sleep prior to lockdown (T1), during the first national lockdown (T2) and as restrictions initially started to ease (T3). For each 24-hour movement behavior, category-shifts are reported (positive, negative or did not change), as well as the percentage of participants recording positive/negative changes across clusters of behaviors and the percentage of participants recording improvement or maintenance of change across time. From T1 to T2 walking decreased, whereas MVPA, sitting and sleep increased, from T2 to T3 levels returned to pre-lockdown for all but MVPA. Participants who changed one behavior positively were more likely to report a positive change in another and 50% of those who reported positive changes from T1 to T2 maintained or improved further when restrictions started to ease. The current study showed that a large proportion of the sample reported positive changes, most notably those displaying initially poor levels of each behavior. These findings will inform salutogenic intervention development.

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Back, Kurt W., and James B. Duke. "Theories of behavior therapy: Exploring behavior change." Journal of the History of the Behavioral Sciences 35, no. 1 (1999): 56–57. http://dx.doi.org/10.1002/(sici)1520-6696(199924)35:1<56::aid-jhbs9>3.0.co;2-j.

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Mull, J. Dennis. "INNOVATION AND BEHAVIOR CHANGE." Infectious Disease Clinics of North America 9, no. 2 (June 1995): 245–64. http://dx.doi.org/10.1016/s0891-5520(20)30657-7.

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23

Lokhorst, Anne Marike, Carol Werner, Henk Staats, Eric van Dijk, and Jeff L. Gale. "Commitment and Behavior Change." Environment and Behavior 45, no. 1 (June 9, 2011): 3–34. http://dx.doi.org/10.1177/0013916511411477.

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Schuman-Olivier, Zev, Marcelo Trombka, David A. Lovas, Judson A. Brewer, David R. Vago, Richa Gawande, Julie P. Dunne, Sara W. Lazar, Eric B. Loucks, and Carl Fulwiler. "Mindfulness and Behavior Change." Harvard Review of Psychiatry 28, no. 6 (November 2020): 371–94. http://dx.doi.org/10.1097/hrp.0000000000000277.

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25

Levav, Jonathan, and Gavan J. Fitzsimons. "When Questions Change Behavior." Psychological Science 17, no. 3 (March 2006): 207–13. http://dx.doi.org/10.1111/j.1467-9280.2006.01687.x.

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Scott, Marcia K., M. Grill, S. R. Cullen, and P. Wright. "Coaching for Behavior Change." Journal of Nutrition Education and Behavior 46, no. 4 (July 2014): S155. http://dx.doi.org/10.1016/j.jneb.2014.04.151.

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27

Iversen, Iver H. "How to change Behavior?" Behavioral and Brain Sciences 11, no. 03 (September 1988): 457. http://dx.doi.org/10.1017/s0140525x00058386.

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Blume, Arthur W., Karen B. Schmaling, and G. Alan Marlat. "Motivating drinking behavior change." Addictive Behaviors 26, no. 2 (March 2001): 267–72. http://dx.doi.org/10.1016/s0306-4603(00)00087-3.

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Smith, Bill. "Perspectives on Behavior Change." Social Marketing Quarterly 14, no. 3 (August 26, 2008): 148–51. http://dx.doi.org/10.1080/15245000802317423.

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Glasgow, Russell E., Sheana S. Bull, John D. Piette, and John F. Steiner. "Interactive behavior change technology." American Journal of Preventive Medicine 27, no. 2 (August 2004): 80–87. http://dx.doi.org/10.1016/j.amepre.2004.04.026.

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Edmondson, Donald, David Conroy, Rainer Romero-Canyas, Molly Tanenbaum, and Susan Czajkowski. "Climate change, behavior change and health: a multidisciplinary, translational and multilevel perspective." Translational Behavioral Medicine 12, no. 4 (April 1, 2022): 503–15. http://dx.doi.org/10.1093/tbm/ibac030.

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Abstract The climate crisis provides a critical new lens through which health and health behaviors need to be viewed. This paper has three goals. First, it provides background on the climate crisis, the role of human behavior in creating this crisis, and the health impacts of climate change. Second, it proposes a multilevel, translational approach to investigating health behavior change in the context of the climate crisis. Third, it identifies specific challenges and opportunities for increasing the rigor of behavioral medicine research in the context of the climate crisis. The paper closes with a call for behavioral medicine to be responsive to the climate crisis.

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Hughes, Jaime, Janet Bettger, Susan Hughes, and Mina Raj. "Interdisciplinary Perspectives on Maintenance of Health Behavior Change." Innovation in Aging 4, Supplement_1 (December 1, 2020): 817. http://dx.doi.org/10.1093/geroni/igaa057.2976.

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Abstract Modifying health behaviors can be difficult, especially for older adults who are challenged by multiple chronic conditions, reduced functional and/or cognitive capacity, and limited social support. Although much attention has been given to the theories, skills, and resources behind initiating and achieving behavior change, less work has focused on maintenance of health behaviors over time. This presentation will showcase pilot research inspired by RCCN’s first workshop, Achieving and Sustaining Behavior Change. Specifically, this pilot brings together an interdisciplinary team of behavioral scientists and health services researchers working at the intersection of intervention science and implementation science to better understand the construct of maintenance and discuss emerging methods for intervention development and evaluation. The presentation will utilize physical activity as an example behavior to demonstrate the value of interdisciplinary research, including recommendations on how some of the six NIA research centers can make unique contributions to understanding health behavior maintenance.

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Shiomi, Masahiro, Kayako Nakagawa, and Norihiro Hagita. "Design of a gaze behavior at a small mistake moment for a robot." Interaction Studies 14, no. 3 (December 31, 2013): 317–28. http://dx.doi.org/10.1075/is.14.3.01shi.

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A change of gaze behavior at a small mistake moment is a natural response that reveals our own mistakes and suggests an apology to others with whom we are working or interacting. In this paper we investigate how robot gaze behaviors at small mistake moments change the impressions of others. To prepare gaze behaviors for a robot, first, we identified by questionnaires how human gaze behaviors change in such situations and extracted three kinds: looking at the other, looking down, and looking away. We prepared each gaze behavior, added a no-gaze behavior, and investigated how a robot’s gaze behavior at a small mistake moment changes the impressions of the interacting people in a simple cooperative task. Experiment results show that the ‘looking at the other’ gaze behavior outperforms the other gaze behaviors and indicates the degrees of perceived apologeticness and friendliness. Keywords: Communication robots; gaze; mistake; mitigation

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Schmitter-Edgecombe, Maureen, and Brenna Boyd. "One-Year Impact of a Multidomain Brain Health Intervention on Cognition and Behavior Change for Midlife and Older Adults: A Pilot Clinical Trial." Journal of Geriatric Medicine 6, no. 1 (April 19, 2024): 18–34. http://dx.doi.org/10.30564/jgm.v6i1.6303.

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Objectives. At one-year follow-up, this study explored whether a multidomain brain-health intervention resulted in maintained behavior change, provided cognitive benefits, increased self-efficacy for behavior change, and whether participants intended to continue with these changes. Methods. One-hundred thirty midlife and older adults were assigned to one of three conditions: brain fitness (B-Fit) intervention utilizing education and goal setting, education-only, or waitlist control. Questionnaires and cognitive measures were administered. Results. Both B-Fit and education-only participants maintained increased levels of health behavior changes at follow-up testing. There were no clinically meaningful cognitive benefits nor impact on self-efficacy. B-Fit participants reported greater intention to increase health behaviors in the coming year compared to education-only. Discussion. The B-Fit intervention helped participants change their behaviors and maintain these changes over time; however, it was not more effective than the education-only condition. Although, B-Fit participants self-reported a greater likelihood to increase these behavior changes over time.

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Wong, Stephen E. "Scientific Discovery, Social Change, and Individual Behavior Change." Behavior and Social Issues 16, no. 2 (October 2007): 190–96. http://dx.doi.org/10.5210/bsi.v16i2.1951.

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Vinci, Debra M. "Stages of Change: Effective Strategies for Behavior Change." Athletic Therapy Today 8, no. 6 (November 2003): 27–29. http://dx.doi.org/10.1123/att.8.6.27.

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Sullivan, Claire F. "Health Behavior Change Challenge: Understanding Stages of Change." Communication Teacher 25, no. 2 (April 2011): 108–14. http://dx.doi.org/10.1080/17404622.2010.528006.

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Amato, Katie, Eunhee Park, and Claudio R. Nigg. "Prioritizing multiple health behavior change research topics: expert opinions in behavior change science." Translational Behavioral Medicine 6, no. 2 (January 6, 2016): 220–27. http://dx.doi.org/10.1007/s13142-015-0381-5.

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Arjoranta, Jonne, Tuomas Kari, and Markus Salo. "Exploring Features of the Pervasive Game Pokémon GO That Enable Behavior Change: Qualitative Study." JMIR Serious Games 8, no. 2 (May 25, 2020): e15967. http://dx.doi.org/10.2196/15967.

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Background Digital gaming is one of the most popular forms of entertainment in the world. While prior literature concluded that digital games can enable changes in players’ behaviors, there is limited knowledge about different types of behavior changes and the game features driving them. Understanding behavior changes and the game features behind them is important because digital games can motivate players to change their behavior for the better (or worse). Objective This study investigates the types of behavior changes and their underlying game features within the context of the popular pervasive game Pokémon GO. Methods We collected data from 262 respondents with a critical incident technique (CIT) questionnaire. We analyzed the responses with applied thematic analysis with ATLAS.ti (ATLAS.ti Scientific Software Development GmbH) software. Results We discovered 8 types of behavior changes and 13 game features relevant to those behavior changes. The behavior changes included added activity in life, enhancing routines, exploration, increased physical activity, strengthening social bonds, lowering social barriers, increased positive emotional expression and self-treatment. The game features included reaching a higher level, catching new Pokémon, evolving new Pokémon, visiting PokéStops, exploring PokéStops, hatching eggs, fighting in gyms, collaborative fighting, exploiting special events, finding specific Pokémon, using items, Pokémon theme, and game location tied to physical location. The behavior changes were connected to specific game features, with game location tied to physical location and catching new Pokémon being the most common and connected to all behavior changes. Conclusions Our findings indicate that the surveyed players changed their behaviors while or after playing Pokémon GO. The respondents reported being more social, expressed more positive emotions, found more meaningfulness in their routines, and had increased motivation to explore their surroundings.

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Woodruff, Susan I., and Terry L. Conway. "A Longitudinal Assessment of the Impact of Health/Fitness Status and Health Behavior on Perceived Quality of Life." Perceptual and Motor Skills 75, no. 1 (August 1992): 3–14. http://dx.doi.org/10.2466/pms.1992.75.1.3.

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This study extended cross-sectional research associating quality of life with health and fitness factors. Longitudinal analyses were performed on data collected from 519 U.S. Navy personnel to assess changes in quality of life with changes in health/fitness status and health behavior dimensions at 1-yr. and 2-yr. intervals. Multiple regression results showed that such changes were positively associated with changes in health/fitness status and behaviors related to accident control and wellness maintenance, with these predictors accounting for 8% of the variance in change in quality of life at the 1-yr. interval. At the 2-yr. interval, such change was associated with health/ fitness status and accident control behaviors, accounting for 11% of the variance. Health behavior change made a unique contribution to change in quality of life after controlling for changes in health/fitness status at both intervals. Findings affirm modest yet consistent associations between changes in fitness and health variables and quality of life and suggest that improvements in health behavior influence quality of life independently of one's health/fitness status.

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Boulton, Kelly, Eric Pallant, Casey Bradshaw-Wilson, Beth Choate, and Ian Carbone. "Energy challenges: isolating results due to behavior change." International Journal of Sustainability in Higher Education 18, no. 1 (January 3, 2017): 116–28. http://dx.doi.org/10.1108/ijshe-08-2015-0144.

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Purpose Approximately 700 colleges and universities have committed to climate neutrality, which will require significant reductions in energy consumption. This paper aims to explore the effectiveness of an Annual Energy Challenge in curtailing electricity use by changing consumption behaviors at one liberal arts college. Design/methodology/approach From 2010 to 2014, Allegheny College (Meadville, PA, USA) ran four-week energy challenges. Electricity consumption was measured and compared to a baseline year of 2008. An alternate baseline, more granular data for 20 sub-metered buildings and historic utility bill consumption trends were further analyzed to identify any persisting change and understand the impact of behavior change separate from efficiency retrofits, changes in population and normal seasonal shifts. Findings Electricity consumption during the challenge period dropped an average of 9 per cent compared to the 2008 baseline and 6 per cent compared to the baseline of the 4 weeks preceding each challenge. Consumption trends changed in the years during challenge implementation compared to the years before engaging the campus community. All analyses reinforce that the challenge reduces electricity consumption. However, results must be analyzed in multiple ways to isolate for behavior change. Practical implications The analyses used to isolate energy challenge results due to behavior change are replicable at other institutions and would allow campuses to compare results and share proven strategies. Originality/value While many campuses organize energy challenges, few have published details about the results both during the challenge and continuing afterwards. Nor has a research explored the need to put results into contexts such as natural seasonal trends to isolate the impacts of behavior change.

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Tarmana, Dede, Nadiroh ., and Agung Purwanto. "Path Model: Effect of Knowledge, Personality and Behavior Intentions toward Climate Change Adaptive Behavior." International Journal of Membrane Science and Technology 10, no. 2 (July 7, 2023): 1450–57. http://dx.doi.org/10.15379/ijmst.v10i2.1496.

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This study was aimed at modelling of climate change adaptive behaviour from basic model theory Responsible Environmental Behaviour (REB) by Hines, and to know the effect of knowledge about climate change (CC), personality, behavior intention toward CC adaptive behavior. A survey method was used by involving 481 Cadets at The School of Meteorology Climatology and Geophysics (STMKG) as sample in this study. There were 4 instruments in this research for measuring knowledge about CC (24 items, reliability .923), personality (25 items, reliability .933), behavior intention (34 items, reliability .976) and CC adaptive behavior of cadets (24 items, reliability .964). Data was analyzed by descriptive statistical, path modelling analysis and inferential statistics. The results revealed that path model at CC adaptive behavior have the significant suitability model. CC adaptive behavior significantly direct and indirect affected knowledge about CC, personality, and behaviour intention, but unaffected indirectly by knowledge about CC. Based on those findings, it can be concluded that variations in CC adaptive behavior of STMKG cadets are affected by variations in knowledge about CC, personality, and behaviour intention. It means that Path model can used to predict CC adaptive behavior based on knowledge about CC, personality, and behavior intention.

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Latifah, Latifah. "PERUBAHAN TINGKAH LAKU SISWA MELALUI KOMUNIKASI ANTAR PRIBADI GURU PENDIDIKAN AGAMA ISLAM." Al-KALAM JURNAL KOMUNIKASI, BISNIS DAN MANAJEMEN 8, no. 2 (July 1, 2021): 112. http://dx.doi.org/10.31602/al-kalam.v8i2.5301.

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This study describes systematically changes in student behavior through interpersonal communication of Islamic Education teachers at Madrasah Ibtidaiyah Assalam Martapura. Focusing on changing behavior and helping teachers to implement good behavior to their students. This behavior change includes: a) Interactive involvement, this behavior change determines the level of one's participation and participation in communication with other people, including: responsiveness, perceptive attitude and attentiveness. b). Interaction management, this change in behavior helps a person be able to take useful actions for someone to achieve communication goals. c) Behavioral flexibility, this change in behavior helps a person to carry out various possible behaviors that can be taken to achieve communication goals. d) Listening, this behavior change helps someone to be able to listen to people who communicate with someone not only content, but also the feelings, concerns, and worries that accompany it. e) Social style, this behavior change helps someone to behave attractively, distinctively, and can be accepted by those who communicate with that person. f) Communication anxiety, this behavior change can overcome fear, confusion, and confusion of thought, body shaking, and stage fright that appears in communicating.

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Baik, Soo, Hae-Deok Song, and Ah Hong. "Craft Your Job and Get Engaged: Sustainable Change-Oriented Behavior at Work." Sustainability 10, no. 12 (November 25, 2018): 4404. http://dx.doi.org/10.3390/su10124404.

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Employees’ change-oriented behavior is known to be critical in promoting organizational changes for a sustainable organization. However, few studies have explored how this behavior can be potentially promoted by job-crafting and work engagement. This study examined the relationship between job-crafting and change-oriented behaviors (adaptive and proactive behaviors), as well as the mediating effect of work engagement. Hypotheses were tested with a structural equation modeling analysis. A total of 459 employees in the Korean automobile manufacturing industry participated in the study. The results show that job-crafting had a positive effect on adaptive behaviors and proactive behaviors. Moreover, seeking job resources and seeking job challenges promoted change-oriented behaviors through work engagement. Based on these results, practical implications are suggested for the development of a sustainable organization.

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Musgrave, Loretta M., Alison Baum, Nilushka Perera, Caroline SE Homer, and Adrienne Gordon. "Baby Buddy App for Breastfeeding and Behavior Change: Retrospective Study of the App Using the Behavior Change Wheel." JMIR mHealth and uHealth 9, no. 4 (April 15, 2021): e25668. http://dx.doi.org/10.2196/25668.

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Background Breastfeeding plays a major role in the health of mothers and babies and has the potential to positively shape an individual’s life both in the short and long term. In the United Kingdom (UK), although 81% of women initiate breastfeeding, only 1% of women breastfeed exclusively to 6 months as recommended by the World Health Organization. In the UK, women who are socially disadvantaged and younger are less likely to breastfeed at 6 to 8 weeks postpartum. One strategy that aims to improve these statistics is the Baby Buddy app, which has been designed and implemented by the UK charity Best Beginnings to be a universal intervention to help reduce health inequalities, including those in breastfeeding. Objective This study aimed to retrospectively examine the development of Baby Buddy by applying the Behavior Change Wheel (BCW) framework to understand how it might increase breastfeeding self-efficacy, knowledge, and confidence. Methods Retrospective application of the BCW was completed after the app was developed and embedded into maternity services. A three-stage process evaluation used triangulation methods and formalized tools to gain an understanding of the potential mechanisms and behaviors used in apps that are needed to improve breastfeeding rates in the UK. First, we generated a behavioral analysis by mapping breastfeeding barriers and enablers onto the Capability, Opportunity, and Motivation-Behavior (COM-B) system using documents provided by Best Beginnings. Second, we identified the intervention functions and policy categories used. Third, we linked these with the behavior change techniques identified in the app breastfeeding content using the Behavior Change Techniques Taxonomy (BCTTv1). Results Baby Buddy is a well-designed platform that could be used to change breastfeeding behaviors. Findings from stage one showed that Best Beginnings had defined breastfeeding as a key behavior requiring support and demonstrated a thorough understanding of the context in which breastfeeding occurs, the barriers and enablers of breastfeeding, and the target actions needed to support breastfeeding. In stage two, Best Beginnings had used intervention and policy functions to address the barriers and enablers of breastfeeding. In stage three, Baby Buddy had been assessed for acceptability, practicability, effectiveness, affordability, safety, and equity. Several behavior change techniques that could assist women with decision making around breastfeeding (eg, information about health consequences and credible sources) and possibly affect attitudes and self-efficacy were identified. Of the 39 videos in the app, 19 (49%) addressed physical capabilities related to breastfeeding and demonstrated positive breastfeeding behaviors. Conclusions Applying a theoretical framework retrospectively to a mobile app is possible and results in useful information to understand potential health benefits and to inform future development. Future research should assess which components and behavioral techniques in the app are most effective in changing behavior and supporting breastfeeding.

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Verplanken, Bas, and Sheina Orbell. "Attitudes, Habits, and Behavior Change." Annual Review of Psychology 73, no. 1 (January 4, 2022): 327–52. http://dx.doi.org/10.1146/annurev-psych-020821-011744.

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Efforts to guide peoples’ behavior toward environmental sustainability, good health, or new products have emphasized informational and attitude change strategies. There is evidence that changing attitudes leads to changes in behavior, yet this approach takes insufficient account of the nature and operation of habits, which form boundary conditions for attitude-directed interventions. Integration of research on attitudes and habits might enable investigators to identify when and how behavior change strategies will be most effective. How might attitudinally driven behavior change be consolidated into lasting habits? How do habits protect the individual against the vicissitudes of attitudes and temptations and promote goal achievement? How might attitudinal approaches aiming to change habits be improved by capitalizing on habit discontinuities and strategic planning? When and how might changing or creating habit architecture shape habits directly? A systematic approach to these questions might help move behavior change efforts from attitude change strategies to habit change strategies.

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Aggleton, P., K. O'Reilly, G. Slutkin, and P. Davies. "Risking everything? Risk behavior, behavior change, and AIDS." Science 265, no. 5170 (July 15, 1994): 341–45. http://dx.doi.org/10.1126/science.8023156.

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Hirvonen, Noora, Maija-Leena Huotari, Raimo Niemelä, and Raija Korpelainen. "Information behavior in stages of exercise behavior change." Journal of the American Society for Information Science and Technology 63, no. 9 (August 17, 2012): 1804–19. http://dx.doi.org/10.1002/asi.22704.

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Prusaczyk, Artur, Joanna Oberska, Paweł Żuk, Marika Guzek, and Magdalena Bogdan. "Behaviorism and the concepts of influencing the attitudes of patients towards health behaviors." Journal of Education, Health and Sport 13, no. 4 (February 16, 2023): 108–14. http://dx.doi.org/10.12775/jehs.2023.13.04.011.

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Health professionals in primary care teams need to know how to effectively encourage patients to change health behaviors to achieve treatment goals. Understanding the behavioral patterns and psychological underpinnings of making changes can help healthcare professionals deliver interventions with a higher success rate. The aim of the study was to assess the importance of behavior patterns in shaping patients' health attitudes and behaviors. There are three types of theoretical models that explain how health behaviors are initiated and changed: motivational, post-intentional, and multistage models. Motivation models describe the role of individual cognitive variables in the process of creating the intention to change behavior. Changing habits in the context of health promotion takes place by influencing the perception of patients. This can be achieved using the TRA (Theory of Reasoned Action), TBP (Theory of Planned Behavior) and HBM (Health Belief Model) models by convincing the public about susceptibility to a given disease, disseminating knowledge about effective methods of prevention and generating persuasion, support, admiration or recognition in society after positive behaviors such as smoking cessation or regular physical activity. Post-intentional models indicate factors that increase the chance of translating motivation into action. They commit the individual to a certain action when certain environmental circumstances are met, thus helping to translate the intention of the goal into action. Multi-stage models describe health behaviors as involving several separate stages. These theories are based on the assumption that people at different stages will behave differently, so the types of interventions and information needed to change behavior will vary depending on the stage they are at. Helping patients set realistic goals, such as moving to the next stage, can facilitate the change process. Effective behavioral interventions must be based on changing the approach of medical staff to the interpersonal process with the patient. This approach should be patient-centred and collaborative. Medical staff should assess the importance that the patient attaches to his health and the treatment process, and thus also the willingness and motivation to comply with the recommendations. Merely providing information will not guarantee a change in their behavior. Healthcare professionals should use active listening techniques (using open-ended questions, explanations, reflective and summarizing statements), should encourage patients to express concerns, and should be able to weigh the pros and cons of different treatment approaches.

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Kanouse, David E., and Itzhak Jacoby. "When Does Information Change Practitioners' Behavior?" International Journal of Technology Assessment in Health Care 4, no. 1 (January 1988): 27–33. http://dx.doi.org/10.1017/s0266462300003214.

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AbstractPrograms that disseminate information to health care practitioners often do so partly to encourage appropriate changes in practice. However, merely providing information is seldom enough to accomplish such changes. If information transfer programs are to influence practice, they must be designed to maximize the conditions facilitating change. Reliance on a diffusion model for thinking about how information reaches practitioners has led researchers to over-emphasize the importance of exposure to information and ignore other factors that determine whether change will occur, such as practitioners' motivation to change, the context in which clinical decisions are made, and how information is presented. The fact that successful dissemination will not necessarily produce change also has implications for how information transfer programs should be monitored and evaluated.

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