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Evaluation of mass-reach physical activity campaigns: considering automatic processes

  • Tanya R. BerryEmail author
  • Lira Yun
Review
  • 131 Downloads

Abstract

Mass-reach physical activity campaigns are usually evaluated based on the assumption that behavior change is an outcome of higher-level cognitive operations such as intentions. However, elements in a mass-reach physical activity promotion campaign may automatically attract attention or activate associated concepts; these, in turn, can influence targeted attitudes, intentions, or behavior. Repeated exposure to campaigns may also create physical activity associations. However, there is limited guidance for the inclusion of measurement of automatic processes when evaluating campaigns. The purpose of this article is to argue that automatic processes should be considered when evaluating mass-reach physical activity promotion campaigns, and to propose hypotheses regarding how automatic processes may relate to campaign effects. The proposed hypotheses build on the physical activity-specific hierarchy-of-effects model, which has been used to evaluate mass-reach campaigns. Points along the hierarchy of effects are suggested where automatic processes may be incorporated into the evaluation of mass-reach physical activity promotion campaigns. Thus, broad hypotheses are offered regarding how automatic processes can be moderators of campaign effects or can emerge as a result of the campaign. By testing the proposed hypotheses, it is hoped that mass-reach physical activity promotion campaigns can be better understood with the goal of having more effective campaigns.

Keywords

Dual-processing Evaluation Physical activity Hierarchy-of-effects model Attentional bias Automatic associations 

Beurteilung von Werbeaktionen für körperliche Aktivität mit Breitenwirkung: Berücksichtigung automatischer Prozesse

Zusammenfassung

Auf Breitenwirkung abzielende Kampagnen für körperliche Aktivität werden gewöhnlich auf der Grundlage der Annahme beurteilt, dass Verhaltensänderungen das Ergebnis kognitiver Vorgänge auf höherer Ebene, z. B. Intentionen, sind. Allerdings können Elemente in einer auf Breitenwirkung abzielenden Werbeaktion für körperliche Aktivität automatisch die Aufmerksamkeit auf sich ziehen oder assoziierte Konzepte aktivieren; diese können umgekehrt gezielte Haltungen, Intentionen oder Verhalten beeinflussen. Die wiederholte Exposition gegenüber Werbeaktionen kann ebenfalls Assoziationen zu körperlicher Aktivität erzeugen. Es bestehen jedoch nur begrenzte Orientierungshilfen für den Einschluss der Messung automatischer Prozesse in die Beurteilung von Werbeaktionen. Der Zweck des vorliegenden Beitrags besteht darin darzulegen, dass automatische Prozesse berücksichtigt werden sollten, wenn es um die Beurteilung von auf Breitenwirkung abzielende Werbeaktionen für körperliche Aktivität geht, und Hypothesen in Bezug darauf vorzustellen, wie automatische Prozesse mit der Wirkung von Werbeaktionen in Zusammenhang stehen. Die vorgestellten Hypothesen beruhen auf dem – hier speziell auf körperliche Aktivität bezogenen – Hierarchy-of-Effects-Modell, einem Modell zur hierarchischen Abfolge von Werbewirkungen, welches zur Beurteilung auf Breitenwirkung abzielender Werbeaktionen eingesetzt wird. Es werden Punkte in der Hierarchie der Wirkungen vorgeschlagen, an denen automatische Prozesse in die Beurteilung auf Breitenwirkung abzielender Promotion-Aktionen integriert werden können. Somit werden weitreichende Hypothesen hinsichtlich dessen dargestellt, wie automatische Prozesse Moderatoren der Wirkungen von Kampagnen sein können oder als ein Ergebnis der Kampagne entstehen können. Durch Testung der vorgestellten Hypothesen ist zu hoffen, dass ein besseres Verständnis auf Breitenwirkung abzielender Werbeaktionen erreicht wird mit dem Ziel, wirksamere Werbekampagnen durchzuführen.

Schlüsselwörter

Separate Verarbeitung Auswertung Sportliche Betätigung Hierarchie-von-Effekten-Modell Aufmerksamkeitsverzerrung Automatische Assoziationen 

Notes

Funding

This work was undertaken, in part, thanks to funding from the Canada Research Chairs program provided to Tanya Berry.

Compliance with ethical guidelines

Conflict of interest

T.R. Berry and L. Yun declare that they have no competing interests.

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Antoniewicz, F., & Brand, R. (2016). Learning to like exercising: evaluative conditioning changes automatic evaluations of exercising and influences subsequent exercising behavior. Journal of Sport and Exercise Psychology, 38, 138–148.  https://doi.org/10.1123/jsep.2015-0125.CrossRefGoogle Scholar
  2. Banting, L. K., Dimmock, J. A., & Lay, B. S. (2009). The role of implicit and explicit components of exerciser self-schema in the prediction of exercise behaviour. Psychology of Sport and Exercise, 10, 80–86.  https://doi.org/10.1016/j.psychsport.2008.07.007.CrossRefGoogle Scholar
  3. Bar-Haim, Y., Lamy, D., Pergamin, L., Bakermans-Kranenburg, M. J., & van IJzendoorn, M. H. (2007). Threat-related attentional bias in anxious and nonanxious individuals: a meta-analytic study. Psychological Bulletin, 133, 1–24.  https://doi.org/10.1037/0033-2909.133.1.1.CrossRefGoogle Scholar
  4. Bauman, A., & Chau, J. (2009). The role of media in promoting physical activity. Journal of Physical Activity and Health, 6(Suppl 2), S196–S210.  https://doi.org/10.1123/jpah.6.s2.s196.CrossRefGoogle Scholar
  5. Bauman, A., Bowles, H. R., Huhman, M., Heitzler, C. D., Owen, N., Smith, B. J., & Reger-Nash, B. (2008). Testing a hierarchy of effects model: pathways from awareness to outcomes in the VERB campaign 2002–2003. American Journal of Preventive Medicine, 34, S249–S256.  https://doi.org/10.1016/j.amepre.2008.03.015.CrossRefGoogle Scholar
  6. Berry, T. R. (2006). Who’s even interested in the exercise message? Attentional bias for exercise and sedentary-lifestyle related words. Journal of Sport and Exercise Psychology, 28, 4–17.  https://doi.org/10.1123/jsep.28.1.4.CrossRefGoogle Scholar
  7. Berry, T. R. (2018). Automatically activated cognitions and physical activity messaging. In B. Jackson, J. A. Dimmock & J. Compton (Eds.), Persuasion and communication in sport, exercise, and physical activity (pp. 104–118). New York: Routledge.Google Scholar
  8. Berry, T. R., Craig, C. L., Faulkner, G., Latimer, A., Rhodes, R., Spence, J. C., & Tremblay, M. S. (2014). Mothers’ intentions to support children’s physical activity related to attention and implicit agreement with advertisements. International Journal of Behavioral Medicine, 21, 131–138.  https://doi.org/10.1007/s12529-012-9279-5.CrossRefGoogle Scholar
  9. Berry, T. R., Rodgers, W., Divine, A., & Hall, C. (2018). The relationship of explicit-implicit evaluative discrepancy to exercise drop-out in middle-aged adults. Journal of Sport and Exercise Psychology, 40, 92–100.  https://doi.org/10.1123/jsep.2017-0267.CrossRefGoogle Scholar
  10. Berry, T. R., Rodgers, W. M., Markland, D., & Hall, C. R. (2016). Moderators of implicit-explicit exercise cognition concordance. Journal of Sport and Exercise Psychology, 38, 579–589.  https://doi.org/10.1123/jsep.2016-0174.CrossRefGoogle Scholar
  11. Brand, R., & Antoniewicz, F. (2016). Affective evaluations of exercising: the role of automatic-reflective evaluation discrepancy. Journal of Sport and Exercise Psychology, 38, 631–638.  https://doi.org/10.1123/jsep.2016-0171.CrossRefGoogle Scholar
  12. Brand, R., & Ekkekakis, P. (2018). Affective-reflective theory of physical inactivity and exercise. German Journal of Exercise and Sport Research, 48, 48–58.  https://doi.org/10.1007/s12662-017-0477-9.CrossRefGoogle Scholar
  13. Calitri, R., Lowe, R., Eves, F. F., & Bennett, P. (2009). Associations between visual attention, implicit and explicit attitude and behaviour for physical activity. Psychology and Health, 24, 1105–1123.  https://doi.org/10.1080/08870440802245306.CrossRefGoogle Scholar
  14. Cavill, N., & Bauman, A. (2004). Changing the way people think about health-enhancing physical activity: Do mass media campaigns have a role. Journal of Sports Sciences, 22, 771–790.  https://doi.org/10.1080/02640410410001712467.CrossRefGoogle Scholar
  15. Cheval, B., Radel, R., Neva, J. L., Boyd, L. A., Swinnen, S. P., Sander, D., & Boisgontier, M. P. (2018). Behavioral and neural evidence of the rewarding value of exercise behaviors: a systematic review. Sports Medicine, 48, 1389–1404.  https://doi.org/10.1007/s40279-018-0898-0.CrossRefGoogle Scholar
  16. Cheval, B., Sarrazin, P., Isoard-Gautheur, S., Radel, R., & Friese, M. (2015). Reflective and impulsive processes explain (in) effectiveness of messages promoting physical activity: a randomized controlled trial. Health Psychology, 34, 10–19.  https://doi.org/10.1037/hea0000102.CrossRefGoogle Scholar
  17. Cheval, B., Sarrazin, P., Isoard-Gautheur, S., Radel, R., & Friese, M. (2016). How impulsivity shapes the interplay of impulsive and reflective processes involved in objective physical activity. Personality and Individual Differences, 96, 132–137.  https://doi.org/10.1016/j.paid.2016.02.067.CrossRefGoogle Scholar
  18. Chevance, G., Yannick, S., Heraud, N., & Boiche, J. (2018). Interaction between self-regulation, intentions and implicit attitudes in the prediction of physical activity among persons with obesity. Health Psychology, 37, 257–261.  https://doi.org/10.1037/hea0000572.CrossRefGoogle Scholar
  19. Cope, K., Vandelanotte, C., Short, C. E., Conroy, D. E., Rhodes, R. E., Jackson, B., Dimmock, J. A., & Rebar, A. L. (2018). Reflective and non-conscious responses to exercise images. Frontiers in Psychology, 8, 2272.  https://doi.org/10.3389/fpsyg.2017.02272.CrossRefGoogle Scholar
  20. Craig, C. L., Bauman, A., & Reger-Nash, B. (2010). Testing the hierarchy of effects model: ParticipACTION’s serial mass communication campaigns on physical activity in Canada. Health Promotion International, 25, 14–23.  https://doi.org/10.1093/heapro/dap048.CrossRefGoogle Scholar
  21. Conroy, D. E., & Berry, T. R. (2017). Automatic affective evaluations of physical activity. Exercise and Sports Science Reviews, 45, 230–237.  https://doi.org/10.1249/JES.0000000000000120.CrossRefGoogle Scholar
  22. Conroy, D. E., Hyde, A. L., Doerksen, S. E., & Ribeiro, N. F. (2010). Implicit attitudes and explicit motivation prospectively predict physical activity. Annals of Behavioral Medicine, 39(7), 112–118.  https://doi.org/10.1007/s12160-010-9161-0.CrossRefGoogle Scholar
  23. Eisend, M., & Tarrahi, F. (2016). The effectiveness of advertising: A meta-meta-analysis of advertising inputs and outcomes. Journal of Advertising, 45, 519–531.  https://doi.org/10.1080/00913367.2016.1185981.CrossRefGoogle Scholar
  24. Evans, J. S., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: advancing the debate. Perspectives in Psychological Science, 8, 223–241.  https://doi.org/10.1177/1745691612460685.CrossRefGoogle Scholar
  25. Fiedler, K., & Hutter, M. (2014). The limits of automaticity. In J. Sherman, B. Gawronski & Y. Trope (Eds.), Dual-process theories of the social mind (pp. 497–513). New York: Guilford.Google Scholar
  26. Field, M., & Cox, W. M. (2008). Attentional bias in addictive behaviors: a review of its development, causes, and consequences. Drug and Alcohol Dependence, 97, 1–20.  https://doi.org/10.1016/j.drugalcdep.2008.03.030.CrossRefGoogle Scholar
  27. Gawronski, B., & Bodenhausen, G. V. (2006). Associative and propositional processes in evaluation: an integrative review of implicit and explicit attitude change. Psychological Bulletin, 132, 692–731.  https://doi.org/10.1037/0033-2909.132.5.692.CrossRefGoogle Scholar
  28. Gawronski, B., & Bodenhausen, G. V. (2014). The associative-propositional evaluation model: Operating principles and operating conditions of evaluation. In J. Sherman, B. Gawronski & Y. Trope (Eds.), Dual-process theories of the social mind (pp. 188–203). New York: Guilford.Google Scholar
  29. Glockner, A., & Witteman, C. (2010). Beyond dual-process models: a categorization of processes underlying intuitive judgement and decision making. Thinking & Reasoning, 16, 1–25.  https://doi.org/10.1080/13546780903395748.CrossRefGoogle Scholar
  30. Guthold, R., Stevens, G. A., Riley, L. M., & Bull, F. C. (2018). Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. The Lancet: Global Health, 6, E1077–E1086.  https://doi.org/10.1016/S2214-109X(18)30357-7.Google Scholar
  31. Jarvis, J. W., Rhodes, R. E., Deshpande, S., Berry, T. R., Chulak-Bozzer, T., Faulkner, G., Latimer-Cheung, A. E., et al. (2014). Investigating the role of brand equity in predicting the relationship between message exposure and parental support for their child’s physical activity. Social Marketing Quarterly, 20, 103–115.  https://doi.org/10.1177/1524500414528183.CrossRefGoogle Scholar
  32. Kahneman, D. (2011). Thinking fast and slow. Canada: Doubleday.Google Scholar
  33. Kendzierski, D. (1988). Self-schemata and exercise. Basic and Applied Social Psychology, 91, 45–59.  https://doi.org/10.1207/s15324834basp0901_4.CrossRefGoogle Scholar
  34. Klein, W. M. P., & Harris, P. R. (2009). Self-affirmation enhances attentional bias toward threatening components of a persuasive message. Psychological Science, 20, 1463–1467.  https://doi.org/10.1111/j.1467-9280.2009.02467.x.CrossRefGoogle Scholar
  35. Lang, A. (2000). The limited capacity model of mediated message processing. Journal of Communication, 50, 46–70.  https://doi.org/10.1111/j.1460-2466.2000.tb02833.x.CrossRefGoogle Scholar
  36. Maibach, E. (2007). The influence of the media environment on physical activity: looking for the big picture. American Journal of Health Promotion, 21, 353–362.  https://doi.org/10.4278/0890-1171-21.4s.353.CrossRefGoogle Scholar
  37. Markus, H. (1977). Self-schemata and processing information about the self. Journal of Personality and Social Psychology, 35, 63–78.CrossRefGoogle Scholar
  38. Marteau, T. M., Hollands, G. J., & Fletcher, P. C. (2012). Changing human behavior to prevent disease: the importance of targeting automatic processes. Science, 337, 1492–1495.  https://doi.org/10.1126/science.1226918.CrossRefGoogle Scholar
  39. Melnikoff, D. E., & Bargh, J. A. (2018). The mythical number two. Trends in Cognitive Science, 22, 280–293.  https://doi.org/10.1016/j.tics.2018.02.001.CrossRefGoogle Scholar
  40. Michie, S., van Stralen, M. M., & West, R. (2011). The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implementation Science, 6, 42.  https://doi.org/10.1186/1748-5908-6-42.CrossRefGoogle Scholar
  41. Mogg, K., Bradley, B. P., Hyare, H., & Lee, S. (1998). Selective attention to food-related stimuli in hunger: are attentional biases specific to emotional and psychopathological states, or are they also found in normal drive states? Behavior Research and Therapy, 36, 227–237.CrossRefGoogle Scholar
  42. Padin, A. C., Emery, C. F., Vasey, M., & Kiecolt-Glaser, J. K. (2017). Self-regulation and implicit attitudes toward physical activity influence exercise behavior. Journal of Sport and Exercise Psychology, 39, 237–248.  https://doi.org/10.1123/jsep.2017-0056.CrossRefGoogle Scholar
  43. Papies, E. K. (2016). Health goal priming as a situated intervention tool: How to benefit from nonconscious motivational routes to health behaviour. Health Psychology Review, 10, 408–424.  https://doi.org/10.1080/17437199.2016.1183506.CrossRefGoogle Scholar
  44. Perkins, A., & Forehand, M. (2010). Implicit social cognition and indirect measures in consumer behavior. In B. Gawronski & B. K. Payne (Eds.), Handbook of implicit social cognition: measurement, theory, and applications (pp. 535–547). New York: Guilford.Google Scholar
  45. Petty, R. E., Priester, J. R., & Brinol, P. (2002). Mass media attitude change: implications of the elaboration likelihood model of persuasion. In B. Jennings & D. Zillmann (Eds.), Media effects: advances in theory and research (pp. 155–198). New York: Guilford.Google Scholar
  46. Rhodes, R. E., Kaushal, N., & Quinlan, A. (2016). Is physical activity a part of who I am? A review and meta-analysis of identity, schema and physical activity. Health Psychology Review, 10, 204–225.  https://doi.org/10.1080/17437199.2016.1143334.CrossRefGoogle Scholar
  47. Samson, A., & Voyer, B. B. (2012). Two minds, three ways: dual system and dual process models in consumer psychology. AMS Review, 2, 48–71.  https://doi.org/10.1007/s13162-012-0030-9.CrossRefGoogle Scholar
  48. Senay, I., & Kaphingst, K. A. (2009). Anchoring-and-adjustment bias in communication of disease risk. Medical Decision Making, 29, 193–201.  https://doi.org/10.1177/0272989X08327395.CrossRefGoogle Scholar
  49. Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220–247.  https://doi.org/10.1207/s15327957pspr0803_1.CrossRefGoogle Scholar
  50. Turner, M. M., Skubisz, C., Pandya, S. P., Silverman, M., & Austin, L. L. (2014). Predicting visual attention to nutrition information for food products: the influence of motivation and ability. Journal of Health Communication, 19, 1017–1029.  https://doi.org/10.1080/10810730.2013.864726.CrossRefGoogle Scholar
  51. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: heuristics and biases. Science, 185, 1124–1131.CrossRefGoogle Scholar
  52. Van Bockstaele, B., Verschuere, B., Tibboel, H., De Houwer, J., Crombez, G., & Koster, E. H. W. (2014). A review of current evidence for the causal impact of attentional bias on fear and anxiety. Psychological Bulletin, 140, 682–721.  https://doi.org/10.1037/a0034834.CrossRefGoogle Scholar
  53. Yun, L., & Berry, T. (2018). Examining implicit cognitions in the evaluation of a community-wide physical activity program. Evaluation and Program Planning, 69, 10–17.  https://doi.org/10.1016/j.evalprogplan.2018.04.001.CrossRefGoogle Scholar
  54. Yun, L., Ori, E., Lee, Y., Berry, T. R., & Sivak, A. (2017). A systematic review of mass media campaigns to promote physical activity: an update from 2010. Journal of Physical Activity and Health, 14, 552–570.  https://doi.org/10.1123/jpah.2016-0616.CrossRefGoogle Scholar
  55. Williams, D. M., & Evans, D. R. (2014). Current emotion research in health behavior science. Emotion Review, 6, 277–287.  https://doi.org/10.1177/1754073914523052.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Kinesiology, Sport, and RecreationUniversity of AlbertaEdmontonCanada

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