Abstract
This paper presents the results of a pilot study examining the effectiveness of an interpretive nutrition label as an information nudge in the context of Human-Computer Interaction (HCI) with a mobile Health application (mHealth app). Thirty subjects from two age groups were recruited to complete a healthy food discrimination task on the web-based app. The primary factor was the availability of an interpretative front-of-package (FOP) nutrition label or the back-of-package (BOP) Nutrition Facts Panel (NFP) for each choice. Additionally, the number of food options and a default nudge were examined. Results indicate both the potential usefulness of FOP nutrition labels as an information nudge in the mHealth app context and the discriminability metric adopted from Signal Detection Theory for similar experiments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bauer, J.M., Reisch, L.A.: Behavioural insights and (un)healthy dietary choices: a review of current evidence. J. Consum. Policy (2018). https://doi.org/10.1007/s10603-018-9387-y
Bleich, S.N., Economos, C.D., Spiker, M.L., Vercammen, K.A., VanEpps, E.M., Block, J.P., Elbel, B., Story, M., Roberto, C.A.: A systematic review of calorie labeling and modified calorie labeling interventions: impact on consumer and restaurant behavior. Obesity 25, 2018–2044 (2017). https://doi.org/10.1002/oby.21940
Campos, S., Doxey, J., Hammond, D.: Nutrition labels on pre-packaged foods: a systematic review. Public Health Nutr. 14, 1496–1506 (2011). https://doi.org/10.1017/S1368980010003290
Talati, Z., Pettigrew, S., Kelly, B., Ball, K., Dixon, H., Shilton, T.: Consumers’ responses to front-of-pack labels that vary by interpretive content. Appetite 101, 205–213 (2016). https://doi.org/10.1016/j.appet.2016.03.009
World Health Organization: “Best buys” and other recommended interventions for the prevention and control of noncommunicable diseases. Updated (2017) appendix 3 of the global action plan for the prevention and control of noncommunicable diseases 2013–2020 (2017)
Cecchini, M., Warin, L.: Impact of food labelling systems on food choices and eating behaviours: a systematic review and meta-analysis of randomized studies. Obes. Rev. 17, 201–210 (2016). https://doi.org/10.1111/obr.12364
Egnell, M., Ducrot, P., Touvier, M., Allès, B., Hercberg, S., Kesse-Guyot, E., Julia, C.: Objective understanding of Nutri-Score Front-Of-Package nutrition label according to individual characteristics of subjects: comparisons with other format labels. PLoS ONE 13, e0202095 (2018). https://doi.org/10.1371/journal.pone.0202095
Khandpur, N., Graham, D.J., Roberto, C.A.: Simplifying mental math: changing how added sugars are displayed on the nutrition facts label can improve consumer understanding (2017). https://doi.org/10.1016/j.appet.2017.03.015
Drichoutis, A.C.: Nutrition knowledge and consumer use of nutritional food labels. Eur. Rev. Agric. Econ. 32, 93–118 (2005). https://doi.org/10.1093/erae/jbi003
Green, D.M., Swets, J.A.: Signal Detection Theory and Psychophysics. Peninsula Publishing, Los Altos (1988)
McLaughlin, A.C., Whitlock, L.A., Lester, K.L., McGraw, A.E.: Older adults’ self-reported barriers to adherence to dietary guidelines and strategies to overcome them. J. Health Psychol. 22, 356–363 (2017). https://doi.org/10.1177/1359105315603472
Boot, W.R., Charness, N., Czaja, S.J., Sharit, J., Rogers, W.A., Fisk, A.D., Mitzner, T., Lee, C.C., Nair, S.: Computer proficiency questionnaire: assessing low and high computer proficient seniors. Gerontologist 55, 404–411 (2015). https://doi.org/10.1093/geront/gnt117
Weiss, B.D., Mays, M.Z., Martz, W., Castro, K.M., DeWalt, D.A., Pignone, M.P., Mockbee, J., Hale, F.A.: Quick assessment of literacy in primary care: the newest vital sign. Ann. Fam. Med. 3, 514–522 (2005). https://doi.org/10.1370/afm.405
Davis, F.D.: A technology acceptance model for empirically testing new end-user information systems: theory and results (1985). http://dspace.mit.edu/handle/1721.1/15192
Lund, A.M.: Measuring usability with the USE questionnaire. Usability Interface 8, 3–6 (2001). https://doi.org/10.1177/1078087402250360
Macmillan, N.A., Creelman, C.D.: Detection Theory: A User’s Guide, 2nd edn. Lawrence Erlbaum Associates (2004). https://doi.org/10.4324/9781410611147
Longo, L.: Subjective usability, mental workload assessments and their impact on objective human performance. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 202–223. Springer (2017). https://doi.org/10.1007/978-3-319-67684-5_13
Hart, S.G., Staveland, L.E.: Development of NASA-TLX (task load index): results of empirical and theoretical research. Adv. Psychol. 52, 139–183 (1988). https://doi.org/10.1016/S0166-4115(08)62386-9
Malloy-Weir, L., Cooper, M.: Health literacy, literacy, numeracy and nutrition label understanding and use: a scoping review of the literature. J. Hum. Nutr. Diet. 30, 309–325 (2017). https://doi.org/10.1111/jhn.12428
Burton-Jones, A., Grange, C.: From use to effective use: a representation theory perspective. Inf. Syst. Res. 24, 632–658 (2013). https://doi.org/10.1287/isre.1120.0444
Zapata, B.C., Fernández-Alemán, J.L., Idri, A., Toval, A.: Empirical studies on usability of mHealth apps: a systematic literature review. J. Med. Syst. 39, 1–19 (2015). https://doi.org/10.1007/s10916-014-0182-2
Acknowledgments
This work was conducted in and supported by Discovery & Learning Research Center, Purdue University and Tippecanoe Senior Center, Lafaytte, Indiana. Authors are grateful for the staffs and those volunteers who have given great support to the recruitment process.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chao, WY., Lehto, M., Pitts, B., Hass, Z. (2021). Evaluation of the Effectiveness of an Interpretive Nutrition Label Format in Improving Healthy Food Discrimination Using Signal Detection Theory. In: Ayaz, H., Asgher, U. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1201. Springer, Cham. https://doi.org/10.1007/978-3-030-51041-1_45
Download citation
DOI: https://doi.org/10.1007/978-3-030-51041-1_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51040-4
Online ISBN: 978-3-030-51041-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)