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There is no single measure of affect that is "best" or appropriate in all research scenarios and for all study purposes. We recommend a three-step decision-making process, to help you choose an appropriate measure for your study. 

Step 1: Decide whether you wish to study core affect, emotion, or mood
The first, most fundamental decision pertains to identifying the target construct of your study, deciding among core affect, emotion, or mood. The boundaries between these three constructs are fuzzy and the lines of demarcation between the three domains are the subject of heated debates in the literature. To understand the differences between these substantively distinct constructs, it is crucial to study this issue in greater depth by referring to a guidebook (such as the corresponding chapter in The Measurement of Affect, Mood, and Emotion: A Guide for Health-Behavioral Research). 

Step 2: Choose the most appropriate theoretical framework for the chosen construct

Unlike many other concepts in psychology, which may be approached from a single theoretical perspective and assessed with a single measure, the domain of affect, mood, and emotion is characterized by enormous diversity of theoretical viewpoints and associated measures. From some perspectives, affective states are conceptualized as distinct. In other perspectives, affective states lay along dimensions that define a broad domain of content. In some cases, the dimensions are unipolar, whereas in other cases bipolar. In some cases, the dimensions are theorized to be orthogonal to each other. In other cases, affective states are theorized to have a specific pattern of interrelationships (e.g., forming a circumplex configuration). The differences between these theoretical perspectives are neither subtle nor trivial; the different perspectives are usually based on research traditions that extend over decades and their implications on the structure of the resultant measures (and therefore the interpretation of the resultant data) can be profound. Therefore, researchers are strongly encouraged to study the differences between the various theoretical perspectives, and understand their implications, before making a decision. 

Step 3: Select the psychometrically strongest measure based on the chosen theoretical framework

In most published reports, the measures of core affect, emotion, or mood appear as de facto choices, unaccompanied by any supporting conceptual rationale. In an effort to provide at least some type of evidence to support their choice, authors occasionally add certain psychometric data (e.g., an index of internal consistency or an index of the goodness of fit). While offering some rationale may seem better than offering no rationale at all, presenting psychometric data is often meaningless, even misleading, if this step is not preceded by the two previous steps outlined here. It makes little sense, for example, to argue that a certain factor model has been found to fit the data well if readers have been given no explanation for why that particular theoretical model was deemed appropriate for the study in the first place. Therefore, supporting psychometric information should be given only after it has been made clear (a) what the target construct of the study is and why, and (b) which theoretical model of the target construct was selected and why. It makes no sense to say that a certain measure has been found to be a "valid" measure of (for example) mood; it only makes sense to say that the measure has been found to be a "valid" measure of a particular theoretical model of the domain of mood. 

Three-step process for selecting a measure of core affect, mood, or emotion

An example of how this three-step decision-making process can be implemented in practice is shown below. 

Step 1


Step 2

Step 3


Given the specific goals of our research, we have determined that our target construct (in most cases) is core affect and the most appropriate conceptualization of the domain of core affect is the circumplex model. 

The circumplex model of core affect


The circumplex model of core affect serves as the template upon which the affective responses to exercise are mapped. According to the circumplex model, the affective space can be adequately defined by two orthogonal and bipolar dimensions, namely affective valence (pleasure-displeasure; see the horizontal dimension in the schematic) and activation (also referred to as arousal; see the vertical dimension in the schematic). When combined, these two dimensions divide the affective space into four meaningful quadrants: (a) pleasant high activation (e.g., excitement, energy), (b) pleasant low activation (e.g., calmness, relaxation), (c) unpleasant low activation (e.g., boredom, fatigue), and (d) unpleasant high activation (e.g., tension, distress). Because of its broad scope, balance, and unparalleled parsimony, the circumplex is a very useful investigative platform for studying the effects of various exercise stimuli on affect. Regardless of their exact nature and direction, which, in most cases, cannot be accurately predicted, affective responses to exercise can be plotted within this two-dimensional space, enabling the identification and basic description of their most salient experiential features.

For the assessment of the valence dimension, we primarily use either the Feeling Scale (FS; Hardy & Rejeski, 1989) or the Empirical Valence Scale (EVS; Lishner, Cooter, & Zald, 2008). For the assessment of the dimension of perceived activation, we use the Felt Arousal Scale (FAS; Svebak & Murgatroyd, 1985). Because the dimensions of affective valence and perceived activation are theorized to be orthogonal to each other within the framework of the circumplex model, in order to minimize common method variance, we strongly encourage researchers to present the scales assessing these dimensions (a) on separate pages (i.e., not side-by-side), (b) with different orientations (e.g., horizontally for the scale assessing valence, vertically for the scale assessing perceived activation), and (c) in randomized order (i.e., valence first, then activation, or activation first, then valence, in a random sequence). 

Sources of Cited Measures

  • Hardy, C. J., & Rejeski, W. J. (1989). Not what, but how one feels: The measurement of affect during exercise. Journal of Sport & Exercise Psychology, 11(3), 304–317. [DOI] 
  • Lishner, D. A., Cooter, A. B., & Zald, D. H. (2008). Addressing measurement limitations in affective rating scales: Development of an empirical valence scale. Cognition and Emotion, 22(1), 180–192. [DOI] 
  • Svebak, S., & Murgatroyd, S. (1985). Metamotivational dominance: A multimethod validation of reversal theory constructs. Journal of Personality and Social Psychology, 48(1), 107–116. [DOI] 


Recommended Further Readings on the Measurement of Affect

  • Ekkekakis, P. (2013). The measurement of affect, mood, and emotion: A guide for health-behavioral research. New York: Cambridge University Press. [DOI]
  • Ekkekakis, P., Ladwig, M.A., & Hartman, M.E. (2019). Physical activity and the "feel-good" effect: Challenges in researching the pleasure and displeasure people feel when they exercise. In S.R Bird (Ed.), Research methods in physical activity and health (pp. 210-229). New York: Routledge. [Link]
  • Ekkekakis, P., Zenko, Z., Ladwig, M.A., & Hartman, M.E. (2018). Affect as a potential determinant of physical activity and exercise: Critical appraisal of an emerging research field. In D.M. Williams, R.E. Rhodes, & M. Conner (Eds.), Affective determinants of health behavior (pp. 237-261). New York: Oxford University Press. [DOI]
  • Ekkekakis, P., & Zenko, Z. (2016). Measurement of affective responses to exercise: From "affectless arousal" to "the most well-characterized" relationship between the body and affect. In H.L. Meiselman (Ed.), Emotion measurement (pp. 299-321). Duxford, United Kingdom: Woodhead. [DOI]
  • Ekkekakis, P. (2012). Affect, mood, and emotion. In G. Tenenbaum, R.C. Eklund, & A. Kamata (Eds.), Measurement in sport and exercise psychology (pp. 321-332). Champaign, IL: Human Kinetics. [Link]



You can download the formatted version of the PRETIE-Q in PDF from this link

The PRETIE-Q gives 2 scores. One is the Preference score and the other is the Tolerance score. The 8 odd-numbered items are Tolerance items (1, 3, 5, 7, 9, 11, 13, 15), whereas the 8 even-numbered items (2, 4, 6, 8, 10, 12, 14,16) are Preference items.

However, half of the Preference items (2, 4, 8, 12) measure LOW Preference and half of the Tolerance items (1, 3, 9, 13) measure LOW Tolerance. These items need to be reverse-scored. This means that a higher score on these items should count as a lower score for Preference/Tolerance and vice versa. So...

  • a score of 1 should be counted as a 5
  • a score of 2 should be counted as a 4
  • a score of 3 should be counted as a 3
  • a score of 4 should be counted as a 2
  • a score of 5 should be counted as a 1


Then, you simply add the reverse-scored and directly scored items together and get the score for the whole factor. If you use SPSS to do the scoring, you can run this script to do it automatically (assuming that the 16 PRETIE-Q items are named PT1 through PT16).

pt1 pt2 pt3 pt4 pt8 pt9 pt12 pt13
(1=5) (2=4) (3=3) (4=2) (5=1).

COMPUTE pref = pt2 + pt4 + pt6 + pt8 +pt10 + pt12 + pt14 + pt16.
COMPUTE tol = pt1 + pt3 + pt5 + pt7 + pt9 + pt11 + pt13 + pt15.

Ηere are the articles on the PRETIE-Q that we have published so far (and links to PDF copies).


Ekkekakis, P., Hall, E.E., & Petruzzello, S.J. (2005). Some like it vigorous: Individual differences in the preference for and tolerance of exercise intensity. Journal of Sport and Exercise Psychology, 27(3), 350-374. [PDF]



Ekkekakis, P., Thome, J., Hall, E.E., & Petruzzello, S.J. (2008). The Preference for and Tolerance of the Intensity of Exercise Questionnaire: A psychometric evaluation among college women. Journal of Sports Sciences, 26(5), 499-510. [PDF]


Hall, E.E., Petruzzello, S.J., Ekkekakis, P., Miller, P.C., & Bixby, W.R. (2014). The role of self-reported individual differences in preference for and tolerance of exercise intensity in fitness-testing performance. Journal of Strength and Conditioning Research, 28(9), 2443-2451. [PDF]



Ekkekakis, P., Lind, E., & Joens-Matre, R.R. (2006). Can self-reported preference for exercise intensity predict physiologically defined self-selected exercise intensity? Research Quarterly for Exercise and Sport, 77(1), 81-90. [PDF]



Ekkekakis, P., Lind, E., Hall, E.E., & Petruzzello, S.J. (2007). Can self-reported tolerance of exercise intensity play a role in exercise testing? Medicine and Science in Sports and Exercise, 39(7), 1193-1199. [PDF]



Smirmaul, B.P.C., Ekkekakis, P., Teixeira, I.P., Nakamura, P.M., & Kokubun, E. (2015). Preference for and Tolerance of the Intensity of Exercise Questionnaire: Brazilian Portuguese version. Brazilian Journal of Kinanthropometry and Human Performance, 17(5), 550-564. [PDF]



  1. Patterson, M.S., Heinrich, K.M., Prochnow, T., Graves-Boswell, T., & Spadine, M.N. (2020). Network analysis of the social environment relative to preference for and tolerance of exercise intensity in Crossfit gyms. International Journal of Environmental Research and Public Health, 17, 8370. [DOI]
  2. Box, A.G., Petruzzello, S.J. (2020). Why do they do it? Differences in high-intensity exercise-affect between those with higher and lower intensity preference and tolerance. Psychology of Sport and Exercise, 47, 101521. [DOI]
  3. Bradley, C., Niven, A., & Phillips, S.M. (2019). Self-reported tolerance of the intensity of exercise influences affective responses to and intentions to engage with high-intensity interval exercise. Journal of Sports Sciences,  37(13), 1472-1480. [DOI]
  4. Jones, L., & Ekkekakis, P. (2019). Affect and prefrontal hemodynamics during exercise under immersive audiovisual stimulation. Journal of Sport and Health Science, 8(4), 325-338. [DOI]
  5. Flack, K., Pankey, C., Ufholz, K., Johnson, L., & Roemmich, J.N. (2019). Genetic variations in the dopamine reward system influence exercise reinforcement and tolerance for exercise intensity. Behavioural Brain Research, 375, 112148. [DOI]
  6. Jones, L., Hutchinson, J.C., & Mullin, E.M. (2018). In the zone: An exploration of personal characteristics underlying affective responses to heavy exercise. Journal of Sport and Exercise Psychology, 40(5), 249-258. [DOI]
  7. Flack, K.D., Johnson, L.A., & Roemmich, J.N. (2017). Aerobic and resistance exercise reinforcement and discomfort tolerance predict meeting activity guidelines. Physiology and Behavior, 170, 32-36. [DOI]
  8. Carlier, M., & Delevoye-Turrell, Y. (2017). Tolerance to exercise intensity modulates pleasure when exercising in music: The upsides of acoustic energy for High Tolerant individuals. PLoS ONE, 12(3), e0170383. [DOI]
  9. Tempest, G., & Parfitt, G. (2016). Self-reported tolerance influences prefrontal cortex hemodynamics and affective responses. Cognitive, Affective, and Behavioral Neuroscience, 16(1), 63-71. [DOI]
  10. Smith, A. E., Eston, R., Tempest, G. D., Norton, B., & Parfitt, G. (2015). Patterning of physiological and affective responses in older active adults during a maximal graded exercise test and self-selected exercise. European Journal of Applied Physiology115(9), 1855–1866. [DOI]
  11. Baiamonte, B. A., Kraemer, R. R., Chabreck, C. N., Reynolds, M. L., McCaleb, K. M., Shaheen, G. L., & Hollander, D. B. (2017). Exercise-induced hypoalgesia: Pain tolerance, preference and tolerance for exercise intensity, and physiological correlates following dynamic circuit resistance exercise. Journal of Sports Sciences35(18), 1–7. [DOI]
  12. Schneider, M. L., & Graham, D. J. (2009). Personality, physical fitness, and affective response to exercise among adolescents. Medicine and Science in Sports and Exercise41(4), 947–955. [DOI]