Motivational profiles and change in physical activity during a weight loss intervention: a secondary data analysis

Danielle M Ostendorf, Sarah J Schmiege, David E Conroy, Suzanne Phelan, Angela D Bryan, Victoria A Catenacci, Danielle M Ostendorf, Sarah J Schmiege, David E Conroy, Suzanne Phelan, Angela D Bryan, Victoria A Catenacci

Abstract

Background: High levels of moderate-to-vigorous intensity physical activity (MVPA) are strongly associated with sustained weight loss, however the majority of adults are unsuccessful in maintaining high levels of MVPA long-term. Our goal was to identify profiles based on exercise motives, and examine the association between motivational profile and longitudinal changes in MVPA during a weight loss intervention.

Methods: Adults with overweight or obesity (n = 169, mean ± SE; age 39 ± 0.7 years, BMI 34.4 ± 0.3 kg/m2, 83% female) underwent an 18-month behavioral weight loss program, including 6 months of supervised exercise, followed by 6 months of unsupervised exercise. Participants self-reported behavioral regulations for exercise at baseline (BREQ-2). Latent profile analysis identified subgroups from external, introjected, identified, and intrinsic regulations measured at baseline. Mean differences in device-measured total MVPA were compared across motivational profiles at baseline, after 6 months of supervised exercise and after a subsequent 6 months of unsupervised exercise.

Results: Three motivational profiles emerged: high autonomous (high identified and intrinsic, low external regulations; n = 52), high combined (high scores on all exercise regulations; n = 25), and moderate combined (moderate scores on all exercise regulations; n = 92). Motivational profile was not associated with baseline level of MVPA or the increase in MVPA over the 6-month supervised exercise intervention (high autonomous: 21 ± 6 min/d; high combined: 20 ± 9 min/d; moderate combined: 33 ± 5 min/d; overall P > 0.05). However, during the transition from supervised to unsupervised exercise, MVPA decreased, on average, within all three profiles, but the high autonomous profile demonstrated the least attenuation in MVPA (- 3 ± 6 min/d) compared to the moderate combined profile (- 20 ± 5 min/d; P = 0.043).

Conclusions: Results were in alignment with the Self-Determination Theory. Adults motivated by autonomous reasons (value benefits of exercise, intrinsic enjoyment) may be more likely to sustain increases in MVPA once support is removed, whereas participants with moderate-to-high scores on all types of exercise regulations may need additional long-term support in order to sustain initial increases in MVPA.

Clinical trial registration: NCT01985568. Registered 24 October 2013.

Keywords: Exercise; Latent profile analysis; Motivation; Obesity; Weight loss.

Conflict of interest statement

The results of this study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. DC reports consulting income from Gelesis, Inc., a company that has developed a weight loss device. SP has a grant from WW International unrelated to this work.

© 2021. The Author(s).

Figures

Fig. 1
Fig. 1
Aligned Assessment Period by Randomized Group. To ensure equal exposure to PA in analyses, PA was captured during two time points: 1) after 6 months of supervised exercise (month 6 for standard, month 12 for sequential), and 2) after 6 months of subsequent unsupervised exercise (month 12 for standard, month 18 for sequential); PA: physical activity
Fig. 2
Fig. 2
Consort Diagram. PA: physical activity
Fig. 3
Fig. 3
Mean Exercise Regulation Score across Baseline Motivational Profiles. Exercise regulation score (mean) across the four motivational profiles; Exercise regulations included external category (range 0–3; 4 categories include: 0 (score = 0), 1 (score > 0 but ≤ 0.5), 2 (score > 0.5 but ≤ 1.25), and 3 (score > 1.25), introjected (range 0–4), identified (range 0–4), and intrinsic regulations (range 0–4); n = 92 for Moderate Combined; n = 52 for High Autonomous; n = 25 for High Combined
Fig. 4
Fig. 4
A-C Change in Mean Total MVPA over time across Baseline Motivational Profiles. Mean difference ( ± SE) in change in total MVPA (min/d) across profiles tested with Wald test and subsequent between group comparisons; * indicates significant difference (P < 0.05) from moderate combined profile; MVPA: minutes of moderate-to-vigorous physical activity. a For Panel A sample sizes are as follows: n = 64 for Moderate Combined; n = 36 for High Autonomous; n = 13 for High Combined. b For Panel B sample sizes are as follows: n = 57 for Moderate Combined; n = 30 for High Autonomous; n = 11 for High Combined. c For Panel C sample sizes are as follows: n = 58 for Moderate Combined; n = 30 for High Autonomous; n = 12 for High Combined

References

    1. Donnelly JE, Blair SN, Jakicic JM, Manore MM, Rankin JW, Smith BK. American College of Sports Medicine Position Stand. Appropriate physical activity intervention strategies for weight loss and prevention of weight regain for adults. Med Sci Sports Exerc. 2009;41(2):459–471.
    1. Saris WH, Blair SN, van Baak MA, Eaton SB, Davies PS, Di Pietro L, et al. How much physical activity is enough to prevent unhealthy weight gain? Outcome of the IASO 1st stock conference and consensus statement. Obes Rev. 2003;4(2):101–114.
    1. U.S. Department of Health and Human Services. Physical activity guidelines for Americans, 2nd edition. Washington, DC: U.S. Department of Health and Human Services; 2018.
    1. Jakicic JM, Marcus BH, Lang W, Janney C. Effect of exercise on 24-month weight loss maintenance in overweight women. Arch Intern Med. 2008;168(14):1550–1559.
    1. Tate DF, Jeffery RW, Sherwood NE, Wing RR. Long-term weight losses associated with prescription of higher physical activity goals. Are higher levels of physical activity protective against weight regain? Am J Clin Nutr. 2007;85(4):954–959.
    1. Garber CE, Blissmer B, Deschenes MR, Franklin BA, Lamonte MJ, Lee IM, et al. American College of Sports Medicine position stand. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med Sci Sports Exerc. 2011;43(7):1334–1359.
    1. Relapse prevention: maintenance strategies in the treatment of addictive behaviors, 2nd edition. Marlatt GA, Donovan DM, editors. New York: The Guilford Press; 2005. .
    1. Dishman RK, Sallis JF, Orenstein DR. The determinants of physical activity and exercise. Public Health Rep. 1985;100(2):158–171.
    1. Varkevisser RDM, van Stralen MM, Kroeze W, Ket JCF, Steenhuis IHM. Determinants of weight loss maintenance: a systematic review. Obes Rev. 2019;20(2):171–211.
    1. Teixeira PJ, Carraca EV, Markland D, Silva MN, Ryan RM. Exercise, physical activity, and self-determination theory: a systematic review. Int J Behav Nutr Phys Act. 2012;9:78.
    1. Deci EL, Ryan RM. Intrinsic motivation and self-determination in human behavior. New York: Plenum; 1985. p. 371.
    1. Deci EL, Ryan RM. The “what” and “why” of goal pursuits: human needs and the self-determination of behavior. Psychol Inq. 2000;11(4):227–268.
    1. Standage M, Sebire SJ, Loney T. Does exercise motivation predict engagement in objectively assessed bouts of moderate-intensity exercise? A self-determination theory perspective. J Sport Exerc Psychol. 2008;30(4):337–352.
    1. Silva MN, Vieira PN, Coutinho SR, Minderico CS, Matos MG, Sardinha LB, et al. Using self-determination theory to promote physical activity and weight control: a randomized controlled trial in women. J Behav Med. 2010;33(2):110–122.
    1. Guerin E, Fortier MS. Situational motivation and perceived intensity: their interaction in predicting changes in positive affect from physical activity. J Obes. 2012;2012:269320.
    1. Wasserkampf A, Silva MN, Santos IC, Carraca EV, Meis JJ, Kremers SP, et al. Short- and long-term theory-based predictors of physical activity in women who participated in a weight-management program. Health Educ Res. 2014;29(6):941–952.
    1. Covington MV, Mueller KJ. Intrinsic versus extrinsic motivation: an approach/avoidance reformulation. Educ Psychol Rev. 2001;13(2):157–176.
    1. Masyn KE. Latent class analysis and finite mixture modeling. The Oxford handbook of quantitative methods in psychology. New York: Oxford University Press; 2013.
    1. Friederichs SA, Bolman C, Oenema A, Lechner L. Profiling physical activity motivation based on self-determination theory: a cluster analysis approach. BMC Psychol. 2015;3(1):1.
    1. Gourlan M, Trouilloud D, Boiche J. Motivational profiles for physical activity practice in adults with type 2 diabetes: a self-determination theory perspective. Behav Med. 2016;42(4):227–237.
    1. Guerin E, Fortier M. Motivational profiles for physical activity: cluster analysis and links with enjoyment. PHEnex J. 2012;4(2):1–21.
    1. Matsumoto H, Takenaka K. Motivational profiles and stage of exercise behavior change. Int J Sport Health Sci. 2004;2:89–96.
    1. Stephan Y, Boiche J, Le Scanff C. Motivation and physical activity behaviors among older women: a self-determination perspective. Psychol Women Quart. 2010;34(3):339–348.
    1. Lindwall M, Ivarsson A, Weman-Josefsson K, Jonsson L, Ntoumanis N, Patrick H, et al. Stirring the motivational soup: within-person latent profiles of motivation in exercise. Int J Behav Nutr Phys Act. 2017;14(1):4.
    1. Friel CP, Garber CE. An examination of the relationship between motivation, physical activity, and wearable activity monitor use. J Sport Exerc Psychol. 2020;42(2):1–8.
    1. Castonguay A, Miquelon P. Motivational profiles, accelerometer-derived physical activity, and acute diabetes-related symptoms in adults with type 2 diabetes. BMC Public Health. 2018;18(1):469.
    1. Sallis JF, Saelens BE. Assessment of physical activity by self-report: status, limitations, and future directions. Res Q Exerc Sport. 2000;71(2 Suppl):S1–14.
    1. Catenacci VA, Ostendorf DM, Pan Z, Bing K, Wayland LT, Seyoum E, et al. The impact of timing of exercise initiation on weight loss: an 18-month randomized clinical trial. Obesity. 2019;27(11):1828–38.
    1. Jensen MD, Ryan DH, Apovian CM, Ard JD, Comuzzie AG, Donato KA, et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Obesity Society. J Am Coll Cardiol. 2014;63(25 Pt B):2985–3023.
    1. Pescatello LS, Arena R, Riebe D, PD. T, editors. ACSM's guidelines for exercise testing and prescription. 9. Philadelphia: Wolters Kluwer/Lippincott Williams & Wilkins Health; 2014.
    1. Services. USDoHaH . In: 2008 physical activity guidelines for Americans: be active, health and happy! Services HaH, editor. Washington, DC: U.S. Dept. of Heath and Human Services, National Institutes of Health; 2008. p. 61.
    1. Johannsen DL, Calabro MA, Stewart J, Franke W, Rood JC, Welk GJ. Accuracy of armband monitors for measuring daily energy expenditure in healthy adults. Med Sci Sports Exerc. 2010;42(11):2134–2140.
    1. Brazeau AS, Karelis AD, Mignault D, Lacroix MJ, Prud'homme D, Rabasa-Lhoret R. Test-retest reliability of a portable monitor to assess energy expenditure. Appl Physiol Nutr Metab. 2011;36(3):339–343.
    1. St-Onge M, Mignault D, Allison DB, Rabasa-Lhoret R. Evaluation of a portable device to measure daily energy expenditure in free-living adults. Am J Clin Nutr. 2007;85(3):742–749.
    1. Matthews CE, Hagstromer M, Pober DM, Bowles HR. Best practices for using physical activity monitors in population-based research. Med Sci Sports Exerc. 2012;44(1 Suppl 1):S68–S76.
    1. Markland D, Tobin V. A modification of the behavioral regulation in exercise questionnaire to include an assessment of amotivation. J Sport Exercise Psy. 2004;26:191–196.
    1. Ingledew DK, Markland D. The role of motives in exercise participation. Psychol Health. 2008;23(7):807–828.
    1. Mullan E, Markland D, Ingledew DK. A graded conceptualisation of self-determination in the regulation of exercise behavior: development of a measure using confirmatory factor analytic procedures. Pers Indiv Differ. 1997;23(5):745–752.
    1. Wilson PM, Rodgers WM, Fraser SN. Examining the psychometric properties of teh behavioral regulation in exercise questionnaire. Meas Phys Educ Exerc Sci. 2002;6(1):1–21.
    1. Meyer JP, Morin AJS. A person-centered approach to commitment research: theory, research, and methodology. J Organ Behav. 2016;37(4):584–612.
    1. Henson JM, Reise SP, Kim KH. Detecting mixtures from structural model differences using latent variable mixture modeling: a comparison of relative model fit statistics. Struct Equ Model Multidiscip J. 2007;14:202–226.
    1. Wang C-P, Brown CH, Bandeen-Roche K. Residual diagnostics for growth mixture models: examining the impact of a preventive intervention on multiple trajectories of aggressive behavior. J Am Stat Assoc. 2005;100(3):1054–1076.
    1. Asparouhov T, Muthen B. Auxiliary variables in mixture modeling: using the BCH method in Mplus to estimate a distal outcome model and an arbitrary secondary model. Mplus web notes: no 21. 2014.
    1. Jakicic JM, Winters C, Lang W, Wing RR. Effects of intermittent exercise and use of home exercise equipment on adherence, weight loss, and fitness in overweight women: a randomized trial. Jama. 1999;282(16):1554–1560.
    1. McEwan D, Rhodes RE, Beauchamp MR. What happens when the party is over?: sustaining physical activity behaviors after intervention cessation. Behav Med. 2020:1–9. .
    1. Teixera P, Marques MM, Silva MN, Brunet J, Duda J, Haerens L, et al. A classification of motivation and behavior change techniques used in self-determination theory-based interventions in health contexts. Motiv Sci. 2020;6(4):438–55.
    1. Rhee H, Belyea MJ, Elward KS. Patterns of asthma control perception in adolescents: associations with psychosocial functioning. J Asthma. 2008;45(7):600–606.
    1. Heino MTJ, Knittle K, Noone C, Hasselman F, Hankonen N. Studying behaviour change mechanisms under complexity. Behav Sci. 2021;11(5):77.
    1. Chevance G, Perski O, Hekler EB. Innovative methods for observing and changing complex health behaviors: four propositions. Transl Behav Med. 2020;11(2):676–685.
    1. Dowd KP, Szeklicki R, Minetto MA, Murphy MH, Polito A, Ghigo E, et al. A systematic literature review of reviews on techniques for physical activity measurement in adults: a DEDIPAC study. Int J Behav Nutr Phys Act. 2018;15(1):15.

Source: PubMed

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