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Behavior, psychology and sociology

Acceptability of technology-based physical activity intervention profiles and their motivational factors in obesity care: a latent profile transition analysis

Abstract

Objectives

This study aimed to: (a) identify the acceptability profiles for three technology-based physical activity interventions (TbPAI) in obesity treatment (active video games, mobile applications, telehealth), (b) examine the issues of consistency or change in these profiles for the same individual across technologies, and (c) determine whether acceptability profiles are related to motivational factors.

Methods

Three hundred and twelve women (Mage = 30.7, SD = 7.1 years; MBMI = 34.5, SD = 7.8 kg/m²) using obesity services were recruited for this cross-sectional survey. They completed an online survey including sociodemographic data and measures related to physical activity: level, stage of change, motivation, and general causality orientations. The women read descriptions of the three technologies and rated their acceptability. We used a latent profile transition analysis (LPTA) approach.

Results

A 2-class model (high and low acceptability) best described the profiles for each technology. Intra-individual analysis revealed that the profiles exhibited both changes and stability across TbPAI. Women with high scores on impersonal orientation were more likely to be in the high acceptability telehealth profile, whereas those reporting high scores on control orientation were more likely to be in the high acceptability active video games profile. Women with high scores on control orientation and low scores on impersonal orientation were more likely to be in the high acceptability mobile applications profile.

Conclusions

Results showed that the causality orientations were factors related to the TbPAI acceptability profiles, suggesting that clinicians should consider these psychological characteristics in TbPAI counseling.

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Fig. 1

Data availability

Participants of this study did not agree to have their data shared publicly. Moreover, as the datasets contained health information, they are protected by the French laws. The data that support the findings of this study are available from the corresponding author, MH, upon reasonable request.

References

  1. 1.

    Boddu R, Wilson E, Snyder B, Wilson T. Optimizing weight loss outcomes for bariatric surgery patients: the role of physical activity. BMC Proc. 2012;6:O1.

  2. 2.

    Gourlan MJ, Trouilloud DO, Sarrazin PG. Interventions promoting physical activity among obese populations: a meta-analysis considering global effect, long-term maintenance, physical activity indicators and dose characteristics. Obes Rev. 2011;12:e633–45.

    CAS  PubMed  Google Scholar 

  3. 3.

    King WC, Bond DS. The importance of preoperative and postoperative physical activity counseling in bariatric surgery. Exerc Sport Sci Rev. 2013;41:26–35.

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. Lancet Glob Health. 2018;6:e1077–86.

    PubMed  Google Scholar 

  5. 5.

    Baruth M, Sharpe PA, Hutto B, Wilcox S, Warren TY. Patterns of sedentary behavior in overweight and obese women. Ethn Dis. 2013;23:336–42.

    PubMed  Google Scholar 

  6. 6.

    Hayotte M, Nègre V, Gray L, Sadoul J-L, d’Arripe-Longueville F. The transtheoretical model (TTM) to gain insight into young women’s long-term physical activity after bariatric surgery: a qualitative study. Obes Surg. 2020;30:595–602.

    PubMed  Google Scholar 

  7. 7.

    Zabatiero J, Hill K, Gucciardi DF, Hamdorf JM, Taylor SF, Hagger MS, et al. Beliefs, barriers and facilitators to physical activity in bariatric surgery candidates. Obes Surg. 2016;26:1097–109.

    PubMed  Google Scholar 

  8. 8.

    Bauer S, Goldschmidt AB. Introduction to the special issue on advancing assessment of, and interventions for, eating disorders via innovative uses of technology. Int J Eat Disord. 2019;52:1073–6.

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Gao Z, Lee JE. Emerging technology in promoting physical activity and health: challenges and opportunities. J Clin Med. 2019;8:1830.

    PubMed Central  Google Scholar 

  10. 10.

    Graham DJ, Hipp JA Emerging technologies to promote and evaluate physical activity: Cutting-edge research and future directions. Front Public Health. 2014;2. https://doi.org/10.3389/fpubh.2014.00066/abstract.

  11. 11.

    O’Reilly GA, Spruijt-Metz D. Current mHealth technologies for physical activity assessment and promotion. Am J Prevent Med. 2013;45:501–7.

    Google Scholar 

  12. 12.

    Michie S, Ashford S, Sniehotta FF, Dombrowski SU, Bishop A, French DP. A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: the CALO-RE taxonomy. Psychol Health. 2011;26:1479–98.

    PubMed  Google Scholar 

  13. 13.

    Middelweerd A, Mollee JS, van der Wal CN, Brug J, te Velde SJ. Apps to promote physical activity among adults: a review and content analysis. Int J Behav Nutr Phys Act. 2014;11:1–9.

    Google Scholar 

  14. 14.

    Mollee JS, Middelweerd A, Kurvers RL, Klein MCA. What technological features are used in smartphone apps that promote physical activity? A review and content analysis. Pers Ubiquit Comput. 2017;21:633–43.

    Google Scholar 

  15. 15.

    Petit A, Cambon L. Exploratory study of the implications of research on the use of smart connected devices for prevention: a scoping review. BMC Public Health. 2016;16:552.

  16. 16.

    Nikolaou CK, Lean MEJ. Mobile applications for obesity and weight management: current market characteristics. Int J Obes. 2017;41:200–2.

    CAS  Google Scholar 

  17. 17.

    Halbrook YJ, O’Donnell AT, Msetfi RM. When and how video games can be good: A review of the positive effects of video games on well-being. Perspect Psychol Sci. 2019;14:1096–104.

    PubMed  Google Scholar 

  18. 18.

    Zhou C, Occa A, Kim S, Morgan S. A meta-analysis of narrative game-based interventions for promoting healthy behaviors. J Health Commun. 2020;25:54–65.

    PubMed  Google Scholar 

  19. 19.

    Hinman RS, Lawford BJ, Bennell KL. Harnessing technology to deliver care by physical therapists for people with persistent joint pain: Telephone and video‐conferencing service models. J Appl Behav Res. 2019;24:e12150.

  20. 20.

    Cotie LM, Prince SA, Elliott CG, Ziss MC, McDonnell LA, Mullen KA, et al. The effectiveness of eHealth interventions on physical activity and measures of obesity among working-age women: a systematic review and meta-analysis. Obes Rev. 2018;19:1340–58.

    CAS  PubMed  Google Scholar 

  21. 21.

    Alexandre B, Reynaud E, Osiurak F, Navarro J. Acceptance and acceptability criteria: a literature review. Cogn Technol Work. 2018;20:165–77.

    Google Scholar 

  22. 22.

    Alkhwaldi M, Kamala M. Why do users accept innovative technologies? A critical review of technology acceptance models and theories. J Multidiscip. Eng Sci. 2017;4:7962–71.

    Google Scholar 

  23. 23.

    Chang A. UTAUT and UTAUT 2: a review and agenda for future research. J. Win. 2012;13:106–14.

    Google Scholar 

  24. 24.

    Venkatesh V, Thong JYL, Xu X. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. 2012;36:157–78.

    Google Scholar 

  25. 25.

    Agarwal R, Prasad J. Are individual differences germane to the acceptance of new information technologies? Decis Sci. 1999;30:361–91.

    Google Scholar 

  26. 26.

    Lee Y, Lee J, Hwang Y. Relating motivation to information and communication technology acceptance: Self-determination theory perspective. Comput Hum Behav. 2015;51:418–28.

    Google Scholar 

  27. 27.

    Nikou SA, Economides AA. Mobile-based assessment: Integrating acceptance and motivational factors into a combined model of self-determination theory and technology acceptance. Comput Hum Behav. 2017;68:83–95.

    Google Scholar 

  28. 28.

    Teixeira PJ, Carraça 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.

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Teixeira PJ, Carraça EV, Marques MM, Rutter H, Oppert J-M, De Bourdeaudhuij I, et al. Successful behavior change in obesity interventions in adults: a systematic review of self-regulation mediators. BMC Med. 2015;13:84.

  30. 30.

    Teixeira PJ, Marques MM. Health behavior change for obesity management. Obes Facts. 2017;10:666–73.

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Deci EL, Ryan RM. The ‘what’ and ‘why’ of goal pursuits: Human needs and the self-determination of behavior. Psychol Inq. 2000;11:227–68.

    Google Scholar 

  32. 32.

    Ryan RM, Deci EL. Self-determination theory: basic psychological needs in motivation, development, and wellness. New York: Guilford Press; 2017. 756 p.

  33. 33.

    Deci EL, Ryan RM. The general causality orientations scale: self-determination in personality. J Res Pers. 1985;19:109–34.

    Google Scholar 

  34. 34.

    Hagger MS, Hamilton K. General causality orientations in self-determination theory: meta-analysis and test of a process model. Eur J Pers. 2020;0:1–26.

    Google Scholar 

  35. 35.

    Schäfer L, Hübner C, Carus T, Herbig B, Seyfried F, Kaiser S, et al. Identifying prebariatric subtypes based on temperament traits, emotion dysregulation, and disinhibited eating: a latent profile analysis. Int. J Eat Disord. 2017;50:1172–82.

    PubMed  Google Scholar 

  36. 36.

    Sultson H, Akkermann K. Investigating phenotypes of emotional eating based on weight categories: A latent profile analysis. Int J Eat Disord. 2019;52:1024–34.

    PubMed  Google Scholar 

  37. 37.

    Emm-Collison LG, Sebire SJ, Salway R, Thompson JL, Jago R. Multidimensional motivation for exercise: A latent profile and transition analysis. Psychol Sport Exerc. 2020;47:101619.

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Martinent G, Nicolas M. A latent profile transition analysis of coping within competitive situations. Sport, Exercise, and Performance. Psychology. 2016;5:218–31.

    Google Scholar 

  39. 39.

    Gupta N, Hallman DM, Dumuid D, Vij A, Rasmussen CL, Jørgensen MB, et al. Movement behavior profiles and obesity: a latent profile analysis of 24-h time-use composition among Danish workers. Int J Obes. 2020;44:409–17.

    Google Scholar 

  40. 40.

    Hayotte M, Thérouanne P, Gray L, Corrion K, d’Arripe-Longueville F. The French eHealth Acceptability Scale using the unified theory of acceptance and use of technology 2 model: instrument validation study. J Med Internet Res. 2020;22:e16520.

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Boiché J, Gourlan M, Trouilloud D, Sarrazin P. Development and validation of the ‘Echelle de Motivation envers l’Activité Physique en contexte de Santé’: a motivation scale towards health-oriented physical activity in French. J Health Psychol. 2016.135910531667662.

  42. 42.

    Deci EL, Ryan RM. Intrinsic motivation and self-determination in human behavior. Springer Science & Business Media; 1985. 371 p.

  43. 43.

    Hodge K, Lonsdale C. Prosocial and antisocial behavior in sport: The role of coaching style, autonomous vs. controlled motivation, and moral disengagement. J Sport Exerc Psychol. 2011;33:527–47.

    PubMed  Google Scholar 

  44. 44.

    Lonsdale C, Hodge K, Rose E. Athlete burnout in elite sport: a self-determination perspective. J Sports Sci. 2009;27:785–95.

    PubMed  Google Scholar 

  45. 45.

    Taber KS. The use of Cronbach’s alpha when developing and reporting research instruments in science education. Res Sci Educ. 2018;48:1273–96.

    Google Scholar 

  46. 46.

    Vallerand RJ, Blais MR, Lacouture Y, Deci EL. L’échelle des orientations générales à la causalité: Validation canadienne française du general causality orientations scale. Can J Behav Sci. 1987;19:1–15.

    Google Scholar 

  47. 47.

    Rivière F, Widad FZ, Speyer E, Erpelding M-L, Escalon H, Vuillemin A. Reliability and validity of the French version of the global physical activity questionnaire. J Sport Health Sci. 2018;7:339–45.

    PubMed  Google Scholar 

  48. 48.

    Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54:1451–62.

    PubMed  Google Scholar 

  49. 49.

    Prochaska JO, DiClemente CC. Stages and processes of self-change of smoking: toward an integrative model of change. J Consult Clin Psychol. 1983;51:390–5.

    CAS  PubMed  Google Scholar 

  50. 50.

    Romain AJ, Bernard P, Attalin V, Gernigon C, Ninot G, Avignon A. Health-related quality of life and stages of behavioural change for exercise in overweight/obese individuals. Diabetes Metab. 2012;38:352–8.

    CAS  PubMed  Google Scholar 

  51. 51.

    Marcus BH, Lewis BA. Physical activity and the stages of motivational readiness for change model. Pres Counc Phys Fit Sports Res Dig. 2003;4:1–8.

    Google Scholar 

  52. 52.

    Schafer JL. Multiple imputation: a primer. Stat Methods Med Res. 1999;8:3–15.

    CAS  PubMed  Google Scholar 

  53. 53.

    Lanza ST, Bray BC, Collins LM. An introduction to latent class and latent transition analysis. In: Weiner I, editor. Handbook of Psychology, Second Edition. Hoboken, NJ: John Wiley & Sons, Inc.; 2012.

  54. 54.

    Martinent G, Gareau A, Lienhart N, Nicaise V, Guillet-Descas E. Emotion profiles and their motivational antecedents among adolescent athletes in intensive training settings. Psychol Sport Exerc. 2018;35:198–206.

    Google Scholar 

  55. 55.

    Martinent G, Nicolas M. Athletes’ affective profiles within competition situations: a two-wave study. Sport, exercise, and performance. Psychology. 2017;6:143–57.

    Google Scholar 

  56. 56.

    Muthén LK, Muthén BO. Mplus user’s guide. Eighth ed. Los Angeles, CA: Muthén & Muthén; 2017. 944 p.

  57. 57.

    González-García H, Martinent G. Perceived anger profiles in table tennis players: Relationship with burnout and coping. Psychol Sport Exerc. 2020;50:101743.

    Google Scholar 

  58. 58.

    Tabak M, Dekker-van Weering M, van Dijk H, Vollenbroek-Hutten M. Promoting daily physical activity by means of mobile gaming: A review of the state of the art. Games Health J. 2015;4:460–9.

    PubMed  Google Scholar 

  59. 59.

    Romain AJ, Chevance G, Caudroit J, Bernard P. The transtheoretical model: description, interests and application in the motivation to physical activity among population with overweight and obesity. Obésité. 2016;11:47–55.

    Google Scholar 

  60. 60.

    Berglind D, Willmer M, Tynelius P, Ghaderi A, Näslund E, Rasmussen F. Accelerometer-Measured Versus Self-Reported Physical Activity Levels and Sedentary Behavior in Women Before and 9 Months After Roux-en-Y Gastric Bypass. Obes Surg. 2016;26:1463–70.

    PubMed  Google Scholar 

  61. 61.

    Possmark S, Sellberg F, Willmer M, Tynelius P, Persson M, Berglind D. Accelerometer-measured versus self-reported physical activity levels in women before and up to 48 months after Roux-en-Y Gastric Bypass. BMC Surg. 2020;20:39.

    PubMed  PubMed Central  Google Scholar 

  62. 62.

    Lewis ZH, Swartz MC, Lyons EJ. What’s the point?: A review of reward systems implemented in gamification interventions. Games Health J. 2016;5:93–9.

    PubMed  Google Scholar 

  63. 63.

    Modave F, Bian J, Leavitt T, Bromwell J, Harris C III, Vincent H. Low quality of free coaching apps with respect to the american college of sports medicine guidelines: a review of current mobile apps. JMIR mHealth uHealth. 2015;3:e77.

    PubMed  PubMed Central  Google Scholar 

  64. 64.

    Kwan BM, Hooper AEC, Magnan RE, Bryan AD. A longitudinal diary study of the effects of causality orientations on exercise-related affect. Self Identity. 2011;10:363–74.

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

MH was supported by a Ph.D. grant from the Région Sud Provence-Alpes Côte d’Azur, France, and co-supported by the association “Azur Sport Santé”. This work was supported by the French government and managed by the “Agence Nationale de la Recherche” as part of the UCAJEDI Future Investments project, reference number ANR-15-IDEX-01. The authors express their sincere gratitude to all the volunteers who participated in this study. We sincerely thank Professor Jean-Louis Sadoul for his contributions to the study conception and to Valentine Filleul and Raphaëlle Ladune for their valuable assistance in the data collection. We also express our gratitude to the following for their aid in contacting the volunteers: the Specialized Center for Obesity of East Provence-Alpes Côte d’Azur and the Nice University Hospital Center; the Specialized Center for Obesity of West Provence-Alpes Côte d’Azur and the Marseille University Hospital Center; the Nutrition Center of Pegomas; the Cérès Nutrition Center of Nice; the Antibes Hospital Center; Dr. Corinne Godenir, nutrition physician in Valbonne; the Val Prévert Nutrition Center in Mimet; the Korian les Palmiers Nutrition Center in Ceyreste; the Physical Activity in Obesity Group of Fettle Studio in Nice; the Physical Activity in Obesity Association (APAO-P) in Le Pradet; Les Oiseaux Nutrition Center in Sanary-sur-Mer; Marie-Christine Sabinen, dietitian in Saint Laurent du Var; and Dr. Catherine Sosset, endocrinologist in Grasse.

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Correspondence to Meggy Hayotte.

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Hayotte, M., Martinent, G., Nègre, V. et al. Acceptability of technology-based physical activity intervention profiles and their motivational factors in obesity care: a latent profile transition analysis. Int J Obes 45, 1488–1498 (2021). https://doi.org/10.1038/s41366-021-00813-6

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