In the field of artificial intelligence-based fitness apps, the effective integration of behavior change techniques (BCTs) is critical for promoting physical activity and improving health outcomes. However, the specific BCTs employed by apps and their impact on user engagement and behavior change are not explored sufficiently. This study investigates the Freeletics fitness app through a mixed-methods approach to evaluate the use of BCTs. In the quantitative analysis, fifteen unique BCTs were identified based on the Behavior Change Technique Taxonomy (V1). In the qualitative analysis, user reviews (n=400) were examined to understand perspectives on the app’s effectiveness in promoting behavior change. Goal setting, action planning, self-monitoring of behavior, and social support were among the most prevalent BCTs identified in the Freeletics app, and their effectiveness in enhancing user engagement and promoting behavior change was also highlighted by user reviews. Among the areas of improvement identified in the study were the need for simplifying personalization options and addressing user concerns regarding the specificity of feedback. The study underscores the importance of integrating BCTs effectively within AI-based fitness apps to drive user engagement and facilitate behavior change. It contributes valuable insights into the design and implementation of BCTs in fitness apps and offers recommendations for developers, emphasizing the significance of goal setting, feedback mechanisms, self-monitoring, and social support. By understanding the impact of specific BCTs on user behavior and addressing user concerns, developers can create more effective fitness apps, ultimately promoting healthier lifestyles and positive behavior change.
AidmanE.FogartyG. J.CramptonJ.BondJ.TaylorP.HeathcoteA.ZaichkowskyL. (2022). An app-enhanced cognitive fitness training program for athletes: The rationale and validation protocol. Frontiers in Psychology, 13, Article 957551.
BaileyR. R. (2019). Goal setting and action planning for health behavior change. American Journal of Lifestyle Medicine, 13(6), 615–618.
4.
BanduraA. (1997). Self-efficacy: The exercise of control. W.H. Freeman and Company.
5.
BelcherB. R.ZinkJ.AzadA.CampbellC. E.ChakravarttiS. P.HertingM. M. (2021). The roles of physical activity, exercise, and fitness in promoting Resilience during adolescence: Effects on mental well-being and brain development. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6(2), 225–237.
6.
BeleigoliA.AndradeA. Q.DinizM. D. F.RibeiroA. L. (2020). Personalized web-based weight loss behavior change program with and without dietitian online coaching for adults with overweight and obesity: Randomized controlled trial. Journal of Medical Internet Research, 22(11), e17494.
7.
BelmonL. S.MiddelweerdA.Te VeldeS. J.BrugJ. (2015). Dutch young adults ratings of behavior change techniques applied in mobile phone apps to promote physical activity: A cross-sectional survey. JMIR mHealth and uHealth, 3(4), e4383.
8.
BenitezT. J.DunsigerS.MarquezB.LarsenB.PekmeziD.MarcusB. H. (2022). Increases in muscle-strengthening activities among Latinas in Seamos Saludables. Health Education & Behavior, 49(3), 446–454.
9.
BertF.GiacomettiM.GualanoM. R.SiliquiniR. (2014). Smartphones and health promotion: A review of the evidence. Journal of Medical Systems, 38, 1–11.
10.
BleckJ.DeBateR.GarciaJ.GattoA. (2023). A pilot evaluation of a university health and wellness coaching program for college students. Health Education & Behavior, 50, 613–621.
Bovend’EerdtT. J.BotellR. E.WadeD. T. (2009). Writing SMART rehabilitation goals and achieving goal attainment scaling: A practical guide. Clinical Rehabilitation, 23(4), 352–361.
13.
BrackenM. L.WaiteB. M. (2020). Self-efficacy and nutrition-related goal achievement of MyFitnessPal users. Health Education & Behavior, 47(5), 677–681.
14.
BraunV.ClarkeV. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
15.
ButrynM. L.GodfreyK. M.MartinelliM. K.RobertsS. R.FormanE. M.ZhangF. (2020). Digital self-monitoring: Does adherence or association with outcomes differ by self-monitoring target?Obesity Science & Practice, 6(2), 126–133.
16.
ConroyD. E.YangC.-H.MaherJ. P. (2014). Behavior change techniques in top-ranked mobile apps for physical activity. American Journal of Preventive Medicine, 46(6), 649–652.
17.
CreswellJ. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches. Sage.
18.
CreswellJ. W. (2014). A concise introduction to mixed methods research. Sage.
19.
DeciE. L.RyanR. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268.
20.
DenzinN. K.LincolnY. S. (2011). The Sage handbook of qualitative research. Sage.
21.
DeterdingS. (2012). Gamification: Designing for motivation. Interactions, 19(4), 14–17.
22.
DireitoA.DaleL. P.ShieldsE.DobsonR.WhittakerR.MaddisonR. (2014). Do physical activity and dietary smartphone applications incorporate evidence-based behaviour change techniques?BMC Public Health, 14, Article 646. https://doi.org/10.1186/1471-2458-14-646
23.
DireitoA.WalshD.HinbarjiM.AlbatalR.TooleyM.WhittakerR.MaddisonR. (2018). Using the intervention mapping and behavioral intervention technology frameworks: Development of an mHealth intervention for physical activity and sedentary behavior change. Health Education & Behavior, 45(3), 331–348. https://doi.org/10.1177/1090198117742438
24.
FarrokhiA.FarahbakhshR.RezazadehJ.MinervaR. (2021). Application of internet of things and artificial intelligence for smart fitness: A survey. Computer Networks, 189, 107859.
FranzeC.FunkL.StrasserL.DiefenbachS. (2015). The power of Freeletics: A sports mobile application through the lens of psychological theory and the user perspective. i-com, 14(3), 244–250.
27.
GoldsteinC. M.ThomasJ. G.WingR. R.BondD. S. (2017). Successful weight loss maintainers use health-tracking smartphone applications more than a nationally representative sample: Comparison of the National Weight Control Registry to Pew Tracking for Health. Obesity Science & Practice, 3(2), 117–126.
28.
HarringtonC. N.WilcoxL.ConnellyK.RogersW.SanfordJ. (2018). Designing health and fitness apps with older adults: Examining the value of experience-based co-design. In PervasiveHealth ‘18: Proceedings of the 12th EAI international conference on pervasive computing technologies for healthcare (pp. 15–24). https://doi.org/10.1145/3240925.3240929
29.
HaugS.MeyerC.SchorrG.BauerS.JohnU. (2009). Continuous individual support of smoking cessation using text messaging: A pilot experimental study. Nicotine & Tobacco Research, 11(8), 915–923.
30.
HerbertC.MeixnerF.WiebkingC.GilgV. (2020). Regular physical activity, short-term exercise, mental health, and well-being among university students: The results of an online and a laboratory study. Frontiers in Psychology, 11, Article 509.
31.
HerrmannL. K.KimJ. (2017). The fitness of apps: A theory-based examination of mobile fitness app usage over 5 months. Mhealth, 3(2), 1–9. https://doi.org/10.21037/mhealth.2017.01.03
32.
HuangG.ZhouE. (2018). Time to work out! Examining the behavior change techniques and relevant theoretical mechanisms that predict the popularity of fitness mobile apps with Chinese-language user interfaces. Health Communication, 34(12), 1502–1512.
33.
HystadP.CarpianoR. M. (2012). Sense of community-belonging and health-behaviour change in Canada. Journal of Epidemiology and Community Health, 66(3), 277–283.
34.
JamesD. C.HarvilleC. (2017). Barriers and motivators to participating in mHealth research among African American men. American Journal of Men’s Health, 11(6), 1605–1613.
35.
KonradA.BellottiV.CrenshawN.TuckerS.NelsonL.DuH.PirolliP.WhittakerS. (2015). Finding the adaptive sweet spot: Balancing compliance and achievement in automated stress reduction. In CHI ‘15: Proceedings of the 33rd annual ACM conference on human factors in computing systems (pp. 3829–3838). https://doi.org/10.1145/2702123.2702512
36.
LeeA. M.ChavezS.BianJ.ThompsonL. A.GurkaM. J.WilliamsonV. G.ModaveF. (2019). Efficacy and effectiveness of mobile health technologies for facilitating physical activity in adolescents: Scoping review. JMIR mHealth and uHealth, 7(2), e11847. https://doi.org/10.2196/11847
37.
LeeH. E.ChoJ. (2017). What motivates users to continue using diet and fitness apps? Application of the uses and gratifications approach. Health Communication, 32(12), 1445–1453.
38.
LockeE. A.LathamG. P. (2002). Building a practically useful theory of goal setting and task motivation. A 35-year odyssey. The American Psychologist, 57(9), 705–717. https://doi.org/10.1037/0003-066X.57.9.705
39.
MichieS.JohnstonM.CareyR. (2020). Behavior change techniques. In GellmanM. D. (Ed.), Encyclopedia of behavioral medicine (pp. 206–213). Springer.
40.
MichieS.RichardsonM.JohnstonM.AbrahamC.FrancisJ.HardemanW.EcclesM. P.CaneJ.WoodC. E. (2013). The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: Building an international consensus for the reporting of behavior change interventions. Annals of Behavioral Medicine, 46(1), 81–95.
41.
MiddelweerdA.MolleeJ. S.van der WalC. N.BrugJ.Te VeldeS. J. (2014). Apps to promote physical activity among adults: A review and content analysis. The International Journal of Behavioral Nutrition and Physical Activity, 11, 97. https://doi.org/10.1186/s12966-014-0097-9
42.
MokminN. A. M. (2020). The effectiveness of a personalized virtual fitness trainer in teaching physical education by applying the artificial intelligent algorithm. International Journal of Human Movement and Sports Sciences, 8(5), 258–264.
43.
MokminN. A. M.JamiatN. (2021). The effectiveness of a virtual fitness trainer app in motivating and engaging students for fitness activity by applying motor learning theory. Education and Information Technologies, 26(2), 1847–1864. https://doi.org/10.1007/s10639-020-10337-7
44.
MolinaM. D.MyrickJ. G. (2021). The “how” and “why” of fitness app use: Investigating user motivations to gain insights into the nexus of technology and fitness. Sport in Society, 24(7), 1233–1248.
45.
Monteiro-GuerraF.Rivera-RomeroO.Fernandez-LuqueL.CaulfieldB. (2019). Personalization in real-time physical activity coaching using mobile applications: A scoping review. IEEE Journal of Biomedical and Health Informatics, 24(6), 1738–1751.
46.
Muntaner-MasA.Martinez-NicolasA.LavieC. J.BlairS. N.RossR.ArenaR.OrtegaF. B. (2019). A systematic review of fitness apps and their potential clinical and sports utility for objective and remote assessment of cardiorespiratory fitness. Sports Medicine, 49, 587–600.
47.
ProchaskaJ. O.DiClementeC. C. (1983). Stages and processes of self-change of smoking: Toward an integrative model of change. Journal of Consulting and Clinical Psychology, 51(3), 390–395.
48.
ReadingJ. M.BuhrK. J.StuckeyH. L. (2019). Social experiences of adults using online support forums to lose weight: A qualitative content analysis. Health Education & Behavior, 46(Suppl. 2), 129S–133S.
49.
RileyW. T.OhA.AklinW. M.Wolff-HughesD. L. (2019). National Institutes of Health support of digital health behavior research. Health Education & Behavior, 46(Suppl. 2), 12S–19S.
50.
SaboiaI. F.AlmeidaA. M.PernencarC.VelosoA. I. (2018). Gamification strategies in weight control applications, where “losing (weight) is winning.”2018 IEEE 6th international conference on serious games and applications for health (SeGAH)Vienna, Austria, 2018, pp. 1–8. https://doi.org/10.1109/SeGAH.2018.8401375
51.
SardiL.IdriA.Fernández-AlemánJ. L. (2017). A systematic review of gamification in e-Health. Journal of Biomedical Informatics, 71, 31–48.
52.
SchneiderH.MoserK.ButzA.AltF. (2016). Understanding the mechanics of persuasive system design: A mixed-method theory-driven analysis of Freeletics. In CHI ‘16: Proceedings of the 2016 CHI conference on human factors in computing systems (pp. 309–320). https://doi.org/10.1145/2858036.2858290
53.
ShiddiqA. N.KristiyantoA.DoewesM. (2019). Freeletics as sports activities community recreation (phenomenological study of community groups that carry out recreational sports in the city of Yogyakarta). Journal of Education, Health and Sport, 9(4), 468–474.
54.
SkinnerB. F. (1965). Science and human behavior (Issue 92904). Simon & Schuster.
55.
TrabocoL.PandianH.NikiphorouE.GuptaL. (2022). Designing infographics: Visual representations for enhancing education, communication, and scientific research. Journal of Korean Medical Science, 37(27), e214.
56.
VenkatachalamP.RayS. (2022). How do context-aware artificial intelligence algorithms used in fitness recommender systems? A literature review and research agenda. International Journal of Information Management Data Insights, 2(2), 100139.
57.
ZhaoJ.FreemanB.LiM. (2016). Can mobile phone apps influence people’s health behavior change? An evidence review. Journal of Medical Internet Research, 18(11), e287.
58.
ZhengE. L. (2021). Interpreting fitness: Self-tracking with fitness apps through a postphenomenology lens. AI & Society, 1–12. https://doi.org/10.1007/s00146-021-01146-8
59.
ZhuJ.DallalD. H.GrayR. C.VillarealeJ.OntañónS.FormanE. M.ArigoD. (2021). Personalization paradox in behavior change apps: Lessons from a social comparison-based personalized app for physical activity. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1–21.
60.
ZimmermanK. N.TorelliJ. N.ChowJ. C. (2022). Planning positive reinforcement cycles in behavior intervention plans. Behavior Analysis in Practice, 15(3), 924–937. https://doi.org/10.1007/s40617-021-00663-8
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
