The impact of healthy lifestyle apps' quality on satisfaction and perceived impact using PLS SEM

Authors

DOI:

https://doi.org/10.14267/VEZTUD.2025.05.05

Keywords:

m-health, LS app, uMARS scale, quality, satisfaction, perceived impact

Abstract

Lifestyle and wellbeing (LS) apps represent a popular form of m-health apps that facilitate the maintenance and improvement of health by encouraging active lifestyles. This study aims to adapt the uMARS scale, developed for evaluating the quality of m-health applications, and to investigate the relationship between quality, satisfaction, and behaviour factors in LS applications. Data collection was conducted using a survey in autumn 2023, with a sample size of 157 respondents. The PLS-SEM method was employed to test the hypotheses. The results indicate that four qualitative dimensions of LS apps have a direct impact on overall satisfaction. The qualitative dimensions of LS apps, including functionality, information content and aesthetics, directly impact users’ perceived health-related knowledge, attitudes and behaviour. Furthermore, the quality dimensions indirectly influence users’ perceived health-related factors through satisfaction.

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Author Biographies

  • Ida Ercsey, University of Győr

    associate professor

  • Veronika Keller, University of Győr

    associate professor

References

Agher, D., Sedki, K., Despres, S., Albinet, J., Jaulent, C.M., & Tsopra, R. (2022). Encouraging Behavior Changes and Preventing Cardiovascular Diseases Using the Prevent Connect Mobile Health App: Conception and Evaluation of App Quality. Journal of Medical Internet Research, 24(1), e25384. https://doi.org/10.2196/25384

Akter, Sh., D’Ambra, J., Ray, P., & Hani, U. (2013). Modelling the impact of mHealth service quality on satisfaction, continuance and quality of life. Behaviour & Information Technology, 32, 1225-1241. https://doi.org/10.1080/0144929X.2012.745606.

Andaleeb, S.S. (2001). Service Quality Perceptions and Patient Satisfaction: A Study of Hospitals in a Developing Country. Social Science and Medicine, 52(9), 1359-1370. https://doi.org/10.1016/S0277-9536(00)00235-5

Bettinghaus, E.P. (1986). Health promotion and the knowledge- attitude-behavior continuum. Preventive Medicine, 15(5), 475–491. https://doi.org/10.1016/0091-7435(86)90025-3

Bhattacherjee, A. (2001). Understanding information systems continuance. An expectation–confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921

Buckingham, S.A, Williams, A.J, Morrissey, K, Price, L, & Harrison. J. (2019). Mobile health interventions to promote physical activity and reduce sedentary behaviour in the workplace: a systematic review. Digit Health, 5, 2055207619839883. https://doi.org/10.1177/2055207619839883

Calik, G., Kartal, B. B., Stoyanov, S., Gravas, S., Othman, L., de la Rosette, J., Albayrak, S., & Laguna, P. (2022). Turkish validation of the user version of the mobile application rating scale. Turkish Journal of Urology, 48(3), 236-242. https://doi.org/10.5152/tud.2022.21324

Chasiotis, G., Stoyanov, S.R., Karatzas, A., & Gravas S. (2023). Greek validation of the user version of the Mobile Application Rating Scale (uMARS). The Journal of International Medical Research, 51(3), 3000605231161213. https://doi.org/10.1177/03000605231161213

Choi, K.S., Cho, W.H., Lee, S., Lee, H. K., & Kim, C. (2004). The relationships among quality, value, satisfaction and behavioural intention in health care provider choice: a South Korean study. Journal of Business Research, 57(8), 913–21. https://doi.org/10.1016/S0148-2963(02)00293-X

Dagger, T.S., Sweeney, J.C., & Johnson, L.W. (2007). A hierarchical model of health service quality: scale development and investigation of an integrated model. Journal of Service Research, 10(2), 123–142. https://doi.org/10.1177/1094670507309594

Dagger, T.S., & Sweeney, J.C. (2006). The effect of service evaluations on behavioural intentions and quality of life. Journal of Service Research, 9(1), 3–18. https://doi.org/10.1177/1094670506289528

Davis, A., & Ellis, R. (2019). A quasi-experimental investigation of college students’ ratings of two physical activity mobile apps with varied behavior change technique quantity. Digit Health, 27(5), 2055207619891347. https://doi.org/10.1177/2055207619891347

Dijkstra, K.T., & Henseler, J. (2015). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics & Data Analysis, 81, 10–23. https://doi.org/10.1016/j.csda.2014.07.008

Direito, A., Carraça, E., Rawstorn, J., Whittaker, R., & Maddison, R. (2017). mHealth technologies to influence physical activity and sedentary behaviors: behavior change techniques, systematic review and metaanalysis of randomized controlled trials. Annals of Behavioral Medicine: a publication of the Society of Behavioral Medicine, 51(2), 226–239. https://doi.org/10.1007/s12160-016-9846-0

Early, J., Gonzalez, C., Gordon-Dseagu, V., & Robles-Calderon L. (2019). Use of mobile health (mhealth) technologies and interventions among community health workers globally: A scoping review. Health Promotion Practice, 20(6), 805–817. https://doi.org/10.1177/1524839919855391

Ercsey, I. & Keller, V. (2023). Az életstílus applikációk használatának megítélése egy kísérlet tapasztalatai alapján. Marketing & Menedzsment, 57(Különszám EMOK 1), 23–32. https://doi.org/10.15170/MM.2023.57

Fedele, D.A., Cushing, C.C., Fritz, A., Amaro, C.M., & Ortega, A. (2017). Mobile health interventions for improving health outcomes in youth: a meta-analysis. JAMA Pediatrics, 171(5), 461–69. https://doi.org/10.1001/jamapediatrics.2017.0042

Firth, J., Torous, J., Nicholas, J., Carney, R., Rosenbaum, S., & Sarris J. (2017). Can smartphone mental health interventions reduce symptoms of anxiety? A meta- analysis of randomized controlled trials. Journal of Affective Disorders, 218, 15–22. https://doi.org/10.1016/j.jad.2017.04.046

Girasek, E., Boros, J., Döbrössy, B., Susánszky, A., & Győrffy, Z. (2022). E-páciensek Magyarországon: Digitális egészséggel kapcsolatos ismeretek, szokások egy országos reprezentatív felmérés tükrében. Orvosi Hetilap, 163(29), 1159-1165. https://doi.org/10.1556/650.2022.32512

Hair, J., & Alamer, A. (2022). Partial Least Squares Structural Equation Modelling (PLS-SEM) in Second Language and Education Research: Guidelines Using an Applied Example. Research Methods in Applied Linguistics, 1, 100027. https://doi.org/10.1016/j.rmal.2022.100027

Hair, J.F., Risher, J.J., Sarstedt, M. & Ringle, C.M. (2019). When to use and how to report the results of PLSSEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203

Henseler, J. (2021). Composite-Based Structural Equation Modeling: Analyzing Latent and Emergent Variables. The Guilford Press.

Henseler, J., Hubona, G., & Ray, P.A. (2016). Using PLS path modeling in new technology research: updated guidelines, Industrial Management & Data Systems, 116(1), 2-20. https://doi.org/10.1108/IMDS-09-2015-0382

Iribarren, S.J., Schnall, R., Stone, P.W., & Carballo-Dieguez, A. (2016). Smartphone applications to support tuberculosis prevention and treatment: review and evaluation. JMIR MHealth Uhealth, 4(2), e25. https://doi.org/10.2196/mhealth.5022

Kemény, I., Kulhavi, N., & Kun, Zs. (2022). A távorvoslás igénybevételét befolyásoló tényezők a COVID-19 járvány miatti félelem tükrében. Statisztikai Szemle, 100(1), 7-43. https://doi.org/10.20311/stat2022.1.hu0007

Kovács, T., & Várallyai, L. (2021). Egészségügyi mobilapplikációkra történő használati szándék mérése UTAUT-modellben: tanulmány egy online felmérés eredményei alapján. Információs Társadalom: Társadalomtudományi Folyóirat, 21(1), 166-187. http://doi.org/10.22503/inftars.XXI.2021.1.7

Kovácsné Tóth, Á., Ercsey, I., & Keller, V. (2023). Az életmód applikációk használatának akadályai egy feltáró kutatás tapasztalatai alapján. Egészségfejlesztés, 64(2), 17-29. https://doi.org/10.24365/ef.12109

Lányi, B., & Törőcsik, M. (2022). Az e-egészségügyi megoldások fogyasztói fogadtatása Magyarországon. Vezetéstudomány, 53(7), 63-78. https://doi.org/10.14267/VEZTUD.2022.07.06

Li, Y., Ding, J., Wang, Y., Tang, C., & Zhang, P. (2019). Nutrition-Related Mobile Apps in the China App Store: Assessment of Functionality and Quality. JMIR Mhealth Uhealth, 7(7), e13261. https://doi.org/10.2196/13261

Limayem, M., Hirt, S.G., & Cheung, C.M.K. (2007). How habit limits the predictive power of intention: the case of information systems continuance. MIS Quarterly, 31(4), 705–737. https://doi.org/10.2307/25148817

Martinez-Perez, B., de la Torre-Diez, I., & Lopez-Coronado, M. (2015). Experiences and results of applying tools for assessing the quality of a mHealth app named Heartkeeper. Journal of Medical Systems, 39(11), 142. https://doi.org/10.1007/s10916-015-0303-6

Martin-Payo, R., Carrasco-Santos, S., Cuesta, M., Stoyan, S., Gonzalez-Mendez, X., & Fernandez-Alvarez, M.D.M. (2021). Spanish adaptation and validation of the User Version of the Mobile Application Rating Scale (uMARS). Journal of the American Medical Informatics Association: JAMIA, 28(12), 2681–2686. https://doi.org/10.1093/jamia/ocab216

Morselli, S., Sebastianelli, A., Domnich, A., Bucchi, C., Spatafora, P., Liaci, A., Gemma, L, Gravas, S., Panatto, D., Stoyanov, S., Serni, S., & Gacci, M. (2021). Translation and validation of the Italian version of the user version of the Mobile Application Rating Scale (uMARS). Journal of Preventive Medicine and Hygiene, 62(1), E243-E248. https://doi.org/10.15167/2421-4248/jpmh2021.62.1.1894

Nagy, Á., Kemény, I., Szűcs, K., Simon, J., & Kehl, D. (2019). A véleményformáló magatartás mint másodrendű látens változó modellezése PLS-alapú strukturális egyenletek módszerével. Statisztikai Szemle, 97(9), 827–854. https://doi.org/10.20311/stat2019.9.hu0827

Overdijkink, S.B., Velu, A.V., Rosman, A.N., van Beukering, M.D., Kok, M., & Steegers-Theunissen, R.P. (2018). The usability and effectiveness of mobile health technology-based lifestyle and medical intervention apps supporting health care during pregnancy: systematic review. JMIR mHealth uHealth, 6(4), e109, https://doi.org/10.2196/mhealth.8834

Peek, J., Hay, K., Hughes, P., Kostellar A., Kumar S., & Bhikoo, Z. (2021). Feasibility and Acceptability of a Smoking Cessation Smartphone App (My QuitBuddy) in Older Persons: Pilot Randomized Controlled Trial. JMIR Formative Research, 5(4), e24976. https://doi.org/10.2196/24976

Powell, A.C., Torous, J., Chan, S., Raynor, G.S., Shwarts, E., Shanahan, M., & Landman, A.B. ( 2016). Interrater reliability of mHealth app rating measures: analysis of top depression and smoking cessation apps. JMIR Mhealth Uhealth, 4(1), e15. https://doi.org/10.2196/mhealth.5176

Ren, S.M., & Li, L.J. (2010). Application of knowledge-attitude- behavior three level of target theory model in health education of patients with breast tumors. Journal of Nursing Science, 16, 1308–1309.

Schang, L., Blotenberg, I., & Boywitt, D. (2021). What makes a good quality indicator set? A systematic review of criteria. International Journal for Quality in Health Care: Journal of the International Society for Quality in Health Care, 33(3), 1–10. https://doi.org/10.1093/intqhc/mzab107

Schnall, R., Rojas, M., Bakken, S., Brown, W., Carballo- Dieguez, A., Carry, M., Gelaude, D., Mosley, J. P., & Travers, J. (2016). A user-centered model for designing consumer mobile health (mHealth) applications (apps). Journal of Biomedical Informatics, 60, 243–51. https://doi.org/10.1016/j.jbi.2016.02.002

Seddon, P.B. (1997). A respecification and extension of the DeLone and McLean model of IS success. Information Systems Research, 8(3), 240–53. https://doi.org/10.1287/isre.8.3.240

Shinohara, Y., Yamamoto, K., Ito, M., Sakata, M., Koizumi, S., Hashisako, M., Sato, M., Wannous, M., Stoyanov, S.R., Nakajima, J., & Furukawa, T.A. (2022). Development and validation of the Japanese version of the umars (user version of the Mobile App Rating System). International Journal of Medical Informatics, 165, 104809, https://doi.org/10.1016/j.ijmedinf.2022.104809

Silva, B.M.C., Rodrigues, J.J.P.C., de la Torre Dıez, I., Lopez-Coronado, M., & Saleem, K. (2015). Mobile- health: a review of current state in 2015. Journal of Biomedical Informatics, 56, 265–72. https://doi.org/10.1016/j.jbi.2015.06.003

Simon, J. (2016). Marketing az egészségügyben. Akadémiai Kiadó.

Statista. (2024a). Statista Market Insights, Digital Health – Hungary, Users. https://www.statista.com/outlook/dmo/digital-health/hungary?currency=usd#users

Statista. (2024b). Statista Market Insights, Digital Health – Hungary, Key Players. https://www.statista.com/outlook/hmo/digital-health/hungary#key-players

Stoyanov, S.R., Hides, L., Kavanagh, D.J, & Wilson, H. (2016). Development and validation of the user version of the mobile application rating scale (uMARS). JMIR mHealth uHealth. 4(2), e72. https://doi.org/10.2196/mhealth.5849

Stoyanov, S.R., Hides, L., Kavanagh, D.J., Zelenko, O., Tjondronegoro, D., & Mani, M. (2015). Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth, 3(1), e27, https://doi.org/10.2196/mhealth.3422

Whittaker, R., McRobbie, H., Bullen, C., Rodgers, A., Gu, Y., & Dobson, R. (2019). Mobile phone text messaging and app-based interventions for smoking cessation. Cochrane Database Systematic Review, 10(10), CD006611. https://doi.org/10.1002/14651858.CD006611.pub5

Woodside, A.G., Frey, L.L., & Daly, R.T. (1989). Linking service quality, customer satisfaction, and behavioural intention. Journal of Health Care Marketing, 9(4), 5 –17.

Yu-Ting, Y., Yong-Wei, Y., Miao, Y., Qiong, Y., Meng-Yu, W., & Ting, L. (2023). Knowledge, attitude, behaviour, and influencing factors of home-based medication safety among community-dwelling older adults witchronic diseases: a cross-sectional study. BMC Geriatrics, 23(1), 256. https://doi.org/10.1186/s12877-023-03966-3

Zapata, B.C., Fernandez-Aleman, J.L., Idri, A., & Toval, A. (2015). Empirical Studies on Usability of mHealth Apps: A Systematic Literature Review. Journal of Medical Systems, 39(2). https://doi.org/10.1007/s10916-014-0182-2

Zhao, S.H., Zhang, M.H., & Wang, Y.N. (2011). Effects of Knowledge-Attitude-Behavior health education mode on improvement of oral drug compliance in patients with epilepsy. Journal of Nursing Science, 26, 84–85.

Published

2025-05-15

Issue

Section

Studies and Articles

How to Cite

Ercsey, I., & Keller, V. (2025). The impact of healthy lifestyle apps’ quality on satisfaction and perceived impact using PLS SEM. Vezetéstudomány Budapest Management Review, 56(5), 57-71. https://doi.org/10.14267/VEZTUD.2025.05.05