literature review of fitness center

Revista Intercontinental de Gestão Desportiva - RIGD (Intercontinental Journal of Sport Management) ISSN 2237-3373

Quality of service in gyms - fitness centers: literature review, jorge mario rondón herrán , victor alfonso ortiz villavicencio.

The aim of this article was to compile updated and systematized scientific literature on the quality of service in gyms - fitness centers. For this purpose, a thorough Boolean search was carried out, using keywords such as service quality, gyms - fitness centers, satisfaction, perceived value in various databases: EbscoHost, Scopus, Redalyc, Dialnet, DOAJ, Physical Therapy & Sports Medicine, ProQuest, Sport Discus and Google Scholar. Subsequently, a total of 13266 articles were found, of which 4846 were eliminated and the remaining articles were filtered according to the inclusion and exclusion criteria, obtaining 50 articles plus 10 official reports from national and international sports institutions. Ten articles were then selected to answer the questions and objectives of this study, finally using a total of 60 documents. Consequently, the information analyzed made it possible to identify which aspects are connected to a higher perceived value by members and, therefore, to a better quality of service: equipment and environments in good condition, trained instructors, economic offers, safety, diverse training programs and accessible schedules. Therefore, it is recommended that gyms - fitness centers create a positive social environment between their facilities and their members, offering quality services based on the characteristics and requirements of the members, thus obtaining satisfied, loyal, committed and committed users with a high perceived value, generating in return, re-registration of members, new customers and a higher economic income.

Aldana, J., & Piña, J. (2017). Calidad del servicio prestado al cliente por los instructores de gimnasios. Revista Arbitrada Interdisciplinaria Koinonía, 2(3), 172–197. https://www.redalyc.org/articulo.oa? id=576866904006

Alguacil, M., Núñez-Pomar, J., Pérez-Campos, C., & Prado-Gascó, V. (2019). Perceived value, satisfaction and future intentions in sport services: Putting congruence and brand trust in the equation–linear models vs QCA. Academia Revista Latinoamericana de Administración, 32(4), 566–579. https://doi.org/https://doi.org/10.1108/ARLA -04-2019-0099

Alves, A., Barbosa, H., & Loureiro, V. (2019). Calidad y satisfacción en gimnasios y health clubs. Materiales Para La Historia Del Deporte, 18, 115–121. https://dialnet.unirioja.es/servlet/articulo? codigo=6834109

Batista, Y., Barros, M., Rodrigues, V., De Queiroz, C., & Sarmento, J. (2021). Determinantes da satisfação e comportamento positivo de clientes em academias fitness. Motricidade, 17(2), 140– 147. https://doi.org/https://doi.org/10.6063/motrici dade.20570

Behnam, M., Sato, M., & Baker, B. (2021). The role of consumer engagement in behavioral loyalty through value cocreation in fitness clubs. Sport Management Review, 24(4), 567–593. https://doi.org/https://doi.org/10.1080/14413 523.2021.1880772

Calesco, V., & Both, J. (2019). Escala de avaliação da qualidade dos serviços prestados por academias de ginástica (QUASPA). Journal of Physical Education and Sport, 31(1). 13 Rondón & Ortiz 2023, Vol. 13, n. 3, e110065 https://doi.org/https://doi.org/10.4025/jphyse duc.v29i1.3011.

Calesco, V., & Both, J. (2021). Qualidade dos serviços prestados pelas academias de ginástica. Retos. Nuevas Tendencias En Educación Física, Deporte y Recreación, 39, 18–23. https://doi.org/https://doi.org/10.47197/retos. v0i39.77659

Casterad, J., Estrada, N., Sanz, G., Simón, J., & Roso, A. (2019). The Fitness Centres of the City of Zaragoza. Apunts. Educación Física y Deportes, 135, 118–136. https://doi.org/https://dx.doi.org/10.5672/apu nts.2014-0983.es.(2019/1).135.08

Choi, E., & Kim, S. (2013). The study of the impact of perceived quality and value of social enterprises on customer satisfaction and re-purchase intention. International Journal of Smart Home, 7(1), 239–252. https://gvpress.com/journals/IJSH/vol7_no1/ 22.pdf

Clavel, I., García, J., Iglesias, E., Felipe, J., & Gallardo, L. (2018). Prediction of abandonment in Spanish fitness centres. European Journal of Sport Science, 19(2), 217–224. https://doi.org/https://doi.org/10.1080/17461 391.2018.1510036

de Azevedo, R. (2020). Estratégias para aumentar captação, retenção e diminuir a evasão de Clientes em Academias. Revista Intercontinental de Gestão Desportiva, 10(3), 1–13. https://www.researchgate.net/publication/346 644288_Estrategias_para_aumentar_captacao _retencao_e_diminuir_a_evasao_de_Clientes _em_Academias_Strategies_to_increase_capt ure_retention_and_reduce_evasion_of_Custo mers_in_Gyms_Estrategias_para_aumentar_l a_c

de Farias, E., Mendes, L., Nascimento, M., & Morais, E. (2020). Relevance of service quality attributes in Fitness Centres in Rio de Janeiro: Comparing stakeholders’ perceptions. International Journal for Quality Research, 15(1), 329–352. https://doi.org/https://doi.org/10.24874/IJQR 15.01-19

de Souza-Dias, P., & Reis-Monteiro, P. (2020). Sports marketing and perceived value: An application of the conjoint analysis in the fan membership programs of football. BBR. Brazilian Business Review, 17(3), 253–274. https://doi.org/https://doi.org/10.15728/bbr.2 020.17.3.1

Deloitte. (2016). European Health & Fitness Market Report 2016. Deloitte. https://www2.deloitte.com/content/dam/Delo itte/de/Documents/consumer-business/SBEuropeActive and Deloitte. European Health and Fitness Market 2016_DE.pdf

Deloitte. (2019). European Health & Fitness Market Report 2019. https://www2.deloitte.com/content/dam/Delo itte/es/Documents/acerca-de-deloitte/ Deloitte-ES-TMT-European-Health-FitnessMarket-2019.pdf

Deloitte. (2021). European Health & Fitness Market Report 2021. https://www2.deloitte.com/content/dam/Delo itte/de/Documents/consumer-business/ European Health and Fitness Market_Reportauszug 2021.pdf

Deloitte. (2022). European Health & Fitness Market Report 2022. https://www2.deloitte.com/content/dam/Delo itte/de/Documents/consumer-business/ EHFMR_2022_Auszug_Report.pdf

Dian, M., & Tomoliyus, T. (2021). Factors Affecting Satisfaction and Loyalty of Health and Fitness Club Customers. KnE Publishing Platform, 2, 381–393. https://doi.org/https://doi.org/10.18502/kss.v 6i2.10002

Dias, C., Ferreira, A., Romao, A., & Fonseca, A. (2019). Examining the relationship between perceived service quality, satisfaction, and renewal intention in Portuguese fitness centers. Journal of Sport Psychology, 28(2), 49–58. https://eds.p.ebscohost.com/eds/pdfviewer/pd fviewer?vid=1&sid=1922cec5-9c43-4713- 9c6a-d530445f11aa%40redis

El Rafei, S., Nassereddine, M., Hammoud, A., & Olleik, A. (2020). Evaluación de la preparación de los gimnasios e instalaciones deportivas libanesas de acuerdo con ISO97.220 – Equipos e instalaciones deportivas. The Sport Journal, 1–8. https://web.p.ebscohost.com/ehost/detail/deta il?vid=0&sid=193353bc-452a-4a51-9e2f388c01bc474a %40redis&bdata=Jmxhbmc9ZXMmc2l0ZT1 laG9zdC1saXZl#AN=146599026&db=eue

Eskiler, E., & Altunışık, R. (2021). The Moderating Effect of Involvement in the Relationship Between Customer Behavioral Intentions and Its Antecedents. SAGE Open, 11(2). https://doi.org/https://doi.org/10.1177/21582 440211014495 

Espada, M., Jiménez, J., Remón, Á., & Santacruz, J. (2021). Spanish sport facilities: differences between public and private, and according to their business model. Retos. Nuevas Tendencias En Educación Física, Deporte y Recreación, 39, 38–45. https://doi.org/https://doi.org/10.47197/retos. v0i39.74842

Felix, R. (2017). Service quality and customer satisfaction in selected banks in Rwanda. Journal of Business & Financial Affairs, 6(1), 1–11. https://doi.org/https://doi.org/10.4172/2167- 0234.1000246

Ferreira-Barbosa, H., Barbosa, J., Sabino, B., & Loureiro, V. (2022). Determinants of service quality influencing customer satisfaction in fitness centers: A systematic review. European Journal of Human Movement, 49, 29–45. https://doi.org/https://doi.org/10.21134/eurjh m.2022.49.3

Foroughi, B., Iranmanesh, M., Gholipour, H., & Hyun, S. (2019). Examining relationships amongprocess quality, outcome quality,delight, satisfaction andbehavioural intentions in fitnesscentres in Malaysia. International Journal of Sports Marketing and Sponsorship, 20(1). https://doi.org/https://doi.org/10.1108/IJSMS -08-2018-0078

García, F., Pérez, C., García, J., Soto, A., & Aguado, S. (2021). Models of Sports Management in Fitness Centres. Influence of Sex, Age and Sport Frequency. Linear Models vs. Qualitative Comparative Analysis. Sustainability, 13(16), 1–11. https://doi.org/https:// doi.org/10.3390/su13168995

García, J., Gálvez, P., Sánchez, A., Fernández, J., Pitts, B., & Grimaldi, M. (2019). An analysis of new social fitness activities: loyalty in female and male CrossFit users. Sport in Society, 23(2), 204–221. https://doi.org/https://doi.org/10.1080/17430 437.2019.1625332

García, J., & Zamorano, S. (2018). El análisis Importancia - Valoración según género y permanencia: el caso de los centros de fitness. Materiales Para La Historia Del Deporte, 16, 24–35. https://doaj.org/article/a9e0f34439ac451d9eb 86431318298c2

Gola, M., Gaviraghi, L., Capasso, L., Cuda, A., D’Alessandro, D., Bertolini, C., Riboli, S., & Capolongo, S. (2019). Design and hygiene issues in sports facilities. A pilot study which investigates fitness centres by using a multidisciplinary tool. Ann Ist Super Sanita, 55(3), 224–232. https://doi.org/https://doi.org/10.4415/ANN_ 19_03_05

Gonçalves, C., Sousa, M., & Carvalho, M. (2019). Segmentation for retain at fitness centres: Contribution of service quality, expectations and satisfaction in the club. Motricidade, 15(1). https://doi.org/https://dx.doi.org/10.6063/mot ricidade.16967

International Health Racquet and Sportsclub Association (IHRSA). (2014). 2014 IHRSA profiles of success: The annual industry data survey of the health and fitness club industry. IHRSA.

International Health Racquet and Sportsclub Association (IHRSA). (2019). The 2019 IHRSA Global Report. IHRSA. https://es.ihrsa.org/publications/the-2019- ihrsa-global-report/

International Health Racquet and Sportsclub Association (IHRSA). (2022). The 2022 IHRSA Global Report. https://www.ihrsa.org/publications/the-2022- ihrsa-global-report/

International Health Racquet and Sportsclub Association (IHRSA) Latin American Report. (2018). Evolución, actualidad y futuro del sector de clubes y gimnasios (Segunda ed). IHRSA. https://mercadofitness.com/ihrsa/Informe_IH RSA_LATIN_REPORT-2ed.pdf

Kunkel, T., Doyle, J., & Berlin, A. (2017). Consumers’ perceived value of sport team games: A multidimensional approach. Journal of Sport Management, 31(1), 80–95. https://doi.org/https://doi.org/10.1123/jsm.20 16-0044

León, J., García, J., & Burillo, P. (2018). Prioridades en la gestión de Centros Fitness. Aplicación del análisis de Importancia – Valoración. Journal of Sports Economics & Management, 8(3), 116–136. https://eds.p.ebscohost.com/eds/detail/detail? vid=0&sid=26c755b1-d3fa-4392-bc2e8ed78ca9e09e %40redis&bdata=Jmxhbmc9ZXMmc2l0ZT1 lZHMtbGl2ZQ%3D %3D#AN=edsbas.CEC1CB79&db=edsbas

León, J., García, J., & Burillo, P. (2020a). Análisis de Importancia-Valoración (IPA) y Modelo Kano aplicados a centros fitness de la Comunidad de Madrid. Cultura, Ciencia y Deporte, 15(44), 223–234. 15 Rondón & Ortiz 2023, Vol. 13, n. 3, e110065 https://doi.org/https://doi.org/10.12800/ccd.v 15i44.1464

León, J., García, J., & Burillo, P. (2020b). Best Practices for Fitness Center Business Sustainability: A Qualitative Vision. Sustainability, 12(12), 1–17. https://doi.org/https://doi.org/10.3390/su1212 5067

León, J., García, J., & Burillo, P. (2020c). Service Perceptions in Fitness Centers: IPA Approach by Gender and Age. International Journal of Environmental Research and Public Health, 17(8), 1–10. https://doi.org/https://doi.org/10.3390/ijerph1 7082844

Li, X., & Petrick, J. (2010). Towards an Integrative Model of Loyalty Formation: The Role of Quality and Value. Leisure Sciences, 32(3), 201–221. https://doi.org/https://doi.org/10.1080/01490 401003709123

Li, Y., Kim, A., Liu, O., Mastromartino, B., Wang, Y., & Zhang, J. (2021). Impact of perceived value on behaviour intention of parent-child runners in a marathon event. Sport in Society, 24(10), 1–19. https://doi.org/https://doi.org/10.1080/17430 437.2021.1916246

Mehmet, F. (2018). Satisfaction Levels of Individuals Who Go to Fitness Centers. Universal Journal of Educational Research, 6(10), 2266–2270. https://doi.org/https://doi.org/10.13189/ujer.2 018.061025

Mosquera, D., Patiño, O., Sánchez, D., Agudelo, J., Ospina, D., & Bermúdez, J. (2019). Factores asociados a la calidad en el servicio en Centros de Acondicionamiento Físico a partir del modelo SERVQUAL. Revista CEA, 5(9), 13–32. https://doi.org/https://doi.org/10.22430/2422 3182.1253

Natus, S., Curth, M., Kerber, L., & Gonçalves, M. (2021). The Influence of Perceived Value on the construct “Word of Mouth” in Fitness Center. PODIUM Sport, Leisure and Tourism Review, 10(2), 139–159. https://doi.org/https://doi.org/10.5585/podiu m.v10i2.17190.

Ndayisenga, T. (2019). Effect of service quality and rates on satisfaction and loyalty of customer behavior at fitness. International Journal of Human Movement and Sports Sciences, 7(2), 25–32. https://doi.org/https://doi.org/10.13189/saj.20 19.070202

Ortega, J., Lourenço, F., González, S., & Campos, F. (2021). Estudio sobre la calidad percibida de los usuarios de fitness: a partir de sus características y preferencias según sexo y edad. Retos. Nuevas Tendencias En Educación Física, Deporte y Recreación, 39, 477–482. https://doi.org/https://doi.org/10.47197/retos. v0i39.80061

Peitzika, E., Chatzi, S., & Kissa, D. (2020). Service Quality Expectations in the Fitness Center Context: A Validation of the Expectations Component of the SERVQUAL Scale in Greece. Services Marketing Quarterly, 41(2), 89–104. https://doi.org/https://doi.org/10.1080/15332 969.2020.1742977

Policani, A., & Silva, T. (2018). Fitness Centers: What are the most important attributes in this sector? International Journal for Quality Research, 13(1), 177–192. https://doi.org/https://doi.org/10.24874/IJQR 13.01-11

Polyakova, O., & Ramchandan, G. (2020). Perceived service quality among regular users of gyms in publicsports centres in the UK. Managing Sport and Leisure. https://doi.org/https://doi.org/10.1080/23750 472.2020.1853594

Rita, P., Oliveira, T., & Farisa, A. (2019). The impact of e-service quality and customer satisfaction on customer behavior in online shopping. Heliyon, 5(10). https://doi.org/https://doi.org/10.1016/j.heliy on.2019.e02690

Sanabria, J., Silveira, Y., Pereira, L., & Rosero, M. (2020). Gestión de la confianza en Gimnasios deportivos. Retos. Nuevas Tendencias En Educación Física, Deporte y Recreación, 37, 100–107. https://doi.org/https://doi.org/10.47197/retos. v37i37.71000

Serdar, E. (2021). The relationship between perceived health outcomes of recreation, exercise dependence, and life satisfaction: A study with fitness center members. Baltic Journal of Health and Physical Activity, 1, 43–50. https://doi.org/https://doi.org/10.29359/BJHP A.2021.Suppl.1.05

Sevilmiş, A., Özdemir, İ., García-Fernández, J., & Zhang, J. (2022). Examining the Relationships Among Perceived Quality, Perceived Value, Customer Satisfaction, and Behavioral Intention in Turkish Fitness Centers. Physical Culture and Sport. Studies and Research, 96(1), 40–54. https://doi.org/https://doi.org/10.2478/pcssr2022-0018

Silva, A., Monteiro, D., & Sobreiro, P. (2019). Effects of sports participation and the perceived value of elite sport on subjective well-being. Sport in Society, 23(7), 1202– 1216. https://doi.org/https://doi.org/10.1080/17430 437.2019.1613376 Sistema Deportivo Nacional. (2011). Plan Nacional del Deporte 2011 - 2030. http://www.ipd.gob.pe/images/documentosdigitales/documentos-mapasitio/plannacional-deporte-2011-2030.pdf

United Nations Educational Scientific and Cultural Organization (UNESCO). (2015). International Charterof Physical Education, Physical Activity and Spor. http://portal.unesco.org/es/ev.phpURL_ID=13150&URL_DO=DO_TOPIC&U RL_SECTION=201.html

Varmus, M., Kubina, M., Koman, G., & Ferenc, P. (2018). Ensuring the Long-Term Sustainability Cooperation with Stakeholders of Sports Organizations in SLOVAKIA. Sustainability, 10(6), 1833. https://doi.org/https://doi.org/10.3390/su1006 1833

Vieira, E., & Ferreira, J. (2017). Strategic framework of fitness clubs based on quality dimensions: the blue ocean strategy approach. Total Quality Management and Business Excellence, 29(13), 1648–1667. https://doi.org/https://doi.org/10.1080/14783 363.2017.1290523

Yong, W., & Choi, K. (2018). Factors influencing choice when enrolling at a Fitness Center. Social Behavior and Personality, 46(6), 1043–1056. https://doi.org/https://doi.org/10.2224/sbp.71 04

Yu, H., Zhang, J., Kim, D., Chen, K., Henderson, C., Min, S., & Huang, H. (2014). Service quality, perceived value, customer satisfaction, and behavioral intention among fitness center members aged 60 years and over. Social Behavior and Personality: An International Journal, 42(5), 757–767. https://doi.org/https://doi.org/10.2224/sbp.20 14.42.5.757

Submitted date: 06/05/2023

Accepted date: 07/16/2023

Europe PMC requires Javascript to function effectively.

Either your web browser doesn't support Javascript or it is currently turned off. In the latter case, please turn on Javascript support in your web browser and reload this page.

Search life-sciences literature (44,070,555 articles, preprints and more)

• Free full text
• Citations & impact
• Similar Articles

Fitness center use and subsequent achievement of exercise goals. A prospective study on long-term fitness center members.

Author information, affiliations.

• Lund Nilsen TI 1
• Steinsbekk A 1
• Hatlen Nøst T 2

BMC Sports Science, Medicine & Rehabilitation , 13 Jan 2022 , 14(1): 9 https://doi.org/10.1186/s13102-022-00400-w   PMID: 35027081  PMCID: PMC8756662

Abstract 

Conclusions, free full text .

Fitness center use and subsequent achievement of exercise goals. A prospective study on long-term fitness center members

1 Department of Public Health and Nursing, Norwegian University of Science and Technology, Post box 8905, 7491 Trondheim, Norway

2 3T-Fitness Center, Vestre Rosten 80, 7075 Tiller, Norway

Tom Ivar Lund Nilsen

3 Clinic of Anesthesia and Intensive Care, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway

Torunn Hatlen Nøst

5 Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway

6 Norwegian Advisory Unit on Complex Symptom Disorders, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway

Aslak Steinsbekk

4 Digital Health Care Unit, Norwegian Center for E-Health Research, Tromsø, Norway

Associated Data

The anonymized data files are available from the corresponding author on reasonable request.

Knowledge on the relationship between fitness center use and long-term members’ subsequent goal achievement is limited. Therefore, the aim was to investigate the prospective association between the use of fitness centers during 18 months and subsequent self-reported goal achievement among long-term members.

This was a registry- and survey-based longitudinal study of 2851 people who had been members at a Norwegian fitness center chain for more than two years. Fitness center use from December 2016 to June 2018 was obtained from registry data. Subsequent goal achievement was measured in a survey in June 2018, assessed by a 1–100 visual analogue scale, and a score between 0 and 50 was defined as low goal achievement.

Frequent and regular long-term fitness center use were associated with higher subsequent self-reported goal achievement.

Despite overwhelming evidence of physical activity’s beneficial effect on health and well-being [ 1 ], the proportion of the adult population who reaches WHO's [ 2 ] recommended level of at least 150 min of moderate or 75 min of vigorous physical activity throughout the week is low [ 3 ]. The same contradiction is also reflected in the use of fitness centers. Although members of fitness centers intend to use the centers regularly [ 3 , 4 ], a substantial proportion of members has infrequent and irregular use of the facilities [ 5 ]. A large proportion also terminates the membership after a short period [ 6 ].

Increasing physical activity and reducing the proportion of people who are physically inactive are significant public health priorities in many countries [ 8 ]. To achieve this, knowledge of factors that can motivate people to become and maintain physically active as recommended is therefore warranted [ 9 ]. Studies have reported that satisfaction with own goal realization can motivate people to maintain physically active over time [ 9 , 10 ]. On the other hand, low goal achievement has been associated with negative emotions, reduced self-efficacy, and perceptions of failure that may reduce participation in physical activity [ 11 ]. Preventing low motivation and poor goal achievement could therefore be key factors to promote regular and sustained physical activity.

Fitness centers are essential arenas for physical activity in many countries [ 12 ]. They offer various exercise options in a safe environment, they facilitate social interaction, and the members often have access to qualified exercise guidance and coaching [ 13 , 14 ]. Thus, use of such exercise facilities could motivate members to regular and sustained physical activity and aid people to reach their exercise goals.

Therefore, the aim was to investigate the prospective association between the use of fitness centers during 18 months and subsequent self-reported goal achievement among people who had been members at a fitness center for two years or more.

This was a registry- and survey-based longitudinal study with membership registry data from December 1st 2016 to May 31st 2018, and survey data collected in June 2018. The "Strengthening the Reporting of Observational Studies in Epidemiology" guidelines were consulted for the reporting of the study [ 15 ].

The study was conducted within the setting of a fitness center chain ( www.3T.no ) in Central Norway. Twelve of 3T’s fitness centers are located in the city of Trondheim, which had approximately 205,000 inhabitants in 2020, and these centers had around 40,000 members at the time of data collection. All centers have a primary workout area, and most of the centers have group classes, fitness trainers, personal trainers, saunas, and member lounges with simple café services. Members can book a free-of-charge session with a fitness trainer to get help and guidance and a personalized exercise program, or choose to pay for a personal trainer for closer follow-up and guidance. Group activities in this chain are also numerous and various, including yoga, stretching, water gymnastics, spinning, strength training and aerobics, lasting from 20 to 90 min.

Participants and procedures

The inclusion criteria were all adult members, aged 18 years or older at the time of survey distribution, who were registered with an e-mail address, been a paying ordinary member for a minimum of the two previous years and who allowed linkage to their membership data on use of the fitness center. Members with free or employee contracts, who were younger than 18 years of age, or whose memberships had not lasted 2 years were excluded.

3 T’s head office sent an email to all 15,273 eligible members with information about the study and a link to the survey. The landing page for the survey included additional information about the study. Members who were willing to participate could tick two boxes: one confirming that they agreed to participate, the other confirming that they consented to having their membership data on the use of fitness centers be linked to their survey record. A reminder was sent to those who had not answered four days after the first request.

Data collection

Fitness center use.

Data on the use of fitness centers was collected from the membership registry, including timestamps for visits, bookings of group activity, and fitness trainer bookings. The number of days with visits during 18 months were categorized into thirds (low (1–57), medium (58–117), and high (118–543)), as well as a separate category for those with zero visits. Similarly, participants were categorized into thirds based on number of group activity bookings (low (1–26), medium (27–80), and high (81–550)), as well as a fourth category with zero bookings. Fitness trainer bookings were categorized into none, one, and two or more bookings. To measure regularity in fitness center visits during the 18-month period, members were classified into three groups based on the number of three-month periods in which they had visited the fitness center at least once: ((1) all six periods, (2) four and five periods, and (3) less than four periods).

Self-reported goal achievement

Demographic data, statistical analyses.

Of the 11,139 eligible members, 2851 (26%) opened the email, accepted the invitation, completed the survey, consented to the linkage to the membership registry, and were available for statistical analyses (Fig.  1 ). Descriptive statistics of the study population appear in Table ​ Table1 1 .

Flow chart of participants

Characteristics of the 2851 long-term members in the study population at the time of survey participation in June 2018

a Education: Compulsory (primary school and middle school graduation or lower), middle (high school graduation (duration 1–2 years), high school graduation (duration three years), certificate of apprenticeship (duration four years)), and higher (college/university graduation)

b Not in work: occupational rehabilitation, unemployed or laid off, disability benefits or retired

Fitness center use during 18 months associated with subsequent mean goal achievement score among 2851 long-term fitness members

a Geometric mean from an inverted VAS (0–100). Higher score indicates lower self-reported goal achievement

b Ratio between geometric means

d 95% CI for adjusted ratio

Fitness center use during 18 months associated with subsequent goal achievement among 2851 long-term fitness members. Crude and adjusted odds ratio for reporting low goal achievement a

CI: Confidence interval

a Low goal achievement was defines as scores between 0 and 50 on the VAS-scale

c 95% CI for adjusted ratio

In this registry- and survey-based longitudinal study among long-term members of fitness centers, a higher number of days with fitness center visits and regular use of fitness centers during and 18 month period were associated with higher subsequent self-reported achievement of exercise goals. Regarding the use of services within the fitness centers, the number of group activity bookings had associations similar to those observed for number of visits, whereas number of bookings with a fitness trainer was not consistently associated with goal achievement.

This study provides new knowledge on the association between the use of fitness centers and goal achievement. Goal achievement has typically been measured on whether concrete goals, such as number of daily steps [ 16 , 17 ], minutes of physical activity per week, and changes in body weight [ 18 ], are met. However, such concrete measures omits the overall experiences of own goal achievement. Thus, the use of a single question to measure overall goal achievement could be used in other studies either alone or in addition to measures of concrete goals.

Visiting a fitness center at least once every third month was associated with higher goal achievement. Research has shown that few members use fitness centers regularly without periods of relapse [ 6 ]. Thus, measuring the regularity of visits, as done in this study, adds important knowledge about the maintenance of physical activity over time, which also is relevant for public health [ 19 ]. We have not found any other studies measuring regularity in this manner. Other studies tend to be cross sectional without clearly distinguishing frequency from regularity [ 20 , 21 ].

It was also found that frequent fitness center visits were associated with higher goal achievement. A Danish report using cross-sectional questionnaire data asked about "regular exercise several times a week" and found that a higher proportion of members who answered yes to this also reported to achieve their exercise goals than members reporting irregular exercise (18% vs. 5%) [ 20 ]. In a published cross-sectional survey from Portugal measuring the association between self-reported weekly frequency of fitness center use and satisfaction, found no association [ 21 ].

Given the longitudinal design of our study, it seems fair to state that current available evidence points to frequent and regular fitness center visits is associated with higher goal achievement among long-term members. However, the bi-directional nature of goal achievement and the use of fitness centers needs to be kept in mind. Since we do not know the initial goal achievement status, the members` perceived goal achievement level at the study`s start might have caused the members` fitness center use, instead of the other way around.

Thus, future longitudinal studies should assess goal achievement both before and after measuring the use of fitness centers.

In addition to visits, we also measured the use of fitness center services, group activity and fitness trainer bookings to further understand which aspects of the use of fitness centers were associated with goal achievement. Among our results, a higher number of group activity bookings was associated with higher self-reported goal achievement.

In a qualitative study among long-term members, joining group activities was told to demand less self-motivation, help to commit more to exercise, and induce more vigorous exercise than self-training [ 13 ]. Other studies have found that participating in group activities can improve health-related quality of life [ 22 ], enhance aerobic capacity [ 23 ], and affect physical activity behavior [ 24 ], which might help to explain why members using that specific service reported higher goal achievement.

Previous research has revealed that one-to-one support can contribute to regular fitness center use [ 25 ]. In addition, help and instructions given by fitness trainers have been described positively by long-term members in a qualitative study and said to increase their understanding of using the fitness center as a means for physical activity [ 13 ]. However, in the current study fitness trainer bookings were not consistently associated with members` subsequent goal achievement. One reason may be that very few members used that specific service and that most had only one booking during the 18-month period.

Strengths and limitations

The longitudinal data and large number of participants are considerable strengths of the current study. In contrast to self-reported activity, registry data provides objectively measured activities and avoids measurement errors [ 26 ]. However, this only provided data on number of activities and not the length of or type of activity. Furthermore, the study only focused on activities related to the fitness center, and did not measure the influence of daily life, other physical activities, diet or other external factors.

Another limitation was that only 26% of eligible members who opened the invitation email chose to participate. Low response rates can increase the risk of selection bias and reduce external validity. Furthermore, the participants were all members at one fitness center chain. Caution should therefore be used when generalizing the findings to other fitness center members.

In this longitudinal study on long-term fitness center members, more days with fitness center visits and regular use of the fitness centers during an 18-month period were associated with higher subsequent self-reported exercise goal achievement.

Acknowledgements

We want to thank all contributors, the study participants, Hilde Thommesen Holck (CEO at 3T), Tor Albert Thommesen (Chairman of the Board at 3T), Monica Leinum Bye (employee at 3T), Johnny Gaustad (employee at 3T), Bjørn Tore Norum (employee at 3T), Hildbjørg Fosse (employee at 3T), The Research Council of Norway and 3T-Fitness Center for involvement and contribution in the project.

Abbreviations

Authors' contributions.

LR and AS designed and planned the project. LR collected data, performed data analysis, and drafted and completed the manuscript. AS, TILN, and THN participated in every part of the data analysis and commented on the manuscript. All authors read and approved the final version to be published.

This work was supported by the 3T-Fitness Center and The Research Council of Norway with Grant Number 239657.

Availability of data and materials

Declarations.

The study was conducted in accordance with the World Medical Association (WMA) Declaration of Helsinki—Ethical Principles for Medical Research Involving Human subjects. A request was sent to The Regional Committee for Medical and Health Research Ethics in Central Norway and there was no need for approval (2014/1870 REK Midt). This because the study did not include health research. The study was approved by NSD—Norwegian Centre for Research Data (NSD 40604/3/SSA). The Informants received information about the project, including contact information which was signed and accepted online before participation. The informed consent ensured that the participation was voluntarily and gave information about their right to at any time withdraw from the study. This study was considered to have low or no risk for the participants due to the nature of the intervention (questionnaire) and no collection of sensitive data.

Not applicable.

The authors declare that they have no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Liv Riseth, Email: [email protected] .

Tom Ivar Lund Nilsen, Email: [email protected] .

Torunn Hatlen Nøst, Email: [email protected] .

Aslak Steinsbekk, Email: [email protected] .

Full text links 

Read article at publisher's site: https://doi.org/10.1186/s13102-022-00400-w

Citations & impact 

This article has not been cited yet.

Impact metrics

Alternative metrics.

Similar Articles 

To arrive at the top five similar articles we use a word-weighted algorithm to compare words from the Title and Abstract of each citation.

The effect of initial support on fitness center use in new fitness center members. A randomized controlled trial.

Riseth L , Ivar Lund Nilsen T , Mittet Ø , Steinsbekk A

Prev Med Rep , 24:101605, 19 Oct 2021

Cited by: 1 article | PMID: 34976662 | PMCID: PMC8683950

Comparing current fitness center members' perceptions of the motivational climate with non-members.

Brown TC , Volberding J , Baghurst T , Sellers J

Glob Health Promot , 24(1):5-13, 09 Jul 2016

Cited by: 2 articles | PMID: 26156579

Long-term members' use of fitness centers: a qualitative study.

Riseth L , Nøst TH , Nilsen TIL , Steinsbekk A

BMC Sports Sci Med Rehabil , 11:2, 21 Feb 2019

Cited by: 13 articles | PMID: 30828457 | PMCID: PMC6383217

Patient-centered goals for pelvic floor dysfunction surgery: long-term follow-up.

Hullfish KL , Bovbjerg VE , Steers WD

Am J Obstet Gynecol , 191(1):201-205, 01 Jul 2004

Cited by: 61 articles | PMID: 15295366

Physical Activity, Fitness, Cognitive Function, and Academic Achievement in Children: A Systematic Review.

Donnelly JE , Hillman CH , Castelli D , Etnier JL , Lee S , Tomporowski P , Lambourne K , Szabo-Reed AN

Med Sci Sports Exerc , 48(6):1197-1222, 01 Jun 2016

Cited by: 529 articles | PMID: 27182986 | PMCID: PMC4874515

Review Free full text in Europe PMC

Funding 

Funders who supported this work.

3T- Fitness Center (1)

Grant ID: 239657

1 publication

The Research Council of Norway (1)

2 publication s

Europe PMC is part of the ELIXIR infrastructure

• Research article
• Open access
• Published: 21 February 2019

Long-term members’ use of fitness centers: a qualitative study

• Liv Riseth   ORCID: orcid.org/0000-0002-3072-2317 1 , 2 ,
• Torunn Hatlen Nøst 1 ,
• Tom I. L. Nilsen 1 , 3 &
• Aslak Steinsbekk 1  

BMC Sports Science, Medicine and Rehabilitation volume  11 , Article number:  2 ( 2019 ) Cite this article

12k Accesses

24 Citations

Metrics details

Although the health benefits of physical activity are well documented, a large proportion of the population remains less active than recommended by current guidelines. Commercial fitness centers provide an opportunity to perform physical activity and exercise, but there has been little research focusing on ordinary members at commercial fitness centers. The aim of this study was therefore to explore what long-term members (> 2 years) wanted to achieve with their membership and to identify important factors that influenced them to use the fitness center as a means for physical activity.

This was a qualitative study with 21 semi-structured individual interviews of adult long-term fitness center members in Trondheim, a city in Central Norway with approximately 190,000 inhabitants. The participants had been continuous fitness center members for more than two years and were asked about their experiences using a fitness center and what they wanted to achieve with the membership. The data was analyzed thematically with the method of systematic text condensation.

The results were categorized into three main themes: “Health benefits and physical appearance”; “Accessible, safe, and comfortable to use”; and “Variety, flexibility, and support.” The participants stated that they wanted to achieve health benefits, but they also talked about physical appearance. The fitness center was mainly described as easily accessible and a comfortable place for physical activity. Some female participants emphasized the feeling of safety compared to outdoor activity. Variation in activities, making commitments, and getting support from staff and other members were factors contributing to use of the fitness center for physical activity.

Achieving desired health benefits and improving physical appearance were the main drivers for long-term members’ use of the fitness center. The fitness center was preferred due to the comfort of the facilities and the possibility to commit to specific exercise times and activities.

Peer Review reports

The effects of physical activity to improve or maintain good health are well documented [ 1 ]. Despite public health recommendations and encouraging advice to stay physically active [ 2 , 3 ], approximately 30% of adults worldwide are physically inactive [ 4 ]. Physical activity behavior depends on a multitude of barriers and facilitators [ 5 ], such as accessibility [ 6 , 7 , 8 ], weather [ 9 ], and social support [ 10 , 11 ]. In addition, it includes many different psychological components, such as habits [ 12 , 13 ], planning [ 14 , 15 , 16 ], perceived behavioral control [ 17 ], motivation [ 18 ], physical activity identity [ 17 ], personality [ 19 ], and self-efficacy [ 16 , 17 ]. Due to the high prevalence of physical inactivity, increased levels of physical activity are a global public health priority [ 20 ].

Commercial fitness centers represent one opportunity to be physically active. The majority of fitness centers offer both group and individual activities [ 21 , 22 ]. In recent years, there has been an increase in the proportion of people who attend commercial fitness centers, with 15% of the European adult population doing so in 2013 [ 23 ]. This number is higher in some countries; for instance, it is 19.4% in Norway [ 24 ]. Statistics show a steady increase in attendance at private fitness centers in European countries; the number of members was 56.4 million at the end of 2016, which was an increase of 4.4% from the previous year [ 24 ]. Thus, fitness centers are an important arena for physical activity. However, it has been found that members would like to exercise more regularly than they do [ 25 ].

Former studies among fitness center members have focused on reasons to become and remain a member [ 25 , 26 ] and have highlighted motivational differences between fitness center and sports club members, e.g., sports club members are reported to be more motivated by competition, pleasure, and social factors and less concerned with appearance than fitness center members [ 21 , 27 , 28 ]. It has also been found that women report a slightly higher desire for wellness, a well-trained body, and weight loss than men do, while men report a desire for improved physical fitness [ 22 , 25 , 27 , 29 , 30 ].

Although a large proportion of the population are fitness center members, research on exercise behavior of members in fitness centers is limited in quantity and quality [ 31 ]. Therefore, it is important to gain in-depth knowledge of ordinary members (those who pay regular membership fees). Long-term members are an especially interesting group as they might have found a way to use the fitness center to achieve their goals for physical activity over time. However, we have not found any studies focusing exclusively on long-term members.

The aim of this study was therefore to explore what long-term members (> 2 years) wanted to achieve with their membership and to identify important factors that influenced them to use the fitness center as a means for physical activity.

This was a qualitative study with semi-structured face-to-face individual interviews conducted between March 2015 and November 2016.

In Norway, with 5.2 million inhabitants, it is estimated that there are 1079 fitness centers [ 24 ]. Within physical activity, there is also a strong tradition for participating in voluntary organizations in Norway, where sports clubs constitute the largest proportion [ 32 ].

This study took place among members of different fitness centers in Trondheim, a city in Central Norway with approximately 190,000 inhabitants, within the 3 T-Fitness Center chain ( www.3t.no ). 3 T established its first fitness center in 1985, and the chain is now the largest fitness center chain in Central Norway, with 16 fitness centers and approximately 40,000 members. During data collection, the fitness center chain had a total of 12 fitness centers, with eight of them located in Trondheim. A membership in this fitness center chain has a cost of 350–550 NOK (approximately 40–60 EUR) per month. To become a member, one has to sign a one-year contract; thereafter, one can terminate the contract one month after giving notice. The centers in this chain have a diversity of training opportunities, with a main workout area that primarily consists of free weights, weight machines, rowing machines, stationary exercise bikes, elliptical trainers, and treadmills. Nearly all centers offer group classes, personal trainers, saunas, and member lounges with simple café services. Some centers offer squash, childcare, and physical therapy. The largest center also has a swimming pool and a wellness area.

Participants

The inclusion criteria for this study required that each participant was older than 18 years and had been a paying member for more than two years continuously (to avoid the large proportion terminating their membership after the obligatory one-year contract) at one of the eight 3T fitness centers in Trondheim. We sought a sample with diversity in gender, age, frequency of visits, and types of services used, ranging from those included in the membership to services with extra cost such as hiring a personal trainer.

The first step in recruiting participants consisted of randomly selecting from the membership register 24 members who met the inclusion criteria. Each of these prospects received a letter or an email with information about the study and a request to participate. The message also stated that they would be contacted by phone after two weeks if they did not respond before that time. Of these 24, 16 responded positively and were interviewed. However, none of these participants were under the age of 27, nor had they used additional services with extra cost. Therefore, eight additional prospects were identified in the member register or by employees at the fitness center. They were contacted as described above, resulting in two more participants who were in their early 20s and three participants (age range 30–57 years) who had used services with extra cost (personal trainer, physiotherapist, and / or nutrition supervisor).

Data collection

The interviews were conducted by the first author at a place chosen by the participants and lasted from 32 to 62 min (average 48 min). The interviews were audiotaped and transcribed verbatim.

An interview guide with open-ended questions was developed by the first author, based on former literature about fitness center use, goal achievement, motivation, and the patient experiences questionnaire (PEQ) [ 26 , 29 , 31 , 33 – 35 ], as well as discussions among the authors and a research group three of the authors belong to. The four main questions were: “Can you tell me about your experiences of being a member at the fitness center?”, “What contributes to your use of the fitness center?”, “What do you want to achieve with the membership?”, and “Is there anything the fitness center can do to make it easier for you to achieve what you want with the membership?”. The following topics were introduced if the participant did not spontaneously talk about them: description of your own physical activity behavior; what affects your use of the fitness center (facilities, opening hours, activities available, social interactions, family, friends, and support and presence of staff); and reasons why you achieve / do not achieve what you want with the membership.

Data analysis

The data was analyzed thematically with the method of systematic text condensation (STC) by Malterud, which is inspired by Giorgi’s psychological phenomenological method [ 36 ]. An illustration of the STC process and how the participant responses were coded and categorized is given in Table  1 . The STC process is an iterative four-step method suitable for descriptive cross-case analysis of qualitative data. The method was chosen because it is well suited to present participants’ experiences, rather than the possible underlying opinions of what was told [ 36 ]. All interviews were held in Norwegian and the material was kept in its original language throughout the analysis. The data analysis was conducted by the first author in cooperation with the co-authors and discussed twice in an established research group.

In the first step of the analysis, the first author and the co-authors read the transcripts from a bird’s-eye perspective to identify preliminary themes. In the second step, meaning units (text segments) relevant to the aim of the study were identified by the first author, then coded and sorted into code groups based on the preliminary themes. The second step was done repeatedly, with several meetings and discussions among co-authors and the research group. MindManager [ 37 ] was used as a systematization tool in this part of the analysis. In the third step, a condensed description of the citations in each code group was made using the participants’ original phrases. Finally, in the fourth step, the descriptions were rephrased into analytical text.

The first sequence of analysis included the first thirteen interviews and was performed to identify areas that needed to be explored in more detail in further interviews. This was done because analyzing the data stepwise may contribute to systematic improvement of data collection, facilitate reflection, and reduce the number of participants needed [ 36 , 38 ]. The four steps of the STC were performed repeatedly in both the preliminary stages and the final analysis, leading to several changes and modifications before the final themes were agreed upon.

The analysis was validated by continuously checking the findings against the transcripts, especially after the final analysis. The first author identified illustrative citations and discussed them with the co-authors to choose the ones that best illustrated the themes. These were translated from Norwegian to English by the first author and checked by the other authors.

A total of 21 long-term members (11 females and 10 males) from eight different fitness centers were interviewed (Table  2 ). They had been members for 2–20 years and their average age was 43 years (range 20–71 years).

What long-term members wanted to achieve with their membership is described in the theme “Health benefits and physical appearance.” Their experiences with factors affecting their use of the fitness center as a means for physical activity were categorized into the themes “Accessible, safe, and comfortable to use” and “Variety, flexibility, and support.”

Health benefits and physical appearance

The long-term members said that they used the fitness center to achieve desired health benefits. The main examples of health benefits were more energy, improved mood and sleep, reduced stress, better well-being, or feeling happier after the workout. Some participants with health complaints said that use of the fitness center was a necessity for their daily function, e.g., to prevent deterioration of their health complaint, and as an aid in pain management.

Yes, I feel the positive effects exercise have for me, in relation to both energy and my mood. And my anxiety, it affects that a lot. I have had years where I only have been sitting indoors, without getting out of the house. (Female, 30–49 years, member for 8 years).

However, during the interviews some long-term members mentioned that they wanted to achieve a better look. Physical appearance was seldom directly talked about as a reason. The participants rather mentioned it in passing, e.g., adding it after having talked about another reason, followed by a joke or laughter. The things they said that concerned physical appearance included a fear of becoming overweight, wanting to get into the clothes they had worn before, reducing abdominal size, and becoming more muscular.

I have never been concerned about how I look and exercise solely in order to be able to continue to work and avoid a new knee replacement. But I should perhaps have reduced this slightly [laughs and pats the stomach]. (Male, 50–71 years, member for 4 years).

Male participants, in particular younger males, talked more about wanting to achieve a muscular body and becoming stronger, while female participants talked more about weight loss. When asked about the reason for their focus on physical appearance in relation to the use of the fitness center, some female participants talked about increased emphasis in society on being thinner and fit.

It feels like we should have been a little leaner, yes actually a little better at everything. If you do not exercise and stay healthy and slim, you are almost a bit questionable. According to everything that is communicated from the fitness center and health authorities (short pause). I do not like that it affects me so much. (Female, 30–49 years, member for 6 years).

Accessible, safe, and comfortable to use

The fitness center was primarily described as a comfortable place to be physically active. It was common to talk about physical activity in the fitness center as easier than being active outdoors. Due to the indoor comfort, avoiding bad weather and winter darkness, they said the fitness center made it easier to motivate themselves to be physically active. Living or working near the fitness center, ample opportunities for parking, and public transport were also given as reasons. Some female participants emphasized the importance of increased security when using the fitness center for physical activity compared to exercising outdoors on their own.

I feel safe when I visit the fitness center and it is cozy getting inside to the reception area with those flames from the fireplace and the friendly staff in the reception. I am afraid of the dark and yes, I am a little afraid of being assaulted by someone when running outside. (Female, 20–29 years, member for 3 years).

Even though some participants talked about how they enjoyed using the fitness center for physical activity, they described different challenges and barriers that hindered them in using the fitness center as much as they wanted, e.g., too little time, no childcare, and low motivation to get out of the house. Participants with children living at home especially expressed difficulties finding time and energy. It was said that having commitments such as appointments with others, pre-booked activities, or payment of no-show fees could help them to prioritize physical activity at the fitness center.

It is a bit odd because I like it when I am at the fitness center, but sometimes or actually quite often, it is hard to get out of the door at home. It is very positive that one must pay if one does not meet for (pre-booked) group classes, because then I have to go. (Female 20–29 years, member for 2 years).

Payment of membership fees and fees for working with personal trainers were said to affect use of the fitness center for some long-term members; typically, they wanted to use the services they had paid for. However, most participants stated that the fee was not something they thought of. One participant who was receiving disability benefits talked about the fitness center as an affordable option for exercise and as the only possibility to be regularly physically active.

It is cheap for me to be a member of the fitness center and it means a lot to me, since I have limited money available. I do not have much money to spend when all expenses are paid, but it is working fine for me to pay a small amount each month [to be a member of the fitness center]. (Female, 50–71 years, member for 4 years).

Some of the long-term members said that they valued the fitness center as a social meeting place where they could spend time with friends, family, and colleagues and make new acquaintances. This was facilitated by a friendly atmosphere at the fitness center and services like the opportunity to buy a cup of coffee and sit down for a chat.

I appreciate having the opportunity to socialize with my friend and that we can sit down, relax, and have a chat after the workout. In fact, I always work out with someone. It's social. (Male, 50–71 years, member for 9 years).

Variety, flexibility, and support

Variety, with both group classes and various possibilities for self-training, was said by some long-term members to be important with regard to regular use of the fitness center for physical activity. Those using mainly group classes or self-training had different explanations for their use of the fitness center facilities as a means for physical activity.

A typical argument given for participating in group classes was as a help to exercise more vigorously compared to self-training. Some also said that they preferred group classes because they were time limited and followed a fixed structure. Furthermore, some spoke about the boost they got from the atmosphere and enthusiasm in the group classes. This was also mentioned as a reason for going to the fitness center in general, since it required less self-motivation. Other things mentioned were enjoyable experiences, being in a group with others, mastering a new step in a choreography, or a pleasant conversation.

As an example, if I had chosen a fitness center without classes, instructors, or those types of facilities, I would have needed the inner motivation for exercising, and to be honest — that is not strong enough. I need something and someone to motivate me. (Male, 30–49 years, member for 5 years).

The reasons for preferring self-training were described somewhat differently from group classes. The arguments given for self-training were flexibility, an all-in-one-place access to equipment, exercise at their own pace, and opportunities for targeted exercise in both strength and endurance. On the other hand, participants who did not perform self-training regularly spoke about challenges to implement it, e.g., because they found it boring, a duty, and not enjoyable.

I have got an exercise program; everything else is too hard, group classes and such. I take my program at my own pace and it strengthens me. It is a bit similar to what I do with the physiotherapist, but here I do not have to see so many sick people. (Female, 50–71 years, member for 4 years).

Some long-term members spoke positively about the help and the individual instructions they received from staff in the main workout area. It had helped them to understand what to do and how to do it when they used the fitness center. The staff were generally described as friendly, helpful, and knowledgeable if the members made appointments, but some perceived them as not very available for questions and help during workouts. Some participants had also used personal trainers and found it helpful for recognizing and understanding their physical capacity and getting a more efficient workout. Personal training was also described by some as helpful when implementing more regular use of the fitness center.

Yes, I thought I was exercising. However, I realized with guidance from a personal trainer that I previously had been far from being able to call it exercise. So, everyone should try a personal trainer to really understand what one should do and how. (Female, 30–49 years, member for 15 years).

The participants stated that they wanted to achieve health benefits, but they also talked about physical appearance. The fitness center was mainly described as easily accessible and a comfortable place for physical activity. Some female participants emphasized the feeling of safety compared to outdoor activity. Variation in activities, making commitments, and getting support from staff and other members were factors contributing to using the fitness center for physical activity.

There was a duality in what the long-term members wanted to achieve, between health benefits and appearance. The participants talked about the health benefits of physical activity as a main reason to use the fitness center. A survey has also found that members of fitness centers in Norway report they exercise to become fit rather than to gain a better-looking body [ 27 ], and the author suggested that it might feel better to say they exercise for fitness rather than for appearance. Similarly, in our study, appearance was mentioned as an additional reason after talking about health benefits. However, findings in another survey among fitness club members showed that seeing physical change as “becoming stronger” or “being able to see improvement in the way I look” were the main reasons for being physically active across age and gender [ 29 ].

This raises a question on whether long-term members of fitness centers are more occupied with their appearance than others doing physical activity in other settings. One study found this to be the case, with members of sports clubs being less concerned with appearance than fitness center members [ 27 ]. Similarly, a study on college students reported that those who engaged in exercise (e.g., aerobics, cycling, weight training) were more focused on appearance than those engaging in sports (e.g., tennis, basketball, soccer) [ 28 ]. It may be positive for fitness center members to focus on looks if it leads to a physically active lifestyle. However, for some, the focus on appearance might lead to a negative attitude towards oneself and make one exercise excessively [ 39 , 40 , 41 ]. It is therefore reasonable to question whether fitness centers should focus on appearance when marketing and promoting activities at the centers. In Norway, the main enterprise federation for fitness centers encourages them to be aware of the risk of excessive exercise in relation to disordered eating among their members.

Some of the participants said that they did not visit the fitness center as often as they wanted. A Danish report also found that most members of fitness centers would have liked to work out more frequently than they did [ 25 ]. An interesting finding in our study was that having committed to exercise through actions like pre-booking an activity, making a binding agreement with others, or making a payment was said to be the pressure they needed to visit the fitness center. This has not previously been reported, although it has been shown that making personal commitments using commitment devices with rewards or punishments for success or failure has been beneficial for behavioral changes [ 42 , 43 ]. This can indicate that binding agreements such as pre-booking of activities, no-show fees, appointments, and even payments can be tested by the fitness centers to see if they promote increased use and consequently more regular physical activity among the members.

According to some participants in the present study, an important motivational factor for using the fitness center was the opportunity for social interaction. Participants reported benefits from social support from employees, group classes, and other members, but also from the possibility to be in a social setting. A review also concluded social support to be positively associated with levels of physical activity among adolescents [ 10 ], and another review on qualitative studies also found that development and maintenance of social support networks were important for participation in sport and physical activity [ 11 ]. Even if these reviews focus on physical activity in general, together with this study it is reasonable to hypothesize that fitness centers can help members to increase their motivation to use the fitness center if they facilitate more opportunities for social interaction. Moreover, a conscientious use of both social interaction and binding agreements might be even more helpful and motivating for some members.

As expected, all participants appreciated that the fitness center was easily accessible, safe, and a comfortable place to be, especially during the fall and winter months. It has also been suggested in a review that levels of physical activity vary with seasons [ 9 ]. On the other hand, one study found that weather showed a weaker relationship with physical activity than did accessibility [ 6 ]. Similarly, another study found that weather had modest effects on physical activity [ 44 ]. In general, easily accessible opportunities for physical activity are positively correlated with the level of activity [ 6 , 7 , 8 ]. Thus, in a public health perspective it is important to emphasize that having safe and easily accessible fitness centers might be a driver for more regularly physical activity in the population.

The factors identified in this study are based on experiences from long-term members who have chosen to continue as members for an extended period and naturally are quite satisfied with the fitness center as an arena for physical activity. However, the factors identified are most likely quite similar for all members, regardless of membership duration [ 26 ]. It is also possible that the identified factors are important factors generally to maintain physical activity over time.

Strengths and limitations

A strength in the study is that it is the first to investigate what long-term members want to achieve with their membership and factors affecting their use of the fitness center as a means for physical activity. Another strength lies in the diversity of the sample. A limitation was that the study was done in a restricted geographical area and in only one fitness center chain. It also focused on long-term members and thus not those who for various reasons have terminated their membership. Moreover, it is possible that invited members who did not want to participate in the study are different from those who enrolled. Furthermore, younger participants were few in number. Younger participants might have had different experiences due to having other motives for participation in physical activity [ 45 ]. Given the similarity with findings in other studies on maintaining physical activity [ 26 ], it is not likely that the lack of younger participants has influenced the findings in the current study.

We consider it as a strength that the authors, who all took part in the analysis, have different backgrounds and experiences. Having researchers with other backgrounds, using a theory-driven approach, or doing member checking by inviting the participants to comment on the results could have produced other understandings and explanations.

At the time of the completion of the study, the first author worked in the administration of the fitness center chain, which might have influenced the research process. This was duly handled by having the co-authors participate in all steps of the research process, paying attention to the possibility of biases.

This study indicates that the main drivers for long-term members’ use of a fitness center is to achieve desired health benefits and improve physical appearance. The prominent factors for using the fitness center were the comforts of the facilities and the ability to commit to exercise through fixed times for group activities, bookings, payments, and training agreements. Female members also valued the fitness center as a safe place for physical activity. Still, being physically active to the degree one wants is challenging, even for some long-term members.

From a public health perspective, the findings in this study point to commitment and having access to safe and easily accessible arenas for physical activity as being possible drivers for physical activity maintenance.

Further research is required to quantify the knowledge from this study. Doing a questionnaire-based survey with a randomly selected sample of fitness center users is recommended.

Abbreviations

Systematic text condensation

Warburton D, Nicol C, Bredin S. Health benefits of physical activity: the evidence. CMAJ. 2006;174(6):801–9.

Article   Google Scholar  

World Health Organization. Global recommendations on physical activity for health. Geneva: World Health Organization; 2010.

Google Scholar  

Kahlmeier S, Wijnhoven T, Alpiger P, Schweizer C, Breda J, Martin B. National physical activity recommendations: systematic overview and analysis of the situation in European countries. BMC Public Health. 2015;15:133.

Hallal P, Andersen L, Bull F, Guthold R, Haskell W, Ekelund U. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet. 2012;380(9838):247–57.

Bauman A, Reis R, Sallis J, Wells J, Loos RJ, Martin BW. Correlates of physical activity: why are some people physically active and others not? Lancet. 2012;380(9838):258–71.

Humpel N, Owen N, Leslie E. Environmental factors associated with adults’ participation in physical activity: a review. Am J Prev Med. 2002;22(3):188–99.

Powell LM, Chaloupka FJ, Slater SJ, Johnston LD, O'Malley PM. The availability of local-area commercial physical activity-related facilities and physical activity among adolescents. Am J Prev Med. 2007;33(4 Suppl):S292–300.

Eriksson U, Arvidsson D, Sundquist K. Availability of exercise facilities and physical activity in 2,037 adults: cross-sectional results from the Swedish neighborhood and physical activity (SNAP) study. BMC Public Health. 2012;12:607.

Tucker P, Gilliland J. The effect of season and weather on physical activity: a systematic review. Public Health. 2007;121(12):909–22.

Article   CAS   Google Scholar  

Mendonca G, Cheng LA, Melo EN, De Farias Junior JC. Physical activity and social support in adolescents: a systematic review. Health Educ Res. 2014;29(5):822–39.

Allender S, Cowburn G, Foster C. Understanding participation in sport and physical activity among children and adults: a review of qualitative studies. Health Educ Res. 2006;21(6):826–35.

Tappe K, Tarves E, Oltarzewski J, Frum D. Habit formation among regular exercisers at fitness centers: an exploratory study. J Phys Act Health. 2013;10(4):607–13.

Maher JP, Conroy DE. Habit strength moderates the effects of daily action planning prompts on physical activity but not sedentary behavior. J Sport Exerc Psychol. 2015;37(1):97–107.

Koring M, Richert J, Parschau L, Ernsting A, Lippke S, Schwarzer R. A combined planning and self-efficacy intervention to promote physical activity: a multiple mediation analysis. Psychol Health Med. 2012;17(4):488–98.

Caudroit J, Boiche J, Stephan Y. The role of action and coping planning in the relationship between intention and physical activity: a moderated mediation analysis. Psychol Health. 2014;29(7):768–80.

Barz M, Lange D, Parschau L, Lonsdale C, Knoll N, Schwarzer R. Self-efficacy, planning, and preparatory behaviours as joint predictors of physical activity: a conditional process analysis. Psychol Health. 2016;31(1):65–78.

Hansen BH, Ommundsen Y, Holme I, Kolle E, Anderssen SA. Correlates of objectively measured physical activity in adults and older people: a cross-sectional study of population-based sample of adults and older people living in Norway. Int J Public Health. 2014;59(2):221–30.

Owen KB, Smith J, Lubans DR, Ng JY, Lonsdale C. Self-determined motivation and physical activity in children and adolescents: a systematic review and meta-analysis. Prev Med. 2014;67:270–9.

Rhodes RE, Smith NE. Personality correlates of physical activity: a review and meta-analysis. Br J Sports Med. 2006;40(12):958–65.

Kohl HW, Craig CL, Lambert EV, Inoue S, Alkandari JR, Leetongin G, Kahlmeier S. The pandemic of physical inactivity: global action for public health. Lancet. 2012;380(9838):294–305.

Ulseth A. Social integration in modern Sport: commercial fitness Centres and voluntary sports clubs. ESMQ. 2004;4:95–115.

Ulseth A. Treningssentre og idrettslag. Konkurrerende eller supplerende tilbud? Institute for Social Research, Norway. ISF. 2003:2.

Eurobarometer S. Sport and Physical Activity. Brussel: TNS Opinion & Social; 2014.

Thidemann A, Rekdal, KE. Treningssenterbransjen 2016. VIRKE Trening, 2016.

Kirkegaard KL, Østerlund K. Fitnesskultur mellem forening og forretning. Danish Institute for Sports Studies: Aktive fitnessudøveres træningsmotiver, tilfredshed og selvvurderede sundhed; 2010.

MacIntosh E, Law B. Should I stay or should I go? Exploring the decision to join, maintain, or cancel a fitness membership. Managing Sport & Leisure. 2015;20(3):191–210.

Ulseth A. New opportunities- Complex Motivations: Gender Differences in Motivation for Physical Activity in the Context of Sports Clubs and Fitness Centers. IJASS. 2008;Vol. 20(1):44–66.

Kilpatrick M, Hebert E, Bartholomew J. College students’ motivation for physical activity: differentiating men’s and women’s motives for sport participation and exercise. J Am Coll Heal. 2005;54(2):87–94.

Mullen S, Whaley D. Age, gender, and fitness Club membership: factors related to initial involvement and sustained participation. IJSEP. 2010;8(1):24–35.

Ashton LM, Hutchesson MJ, Rollo ME, Morgan PJ, Thompson DI, Collins CE. Young adult males’ motivators and perceived barriers towards eating healthily and being active: a qualitative study. Int J Behav Nutr Phys Act. 2015;12:1–10.

Middelkamp PJC, Steenberg B. The transtheoretical model and exercise behaviour of members in fitness clubs. J Fitness Res. 2015;4(2).

Sivesind KH, Lorentzen H, Selle P, Wollebæk D. The voluntary sector in Norway: Composition, Changes, and Causes. Oslo: Institute for Social Research. Report. 2002:2.

Roberts GC, Treasure D, Conroy DE. Understanding the dynamics of motivation in sport and physical activity: an achievement goal interpretation. In: Handbook of sport psychology. 3rd ed. Hoboken: John Wiley & Sons Inc; 2007.

Gollwitzer PM, Sheeran P. Implementation Intentions and Goal achievement: a meta-analysis of effects and processes. Advances in experimental social psychology: Academic Press; 2006.

Garratt A, Bjertnaes OA, Krogstad U, Gulbrandsen P. The patient experiences questionnaire PasOpp in somatic outpatient clinics. Tidsskr Nor Laegeforen. 2005;125(4):421–4.

PubMed   Google Scholar  

Malterud K. Systematic text condensation: a strategy for qualitative analysis. Scand J Public Health. 2012;40(8):795–805.

MindManager. https://www.mindjet.com/mindmanager/ . Accessed 29 Oct 2015.

Malterud K. Qualitative research: standards, challenges, and guidelines. Lancet. 2001;358(9280):483–8.

Lichtenstein MB, Griffiths MD, Hemmingsen SD, Stoving RK. Exercise addiction in adolescents and emerging adults - validation of a youth version of the exercise addiction inventory. J Behav Addict. 2018;7(1):117–25.

Eriksson L, Baigi A, Marklund B, Lindgren EC. Social physique anxiety and sociocultural attitudes toward appearance impact on orthorexia test in fitness participants. Scand J Med Sci Sports. 2008;18(3):389–94.

Bratland-Sanda S, Sundgot-Borgen J. “I'm concerned - what do I do?” recognition and management of disordered eating in fitness center settings. Int J Eat Disord. 2015;48(4):415–23.

Bryan G, Karlan D, Nelson S. Commitment Devices. Annual Review of Economics. 2010;2(1):671–98.

Rogers T. Commitment Devices. Using initatives to change healt behavior. JAMA. 2014;311(20):2065–6.

Chan CB, Ryan DA, Tudor-Locke C. Relationship between objective measures of physical activity and weather: a longitudinal study. Int J Behav Nutr Phys Ac. 2006;3:21.

Molanorouzi K, Khoo S, Morris T. Motives for adult participation in physical activity: type of activity, age, and gender. BMC Public Health. 2015;15:66.

Download references

Acknowledgements

We thank all the participants, Hilde Thommesen Holck (CEO of 3 T-Fitness Center), Tor Albert Thommesen (Chairman of the Board of 3 T-Fitness Center), The Research Council of Norway, and 3 T-Fitness Center for involvement in and contributions to the project.

This research was funded 50/50 by 3 T-Fitness Center and The Research Council of Norway with grant number 239657.

Availability of data and materials

In the anonymized transcripts it is possible to identify the participants, and restrictions therefore apply to the availability of these data. Due to regulations of The Regional Committee for Medical and Health Research Ethics, we have to secure the anonymity of the participants. Parts of the anonymized transcripts are available from the corresponding author on reasonable request.

Author information

Authors and affiliations.

Department of Public Health and Nursing, Norwegian University of Science and Technology, P.O.Box 8905, 7491, Trondheim, Norway

Liv Riseth, Torunn Hatlen Nøst, Tom I. L. Nilsen & Aslak Steinsbekk

3T- Fitness Center, Vestre Rosten 80, 7075, Tiller, Norway

Clinic of Anaesthesia and Intensive Care, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway

Tom I. L. Nilsen

You can also search for this author in PubMed   Google Scholar

Contributions

LR and AS designed and planned the project. LR collected data, performed data analysis, and drafted and completed the manuscript. AS participated in every part of the data analysis. AS, TILN, and THN participated in analysis meetings and commented on the manuscript. All authors read and approved the final version to be published.

Corresponding author

Correspondence to Liv Riseth .

Ethics declarations

Ethics approval and consent to participate.

The study was submitted to The Regional Committee for Medical and Health Research Ethics in Central Norway (2014/1870 REK Midt) and they assessed it as not being health research and therefore not needing their approval. The study was approved by NSD — Norwegian Centre for Research Data (NSD 40604/3/SSA).

The study was conducted in accordance with the Declaration of Helsinki. Participants received written and oral information about the project before the consent form was signed. The interviewer informed the participants of their right to withdraw from the study at any time. The project was considered to have low or no risk for the participants. Confidentiality was ensured by keeping transcripts anonymized and audio files locked in a filing cabinet. Data was only available to the authors.

Consent for publication

Not applicable.

Competing interests

First author is an Industrial Ph.D. candidate and was employed in the administration of 3 T-Fitness Center at the time of data collection and writing of this article. The authors declare that there is no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Cite this article.

Riseth, L., Nøst, T.H., Nilsen, T.I.L. et al. Long-term members’ use of fitness centers: a qualitative study. BMC Sports Sci Med Rehabil 11 , 2 (2019). https://doi.org/10.1186/s13102-019-0114-z

Download citation

Received : 06 December 2017

Accepted : 15 February 2019

Published : 21 February 2019

DOI : https://doi.org/10.1186/s13102-019-0114-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Fitness center
• Physical activity
• Long-term members
• Qualitative methods

BMC Sports Science, Medicine and Rehabilitation

ISSN: 2052-1847

• General enquiries: [email protected]

• Systematic Review
• Open access
• Published: 17 May 2024

Effect of exercise-based cancer rehabilitation via telehealth: a systematic review and meta-analysis

• Ladislav Batalik 1 , 2 , 3 ,
• Katerina Chamradova 1 , 4 ,
• Petr Winnige 1 ,
• Filip Dosbaba 1 , 3 ,
• Katerina Batalikova 1 ,
• Daniela Vlazna 1 , 3 , 5 ,
• Andrea Janikova 6 ,
• Garyfallia Pepera 7 ,
• Hammoda Abu-Odah 8 &
• Jing Jing Su 8 , 9  

BMC Cancer volume  24 , Article number:  600 ( 2024 ) Cite this article

Metrics details

Exercise-based cancer rehabilitation via digital technologies can provide a promising alternative to centre-based exercise training, but data for cancer patients and survivors are limited. We conducted a meta-analysis examining the effect of telehealth exercise-based cancer rehabilitation in cancer survivors on cardiorespiratory fitness, physical activity, muscle strength, health-related quality of life, and self-reported symptoms.

PubMed, Web of Science, and reference lists of articles related to the aim were searched up to March 2023. Randomized controlled clinical trials were included comparing the effect of telehealth exercise-based cancer rehabilitation with guideline-based usual care in adult cancer survivors. The primary result was cardiorespiratory fitness expressed by peak oxygen consumption.

A total of 1510 participants were identified, and ten randomized controlled trials ( n  = 855) were included in the meta-analysis. The study sample was 85% female, and the mean age was 52.7 years. Meta-analysis indicated that telehealth exercise-based cancer rehabilitation significantly improved cardiorespiratory fitness (SMD = 0.34, 95% CI 0.20, 0.49, I2 = 42%, p  < 0.001) and physical activity (SMD = 0.34, 95% CI, 0.17, 0.51, I2 = 71%, p  < 0.001). It was uncertain whether telehealth exercise-based cancer rehabilitation, compared with guideline-based usual care, improved the quality of life (SMD = 0.23, 95%CI, -0.07, 0.52, I2 = 67%, p  = 0.14) body mass index (MD = 0.46, 95% CI, -1.19, 2.12, I2 = 60%, p  = 0.58) and muscle strength (SMD = 0.07, 95% CI, -0.14, 0.28, I2 = 37%, p  = 0.51).

This meta-analysis showed that telehealth exercise cancer rehabilitation could significantly increase cardiorespiratory fitness and physical activity levels and decrease fatigue. It is uncertain whether these interventions improve quality of life and muscle strength. High-quality and robust studies are needed to investigate specific home-based exercise regimens in different cancer subgroups to increase the certainty of the evidence.

Peer Review reports

Introduction

According to the latest global cancer data estimates cancer burden rose by approximately 19 million new cases and ten million cancer deaths in 2020. Cancer incidence is expected to continue to rise, with the global cancer burden projected to be 50% higher in 2040 than in 2020 [ 1 , 2 ]. Therefore, it is essential to develop sustainable approaches to cancer treatment and prevention. While improving cancer treatment and supportive care has reduced mortality rates, many individuals still experience continuing physical and psychological cancer treatment-related side effects, especially fatigue, pain, muscle loss, or depression [ 3 , 4 , 5 , 6 ]. In addition, adverse effects on the cardiovascular system and worsened cardiovascular risk of survivors have recently been demonstrated, supporting the development of the field of cancer [ 7 , 8 ]. Therefore, there is a need for long-term systematic supportive care for cancer, highlighting the need for evidence-based rehabilitation interventions tailored to this population.

Exercise-based interventions are increasingly recognized as a cornerstone of rehabilitation for cancer patients and survivors [ 9 ]. Evidence from meta-analyses demonstrates that exercise and physical activity can provide a range of physical and psychosocial benefits that can reduce the side effects of cancer treatment [ 10 , 11 ]. The latest research has demonstrated that exercise-based rehabilitation can significantly enhance cancer survivors' quality of life, cardiorespiratory fitness, and fatigue [ 12 ]. Despite the benefits, geographic barriers, a deficit of rehabilitation centers, low referrals, and other factors limit access to exercise-based rehabilitation programs [ 13 ].

Telehealth has the potential to revolutionize healthcare delivery by enhancing accessibility, reducing costs, improving quality, and personalizing medicine for patients [ 14 ]. In recent years, telehealth has gained popularity as a viable solution to the challenges faced by cancer survivors seeking access to exercise-based rehabilitation programs. An increasing number of smartphone users and internet coverage have made telehealth an attractive approach to the challenges of a resource-limited healthcare system [ 15 ]. The integration of telehealth faces several challenges, including regulatory issues, security concerns, and a need for more scientific recommendations [ 16 ]. Despite these challenges, telehealth has been shown to be a feasible and effective alternative in different healthcare fields, including cancer rehabilitation [ 17 ]. Furthermore, telehealth has enabled access to rehabilitation delivery during the pandemic, providing increased safety and convenience for a burdened patient population and holding the potential to elevate beyond the current best practice [ 18 ].

This topic delves into the effectiveness of exercise-based cancer rehabilitation via telehealth and its potential to improve outcomes for cancer survivors. The aim is to provide insights into the feasibility and efficacy of telehealth-based rehabilitation interventions and analyse their impact on cancer survivors' physical and psychological outcomes.

A comprehensive literature search was carried out to determine the impact of telehealth-based exercise interventions on cancer patients and survivors. The systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Guidelines 2020 [ 19 ], and the review protocol was registered in the Prospective Register of Systematic Reviews (PROSPERO) registry (CRD42023395521).

Eligibility criteria

The Populations, Interventions, Comparisons, Outcomes, and Study Designs (PICOS) framework was used to describe eligibility criteria. The inclusion criteria were: 1) P – patients or survivors with a medical diagnosis of cancer during or post treatment (e.g., chemotherapy, surgery); 2) I – intervention arm received telehealth exercise-based cancer rehabilitation intervention delivered by Information and communication technologies (e.g., smartphone, web-platform, internet, or video-monitoring) including the use of telemonitoring and telecoaching tools (e.g., telephone calls, text messages, emails); 3) C – control comparator group received conventional treatment/ rehabilitation, usual care, or waitlist intervention. 4) O – outcomes reported were cardiorespiratory fitness, physical activity levels, quality of life, fatigue, pain, muscle strength, body mass index and occurrence of adverse events; and 5) S – randomized controlled trials (RCTs). The searches were limited to studies published in English. The description of the exercise intervention consisted of identifying the following components: telemonitoring of the exercise and the telecoaching method. Further, a description of the exercise was included: prescription of intensity and session duration using wearable sensors (e.g., heart rate monitors, accelerometers, or pedometers). Description of telecoaching/teleconsulting: the variability of supervising, educating, and motivating approach for physical exercise.

The criteria for exclusion were defined as 1) quasi-experimental, qualitative, or case studies; 2) study protocols; 3) conference abstracts; and 4) full-text articles that were unavailable even after contacting the authors.

Search methods

An electronic literature search was conducted in March 2023 through the PubMed database and the Web of Science metasearch engine. The inclusion of these two databases is to allow a comprehensive search as PubMed focuses mainly on medicine and biomedical sciences with large number of keywords per search, whereas Web of Science cover most scientific fields. Web of Science includes the oldest publications with archived records dated back to 1900 [ 20 ]. The search was structured to identify the effect of telehealth exercise interventions published since 2000 in English. Search terms included exercise-based terminology, cancer, and telehealth medicine terms. Telehealth, in exercise-based terminology, involves the use of technology to deliver remote healthcare services, including virtual consultations, exercise prescriptions, and monitoring. The detailed selection process involved a keyword search summarized in Supplementary Table S1. Authors conducting the study selection process hand-searched the references of topical systematic reviews to identify relevant studies not captured in the search.

The relevant articles were chosen based on keywords after an initial literature search. Two independent reviewers (KF & PW) manually assessed the articles based on their titles and abstract. After the first round, the full text of the articles was assessed for relevance based on the inclusion criteria. Any disagreements were resolved by consensus or by discussing with a third reviewer (KB). All reports were combined in cases with multiple publications for a study, and the version with the most systematic data was selected for analysis. The authors of the studies were also contacted to request additional information when necessary.

Study quality and risk of bias

Two researchers (JJS & LB) assessed methodological quality independently using the Cochrane risk of bias tool 2.0. Study quality was assessed concerning seven domains of bias: allocation bias, selection bias, performance bias, attrition bias, detection bias, reporting bias, and an auxiliary domain (other bias) [ 21 ]. The bias domains of the tool were identified to purposefully cover all fundamental bias mechanisms in RCTs and it is the most commonly used tool (about 100% for Cochrane reviews and 31% for non-Cochrane reviews) [ 21 ]. Risk of bias was judged as unclear or high when data were insufficient or uncertain. The funnel plots analysis was conducted to evaluate publication bias.

Data extraction

All study data were extracted independently by two authors (KCH & PW), and any disagreement was resolved by discussing with a third author (KB). The authors extracted data, including ( a ) origin of the articles: authors, year, and country; ( b ) sample characteristics: sample size, age, sex, treatment, and diagnosis; ( c ): group design: intervention given to experimental and control group, ( d ) duration, ( e ) health outcomes and instruments, and ( f ) completion rate, ( g ) Frequency, Intensity, Type, and Time within the exercise group.

Data analysis

Review Manager 5.3 (Nordic Cochrane Centre, Cochrane Collaboration) was used for data pooling when three or more studies reported the same outcome. Cardiorespiratory fitness was the primary outcome. Data from immediately after intervention completion was used for studies that reported outcomes at different endpoints. An intervention effect was expressed as Cohen’s d when studies used different tools/questionnaires to assess the same outcome, calculating standard mean difference (SMD) with a 95% confidence interval (CI) of post-intervention results between groups. Mean difference (MD) was used for pooling studies using the same instrument/tool. Cohen's d > 0.8 represents a large, 0.5–0.8 a medium, and 0.2–0.5 a small effect [ 22 ]. Risk ratios were calculated for dichotomous outcomes with the Mantel–Haenszel method. Heterogeneity was evaluated using I 2 and τ2; I 2  > 50% and τ2 with p ‐value < 0.1 suggested significant heterogeneity, and thus, a random effect model was used. Leave-one-out sensitivity analysis was conducted when the pooled effect showed significant heterogeneity (I 2  > 50%) [ 23 ]. Subgroup analysis was performed further to investigate the intervention effect across different study characteristics (e.g., study duration, control group intervention) for outcome variables with at least three studies in each subgroup. In this study subgroup analysis was only conducted for the cardiorespiratory fitness outcome regarding different intervention duration for the limited number of studies available for other outcome parameters.

A database and meta-search engine search were performed and identified 4981 records. After screening the titles and abstracts, it was found that 719 publications did not meet the inclusion criteria. Of the 101 full-text publications, 91 records were excluded. Finally, ten publications met the inclusion criteria for this systematic review and meta-analysis. Figure  1 provides an overview of the study flow process.

Flow diagram detailing the search strategy

Studies included

The characteristics and findings of the ten included studies [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ] are shown in Table  1 . All were of RCT design. The methodological characteristics of studies and adherence and safety outcomes within the studies are presented in Supplementary Table S2 and S3. Three of the ten studies were conducted in the USA [ 30 , 32 , 33 ]; one each was conducted in the United Kingdom [ 29 ], Canada [ 31 ], France [ 24 ], Spain [ 27 ], Netherlands [ 28 ], Germany [ 26 ], and China [ 25 ].

Risk of bias assessment

Figure  2 a,b summarizes the risks of bias assessment. Two major concerns were the inadequate report of randomization or allocation processes and the selective reporting bias. Referring to trial registration and/or study protocol, three studies were at risk of selective reporting for omitting some proposed outcomes [ 25 , 26 , 33 ]; and the rest provided no registration records for evaluation. Participants were not blinded across the studies, which is understood as a common challenge for eHealth interventions to ensure informed consent. In addition, between-group comparability at baseline was unclear in three studies [ 26 , 29 , 31 ].

a Risk of bias summary. b  Risk of bias graph

Sampling and recruitment

Sample size calculation was reported in 9/10 studies. The number of participants in the included studies ranged from 34 to 222 participants. A total of 855 participants, with 84.9% representation of the female population, were included in the systematic review. A few studies [ 28 , 32 ] reported recruitment difficulties and needed to include the target number of participants. Reasons were given as insufficient patient motivation or competing demands on time. Recruitment strategies were reported via email [ 31 ]; hospital database and/or hospital referral [ 28 ]; a combination of methods, personal recruitment, media and community presentation [ 32 ]; postoperatively before discharge [ 26 ]; oncologist discussion [ 27 , 30 ]; specialist nurses [ 29 ] or community and clinic-based recruitment [ 33 ]. In 2/10 studies, the recruitment method was not specified.

Characteristics of participants

The participants in the studies were cancer survivors of various types. Table 1 reports information on treatments. Breast cancer was the most common 6/10. Other studies included patients with glioma, colorectal cancer, or combined studies of participants with colorectal/breast cancer or colorectal/breast/prostate cancer. The age of the patients ranged from 37 to 73, with an average of 52.7 years. A wide range of age categories was seen most often.

Intervention

Telehealth cancer exercise interventions were different in the included studies. The intervention period ranged from 8 to 27 weeks. The studies used various forms of telemonitoring of exercises and telecoaching (consultation, feedback). In most 8/10 studies, the exercise intervention was carried out completely autonomously with post exercise telecoaching [ 24 , 26 , 28 , 29 , 30 , 31 , 32 , 33 ]. The supervised real-time telemonitoring of the exercise was included in 2/10 interventions [ 25 , 27 ]. Usually, exercise protocols use telemonitoring and post-exercise telecoaching methodology. Half of the studies used HR monitors for telemonitoring exercise intensity [ 24 , 25 , 28 , 31 , 33 ]. Telecoaching was most often carried out in a telephone call, text messages, or a combination. Telemonitoring and telecoaching in the studies were performed by physiotherapists [ 26 , 28 ], exercise physiologists [ 24 , 31 ], exercise specialists [ 25 , 33 ], or research staff [ 27 , 29 , 32 ]. Telecoaching was provided in the range of 1—4 times a week, most often once weekly. The content of telecoaching included checking compliance with the exercise prescription, feedback, the occurrence of adverse events, technical support, or solving obstacles to exercise.

The included studies varied exercise prescriptions (Frequency, Intensity, Time, and Type). The frequency most often prescribed was 3 exercise sessions per week [ 24 , 26 , 27 , 28 , 29 , 33 ]. The total range was 2—5 sessions per week. The methodology for prescribing exercise intensity was determined differently, usually at a moderate intensity level [ 24 , 25 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. Furthermore, the maximum heart rate obtained by exercise test [ 27 , 28 , 30 , 31 ], anaerobic threshold, [ 24 ] or evaluation of the rate of perceived exertion on the Borg scale (6—20) [ 25 , 27 ] was used to reach moderate intensity level. The duration of the exercise session was most often in the range of 30—50 min [ 24 , 25 , 26 , 27 , 29 , 32 ]. There were 3/10 studies that prescribed a target time exercise threshold per week [ 30 , 31 , 33 ]. The prescription of the intensity or duration of the exercise session was progressive and individualized in 5/10 studies [ 24 , 26 , 27 , 32 , 33 ].

There were 3/10 studies that included a combination of aerobic strength training where the aerobic component dominated [ 24 , 30 , 32 ]. The rest of the studies were designed purely aerobically on the principle of exercise-based rehabilitation. Three studies prescribed the exercise modality of walking, cycling, or home exercise on ergometers [ 24 , 30 , 32 ]. Two studies included a range of variable aerobic methods, including walking, cycling, ball games, and swimming [ 28 , 31 ]. The exercise modality was not adequately defined in four studies in the methodological description [ 25 , 30 , 31 , 33 ]. Studies involving resistance training components used bodyweight exercises [ 26 , 27 ] or resistance bands [ 24 ]. Resistance prescription with bodyweight exercises was prescribed two to three times each week and consisted of two sets of 8–15 repetitions for major muscle groups at moderate to vigorous intensity [ 26 , 27 ]. Exercise using resistance bands was prescribed once weekly and consisted of two sets of 8–12 repetitions on five muscle groups (abdominal, hamstring, quadriceps, triceps, and gluteus maximus) [ 24 ]. In half of the studies, warm-up and cool-down phases were included in exercise prescriptions [ 24 , 25 , 26 , 27 , 32 ].

Control groups

Instructions for control group participants were provided in all studies. Participants from six included studies [ 24 , 26 , 27 , 28 , 29 , 30 ] received a physical activity recommendation based on guidelines. Two studies recommended participants to maintain their regular PA [ 31 , 32 ] and the other two involved supervised exercise in a center [ 25 , 33 ]. In four studies [ 28 , 29 , 30 , 32 ], the participants in the control groups were offered an exercise intervention after the end of the study.

Completion rate

Training diaries [ 24 , 30 , 32 ] or web platform training logs [ 26 , 27 , 28 , 31 ] assessed adherence to the exercise prescription. Adherence to the exercise protocol was reported relatively consistently (Supplementary Table S2), with only 2/10 studies not reporting adherence to the exercise protocol [ 25 , 29 ]. A high retention rate (range 74 – 94%) was reported in five studies [ 24 , 26 , 27 , 28 , 29 , 33 ]. One study reported high levels of adherence but did not define their methodology [ 31 ]. Half of the studies report moderate to high adherence (65—90%) with target exercise intensity [ 24 , 26 , 28 , 30 , 32 ]. Another study reports high adherence with target exercise time at the correct intensity per week [ 31 ]. The remaining 4/10 studies did not report exercise adherence outcomes.

Occurrence of adverse effects

The occurrence of adverse effects was not reported in half of the studies. Three out of five studies reported no adverse events or death recorded [ 24 , 26 , 27 ]. Gering et al. reported one mild adverse effect associated with exercise, specifically an aggravation of pre-existing osteoarthritis-related knee pain [ 28 ], and Rogers et al. reported one serious adverse event associated with exercise, specifically a bone fracture. Another 12 unrelated adverse severe events that occurred were reported [ 33 ].

Cardiorespiratory fitness

All included studies investigated the effect of telehealth exercise-based rehabilitation intervention on cardiorespiratory fitness levels measured by pVO2 and a six-minute walk test. Data pooling of these studies indicated that exercise significantly improved cardiorespiratory fitness [ n  = 10, SMD = 0.34, 95% CI 0.20, 0.49, I2 = 42%, p  < 0.001] (Fig.  3 ). Subgroup analysis showed that studies with intervention duration of more than 12 weeks showed a slightly higher magnitude of effect [ n  = 4, SMD = 0.39, 95% CI (0.04, 0.74), I 2  = 27%, p  = 0.006] compared with intervention duration ≤ 12wks [ n  = 6, SMD = 0.36, 95% CI (0.08, 0.63), I 2  = 54%, p  < 0.001]; both with small effect size. The selection of a 12-week timeframe is based on the frequent use of this duration in early cancer rehabilitation interventions, which allows for a significant improvement in cardiorespiratory fitness level during/immediately after cancer treatment. The funnel plot analysis of all the included studies observed a symmetrical and pyramid-like scatter of points at both sides of the weighted average standard mean difference, indicating low publication bias (Fig.  4 ). The subgroup analysis figures are presented in Supplementary Fig. 1.

Effect of telehealth exercise on cardiorespiratory fitness

Funnel plot of comparison: cardiorespiratory fitness

Physical activity

Seven studies measured physical activity level in terms of minutes of activities per week, and the intervention showed significant improvement [ n  = 7; SMD = 0.34, 95% CI (0.17, 0.51), I 2  = 71%, p  < 0.001]. The high heterogeneity was resolved by removing one study with a large effect size [ 32 ] that provided weekly phone calls and an accelerometer to promote daily physical activity; and the significant improvement retained [ n  = 6, SMD = 0.26, 95%CI (0.09, 0.43), P  = 0.003] (Fig.  5 ). By removing one that provided control group supervised exercise training, the significant effect of tele-health exercise-based rehabilitation and the heterogeneity remained [ n  = 6, SMD = 0.33, 95% CI 0.12, 0.54, I 2  = 75%, p  = 0.002].

Effect of telehealth cancer exercise on physical activity

Health-related quality of life

Seven studies measured health-related quality of life using EORTC QLQ C30 (Global status subscale [ 24 , 26 , 27 ] and Physical functioning subscale [ 30 ] and SF-36 (Physical functioning subscale) [ 25 , 32 , 33 ]. Data pooling showed no significant improvement of telehealth cancer exercise intervention on HRQoL [ n  = 7; SMD = 0.23, 95%CI (-0.07, 0.52), I 2  = 67%, p  = 0.14] (Fig.  6 ). The sensitivity analysis did not show significant deviation.

Effect of telehealth cancer exercise on HRQoL

Self-reported fatigue was measured by five studies, and data pooling showed significant improvement for patients receiving telehealth exercise [ n  = 5, SMD = -0.28, 95% CI (-0.47, -0.09), I 2  = 24%, p  = 0.004] (Fig.  7 ).

Effect of telehealth cancer exercise on fatigue

Body mass index

Body mass index was measured by five studies, and data pooling showed no significant improvement [ n  = 5, MD = 0.46, 95% CI (-1.19, 2.12), I 2  = 60%, p  = 0.58]. (Fig.  8 ).

Effect of telehealth cancer exercise on BMI

Strength was measured by four studies, including handgrip strength and leg muscle strength [ 24 , 25 , 27 , 33 ]. Two studies showed significant improvements [ 25 , 27 ]. The detailed descriptive results are shown in Table  2 .

Anxiety and depression

Two studies measured anxiety and depression using Hospital Anxiety and Depression Scale and both showed no significant improvement (Table  2 ) [ 24 , 33 ].

This meta-analysis suggested the effectiveness of telehealth exercise-based rehabilitation intervention in improving cardiorespiratory fitness, physical activity level, and fatigue among cancer patients when compared with conventional intervention. As the toxic cancer treatment effect on the cardio-respiratory system is increasingly recognized, the prominent improvement of cardiorespiratory fitness (1.68 ml/kg/min increment in peak VO2 or 104.54 m distance improvement in 6MWT) should be emphasized for cancer patients. In addition, improving physical activity and cardiorespiratory fitness among cancer patients without triggering/intensifying fatigue is challenging [ 34 ]. The reduction in fatigue suggests that telehealth is an ideal alternative to allow patients feasibility of physical activity at their own pace and convenience. The extensive use of telemonitoring (e.g., heart rate monitor, televideo, and accelerometer) and professional call support should be acknowledged to supervise/reassure the patients and prevent adverse events or non-adherence.

The adherence reported in the exercise interventions was high, averaging around 78.5%, corresponding with center-based cancer rehabilitation exercise interventions, where high adherence of around 92% has been reported [ 35 ]. Furthermore, increased cardiorespiratory fitness levels may correlate with higher exercise adherence [ 36 ]. Therefore, patients may be considered sufficiently motivated to exercise in their environment, despite differences in exercise prescription [ 37 ]. However, exercise prescription may differ between cancer subgroups. Our meta-analysis indicates that an effective aerobic exercise regimen typically involves sessions occurring two to five times per week, lasting between 20 to 50 min each, performed at a moderate intensity ranging from 60 to 80% of maximum heart rate, and may be supplemented with resistance exercise. However, while our analysis highlights the importance of resistance training in systematic cancer rehabilitation, determining the optimal prescription for telehealth cancer rehabilitation resistance exercises requires further investigation due to inconclusive muscle strength results. Previous studies have shown significant improvements in muscle strength with resistance training, underscoring its importance in cancer rehabilitation [ 38 , 39 ].

The results of this meta-analysis demonstrated significant improvements in cardiorespiratory fitness and PA after telehealth cancer exercise rehabilitation compared to the control group but not for HRQoL and BMI change, contrary to previous center-based exercise rehabilitation demonstrating the efficacy of exercise in multiple cancer subgroups [ 40 , 41 , 42 ]. The failure to observe improvements in HRQoL contradicts the positive within-group changes reported by most studies. This discrepancy prompts a closer inspection of the measures employed or the specific aspects of HRQoL affected by telehealth interventions, as well as the potential influence of the control group's instructions from healthcare professionals adhering to international guidelines. Discrepancies in HRQoL may stem from nuanced measurement aspects, while BMI findings are limited by a small study pool and a proportion of patients without baseline abnormalities. The small number of studies and some proportion of patients without BMI abnormality at baseline may explain the lack of effect on BMI. It is also likely that the short duration of exercise would not be expected to necessarily alter BMI, nor would this necessarily be desirable in a group of patients with cancer unless accompanied by improvements in muscle strength. On the other hand, the importance of considering the duration and intensity of interventions in future studies is needed to stress. Finally, improved cardiorespiratory fitness and optimal PA support the clinical relevance of developing telehealth exercise-based cancer rehabilitation interventions because they reduce mortality risk and cancer burden [ 43 , 44 , 45 , 46 ]. A more comprehensive assessment considering psychosocial well-being and disease-related symptoms is essential for a holistic understanding of telehealth interventions in cancer rehabilitation.

In addition, longer-term maintenance is necessary to preserve the health benefits and reduced risk. Part of included study sample examined the long-term effect (six-month to two-year follow-up), and although it would be reasonable to assume that telehealth exercise would lead to longer-term improvements in clinical outcomes as has been demonstrated in chronic populations elsewhere [ 47 , 48 , 49 ], more robust evidence based on a methodologically rigorous design will be needed to provide satisfactory long-term evidence in survivors [ 50 , 51 , 52 ]. However, limited reports suggest that telehealth exercise interventions have the potential to be an effective strategy for maintaining health benefits.

Furthermore, it is important to discuss exercise safety, as many clinicians concerning exercise without direct professional supervision, as is standard with center-based rehabilitation models. Patients may be at risk of safety and adherence to an exercise prescription, leading to unsatisfactory results. In our meta-analysis, half of the studies reported adverse events, and only Rogers et al. reported a single severe exercise-related event consistent with the low rates reported in home-based exercise for cardiac population [ 53 ]. However, comparative research with supervised care in a sufficiently large study sample and inclusion of adverse event reporting in future controlled trials is needed to conclude exercise-related risk in cancer survivors.

As noted above, increased cardiovascular risk and a higher prevalence of cardiovascular risk factors have been found in survivors [ 54 ]. In focus on core cardiac preventive components [ 55 , 56 ], only minor studies included cardiac output [ 26 , 29 ]. Therefore, future research needs to include the systematic cardiac assessment of core prevention components that can potentially optimize cardiovascular risk, especially lipid, blood pressure, or diabetes management, through exercise-based rehabilitation [ 57 , 58 , 59 ]. Ultimately, exercise-based telehealth is an alternative form to delivering cancer rehabilitation exercise services through information and telecommunication technologies (PC, smartphone, internet, and videoconferencing) [ 8 ]. Based on this, the use of telehealth platforms may lead to increased attractiveness and utility in cancer exercise rehabilitation [ 60 ]. However, the acceptability and usefulness of the telemedicine approach may be limited by factors such as the validity of technological tools, technological literacy or legal clarity, and data protection [ 12 ]. Despite these telehealth challenges, the recent pandemic has dramatically promoted the use of providing digital healthcare strategies that have the potential to overcome barriers, reduce costs, and increase overall cancer exercise rehabilitation utilization [ 61 , 62 , 63 ].

The COVID-19 pandemic has had a significant impact on the normalization and adoption of telehealth-delivered cancer exercise interventions. The need for physical distancing and minimizing in-person interactions during the pandemic has accelerated the use of telehealth as a means of delivering healthcare services, including rehabilitation exercise interventions, to cancer patients. Telehealth allows healthcare providers to remotely monitor and guide exercise interventions, reducing the need for in-person visits and minimizing the risk of exposure to the virus. This has not only ensured the safety of cancer patients but has also provided them with the necessary support and guidance to maintain their physical activity levels during a time when access to traditional exercise facilities may be limited [ 64 ].

Ultimately, the COVID-19 pandemic has had a transformative impact on telehealth-delivered cancer exercise interventions. It has accelerated the adoption and acceptance of telehealth platforms, highlighted the importance of telehealth in ensuring continuity of care, and prompted policy changes to support its widespread use. Telehealth has become a vital tool in delivering exercise interventions to cancer patients, providing them with safe and accessible care during a time of restricted in-person interactions. The lessons learned from the pandemic will likely shape the future of cancer care, with telehealth playing an increasingly prominent role in delivering exercise interventions and supporting the overall well-being of cancer patients.

Limitations

Since a wide range of eligible RCTs were identified to provide a broad perspective on this new area, there are limitations. Firstly, there was language bias as only evidence studies in English were included. For another, only RCT designs are limiting for pragmatic studies in clinical settings. The sample size of some studies analyzed was small, which may lead to the reliability of the results. Also, the limitation of methodologically different exercise prescriptions and heterogeneity of the measurement instruments should be mentioned, which may have affected the results. Secondly, there is heterogeneity in the definition of telehealth used in cancer exercise interventions. Pilot studies have found telehealth exercise interventions to be feasible and generally accepted among participants, but there is marked heterogeneity in specific exercise activities and telehealth modalities [ 18 ]. Thirdly, the different PA recommendations of the control groups could have affected the study findings by potentially reducing the observed differences between the control and intervention groups, impacting participant motivation levels, and introducing confounding variables related to baseline activity levels and the effectiveness of the intervention.

Finally, the meta-analysis results underscore the effectiveness of telehealth exercise-based cancer rehabilitation in improving cardiorespiratory fitness and PA. However, an important avenue for discussion revolves around determining the most effective doses needed to achieve these benefits. The study lacks detailed insights into optimal exercise duration, frequency, and intensity for meaningful outcomes. Future research should delve into these specifics to establish evidence-based guidelines, ensuring the maximum benefits are derived from telehealth interventions in cancer rehabilitation. Clarifying dosage parameters [ 50 ] will contribute to the refinement and standardization of telehealth programs, enhancing their impact on patients' well-being and overall outcomes.

In summary, the results of this meta-analysis showed that telehealth exercise-based cancer rehabilitation interventions could significantly increase cardiorespiratory fitness and PA levels and reduce fatigue. However, these interventions did not significantly improve BMI, quality of life, and muscle strength. Nevertheless, these exercise interventions confirmed high adherence, which lends to further research needed to ensure development in this area. Given the limitations of this meta-analysis and the flaws in methodology rigor (unclear description in randomization and allocation and a lack of blinding), the results need to be interpreted cautiously. High-quality and robust RCTs are needed to investigate specific home-based exercise regimens in different subgroups of patients and cancer survivors.

Availability of data and materials

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Abbreviations

Populations, interventions, comparisons, outcomes, and study designs

Randomized controlled trials

Standard mean difference

Mean difference

Confidence interval

Health related quality of life

Six-minute walk test

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. https://doi.org/10.3322/caac.21660 .

Article   CAS   PubMed   Google Scholar  

Arnold M, Morgan E, Rumgay H, Mafra A, Singh D, Laversanne M, et al. Current and future burden of breast cancer: global statistics for 2020 and 2040. Breast. 2022;66:15–23. https://doi.org/10.1016/j.breast.2022.08.010 .

Article   PubMed   PubMed Central   Google Scholar  

Schmitz KH, Campbell AM, Stuiver MM, Pinto BM, Schwartz AL, Morris GS, et al. Exercise is medicine in oncology: Engaging clinicians to help patients move through cancer. CA Cancer J Clin. 2019N;69(6):468–84. https://doi.org/10.3322/caac.21579 .

Moslehi JJ. Cardiovascular toxic effects of targeted cancer therapies. N Engl J Med. 2016;375(15):1457–67. https://doi.org/10.1056/NEJMra1100265 .

Stein KD, Syrjala KL, Andrykowski MA. Physical and psychological long-term and late effects of cancer. Cancer. 2008;112(11 Suppl):2577–92. https://doi.org/10.1002/cncr.23448 .

Article   PubMed   Google Scholar  

Berman R, Davies A, Cooksley T, Gralla R, Carter L, Darlington E, Scotté F, Higham C. Supportive care: an indispensable component of modern oncology. Clin Oncol (R Coll Radiol). 2020;32(11):781–8. https://doi.org/10.1016/j.clon.2020.07.020 .

Campbell KL, Winters-Stone KM, Wiskemann J, May AM, Schwartz AL, Courneya KS, Zucker DS, Matthews CE, Ligibel JA, Gerber LH, Morris GS, Patel AV, Hue TF, Perna FM, Schmitz KH. Exercise guidelines for cancer survivors: consensus statement from international multidisciplinary roundtable. Med Sci Sports Exerc. 2019;51(11):2375–90. https://doi.org/10.1249/MSS.0000000000002116 .

Batalik L, Filakova K, Radkovcova I, Dosbaba F, Winnige P, Vlazna D, et al. Cancer rehabilitation and telehealth: rationale for future integration in supportive care of cancer survivors. Front Cardiovasc Med. 2022A;15(9):858334. https://doi.org/10.3389/fcvm.2022.858334 .

Article   CAS   Google Scholar  

Dittus KL, Lakoski SG, Savage PD, Kokinda N, Toth M, Stevens D, et al. Exercise-based oncology rehabilitation: leveraging the cardiac rehabilitation model. J Cardiopulm Rehabil Prev. 2015;35(2):130–9. https://doi.org/10.1097/HCR.0000000000000091 .

Spence RR, Heesch KC, Brown WJ. Exercise and cancer rehabilitation: a systematic review. Cancer Treat Rev. 2010;36(2):185–94. https://doi.org/10.1016/j.ctrv.2009.11.003 .

Salisbury CE, Hyde MK, Cooper ET, Stennett RC, Gomersall SR, Skinner TL. Physical activity behaviour change in people living with and beyond cancer following an exercise intervention: a systematic review. J Cancer Surviv. 2023. https://doi.org/10.1007/s11764-023-01377-2 .

Batalik L, Winnige P, Dosbaba F, Vlazna D, Janikova A. Home-based aerobic and resistance exercise interventions in cancer patients and survivors: a systematic review. Cancers (Basel). 2021;13(8):1915. https://doi.org/10.3390/cancers13081915 .

Yang EJ, Chung SH, Jeon JY, Seo KS, Shin HI, Hwang JH, et al. Current practice and barriers in cancer rehabilitation: perspectives of korean physiatrists. Cancer Res Treat. 2015;47(3):370–8. https://doi.org/10.4143/crt.2014.084 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Ye J, He L, Beestrum M. Implications for implementation and adoption of telehealth in developing countries: a systematic review of China's practices and experiences. NPJ Digit Med. 2023;6(1):174. https://doi.org/10.1038/s41746-023-00908-6 .

Osei E, Mashamba-Thompson TP. Mobile health applications for disease screening and treatment support in low-and middle-income countries: a narrative review. Heliyon. 2021;7(3):e06639. https://doi.org/10.1016/j.heliyon.2021.e06639 .

Jonasdottir SK, Thordardottir I, Jonsdottir T. Health professionals’ perspective towards challenges and opportunities of telehealth service provision: a scoping review. Int J Med Inform. 2022;167:104862. https://doi.org/10.1016/j.ijmedinf.2022.104862 .

Seron P, Oliveros MJ, Gutierrez-Arias R, Fuentes-Aspe R, Torres-Castro RC, Merino-Osorio C, et al. Effectiveness of telerehabilitation in physical therapy: a rapid overview. Phys Ther. 2021;101(6):pzab053. https://doi.org/10.1093/ptj/pzab053 .

Chang P, Zheng J. Updates in cancer rehabilitation telehealth. Curr Phys Med Rehabil Rep. 2022;10(4):332–8. https://doi.org/10.1007/s40141-022-00372-5 .

Page MJ, Moher D, Bossuyt PM, et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. 2021;372:n160 . https://doi.org/10.1136/bmj.n160 . Published 2021 Mar 29.

Falagas ME, Pitsouni EI, Malietzis GA, Pappas G. Comparison of PubMed, Scopus, web of science, and Google scholar: strengths and weaknesses. FASEB J. 2008;22(2):338–42.

Jørgensen L, Paludan-Müller AS, Laursen DR, Savović J, Boutron I, Sterne JA, Higgins JP, Hróbjartsson A. Evaluation of the cochrane tool for assessing risk of bias in randomized clinical trials: overview of published comments and analysis of user practice in cochrane and non-cochrane reviews. Syst Rev. 2016;5:1–3.

Article   Google Scholar  

Cohen J. Statistical Power Analysis for the Behavioral Sciences. Routledge; 1988. https://doi.org/10.4324/9780203771587 .

Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–58. https://doi.org/10.1002/sim.1186 .

Cornette T, Vincent F, Mandigout S, Antonini MT, Leobon S, Labrunie A, et al. Effects of home-based exercise training on VO2 in breast cancer patients under adjuvant or neoadjuvant chemotherapy (SAPA): a randomized controlled trial. Eur J Phys Rehabil Med. 2016;52(2):223–32.

PubMed   Google Scholar  

Dong X, Yi X, Gao D, Gao Z, Huang S, Chao M, et al. The effects of the combined exercise intervention based on internet and social media software (CEIBISMS) on quality of life, muscle strength and cardiorespiratory capacity in Chinese postoperative breast cancer patients:a randomized controlled trial. Health Qual Life Outcomes. 2019J 26;17(1):109. https://doi.org/10.1186/s12955-019-1183-0 .

Falz R, Bischoff C, Thieme R, Tegtbur U, Hillemanns P, Stolzenburg J, et al. Effect of home-based online training and activity feedback on oxygen uptake in patients after surgical cancer therapy: a randomized controlled trial. BMC Med. 2023;21(1):293. https://doi.org/10.1186/s12916-023-03010-6 .

Galiano-Castillo N, Cantarero-Villanueva I, Fernández-Lao C, Ariza-García A, Díaz-Rodríguez L, Del-Moral-Ávila R, et al. Telehealth system: A randomized controlled trial evaluating the impact of an internet-based exercise intervention on quality of life, pain, muscle strength, and fatigue in breast cancer survivors. Cancer. 2016;122(20):3166–74. https://doi.org/10.1002/cncr.30172 .

Gehring K, Kloek CJ, Aaronson NK, Janssen KW, Jones LW, Sitskoorn MM, et al. Feasibility of a home-based exercise intervention with remote guidance for patients with stable grade II and III gliomas: a pilot randomized controlled trial. Clin Rehabil. 2018;32(3):352–66. https://doi.org/10.1177/0269215517728326 .

Lahart IM, Carmichael AR, Nevill AM, Kitas GD, Metsios GS. The effects of a home-based physical activity intervention on cardiorespiratory fitness in breast cancer survivors; a randomised controlled trial. J Sports Sci. 2018;36(10):1077–86. https://doi.org/10.1080/02640414.2017.1356025 .

Ligibel JA, Meyerhardt J, Pierce JP, Najita J, Shockro L, Campbell N, Newman VA, Barbier L, Hacker E, Wood M, Marshall J, Paskett E, Shapiro C. Impact of a telephone-based physical activity intervention upon exercise behaviors and fitness in cancer survivors enrolled in a cooperative group setting. Breast Cancer Res Treat. 2012;132(1):205–13. https://doi.org/10.1007/s10549-011-1882-7 .

McNeil J, Brenner DR, Stone CR, O’Reilly R, Ruan Y, Vallance JK, Courneya KS, Thorpe KE, Klein DJ, Friedenreich CM. Activity tracker to prescribe various exercise intensities in breast cancer survivors. Med Sci Sports Exerc. 2019;51(5):930–40. https://doi.org/10.1249/MSS.0000000000001890 .

Pinto BM, Papandonatos GD, Goldstein MG, Marcus BH, Farrell N. Home-based physical activity intervention for colorectal cancer survivors. Psychooncology. 2013;22(1):54–64. https://doi.org/10.1002/pon.2047 .

Rogers LQ, Courneya KS, Oster RA, Anton PM, Phillips S, Ehlers DK, McAuley E. Physical activity intervention benefits persist months post-intervention: randomized trial in breast cancer survivors. J Cancer Surviv. 2023. https://doi.org/10.1007/s11764-022-01329-2 .

Jones LW, Alfano CM. Exercise-oncology research: Past, present, and future. Acta Oncol. 2013A;52(2):195–215. https://doi.org/10.3109/0284186X.2012.742563 .

Viamonte SG, Joaquim A, Alves A, Vilela E, Capela A, Ferreira C, et al. Adherence, safety, and satisfaction of a cancer rehabilitation program framework versus community exercise training for cancer survivors: findings from the CORE trial. Support Care Cancer. 2023;31(3):173. https://doi.org/10.1007/s00520-023-07638-1 .

Kampshoff CS, Jansen F, van Mechelen W, May AM, Brug J, Chinapaw MJ, et al. Determinants of exercise adherence and maintenance among cancer survivors: a systematic review. Int J Behav Nutr Phys Act. 2014;2(11):80. https://doi.org/10.1186/1479-5868-11-80 .

Courneya KS, Segal RJ, Gelmon K, Mackey JR, Friedenreich CM, Yasui Y, et al. Predictors of adherence to different types and doses of supervised exercise during breast cancer chemotherapy. Int J Behav Nutr Phys Act. 2014;6(11):85. https://doi.org/10.1186/s12966-014-0085-0 .

Gilchrist SC, Barac A, Ades PA, Alfano CM, Franklin BA, Jones LW, et al. Cancer rehabilitation to manage cardiovascular outcomes in cancer patients and survivors: a scientific statement from the American heart association. Circulation. 2019;139(21):e997–1012. https://doi.org/10.1161/CIR.0000000000000679 .

Strasser B, Steindorf K, Wiskemann J, Ulrich CM. Impact of resistance training in cancer survivors: a meta-analysis. Med Sci Sports Exerc. 2013;45(11):2080–90. https://doi.org/10.1249/MSS.0b013e31829a3b63 .

Brown JC, Huedo-Medina TB, Pescatello LS, Pescatello SM, Ferrer RA, Johnson BT. Efficacy of exercise interventions in modulating cancer-related fatigue among adult cancer survivors: a meta-analysis. Cancer Epidemiol Biomarkers Prev. 2011J;20(1):123–33. https://doi.org/10.1158/1055-9965.EPI-10-0988 .

Kessels E, Husson O, van der Feltz-Cornelis CM. The effect of exercise on cancer-related fatigue in cancer survivors: a systematic review and meta-analysis. Neuropsychiatr Dis Treat. 2018F;9(14):479–94. https://doi.org/10.2147/NDT.S150464 .

Lake B, Damery S, Jolly K. Effectiveness of weight loss interventions in breast cancer survivors: a systematic review of reviews. BMJ Open. 2022;12(10):e062288. https://doi.org/10.1136/bmjopen-2022-062288 .

Morishita S, Hamaue Y, Fukushima T, Tanaka T, Fu JB, Nakano J. Effect of Exercise on mortality and recurrence in patients with cancer: a systematic review and meta-analysis. Integr Cancer Ther. 2020;19:1534735420917462. https://doi.org/10.1177/1534735420917462 .

Loprinzi PD, Cardinal BJ, Winters-Stone K, Smit E, Loprinzi CL. Physical activity and the risk of breast cancer recurrence: a literature review. Oncol Nurs Forum. 2012;39(3):269–74. https://doi.org/10.1188/12.ONF.269-274 .

Paluch AE, Bajpai S, Bassett DR, Carnethon MR, Ekelund U, Evenson KR, et al. Daily steps and all-cause mortality: a meta-analysis of 15 international cohorts. Lancet Public Health. 2022;7(3):e219–28. https://doi.org/10.1016/S2468-2667(21)00302-9 .

Dennett AM, Hirko KA, Porter KJ, Loh KP, Liao Y, Yang L, et al. Embedding lifestyle interventions into cancer care: has telehealth narrowed the equity gap? J Natl Cancer Inst Monogr. 2023;2023(61):133–9. https://doi.org/10.1093/jncimonographs/lgac028 .

Frederix I, Hansen D, Coninx K, Vandervoort P, Van Craenenbroeck EM, Vrints C, et al. Telerehab III: a multi-center randomized, controlled trial investigating the long-term effectiveness of a comprehensive cardiac telerehabilitation program–rationale and study design. BMC Cardiovasc Disord. 2015;7(15):29. https://doi.org/10.1186/s12872-015-0021-5 .

Batalik L, Dosbaba F, Hartman M, Konecny V, Batalikova K, Spinar J. Long-term exercise effects after cardiac telerehabilitation in patients with coronary artery disease: 1-year follow-up results of the randomized study. Eur J Phys Rehabil Med. 2021;57(5):807–14. https://doi.org/10.23736/S1973-9087.21.06653-3 .

Tan GA, Peiris CL, Dennett AM. Cancer survivors maintain health benefits 6 to 12 months after exercise-based rehabilitation: a systematic review and meta-analysis. J Cancer Surviv. 2022. https://doi.org/10.1007/s11764-022-01322-9 .

Dennett AM, Harding KE, Peiris CL, Shields N, Barton C, Lynch L, et al. Efficacy of group exercise-based cancer rehabilitation delivered via telehealth (TeleCaRe): protocol for a randomized controlled trial. JMIR Res Protoc. 2022;11(7):e38553. https://doi.org/10.2196/38553 .

Lee K, Nathwani N, Shamunee J, Lindenfeld L, Wong FL, Krishnan A, et al. Telehealth exercise to Improve Physical function and frailty in patients with multiple myeloma treated with autologous hematopoietic Stem cell transplantation (TIPS): protocol of a randomized controlled trial. Trials. 2022;23(1):921. https://doi.org/10.1186/s13063-022-06848-y .

Culos-Reed N, Wagoner CW, Dreger J, McNeely ML, Keats M, Santa Mina D, et al. Implementing an exercise oncology model to reach rural and remote individuals living with and beyond cancer: a hybrid effectiveness-implementation protocol for project EXCEL (EXercise for Cancer to Enhance Living Well). BMJ Open. 2022D 29;12(12):e063953. https://doi.org/10.1136/bmjopen-2022-063953 .

Stefanakis M, Batalik L, Antoniou V, Pepera G. Safety of home-based cardiac rehabilitation: a systematic review. Heart Lung. 2022;55:117–26. https://doi.org/10.1016/j.hrtlng.2022.04.016 .

Koene RJ, Prizment AE, Blaes A, Konety SH. Shared risk factors in cardiovascular disease and cancer. Circulation. 2016;133(11):1104–14. https://doi.org/10.1161/CIRCULATIONAHA.115.020406 .

Cowie A, Buckley J, Doherty P, Furze G, Hayward J, Hinton S, et al. Standards and core components for cardiovascular disease prevention and rehabilitation. Heart. 2019;105(7):510–5. https://doi.org/10.1136/heartjnl-2018-314206 .

Winnige P, Vysoky R, Dosbaba F, Batalik L. Cardiac rehabilitation and its essential role in the secondary prevention of cardiovascular diseases. World J Clin Cases. 2021;9(8):1761–84. https://doi.org/10.12998/wjcc.v9.i8.1761 .

Batalik L, Pepera G, Su JJ. Cardiac telerehabilitation improves lipid profile in the long term: Insights and implications. Int J Cardiol. 2022;367:117–8. https://doi.org/10.1016/j.ijcard.2022.08.055 .

Antoniou V, Davos CH, Kapreli E, Batalik L, Panagiotakos DB, Pepera G. Effectiveness of home-based cardiac rehabilitation, using wearable sensors, as a multicomponent, cutting-edge intervention: a systematic review and meta-analysis. J Clin Med. 2022;11(13):3772. https://doi.org/10.3390/jcm11133772

Díaz-Balboa E, González-Salvado V, Rodríguez-Romero B, Martínez-Monzonís A, Pedreira-Pérez M, Palacios-Ozores P, et al. A randomized trial to evaluate the impact of exercise-based cardiac rehabilitation for the prevention of chemotherapy-induced cardiotoxicity in patients with breast cancer: ONCORE study protocol. BMC Cardiovasc Disord. 2021;21(1):165. https://doi.org/10.1186/s12872-021-01970-2 .

Filakova K, Janikova A, Felsoci M, Dosbaba F, Su JJ, Pepera G, et al. Home-based cancer rehabilitation using a telerehabilitation platform in hematological cancer survivors: a feasibility study. BMC Sports Sci Med Rehabil. 2023M 23;15(1):38. https://doi.org/10.1186/s13102-023-00650-2 .

Champion S, Clark RA, Tirimacco R, Tideman P, Gebremichael L, Beleigoli A. The impact of the SARS-CoV-2 Virus (COVID-19) pandemic and the rapid adoption of telehealth for cardiac rehabilitation and secondary prevention programs in rural and remote Australia: a multi-method study. Heart Lung Circ. 2022;31(11):1504–12. https://doi.org/10.1016/j.hlc.2022.07.006 .

Stefanakis M, Batalik L, Papathanasiou J, Dipla L, Antoniou V, Pepera G. Exercise-based cardiac rehabilitation programs in the era of COVID-19: a critical review. Rev Cardiovasc Med. 2021;22(4):1143–55. https://doi.org/10.31083/j.rcm2204123 .

Dennett A, Harding KE, Reimert J, Morris R, Parente P, Taylor NF. Telerehabilitation’s safety, feasibility, and exercise uptake in cancer survivors: process evaluation. JMIR Cancer. 2021;7(4):e33130. https://doi.org/10.2196/33130 .

Fisk M, Livingstone A, Pit SW. Telehealth in the context of COVID-19: changing perspectives in Australia, the United Kingdom, and the United States. J Med Internet Res. 2020;22(6):e19264. https://doi.org/10.2196/19264 .

Download references

Acknowledgements

Supported by Ministry of Health of the Czech Republic, grant nr. NU23-09–00048. All rights reserved.“

Author information

Authors and affiliations.

Department of Rehabilitation, University Hospital Brno, Brno, Czech Republic

Ladislav Batalik, Katerina Chamradova, Petr Winnige, Filip Dosbaba, Katerina Batalikova & Daniela Vlazna

Department of Public Health, Faculty of Medicine, Masaryk University, Brno, Czech Republic

Ladislav Batalik

Department of Physiotherapy and Rehabilitation, Faculty of Medicine, Masaryk University, Brno, Czech Republic

Ladislav Batalik, Filip Dosbaba & Daniela Vlazna

Department of Rehabilitation and Sports Medicine, Second Faculty of Medicine, Motol University Hospital, Charles University, Prague, Czech Republic

Katerina Chamradova

Department of Neurology, Center for Neuromuscular Diseases (Associated National Center in the European Reference Network ERN EURO-NMD), University Hospital Brno, Brno, Czech Republic

Daniela Vlazna

Department of Internal Medicine-Hematology and Oncology, University Hospital Brno, Brno, Czech Republic

Andrea Janikova

Clinical Exercise Physiology and Rehabilitation Research Laboratory Department of Physiotherapy, Faculty of Health Sciences, University of Thessaly, Lamia, Greece

Garyfallia Pepera

School of Nursing, The Hong Kong Polytechnic University, Hong Kong SAR, China

Hammoda Abu-Odah & Jing Jing Su

School of Nursing, Tung Wah College, Hong Kong, China

Jing Jing Su

You can also search for this author in PubMed   Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Ladislav Batalik], [Katerina Chamradova] and [Jing Jing Su]. The first draft of the manuscript was written by [Ladislav Batalik] and all authors (Ladislav Batalik, Katerina Chamradova, Petr Winnige ,Filip Dosbaba ,Katerina Batalikova ,Daniela Vlazna ,Andrea Janikova,Garyfallia Pepera,Hammoda Abu-Odah, Jing Jing Su) commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Ladislav Batalik or Jing Jing Su .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary material 1., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Batalik, L., Chamradova, K., Winnige, P. et al. Effect of exercise-based cancer rehabilitation via telehealth: a systematic review and meta-analysis. BMC Cancer 24 , 600 (2024). https://doi.org/10.1186/s12885-024-12348-w

Download citation

Received : 21 February 2024

Accepted : 07 May 2024

Published : 17 May 2024

DOI : https://doi.org/10.1186/s12885-024-12348-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Exercise-based rehabilitation
• Cancer rehabilitation
• Home-based exercise

ISSN: 1471-2407

• Submission enquiries: [email protected]
• General enquiries: [email protected]

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Int J Environ Res Public Health

Factors Influencing Use of Fitness Apps by Adults under Influence of COVID-19

Yanlong guo.

1 Social Innovation Design Research Centre, Anhui University, Hefei 203106, China

2 Anhui Institute of Contemporary Studies, Anhui Academy of Social Sciences, Hefei 203106, China

Denghang Chen

3 Department of Science and Technology Communication, University of Science and Technology of China, Hefei 203106, China

4 Research Center for Science Communication, Chinese Academy of Sciences, Hefei 203106, China

5 College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, China

Associated Data

The experimental data used to support the findings of this study are included in the article.

During the coronavirus disease 2019 (COVID-19) pandemic, many countries imposed restrictions and quarantines on the population, which led to a decrease in people’s physical activity (PA) and severely damaged their mental health. As a result, people engaged in fitness activities with the help of fitness apps, which improved their resistance to the virus and reduced the occurrence of psychological problems, such as anxiety and depression. However, the churn rate of fitness apps is high. As such, our purpose in this study was to analyze the factors that influence the use of fitness apps by adults aged 18–65 years in the context of COVID-19, with the aim of contributing to the analysis of mobile fitness user behavior and related product design practices. We constructed a decision target program model using the analytic hierarchy process (AHP), and we analyzed and inductively screened 11 evaluation indicators, which we combined with an indicator design questionnaire. We distributed 420 questionnaires; of the respondents, 347 knew about or used fitness apps. Among these 347, we recovered 310 valid questionnaires after removing invalid questionnaires with a short completion time, for an effective questionnaire recovery rate of 89.33%. We used the AHP and entropy method to calculate and evaluate the weight coefficient of each influencing factor and to determine an influencing factor index. Our conclusions were as follows: first, the effect of perceived usefulness on the use of fitness apps by the study groups was the most notable. Second, personal motivation and perceived ease of use considerably influenced the adult group’s willingness to use fitness apps. Finally, the perceived cost had relatively little effect on the use of fitness apps by adults, and the study group was much more concerned with the privacy cost than the expense cost.

1. Introduction

The COVID-19 pandemic has hugely impacted people’s ways of living, intellectual health, and quality of life worldwide [ 1 ]. The imposition of lockdown and quarantine measures on populations has been used to restrict the spread of COVID-19, but such measures have also had many serious consequences [ 2 ]. According to the results of multi-country surveys, measures such as restraint and seclusion have negatively impacted social participation, lifestyle pleasure, mental health, psychosocial and emotional disorders, sleep quality, and employment status [ 3 , 4 , 5 ]. Some authorities announced a stoppage of all services and activities except for a few basic services, which led to necessary adjustments in the lifestyles of the affected populations, which severely damaged their mental health. This was manifested by increased stress in the general population and an increase in the number of depressions [ 6 , 7 ]. These abrupt modifications in people’s lives included, among others, physical activity and exercise. Ammer et al. stated that home confinement during COVID-19 led to a reduction in physical activity (PA) and an increase of approximately 28% in the time spent sitting each day [ 8 ].

How humans coped and found approaches to being physically healthy in the face of pandemic-related restrictions (home isolation and closed gyms, parks, and gymnasiums) needs to be understood. Through health apps, users changed their traditional method of engaging in fitness imposed by time and geographical barriers and could choose to exercise anytime and anywhere, record their physical condition, and more flexibly control their exercise. People’s intention to use fitness apps has substantially increased. However, the churn rate of these apps is high, with over 45% of customers stopping after the novelty wears off, so an in-depth perception of consumer motivation and the elements influencing the use of health apps is required [ 9 , 10 ]. To gain insight into these issues, in this study, we collected user data using a questionnaire and constructed a decision-goal scenario model based on TAM through the analytic hierarchy process (AHP). The questionnaire included questions about the user’s basic information and what factors affected their use of fitness apps. We analyzed the user data to assess the factors that affected their continued use of fitness apps. Next, we reviewed the literature on the impact of the pandemic on physical health and described the factors influencing the use of fitness apps, and then presented the details of our analysis and the final findings.

2. Literature Review

Sports and physical exercise play a vital role in the physical and intellectual health of an individual [ 11 ]. The U.S. Physical Activity Guidelines suggest that all adults, even those with chronic conditions, should engage in at least 150 to 300 min of moderate-intensity exercise per week if they are capable [ 12 ]. Haider stated that decreased PA levels may negatively affect fitness and can be related to an increase in nervousness and despair [ 13 ]. The findings of a study in Austria showed an increase in the duration of predominant depressive signs and symptoms from 3% to 6% between pre- and post-pandemic [ 14 ]. Harleen et al. conducted semi-structured smartphone interviews in 2020 with 22 adults who had usually exercised at a fitness center before the COVID-19 pandemic but who stayed at home at some point during the countrywide lockdown. The results of the analysis showed that participants’ situational perceptions at some stage during the lockdown were extremely negative, and they lacked the motivation to exercise at a gym. They exhibited mental health concerns and an over-reliance on social media. However, performing general health exercises indoors during lockdown remarkably helped them to overcome their psychological problems and fitness issues [ 15 ].

While experiencing a forced adaptation to new norms of maintaining social distancing, health apps can assist humans to manipulate a change in their dietary intake, engaging in both healthy and bodily activity, and promoting a wholesome lifestyle [ 16 ]. Based on the above advantages, humans from all groups seized the opportunities provided by the commercial online health industry, which vigorously improved their offerings of online fitness. This situation actively promoted the digital reform of the ordinary health industry. The Talking Data 2014 Mobile Internet Data Report showed that the number of users of mobile health management on both iOS and Android platforms reached 120 million, which was an increase of 113.4% from January to December 2014, and the growth rate was increasing. Users of apps such as Goudong and Le Power Running have exceeded 10 million in number, and the number of downloads of Nike Training and Super Diet King has increased by more than 300%. Sports and fitness apps have a wide range of people using them. In addition, the use of sports and health apps to assist in guiding exercise will change traditional sports and fitness methods, creating a shift in digital and scientific fitness.

In the context of the rapid development of mobile fitness apps, many scholars have focused on the factors influencing their use, engaging in theoretical research and practical studies. When studying the factors influencing college students’ fitness app use, Yi considered fitness motivation, leisure, entertainment motivation, and structure rationality hardware requirements as antecedent variables of the perceived ease of use (PEOU) and perceived usefulness (PU) based on the technology acceptance model (TAM). Yi considered the perceived value variables as direct elements influencing university students’ mindset toward health app use [ 17 ]. The empirical findings showed that PEOU, PU, and perceived price positively affected college students’ attitudes toward using mobile fitness apps. PEOU was positively influenced by the ease of operation experienced when using the health software program and the rationality of the health software program design, whereas perceived price was influenced by cellular hardware requirement, the cost of the software program, and the value obtained in the course of its use. The factors that positively influenced PU were, in descending order, fitness motivation, PEOU, motivation to acquire fitness knowledge, perceived cost, and motivation to record fitness activities [ 18 ]. Cui investigated the willingness to use mHealth programs based on the technology readiness and acceptance model (TRAM) and extended the model by introducing health awareness. The constructed model was tested by surveying 639 mobile fitness app users and potential users using AMOS 22.0. The test results showed that optimism, revolutionary spirit, and health perception were necessary antecedent variables for the PEOU and PU of cell phone health apps, which indirectly influenced the intention to use. PU and usage mindset directly influenced cell phone health app users’ intention to use them [ 19 , 20 , 21 , 22 ]. Ardion et al. conducted a technology acceptance model (TAM) test considering trust, social influence, and health valuation on 476 German fitness app users, examining the factors influencing the German users’ intentions to continue using specific fitness software. The outcomes of the structural equation modeling showed that the respondents’ intention to use a particular health app was primarily based on three factors: PEOU, PU, and prohibitive social norms [ 23 ].

In summary, in the context of the COVID-19 pandemic, country-wide fitness awareness has increased, and mobile phone fitness app use has become a commonly accepted new form of exercise [ 24 ]. Nowadays, the user and industry scales of mobile fitness apps are rapidly growing, and the mobile fitness industry has broad market prospects and is now an emerging area of general interest in the industry. In this context, we selected fitness apps as the research object and analyzed which factors affected the use of fitness apps through theoretical analysis and empirical testing. Our results benefit the analysis of mobile fitness user behavior and related product design practices.

3. Research Methodology

In this study, we first reviewed a large amount of the literature to determine the content of the study. We then summarized the relevant literature about the theoretical knowledge of technology acceptance, perceived cost, and self-determination theory and analyzed the relationship between them. Next, we selected reasonable judgment indicators to provide a theoretical basis for the subsequent study [ 25 ]. In determining the study population, according to the 2021 United Nations World Health Organization, the classification of age groups placed those aged 0–17, 18–65, 66–79, 80–99, and 100 years or more into the categories of minors, adults, middle-aged people, elderly people, and long-lived people, respectively. Among them, those who should pay the most attention to physical exercise and have a strong ability to make independent choices are adults aged 18–65 years. Therefore, in this study, we distributed a questionnaire to the study group and collected the data from the questionnaires. We screened the initially recovered data and then used SPSSAU for reliability and validity analysis. Finally, we used two assignment methods, AHP, and entropy weighting, to derive the comprehensive weighting results and analyze the relevant indicators affecting the weighting of the use of fitness apps by adults under 65 years old [ 26 ].

3.1. Hierarchical Analysis and Entropy Method

We needed to analyze the factors affecting the use of fitness apps by adults from multiple dimensions, and we selected the AHP method, which is used to combine the qualitative and quantitative aspects of multi-objective complex problems to calculate the decision weights and use the experience of decision makers to judge the relative importance of the weights between the criteria of whether each measurement goal can be achieved. The entropy weighting method is combined with the resynthesis of indicator weights to assign values, and the use of comprehensive weights makes the results more scientific, fair, and persuasive.

3.2. Indicator Construction

In our analysis of the factors influencing the use of fitness apps by adults aged 18–65 years, we needed to consider the current pandemic and policy guidance, the characteristics of health app use, and the relevant research results to build a scientific and reasonable indicator system. A wide range of elements may influence health app use: they may be multilevel, multifactor, and multi-indicator. For evaluation index selection, by collecting the opinions of relevant experts and designers, our final hierarchy of the fitness app-use influencing factors included one target layer, four guideline layers, and eleven program layers.

3.2.1. Establishing Guideline Level Indicators

The technology acceptance model (TAM), proposed by Davis et al. in 1989, is one of the most influential theories in the field of information systems research. In the preliminary TAM, PU, and PEOU are the elements that directly impact the usage attitude and user behavior through attitude intention [ 27 ]. Davis et al. reported that PEOU refers to the effort customers perceive as being required to operate a new technology; PU refers to how many customers accept as true that the technological device will enhance their overall work performance [ 27 ]. Karah anna et al. demonstrated that PEOU and PU affect the users’ use behavior, and PEOU additionally impacts PU [ 28 ]. Bildad et al. found that the ease of using Internet technology plays a key role in improving user faith in software builders [ 29 ]. Therefore, in the specific construction of the corresponding indicators, we used PEOU (B1) and PU (B2) [ 30 , 31 ].

Despite the broad applicability of the TAM ( Figure 1 ), the model can be modified by adding external premises and theoretically sound elements, which can expand the predictive power of the model [ 32 ]. The self-determination principle has been widely used to help encourage physical activity in individuals, and intrinsic motivation represents an archetype of independent activity, where people are motivated by intrinsic motivation and are free to engage in activities independent of external factors [ 33 , 34 , 35 ]. According to self-determination theory, consumer motivation (the reason why a person engages in an activity) and consumer-aim (the purpose for this activity) is intently associated [ 36 ]. In the field of advertising and customer behavior studies, researchers typically agree that customers perceive the cost as a necessary factor influencing purchase decisions: and the greater the perceived cost-utility of a product, the greater the motivation to buy it [ 37 ]. Regarding the elements affecting the perceived value, most researchers have considered the antecedent variables of the perceived cost for empirical analysis. Perceived immediate use advantages (i.e., perceived gains) and perceived sacrifices (i.e., perceived losses) are the antecedent variables of perceived cost [ 38 ]. Some scholars have also used factors such as perceived risk, cost of purchase, quality of service, and the quality of the product as antecedent variables affecting the consumers’ perceived value (Wood and Scheer, 1996; Zhong, K., 2013) [ 39 ]. Regarding the elements impacting the customer’s perceived value, we introduced two achievable variables to the technology acceptance model: perceived cost (B3) and personal motivation (B4) [ 40 ].

Technology acceptance model.

3.2.2. Determination of Program-Level Indicators

We analyzed and inductively screened 11 evaluation indicators from H1 to H11 according to the detailed division of the elements used for evaluating the first-level indicators ( Table 1 ). To measure indicator B1 (PU), we used the scale developed by Yang et al. to set three measurement indicators: content adaptability (H1), content relevance (H2), and content quality (H3) [ 41 ]. To measure indicator B2 (PEOU), we used the scale developed by Gong et al.: the technology level (H4), interaction effectiveness (H5), and system compatibility (H6) [ 42 , 43 , 44 , 45 ]. For B3, the perceived value indicator, we measured the financial cost (H7) and privacy cost (H8) based totally on the evaluation by San et al., who focused on the effects of the perceived advantages and perceived dangers of people’s transactional conduct [ 46 , 47 , 48 ]. To measure B4 (personal motivation indicator), we applied the scale developed by Park et al. and set three measures: health concerns (H9), outcome expectations (H10), and social influence (H11) [ 49 , 50 , 51 ].

Index system used for analyzing factors influencing use of fitness apps by adults aged 18–65 years.

3.3. Questionnaire Design

Based on the literature review of the effect of the pandemic and health apps, we chose eleven attributes to examine the factors influencing the use of health apps amongst adults aged 18–65 years to determine the impact of COVID-19. We assessed these 11 attributes with a questionnaire ( Table 2 ). We built the questionnaire with Questionnaire Star, and the first question required respondents to have used health apps or to have some knowledge of health apps. The questions could be answered on a scale, and every question consisted of a set of statements. Each statement had nine responses, ranging from 1 to 9 according to the evaluation of the degree of the effect, ranging from very unimportant to very important. The questionnaire included basic information (sex, age, education level, and whether they had used or known about fitness apps) and the evaluation of the importance of relevant factors influencing their use.

Description of index conversion questionnaire.

4. Statistics and Analysis

We distributed 420 questionnaires using Questionnaire Star to adults aged 18–65 years. The first part of the questionnaire asked the respondents whether they know about or have used a fitness app; if they responded yes, they continued to the second part containing influencing factor questions; if they responded no, the questionnaire ended. According to the data collected from the questionnaires, 347 out of 420 people had knowledge of or had used a fitness app. Among the 347 questionnaires, those with a shorter filling time and multiple scores of the same response were considered invalid questionnaires and deleted. Of the 347 questionnaires, 310 were valid, with an effective rate of 89.33%. Among them, men and women accounted for 50.32% and 49.68%, respectively, of the respondents, with most being 18–30 years old, followed by 31–40 years old ( Table 3 ). Subsequently, we performed frequency analysis and AHP on the 310-sample data to derive the weight values for each index and perform the consistency test.

Basic information of the questionnaire respondents.

4.1. Confidence and Validity Analysis

Reliability research methods are often used when analyzing research projects to test whether they are reasonable and meaningful ( Table 4 ). Validity analysis is performed using factor analysis methods to verify the validity level of the data with KMO values of commonality, variance explained values, factor loading coefficient values, and other indicators. KMO values are used to select the suitability of the fact extraction, and commonality values are used to eliminate unreasonable items ( Table 5 ).

Cronbach reliability analysis.

KMO and Bartlett test results.

Note: *** represents a significance level of 1%.

4.1.1. Questionnaire Reliability Test

The reliability coefficient, Cronbach’s alpha, is used to measure the internal consistency or reliability of an instrument or questionnaire. This coefficient is often used for questionnaires developed using multiple Likert scales to determine whether the scale is reliable. We used Cronbach’s alpha to determine the reliability using SPSSAU, resulting in a Cronbach’s alpha value of 0.917 ( Table 4 ), which indicated the good reliability and high internal consistency of this questionnaire for additional analysis.

4.1.2. Questionnaire Validity Test

Validity testing involves the measurement of the validity of the questionnaire research data: whether the results obtained through the questionnaire are true and whether the respondents’ evaluations are objective. For questionnaire validity tests, structural validity is used, and the results reflect the accuracy of the questionnaire items. Structural validity reflects the relationship between the questionnaire measurement results and the measured items. The two indicators of structural validity are the KMO and Bartlett’s sphericity test. The coefficients of the KMO range from 0 to 1; the closer the coefficient is to one, the higher the validity of the questionnaire. Bartlett’s sphericity test result needs to be less than 0.01. We imported the questionnaire into SPSSAU for analysis, finding a KMO value of 0.926 and Bartlett’s sphericity test result of 0.000, which indicated that the structural validity of the questionnaire was excellent and all the factors had a strong correlation ( Table 5 ). According to Bartlett’s sphericity check, the significance of this check is infinitely close to zero. Therefore, the questionnaire has appropriate validity and meets the conditions of applicability for factor analysis.

4.2. Determination of Index System Weights Based on Hierarchical Analysis

4.2.1. establishing comparison judgment matrix.

Based on the evaluation scales in the AHP, the elements in the product hierarchy model are compared and assigned. To use mathematical methods for data processing, the data needs to be transformed into a matrix to quantify the results and determine the importance of the design elements. Supposing n influencing elements, b 1 ..., b i ..., b j ..., b n , the project elements are compared with each other in pairs and transformed into a judgment matrix as follows:

The Perron–Fresenius theorem shows that matrix B has a unique nonzero eigenroot, i.e., the largest eigenroot ( λ m a x ) corresponds to the eigenvector ( w ).

The specific steps for calculating the feature vectors using the sum-product method are as follows:

Normalize the data in b by column.

Sum the normalized matrix peers.

Divide the summed vector by n to obtain the weight vector.

Find the maximum characteristic root.

where ( B w ) i denotes its component of the vector B w .

Based on the above Equations (1)–(6), we calculated the weight values of the designed element objectives at the criterion and program levels and then ranked them in terms of importance to complete the decision on the influencing factors.

4.2.2. Calculating Weight Coefficients

Because the hierarchical structure model we constructed had more elements at the program level and the generated judgment matrix order was greater than nine, we used a combination of the AHP and entropy methods for data processing. We formed the evaluation indexes by decomposing the problem and comparing the judgment. We calculated the weightings to obtain the comprehensive weight values of the elements at the program level. We first calculated the AHP-based weight.

Check the consistency of matrix B . Calculate:

where CI is the consistency index; CR is the consistency ratio; and RI is the common random consistency index.

From Equation (8), CR can be calculated. CR < 0.1 indicates that the calculation of matrix B is qualified and valid. If CR > 0.1, the matrix needs to be corrected [ 52 ].

Based on the above-mentioned ideas, we constructed the judgment matrix and calculated the weights of the impact factor ( Table 6 , Table 7 , Table 8 , Table 9 and Table 10 ).

Target layer judgment matrix and weight value of influencing factor.

PU judgment matrix and weight values.

PEOU judgment matrix and weight values.

Perceived cost judgment matrix and weight values.

Personal motivation judgment matrix and weight values.

From the outcomes in Table 6 , Table 7 , Table 8 , Table 9 and Table 10 , we found that the CR values of the judgment matrices were all <0.1, so we skipped the consistency test. From this, we calculated the weighting for the program-level elements to obtain the comprehensive weight values of the program-level elements ( Table 11 ).

Comprehensive weight values of criterion-layer elements.

4.2.3. Consistency Test

We performed consistency tests on the combined weight values of all the design elements in Table 11 , and the operational procedure and results are shown:

Based on Equations (9) and (10), CR = 0 < 0.1. The hierarchical total ranking of matrix B was consistent with the consistency test principle, and we found that the calculations of the comprehensive weight values of the scheme-level elements in Table 9 were scientific and reasonable and so could effectively guide the practical analysis [ 53 ].

4.3. Entropy Method Weights

The entropy approach is a goal-undertaking method, and the weights determined with this method are more accurate than those obtained with the subjective challenge method. Entropy is a measure of the disorder of a system, and by measuring the degree of disorder in the variables, the weights of indicator variables can be obtained by comparing the amount of information possessed by the variables. However, the method is prone to imbalanced weights due to the large dispersion of a certain indicator.

In the entropy weight approach, the entropy weight of the index is first calculated by applying the record’s entropy after standardizing the authentic data. The rank of item X , when the index is positive, is standardized with the following system.

When the indicator is negative, its normalization treatment formula is:

where X i m a x and X i m i n are the maximum and minimum values of the indices, respectively; Y ij is the normalized result setting of the first impact factor affecting prevention and control. For a certain impact factor j , its information entropy calculation formula E j is:

where P ij is the proportion of the standardized value and Y ij is the total standardized value. If the information entropy E j of the factor influencing prevention and control is smaller, the degree of variability in the factor is smaller, the sample data are more orderly, the differentiation ability of the evaluation object is larger, and the information utility value provided by the factor is larger. The stronger the influence on border prevention and control, the higher the weight; conversely, the larger the information entropy E , the larger the degree of variability is for the influence factor, and the information utility value provided by the factor and the weight is smaller.

According to the calculated information entropy of each factor,   E 1 , E 2 , ⋯ , E k , the weight formula W j for each factor can be calculated as follows:

Based on Equations (11)–(15), we calculated the weights of each index ( Table 12 and Table 13 ).

Weight results of each criterion layer based on entropy method.

Weight results of each index based on entropy method.

4.4. Integrated Weight Calculation

In this study, based totally on the reliability and availability of the data, we used two strategies (subjective and goal weight replication) to resynthesize and assign the weights of the influencing factors affecting the use of health apps by adults. We continuously revised the influencing elements. The results indicated a large difference in the weighting of the indicators using the two methods, especially in the process of determining the weighted values of indicators H11 and H3. This difference was due to the difference between the weights calculated by the mathematical model and our understanding of the application of the indicators in practice, which led to the difference in the weight coefficients. Our finding also further confirmed the necessity of studying the assignment of subjective and objective integrated weights.

Based on the results of assigning weights to the indicators by the above two methods, we calculated the combined weight C j :

where w i and w j represent the weights of the evaluation indexes calculated by the hierarchical analysis and entropy value method, respectively. We synthesized and calculated the results of both the subjective and objective assignments ( Table 14 and Table 15 ).

Comprehensive weight results (criterion layer) obtained using two weighting methods.

Comprehensive weight results (scheme layer) obtained by two weighting methods.

4.5. Data Analysis

The weightings of B1 (PU), B2 (PEOU), B3 (perceived cost), and B4 (nonpublic motivation) for the assessment goal layer A were 0.2808, 0.2565, 0.1997, and 0.2630, respectively. The comprehensive weights of H1, H2, and H3 were 0.0948, 0.0874, and 0.0795, respectively. The weights of H4 (technical grade), H5 (interaction effectiveness), and H6 (system compatibility) for B2 PEOU were 0.0857, 0.0900, and 0.0867, respectively. The weight values of H7 (financial cost) and H8 (privacy cost) for B3 (perceived cost) were 0.0859 and 0.1000, respectively. The weight values of H9 (health concern), H10 (outcome expectations), and H11 (social influence) for B4 (personal motivation) had weight values of 0.0867, 0.0862, and 0.1172, respectively.

According to the criterion-stage weight values, we found that the ranking of the elements influencing the use of health apps by adults under the impact of COVID-19 were B1 (PU), B4 (nonpublic motivation), B2 (PEOU), and B3 (perceived cost) ( Figure 2 ). That is, for this group, PU ranked first when people chose or used fitness apps, accompanied by private motivation, which was especially influential, and then the PEOU and perceived value. According to the weight values of the scheme layer, we found that these adults were more influenced by H11 (social), H8 (privacy cost), H1 (content adaptability), H5 (interaction effectiveness), and H2 (content relevance), and less influenced by H6 (system compatibility), H9 (health concerns), H10 (outcome expectation), H7 (financial cost), H4 (technical grade), and H3 (content quality) ( Figure 3 ).

Statistical comprehensive weight values of criterion layer.

Statistical comprehensive weight values of scheme layer.

5. Discussion

First, PU had the most notable effect on the adult use of fitness apps. The indicators we used to measure B1 (PU) were the pair of H1 (content adaptability) and H2 (content relevance), as well as H3 (content quality). Among them, H1 was the influencing factor with the highest weight because the users of fitness apps are of different ages and have different exercise purposes, physical bases, and exercise programs, so users have different requirements for the content adaptability of fitness apps. If the app is not based on scientific and effective assessment data for personalized program settings, the user may perform improper or ineffective exercises. For example, some apps directly recommend HIIT exercise programs for primary training; such training is characterized by high exercise intensity, short duration, and high energy consumption, so is not suitable for most primary fitness, leading to the user feedback of exercise intensity being too high and the exercise program being difficult to implement. However, for people experienced with exercise, this kind of exercise may not meet their fitness needs. Exercise apps should also help users avoid injury due to exercise, allowing users to reduce the difficulty of the exercise and to choose low-risk and low-threshold programs to ensure the safety of exercise; however, this may prevent users from achieving the purpose of the exercise.

Second, personal motivation considerably influenced the study group’s intention to use fitness apps. The indicators measuring B4 (personal motivation) were H9 (health concerns), H10 (outcome expectation), and H11 (social influence). Among them, H11 and H9 had higher weights. The higher weight of H11 indicated that people were more influenced by their community when using fitness apps. Social impact refers to the stress and impact that people experience from the humans around them when they perform a behavior [ 52 ]. The environment and people around an individual, such as family environment and members, friends, work environment, colleagues, etc., can substantially influence their specific behavior. Community influence is more important in Chinese culture. If companies want to improve their social influence, a long-term process is required; they should implement measures to proactively improve the quality of their products and services, improve customer experience, and assume their social role. H9 had a stronger impact on personal motivation, indicating that the study group was aware of the importance of physical health; therefore, concerns about their health will increase their autonomy in fitness. Given the effect of COVID-19, people’s fitness awareness has increased, and their intention to engage in PA is stronger, increasing their motivation to use health apps.

Third, B2 (PEOU) strongly impacted the study participants’ use of fitness apps. PEOU was influenced in decreasing order by interaction effectiveness, system compatibility, and technical level. This indicated that this group preferred software that was easy to operate, appropriate, with a reasonable design, and that had a user-friendly software interface, which could provide a good user experience. The discovery, installation, and login of the software, the use, recording, and uploading of results and sharing in the fitness process should be easy and not require time or effort to learn. In addition, the interface of the software should be reasonably designed, and the content should be relevant so that the user feels that this product is suitable for them. In the design of mobile fitness apps, user-friendliness should be considered.

Fourth, B3 (perceived cost) had relatively little impact on the use of fitness apps by the study group; however, adults of this age were considerably more worried about H8 (privacy cost) than financial cost according to the weights of the scheme-level indicators. Users face many risks in the process of using the mobile Internet; private information may be leaked, and the perception of privacy risks negatively affects the perceived value. Privacy price has a sizable poor impact on the perceived price. Fitness APPsapps should have a clear, effective, and easy-to-understand security privacy policy. Expense cost also affects the users’ experience of perceived cost during use. The perceived economic cost is the users’ perception of objective costs with a certain subjectivity, and the higher the perceived cost, the lower the perceived value. Therefore, enhancing the best of merchandise and offerings and enhancing the value effectiveness is one of the core aggressive benefits of sports activities and health apps. The degree of satisfaction with the merchandise and offerings immediately determines whether or not customers are inclined to use them continuously, and companies should focus on providing users with high-quality products and services.

6. Conclusions

Our results showed that, first, in the criterion layer, the weight of PU was 0.2808, which was much larger than that of the other indicators, indicating that PU most strongly influenced the study group’s use of fitness apps under the influence of COVID-19. Among the criteria that we used for measuring PU, the study group was more concerned about content adaptability. Therefore, developers of health apps need to pay attention to the special traits of users, provide more customized and scientific strategies and content, and select reasonable and scientific fitness programs tailored to users according to their age, occupation, height, weight, personal preferences, etc. Second, the weights of personal motivation and PEOU were 0.2630 and 0.2565, respectively, indicating their stronger impact on the willingness of the study groups to use fitness apps. We recommend that fitness APP developers pay attention to the different characteristics of users and provide more personalized service methods and content, improve the fun of exercise, and reduce the fatigue experienced when users exercise. Third, the perceived cost had the lowest weight of 0.1997, indicating a weaker influence on the group’s use of fitness apps. The data of the indicators measuring the perceived cost showed that the study group was much more worried about privacy than the financial cost, indicating that the group had a strong sense of privacy. Security and privacy policies imply a commitment to users’ personal information. Due to the small operating interface of cell phones and portable devices, companies should proactively and prominently display protection policies so that users can feel the company’s commitment to security and privacy.

Currently, the COVID-19 pandemic is still ongoing. Increasing people’s physical activity during the pandemic to ensure physical and mental health and to improve the well-being of the population remains a difficult task. The data from this study can help subsequent fitness app developers understand user needs and provide an empirical basis for subsequent fitness app development or iterations.

Funding Statement

This study was conducted by the Anhui University 2020 Talent Introduction Scientific Research Start-up Fund Project (Project No. S020318019/001).

Author Contributions

Conceptualization, Y.G. and D.C.; methodology, Y.G. and H.Z.; software, X.M.; validation, Y.G. and D.C.; formal analysis, Y.G.; investigation, Y.G. and X.M.; resources, Y.G.; data curation, X.M.; writing—original draft preparation, Y.G. and H.Z.; writing—review and editing, Y.G.; visualization, X.M.; supervision, Y.G.; project administration, Y.G. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.