Skip to main content
SpringerLink
Log in

Mobile Telepresence Robot as a New Service to Remotely Supervise Older Adults’ Physical Activity: Effectiveness, Acceptance, and Perception

  • Published:
International Journal of Social Robotics Aims and scope Submit manuscript

Abstract

While the benefits of physical activity are well known, many older adults do not have any physical activity because they do not have access to adapted physical activity (APA) structures and teachers. The use of a telepresence robot to remotely supervise older adults’ physical activity may be a suitable solution to break with lack of physical inactivity of older adults. The aim of the present study was to investigate effectiveness, acceptance, and perception of a mobile telepresence robot (MTR) that is remotely controlled by an APA teacher.

Forty older adults (70.7 ± 4.3 years, 21 women, 19 men) were randomly divided in two groups of 20 participants who took part in a single individual learning session of a cognitive-motor task either conducted face-to-face with an APA teacher or with the same APA teacher using the MTR. Task performance, acceptance of the MTR, and perception of the APA teacher were assessed with quantitative and qualitative measures. The results showed that using MTR was as effective as a face-to-face intervention to help older adults learning a cognitive-motor task during a single session. The MTR was well-accepted by older adults and the APA teacher using the MTR was perceived warm, competent, adaptive, and sociable. The participants forgot that they were talking to a robot and they clearly put forward the added values of the MTR, especially its specific functionalities that does not exist in other types of devices such as static videoconference. APA sessions supervised by qualified teachers using the MTR may be implemented for isolated older adults who do not benefit from the services dedicated to supervised physical activity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data Availability

All the data are available on request to the corresponding author.

References

  1. Berchicci M, Lucci G, Di Russo F (2013) Benefits of physical exercise on the aging brain: the role of the prefrontal cortex. J Gerontol A Biol Sci Med Sci 68(11):1337–1341. https://doi.org/10.1093/gerona/glt094

    Article  Google Scholar 

  2. Insee (2019) 68,1 millions d’habitants en 2070: une population un peu plus nombreuse qu’en 2021, mais plus âgée. Insee Première 1881

  3. World Health Organization (2021) Guidelines on physical activity and sedentary Behaviour. World Health Organization, Geneva, Switzerland

    Google Scholar 

  4. Hupin D, Roche F, Gremeaux V, Chatard JC, Oriol M, Gaspoz JM, Barthélémy JC, Edouard P (2015) Even a low-dose of moderate-to-vigorous physical activity reduces mortality by 22% in adults aged ≥ 60 years: a systematic review and meta-analysis. Br J Sports Med 49(19):1262–1267. https://doi.org/10.1136/bjsports-2014-094306

    Article  Google Scholar 

  5. Cunningham C, O’Sullivan R, Caserotti P, Tully MA (2020) Consequences of physical inactivity in older adults: a systematic review of reviews and meta-analyses. Scand J Med Sci Sports 30(5):816–827. https://doi.org/10.1111/sms.13616

    Article  Google Scholar 

  6. Harvey JA, Chastin SF, Skelton DA (2013) Prevalence of sedentary behavior in older adults: a systematic review. Int J Environ Res Public Health 10(12):6645–6661. https://doi.org/10.3390/ijerph10126645

    Article  Google Scholar 

  7. Isabet B, Pino M, Lewis M, Benveniste S, Rigaud AS (2021) Social telepresence robots: a narrative review of experiments involving older adults before and during the COVID-19 pandemic. Int J Environ Res Public Health 18(7):3597. https://doi.org/10.3390/ijerph18073597

    Article  Google Scholar 

  8. World Health Organization (2018) Continuity and coordination of care: a practice brief to support implementation of the WHO Framework on integrated people-centred health services. World Health Organization, Geneva, Switzerland

    Google Scholar 

  9. Trost SG, Owen N, Bauman AE, Sallis JF, Brown W (2002) Correlates of adults’ participation in physical activity: review and update. Med Sci Sports Exerc 34(12):1996–2001. https://doi.org/10.1186/s12889-017-4255-2

    Article  Google Scholar 

  10. Schulz R, Wahl HW, Matthews JT, De Vito Dabbs A, Beach SR, Czaja SJ (2015) Advancing the aging and technology agenda in gerontology. Gerontologist 55(5):724–734. https://doi.org/10.1093/geront/gnu071

    Article  Google Scholar 

  11. Henkel AP, Čaić M, Blaurock M, Okan M (2020) Robotic transformative service research: deploying social robots for consumer well-being during COVID-19 and beyond. J Serv Manag 31(6):131–1148. https://doi.org/10.1108/josm-05-2020-0145

    Article  Google Scholar 

  12. Savage N (2022) Robots rise to meet the challenge of caring for old people. Nature 601(7893):S8–S10. https://doi.org/10.1038/d41586-022-00072-z

    Article  Google Scholar 

  13. Avelino J, Simão H, Ribeiro R, Moreno P, Figueiredo R, Duarte N, Nunes R, Bernardino A, Čaić M, Mahr D, Odekerken-Schröder G (2018) Experiments with Vizzy as a Coach for Elderly Exercise. Procedia Comput Sci 88:505–511. https://doi.org/10.5898/jhri.2.2.fasola

    Article  Google Scholar 

  14. Čaić M, Avelino J, Mahr D, Odekerken-Schröder G, Bernardino A (2020) MTR versus face-to-face coaches for active aging: an automated social presence perspective. Int J Soc Robot 12(4):867–882. https://doi.org/10.1007/s12369-018-0507-2

    Article  Google Scholar 

  15. Fitter NT, Mohan M, Kuchenbecker KJ, Johnson MJ (2020) Exercising with Baxter: preliminary support for assistive social-physical human-robot interaction. J Neuroeng Rehabilitation 17(1):1–22. https://doi.org/10.1186/s12984-020-0642-5

    Article  Google Scholar 

  16. Görer B, Salah AA, Akın HL (2017) An autonomous robotic exercise tutor for elderly people. Auton Robots 41(3):657–678. https://doi.org/10.1007/s10514-016-9598-5

    Article  Google Scholar 

  17. Schneider S, Kümmert F (2016) Exercising with a humanoid companion is more effective than exercising alone. IEEE-RAS 16th International Conference on Humanoid Robots (pp.495–501). Cancun: Mexico. https://doi.org/10.1109/humanoids.2016.7803321

  18. Werner F, Krainer D, Oberzaucher J, Werner K (2013) Evaluation of the acceptance of a social assistive robot for physical training support together with older users and domain experts. Assist Technol 33:137. https://doi.org/10.1109/cisis.2012.36

    Article  Google Scholar 

  19. Tsui KM, Desai M, Yanco HA (2012) Towards measuring the quality of interaction: communication through telepresence robots. Proc workshop Perform metrics Intell Syst 101–108. https://doi.org/10.1145/2393091.2393112

  20. Hong J, Kong HJ, Yoon HJ (2018) Web-based telepresence exercise program for community-dwelling elderly women with a high risk of falling: randomized controlled trial. JMIR mHealth uHealth 6(5):9563. https://doi.org/10.2196/mhealth.9563

    Article  Google Scholar 

  21. Wu GE, Keyes LM (2006) Group tele-exercise for improving balance in elders. Telemed E-Health 12(5):561–570. https://doi.org/10.1089/tmj.2006.12.561

    Article  Google Scholar 

  22. Wu G, Keyes L, Callas P, Ren X, Bookchin B (2010) Comparison of telecommunication, community, and home-based Tai Chi exercise programs on compliance and effectiveness in elders at risk for falls. Arch Phys Med Rehabilitation 91(6):849–856. https://doi.org/10.1016/j.apmr.2010.01.024

    Article  Google Scholar 

  23. Hong J, Kim J, Kim SW, Kong HJ (2017) Effects of home-based tele-exercise on sarcopenia among community-dwelling elderly adults: body composition and functional fitness. Exp Gerontol 87:33–39. https://doi.org/10.1016/j.exger.2016.11.002

    Article  Google Scholar 

  24. Langeard A, Bigot L, Maffiuletti NA, Moussay S, Sesboüé B, Quarck G, Gauthier A (2022) Non-inferiority of a home-based videoconference physical training program in comparison with the same program administered face-to-face in healthy older adults: the MOTION randomised controlled trial. Age Ageing 51(3):afac059. https://doi.org/10.1093/ageing/afac059

    Article  Google Scholar 

  25. Niemelä M, Van Aerschot L, Tammela A, Aaltonen I, Lammi H (2021) Towards ethical guidelines of using telepresence robots in residential care. Int J Soc Robot 13(3):431–439. https://doi.org/10.1007/s12369-019-00529-8

    Article  Google Scholar 

  26. Wu X, Nix LC, Brummett AM, Aguillon C, Oltman DJ, Beer JM (2021) The design, development, and evaluation of telepresence interfaces for aging adults: investigating user perceptions of privacy and usability. Int J Hum Comput Stud 156:102695. https://doi.org/10.1016/j.ijhcs.2021.102695

    Article  Google Scholar 

  27. Lei M, Clemente IM, Liu H, Bell J (2022) The Acceptance of Telepresence Robots in Higher Education. Int J Soc Robot 1–18. https://doi.org/10.1007/s12369-021-00837-y

  28. Hung L, Wong J, Smith C, Berndt A, Gregorio M, Horne N et al (2022) Facilitators and barriers to using telepresence robots in aged care settings: a scoping review. J Rehabilitation Assist Technol Eng 9:20556683211072385. https://doi.org/10.1177/20556683211072385

    Article  Google Scholar 

  29. Bigot L, Langeard A, Moussay S, Gauthier A, Quarck G (2019) Activité physique à domicile pour les seniors: revue de la question et proposition d’une pratique optimisée. Mov Sports Sci - Sci Mot 103:27–37. https://doi.org/10.1051/sm/2019003

    Article  Google Scholar 

  30. Chen K, Chan AHS (2014) Gerontechnology acceptance by elderly Hong Kong Chinese: a senior technology acceptance model (STAM). Ergonomics 57(5):635–652. https://doi.org/10.1080/00140139.2014.895855

    Article  Google Scholar 

  31. Kuo HM, Chen CW, Hsu CH (2012) A study of a B2C supporting Interface Design System for the Elderly. Hum Factors Ergon Manuf 22(6):528–540. https://doi.org/10.1002/hfm.20297

    Article  Google Scholar 

  32. Davis FD, Bagozzi RP, Warshaw PR (1989) User Acceptance of Computer Technology: a comparison of two theoretical models. Manage Sci 35(8):982–1003. https://doi.org/10.1287/mnsc.35.8.982

    Article  Google Scholar 

  33. Venkatesh V, Bala H (2008) Technology Acceptance Model 3 and a Research Agenda on Interventions. Decis Sci 39(2):273–312. https://doi.org/10.1111/j.1540-5915.2008.00192.x

    Article  Google Scholar 

  34. Venkatesh V, Davis FD (2000) A theoretical extension of the Technology Acceptance Model: four Longitudinal Field Studies. Manage Sci 46(2):186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

    Article  Google Scholar 

  35. Mascret N, Delbes L, Voron A, Temprado JJ, Montagne G (2020) Acceptance of a virtual reality headset designed for fall prevention in older adults: questionnaire study. J Med Internet Res 22(12):20691. https://doi.org/10.2196/20691

    Article  Google Scholar 

  36. Scherer R, Siddiq F, Tondeur J (2019) The technology acceptance model (TAM): a meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Comput Educ 128:13–35. https://doi.org/10.1016/j.compedu.2018.09.009

    Article  Google Scholar 

  37. Angosto S, García-Fernández J, Valantine I, Grimaldi-Puyana M (2020) The intention to use fitness and physical activity apps: a systematic review. Sustainability 12(16):6641. https://doi.org/10.3390/su12166641

    Article  Google Scholar 

  38. Mitzner TL, Stuck R, Hartley JQ, Beer JM, Rogers WA (2017) Acceptance of televideo technology by adults aging with a mobility impairment for health and wellness interventions. J Rehabilitation AssistTechnol Eng 4:2055668317692755. https://doi.org/10.1177/2055668317692755

    Article  Google Scholar 

  39. Mascret N, Temprado JJ (2023) Acceptance of a Mobile Telepresence Robot, before Use, to Remotely Supervise Older Adults’ Adapted Physical Activity. Int J Environ Res Public Health 20(4): 3012. https://doi.org/10.3390/ijerph20043012

  40. Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User Acceptance of Information Technology: toward a unified view. MIS Q 27(3):425–478. https://doi.org/10.2307/30036540

    Article  Google Scholar 

  41. Fiske ST, Cuddy AJ, Glick P (2007) Universal dimensions of social cognition: warmth and competence. Trends Cogn Sci 11(2):77–83. https://doi.org/10.1016/j.tics.2006.11.005

    Article  Google Scholar 

  42. Breazeal C (2003) Toward sociable robots. Rob Auton Syst 42(3–4):167–175. https://doi.org/10.1016/s0921-8890(02)00373-1

    Article  MATH  Google Scholar 

  43. Schouten AP, Portegies TC, Withuis I, Willemsen LM, Mazerant-Dubois K (2022) Robomorphism: examining the effects of telepresence robots on between-student cooperation. Comput Hum Behav 126:106980. https://doi.org/10.1016/j.chb.2021.106980

    Article  Google Scholar 

  44. Heerink M, Kröse B, Evers V, Wielinga B (2010) Assessing acceptance of assistive social agent technology by older adults: the almere model. Int J Soc Robot 2(4):361–375. https://doi.org/10.1007/s12369-010-0068-5

    Article  Google Scholar 

  45. Han J, Conti D (2020) The use of UTAUT and post acceptance models to investigate the attitude towards a telepresence robot in an educational setting. Robot 9(2):34. https://doi.org/10.3390/robotics9020034

    Article  Google Scholar 

  46. Podsiadlo D, Richardson S (1991) The timed “Up & Go”: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc 39(2):142–148. https://doi.org/10.1111/j.1532-5415.1991.tb01616.x

    Article  Google Scholar 

  47. Pino M, Boulay M, Jouen F, Rigaud AS (2015) Are we ready for robots that care for us?” Attitudes and opinions of older adults toward socially assistive robots. Front Aging Neurosci 7:141. https://doi.org/10.3389/fnagi.2015.00141

    Article  Google Scholar 

  48. Poizat G, Flandin S, Theureau J (2022) A micro-phenomenological and semiotic approach to cognition in practice: a path toward an integrative approach to studying cognition-in-the-world and from within. Adap Behav. https://doi.org/10.1177/10597123211072352

    Article  Google Scholar 

  49. Folstein MF, Folstein SE, McHugh PR (1975) Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12(3):189–198. https://doi.org/10.1016/0022-3956(75)90026-6

    Article  Google Scholar 

  50. Bohannon RW (2008) Hand-grip dynamometry predicts future outcomes in aging adults. J Geriatr Phys Ther 31(1):3–10. https://doi.org/10.1519/00139143-200831010-00002

    Article  Google Scholar 

  51. Springer BA, Marin R, Cyhan T, Roberts H, Gill NW (2007) Normative values for the unipedal stance test with eyes open and closed. J Geriatr Phys Ther 30(1):8–15. https://doi.org/10.1519/00139143-200704000-00003

    Article  Google Scholar 

  52. Stevens PJ, Syddall HE, Patel HP, Martin HJ, Cooper C, Aihie Sayer A (2012) Is grip strength a good marker of physical performance among community-dwelling older people? J Nutr Health Aging 16(9):769–774. https://doi.org/10.1007/s12603-012-0388-2

    Article  Google Scholar 

  53. Shigematsu R, Okura T (2006) A novel exercise for improving lower-extremity functional fitness in the elderly. Aging Clin Exp Res 18(3):242–248. https://doi.org/10.1007/bf03324655

    Article  Google Scholar 

  54. Shigematsu R, Okura T, Nakagaichi M, Tanaka K, Sakai T, Kitazumi S, Rantanen T (2008) Square-Stepping Exercise and fall risk factors in older adults: a Single-Blind, randomized controlled trial. J Gerontol A Biol Sci Med Sci 63(1):76–82. https://doi.org/10.1249/01.mss.0000274651.98988.33

    Article  Google Scholar 

  55. Li J, Ma Q, Chan AH, Man SS (2019) Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Appl Ergon 75:162–169. https://doi.org/10.1016/j.apergo.2018.10.006

    Article  Google Scholar 

  56. Dunn TJ, Baguley T, Brunsden V (2014) From alpha to omega: a practical solution to the pervasive problem of internal consistency estimation. Br J Psychol 105(3):399–412. https://doi.org/10.1111/bjop.12046

    Article  Google Scholar 

  57. Theureau J (2015) Le cours d’action : L’enaction et l’expérience. Octarès, Toulouse, France

    Google Scholar 

  58. Vors O, Cury F, Marqueste T, Mascret N (2019) Enactive Phenomenological Approach to the Trier Social stress test: a mixed methods point of view. J Vis Exp 143:e58805. https://doi.org/10.3791/58805

    Article  Google Scholar 

  59. Starks H, Brown Trinidad S (2007) Choose your method: a comparison of phenomenology, discourse analysis, and grounded theory. Qual Health Res 17(10):1372–1380. https://doi.org/10.1177/1049732307307031

    Article  Google Scholar 

  60. Curran PJ, West SG, Finch JF (1996) The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychol Methods 1(1):16–29. https://doi.org/10.1037/1082-989x.1.1.16

    Article  Google Scholar 

  61. Durand M (2014) Activité humaine, pratiques sociales, et éducation des adultes. Un dialogue entre concepts et réalité. Raison et passions, Dijon, France

    Google Scholar 

  62. Chen PY, Song CY, Yen HY, Lin PC, Chen SR, Lu LH et al (2021) Impacts of tai chi exercise on functional fitness in community-dwelling older adults with mild degenerative knee osteoarthritis: a randomized controlled clinical trial. BMC Geriatr 21(1):1–9. https://doi.org/10.1186/s12877-021-02390-9

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to warmly thank Amelie Voron and Elodie Navarro for their excellent help in collecting the data.

Funding

This study was funded by AG2R-La Mondiale as part the experimental program developed by the Chair Active Aging.

Author information

Authors and Affiliations

  1. Aix Marseille Univ, CNRS, ISM, Marseille, France

    Nicolas Mascret, Olivier Vors & Jean-Jacques Temprado

Authors
  1. Nicolas Mascret
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Olivier Vors
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jean-Jacques Temprado
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nicolas Mascret.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Compliance with Ethical Standards

The present study was carried out in accordance with the Declaration of Helsinki and has been approved by the National Ethics Committee (N°IRB00012476-2021-10-03-93).

Additional information

Publisher’s Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mascret, N., Vors, O. & Temprado, JJ. Mobile Telepresence Robot as a New Service to Remotely Supervise Older Adults’ Physical Activity: Effectiveness, Acceptance, and Perception. Int J of Soc Robotics 15, 1243–1260 (2023). https://doi.org/10.1007/s12369-023-01025-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12369-023-01025-w

Keywords

Search

Navigation