The Associations between Technology Acceptance, Social Networks, Risk of Falls, and Physical Activity among Older People with Musculoskeletal Conditions during the COVID-19 Pandemic

Authors

  • Jaruporn Chaiwongsa Ramathibodi School of Nursing, Faculty of Medicine Ramathibodi Hospital, Mahidol University
  • Inthira Roopsawang Ramathibodi School of Nursing, Faculty of Medicine Ramathibodi Hospital, Mahidol University
  • Suparb Aree-Ue Ramathibodi School of Nursing, Faculty of Medicine Ramathibodi Hospital, Mahidol University

DOI:

https://doi.org/10.60099/jtnmc.v38i01.259465

Keywords:

Technology acceptance, Social network, Risk of falls, Physical activity, Older people

Abstract

Introduction: The COVID-19 pandemic has had significant impacts on older adults including communication, social networks, physical activity, and adverse health outcomes, especially in those with musculoskeletal conditions who often experience mobility limitations. Technology has been utilized as an alternative approach to providing services during the pandemic. However, research on the use of technology in older people with musculoskeletal conditions is limited. 

Objective: To describe the technology acceptance, social networks, risk of falls, and physical activity and to investigate the relationships between technology acceptance, social networks, risk of falls, and physical activity in older people with musculoskeletal conditions during the COVID-19 pandemic. 

Design: Descriptive correlation research 

Methodology: A purposive sampling method was used to recruit a sample of 194 older adults aged 60 years or older with musculoskeletal conditions who had visited or received medical services through telemedicine at the orthopedic outpatient department of a university hospital in Bangkok. The study utilized several instruments, including a demographic questionnaire, the Senior Technology Acceptance Model 14-item scale, the Lubben Social Network Scale-Six items, the Fall risks assessment-Stopping Elderly Accidents, Deaths & Injuries 12-question checklist, and the Rapid Assessment of Physical Activity. The content validity index was .92, .93, .90, and .95, respectively. The reliability of the Senior Technology Acceptance Model 14-item scale and the Lubben Social Network Scale-Six items were .86 and .86, respectively, as determined by Cronbach’s Alpha. The Fall risks assessment-Stopping Elderly Accidents, Deaths & Injuries 12-question checklist and the Rapid Assessment of Physical Activity were tested by Cohen’s Kappa Coefficient, which yielded .67 and .92, respectively. Data were collected through structured interviews and analyzed using descriptive statistics and the Spearman’s rank correlation coefficient. A significance level of .05 was set for the analysis. 

Results: The majority of the participants (96.91%) reported a high level of technology acceptance (M= 110.08, SD =14.39). More than half of the participants (61.34%) had no social isolation (M= 14.85, SD = 5.42), while 48.97% had an identified risk of falls (M= 3.81, SD = 3.10; Median = 3) and 96.39% had suboptimal habitual activity (M= 3.51, SD = 0.98). Correlational analysis revealed that technology acceptance and social networks had low positive correlations with physical activity, with a significant level of .05 (r = .167, r = .179, respectively). On the other hand, risk of falls had a low negative correlation with physical activity (r = -.163; p < .05). 

Recommendations: Healthcare professionals, especially gerontological nurse practitioners, can apply this information to encourage the acceptance of technology and the development of social networks, as well as to assess the risk of falls among older adults with musculoskeletal conditions. By doing so, they can plan and provide nursing care that effectively promotes physical activity in this population.

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References

Gomez-Belda AB, Fernandez-Garces M, Mateo-Sanchis E, Madrazo M, Carmona M, Piles-Roger L, et al. COVID-19 in older adults: what are the differences with younger patients?. Geriatr Gerontol Int. 2021; 21(1): 60-5.

Pinto AJ, Dunstan DW, Owen N, Bonfa E, Gualano B. Combating physical inactivity during the COVID-19 pandemic. Nat Rev Rheumatol. 2020; 16(7): 347-8.

Mikkola TM, von Bonsdorff MB, Salonen MK, Simonen M, Pohjolainen P, Osmond C, et al. Body composition as a predictor of physical performance in older age: A ten-year follow-up of the Helsinki Birth Cohort Study. Arch Gerontol Geriatr. 2018; 77: 163-8.

Williams A, Kamper SJ, Wiggers JH, O’Brien KM, Lee H, Wolfenden L, et al. Musculoskeletal conditions may increase the risk of chronic disease: a systematic review and meta-analysis of cohort studies. BMC Med. 2018; 16(1): 167. doi: 10.1186/s12916-018-1151-2

Blyth FM, Noguchi N. Chronic musculoskeletal pain and its impact on older people. Best Pract Res Clin Rheumatol. 2017; 31(2): 160-8.

Hicks C, Levinger P, Menant JC, Lord SR, Sachdev PS, Brodaty H, et al. Reduced strength, poor balance and concern about falls mediate the relationship between knee pain and fall risk in older people. BMC Geriatr. 2020; 20(1): 94. doi: 10.1186/s12877- 020-1487-2.

Fallon N, Brown C, Twiddy H, Brian E, Frank B, Nurmikko T, et al. Adverse effects of COVID-19-related lockdown on pain, physical activity and psychological well-being in people with chronic pain. Br J Pain. 2021; 15(3): 357-68.

Hoffman GJ, Malani PN, Solway E, Kirch M, Singer DC, Kullgren JT. Changes in activity levels, physical functioning, and fall risk during the COVID-19 pandemic. J Am Geriatr Soc. 2022; 70(1): 49-59.

Forsman AK, Nordmyr J, Matosevic T, Park AL, Wahlbeck K, McDaid D. Promoting mental wellbeing among older people: technology-based interventions. Health Promot Int. 2018; 33(6): 1042-54.

Strutt PA, Johnco CJ, Chen J, Muir C, Maurice O, Dawes P, et al. Stress and coping in older Australians during COVID-19: health, service utilization, grandparenting, and technology use. Clin Gerontol. 2022; 45(1): 106-19.

Srivastav AK, Khadayat S, Samuel AJ. Mobile-based health apps to promote physical activity during COVID-19 lockdowns. J Rehabil Med Clin Commun. 2021; 4: 1000051. doi: 10.2340/20030711-1000051.

Padala KP, Jendro AM, Wilson KB, Padala PR. Technology use to bridge the gap of social distancing during COVID-19. J Geriatr Med Gerontol. 2020; 6(2): 10.23937/2469-5858/1510092. doi: 10.23937/2469-5858/1510092.

Han M, Tan XY, Lee R, Lee JK, Mahendran R. Impact of social media on health-related outcomes among older adults in singapore: qualitative study. JMIR Aging. 2021; 4(1): e23826. doi: 10.2196/23826.

Matteucci I. Sport, physical activity and social health in older adults. caring with technology in the COVID-19 pandemic. Int Rev Sociol Sport. 2022;57(6): 960-79.

Doraiswamy S, Jithesh A, Mamtani R, Abraham A, Cheema S. Telehealth use in geriatrics care during the COVID-19 pandemic-a scoping review and evidence synthesis. Int J Environ Res Public Health. 2021; 18(4): 1755. doi: 10.3390/ijerph18041755.

Chen K, Lou VWQ. Measuring Senior Technology Acceptance: development of a brief, 14-item scale. Innov Aging. 2020; 4(3): igaa016. doi: 10.1093/ geroni/igaa016.

Knippenberg E, Timmermans A, Palmaers S, Spooren A. Use of a technology-based system to motivate older adults in performing physical activity: a feasibility study. BMC Geriatr. 2021; 21(1): 81. doi: 10.1186/ s12877-021-02021-3.

Hulley SB, Cummings SR, Browner WS, Grady D, Newman TB. Designing clinical research: an epidemiologic approach. 4th ed. Philadelphia, PA: Lippincott Williams and Wilkins; 2013. p.79.

Aree-Ue S, Youngcharoen P. The 6-Item Cognitive Function Test-Thai Version: psychometric property testing. Rama Nurs J. 2020; 26(2): 188–202. (in Thai)

Chang Q, Sha F, Chan CH, Yip PSF. Validation of an abbreviated version of the Lubben Social Network Scale (“LSNS-6”) and its associations with suicidality among older adults in China. PLoS One. 2018; 13(8): e0201612. doi: 10.1371/journal.pone.0201612.

Centers for Disease Control and Prevention. STEADI older adult fall prevention [Internet]. 2017 [cited 2022 Sep 15]. Available from: https://www.cdc.gov/steadi/

Topolski TD, LoGerfo J, Patrick DL, Williams B, Walwick J, Patrick MB. The Rapid Assessment of Physical Activity (RAPA) among older adults. Prev Chronic Dis. 2006; 3(4): A118.

Aree-Ue S, Roopsawang I, Thiamwong L, Kwan RYC. Technology acceptance, face mask wearing behavior, health status, and quality of life among older persons with musculoskeletal conditions during COVID-19 pandemic. Unpublished report, 2021.

O’Connell M, Haase K, Cammer A, Peacock S, Cosco T, Holtslander L. Older adults’ acceptance of technology during the pandemic: the COVID Technology Acceptance Model (TAM). Innov Aging. 2021; 5(Suppl 1): 1012. doi: 10.1093/geroni/igab046.3628.

Hauk N, Göritz AS, Krumm S. The mediating role of coping behavior on the age-technostress relationship: a longitudinal multilevel mediation model. PLoS One. 2019; 14(3): e0213349. doi: 10.1371/journal. pone.0213349.

Afrin N, Honkanen R, Koivumaa-Honkanen H, Sund R, Rikkonen T, Williams L, et al. Role of musculoskeletal disorders in falls of postmenopausal women. Osteoporos Int. 2018; 29(11): 2419-26.

Khadilkar SS. Musculoskeletal disorders and menopause. J Obstet Gynaecol India. 2019; 69(2): 99-103.

Hirase T, Okita M, Nakai Y, Akaida S, Shono S, Makizako H. Pain and physical activity changes during the COVID-19 state of emergency among Japanese adults aged 40 years or older: a cross-sectional study. Medicine (Baltimore). 2021; 100(41): e27533. doi: 10.1097/MD.0000000000 027533.

Ha J, Park HK. Factors affecting the acceptability of technology in health care among older korean adults with multiple chronic conditions: a cross-sectional study adopting the senior technology acceptance model. Clin Interv Aging. 2020; 15: 1873-81.

Hong M, De Gagne JC, Shin H. Social networks, health promoting-behavior, and health-related quality of life in older Korean adults. Nurs Health Sci. 2018; 20(1): 79-88.

Mani R, Adhia DB, Leong SL, Vanneste S, De Ridder D. Sedentary behaviour facilitates conditioned pain modulation in middle-aged and older adults with persistent musculoskeletal pain: a cross-sectional investigation. Pain Rep. 2019; 4(5): e773. doi: 10.1097/PR9.000000000000 0773.

Quintela Cardoso-Carmo PJ, Pontes César AM, Santos MR, de Carvalho MJ. Fall risk prediction model for older men and women based on ambulatory physical activity level – a cross-sectional population-based study from the Oporto Region. Balt J Health Phys Act. 2022; 14(1): Article3. https://doi.org/10.29359/BJHPA.14.1.03

Naumov AV, Khovasova NO, Moroz VI, Tkacheva ON. Falls and pathology of the musculoskeletal system in the older age groups. Zh Nevrol Psikhiatr Im S S Korsakova. 2020; 120(2), 7-14.

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Published

2023-03-21

How to Cite

1.
Chaiwongsa J, Roopsawang I, Aree-Ue S. The Associations between Technology Acceptance, Social Networks, Risk of Falls, and Physical Activity among Older People with Musculoskeletal Conditions during the COVID-19 Pandemic. J Thai Nurse midwife Counc [Internet]. 2023 Mar. 21 [cited 2024 Nov. 5];38(01):37-51. Available from: https://he02.tci-thaijo.org/index.php/TJONC/article/view/259465

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Research Articles