Advertisement

Changes in Internet Use Over Time Among Individuals with Traumatic Spinal Cord Injury

      Abstract

      Objective

      To investigate the changes in total internet and mobile internet use over time and determine how demographic characteristics are related to changes in internet and mobile internet use among individuals with spinal cord injury (SCI).

      Design

      Cross-sectional analysis of a multicenter cohort study.

      Setting

      National SCI Database.

      Participants

      Individuals with traumatic SCI with follow-up data collected between 2012 and 2018 (N=13,622).

      Interventions

      Not applicable.

      Main Outcome Measures

      Proportion of sample reporting internet use at all or through a mobile device over time and specifically in 2018.

      Results

      The proportion of internet users increased from 77.7% in 2012 to 88.1% in 2018. Older participants (P<.001); those with lower annual income (P<.001), less education (P<.001), non-White race or Hispanic ethnicity (P<.001), or motor incomplete tetraplegia (P=.004); and men (P=.035) were less likely to use the internet from 2012-2018. By 2018, there were no longer differences in internet use based on race and ethnicity (P=.290) or sex (P=.066). Mobile internet use increased each year (52.4% to 87.7% of internet users from 2012-2018), with a participant being 13.7 times more likely to use mobile internet in 2018 than 2012. Older age (P<.001), income <$50,000 (P<.001), high school diploma or less (P=.011), or non-Hispanic White race/ethnicity (P=.001) were associated with less mobile internet use over time. By 2018, there were no differences in mobile internet use by education (P=.430), and only participants with incomes >$75,000 per year had greater odds of mobile internet use (P=.016).

      Conclusions

      Disparities associated with internet access are decreasing likely as a result of mobile device use. Increased internet access offers an important opportunity to provide educational and training materials to frequently overlooked groups of individuals with SCI.

      Keywords

      List of abbreviations:

      NSCID (National Spinal Cord Injury Database), SCI (spinal cord injury)
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Archives of Physical Medicine and Rehabilitation
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

      1. Research2Guidance. mHealth economics 2017–current status and future trends in mobile health. Available at:https://research2guidance.com/product/mhealth-economics-2017-current-status-and-future-trends-in-mobile-health/. Accessed June 3, 2020.

        • Griffiths F
        • Lindenmeyer A
        • Powell J
        • Lowe P
        • Thorogood M.
        Why are health care interventions delivered over the internet? A systematic review of the published literature.
        J Med Internet Res. 2006; 8: e10
        • Pires IM
        • Marques G
        • Garcia NM
        • Flórez-Revuelta F
        • Ponciano V
        • Oniani S.
        A research on the classification and applicability of the mobile health applications.
        J Pers Med. 2020; 10: 11
        • Ventola CL.
        Mobile devices and apps for health care professionals: uses and benefits.
        P T. 2014; 39: 356-364
        • Hill ML
        • Cronkite RC
        • Ota DT
        • Yao EC
        • Kiratli BJ.
        Validation of home telehealth for pressure ulcer assessment: a study in patients with spinal cord injury.
        J Telemed Telecare. 2009; 15: 196-202
        • Olney CM
        • Vos-Draper T
        • Egginton J
        • et al.
        Development of a comprehensive mobile assessment of pressure (CMAP) system for pressure injury prevention for veterans with spinal cord injury.
        J Spinal Cord Med. 2019; 42: 685-694
        • Singh G
        • MacGillivray M
        • Mills P
        • Adams J
        • Sawatzky B
        • Mortenson WB.
        Patients’ perspectives on the usability of a mobile app for self-management following spinal cord injury.
        J Med Syst. 2019; 44: 26
        • Van Straaten MG
        • Cloud BA
        • Morrow MM
        • Ludewig PM
        • Zhao KD.
        Effectiveness of home exercise on pain, function, and strength of manual wheelchair users with spinal cord injury: a high-dose shoulder program with telerehabilitation.
        Arch Phys Med Rehabil. 2014; 95 (1810-7.e2)
        • Kryger MA
        • Crytzer TM
        • Fairman A
        • et al.
        The effect of the interactive mobile health and rehabilitation system on health and psychosocial outcomes in spinal cord injury: randomized controlled trial.
        J Med Internet Res. 2019; 21: e14305
        • Mehta S
        • Hadjistavropoulos H
        • Nugent M
        • Karin E
        • Titov N
        • Dear BF
        Guided internet-delivered cognitive-behaviour therapy for persons with spinal cord injury: a feasibility trial.
        Spinal Cord. 2020; 58: 544-552
        • Worobey LA
        • Rigot SK
        • Hogaboom NS
        • Venus C
        • Boninger ML.
        Investigating the efficacy of web-based transfer training on independent wheelchair transfers through randomized controlled trials.
        Arch Phys Med Rehabil. 2018; 99 (9-16.e10)
        • Worobey LA
        • Hibbs R
        • Rigot SK
        • Boninger ML
        • Huzinec R
        • Sung JH
        • et al.
        Intra- and inter-rater reliability of remote assessment of transfers by wheelchair users using the transfer assessment instrument (version 4.0).
        Arch Phys Med Rehabil. 2021 Mar 10; ([Epub ahead of print])
        • Rezaei M
        • Sharifi A
        • Vaccaro AR
        Rahimi-Movaghar V. Home-based rehabilitation programs: promising field to maximize function of patients with traumatic spinal cord injury.
        Asian J Neurosurg. 2019; 14: 634-640
        • Sunthonlap J
        • Monsalvo K
        • Velasco J
        • et al.
        Promoting exercise in wheelchairs through wireless sensing and computing in a mobile app.
        in: Benavente-Pecas C Cam-Winget N Fleury E Ahrens A Sensor networks. Springer, Cham, Switzerland2018: 118-134
        • Graham F
        • Boland P
        • Grainger R
        • Wallace S.
        Telehealth delivery of remote assessment of wheelchair and seating needs for adults and children: a scoping review.
        Disabil Rehabil. 2019; 42: 3538-3548
        • Lam J-F
        • Gosselin L
        • Rushton PW.
        Use of virtual technology as an intervention for wheelchair skills training: a systematic review.
        Arch Phys Med Rehabil. 2018; 99: 2313-2341
        • Giesbrecht EM
        • Miller WC
        • Mitchell IM
        • Woodgate RL.
        Development of a wheelchair skills home program for older adults using a participatory action design approach.
        Biomed Res Int. 2014; 2014172434
        • Coffey NT
        • Weinstein AA
        • Cai C
        • et al.
        Identifying and understanding the health information experiences and preferences of individuals with TBI, SCI, and burn injuries.
        J Patient Exp. 2016; 3: 88-95
        • Hogan TP
        • Hill JN
        • Locatelli SM
        • et al.
        Health information seeking and technology use among veterans with spinal cord injuries and disorders.
        PM R. 2016; 8: 123-130
        • Matter B
        • Feinberg M
        • Schomer K
        • Harniss M
        • Brown P
        • Johnson K.
        Information needs of people with spinal cord injuries.
        J Spinal Cord Med. 2009; 32: 545-554
        • Goodman N
        • Jette AM
        • Houlihan B
        • Williams S.
        Computer and internet use by persons after traumatic spinal cord injury.
        Arch Phys Med Rehabil. 2008; 89: 1492-1498
        • Monden KR
        • Sevigny M
        • Ketchum JM
        • et al.
        Associations between insurance provider and assistive technology use for computer and electronic devices 1 year after tetraplegia: findings from the spinal cord injury model systems national database.
        Arch Phys Med Rehabil. 2019; 100: 2260-2266
        • Post MWM
        • Leenders JMP
        • Tepper M
        • et al.
        Computer and internet use among people with long-standing spinal cord injury: a cross-sectional survey in the Netherlands.
        Spinal Cord. 2019; 57: 396-403
        • Chen Y
        • DeVivo MJ
        • Richards JS
        • SanAgustin TB.
        Spinal cord injury model systems: review of program and national database from 1970 to 2015.
        Arch Phys Med Rehabil. 2016; 97: 1797-1804
        • Hesse BW
        • Nelson DE
        • Kreps GL
        • et al.
        Trust and sources of health information: the impact of the internet and its implications for health care providers: findings from the first Health Information National Trends Survey.
        Arch Intern Med. 2005; 165: 2618-2624
        • Greenberg-Worisek AJ
        • Kurani S
        • Finney Rutten LJ
        • Blake KD
        • Moser RP
        • Hesse BW.
        Tracking healthy people 2020 internet, broadband, and mobile device access goals: an update using data from the health information national trends survey.
        J Med Internet Res. 2019; 21: e13300
      2. Centers for Disease Control and Prevention, National Center for Health Statistics. NCHS urban-rural classification scheme for counties. Available at: https://www.cdc.gov/nchs/data_access/urban_rural.htm. Accessed July 7, 2019.

        • Centers for Disease Control and Prevention
        • National Center for Health Statistics
        • Office of Analysis and Epidemiology
        2013 NCHS urban–rural classification scheme for counties.
        U.S. Department of Health and Human Services, Hyattsville2014
      3. Statistica Research Department. Mobile internet user penetration 2015 to 2025. Available at: https://www.statista.com/statistics/275587/mobile-phone-internet-user-penetration-us/. Accessed December 3, 2020.

      4. Pew Research Center. Internet/broadband fact sheet. Available at: https://www.pewresearch.org/internet/fact-sheet/internet-broadband/. Accessed June 11, 2020.

        • World Health Organization
        Global diffusion of e-health - making universal health coverage achievable: report of the third global survey on e-health.
        World Health Organization, Geneva2017 (Report no. 978-92-4-151178-0)
      5. Federal Communications Commission. 2020 broadband deployment report. Available at: https://docs.fcc.gov/public/attachments/FCC-20-50A1.pdf. Accessed December 3, 2020.

      6. Pew Research Center. Mobile fact sheet. Available at: https://www.pewresearch.org/internet/fact-sheet/mobile/. Accessed June 11, 2020.

        • Gordon NP
        • Crouch E.
        Digital information technology use and patient preferences for internet-based health education modalities: cross-sectional survey study of middle-aged and older adults with chronic health conditions.
        JMIR Aging. 2019; 2: e12243
        • Helbostad JL
        • Vereijken B
        • Becker C
        • et al.
        Mobile health applications to promote active and healthy ageing.
        Sensors (Basel). 2017; 17: 622
        • Jones M
        • DeRuyter F
        • Morris J.
        The digital health revolution and people with disabilities: perspective from the United States.
        Int J Environ Res Public Health. 2020; 17: 381
        • Kim K-I
        • Gollamudi SS
        • Steinhubl S.
        Digital technology to enable aging in place.
        Exp Gerontol. 2017; 88: 25-31
      7. Task Force on Research and Development for Technology to Support Aging Adults, Committee on Techology of the National Science and Technology Council. Emerging technologies to support an aging population. Available at:https://trumpwhitehouse.archives.gov/wp-content/uploads/2019/03/Emerging-Tech-to-Support-Aging-2019.pdf. Accessed July 1, 2020.

        • Drainoni M-L
        • Houlihan B
        • Williams S
        • et al.
        Patterns of Internet use by persons with spinal cord injuries and relationship to health-related quality of life.
        Arch Phys Med Rehabil. 2004; 85: 1872-1879
        • Duplaga M.
        Digital divide among people with disabilities: analysis of data from a nationwide study for determinants of Internet use and activities performed online.
        PLoS One. 2017; 12e0179825
      8. Consumer Expert Group. Consumer Expert Group report into the use of the Internet by disabled people: barriers and solutions. 2009. Available at:https://webarchive.nationalarchives.gov.uk/+/http:/www.culture.gov.uk/images/publications/CEGreport-internet-and-disabled-access2009.pdf. Accessed Jue 10, 2020.

        • van Deursen AJAM
        • van Dijk JAGM.
        Toward a multifaceted model of internet access for understanding digital divides: an empirical investigation.
        Inf Soc. 2015; 31: 379-391
        • Prieger JE.
        The broadband digital divide and the benefits of mobile broadband for minorities.
        J Econ Inequal. 2015; 13: 373-400
        • Saltes N.
        Disability in the digital age: reconfiguring access, inclusion and equality. [dissertation]. Queen's University (Canada).
        2016
        • Chen X
        • Orom H
        • Hay JL
        • et al.
        Differences in rural and urban health information access and use.
        J Rural Health. 2019; 35: 405-417
        • Hale TM
        • Cotten SR
        • Drentea P
        • Goldner M.
        Rural-urban differences in general and health-related internet use.
        Am Behav Sci. 2010; 53: 1304-1325
        • Johnston KJ
        • Wen H
        • Joynt Maddox KE
        Lack of access to specialists associated with mortality and preventable hospitalizations of rural medicare beneficiaries.
        Health Aff (Millwood). 2019; 38: 1993-2002
        • Cai Z
        • Fan X
        • Du J.
        Gender and attitudes toward technology use: a meta-analysis.
        Comput Educ. 2017; 105: 1-13
        • National Spinal Cord Injury Statistical Center
        Facts and figures at a glance.
        University of Alabama at Birmingham, Birmingham2020
        • Ketchum JM
        • Cuthbert JP
        • Deutsch A
        • et al.
        Representativeness of the spinal cord injury model systems national database.
        Spinal Cord. 2018; 56: 126-132