A Novel Instrument for Measuring Older People's Attitudes Toward Technology (TechPH): Development and Validation

Peter Anderberg, Shahryar Eivazzadeh, Johan Sanmartin Berglund, Peter Anderberg, Shahryar Eivazzadeh, Johan Sanmartin Berglund

Abstract

Background: The use of health technology by older people is coming increasingly in focus with the demographic changes. Health information technology is generally perceived as an important factor in enabling increased quality of life and reducing the cost of care for this group. Age-appropriate design and facilitation of technology adoption are important to ensure functionality and removal of various barriers to usage. Development of assessment tools and instruments for evaluating older persons' technology adoption and usage as well as measuring the effects of the interventions are of high priority. Both usability and acceptance of a specific technology or service are important factors in evaluating the impact of a health information technology intervention. Psychometric measures are seldom included in evaluations of health technology. However, basic attitudes and sentiments toward technology (eg, technophilia) could be argued to influence both the level of satisfaction with the technology itself as well as the perception of the health intervention outcome.

Objective: The purpose of this study is to develop a reduced and refined instrument for measuring older people's attitudes and enthusiasm for technology based on relevant existing instruments for measuring technophilia. A requirement of the new instrument is that it should be short and simple to make it usable for evaluation of health technology for older people.

Methods: Initial items for the TechPH questionnaire were drawn from a content analysis of relevant existing technophilia measure instruments. An exploratory factor analysis was conducted in a random selection of persons aged 65 years or older (N=374) on eight initial items. The scale was reduced to six items, and the internal consistency and reliability of the scale were examined. Further validation was made by a confirmatory factor analysis (CFA).

Results: The exploratory factor analysis resulted in two factors. These factors were analyzed and labeled techEnthusiasm and techAnxiety. They demonstrated relatively good internal consistency (Cronbach alpha=.72 and .68, respectively). The factors were confirmed in the CFA and showed good model fit (χ28=21.2, χ2/df=2.65, comparative fit index=0.97, adjusted goodness-of-fit index=0.95, root mean square error of approximation=0.067, standardized root mean square residual=0.036).

Conclusions: The construed TechPH score showed expected relations to external real-world criteria, and the two factors showed interesting internal relations. Different technophilia personality traits distinguish clusters with different behaviors of adaptation as well as usage of new technology. Whether there is an independent association with the TechPH score against outcomes in health technology projects needs to be shown in further studies. The instrument must also be validated in different contexts, such as other countries.

Keywords: aging; eHealth; health technology; internet; technophilia.

Conflict of interest statement

Conflicts of Interest: None declared.

©Peter Anderberg, Shahryar Eivazzadeh, Johan Sanmartin Berglund. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 23.05.2019.

Figures

Figure 1
Figure 1
Scatterplot of techEnthusiasm (y-axis) and techAnxiety (x-axis). Individuals showing high technophilia (TechPH) are found in the second quadrant; low TechPH are found in the fourth quadrant.

References

    1. Schulz R, Wahl H, Matthews JT, De Vito DA, Beach SR, Czaja SJ. Advancing the aging and technology agenda in gerontology. Gerontologist. 2015 Oct;55(5):724–734. doi: 10.1093/geront/gnu071.
    1. Kwon S. In: Gerontechnology: Research, Practice, and Principles in the Field of Technology and Aging. Kwon S, editor. New York: Springer Publishing Company; 2016.
    1. Peine A. Technology and ageing-theoretical propositions from science and technology studies (STS) In: Neves BB, Vetere F, editors. Ageing and Digital Technology: Designing and Evaluating Emerging Technologies for Older Adults. Singapore: Springer Singapore; 2019. pp. 51–64.
    1. Fisk A, Czaja S, Rogers W, Charness N, Sharit J. Designing for Older Adults-Principles and Creative Human Factors Approaches. Boca Raton, FL: CRC Press; 2018. Nov 14,
    1. Mitzner TL, Savla J, Boot WR, Sharit J, Charness N, Czaja SJ, Rogers WA. Technology adoption by older adults: findings from the PRISM trial. Gerontologist. 2019 Dec 09;59(1):34–44. doi: 10.1093/geront/gny113.
    1. Czaja SJ, Charness N, Fisk AD, Hertzog C, Nair SN, Rogers WA, Sharit J. Factors predicting the use of technology: findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE) Psychol Aging. 2006 Jun;21(2):333–352. doi: 10.1037/0882-7974.21.2.333.
    1. Roque NA, Boot WR. A new tool for assessing mobile device proficiency in older adults: the mobile device proficiency questionnaire. J Appl Gerontol. 2018 Feb;37(2):131–156. doi: 10.1177/0733464816642582.
    1. Boot WR, Charness N, Czaja SJ, Sharit J, Rogers WA, Fisk AD, Mitzner T, Lee CC, Nair S. Computer proficiency questionnaire: assessing low and high computer proficient seniors. Gerontologist. 2015 Jun;55(3):404–411. doi: 10.1093/geront/gnt117.
    1. Kamin ST, Lang FR, Beyer A. Subjective technology adaptivity predicts technology use in old age. Gerontology. 2017;63(4):385–392. doi: 10.1159/000471802.
    1. Kamin S, Lang F. The Subjective Technology Adaptivity Inventory (STAI): a motivational measure of technology usage in old age. Gerontechnology. 2013 Oct 18;12(1):16–25. doi: 10.4017/gt.2013.12.1.008.00.
    1. Ramprasad C, Tamariz L, Garcia-Barcena J, Nemeth Z, Palacio A. The use of tablet technology by older adults in health care settings-is it effective and satisfying? A systematic review and meta analysis. Clin Gerontol. 2019;42(1):17–26. doi: 10.1080/07317115.2017.1322162.
    1. Jakobsson E, Nygård L, Kottorp A, Malinowsky C. Experiences from using eHealth in contact with health care among older adults with cognitive impairment. Scand J Caring Sci. 2019 Jan 10;:1–10. doi: 10.1111/scs.12634.
    1. Barbosa Neves B, Franz R, Judges R, Beermann C, Baecker R. Can digital technology enhance social connectedness among older adults? A feasibility study. J Appl Gerontol. 2019 Jan;38(1):49–72. doi: 10.1177/0733464817741369.
    1. Baker S, Warburton J, Waycott J, Batchelor F, Hoang T, Dow B, Ozanne E, Vetere F. Combatting social isolation and increasing social participation of older adults through the use of technology: A systematic review of existing evidence. Australas J Ageing. 2018 Sep;37(3):184–193. doi: 10.1111/ajag.12572.
    1. Enam A, Torres-Bonilla J, Eriksson H. Evidence-based evaluation of eHealth interventions: systematic literature review. J Med Internet Res. 2018 Nov 23;20(11):e10971. doi: 10.2196/10971.
    1. Wakefield B, Turvey C, Nazi K, Holman J, Hogan T, Shimada S, Kennedy D. Psychometric properties of patient-facing eHealth evaluation measures: systematic review and analysis. J Med Internet Res. 2017 Dec 11;19(10):e346. doi: 10.2196/jmir.7638.
    1. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly. 1989 Sep;13(3):319. doi: 10.2307/249008.
    1. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. MIS Quarterly. 2003;27(3):425. doi: 10.2307/30036540.
    1. Holden RJ, Karsh B. The technology acceptance model: its past and its future in health care. J Biomed Inform. 2010 Feb;43(1):159–172. doi: 10.1016/j.jbi.2009.07.002.
    1. Kim J, Park H. Development of a health information technology acceptance model using consumers' health behavior intention. J Med Internet Res. 2012 Oct 01;14(5):e133. doi: 10.2196/jmir.2143.
    1. Chen K, Chan AH. Gerontechnology acceptance by elderly Hong Kong Chinese: a senior technology acceptance model (STAM) Ergonomics. 2014;57(5):635–652. doi: 10.1080/00140139.2014.895855.
    1. Middlemass JB, Vos J, Siriwardena AN. Perceptions on use of home telemonitoring in patients with long term conditions-concordance with the Health Information Technology Acceptance Model: a qualitative collective case study. BMC Med Inform Decis Mak. 2017 Jun 26;17(1):89. doi: 10.1186/s12911-017-0486-5.
    1. Peek STM, Luijkx KG, Rijnaard MD, Nieboer ME, van der Voort CS, Aarts S, van Hoof J, Vrijhoef HJ, Wouters EJ. Older adults' reasons for using technology while aging in place. Gerontology. 2016;62(2):226–237. doi: 10.1159/000430949.
    1. Merkel S, Enste P, Hilbert J, Chen K, Chan A, Kwon S. Technology acceptance and aging. In: Kwon S, editor. Gerontechnology Research, Practice, and Principles in the Field of Technology and Aging. New York: Springer Publishing Company; 2017. pp. 335–350.
    1. Holthe T, Halvorsrud L, Karterud D, Hoel K, Lund A. Usability and acceptability of technology for community-dwelling older adults with mild cognitive impairment and dementia: a systematic literature review. Clin Interv Aging. 2018;13:863–886. doi: 10.2147/CIA.S154717. doi: 10.2147/CIA.S154717.
    1. DeLone WH, McLean ER. Information systems success: the quest for the dependent variable. Inform Syst Res. 1992 Mar;3(1):60–95. doi: 10.1287/isre.3.1.60.
    1. Yusof MM, Paul RJ, Stergioulas LK. Towards a framework for health information systems evaluation. HICSS '06 39th Annual Hawaii International Conference on System Sciences; Jan 4-7, 2006; Kauia, HI. 2006.
    1. Yu P, Li H, Gagnon M. Health IT acceptance factors in long-term care facilities: a cross-sectional survey. Int J Med Inform. 2009 Apr;78(4):219–229. doi: 10.1016/j.ijmedinf.2008.07.006.
    1. Self R, Aquilina C. Advances in Intelligent Systems and Computing. Berlin: Springer; 2013. TechnoStress in the 21st century; does it still exist and how does it affect knowledge management and other information systems initiatives; pp. 117–127.
    1. Tarafdar M, Tu Q, Ragu-Nathan TS. Impact of technostress on end-user satisfaction and performance. J Manage Inform Syst. 2014 Dec 09;27(3):303–334. doi: 10.2753/mis0742-1222270311.
    1. Yen P, Wantland D, Bakken S. Development of a customizable health IT usability evaluation scale. AMIA Annu Symp Proc. 2010 Nov 13;2010:17–21.
    1. Brooke J. Usability Evaluation in Industry. London: Taylor and Francis; 1996. SUS: A quick and dirty usability scale; pp. 189–194.
    1. Georgsson M, Staggers N. Quantifying usability: an evaluation of a diabetes mHealth system on effectiveness, efficiency, and satisfaction metrics with associated user characteristics. J Am Med Inform Assoc. 2016 Jan;23(1):5–11. doi: 10.1093/jamia/ocv099.
    1. Schnall R, Cho H, Liu J. Health Information Technology Usability Evaluation Scale (Health-ITUES) for usability assessment of mobile health technology: validation study. JMIR Mhealth Uhealth. 2018 Jan 05;6(1):e4. doi: 10.2196/mhealth.8851.
    1. Edison SW, Geissler GL. Measuring attitudes towards general technology: Antecedents, hypotheses and scale development. J Target Meas Anal Mark. 2003 Nov 1;12(2):137–156. doi: 10.1057/palgrave.jt.5740104.
    1. Plociennik C, Wandke H, Kirste T. What influences user acceptance of ad-hoc assistance systems? A quantitative study. Proceedings of MMS European Mathematical Information Service; MMS European Mathematical Information Service; Feb 23-25, 2010; Bonn. 2010. pp. 57–70.
    1. Ronit P. Technophilia: A new model for technology adoption. Proceedings of the UK Academy for Information Systems Conference; UK Academy for Information Systems Conference; Apr 11, 2011; Oxford, UK. 2011. p. 41.
    1. Seebauer S, Stolz R, Berger M. Technophilia as a driver for using advanced traveler information systems. Transport Res C-Emer. 2015 Nov;60:498–510. doi: 10.1016/j.trc.2015.10.009.
    1. Osiceanu M. Psychological implications of modern technologies: “technofobia” versus “technophilia”. Procd Soc Behv. 2015 May;180:1137–1144. doi: 10.1016/j.sbspro.2015.02.229.
    1. Martínez-Córcoles M, Teichmann M, Murdvee M. Assessing technophobia and technophilia: development and validation of a questionnaire. Technol Soc. 2017 Nov;51:183–188. doi: 10.1016/j.techsoc.2017.09.007.
    1. Li Y, Fuller B. I'm lovin' IT: toward a technophilia model of user adaptation to ICT. Proceedings Association for Information Systems (AIS); AMCIS 2017; Aug 10, 2017; Boston, MA. 2017.
    1. Donat E, Brandtweiner R, Kerschbaum J. Attitudes and the digital divide: attitude measurement as instrument to predict internet usage. Inform Sci. 2009;12(1):37–56. doi: 10.28945/427.
    1. Nimrod G. Technophobia among older Internet users. Educ Gerontol. 2018 Jan 24;44(2-3):148–162. doi: 10.1080/03601277.2018.1428145.
    1. Devaraj S, Easley RF, Crant JM. How does personality matter? Relating the five-factor model to technology acceptance and use. Inform Syst Res. 2008 Mar;19(1):93–105. doi: 10.1287/isre.1070.0153.
    1. Steinerman J, Lipton R, Rapkin B, Quaranto B, Schwartz C. Factors associated with openness to research participation in an aging community: the importance of technophilia and social cohesion. Gerontechnology. 2013 Mar 21;11(4):504–512. doi: 10.4017/gt.2013.11.4.012.00.
    1. Lagergren M, Fratiglioni L, Hallberg I, Berglund J, Elmståhl S, Hagberg B, Holst G, Rennemark M, Sjölund B, Thorslund M, Wiberg I, Winblad B, Wimo A. A longitudinal study integrating population, care and social services data. The Swedish National study on Aging and Care (SNAC) Aging Clin Exp Res. 2004 Apr;16(2):158–168.
    1. Peterson CH, Peterson NA, Powell KG. Cognitive interviewing for item development: validity evidence based on content and response processes. Meas Eval Couns Dev. 2017 Oct 04;50(4):217–223. doi: 10.1080/07481756.2017.1339564.
    1. DiStefano C, Zhu M, Mindrila D. Understanding and Using Factor Scores: Considerations for the Applied Researcher. Prac Assess Res Eval. 2009 Oct 01;14(20):1–10.
    1. DiIorio CK. Measurement In Health Behavior: Methods For Research And Evaluation. San Francisco, CA: John Wiley & Sons; 2006. Mar 06, pp. 1–336.
    1. Hair J, Black W, Babin B, Anderson R. Multivariate Data Analysis (seventh edition) Upper Saddle River, NJ: Prentice Hall; 2019.
    1. Osborne J, Costello A. Sample size and subject to item ratio in principal components analysis. Prac Assess Res Eval. 2004;9(11):1–10.
    1. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling. 1999 Jan;6(1):1–55. doi: 10.1080/10705519909540118.
    1. Or CK, Karsh B. A systematic review of patient acceptance of consumer health information technology. J Am Med Inform Assoc. 2009 Aug;16(4):550–560. doi: 10.1197/jamia.M2888.
    1. Wild K, Mattek N, Maxwell S, Dodge H, Jimison H, Kaye J. Computer-related self-efficacy and anxiety in older adults with and without mild cognitive impairment. Alzheimers Dement. 2012 Nov;8(6):544–552. doi: 10.1016/j.jalz.2011.12.008.
    1. Shedletsky L. Internet training for older adult learners: an intergenerational mentoring approach. LLI Rev. 2006;1:43.
    1. Seifert A, Schelling H. Seniors online: attitudes toward the internet and coping with everyday life. J Appl Gerontol. 2018 Jan;37(1):99–109. doi: 10.1177/0733464816669805.
    1. Sinkovics RR, Stöttinger B, Schlegelmilch BB, Ram S. Reluctance to use technology-related products: development of a technophobia scale. Thunderbird Int Bus Rev. 2002 Jun 27;44(4):477–494. doi: 10.1002/tie.10033.
    1. Nimrod G. Technostress: measuring a new threat to well-being in later life. Aging Ment Health. 2018 Aug;22(8):1080–1087. doi: 10.1080/13607863.2017.1334037.
    1. Berner J, Rennemark M, Jogréus C, Anderberg P, Sköldunger A, Wahlberg M, Elmståhl S, Berglund J. Factors influencing Internet usage in older adults (65 years and above) living in rural and urban Sweden. Health Informatics J. 2015 Sep;21(3):237–249. doi: 10.1177/1460458214521226.
    1. Bollen KA. Structural Equations With Latent Variables. New York: John Wiley & Sons; 1989. Apr 28,

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