Learning From Older Adults to Promote Independent Physical Activity Using Mobile Health (mHealth)

Camille Nebeker, Zvinka Z Zlatar, Camille Nebeker, Zvinka Z Zlatar

Abstract

Background: Healthy aging is critically important for several reasons, including economic impact and quality of life. As the population of older adults rapidly increases, identifying acceptable ways to promote healthy aging is a priority. Technologies that can facilitate health promotion and risk reduction behaviors may be a solution, but only if these mobile health (mHealth) tools can be used by the older adult population. Within the context of a physical activity intervention, this study gathered participant's opinions about the use of an mHealth device to learn about acceptance and to identify areas for improvement. Methods: The Independent Walking for Brain Health study (NCT03058146) was designed to evaluate the effectiveness of a wearable mHealth technology in facilitating adherence to a physical activity prescription among participants in free-living environments. An Exit Survey was conducted following intervention completion to gauge participant's perceptions and solicit feedback regarding the overall study design, including exercise promotion strategies and concerns specific to the technology (e.g., privacy), that could inform more acceptable mHealth interventions in the future. The Digital Health Checklist and Framework was used to guide the analysis focusing on the domains of Privacy, Access and Usability, and Data Management. Results: Participants (n = 41) were in their early 70's (mean = 71.6) and were predominantly female (75.6%) and White (92.7%). Most were college educated (16.9 years) and enjoyed using technology in their everyday life (85.4%). Key challenges included privacy concerns, device accuracy, usability, and data access. Specifically, participants want to know what is being learned about them and want control over how their identifiable data may be used. Overall, participants were able to use the device despite the design challenges. Conclusions: Understanding participant's perceptions of the challenges and concerns introduced by mHealth is important, as acceptance will influence adoption and adherence to the study protocol. While this study learned from participants at studycompletion, we recommend that researchers consider what might influence participant acceptance of the technology (access, data management, privacy, risks) and build these into the mHealth study design process. We provide recommendations for future mHealth studies with older adults.

Keywords: digital health; interventions; mobile health; older adults; participant perspectives; physical activity; research design; research ethics.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Nebeker and Zlatar.

Figures

Figure 1
Figure 1
Digital Health Checklist and the four domains connected to foundational ethical principles used with permission. Published with permission of C. Nebeker, ReCODE Health The DHC-R is licensed under a Creative Commons Attribution-Non-Commercial 4.0 International License (2018–2020) and available at https://recode.health/tools/.
Figure 2
Figure 2
Exit Survey items that aligned with the Access and Usability domain.
Figure 3
Figure 3
Exit Survey items that aligned with the Privacy domain.
Figure 4
Figure 4
Exit Survey items that aligned with the Data Management domain.
Figure 5
Figure 5
Exit Survey items that aligned with the Data Management domain.

References

    1. World Health Organization . Ageing and Health. (2018). Available online at: (accessed April 26, 2021).
    1. Harmell AL, Jeste D, Depp C. Strategies for successful aging: a research update. Curr Psychiatry Rep. (2014) 16:476. 10.1007/s11920-014-0476-6
    1. World Health Organization . Ageing: Healthy Ageing and Functional Ability. (2020). Available online at: (accessed April 27, 2021).
    1. Bauman A, Merom D, Bull FC, Buchner DM, Singh FAM. Updating the evidence for physical activity: summative reviews of the epidemiological evidence, prevalence, and interventions to promote active aging. Gerontologist. (2016) 56:S268–80. 10.1093/geront/gnw031
    1. Chan WC, Lee ATC, Lam LCW. Exercise for the prevention and treatment of neurocognitive disorders: new evidence and clinical recommendations. Curr Opin Psychiatry. (2021) 34:136–41. 10.1097/YCO.0000000000000678
    1. Haeger A, Costa AS, Schulz JB, Reetz K. Cerebral changes improved by physical activity during cognitive decline: a systematic review on MRI studies. Neuroimage Clin. (2019) 23:101933. 10.1016/j.nicl.2019.101933
    1. Sanders LMJ, Hortobágyi T, Gemert SLB, van der Zee EA, van Heuvelen MJG. Dose-response relationship between exercise and cognitive function in older adults with and without cognitive impairment: a systematic review and meta-analysis. PLoS ONE. (2019). 14:e0210036. 10.1371/journal.pone.0210036
    1. Lee I-M, Shiroma EJ, Kamada M, Bassett DR, Matthews CE, Buring JE. Association of step volume and intensity with all-cause mortality in older women. JAMA Intern Med. (2019) 179:1105–12. 10.1001/jamainternmed.2019.0899
    1. Sabia S, Fayosse A, Dumurgier J, Schnitzler A, Empana J-P, Ebmeier KP, et al. . Association of ideal cardiovascular health at age 50 with incidence of dementia: 25 year follow-up of Whitehall II cohort study. BMJ. (2019) 366:l4414. 10.1136/bmj.l4414
    1. Langhammer B, Bergland A, Rydwik E. The importance of physical activity exercise among older people. Bio Med Res Int. (2018) 2018:e7856823. 10.1155/2018/7856823
    1. Nebeker C. mHealth research applied to regulated and unregulated behavioral health sciences. J Law Med Ethics. (2020) 48:49–59. 10.1177/1073110520917029
    1. Armstrong D, Najafi B, Shahinpoor M. Potential applications of smart multifunctional wearable materials to gerontology. Gerontology. (2017) 63:287–98. 10.1159/000455011
    1. Farivar S, Abouzahra M, Ghasemaghaei M. Wearable device adoption among older adults: a mixed-methods study. Int J Inf Manage. (2020) 55:102209. 10.1016/j.ijinfomgt.2020.102209
    1. Lewis JE, Neider MB. Designing wearable technology for an aging population. Ergonom Design. (2017) 25:4–10. 10.1177/1064804616645488
    1. Vaghefi I, Tulu B. The continued use of mobile health apps: insights from a longitudinal study. JMIR Mhealth Uhealth. (2019) 7:e12983. 10.2196/12983
    1. Nilsen W, Kumar S, Shar A, Varoquiers C, Wiley T, Riley WT, et al. . Advancing the science of mHealth. J Health Commun. (2012) 17:5–10. 10.1080/10810730.2012.677394
    1. Hamideh D, Nebeker C. The digital health landscape in addiction and substance use research: will digital health exacerbate or mitigate health inequities in vulnerable populations? Curr Addict Rep. (2020) 7:317–32. 10.1007/s40429-020-00325-9
    1. Wang S, Bolling K, Mao W, Reichstadt J, Jeste D, Kim H-C, et al. . Technology to support aging in place: older adults' perspectives. Healthcare. (2019) 7:60. 10.3390/healthcare7020060
    1. Nebeker C, Bartlett Ellis RJ, Torous J. Development of a decision-making checklist tool to support technology selection in digital health research. Transl Behav Med. (2020) 10:1004–15. 10.1093/tbm/ibz074
    1. Green C, Woodard L. Validity of the Mattis Dementia Rating Scale for detection of cognitive impairment in the elderly. JNP. (1995) 7:357–60. 10.1176/jnp.7.3.357
    1. Jak AJ, Bondi MW, Delano-Wood L, Wierenga C, Corey-Bloom J, Salmon DP, et al. . Quantification of five neuropsychological approaches to defining mild cognitive impairment. Am J Geriatr Psychiatry. (2009) 17:368–75. 10.1097/JGP.0b013e31819431d5
    1. Mihailidis A, Cockburn A, Longley C, Boger J. The acceptability of home monitoring technology among community-dwelling older adults and baby boomers. Assist Technol. (2008) 20:1–12. 10.1080/10400435.2008.10131927
    1. Wildenbos GA, Peute L, Jaspers M. A framework for evaluating mHealth tools for older patients on usability. Stud Health Technol Inform. (2015) 210:783–7. 10.3233/978-1-61499-512-8-783
    1. Wildenbos GA, Jaspers MWM, Schijven MP, Dusseljee-Peute LW. Mobile health for older adult patients: using an aging barriers framework to classify usability problems. Int J Med Inform. (2019) 124:68–77. 10.1016/j.ijmedinf.2019.01.006
    1. Wildenbos GA, Peute L, Jaspers M. Aging barriers influencing mobile health usability for older adults: a literature based framework (MOLD-US). Int J Med Inform. (2018) 114:66–75. 10.1016/j.ijmedinf.2018.03.012
    1. Parker SJ, Jessel S, Richardson JE, Reid MC. Older adults are mobile too! Identifying the barriers and facilitators to older adults' use of mHealth for pain management. BMC Geriatr. (2013) 13:43. 10.1186/1471-2318-13-43
    1. Harrington CN, Ruzic L, Sanford JA. Universally accessible mHealth apps for older adults: towards increasing adoption and sustained engagement. In: Antona M, Stephanidis C, editors. Universal Access in Human–Computer Interaction. Human and Technological Environments. Cham: Springer International Publishing; (2017) p. 3–12. 10.1007/978-3-319-58700-4_1
    1. Administration on Aging . 2019 Profile of Older Americans (2020).
    1. Matthews KA, Xu W, Gaglioti AH, Holt JB, Croft JB, Mack D, et al. . Racial and ethnic estimates of Alzheimer's disease and related dementias in the United States (2015–2060) in adults aged ≥65 years. Alzheimers Dement. (2019) 15:17–24. 10.1016/j.jalz.2018.06.3063

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