Journal of Gerontological Nursing

Feature Article 

Voice Assistant Reminders for Pain Self-Management Tasks in Aging Adults

Marcia Y. Shade, PhD, BS, RN; Kyle Rector, PhD; Rasila Soumana, BS, BSN, RN; Kevin Kupzyk, PhD

Abstract

Aging adults are impacted by pain. Technology can assist older adults with pain self-management while allowing for independence. The current usability study explored the use of voice assistant reminders for two pain self-management tasks in aging adults. Fifteen community-dwelling older adults with chronic pain and an average age of 65 years used the voice assistant for 4 weeks. Participants had moderate scores for pain severity (mean = 4.6 [SD = 2.3]) and pain interference (mean = 4 [SD = 2.6]). Voice assistant usability was above average (78 of 100). Median time to set up the Google Home Assistant profile was 5 minutes (SD = 7.5), with a median of asking for help two times. Pain self-management task reminders from the voice assistant were perceived as consistent, easy to set up, and helpful for accountability. Voice assistant reminders may be an option to help encourage a variety of pain self-management tasks in aging adults. [Journal of Gerontological Nursing, 46(10), 27–33.]

Abstract

Aging adults are impacted by pain. Technology can assist older adults with pain self-management while allowing for independence. The current usability study explored the use of voice assistant reminders for two pain self-management tasks in aging adults. Fifteen community-dwelling older adults with chronic pain and an average age of 65 years used the voice assistant for 4 weeks. Participants had moderate scores for pain severity (mean = 4.6 [SD = 2.3]) and pain interference (mean = 4 [SD = 2.6]). Voice assistant usability was above average (78 of 100). Median time to set up the Google Home Assistant profile was 5 minutes (SD = 7.5), with a median of asking for help two times. Pain self-management task reminders from the voice assistant were perceived as consistent, easy to set up, and helpful for accountability. Voice assistant reminders may be an option to help encourage a variety of pain self-management tasks in aging adults. [Journal of Gerontological Nursing, 46(10), 27–33.]

According to the U.S. Census Bureau, by 2050, 1.6 billion members of the total population will be adults age 65 years and older (Roberts et al., 2016). Pain is prevalent among older adults, with 60% to 75% reporting persistent pain (Dahlhamer et al., 2018). The impact of pain is significant, with symptoms being associated with three times the risk of disability in older adults (Eggermont et al., 2014; Stubbs et al., 2016). Appropriate pain management is necessary to promote quality of life for aging adults.

Pain Management and Pain Self-Management in Older Adults

Pain management is challenging in older populations. Pain symptoms are frequently underreported and health care provider training in geriatric pain management is limited (Kaye et al., 2014; Malec & Shega, 2015). Older adults use multiple medications and the additional use of pain medications increases the risk for adverse events (American Geriatrics Society [AGS] Beers Criteria® Update Expert Panel et al., 2019; Shade et al., 2017; Shade, Herr, & Kupzyk, 2019). The opioid crisis may contribute to the concern and fear of analgesic use, which may also influence pain management (Malec & Shega, 2015). In under-served areas, these challenges are met by additional barriers with health care access (Eaton et al., 2018). Pain self-management in older adults should be a multicomponent process using nonpharmacological and pharmacological strategies (Reid et al., 2015).

No cure exists for chronic disease or chronic pain symptoms, therefore, the benefits of self-management are the maintenance of quality of life and reduced burden on the health care system (Reid et al., 2008). Historically, self-management activities focus on managing the medical condition, maintenance, changing or creating new behaviors, and coping with emotional aspects of having a disease or symptoms (Lorig & Holman, 2003). Pain-specific tasks for self-management may include adhering to pain medication, modifying physical activities, monitoring symptoms, and coping with feelings associated with pain symptoms. In the literature, a variety of pain self-management strategies have been studied and mostly combine medication, rehabilitation, exercise, and psychological therapies (Reid et al., 2008).

The efficacy of pain self-management strategies has varied in older adults (Chodosh et al., 2005; Reid et al., 2008). This variation may be due to barriers related to the adoption of and adherence to pain self-management strategies (Hadjistavropoulos, 2012; Reid et al., 2008). A review by Reid et al. (2008) found that pain self-management strategies are mostly community-based and face-to-face. The same review found that in-person or face-to-face programs were associated with participant attrition and have limitations with sustainability. In addition, there are time constraints, lack of transportation and knowledge of resources, and cost barriers to adopting pain self-management strategies (Austrian et al., 2005; Booker et al., 2019; Reid et al., 2008). According to Booker et al. (2019), older adults perceive pain self-management strategies as helpful if they are easy to use, less costly, and accessible. The option to deliver pain self-management using technology-based approaches is promising. In fact, internet-based cognitive-behavioral therapy interventions mostly reduced a person's negative conception of pain experiences, and helped with depression and pain (Hadjistavropoulos, 2012). In addition to computers, mobile health (mHealth) is an option using a software application (app) on a smart device. mHealth has evolved to possibly benefit the incorporation of specific self-management activities in aging adults (Parker et al., 2013).

mHealth for Pain Self-Management

Software apps have been developed as an mHealth tool to promote pain self-management. Machado et al. (2016) reviewed self-management apps for low back pain and most were found to be exercise based, not trialed in outcomes research, or regulated. In addition, pain management software apps have limited evidence-based self-management strategies, and limitations with usability in community-dwelling older adults (Bhattarai & Phillips, 2017; Parker et al., 2013). Beyond privacy concerns, usability concerns have been reported as a major barrier for mHealth (Liu et al., 2016; Parker et al., 2013; Zeng et al., 2017). Usability barriers exist with mHealth interventions delivered via mobile smart devices. Physical and sensory age-related changes influence the accessibility of devices with smaller fonts and keys.

Usability may impact app navigation and the adoption of mobile app features. Diaries and tracking can help older adults monitor pain symptoms and reflect on the effectiveness of pain management (Cruise et al., 1996). In a previous study, only 15% of a sample of 15 rural community-dwelling older adults input health outcomes in the measurement function of a medication adherence app. No participants used the diary function, which required texting their own thoughts on a blank screen (Shade, Boron, et al., 2019). mHealth apps for pain self-management need to be functional and accessible to the needs of the growing population of older adults.

Voice assistants, such as Amazon AlexaTM and Google AssistantTM, may eliminate some of the barriers associated with disabilities and changes that occur with aging. It is estimated that approximately 50 million Americans will use voice assistants in 2020 and individuals between ages 45 and 60 are most interested in using voice assistants for health care tasks (Kinsella, 2019; Koetsier, 2018).Voice assistants have been reported to be useful for task management, fitness, cognitive challenges, and stress in populations with disabilities (Chung et al., 2018; Hoy, 2018; Moar, 2017; Wallace & Morris, 2018; Wolters et al., 2016).

Users with a variety of disabilities have been reported to use voice assistants for speech therapy and support for caregivers. Older adults with disabilities associated with visual and disease-related motor impairments had positive feedback about using voice to control smart homes that include mHealth (Callejas & López-Cózar, 2009; Pradhan et al., 2018; Vacher et al., 2013). The continued evolution of voice assistant technology may be valuable for older adults. A recent study found that voice assistants can comprehend the names of medications, which may support the use of these devices to monitor medication adherence (Palanica et al., 2019). Minimal research has been done to study voice assistants for use with health outcomes and self-management of disease (O'Brien et al., 2020). As this body of research is in its early stages, the purpose of the current study was to explore the feasibility of using a voice assistant for pain self-management tasks in older adults.

Method

Design, Sample, and Setting

The current feasibility study had a convenience sample of 15 adults age ≥55 years, recruited from urban and rural communities in the Midwest. In a feasibility study, a sample size between 15 and 20 has been recommended. The lower sample size was warranted due to the feasibility design and recommended sample size for usability studies (Hertzog, 2008; Nielsen & Landauer, 1993; Virzi, 1992). Adults were eligible if they self-reported chronic pain, used daily pain medication, lived independently, lacked cognitive impairment, and had wireless internet in their homes. Study procedures occurred at participants' homes.

Measures

Demographic data were collected on gender, ethnicity, marital status, education, income, self-reported chronic disease, and current strategies used for pain self-management. Scheduled prescribed or over-the-counter (OTC) pain medications were documented and verified using participants' prescribed and OTC medication bottles.

Characteristics of pain and confidence of managing pain were captured using the Brief Pain Inventory Short Form (BPI-SF) and a PROMIS® Self-Efficacy for Managing Symptoms Short Form 8A. The BPI-SF measures pain severity, location, and interference on a 10-point Likert scale and has demonstrated reliability and validity in older adults (Mendoza et al., 2006). Cronbach's alpha in this study was 0.88 for severity and 0.93 for interference. The PROMIS Self-Efficacy for Managing Symptoms Short Form 8A measures the confidence an individual has with self-managing symptoms. The eight questions include item responses on a 5-point Likert scale. The questionnaire is global; therefore, participants were encouraged to respond from the perspective of managing their pain. The PROMIS measures are reliable and valid common data elements (Gruber-Baldini et al., 2017). Cronbach's alpha for this study was 0.84.

The System Usability Scale (SUS) was used to assess subjective ease of use and usefulness of the voice assistant. The 10 question, 5-point Likert scale has scores that range from 0 to 100. Scores >68 are above average (Bangor et al., 2008). Cronbach's alpha for this study was 0.85.

We documented participant difficulties with device set up as noted by number of times asking for help and execution time in minutes. At follow up, participants wrote down (a) if or how they used the voice assistant in other ways to help with pain self-management, (b) if the assistant was consistent with verbal reminders for pain management tasks, and (c) opinions about using the voice assistant for pain self-management.

Procedures

After Institutional Review Board approval, eligible older adults were recruited and invited to take part in the study. Data were collected on demographics, health history, analgesic medications, pain severity, pain interference, and pain self-efficacy. Participants were provided one-on-one instruction and help if needed during set up of the Google HomeTM app and Google Home MiniTM device. Participants created two predetermined reminder tasks to (a) take their daily scheduled pain medication, and (b) write daily in a pain diary. Participants completed tasks using the voice assistant reminders and were encouraged to use the assistant for other activities to help with pain symptoms for a 4-week period. After 4 weeks, follow-up data were collected using the SUS and open-ended questions. Participants logged into their Google Home apps on their mobile devices to confirm the execution of reminder prompts. Pain diaries were collected and reviewed to ensure adherence to diary completion.

Data Analysis

Univariate descriptive statistics were conducted including means, standard deviations, and frequency data of demographics, medications, pain, and voice assistant usability. Spearman's rho correlational statistics were performed to examine the relationships among participant demographics, pain characteristics, and voice assistant usability. IBM SPSS version 25 was used to perform all statistical analyses. Written comments regarding additional voice assistant self-management tasks, voice responses, and opinions were analyzed by three individuals (M.S., K.R., R.S.) to determine commonalities.

Results

Demographic characteristics of the 15 participants are presented in Table 1. Average age of participants was 65, and most were female, Caucasian, and college educated. One half of the sample lived in small town and rural areas, whereas the others lived in urban communities. All participants used a smartphone and six also used a tablet. Eleven participants self-manage their pain using pharmacological and nonpharmacological strategies. Most analgesics used for pain relief were non-steroidal anti-inflammatory drugs (NSAIDs), neuroleptic, and opioid classes of medications. Participants reported that they have taken these pain medications for at least 1 month. Results of the BPISF showed a mean pain severity score of 4.6 (SD = 2.3, range = 1 to 9) and mean pain interference score of 4.0 (SD = 2.6, range = 0 to 8). The mean PROMIS Self-Efficacy for Managing Symptoms score was 50.8 (SD = 8.2, range = 37 to 64). Participants with a higher level of education had lower pain severity (ρ = −0.677, p = 0.01), lower pain interference (ρ = −0.56, p = 0.03), and higher self-efficacy for managing symptoms (ρ = 0.54, p = 0.04).

Sample Characteristics (N = 15)Sample Characteristics (N = 15)

Table 1:

Sample Characteristics (N = 15)

The mean SUS score was 78 (SD = 17.3, range = 48 to 100). The median time to set up the Google Home Assistant profile was 5 minutes (SD = 7.5 minutes) and participants asked for help a median of two times. Seven participants made more than one attempt to download the Google Home app due to slow speed of wireless internet. The median time to make reminder tasks was 3 minutes and participants asked for help a median of two times. Of all participants, three had wireless internet interruption during the month of interacting with the voice assistant. All participants reported completing the tasks after the voice reminder prompt and the voice assistant gave reminders as scheduled according to the profile. No participant demographics or pain characteristics were associated with usability of the voice assistant. A small negative association was found between time to set up the profile and chronic disease. Set up of the Google Home profile took more time for those reporting less chronic diseases (ρ = −0.52, p = 0.05). All participants completed their diaries in addition to giving a self-report of taking medications for pain after the voice assistant reminders. Of all participants, three set up additional reminders for pain self-management tasks for physical activity and distraction (e.g., play music, tell a joke).

There were positive and negative themes found within participant comments after their use of the voice assistant. The voice assistant was considered easy to set up, and reminders were consistent, useful, and helpful for accountability. One participant reported difficulty hearing the voice assistant from another room. Two participants reported occasions when their voices were not recognized by the voice assistant. There was one voice assistant error in the ability to understand user intent during an interaction. Two participants preferred that the voice assistant state specific reminder details instead of giving a vague reminder prompt. Two participants disliked the process for deleting reminders.

Discussion

The goal of the current study was to gain a preliminary understanding of the practical use of a smart speaker to create voice assistant reminders to help encourage pain self-management tasks in older adults. Voice assistants may be more accessible to the aging population with pain, chronic disease, and disability. Usability of the voice assistant for pain self-management reminders was above average. Although there were a few instances of voice assistant error of user conversation intent, additional innovations with the voice assistant may make it helpful to encourage completion of pain self-management tasks.

In the current sample, pain severity was moderate to severe and pain interference was mild to moderate. Consistent with Reid et al. (2015), participants in this study used a combination of pharmacological and nonpharmacological strategies to self-manage pain. Pain symptoms and analgesic medications used for pain relief were consistent with those reported in prior published evidence (Dahlhamer et al., 2018; Shade, Herr, & Kupzyk, 2019). It is recommended that chronic use of NSAIDs be avoided due to a risk for gastrointestinal, cardiovascular, and kidney problems. Opioids should be avoided while taking other medications, such as benzodiazepines and gabapentin, or by individuals with a history of falls or fractures. NSAIDs, opioids, and neuroleptic medications should be monitored closely and used for a limited amount of time by older adults (AGS Beers Criteria® Update Expert Panel et al., 2019). Self-monitoring needs to be promoted among older adults who are using pharmacological medications for pain relief. Given participants' diaries included pain documentation, voice assistant reminders may help with encouraging daily self-monitoring behavior.

Participants reported self-efficacy to manage symptoms that was average to the general population of the United States. Participants were mostly college educated, which was associated with higher self-efficacy for managing symptoms and lower reported pain severity and interference. This finding is synonymous with published evidence related to socioeconomic status and pain. Individuals with higher education may have the knowledge, coping, support, and ability to gain access to pain self-management techniques (Poleshuck & Green, 2008). Self-efficacy for managing symptoms was average, yet participants reported pain at moderate severity. Even though this sample was small, participants may be able to benefit from additional support and guidance with self-management strategies for persistent pain.

Older adults in this study had above average usability scores for using the voice assistant with pain self-management reminders. Set up of this device took less time than downloading and uploading medications within a mobile app (Shade, Boron, et al., 2019). Set up time for smart speaker voice assistant devices can range from 5 to 15 minutes (Cipriani & Conner, 2019). The median time for set up and making reminder tasks for this sample was 8 minutes. Although slower task execution time has been found with older adults using touch screen technology, voice assistant execution time was normal (Vaportzis et al., 2017). No significant relationships were found between participant demographics and pain characteristics and usability. Although this finding should be explored more, it may demonstrate that aging adults from various backgrounds may be able to use the voice assistant. Participants' comments show favorable alignment with the SUS results and seem to suggest the use of a voice assistant reminder is easy for older adults with pain and chronic conditions.

Limitations

This was the first known study to explore voice assistants for pain self-management, and despite the positive findings, there are limitations. This was a usability study and there was no control or comparison group to make the declaration that voice assistants improve pain self-management. The sample size was small, primarily Caucasian, female, educated, and had prior experience with use of mobile devices. The results are not generalizable to all aging adults with pain symptoms. Studies need to be conducted in those with lower education and socioeconomic status. The PROMIS Self-Efficacy scale was a measure of global symptoms and was not pain specific. The study was limited by the measurement of only two pain self-management tasks. Privacy may be an issue as voice assistants can respond to multiple users. Although this was not an issue, multiple household members may create or delete a reminder (Liu et al., 2016). Individuals who use voice assistants should be encouraged to turn off the microphone on smart speakers during sensitive conversations, however, this option may be limited with assistants on smart-phones (Lau et al., 2018). Lastly, the study did not collect longitudinal data to assess abandonment of the voice assistant.

Implications for Nursing and Research

Nurses need to collaborate with older adults and other health providers to identify specific strategies for pain self-management. mHealth with voice interactions can be an additive strategy to encourage the adoption and use of pain self-management interventions. As voice assistants evolve, nurses could propose older adults create voice assistant reminders to adopt strategies beyond medications and diaries. Any errors associated with recognizing the user's voice, misunderstanding user intent, or reminder deletion should be anticipated. Speaker volume can be increased, and device placement should be in the room most occupied in the home. Key pain self-management activities need to be selected, as multiple voice assistant reminders may become overwhelming.

Additional research is warranted to examine if voice assistants reduce adoption barriers of time, transportation, and cost reported with traditional face-to-face pain self-management programs. Additional studies can expand from this pilot and test the use of different pain self-management reminders on self-efficacy to manage pain symptoms. User-centered design studies can be performed to discover how voice assistant capabilities may be tailored to pain self-management for older adults. Future research can explore how tailored voice assistant interactions impact pain symptoms and health outcomes. Lastly, studies can explore the use of voice assistants for pain self-management in underserved areas.

Conclusion

Voice assistants are accessible and popular technologies. The use of voice assistants on smartphones, tablets, and smart speakers is expected to increase in the United States. Older adults in this study used the reminders to complete tasks related to taking pain medications and monitoring of symptoms. Overall, the voice assistant was consistent, helpful, and easy to use. Older users may provide beneficial recommendations for voice assistant software for pain self-management. Voice assistant technology may be a good strategy to remind aging adults to adopt pain self-management strategies.

References

  • American Geriatrics Society Beers Criteria® Update Expert Panel. (2019). American Geriatrics Society 2019 updated AGS beers criteria® for potentially inappropriate medication use in older adults. Journal of the American Geriatrics Society, 67(4), 674–694 doi:10.1111/jgs.15767 [CrossRef]
  • Austrian, J. S., Kerns, R. D. & Reid, M. C. (2005). Perceived barriers to trying self-management approaches for chronic pain in older persons. Journal of the American Geriatrics Society, 53(5), 856–861 doi:10.1111/j.1532-5415.2005.53268.x [CrossRef] PMID:15877564
  • Bangor, A., Kortum, P. T. & Miller, J. T. (2008). An empirical evaluation of the system usability scale. International Journal of Human–Computer Interaction, 24(6), 574–594 doi:10.1080/10447310802205776 [CrossRef]
  • Bhattarai, P. & Phillips, J. L. (2017). The role of digital health technologies in management of pain in older people: An integrative review. Archives of Gerontology and Geriatrics, 68, 14–24 doi:10.1016/j.archger.2016.08.008 [CrossRef] PMID:27584871
  • Booker, S., Herr, K. & Tripp-Reimer, T. (2019). Black American older adults' motivation to engage in osteoarthritis treatment recommendations for pain self-management: A mixed methods study. International Journal of Nursing Studies. Advance online publication. doi:10.1016/j.ijnurstu.2019.103510 [CrossRef] PMID:32169337
  • Callejas, Z. & López-Cózar, R. (2009). Designing smart home interfaces for the elderly. ACM SIGACCESS Accessibility and Computing, 95, 10–16 doi:10.1145/1651259.1651261 [CrossRef]
  • Chodosh, J., Morton, S. C., Mojica, W., Maglione, M., Suttorp, M. J., Hilton, L., Rhodes, S. & Shekelle, P. (2005). Meta-analysis: Chronic disease self-management programs for older adults. Annals of Internal Medicine, 143(6), 427–438 doi:10.7326/0003-4819-143-6-200509200-00007 [CrossRef] PMID:16172441
  • Chung, A. E., Griffin, A. C., Selezneva, D. & Gotz, D. (2018). Health and fitness apps for hands-free voice-activated assistants: Content analysis. JMIR mHealth and uHealth, 6(9), e174 doi:10.2196/mhealth.9705 [CrossRef] PMID:30249581
  • Cipriani, J. & Conner, K. (2019). New Echo Show 5? Everything you need to set up Amazon's smart speaker with a screen. https://www.cnet.com/how-to/new-echo-show-5-everything-you-need-to-set-up-amazons-smart-speaker-with-a-screen/
  • Cruise, C. E., Broderick, J., Porter, L., Kaell, A. & Stone, A. A. (1996). Reactive effects of diary self-assessment in chronic pain patients. Pain, 67(2-3), 253–258 doi:10.1016/0304-3959(96)03125-9 [CrossRef] PMID:8951918
  • Dahlhamer, J., Lucas, J., Zelaya, C., Nahin, R., Mackey, S., DeBar, L., Kerns, R., Von Korff, M., Porter, L. & Helmick, C. (2018). Prevalence of chronic pain and high-impact chronic pain among adults – United States, 2016. Morbidity and Mortality Weekly Report (MMWR), 67(36), 1001–1006 doi:10.15585/mmwr.mm6736a2 [CrossRef] PMID:30212442
  • Eaton, L. H., Langford, D. J., Meins, A. R., Rue, T., Tauben, D. J. & Doorenbos, A. Z. (2018). Use of self-management interventions for chronic pain management: A comparison between rural and nonrural residents. Pain Management Nursing, 19(1), 8–13 doi:10.1016/j.pmn.2017.09.004 [CrossRef]
  • Eggermont, L. H., Leveille, S. G., Shi, L., Kiely, D. K., Shmerling, R. H., Jones, R. N., Guralnik, J. M. & Bean, J. F. (2014). Pain characteristics associated with the onset of disability in older adults: The maintenance of balance, independent living, intellect, and zest in the Elderly Boston Study. Journal of the American Geriatrics Society, 62(6), 1007–1016 doi:10.1111/jgs.12848 [CrossRef] PMID:24823985
  • Gruber-Baldini, A. L., Velozo, C., Romero, S. & Shulman, L. M. (2017). Validation of the PROMIS® measures of self-efficacy for managing chronic conditions. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 26, 1915–1924 doi:10.1007/s11136-017-1527-3 [CrossRef] PMID:28239781
  • Hadjistavropoulos, T. (2012). Self-management of pain in older persons: Helping people help themselves. Pain Medicine, 13(Suppl. 2), S67–S71 doi:10.1111/j.1526-4637.2011.01272.x [CrossRef] PMID: 22497750
  • Hertzog, M. A. (2008). Considerations in determining sample size for pilot studies. Research in Nursing & Health, 31(2), 180–191 doi:10.1002/nur.20247 [CrossRef] PMID:18183564
  • Hoy, M. B. (2018). Alexa, Siri, Cortana, and more: An introduction to voice assistants. Medical Reference Services Quarterly, 37(1), 81–88 doi:10.1080/02763869.2018.1404391 [CrossRef] PMID:29327988
  • Kaye, A. D., Baluch, A. R., Kaye, R. J., Niaz, R. S., Kaye, A. J., Liu, H. & Fox, C. J. (2014). Geriatric pain management, pharmacological and nonpharmacological considerations. Psychology & Neuroscience, 7(1), 15–26 doi:10.3922/j.psns.2014.1.04 [CrossRef]
  • Kinsella, B. (2019). More than half of consumers want to use voice assistants for healthcare – New report from Voicebot and Orbita. https://voicebot.ai/2019/10/29/more-than-half-of-consumers-want-to-use-voice-assistants-forhealthcare-new-report-from-voicebot-andorbita
  • Koetsier, J. (2018).Amazon Echo, Google Home, installed base hit 50 million; Apple has 6% market share, report says. https://www.forbes.com/sites/johnkoetsier/2018/08/02/amazon-echo-google-home-installed-base-hits-50-million-apple-has-6-market-share-report-says/#7f301975769c
  • Lau, J., Zimmerman, B. & Schaub, F. (2018). Alexa, are you listening? Privacy perceptions, concerns and privacy-seeking behaviors with smart speakers. Proceedings of the ACM on Human–Computer Interaction, 2(CSCW), 1–31 doi:10.1145/3274371 [CrossRef]
  • Liu, L., Stroulia, E., Nikolaidis, I., Miguel-Cruz, A. & Rios Rincon, A. (2016). Smart homes and home health monitoring technologies for older adults: A systematic review. International Journal of Medical Informatics, 91, 44–59 doi:10.1016/j.ijmedinf.2016.04.007 [CrossRef] PMID:27185508
  • Lorig, K. R. & Holman, H. (2003). Self-management education: History, definition, outcomes, and mechanisms. Annals of Behavioral Medicine, 26(1), 1–7 doi:10.1207/S15324796ABM2601_01 [CrossRef] PMID:12867348
  • Machado, G. C., Pinheiro, M. B., Lee, H., Ahmed, O. H., Hendrick, P., Williams, C. & Kamper, S. J. (2016). Smartphone apps for the self-management of low back pain: A systematic review. Best Practice & Research. Clinical Rheumatology, 30(6), 1098–1109 doi:10.1016/j.berh.2017.04.002 [CrossRef] PMID:29103552
  • Malec, M. & Shega, J. W. (2015). Pain management in the elderly. The Medical Clinics of North America, 99(2), 337–350 doi:10.1016/j.mcna.2014.11.007 [CrossRef] PMID:25700587
  • Mendoza, T., Mayne, T., Rublee, D. & Cleeland, C. (2006). Reliability and validity of a modified Brief Pain Inventory short form in patients with osteoarthritis. European Journal of Pain (London, England), 10(4), 353–361 doi:10.1016/j.ejpain.2005.06.002 [CrossRef] PMID:16051509
  • Moar, J. (2017).Amazon Echo and Google Home to reside in over 50% of households by 2020, as multi-assistant devices take off. https://www.juniperresearch.com/press/press-releases/amazon-echo-google-home-to-reside
  • Nielsen, J. & Landauer, T. K. (1993). A mathematical model of the finding of usability problems. CHI '93: Proceedings of the INTERACT '93 and CHI '93 Conference on Human Factors in Computing Systems, 206–213. doi:10.1145/169059.169166 [CrossRef]
  • O'Brien, K., Liggett, A., Ramirez-Zohfeld, V., Sunkara, P. & Lindquist, L. A. (2020). Voice-controlled intelligent personal assistants to support aging in place. Journal of the American Geriatrics Society, 68(1), 176–179 doi:10.1111/jgs.16217 [CrossRef]
  • Palanica, A., Thommandram, A., Lee, A., Li, M. & Fossat, Y. (2019). Do you understand the words that are comin outta my mouth? Voice assistant comprehension of medication names. npj Digital Medicine, 2(1), 55 doi:10.1038/s41746-019-0133-x [CrossRef]
  • Parker, S. J., Jessel, S., Richardson, J. E. & Reid, M. C. (2013). Older adults are mobile too! Identifying the barriers and facilitators to older adults' use of mHealth for pain management. BMC Geriatrics, 13(1), 43 doi:10.1186/1471-2318-13-43 [CrossRef] PMID:23647949
  • Poleshuck, E. L. & Green, C. R. (2008). Socioeconomic disadvantage and pain. Pain, 136(3), 235–238 doi:10.1016/j.pain.2008.04.003 [CrossRef] PMID:18440703
  • Pradhan, A., Mehta, K. & Findlater, L. (2018). “Accessibility came by accident”: Use of voice-controlled intelligent personal assistants by people with disabilities. CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, (459), 1–13. doi:10.1145/3173574.3174033 [CrossRef]
  • Reid, M. C., Eccleston, C. & Pillemer, K. (2015). Management of chronic pain in older adults. BMJ (Clinical Research Ed.), 350, h532 doi:10.1136/bmj.h532 [CrossRef] PMID:25680884
  • Reid, M. C., Papaleontiou, M., Ong, A., Breckman, R., Wethington, E. & Pillemer, K. (2008). Self-management strategies to reduce pain and improve function among older adults in community settings: A review of the evidence. Pain Medicine, 9(4), 409–424 doi:10.1111/j.1526-4637.2008.00428.x [CrossRef] PMID:18346056
  • Roberts, A. W., Ogunwole, S. U., Blakeslee, L. & Rabe, M. A. (2016). The population 65 years and older in the United States: 2016. https://www.census.gov/content/dam/Census/library/publications/2018/acs/ACS-38.pdf
  • Shade, M. Y., Berger, A. M., Chaperon, C., Haynatzki, G., Sobeski, L. & Yates, B. (2017). Factors associated with potentially inappropriate medication use in rural community-dwelling older adults. Journal of Gerontological Nursing, 43(9), 21–30 doi:10.3928/00989134-20170406-01 [CrossRef] PMID:28399319
  • Shade, M., Boron, J., Manley, N., Kupzyk, K. & Pullen, C. (2019). Ease of use and usefulness of medication reminder apps among rural aging adults. Journal of Community Health Nursing, 36(3), 105–114 doi:10.1080/07370016.2019.1630960 [CrossRef] PMID:31291770
  • Shade, M. Y., Herr, K. & Kupzyk, K. (2019). Self-reported pain interference and analgesic characteristics in rural older adults. Pain Management Nursing, 20(3), 232–238 doi:10.1016/j.pmn.2019.03.001 [CrossRef] PMID:31080145
  • Stubbs, B., Schofield, P. & Patchay, S. (2016). Mobility limitations and fall-related factors contribute to the reduced health-related quality of life in older adults with chronic musculoskeletal pain. Pain Practice, 16(1), 80–89 doi:10.1111/papr.12264 [CrossRef] PMID:25469983
  • Vacher, M., Lecouteux, B., Istrate, D., Joubert, T., Portet, F., Sehili, M. & Chahuara, P. (2013). Experimental evaluation of speech recognition technologies for voice-based home automation control in a smart home. 4th Workshop on Speech and Language Processing for Assistive Technologies, 99–105. http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=3ABEF0E41E4E5A7AF98CCB0C3B0465B4?doi=10.1.1.377.7017&rep=rep1&type=pdf
  • Vaportzis, E., Clausen, M. G. & Gow, A. J. (2017). Older adults perceptions of technology and barriers to interacting with tablet computers: A focus group study. Frontiers in Psychology, 8, 1687 doi:10.3389/fpsyg.2017.01687 [CrossRef] PMID:29071004
  • Virzi, R. A. (1992). Refining the test phase of usability evaluation: How many subjects is enough?Human Factors, 34(4), 457–468 doi:10.1177/001872089203400407 [CrossRef]
  • Wallace, T. & Morris, J. T. (2018). Smart speaker usability by military service members with mTBI and PTSD. The Journal on Technology and Persons with Disabilities, 127–139. http://scholarworks.csun.edu/bitstream/handle/10211.3/202991/JTPD-2018-ID16-p127-139.pdf?sequence=1
  • Wolters, M. K., Kelly, F. & Kilgour, J. (2016). Designing a spoken dialogue interface to an intelligent cognitive assistant for people with dementia. Health Informatics Journal, 22(4), 854–866 doi:10.1177/1460458215593329 [CrossRef] PMID:26276794
  • Zeng, E., Mare, S. & Roesner, F. (2017). End user security and privacy concerns with smart homes. Proceedings of the Thirteenth Symposium on Usable Privacy and Security (SOUPS 2017), 65–80. https://www.usenix.org/system/files/conference/soups2017/soups2017-zeng.pdf

Sample Characteristics (N = 15)

Variablen (%)
Gender
  Female11 (73)
  Male4 (27)
Race
  Caucasian14 (93)
  Native American1 (7)
Education level
  College degree and higher12 (80)
  High school graduate and lower3 (20)
Marital status
  Single, widowed, or divorced8 (53)
  Married or cohabitating7 (47)
County
  Rural/small town8 (53)
  Urban7 (47)
Income (U.S. dollars)
  <10,0001 (7)
  10,000 to 40,0007 (50)
  41,000 to 80,0005 (35)
  ≥81,0001 (7)
  Declined1 (7)
Insurance
  Medicare and/or Medicaid10 (67)
  Private insurance or self-pay5 (33)
Chronic conditions
  Degenerative joint disease and osteoarthritis14 (93)
  Depression8 (53)
  Hypertension8 (53)
  Anxiety6 (40)
  Thyroid disease5 (33)
  Fibromyalgia4 (27)
  Insomnia4 (27)
  Obstructive sleep apnea3 (20)
  Diabetes2 (13)
  Peripheral vascular disease2 (13)
  Cancer1 (7)
  Chronic obstructive pulmonary disease1 (7)
  Heart disease1 (7)
  Kidney disease1 (7)
  Prior heart attack1 (7)
Mean (SD) (Range)Median
Age (years)65 (7.3) (56 to 80)
BPI pain severity4.6 (2.3) (1 to 9)
BPI pain interference4.0 (2.6) (0 to 8)
PROMIS® Self-Efficacy Scale SF 8A50.8 (8.2) (37 to 64)
System Usability Scale78.2 (17.3) (48 to 100)
Time to set-up profile for voice assistant (minutes)9 (7.5) (1 to 25)5
Time to set reminder tasks (minutes)3.5 (2.4) (1 to 10)3
Number of occasions asked for help (profile)3 (2.3) (0 to 6)2
Number of times asked for help (reminders)2 (2) (0 to 7)2
Authors

Dr. Shade is Assistant Professor, Ms. Soumana is PhD Student, and Dr. Kupzyk is Assistant Professor, University of Nebraska Medical Center College of Nursing, Omaha, Nebraska; and Dr. Rector is Assistant Professor, University of Iowa Department of Computer Science, The University of Iowa, Iowa City, Iowa.

The authors have disclosed no potential conflicts of interest, financial or otherwise. This study was funded by the University of Nebraska Medical Center's Center for Patient, Family, and Community Engagement in Chronic Care Management.

Address correspondence to Marcia Y. Shade, PhD, BS, RN, Assistant Professor, University of Nebraska Medical Center College of Nursing, 985330 Nebraska Medical Center, Omaha, NE 68198; email: marcia.shade@unmc.edu.

Received: December 27, 2019
Accepted: April 28, 2020
Posted Online: August 27, 2020

10.3928/00989134-20200820-03

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