Predictors of Smartphone Ownership Among Post-Stroke Community-Dwelling Adults: Implications for Future Mobile Health in Rehabilitation

PURPOSE: As the onset of stroke is occurring at younger ages, increasingly mild, and resulting in more discharges to the community (Wolf et al., 2009), increased attention in rehabilitation to community activities, such as management of smartphones or technologies, is enabling engagement in valued activities and roles (Gustavsson et al., 2016; Lemke et al., 2019). Although access to mobile health (mHealth) through mobile devices holds the potential to expand rehabilitation services outside of the clinic and promote the transition into communities (Jones et al., 2018), adults with disabilities, including post-stroke impairments, own smartphones and use mHealth at lower rates than their non-disabled peers (Lemke et al., 2019). Factors predicting use of and access to smartphones among post-stroke community-dwelling adults are not well understood (Jones et al., 2018; Lemke et al., 2019). Therefore, the purpose of this study is to identify sociodemographic, functional, and medical factors that predict smartphone use among post-stroke community-dwelling adults.

DESIGN: Cross-sectional analysis of data from telephone interviews and the stroke registry of an academic medical center.

METHOD: We recruited 73 participants from a metropolitan area in the Midwestern United States. Participants’ average age was 65.2 ± 13.0 years, and 78.1% experienced a mild stroke. We also abstracted information such as type of stroke, type of residence, level of education, race, and pre-stroke functional status from the stroke registry. We created a logistic regression model to evaluate factors predicting ownership of a smartphone among post-stroke community-dwelling adults. Last, we analyzed the model’s overall ability to predict and explain smartphone ownership among our sample.

RESULTS: Younger age (B [SE B] = -.08 [.03], Odds Ratio [95% CI] = .92 [.87, .98], p < .01) was the only significant predictor of smartphone ownership. Lower stroke severity, ischemic stroke, and urban/suburban residence were nearly significant factors predicting smartphone ownership. Education, race, and pre-stroke functional status were not significant predictors of smartphone ownership. Accounting for all of these predictors increased the overall classification accuracy of the model from 56.2% to 74.2% and accounted for 32.0% of the variation in the model. A receiver operator characteristic curve analysis indicated that the model had adequate ability to predict ownership of smartphones (Area Under the Curve [95% CI] = .79 [.67, .90], p < .001).

CONCLUSION: Although ownership of smartphones has increased among all adults, a complex set of factors impacts ownership of smartphones and use of mHealth services among post-stroke community-dwelling adults. mHealth services that are accessible through use of smartphones have the potential to increase the quality of community services and rehabilitation for stroke survivors. Post-stroke community-dwelling adults live with lasting physical, cognitive, and psychological impairments that may impact the ability to own and use a smartphone and manage increasingly digitized activities, including mHealth services. This study highlights the role of sociodemographic, functional, and medical factors that impact smartphone ownership among post-stroke community-dwelling adults. Future research is warranted to identify what mHealth services are needed in rehabilitation, as well as to assess the comprehensive influence of sociodemographic, functional, and medical factors on mHealth utilization.

IMPACT STATEMENT: This study overviews complex factors affecting smartphone ownership among post-stroke community-dwelling adults. In community-based post-stroke rehabilitation, clinicians may consider incorporating mobile health technologies into their practices.

References

Gustavsson, M., Ytterberg, C., Nabsen Marwaa, M., Tham, K., & Guidetti, S. (2016). Experiences of using information and communication technology within the first year after stroke—A grounded theory study. Disability and Rehabilitation, 40(5), 1–8. https://doi.org/10.1080/09638288.2016.1264012

Jones, M., Morris, J., & Deruyter, F. (2018). Mobile healthcare and people with disabilities: Current state and future needs. International Journal of Environmental Research and Public Health, 15(3), 515. https://doi.org/10.3390/ijerph15030515

Lemke, M., Rodríguez Ramírez, E., Robinson, B., & Signal, N. (2019). Motivators and barriers to using information and communication technology in everyday life following stroke: A qualitative and video observation study. Disability and Rehabilitation. https://doi.org/10.1080/09638288.2018.1543460

Wolf, T. J., Baum, C., & Conner, L. T. (2009). Changing face of stroke: Implications for occupational therapy practice. American Journal of Occupational Therapy, 63(5), 621–625. https://doi.org/10.5014/ajot.63.5.621