Abstract
Objective:
With mobile health technologies serving as an alternative means of providing healthcare, evaluating patients’ abilities to navigate digital infrastructures is becoming increasingly relevant. The goal of this study is to investigate smartphone use patterns among individuals with history of moderate-to-severe traumatic brain injury (TBI).
Methods:
An anonymous survey was delivered via email or text message to eligible participants who had a history of moderate-to-severe TBI and were prospectively followed at one of the eight participating Traumatic Brain Injury Model Systems centers for at least 1-year post-injury. The survey captured demographic data and included a questionnaire to evaluate smartphone use (calling, texting, web browsing, etc.).
Results:
2665 eligible individuals were contacted to complete the survey, 472 of which responded. 441 of them reported smartphone use. Individuals ages 45 and older were significantly less likely to use their phones for functions other than calling and texting when compared to individuals ages 18–44 (p < 0.05).
Conclusions:
Most individuals with moderate-to-severe TBI in this cohort demonstrated intentional smartphone use, suggesting that mobile health technologies may be feasible as a cost-effective healthcare alternative. However, doing so will require additional interventions to provide further technological education especially in older individuals with TBI.
Keywords: smartphone, brain injuries, traumatic, mobile health
Mobile health (mHealth) refers to the use of mobile communication technology to assess and treat health conditions. Since the COVID-19 pandemic, mHealth technology has blossomed as a potentially cost-effective way to improve access to care, self-management, and long-term outcomes in clinical care and clinical research (1). Due to high ownership rate, portability, and public acceptance, the smartphone has played an important role in wide adoption of mHealth (2). Per a recent study using the Traumatic Brain Injury (TBI) Model Systems National dataset, a majority of participants with moderate to severe TBI (95%) owned a mobile phone and used their smartphone to access the internet (3). However, despite high smartphone ownership rates, there is minimal knowledge regarding how feasible it is to successfully implement mHealth among individuals with TBI (4).
A study in the general population suggests that technology literacy, defined as the ability to use, manage, understand, and assess technology, is one of the important discriminative variables associated with mHealth use and may have direct implications for delivery of high-quality mHealth interventions (5). Individuals with moderate-to-severe TBI often experience impaired memory and difficulty in new information learning and processing as long-term effects of injury which may affect their ability to learn and use new technology (6, 7). Understanding the current technology literacy status of individuals with TBI will help us understand how to implement mHealth in this population. In this study, we performed a post-hoc analysis of an anonymous TBI Model System Database survey among participants with moderate to severe TBI to identify barriers to exercise after TBI.(8) Our goal was to identify respondents’ comfort and experience in using smartphone technology. To date, as there are no definitive measures for technology literacy, we used survey responses about smartphone ownership, smartphone use patterns (i.e., which smartphone functions and applications were used), and internet access as the indicators of technology literacy in this study.
Methods
The structure of the customized survey was designed for literacy level (grade eight level) and was reviewed by two local consumer advisory groups representing the TBI and general patient populations respectively. The full survey was published previously (8). The study was approved by the Institutional Review Board at the primary site for the parent study. Local approvals from other TBIMS sites were obtained if required to support recruitment for the parent study. In the parent study, a link to the anonymous RedCap survey was sent via email or text message to eligible participants enrolled in the TBI Model Systems (TBIMS) (9). Individuals were eligible if they had a history of moderate-to-severe TBI (defined as post-traumatic amnesia ≥24 hours, loss of consciousness ≥ 30 minutes, Glasgow Coma Scale < 13, or intracranial neuroimaging abnormalities within 72 hours of hospital admission), were 18 years and older at time of injury, were English-speaking, were prospectively followed at one of eight TBIMS centers for at least 1-year post-injury, and were consented to be reached via email or text message. Participants were excluded from the study if they: 1) could not complete a survey online or via telephone independently, or 2) were not fluent in English. A total of 2665 eligible participants were identified at these sites.
The following survey questions were used in this post-hoc analysis: 1) demographic information, including age, sex, education level, state of residence, employment, living situation, income (Table 1), 2) functional capacity variables including driving ability and use of assistive devices (Table 1), and 3) technology use information, including internet accessibility, smartphone availability, and application usage (smartphone function and the number of applications used) (Table 2).
Table 1.
Demographic Characters of Survey Responders
| Measures | Level | Total (n, %) | 18–44 year old (n, %) | ≥ 45 year old (n, %) | χ2 Test | p |
|---|---|---|---|---|---|---|
| Sex | Male | 298 (66.8%) | 151 (65.9%) | 147 (67.7%) | 0.163 | 0.689 |
| Female | 148 (33.2%) | 78 (34.1%) | 70 (32.3%) | |||
| Education | ≤High School or GED | 85 (18.9%) | 49 (21.1%) | 36 (16.6%) | 16.099 | 0.003 |
| Some college, vocational training, or associate’s degree | 159 (35.4%) | 93 (40.1%) | 66 (30.4%) | |||
| Bachelor | 115 (25.6%) | 58 (25%) | 57 (26.3%) | |||
| Masters | 70 (15.6%) | 28 (12.1%) | 42 (19.4%) | |||
| Doctorate | 20 (4.5%) | 4 (1.7%) | 16 (7.4%) | |||
| Work status | Full time | 181 (40.3%) | 126 (54.3%) | 55 (25.3%) | 99.984 | <0.001 |
| Student | 15 (3.3%) | 14 (6%) | 1 (0.5%) | |||
| Retired | 80 (17.8%) | 5 (2.2%) | 75 (34.6%) | |||
| Others | 173 (38.5%) | 87 (37.5%) | 86 (39.6%) | |||
| Income groups | < $15,000 | 43 (11.5%) | 27 (14.2%) | 16 (8.6%) | 9.348 | 0.025 |
| $15,000 - $49,999 | 122 (32.5%) | 61 (32.1%) | 61 (33%) | |||
| $50,000 - $100,000 | 115 (30.7%) | 65 (34.2%) | 50 (27%) | |||
| >$100,000 | 95 (25.3%) | 37 (19.5%) | 58 (31.4%) | |||
| Driving* | Everywhere | 342 (76.2%) | 184 (79.3%) | 158 (72.8%) | 2.609 | 0.121 |
| Only Short Distances | 30 (6.7%) | 15 (6.5%) | 15 (6.9%) | 0.036 | 0.853 | |
| Bus/DART | 35 (7.8%) | 25 (10.8%) | 10 (4.6%) | 5.934 | 0.021 | |
| Uber/Lyft | 29 (6.5%) | 26 (11.2%) | 3 (1.4%) | 17.913 | <0.001 | |
| Disability Transportation | 7 (1.6%) | 1 (0.4%) | 6 (2.8%) | 0.0601 | ||
| Family | 78 (17.4%) | 33 (14.2%) | 45 (20.7%) | 3.314 | 0.081 | |
| Do not go out | 5 (1.1%) | 4 (1.7%) | 1 (0.5%) | 0.3741 | ||
| Assistive Devices* | Cane | 42 (9.4%) | 7 (3%) | 35 (16.1%) | 22.734 | <0.001 |
| Wheelchair | 22 (4.9%) | 8 (3.4%) | 14 (6.5%) | 2.170 | 0.189 | |
| Walker | 25 (5.6%) | 6 (2.6%) | 19 (8.8%) | 8.117 | 0.006 |
Participants were allowed to select multiple responses for this category;
Fisher Exact test.
Table 2.
Technology Literacy Survey and Responses
| Measure | Level | Total | 18–44 year old (n, %) | ≥ 45 year old (n, %) | χ2 Test | p |
|---|---|---|---|---|---|---|
| Smartphone Function Uses* | Calls | 441 (98.2%) | 229 (98.7%) | 212 (97.7%) | 0.4911 | |
| Text | 428 (95.3%) | 228 (98.3%) | 200 (92.2%) | 9.389 | 0.003 | |
| Games | 238 (53%) | 150 (64.7%) | 88 (40.6%) | 26.149 | <0.001 | |
| Websites | 375 (83.5%) | 211 (90.9%) | 164 (75.6%) | 19.249 | <0.001 | |
| Reminders | 356 (79.3%) | 199 (85.8%) | 157 (72.4%) | 12.306 | <0.001 | |
| Videos | 279 (62.1%) | 181 (78%) | 98 (45.2%) | 51.448 | <0.001 | |
| Music | 306 (68.2%) | 196 (84.5%) | 110 (50.7%) | 58.986 | <0.001 | |
| Other | 45 (10%) | 25 (10.8%) | 20 (9.2%) | 0.302 | 0.639 | |
| Number of different types of smart phone uses (Median, IQR, SD) | 6 (4–7, 1.76) | 7 (5–7, 1.47) | 5 (4–6, 1.81) | <0.0013 | ||
| Internet Access | Home Wifi | 180 (40.1%) | 76 (32.8%) | 104 (47.9%) | 0.0012 | |
| Data plan on phone | 33 (7.3%) | 18 (7.8%) | 15 (6.9%) | |||
| No Access | 1 (0.2%) | 0 (0%) | 1 (0.5%) | |||
| Donť know how to use internet | 2 (0.4%) | 0 (0%) | 2 (0.9%) | |||
| Other | 1 (0.2%) | 0 (0%) | 1 (0.5%) | |||
| Wifi and data | 232 (51.7%) | 138 (59.5%) | 94 (43.3%) | |||
| Smartphone | Android phone (such as Samsung, Google, Huawei, LG, TCL, OnePlus, Nokie, Motorola) | 184 (41%) | 96 (41.4%) | 88 (40.6%) | 0.032 | 0.924 |
| iPhone | 265 (59%) | 136 (58.6%) | 129 (59.4%) | |||
Participants were allowed to select multiple responses for this category
Fisher Exact test
Fisher-Freeman-Halton Exact Test
Mann-Whitney U test; all other tests were χ2 Tests.
Data were analyzed using descriptive and summary statistics, and chi-squared analyses were used to compare age group differences in categorical variables (Fischer’s exact tests were used if the sample size in one group was less than 5). The number of distinct apps and smartphone functions used was calculated. A multivariable logistic regression was conducted with all potential demographic (sex, education, working status, driving, and use of assistive device) variables. All analyses were performed using IBM SPSS Statistics, Version 27.0 (IBM Corporation, Armonk, NY).
Results
Demographic and socioeconomic characters
Of the 2665 eligible individuals contacted, 472 completed the survey (response rate of 18%). Twenty-three individuals indicated they did not own a smartphone resulting in 449 responses included in subsequent analyses. Approximately two thirds (66.8%) of participants were male which is reflective of male preponderance seen in national statistics on sex and TBI (Table 1). As generally accepted in lifespan and aging literature, participants were further grouped by age: the younger group representing early adulthood age between 18–44 years old, the middle-aged representing middle adulthood (45–64 years), and the oldest age group representing late adulthood (65 years and older).(10, 11) Fifty-one percent of respondents were in early adulthood, while the remaining 49% were in late middle or older adulthood. The demographic and socioeconomic characteristics of responders are summarized in Table 1.
In addition, 53% of responders in middle age or older and 39% in younger age group had a bachelors or higher degree (p = 0.003) (Table 1). The younger group was more likely to hold a full-time job compared to middle age and older group (54.3% vs 25.3%, p < 0.001) (Table 1).” Although most responders were able to drive independently, fewer responders in middle age and older group used public transportation (4.6% vs 10.8%, p = 0.021) or Uber/Lyft (1.4% vs 11.2%, p < 0.001). Thirty one percent of middle-aged and older responders used assistive devices compared to only 9% in the younger group (p < 0.001).
Technology Literacy
Only 3% of responders did not know how to use the internet or had no internet access. All these responders were in the older group. Most responders (95%) owned a smartphone, with 56% and 39% being iPhone and Android users, respectively. Income and education levels were similar between responders with and without smartphones. More responders who owned a smartphone were more likely to have a full-time job compared to the responders without a smartphone (40.3% vs 17.4%, p = 0.02). Ten (43.5%) responders without smartphone and 85 (19.2%) responders with smartphone were retired (p = 0.008). More responders with smartphone were able to drive compared to the responders without smartphone (81% vs 56.5%, p = 0.008).
Of those with smartphones, more than 95% reported regular use for calling and texting, about 80% used their devices for web browsing and reminders, and over 50% reported uses for playing games, watching videos, and listening to music (Table 2). Internet access, functions used, and the numbers of apps regularly used were similar in responders at all income levels, education levels, and driving situations.
Individuals ages 45 and older were significantly less likely to use phone’s function other than making calls (i.e., texting, browsing websites, playing games, and using reminders) when compared to individuals ages 18–44 (p < 0.05) (Table 2). Furthermore, older adults tended to use fewer smartphone applications compared to younger adults (Table 2). After adjusting for education, work and driving situation, being age 45 and older was associated with a fewer number of app functions used (Odds Ratio [95%CI]: 0.698 [0.604 to 0.806], p < 0.001; Hosmer-Lemeshow p=0.871).
Discussion
This survey demonstrated that the most individuals with moderate-to-severe TBI in this cohort owned and were able to use smartphones for many functions. However, those over 45 years old were less likely to use functions beyond telephone calls, and they used a fewer apps compared to younger individuals. The clinical significance of this observed difference is unclear, our finding may indicate lower technology literacy in middle-aged and older adults with TBI or other challenges with smart phone usage (e.g., vision challenges).
Our finding suggests that promoting mHealth in this population may be feasible. Individuals with TBI can use mHealth technology for reminders in everyday tasks, self-management of chronic symptoms, general organization, and social participation (4), and there is emerging evidence to support its efficacy in improving long-term functional outcomes and quality of life (1). However, certain aspects of mHealth may still remain challenging. As shown in a recent focus group study and our pilot exercise study, individuals with a history of stroke or TBI want mobile health applications to be simple and all-in-one (not requiring multiple apps for multiple functions) (12, 13). Future mHealth app design should be user-centered in order to engage individuals with TBI in mHealth. Our survey received responses from both Android and iPhone users. This distribution highlights the importance of availability of the future mHealth app on both platforms, as this will ensure accessibility to most smartphone users. Our data also illustrate that age should be considered in future mHealth app or intervention design, as older adults may be less familiar with technology and unable to successfully navigate mHealth intuitively and/or independently (14). Aging individuals with TBI therefore face a unique intersectionality of disparities resulting from their age and brain injury, including physical, visual, cognitive, and behavioral changes (7), all of which are barriers to productive mHealth adoption.
mHealth will persist within the contemporary healthcare and post-pandemic landscape, but many existing health technology literacy programs are neither theory-based nor do they use high-quality research designs (15). Nevertheless, with literacy education designs becoming an emerging area of research, there have been some recent successes; in one case, student-navigators assisted and prepared patients prior to their telehealth visits during the pandemic (16). Reducing technological stress and increasing direct healthcare provider communication are two major factors implicated in increasing technology utilization and literacy in adults with TBI (4, 12). Our finding suggests that we should keep age difference in mind when we develop the literacy education. For example, the older patients may need more education/instruction on how to use video or communication apps which are common feature of telemedicine. For mHealth to be a viable avenue in caring for TBI patients, cognitively demanding tasks such as planning, remembering appointments, and tracking health information should also be more streamlined on smartphones (1, 4). Additionally, smartphone applications themselves can be better designed with simplicity, accessibility (large displays and large fonts), and engaging application interfaces in mind (12).
Despite our data serving as a useful proxy, it is not a direct representation of the technological skillset necessary for effective mHealth, and there are alternative explanations for the responses the participants reported. Therefore, a more focused evaluation is needed to better elucidate the potential care gaps in virtually serving the TBI population. While individual-level technological obstacles remain a major determinant of access to health care, structural-level barriers should also not be ignored. For instance, the American with Disabilities Act (ADA) improves accessibility in areas such as public transit, grocery stores, and telecommunications. However, some areas such as telecommunications have only been optimized for individuals with certain disabilities such as hearing and speech, leaving out numerous other accessibility needs of individuals served under the ADA, which include many individuals with TBI (6). Accessing today’s digital world through websites, smartphone apps, and other mHealth products therefore requires new accessibility accommodations.
Limitations
Since the survey was administered electronically via text and email, those who had no internet access, who did not have a smartphone, who were physically unable to use smartphones, or those who refused to use the technology altogether would not be well represented in the cohort. Therefore, the responses obtained in this study were contingent on basic levels of technology literacy, thus might have over-estimated smartphone use in individuals with moderate-to-severe TBI. Given the limitations of the initial survey, some variables which may affect measurement of technology literacy (i.e., the duration of TBI, time-post injury, the location they lived, rural or urban) were not available for this analysis. Other technology literacy variables such as the time spent on their phones and the types of apps they used were also not available. These pitfalls limited the generalization of the conclusions. Future study to determine the feasibility of mHealth in individuals with TBI most at risk for lack of technology literacy is warranted.
Conclusions
While our data suggest that mHealth may be a feasible option in caring for individuals with TBI, providers should consider the individual level of technology literacy especially in older adults. Long-term solutions to address systemic barriers are necessary to ensure optimal healthcare delivery via mHealth for individuals with TBI.
Acknowledgements:
We thank all the participants in this study. We also thank all TBIMS participants and investigators: Cynthia Dunklin, Baylor Scott & White Institute for Rehabilitation; Cindy Harrison-Felix and Shelby Mann, Rocky Mountain Regional Brain Injury System; Flora Hammond, Rebecca Runkel, and Darby Dyar, Indiana University/Rehabilitation Hospital of Indiana; Yelena Goldin, JFK Johnson Rehabilitation Institute TBIMS; Ben Dirlikov, Jame Crew, and Thao Duong, Northern California TBIMS; Amy Wagner, Daniel Rusnak, and Kimberli Huster, University of Pittsburgh Medical Center TBIMS; Shanti Pinto, Tami Guerrier, and Kimberly Welsh, Carolinas Rehabilitation TBIMS Follow-up Site; Dmitry Esterov and Allen Brown, Mayo Clinic TBIMS. We also thank Evelyn Kwei for her editing assistance.
Footnotes
Conflicts of interest: The authors declare no conflicts of interest.
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