Abstract
Background:
Approximately half of individuals living with type 2 diabetes mellitus (T2DM) have suboptimal self-management, which could be improved by using assistive technologies in self-management regimes. This study examines patient attitudes and intentions to adopt assistive technologies into T2DM self-management.
Methods:
Forty-four participants (M = 58.7 years) with T2DM were recruited from diabetes education classes in the southwestern Ontario, Canada, between February and April 2014. Participants completed a self-reported in-person survey assessing demographic characteristics, current diabetes management, and attitudes toward using assistive technologies in their diabetes self-management. Demographics, disease characteristics, and current technology use and preferences of the cohort were examined, followed by a correlational analysis of descriptive characteristics and attitudes and intentions to use technology in self-management.
Results:
The majority of (but not all) participants felt that using Internet applications (65%) and smartphone (53.5%) applications for self-management was a good idea. The majority of participants did not currently use an Internet (92.5%) or mobile (96%) application for self-management. Of participants, 77% intended to use an Internet application to manage their diabetes in the future and 58% intended to use mobile applications. Younger age was associated with more positive attitudes (r = –.432, P = .003) and intentions (r = –.425, P = .005) to use assistive technologies in diabetes self-management.
Conclusions:
Findings suggest that patients, especially those younger in age, are favorable toward adopting assistive technologies into management practice. However, attitudes among older adults are less positive, and few currently make use of such technologies in any age group.
Keywords: assistive technology, attitudes, patient, self-management, type 2 diabetes
Self-management is an integral part of type 2 diabetes mellitus (T2DM) treatment.1 Self-management techniques are typically applied to a variety of behaviors, including glucose monitoring, dietary behavior, physical activity, foot care and blood pressure monitoring.1,2 However, it has been found that half of individuals living with T2DM have suboptimal self-management and are not achieving optimal glycated hemoglobin and LDL-C levels.3 Improper management leads to macrovascular and microvascular complications, lowered quality of life, and premature mortality.1,4,5 Therefore, there is a need to adopt approaches into practice to improve self-management for T2DM patients.
One approach with some promise to enhance self-management skills for individuals with T2DM is to integrate assistive technologies into self-management protocols. Assistive technologies include Internet, telephone, mobile phone, or computer applications designed to help self-management of diseases. These technology media create portals for dietary journals, physical activity, and glucose logging and provide opportunities for incidental/scheduled prompting, reminders, and other interventions.6 Such technologies also allow for access to information in the form of reviews of medical products or professionals, podcasts, videos, community discussion boards, and media for communication between care providers and patients.6
In the few studies that have examined technology in T2DM self-management, findings suggest that incorporating assistive technologies into chronic disease management not only improves self-efficacy (an individual’s belief in their ability to control their own health) but also successfully supports health behavior changes.7-9 As the prevalence of diabetes steadily increases, the use of technology is an appealing option to help manage the disease. Among diabetes researchers and clinicians there has been strong interest in integrating assistive technologies into diabetes management.10 This enthusiasm is largely based on the potential benefits of integrating assistive technologies into diabetes self-management, such as greater adherence to clinical and self-management recommenda-tions, increased access to health services, and easier transition to healthier habits.10,11
However, individuals most at risk for developing diabetes, current technology use of those with T2DM, application effectiveness, and sustainability of these technologies must be considered before recommending assistive technologies for diabetes self-management. The vast majority of T2DM cases are over the age of 50.5,12 It is unclear if individuals in this age category find such assistive technologies to be an acceptable means to manage their diabetes given their later-life introduction to such technology as a part of daily life. A report published by Statistics Canada in 2009 found that only 45% of Canadians aged 65 to 74 use the Internet.13 Although the number would be likely higher currently, it is still the case that many individuals in the target T2DM population for technological assistive devices may not be likely to use them.
The clinical effectiveness of assistive technologies among those open to using them has been studied, with preliminary results suggesting improved glycemic control and weight management.7,8,14-17 However, little literature exists that examines patient preferences toward using these technologies in practice. Therefore, the purpose of this study is to examine the attitudes toward and intentions to use assistive technologies in self-management among individuals with type 2 diabetes and to determine if individuals with T2DM are interested in adopting technological self-management strategies for their diabetes.
Methods
Participants were recruited from 2 organizations providing in-person diabetes education classes. All participating organizations and participants provided informed consent to participate in the study. This study received institutional ethics clearance in January 2014. The primary outcome of the study was current patient attitudes toward using assistive technologies, such as Internet and mobile phone applications, in T2DM self-management. Secondary outcomes of this study included if participants currently used technology in their self-management, what management services would be most appealing on technological devices, how confident participants felt in using technological diabetes applications. We also examined relationships between demographic variables and attitudes, intentions and confidence.
Participants
Participants were recruited from 2 community-based organizations providing diabetes education classes in a moderately populated urban region of southwestern Ontario, Canada. To be eligible to participate in the study, participants must have been diagnosed with T2DM and living independently in the community. Participants must have been living independently in the community as those living in assisted-living situations would not be in a position to self-manage their diabetes. Originally participants must have been over the age of 50 to be eligible to participate; however, this age criterion was removed part way through the study to enable more participants to complete the survey.
Procedures
Two organizations providing T2DM education classes in southwestern Ontario were contacted via email and telephone to participate in the study to identify individuals with T2DM living within these communities. Thirteen classes were attended from February 2014 to April 2014 for recruitment purposes. A total of 66 individuals with diabetes attended the 13 classes from which the investigators recruited; of these, 61 were eligible to participate. Five individuals did not meet the eligibility criteria as they were either over 50 years of age before the age restriction was removed or were not formally diagnosed with T2DM. One individual attended 2 of the classes and completed the survey in the first class. Of those eligible, a total of 44 participants completed a self-report, pen and paper survey, yielding a 72% participation rate.
Measures
Participants completed all measures of study variables by self-report. The self-reported survey used standard means for constructing the attitude and intention items in accordance with the theory of planned behavior.18-20 Before use in the study, the survey was assessed by 4 expert diabetes researchers and clinicians and was pilot tested on 2 individuals representative of the target population to determine if readability and survey length was appropriate.
Attitudes Toward Technology Use in Diabetes Management
Attitudes toward using Internet and mobile applications for self-management were assessed by asking “for me, using a specifically developed application to assist with self-management would be: a good idea; enjoyable; comforting; exciting; interesting; helpful; and time saving” for each technology. These questions used a Likert-type scale of 1 to 5, with 1 indicating technology in self-management is a very bad idea and 5 indicating using technology in self-management is a very good idea for each technology. Both the Internet scale (Cronbach’s alpha = .942) and mobile application (Cronbach’s alpha = .980) scale demonstrated strong internal consistency reliability in the current sample.
Participants were asked to indicate diabetes management areas they would be interested in using technology for. Management areas included dietary and physical activity planning, text/image message monitoring and reminders, glucose tracking, and communicating with health professionals or others with T2DM.
Confidence and Intention to Use Technology
Self-efficacy was assessed by participants indicating “I feel __% confident that I could use an Internet application to help me with my diabetes”; this question was repeated for mobile applications. Responses were indicated as a percentage, with higher scores indicating more confidence in the use of technologies for self-management (Cronbach’s alpha = .825).
Intentions to use Internet and mobile applications for self-management were assessed by participants responding to the item, “I intend to use an application for helping me with my diabetes management in the future” for each technology. These questions used a Likert-type scale of 1 to 5, with 1 indicating no intention to use technology and 5 indicating strong intention to use the specific technology for self-management.
Current Technology Use
Participants were asked if they owned a computer, cell phone, smartphone, tablet, or had daily Internet access. For each device, participants were asked how much time they spend using it a day: less than 1 hour, 1-2 hours, 2-3 hours, or over 3 hours. Participants were asked to describe activities they currently used each device for, including emailing/communicating with friends, social media, reading, researching information, watching television/movies, playing games, or business purposes (Cronbach’s alpha = .857). Participants were also asked if they currently used any of the mentioned technologies to help manage their diabetes.
Diabetes Management Practices
Participants indicated their height, weight, duration of diabetes, and other chronic health conditions. Participants indicated areas of diabetes management, including diet, physical activity, glucose monitoring, and communication with health professionals, they felt were most difficult to manage. Current diabetes issues (“which of the following diabetes issues are currently a problem for you?”), adapted from the Problem Area in Diabetes (PAID) questionnaire,17 were assessed through a Likert-type scale with 1 indicating “serious problem” and 5 indicating “not a problem.” Problem areas included not having clear goals for diabetes, feeling discouraged by diabetes treatment, scared about living with diabetes, diabetes was taking up too much physical energy, complications coping with diabetes, and feeling “burned out” by diabetes management (Cronbach’s alpha = .927).21
Demographic Information
Demographic information collected included participant gender, age, race, highest level of education, and employment status.
Data Analysis
Descriptive and inferential statistical were computed using IBM SPSS 22. Frequencies of demographic, disease characteristics, technology preferences, and the overall degree of acceptance or opposition to diabetes assistive technologies for self-management were computed. Items from the Internet and mobile application attitude scales were combined to determine an overall attitude score for each technology, and then combined to determine overall technology attitude score. Scores for intention to use each device were also combined to determine an overall score for intended technology use. The same procedure was done for confidence scores for each technology to determine an overall technology confidence score. Body mass index (BMI) was also calculated based on weight and height information.22 Following descriptive statistics, correlations among demographics and technology confidence with technology attitude and intention variables were computed.
Results
Table 1 contains descriptive statistics for participant characteristics and current technology use. The mean age of participants was 58.7 years of age (SD = 11.02). The majority of the sample was male (56.8%) and Caucasian (83.7%). Most participants held a college degree or diploma as their highest level of education (45.5%) and were retired (54.8%). Over half (51.2%) of participants were diagnosed with T2DM for over 5 years. Participants indicated that they had the most difficulty with making healthy diet choices (73.8%), getting enough physical activity (61.9%), and keeping track of blood glucose levels (35.7%) in their T2DM self-management. Most participants stated that they felt “burned out” by the constant effort needed to manage diabetes (63.2%) and had difficulties coping with complications of their diabetes (55.3%).
Table 1.
Participant Characteristics.
| Characteristics | Mean | n | % |
|---|---|---|---|
| Age | 58.7 (11.02) | 43 | |
| Sex | |||
| Female | 19 | 43.2 | |
| Male | 25 | 56.8 | |
| Ethnicity | |||
| Caucasian | 36 | 83.7 | |
| African descent | 4 | 9.3 | |
| Other | 3 | 7.0 | |
| Education level | |||
| School grade | 5 | 11.4 | |
| High school | 13 | 29.5 | |
| College/degree/diploma | 20 | 45.5 | |
| University undergraduate degree | 4 | 9.1 | |
| University graduate degree | 2 | 4.5 | |
| Employment status | |||
| Retired | 23 | 54.8 | |
| Working full-time | 14 | 33.3 | |
| Working part-time | 3 | 7.1 | |
| Unemployed | 2 | 4.8 | |
| Length of diabetes | |||
| Less than 6 months | 7 | 17.1 | |
| 6 months-1 year | 3 | 7.3 | |
| 1-3 years | 6 | 14.6 | |
| 3-5 years | 4 | 9.8 | |
| Over 5 years | 21 | 51.2 | |
| Other chronic health conditions | |||
| Yes | 17 | 41.5 | |
| No | 24 | 58.5 | |
| BMI | 32.8 (7.55) | 38 | |
| Own a computer | |||
| Yes | 40 | 95.2 | |
| No | 2 | 4.8 | |
| Own a cell phone | |||
| Yes | 31 | 72.1 | |
| No | 12 | 27.9 | |
| Own a smartphone | |||
| Yes | 20 | 46.5 | |
| No | 23 | 53.5 | |
| Own a tablet | |||
| Yes | 17 | 43.6 | |
| No | 22 | 56.4 | |
| Have daily access to the Internet | |||
| Yes | 40 | 93 | |
| No | 3 | 7 | |
| Currently uses a computer to help manage their diabetes | |||
| Yes | 7 | 17.9 | |
| No | 32 | 82.1 | |
| Currently uses a smartphone to help manage their diabetes | |||
| Yes | 2 | 7.1 | |
| No | 26 | 92.9 | |
Demographic variables of the patient sample. Continuous variables such as age and BMI are indicated by their mean and standard deviation. Categorical variables such as gender, ethnicity, education level, employment status, length of diabetes, and current technology preferences are denoted by frequencies and percentage of the sample.
The majority of participants owned a computer (95.2%), cell phone (72.1%), or had daily access to the Internet (93.0%; Table 1). Among those with access to technology (n = 38), participants indicated that they typically spend up to 2 hours a day on the Internet (68.4%) and less than 1 hour per day on their mobile phone (75.0%). Of those with access to a computer (n = 38), participants indicated that they use it to surf the Internet (83%), play games (48%), conduct business (29%), or to read (29%). Participants indicated that they most commonly used the Internet to email or communicate with friends (74%), research information (69%), or use social media websites (43%). Of those with access to a smartphone (n = 20), participants indicated they mostly use it to email or communicate with friends (47%), research information (19%), or social media websites (19%). The majority of participants indicated that they do not currently use the Internet (82.1%) or mobile phones (92.9%) in their diabetes management.
Table 2 highlights participants’ current attitudes and intentions toward using technology in self-management. Most (but not all) participants (65%) thought that using an Internet application to help manage their diabetes was a good idea (Table 2). Although less favorable than the Internet application, a slight majority of participants also indicated that using a smartphone to help manage their diabetes was a good idea (53.5%). Approximately 77% of the sample reported intending to use an Internet application to manage their diabetes in the future and 58% reported intentions to using a smartphone application. The average confidence level of being able to use an Internet and mobile application for self-management was 69% (SD = 34.29) and 63% (SD = 40.5), respectively. If an application was to be used, participants were most interested in having glucose tracking options (90.0%), dietary planning options (87.5%), and the option to communicate with health professionals about their diabetes (85.7%).
Table 2.
Means and Standard Deviations for Participant Attitudes Toward and Intentions to Use Internet and Smartphone Diabetes Applications.
| Characteristics | Internet | Mobile application | Technology overall |
|---|---|---|---|
| Attitudes toward using an applicationa | 3.8 (0.93) | 3.5 (1.37) | 3.5 (0.81) |
| Confidence in using an application to manage diabetesb | 68.74 (34.29) | 62.69 (40.50) | 65.94 (34.07) |
| Intention to use an application to manage diabetes in the futurec | 3.4 (1.63) | 3.0 (1.82) | 3.2 (1.56) |
Means and standard deviations for participant attitudes toward, intentions to use, and confidence in using Internet and mobile applications in self-management. Aggregated calculations were performed to determine overall attitudes toward, intentions to use, and confidence to use assistive technologies in self-management.
Attitudes rated 1-5.
Confidence rated 0-100%.
Intention rated 1-5.
A correlational analysis examined the association between demographic characteristics and attitudes and intentions toward using assistive technologies in self-management (Table 3). It was found that participants younger in age had more favorable attitudes toward using Internet (r = –.352, P = .013) and mobile applications (r = –.458, P = .007), as well as intentions to use the Internet (r = –.314, P = .033) smartphone application for diabetes self-management in the future (r = –.473, P = .004). This trend was also seen in overall attitudes regarding technology for diabetes management (r = –.432, P = .003) and overall intention to use any technology for diabetes management (r = –.425, P = .005). Lastly, it was found that attitudes (r = .745, P = .001) and intentions for using technologies were significantly correlated with confidence in using these technologies (r = .748, P = .001).
Table 3.
Demographic Variables and Attitudes and Intentions to Use Technology in Diabetes Management.
| Internet Attitude | Mobile Phone Attitude | Intended Use Internet | Intended Use Mobile Phone | Technology Attitudes | Intended Use Technology | |
|---|---|---|---|---|---|---|
| Age | −.352* | −.458** | −.314* | −.473** | −.432** | −.425** |
| Education level | .020 | .120 | −.003 | .262 | .129 | .077 |
| Gendera | .016 | −.50 | −.183 | −.132 | −.052 | −.194 |
| Working statusa | .61 | .013 | .041 | .179 | .030 | .160 |
| Length of diabetes | −.169 | −.330* | −.132 | −.270 | −.239 | −.176 |
| Confidence in using technologies | .799** | .549** | .698** | .695** | .745** | .748** |
Correlations between variables and confidence in using technologies were associated with attitudes or intentions to use the Internet or mobile applications in self management. Aggregated calculations were performed to determine overall attitudes toward and intentions to use assistive technologies in self-management. Continuous variables used the Pearson product correlation value, while categorical variables used Spearman’s rho.
Correlation calculated using Spearman’s rho.
Correlation is significant at the .05 level (1-tailed). **Correlation is significant at the .01 level (1-tailed).
Those who had been diagnosed with T2DM for less time had more favorable attitudes toward using smartphones in management (r = –.330, P = .050). Males and females did not differ on their attitudes toward, F(1, 38) = 0.233, P = .632, or intentions to use technologies for diabetes management, F(1, 33) = 1.337, P = .256. No association was found between educational levels and attitudes (r = .129 P = .428) or intentions (r = .077, P = .660). Likewise, no significant association was found for working status and technology attitudes, F(1, 32) = 0.004, P = .947.
Discussion
The use of assistive technologies to aid patients in T2DM self-management has considerable appeal, yet it is unclear whether those living with T2DM are universally interested in adopting such technologies into everyday use. The purpose of this pilot study was to examine patient attitudes toward adopting assistive technologies, such as Internet and mobile applications, into T2DM self-management. We also examined if participants currently used technology in their self-management; what management services would be most appealing on a technological devices; how confident participants felt in using technological diabetes applications; and if demographic factors influenced attitudes or intentions.
Results indicate that participants generally had mixed views of using technology in diabetes management. Relatively speaking, younger participants had more positive attitudes and intentions toward using technology in their treatment than older participants, a finding that corresponds with those of several prior studies.15,23-26 Nes and colleagues,25 for example, found that older participants found smartphone use challenging; this may provide a partial explanation for these age effects.
Interestingly, in the current study almost all participants indicated that they owned technologies such as computers, cell phones, and had daily access to the Internet. However, the overwhelming majority did not currently use Internet or mobile applications in their diabetes management practice. Many (but not all) participants indicated they would be interested in adopting technology, specifically those that provide dietary planning, glucose tracking, and communication with health professionals, into their management.
The results of the present study indicate that—at least for those patients younger in age—there is a gap between patients wanting to use technology in self-management and actually using technology in self-management. This is apparent as the majority of participants owned technology but were not using it within their disease management. This gap may exist for a variety of reasons. For example, participants may simply not be aware of technological applications available to help manage their disease. It is also possible that people are aware of such applications but have low confidence about using it regularly. This question will be of critical interest and examined further via focus groups in a larger scale research study on assistive technologies in T2DM in the near future.
Although results regarding intention to adopt technologies in the future found in this study may be optimistic, these results may also lead to the conclusion that individuals are not opposed to technology, but simply unlikely to adopt it into their management due to its perceived usefulness and ease of use.27 Although previous literature indicates that if technology is easy to use, patients will adopt it into self-management, the varying self-confidence levels reported in the current study for participant ability to use assistive technologies should not be ignored.16,17 Furthermore, it has been found that if patients were to adopt technology compliance and actual use of this technology may be lower than expected.6,28,29
Study Strengths and Limitations
There are several strengths of the current investigation. Given the proliferation of technological devices for assisting those with T2DM in the marketplace, it is useful to know about T2DM patients attitudes toward them. This is among only a few existing studies that document T2DM patient attitudes and intentions to use these technologies. The breadth of information gathered regarding current patterns of everyday technology use, technology use for self-management, as well as self-efficacy and intention to use these technologies for diabetes-related purposes is information that can guide future studies and development of technology designed for diabetes self-management. An additional strength of this study was the use of in-person sampling (as opposed to sampling via the Internet), which reduces bias in preference for technologies.
Several limitations must also be mentioned. As the current study is a pilot study, the small sample size may have yielded low power to detect some effects. For example, some demographic characteristics were not found to be significantly correlated with assistive technology attitudes and intentions in this limited sample, but might have in larger population samples. Secondly, not all individuals chose to complete the survey due to literacy restraints which resulted in a lower response rate and lack of representation of those individuals in the presented results; it is possible that this would bias upward our estimates of technological acceptance and intentions to use, given the language-mediated use of most of such devices. Finally, the survey did not ask about participant income or if participants would use applications if they were required to purchase them. Although participants indicated they would be interested and intended to use technology, the cross-sectional nature of the survey could not examine if participants would actually use technology in their self-management after survey completion or for how long.
Conclusions
Results of this pilot study indicate that individuals with T2DM generally have a mixed view toward integration of assistive technologies into self-management. Relatively speaking, younger age groups have more favorable attitudes and intentions toward adopting assistive technologies into self-management practices than those in older age groups. The vast majority of participants did not currently use technology in their disease management, despite having access to the required technology devices to do so. These results indicate that further research is required to determine the actual use of application services are most of interest to this population, specifically focusing on Internet and mobile phone applications.
In terms of clinical implications, diabetes clinicians should query about patients’ interest in technology, and provide examples for when/how it can be accessed to their benefit, among those who indicate interest. Older adults with T2DM may be less oriented toward such resources than younger adults with T2DM. Further investigation of facilitators and barriers to the adoption of assistive technology in T2DM self-management should be explored within the context of larger survey studies with more representative samples. In such studies, use of low technology administration strategies will be necessary to reduce selection bias in the direction of more positive attitudes toward technology.
Acknowledgments
The authors would like to thank Langs Organization as well as the A.R. Kaufman YMCA for their support in participant recruitment. Furthermore, the authors wish to thank Cassandra Lowe, Corita Vincent, and the members of the University of Waterloo’s Social Health and Neuroscience Laboratory for their support of this study.
Footnotes
Declaration of Conflicting Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The authors received no financial support for the research, authorship, and/or publication of this article.
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