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Technology profiles as proxies for measuring functional and frailty status
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Procedia Computer Science 111 (2017) 77–86
8th on in Information Technology, 8th International International Conference ConferenceDecember on Advances Advances inMacau, Information Technology, IAIT2016, IAIT2016, 19-22 19-22 2016, China December 2016, Macau, China
Technology Technology profiles profiles as as proxies proxies for for measuring measuring functional functional and and frailty frailty status status a, a b c Tiffany Sirois Tiffany Tong Tonga,*, *,dMark Mark Chignell Chignella,, e,Mary Mary C. C. Tierney Tierneyb,, Marie-Josée Marie-Josée Siroiscb,, Judah Judah f Goldstein Goldsteind,, Marcel Marcel Émond Émonde, Kenneth Kenneth Rockwood Rockwoodf,, Jacques Jacques S. S. Lee Leeb a University of Toronto, Toronto, Canada a University of Toronto, Toronto, Canada Sunnybrook Research Institute, Toronto, Canada Sunnybrook Research Institute, Toronto, Canada c Centre Hospitalier Universitaire de Québec, Québec City, Canada c Centred Hospitalier Universitaire de Québec, Québec City, Canada Nova Scotia Health Authority, Nova Scotia, Canada d NovaeScotia Health Authority, Université Laval, QuébecNova City,Scotia, CanadaCanada e f Université Laval, Québec City, Canada Dalhousie University, Halifax, Canada f Dalhousie University, Halifax, Canada b b
Abstract Abstract Technology questionnaires can assist in developing profiles that characterize the ability of people to use new technologies. Technology questionnaires can assist in developing profiles that characterize the ability of people to use new technologies. Technology profiles may also be related to physical and cognitive abilities, and may possibly serve as proxies for constructs that Technology profiles may also be related to physical and cognitive abilities, and may possibly serve as proxies for constructs that may be more difficult to measure. The purpose of the research reported in this paper was to examine possible relationships may be more difficult to measure. The purpose of the research reported in this paper was to examine possible relationships between responses to questions about use of technologies and functional and frailty status as measured using a digital, tabletbetween responses to questions about use of technologies and functional and frailty status as measured using a digital, tabletbased test battery. A battery of digitized cognitive and functional assessments was administered on a tablet, along with a based test battery. A battery of digitized cognitive and functional assessments was administered on a tablet, along with a technology questionnaire, to Canadian adults over 65 years who called 911 for paramedic services or who presented to an technology questionnaire, to Canadian adults over 65 years who called 911 for paramedic services or who presented to an emergency department. 330 people between the ages of 65 and 97 years (mean = 75.8 years, standard deviation = 7.6) emergency department. 330 people between the ages of 65 and 97 years (mean = 75.8 years, standard deviation = 7.6) participated in the study. We observed significant relationships between elderly adults’ responses to questions about their participated in the study. We observed significant relationships between elderly adults’ responses to questions about their technology use and their functional status and frailty scores with more technology use implying better functional status and less technology use and their functional status and frailty scores with more technology use implying better functional status and less frailty. It is suggested that the present findings may lead to the use of more detailed technology profiles as efficient proxy frailty. It is suggested that the present findings may lead to the use of more detailed technology profiles as efficient proxy estimates of overall functional ability and frailty status in elderly adults. estimates of overall functional ability and frailty status in elderly adults. © 2015 The Authors. Published by Elsevier B.V. © 2015 The Authors. Published by Elsevier B.V. Peer-review responsibility of Elsevier the organizing of the 8th International Conference on Advances in Information © 2017 The under Authors. Published by B.V. committee Peer-review under responsibility of the organizing committee of the 8th International Conference on Advances in Information Technology. Peer-review under responsibility of the organizing committee of the 8th International Conference on Advances in Information Technology. Technology
* Corresponding author. Tel.: 416 978 7581. * Corresponding author. Tel.: 416 978 7581. E-mail address: [email protected] E-mail address: [email protected] 1877-0509 © 2015 The Authors. Published by Elsevier B.V. 1877-0509 ©under 2015responsibility The Authors. of Published by Elsevier B.V. of the 8th International Conference on Advances in Information Technology. Peer-review the organizing committee Peer-review under responsibility of the organizing committee of the 8th International Conference on Advances in Information Technology.
1877-0509 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the organizing committee of the 8th International Conference on Advances in Information Technology 10.1016/j.procs.2017.06.013
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Keywords: Frailty assessment; functional assessment; human computer interaction; human factors; technology use; user experience
1. Introduction Technology profiles are commonly used to characterize a user’s familiarity, experience, and preferences related to technologies. They provide a method to help predict user behaviors, and responses to technology. They also enable researchers to separate a user’s attitudes towards a tool from other personal attributes. For example, a user who is uncomfortable and unfamiliar with new technology may not perform as well with a new device or system compared to another user who is more comfortable with technology, even if both users might otherwise share similar cognitive and motor abilities. 1.1. Previous work Early technology profile measures focused on the use of personal computers (e.g., 1–3) and were created before personal computers and the Internet became widely available. However, as computers and related technologies such as the Internet and mobile devices became pervasive in users’ professional and personal lives, measures of technology attitudes changed and expanded accordingly. More recent scales such as the technology profile index by 4 and the scale developed by 5 consider factors such as confidence with, approval of, and interest in various technologies. The scale developed by 4 also considers variables such as gender, socio-economic status, and expertise. Based on a review of technology profile questionnaires, 6 identified common themes across various scales such as experience, use, and anxiety. As an extension of this work, 7 proposed that existing measures of technology attitude scales can be grouped into the following three categories: confidence, approval, and interest. 1.2. Technology use and functional ability Technology scales have been used in cognitive aging research. A study examining the role of the Computerized Assessment of Mild Cognitive Impairment 8 included a questionnaire examining the technology profile of patients. The questionnaire assessed experience with technologies such as automatic banking machines (ABMs) and mobile devices such as tablets. Information about experience and attitude towards technology can inform the design of technology for users with unique needs. User technology profiles can also provide information regarding lifestyle and functional abilities. For example, previous research has demonstrated that older adults who participated in online communities or used the Internet benefitted from improved social support and general well-being9 and experience decreased loneliness and increased psychosocial well-being10,11. Factors that contribute to computer use for older adults include younger age, higher education level, and general physical health12,13. Overall, the use of technology can provide insight into an elderly adult’s cognitive and functional abilities. Previous research has focused on the use of technology by both healthy and frail elderly adults, but has not looked into how technology profiles relate to clinical measures such as scores on functional and frailty assessments. 1.3. Objective The goal of using a brief technology profiling questionnaire in this study was to examine the technology experience of older users, and how this relates to their functional and frailty status, as assessed by their performance on a digital test battery. 2. Methods 2.1. Creation of the modified technology history questionnaire We created a short, five-item questionnaire based on a subset of the technology use items used in a previous technology questionnaire targeted for older people8.
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2.2. Procedure A battery of digitized cognitive and functional assessments, as well as a serious game-based cognitive screening tool, were implemented on a tablet, and were provided in both French and English. The study included adults over 65 years of age who called 911 for paramedic services, or who presented to an emergency department (ED). A series of tests were administered to participants, by trained research personnel (RPs). These tests included the Alzheimer’s Disease 814, activities of daily living (ADL)15, Brief Alzheimer’s Screen16, Confusion Assessment Method17, Canadian Emergency Team Initiative, Clinical Frailty Score (CFS)18, delirium index19, instrumental activities of daily living (IADL)15, Montreal Cognitive Assessment20, Mini-Mental State Examination21, Ottawa 3 Date and Year22, Richmond Agitation-Sedation Scale23, and the Short Blessed Test24. Participants were then asked to selfassess their level of frailty and ADL using a digital tablet. Where possible, the frailty level of the participant was also assessed by a physician, and by a caregiver. At the end of the study, participants were asked to complete the five-item modified technology questionnaire, and a system usability scale (SUS)25, based on their experience in using the tablet-based data collection software. Our research protocol was approved by ethic review boards at the University of Toronto, and Sunnybrook Health Sciences Center. In the research, we considered the relationship between the CFS, ADL, and IADL measures and items on the modified technology history use questionnaire. 2.3. Recruitment criteria The patient inclusion criteria for our study included adults 65 years of age and older who called 911 for paramedic services or who presented to an ED. Exclusion criteria included patients who: 1) lived in a full care nursing home; 2) had a critical illness rendering them unable to communicate or provide consent; 3) had moderate to severe cognitive impairment based on their Montreal Cognitive Assessment score; 4) had visual impairment that made them unable to use the tablet; or 5) had other communication difficulties preventing use of the tablet. Patients who met the inclusion criteria and were not excluded were invited to participate, but could decline for any reason. 2.4. Apparatus The tablets used in this study were 10.1-inch screen tablets manufactured by Samsung26. 2.5. Data analysis Scores from the functional (e.g., ADL, IADL, and CFS), and technology assessments were treated as interval data. Age data were treated as continuous, and sex data were treated as dichotomous. The assumption of normality was tested for the continuous data collected in this study (e.g., age). We carried out a visual inspection of the histograms, P-P plots, and Q-Q plots as well as referring to the values of skewness and kurtosis of the data sets. As is typical in this type of sample, the age data exhibited a positive skew. 3. Results 3.1. Sample population 330 participants between 65-97 years (mean = 75.8 years, standard deviation = 7.6), were enrolled in this study. 3.2. Modified technology questionnaire results A summary of the descriptive data is shown in Table 1 (the questionnaire items are shown in the first column of the table). Around two-thirds (66.4%) of the sample indicated that they had a computer in their home. Over half indicated that they used a computer daily and 42.6% indicated that they had a tablet/smartphone. Close to half (over 40%) of those owning a tablet/smartphone reported playing games and a similar proportion reported using the
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device daily. A large majority of our sample (80.7%) indicated that they had used an ABM previously, of whom over 90% indicated that they had been using ABMs for five or more years. Around 39.1% of the patients in the sample indicated that they were very comfortable with using a computer, tablet, or other electronic device. Table 1. Summary of modified technology questionnaire results. Question Q1. Do you own your own computer or have one in your home? Q2. How often do you use a computer?
Q3. Do you own your own tablet or have one in your home? Q3a. If yes, do you play games on your tablet/smartphone? Q3b. If yes, how often do you use a tablet/smartphone?
Q4. Have you ever used an Automated Banking Machine (ABM)? Q4a. If yes, how long have you being using them?
Q5. I am very comfortable using a computer, tablet or other electronic devices.
Response No: 102/304 (33.6%) Yes: 202/304 (66.4%) Daily: 139/244 (57%) Weekly: 25/244 (10.2%) Monthly: 4/244 (1.6%) Rarely: 19/244 (7.8%) Never: 57/244 (23.4%) No: 174/303 (57.4%) Yes: 129/303 (42.6%) No: 94/160 (58.8%) Yes: 66/160 (41.2%) Daily: 79/168 (47%) Weekly: 25/168 (14.9%) Monthly: 4/168 (2.4%) Rarely: 14/168 (8.3%) Never: 46/168 (27.4%) No: 59/305 (19.3%) Yes: 246/305 (80.7%) Within the last month: 6/252 (2.4%) 6 months - 1 year: 3/252 (1.2%) 1 - 2 years: 2/252 (0.8%) 2 - 5 years: 10/252 (4%) Greater than or equal to 5 years: 231/252 (91.7%) Strongly agree: 117/299 (39.1%) Agree: 65/299 (21.7%) Neither agree nor disagree: 35/299 (11.7%) Disagree: 34/299 (11.4%) Strongly disagree: 48/299 (16.1%)
A series of two-way ANOVAs were carried out using questions 1, 3, and 4 to determine mean differences in CFS scores rated by physicians, RPs, and patients. In a two-way ANOVA with computer ownership (question 1) and ABM usage (question 3), there was one statistically significant simple main effect of computer ownership (question 1) on frailty assessed by physicians, F(2, 287) = 4.828, p = .009. There were two significant interactions for frailty scores assessed by RPs. 1. There was a statistically significant interaction between the effects of computer (question 1) and tablet/smartphone ownership (question 3) on frailty, F(3, 289) = 4.148, p = .007. 2. There was a statistically significant interaction between the effects of tablet/smartphone ownership (question 3) and ABM usage (question 4) on frailty, F(3 289) = 3.081, p = .028. For the self-assessments of frailty (by patients) there were two significant interactions. 1. There was a statistically significant interaction between the effects of computer (question 1) and tablet/smartphone ownership (question 3) on frailty, F(3, 283) = 4.518, p = .004. 2. There was a statistically significant interaction between the effects of tablet/smartphone ownership (question 3) and ABM usage (question 4) on frailty, F(3, 283) = 3.224, p = .023. These results suggested that the way that the person was answering the technology use questions was predicting differences in frailty. To understand the relationship between self-rated technology use and frailty, we classified people into technology use types (in the following section) based on their responses to questions 1, 3, and 4.
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3.3. Forming patient types based on technology use An analysis was carried out to develop technology use types based on the use of computers, tablets/smartphones, and ABMs. There were 8 combinations of user types based on their responses to questions 1, 3, and 4 (since each of the three questions had dichotomous responses there were 2x2x2 = 8 possible combinations of responses). The numbers of people in each of the 8 combinations is shown in Fig. 1. Eight patient types based on responses to questions 1, 3 and 4. Fig. 1. There were 301 patients who responded to all three of the questions. Five of these combinations represented at least 15 individuals in the sample and will be the focus of the analysis below. There were 96 patients who answered “yes” to owning a computer, tablet/smartphone, and having used an ABM. Another 82 patients owned a computer but not a tablet/smartphone, and had used an ABM. 17 patients responded that they did not own a computer but did own a tablet/smartphone, and had used an ABM. 49 patients did not own a computer or use a tablet/smartphone but had used an ABM. Finally 33 participants responded “no” to all three of the questions (not owning a computer and tablet/smartphone, and never using an ABM). The five most frequently occurring technology use combinations were interpreted as types: • Type 1: Computer and tablet/smartphone users with ABM experience (N=96) • Type 2: Computer users with ABM experience but no tablet/smartphone experience (N=82) • Type 3: Tablet/smartphone users with ABM experience but not computer experience (N=17) • Type 4: ABM users with no computer or tablet/smartphone experience (N=49) • Type 5: No computer, tablet/smartphone or ABM experience (N=33) Patient types 1 through 5 captured 92.0% (277/301) of the sample and were the focus of the analyses.
Fig. 1. Eight patient types based on responses to questions 1, 3 and 4.
3.4. System usability scale Types 1, 2, and 3 patients generally had high SUS scores as shown in Fig. 2, which shows boxplots of SUS scores across the different patient types. About 1/3 of the Type 1 and 2 patients had SUS scores above 80. As technologically savvy users, they generally found the tablet-based applications to be useful. In contrast, patient types 4 and 5 had more SUS scores below 80.Patient types 1 and 3 had the highest median SUS total score of 87.5, with
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type 2 median scores being slightly lower, and type 4 and 5 median scores being considerably lower. A KruskalWallis H test showed that there was a statistically significant difference in SUS scores between the different patient types, χ2(4) = 39.722, p = .000, with a mean rank SUS total score of 145.42 for type 1, 124.79 for type 2, 144.59 for type 3, 67.80 for type 4 and 103.20 for type 5.
Fig. 2. Boxplots comparing SUS scores across all five patient types.
Fig. 3. Boxplots comparing age of patients across all five patient types.
Patient type 1 had the youngest median age (72 years) compared to patient type 5, which had the oldest median age (81 years) as shown in Fig. 3. Since type 4 and 5 people did not own a computer or tablet/smartphone they would likely have found the tablet-based software harder to use, which would account for their low SUS scores. Total ADL scores across the patient types are shown in 错误!未找到引用源。. ADL scores were highest for patient types 1 and 2. These two patient types have experience with ABMs and own or have used a computer. A Kruskal-Wallis H test showed that there was a statistically significant difference in ADL scores between the different patient types, χ2(4) = 36.191, p = .000, with a mean rank ADL score of 160.76 for type 1, 153.59 for type 2, 125.91 for type 3, 98.95 for type 4 and 98.26 for type 5. IADL scores were highest for patient types 1, and 2 as shown in Fig. 4 and Fig. 5. Comparing Fig. 4 and Fig. 5, it can be seen that the pattern of results for the ADL and IADL measures are generally similar except that the type 5 patients do relatively worse on the IADL, in comparison to the other patient types. Frailty scores will generally be inversely related to ADL and IADL scores and this was also observed in the present study. A Kruskal-Wallis H test showed that there was a statistically significant difference in IADL scores between the different patient types, χ2(4) = 34.653, p = .000, with a mean rank IADL score of 160.16 for type 1, 155.50 for type 2, 121.44 for type 3, 106.82 for type 4 and 90.59 for type 5.
Fig. 4. Boxplots comparing ADL total scores of patients across all five patient types.
Fig. 5. Boxplot comparing IADL total scores of patients across all five patient types.
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As can be seen in Figures 6 through 9, regardless of who is doing the rating (physician, RP, caregiver, self) the frailty scores as assessed by the CFS tend to rise across the five patient types. A potentially interesting point of disagreement is that physicians and RPs rated the frailty of type 5 patients as higher than did the patients and their caregivers. One possible explanation of this result is that physicians and RPs may have overestimated frailty in some cases where the patient was experiencing usability issues with the tablet-based software. Kruskal-Wallis H tests were carried out to determine if there were significant differences in CFS scores across the different patient types as assessed by different raters. 1. There was a statistically significant difference in CFS scores assessed by MDs between the different patient types, χ2(4) = 31.216, p = .000, with a mean rank CFS score of 105.92 for type 1, 117.27 for type 2, 147.57 for type 3, 167.18 for type 4 and 162.16 for type 5. 2. There was a statistically significant difference in CFS scores assessed by RPs between the different patient types, χ2(4) = 39.392, p = .000, with a mean rank CFS score of 106.33 for type 1, 116.85 for type 2, 154.41 for type 3, 169.65 for type 4 and 177.33 for type 5. 3. There was no significant difference in CFS scores assessed by caregivers between the different patient types, χ2(4) = 2.869, p = .580. 4. There was a statistically significant difference in CFS scores self-assessed by patients between the different patient types, χ2(4) = 29.301, p = .000, with a mean rank CFS score of 111.31 for type 1, 112.60 for type 2, 146.82 for type 3, 171.21 for type 4 and 154.40 for type 5. Type 1 through 3 patients were generally least frail, as assessed by the CFS, exhibiting a low median score, as shown in Fig. 6, Fig. 7, Fig. 8, and Fig. 9.
Fig. 6. CFS scores assessed by physicians. The median CFS score for patient types 1 through 3was 3. For patient types 4 and 5, it was 4.
Fig. 7. CFS scores assessed by RPs. The median CFS score for patient types 1 and 2 was 3. For patient types 3, 4 and 5, the median CFS score was 4.
Fig. 8. CFS scores assessed by caregivers. The median CFS score for patient types 1, 2, 4 and 5 was 3. For patient type 3, the median CFS score is 4.
Fig. 9. CFS scores assessed by patients. The median CFS score for patient types 1, 2, 3, and 5 was 3. For patient type 4, the median CFS score was 4.
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3.5. Correlations of measures within the patient types A two-tailed Pearson’s r correlation analysis was carried out to compare the relationships between performance on the CFS, ADL, IADL, SUS as shown in Table 2. This analysis included all of the patients in the sample. There were significant correlations observed between CFS scores by different raters (e.g., physicians, RPs, caregivers and patients). The ADL and IADL scores also exhibited strong relationships with each other. Performance on the SUS was correlated with scores on the ADL, IADL, and CFS ratings by MDs, RPs, and patients. Age was strongly related with all CFS ratings, and scores on the ADL and IADL. Sex was only related to IADL performance, where males had a higher IADL score than females (Fig. 10).
Fig. 10. IADL Performance Based On Sex. Table 2. Correlation Analysis Carried Out Using Pearson’s R (Two-Tailed) For Patient Type 1. * p < .05, ** p < .01.
CFS Caregivers CFS Physicians CFS Patients CFS RPs ADL IADL SUS Age
r p N r p N r p N r p N r p N r p N r p N r p N
CFS Physicians CFS Patients CFS RPs ADL IADL SUS .766** .661** .783** -.343* -.588** .000 .000 .000 .026 .000 41 39 39 42 42 1 .581** .695** -.289** -.409** .000 .000 .000 .000 270 277 288 287 1 .745** -.282** -.385** .000 .000 .000 281 286 286 1 -.267** -.412** .000 .000 292 292 1 .812** .000 308 1
Sex -.304 .072 36 -.299** .000 253 -.204** .001 258 -.353** .000 258 .134* .029 268 .177** .004 268 1
Age .404* .018 34 .260** .000 187 .190** .010 183 .348** .000 189 -.162* .022 200 -.260** .000 199 -.241** .001 176 1
.221 .196 36 .049 .505 191 .024 .746 187 -.044 .550 191 -.121 .087 203 -.147* .036 202 .156* .036 180 .023 .733 215
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4. Discussion We created five patient types based on the grouping of responses from the survey questions on computer usage, tablet/smartphone usage, and ABM experience. Using these patient types, we then carried out statistical analyses to examine trends across and within these groups. Our sample exhibited strong correlations between their use of technology and their functional ability (assessed using the ADL) as well as with reported technology usability (assessed using the SUS). Gender-specific relationships were only observed with performance on the IADL. There were also differences between the five technology use types that we identified in terms of their ADL, IADL, SUS, and CFS scores. The ability to use technology seems to be a key predictor of frailty. For people who frequently use technology such as computers, tablets/smartphones, and ABMs, the frequency of the technology use is related to the ability to live independently, with more technology use indicating less frailty and greater functional ability. Technology use may provide a virtuous cycle, where frequent computer/tablet/smartphone use offers a means to communicate with friends and family, thereby promoting social interaction as well as providing opportunities for the use of physical and cognitive abilities that might otherwise degrade with disuse. More frequent usage of technology may indicate that adults are more cognitively and physically active27. Similar relationships between technology use and functional status in aging adults has been observed previously, with technology users having higher cognitive and physical statuses compared to a control group28. In addition, as computer technology is more common than tablets and smartphone devices, it is possible that using one of these “newer” devices suggests a higher level of computer/technology-efficacy27. This work demonstrates the ability to use technology questions to categorize patients into different groups. It illustrates the potential to use relatively few screening questions to assist in predicting a patient’s frailty status without the need to carry out a full technology profile questionnaire. In addition, questions on computers, tablets/smartphones, and ABM experience can potentially be answered by a patient’s caregiver, which can assist healthcare staff in assessing the patient’s health status. This study used only a few technology profiling questions in the context of a much larger study that focused on frailty of emergency patients. Future research should examine if more detailed technology profiling can provide more sensitive proxy assessments of frailty. 5. Conclusion This work examined the relationship between a patient’s self-reported technology use and their frailty status. Three questions concerning computer, tablet/smartphone, and ABM usage, were used to separate users into five different subtypes. Using these groupings, we examined how differences in technology use predicted differences in frailty assessed using the CFS, and functional status assessed using the ADL and IADL. This work demonstrates the potential to use a relatively small set of questions on technology use as a preliminary screening tool for estimating functional and frailty status in the elderly. Acknowledgements MCT is supported by a Clinician Scientist Award from the Department of Family & Community Medicine, University of Toronto. TT and MC are supported by a grant from the AGE-WELL National Center of Excellence (WP 6.1). This research was funded by a Canadian Frailty Network Grant (application number FRA 2015-B-09). References 1. Loyd, B. H. & Gressard, C. Reliability and Factorial Validity of Computer Attitude Scales. Educ. Psychol. Meas. 44, 501–505 (1984). 2. Nickell, G. S. & Pinto, J. N. The computer attitude scale. Comput. Human Behav. 2, 301–306 (1986). 3. Popovich, P. M., Hyde, K. R., Zakrajsek, T. & Blumer, C. The Development of the Attitudes toward Computer Usage Scale. Educ. Psychol. Meas. 47, 261–269 (1987). 4. Spence, I., DeYoung, C. G. & Feng, J. The technology profile inventory: Construction, validation, and application. Comput. Human Behav. 25, 458–465 (2009).
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5. Liaw, S.-S. An Internet survey for perceptions of computers and the World Wide Web: relationship, prediction, and difference. Comput. Human Behav. 18, 17–35 (2002). 6. Smith, B., Caputi, P., Crittenden, N., Jayasuriya, R. & Rawstorne, P. A review of the construct of computer experience. Comput. Human Behav. 15, 227–242 (1999). 7. DeYoung, C. G. & Spence, I. Profiling information technology users: en route to dynamic personalization. Comput. Human Behav. 20, 55–65 (2004). 8. Tierney, M. C. et al. Feasibility and validity of the self-administered computerized assessment of mild cognitive impairment with older primary care patients. Alzheimer Dis. Assoc. Disord. 28, 311–9 (2014). 9. Nimrod, G. Seniors’ online communities: A quantitative content analysis. Gerontologist 50, 382–392 (2010). 10. White, H. et al. Surfing the Net in Later Life: A Review of the Literature and Pilot Study of Computer Use and Quality of Life. J. Appl. Gerontol. 18, 358–378 (1999). 11. Malcolm, M. et al. Computer and Internet Use in Physically Frail Elders. Phys. Occup. Ther. Geriatr. 19, 15–32 (2002). 12. Werner, J. M., Carlson, M., Jordan-Marsh, M. & Clark, F. Predictors of computer use in community-dwelling, ethnically diverse older adults. Hum. Factors 53, 431–47 (2011). 13. Carpenter, B. D. & Buday, S. Computer use among older adults in a naturally occurring retirement community. Comput. Human Behav. 23, 3012–3024 (2007). 14. Galvin, J. E. et al. The AD8: A brief informant interview to detect dementia. Neurology 65, 559–564 (2005). 15. Katz, S. Assessing self-maintenance: activities of daily living, mobility, and instrumental activities of daily living. J. Am. Geriatr. Soc. 31, 721–7 (1983). 16. Mendiondo, M. S., Ashford, J. W., Kryscio, R. J. & Schmitt, F. A. Designing a Brief Alzheimer Screen (BAS). J. Alzheimers. Dis. 5, 391– 398 (2003). 17. Inouye, S. K. et al. Clarifying confusion: the confusion assessment method. A new method for detection of delirium. Ann. Intern. Med. 113, 941–8 (1990). 18. Rockwood, K. et al. A global clinical measure of fitness and frailty in elderly people. CMAJ 173, 489–95 (2005). 19. McCusker, J., Cole, M. G., Dendukuri, N. & Belzile, E. The delirium index, a measure of the severity of delirium: new findings on reliability, validity, and responsiveness. J. Am. Geriatr. Soc. 52, 1744–9 (2004). 20. Nasreddine, Z. S. et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J. Am. Geriatr. Soc. 53, 695–9 (2005). 21. Folstein, M. F., Folstein, S. E. & McHugh, P. R. ‘Mini-mental state’ A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12, 189–198 (1975). 22. Molnar, F. J., Wells, G. A. & Mcdowell, I. The Derivation and Validation of the Ottawa 3D and Ottawa 3DY Three- and Four-Question Screens for Cognitive Impairment. Geriatrics 2008, 1–11 (2008). 23. Sessler, C. N. et al. The Richmond Agitation-Sedation Scale: validity and reliability in adult intensive care unit patients. Am. J. Respir. Crit. Care Med. 166, 1338–44 (2002). 24. Katzman, R. et al. Validation of a short orientation-memory-concentration test of cognitive impairment. Am. J. Psychiatry 140, 734–739 (1983). 25. Brooke, J. in Usability Evaluation in Industry (eds. Jordan, P. W., Thomas, B., Weerdmeester, B. A. & McClelland, A. L.) 189–194 (Taylor and Francis, 1996). 26. Czaja, S. J. et al. Factors predicting the use of technology: Findings from the center for research and education on aging and technology enhancement (create). Psychol. Aging 21, 333–352 (2006). 27. Tomita, M. R., Mann, W. C., Stanton, K., Tomita, A. D. & Sundar, V. Use of currently available smart home technology by frail elders: process and outcomes. Top. Geriatr. Rehabil. 23, 24–34 (2007).
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8th on in Information Technology, 8th International International Conference ConferenceDecember on Advances Advances inMacau, Information Technology, IAIT2016, IAIT2016, 19-22 19-22 2016, China December 2016, Macau, China
Technology Technology profiles profiles as as proxies proxies for for measuring measuring functional functional and and frailty frailty status status a, a b c Tiffany Sirois Tiffany Tong Tonga,*, *,dMark Mark Chignell Chignella,, e,Mary Mary C. C. Tierney Tierneyb,, Marie-Josée Marie-Josée Siroiscb,, Judah Judah f Goldstein Goldsteind,, Marcel Marcel Émond Émonde, Kenneth Kenneth Rockwood Rockwoodf,, Jacques Jacques S. S. Lee Leeb a University of Toronto, Toronto, Canada a University of Toronto, Toronto, Canada Sunnybrook Research Institute, Toronto, Canada Sunnybrook Research Institute, Toronto, Canada c Centre Hospitalier Universitaire de Québec, Québec City, Canada c Centred Hospitalier Universitaire de Québec, Québec City, Canada Nova Scotia Health Authority, Nova Scotia, Canada d NovaeScotia Health Authority, Université Laval, QuébecNova City,Scotia, CanadaCanada e f Université Laval, Québec City, Canada Dalhousie University, Halifax, Canada f Dalhousie University, Halifax, Canada b b
Abstract Abstract Technology questionnaires can assist in developing profiles that characterize the ability of people to use new technologies. Technology questionnaires can assist in developing profiles that characterize the ability of people to use new technologies. Technology profiles may also be related to physical and cognitive abilities, and may possibly serve as proxies for constructs that Technology profiles may also be related to physical and cognitive abilities, and may possibly serve as proxies for constructs that may be more difficult to measure. The purpose of the research reported in this paper was to examine possible relationships may be more difficult to measure. The purpose of the research reported in this paper was to examine possible relationships between responses to questions about use of technologies and functional and frailty status as measured using a digital, tabletbetween responses to questions about use of technologies and functional and frailty status as measured using a digital, tabletbased test battery. A battery of digitized cognitive and functional assessments was administered on a tablet, along with a based test battery. A battery of digitized cognitive and functional assessments was administered on a tablet, along with a technology questionnaire, to Canadian adults over 65 years who called 911 for paramedic services or who presented to an technology questionnaire, to Canadian adults over 65 years who called 911 for paramedic services or who presented to an emergency department. 330 people between the ages of 65 and 97 years (mean = 75.8 years, standard deviation = 7.6) emergency department. 330 people between the ages of 65 and 97 years (mean = 75.8 years, standard deviation = 7.6) participated in the study. We observed significant relationships between elderly adults’ responses to questions about their participated in the study. We observed significant relationships between elderly adults’ responses to questions about their technology use and their functional status and frailty scores with more technology use implying better functional status and less technology use and their functional status and frailty scores with more technology use implying better functional status and less frailty. It is suggested that the present findings may lead to the use of more detailed technology profiles as efficient proxy frailty. It is suggested that the present findings may lead to the use of more detailed technology profiles as efficient proxy estimates of overall functional ability and frailty status in elderly adults. estimates of overall functional ability and frailty status in elderly adults. © 2015 The Authors. Published by Elsevier B.V. © 2015 The Authors. Published by Elsevier B.V. Peer-review responsibility of Elsevier the organizing of the 8th International Conference on Advances in Information © 2017 The under Authors. Published by B.V. committee Peer-review under responsibility of the organizing committee of the 8th International Conference on Advances in Information Technology. Peer-review under responsibility of the organizing committee of the 8th International Conference on Advances in Information Technology. Technology
* Corresponding author. Tel.: 416 978 7581. * Corresponding author. Tel.: 416 978 7581. E-mail address: [email protected] E-mail address: [email protected] 1877-0509 © 2015 The Authors. Published by Elsevier B.V. 1877-0509 ©under 2015responsibility The Authors. of Published by Elsevier B.V. of the 8th International Conference on Advances in Information Technology. Peer-review the organizing committee Peer-review under responsibility of the organizing committee of the 8th International Conference on Advances in Information Technology.
1877-0509 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the organizing committee of the 8th International Conference on Advances in Information Technology 10.1016/j.procs.2017.06.013
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Keywords: Frailty assessment; functional assessment; human computer interaction; human factors; technology use; user experience
1. Introduction Technology profiles are commonly used to characterize a user’s familiarity, experience, and preferences related to technologies. They provide a method to help predict user behaviors, and responses to technology. They also enable researchers to separate a user’s attitudes towards a tool from other personal attributes. For example, a user who is uncomfortable and unfamiliar with new technology may not perform as well with a new device or system compared to another user who is more comfortable with technology, even if both users might otherwise share similar cognitive and motor abilities. 1.1. Previous work Early technology profile measures focused on the use of personal computers (e.g., 1–3) and were created before personal computers and the Internet became widely available. However, as computers and related technologies such as the Internet and mobile devices became pervasive in users’ professional and personal lives, measures of technology attitudes changed and expanded accordingly. More recent scales such as the technology profile index by 4 and the scale developed by 5 consider factors such as confidence with, approval of, and interest in various technologies. The scale developed by 4 also considers variables such as gender, socio-economic status, and expertise. Based on a review of technology profile questionnaires, 6 identified common themes across various scales such as experience, use, and anxiety. As an extension of this work, 7 proposed that existing measures of technology attitude scales can be grouped into the following three categories: confidence, approval, and interest. 1.2. Technology use and functional ability Technology scales have been used in cognitive aging research. A study examining the role of the Computerized Assessment of Mild Cognitive Impairment 8 included a questionnaire examining the technology profile of patients. The questionnaire assessed experience with technologies such as automatic banking machines (ABMs) and mobile devices such as tablets. Information about experience and attitude towards technology can inform the design of technology for users with unique needs. User technology profiles can also provide information regarding lifestyle and functional abilities. For example, previous research has demonstrated that older adults who participated in online communities or used the Internet benefitted from improved social support and general well-being9 and experience decreased loneliness and increased psychosocial well-being10,11. Factors that contribute to computer use for older adults include younger age, higher education level, and general physical health12,13. Overall, the use of technology can provide insight into an elderly adult’s cognitive and functional abilities. Previous research has focused on the use of technology by both healthy and frail elderly adults, but has not looked into how technology profiles relate to clinical measures such as scores on functional and frailty assessments. 1.3. Objective The goal of using a brief technology profiling questionnaire in this study was to examine the technology experience of older users, and how this relates to their functional and frailty status, as assessed by their performance on a digital test battery. 2. Methods 2.1. Creation of the modified technology history questionnaire We created a short, five-item questionnaire based on a subset of the technology use items used in a previous technology questionnaire targeted for older people8.
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2.2. Procedure A battery of digitized cognitive and functional assessments, as well as a serious game-based cognitive screening tool, were implemented on a tablet, and were provided in both French and English. The study included adults over 65 years of age who called 911 for paramedic services, or who presented to an emergency department (ED). A series of tests were administered to participants, by trained research personnel (RPs). These tests included the Alzheimer’s Disease 814, activities of daily living (ADL)15, Brief Alzheimer’s Screen16, Confusion Assessment Method17, Canadian Emergency Team Initiative, Clinical Frailty Score (CFS)18, delirium index19, instrumental activities of daily living (IADL)15, Montreal Cognitive Assessment20, Mini-Mental State Examination21, Ottawa 3 Date and Year22, Richmond Agitation-Sedation Scale23, and the Short Blessed Test24. Participants were then asked to selfassess their level of frailty and ADL using a digital tablet. Where possible, the frailty level of the participant was also assessed by a physician, and by a caregiver. At the end of the study, participants were asked to complete the five-item modified technology questionnaire, and a system usability scale (SUS)25, based on their experience in using the tablet-based data collection software. Our research protocol was approved by ethic review boards at the University of Toronto, and Sunnybrook Health Sciences Center. In the research, we considered the relationship between the CFS, ADL, and IADL measures and items on the modified technology history use questionnaire. 2.3. Recruitment criteria The patient inclusion criteria for our study included adults 65 years of age and older who called 911 for paramedic services or who presented to an ED. Exclusion criteria included patients who: 1) lived in a full care nursing home; 2) had a critical illness rendering them unable to communicate or provide consent; 3) had moderate to severe cognitive impairment based on their Montreal Cognitive Assessment score; 4) had visual impairment that made them unable to use the tablet; or 5) had other communication difficulties preventing use of the tablet. Patients who met the inclusion criteria and were not excluded were invited to participate, but could decline for any reason. 2.4. Apparatus The tablets used in this study were 10.1-inch screen tablets manufactured by Samsung26. 2.5. Data analysis Scores from the functional (e.g., ADL, IADL, and CFS), and technology assessments were treated as interval data. Age data were treated as continuous, and sex data were treated as dichotomous. The assumption of normality was tested for the continuous data collected in this study (e.g., age). We carried out a visual inspection of the histograms, P-P plots, and Q-Q plots as well as referring to the values of skewness and kurtosis of the data sets. As is typical in this type of sample, the age data exhibited a positive skew. 3. Results 3.1. Sample population 330 participants between 65-97 years (mean = 75.8 years, standard deviation = 7.6), were enrolled in this study. 3.2. Modified technology questionnaire results A summary of the descriptive data is shown in Table 1 (the questionnaire items are shown in the first column of the table). Around two-thirds (66.4%) of the sample indicated that they had a computer in their home. Over half indicated that they used a computer daily and 42.6% indicated that they had a tablet/smartphone. Close to half (over 40%) of those owning a tablet/smartphone reported playing games and a similar proportion reported using the
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device daily. A large majority of our sample (80.7%) indicated that they had used an ABM previously, of whom over 90% indicated that they had been using ABMs for five or more years. Around 39.1% of the patients in the sample indicated that they were very comfortable with using a computer, tablet, or other electronic device. Table 1. Summary of modified technology questionnaire results. Question Q1. Do you own your own computer or have one in your home? Q2. How often do you use a computer?
Q3. Do you own your own tablet or have one in your home? Q3a. If yes, do you play games on your tablet/smartphone? Q3b. If yes, how often do you use a tablet/smartphone?
Q4. Have you ever used an Automated Banking Machine (ABM)? Q4a. If yes, how long have you being using them?
Q5. I am very comfortable using a computer, tablet or other electronic devices.
Response No: 102/304 (33.6%) Yes: 202/304 (66.4%) Daily: 139/244 (57%) Weekly: 25/244 (10.2%) Monthly: 4/244 (1.6%) Rarely: 19/244 (7.8%) Never: 57/244 (23.4%) No: 174/303 (57.4%) Yes: 129/303 (42.6%) No: 94/160 (58.8%) Yes: 66/160 (41.2%) Daily: 79/168 (47%) Weekly: 25/168 (14.9%) Monthly: 4/168 (2.4%) Rarely: 14/168 (8.3%) Never: 46/168 (27.4%) No: 59/305 (19.3%) Yes: 246/305 (80.7%) Within the last month: 6/252 (2.4%) 6 months - 1 year: 3/252 (1.2%) 1 - 2 years: 2/252 (0.8%) 2 - 5 years: 10/252 (4%) Greater than or equal to 5 years: 231/252 (91.7%) Strongly agree: 117/299 (39.1%) Agree: 65/299 (21.7%) Neither agree nor disagree: 35/299 (11.7%) Disagree: 34/299 (11.4%) Strongly disagree: 48/299 (16.1%)
A series of two-way ANOVAs were carried out using questions 1, 3, and 4 to determine mean differences in CFS scores rated by physicians, RPs, and patients. In a two-way ANOVA with computer ownership (question 1) and ABM usage (question 3), there was one statistically significant simple main effect of computer ownership (question 1) on frailty assessed by physicians, F(2, 287) = 4.828, p = .009. There were two significant interactions for frailty scores assessed by RPs. 1. There was a statistically significant interaction between the effects of computer (question 1) and tablet/smartphone ownership (question 3) on frailty, F(3, 289) = 4.148, p = .007. 2. There was a statistically significant interaction between the effects of tablet/smartphone ownership (question 3) and ABM usage (question 4) on frailty, F(3 289) = 3.081, p = .028. For the self-assessments of frailty (by patients) there were two significant interactions. 1. There was a statistically significant interaction between the effects of computer (question 1) and tablet/smartphone ownership (question 3) on frailty, F(3, 283) = 4.518, p = .004. 2. There was a statistically significant interaction between the effects of tablet/smartphone ownership (question 3) and ABM usage (question 4) on frailty, F(3, 283) = 3.224, p = .023. These results suggested that the way that the person was answering the technology use questions was predicting differences in frailty. To understand the relationship between self-rated technology use and frailty, we classified people into technology use types (in the following section) based on their responses to questions 1, 3, and 4.
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3.3. Forming patient types based on technology use An analysis was carried out to develop technology use types based on the use of computers, tablets/smartphones, and ABMs. There were 8 combinations of user types based on their responses to questions 1, 3, and 4 (since each of the three questions had dichotomous responses there were 2x2x2 = 8 possible combinations of responses). The numbers of people in each of the 8 combinations is shown in Fig. 1. Eight patient types based on responses to questions 1, 3 and 4. Fig. 1. There were 301 patients who responded to all three of the questions. Five of these combinations represented at least 15 individuals in the sample and will be the focus of the analysis below. There were 96 patients who answered “yes” to owning a computer, tablet/smartphone, and having used an ABM. Another 82 patients owned a computer but not a tablet/smartphone, and had used an ABM. 17 patients responded that they did not own a computer but did own a tablet/smartphone, and had used an ABM. 49 patients did not own a computer or use a tablet/smartphone but had used an ABM. Finally 33 participants responded “no” to all three of the questions (not owning a computer and tablet/smartphone, and never using an ABM). The five most frequently occurring technology use combinations were interpreted as types: • Type 1: Computer and tablet/smartphone users with ABM experience (N=96) • Type 2: Computer users with ABM experience but no tablet/smartphone experience (N=82) • Type 3: Tablet/smartphone users with ABM experience but not computer experience (N=17) • Type 4: ABM users with no computer or tablet/smartphone experience (N=49) • Type 5: No computer, tablet/smartphone or ABM experience (N=33) Patient types 1 through 5 captured 92.0% (277/301) of the sample and were the focus of the analyses.
Fig. 1. Eight patient types based on responses to questions 1, 3 and 4.
3.4. System usability scale Types 1, 2, and 3 patients generally had high SUS scores as shown in Fig. 2, which shows boxplots of SUS scores across the different patient types. About 1/3 of the Type 1 and 2 patients had SUS scores above 80. As technologically savvy users, they generally found the tablet-based applications to be useful. In contrast, patient types 4 and 5 had more SUS scores below 80.Patient types 1 and 3 had the highest median SUS total score of 87.5, with
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type 2 median scores being slightly lower, and type 4 and 5 median scores being considerably lower. A KruskalWallis H test showed that there was a statistically significant difference in SUS scores between the different patient types, χ2(4) = 39.722, p = .000, with a mean rank SUS total score of 145.42 for type 1, 124.79 for type 2, 144.59 for type 3, 67.80 for type 4 and 103.20 for type 5.
Fig. 2. Boxplots comparing SUS scores across all five patient types.
Fig. 3. Boxplots comparing age of patients across all five patient types.
Patient type 1 had the youngest median age (72 years) compared to patient type 5, which had the oldest median age (81 years) as shown in Fig. 3. Since type 4 and 5 people did not own a computer or tablet/smartphone they would likely have found the tablet-based software harder to use, which would account for their low SUS scores. Total ADL scores across the patient types are shown in 错误!未找到引用源。. ADL scores were highest for patient types 1 and 2. These two patient types have experience with ABMs and own or have used a computer. A Kruskal-Wallis H test showed that there was a statistically significant difference in ADL scores between the different patient types, χ2(4) = 36.191, p = .000, with a mean rank ADL score of 160.76 for type 1, 153.59 for type 2, 125.91 for type 3, 98.95 for type 4 and 98.26 for type 5. IADL scores were highest for patient types 1, and 2 as shown in Fig. 4 and Fig. 5. Comparing Fig. 4 and Fig. 5, it can be seen that the pattern of results for the ADL and IADL measures are generally similar except that the type 5 patients do relatively worse on the IADL, in comparison to the other patient types. Frailty scores will generally be inversely related to ADL and IADL scores and this was also observed in the present study. A Kruskal-Wallis H test showed that there was a statistically significant difference in IADL scores between the different patient types, χ2(4) = 34.653, p = .000, with a mean rank IADL score of 160.16 for type 1, 155.50 for type 2, 121.44 for type 3, 106.82 for type 4 and 90.59 for type 5.
Fig. 4. Boxplots comparing ADL total scores of patients across all five patient types.
Fig. 5. Boxplot comparing IADL total scores of patients across all five patient types.
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As can be seen in Figures 6 through 9, regardless of who is doing the rating (physician, RP, caregiver, self) the frailty scores as assessed by the CFS tend to rise across the five patient types. A potentially interesting point of disagreement is that physicians and RPs rated the frailty of type 5 patients as higher than did the patients and their caregivers. One possible explanation of this result is that physicians and RPs may have overestimated frailty in some cases where the patient was experiencing usability issues with the tablet-based software. Kruskal-Wallis H tests were carried out to determine if there were significant differences in CFS scores across the different patient types as assessed by different raters. 1. There was a statistically significant difference in CFS scores assessed by MDs between the different patient types, χ2(4) = 31.216, p = .000, with a mean rank CFS score of 105.92 for type 1, 117.27 for type 2, 147.57 for type 3, 167.18 for type 4 and 162.16 for type 5. 2. There was a statistically significant difference in CFS scores assessed by RPs between the different patient types, χ2(4) = 39.392, p = .000, with a mean rank CFS score of 106.33 for type 1, 116.85 for type 2, 154.41 for type 3, 169.65 for type 4 and 177.33 for type 5. 3. There was no significant difference in CFS scores assessed by caregivers between the different patient types, χ2(4) = 2.869, p = .580. 4. There was a statistically significant difference in CFS scores self-assessed by patients between the different patient types, χ2(4) = 29.301, p = .000, with a mean rank CFS score of 111.31 for type 1, 112.60 for type 2, 146.82 for type 3, 171.21 for type 4 and 154.40 for type 5. Type 1 through 3 patients were generally least frail, as assessed by the CFS, exhibiting a low median score, as shown in Fig. 6, Fig. 7, Fig. 8, and Fig. 9.
Fig. 6. CFS scores assessed by physicians. The median CFS score for patient types 1 through 3was 3. For patient types 4 and 5, it was 4.
Fig. 7. CFS scores assessed by RPs. The median CFS score for patient types 1 and 2 was 3. For patient types 3, 4 and 5, the median CFS score was 4.
Fig. 8. CFS scores assessed by caregivers. The median CFS score for patient types 1, 2, 4 and 5 was 3. For patient type 3, the median CFS score is 4.
Fig. 9. CFS scores assessed by patients. The median CFS score for patient types 1, 2, 3, and 5 was 3. For patient type 4, the median CFS score was 4.
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3.5. Correlations of measures within the patient types A two-tailed Pearson’s r correlation analysis was carried out to compare the relationships between performance on the CFS, ADL, IADL, SUS as shown in Table 2. This analysis included all of the patients in the sample. There were significant correlations observed between CFS scores by different raters (e.g., physicians, RPs, caregivers and patients). The ADL and IADL scores also exhibited strong relationships with each other. Performance on the SUS was correlated with scores on the ADL, IADL, and CFS ratings by MDs, RPs, and patients. Age was strongly related with all CFS ratings, and scores on the ADL and IADL. Sex was only related to IADL performance, where males had a higher IADL score than females (Fig. 10).
Fig. 10. IADL Performance Based On Sex. Table 2. Correlation Analysis Carried Out Using Pearson’s R (Two-Tailed) For Patient Type 1. * p < .05, ** p < .01.
CFS Caregivers CFS Physicians CFS Patients CFS RPs ADL IADL SUS Age
r p N r p N r p N r p N r p N r p N r p N r p N
CFS Physicians CFS Patients CFS RPs ADL IADL SUS .766** .661** .783** -.343* -.588** .000 .000 .000 .026 .000 41 39 39 42 42 1 .581** .695** -.289** -.409** .000 .000 .000 .000 270 277 288 287 1 .745** -.282** -.385** .000 .000 .000 281 286 286 1 -.267** -.412** .000 .000 292 292 1 .812** .000 308 1
Sex -.304 .072 36 -.299** .000 253 -.204** .001 258 -.353** .000 258 .134* .029 268 .177** .004 268 1
Age .404* .018 34 .260** .000 187 .190** .010 183 .348** .000 189 -.162* .022 200 -.260** .000 199 -.241** .001 176 1
.221 .196 36 .049 .505 191 .024 .746 187 -.044 .550 191 -.121 .087 203 -.147* .036 202 .156* .036 180 .023 .733 215
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4. Discussion We created five patient types based on the grouping of responses from the survey questions on computer usage, tablet/smartphone usage, and ABM experience. Using these patient types, we then carried out statistical analyses to examine trends across and within these groups. Our sample exhibited strong correlations between their use of technology and their functional ability (assessed using the ADL) as well as with reported technology usability (assessed using the SUS). Gender-specific relationships were only observed with performance on the IADL. There were also differences between the five technology use types that we identified in terms of their ADL, IADL, SUS, and CFS scores. The ability to use technology seems to be a key predictor of frailty. For people who frequently use technology such as computers, tablets/smartphones, and ABMs, the frequency of the technology use is related to the ability to live independently, with more technology use indicating less frailty and greater functional ability. Technology use may provide a virtuous cycle, where frequent computer/tablet/smartphone use offers a means to communicate with friends and family, thereby promoting social interaction as well as providing opportunities for the use of physical and cognitive abilities that might otherwise degrade with disuse. More frequent usage of technology may indicate that adults are more cognitively and physically active27. Similar relationships between technology use and functional status in aging adults has been observed previously, with technology users having higher cognitive and physical statuses compared to a control group28. In addition, as computer technology is more common than tablets and smartphone devices, it is possible that using one of these “newer” devices suggests a higher level of computer/technology-efficacy27. This work demonstrates the ability to use technology questions to categorize patients into different groups. It illustrates the potential to use relatively few screening questions to assist in predicting a patient’s frailty status without the need to carry out a full technology profile questionnaire. In addition, questions on computers, tablets/smartphones, and ABM experience can potentially be answered by a patient’s caregiver, which can assist healthcare staff in assessing the patient’s health status. This study used only a few technology profiling questions in the context of a much larger study that focused on frailty of emergency patients. Future research should examine if more detailed technology profiling can provide more sensitive proxy assessments of frailty. 5. Conclusion This work examined the relationship between a patient’s self-reported technology use and their frailty status. Three questions concerning computer, tablet/smartphone, and ABM usage, were used to separate users into five different subtypes. Using these groupings, we examined how differences in technology use predicted differences in frailty assessed using the CFS, and functional status assessed using the ADL and IADL. This work demonstrates the potential to use a relatively small set of questions on technology use as a preliminary screening tool for estimating functional and frailty status in the elderly. Acknowledgements MCT is supported by a Clinician Scientist Award from the Department of Family & Community Medicine, University of Toronto. TT and MC are supported by a grant from the AGE-WELL National Center of Excellence (WP 6.1). This research was funded by a Canadian Frailty Network Grant (application number FRA 2015-B-09). References 1. Loyd, B. H. & Gressard, C. Reliability and Factorial Validity of Computer Attitude Scales. Educ. Psychol. Meas. 44, 501–505 (1984). 2. Nickell, G. S. & Pinto, J. N. The computer attitude scale. Comput. Human Behav. 2, 301–306 (1986). 3. Popovich, P. M., Hyde, K. R., Zakrajsek, T. & Blumer, C. The Development of the Attitudes toward Computer Usage Scale. Educ. Psychol. Meas. 47, 261–269 (1987). 4. Spence, I., DeYoung, C. G. & Feng, J. The technology profile inventory: Construction, validation, and application. Comput. Human Behav. 25, 458–465 (2009).
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