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Information overload in patients with atrial fibrillation: Can the cancer information overload (CIO) scale be used?
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Contents lists available at ScienceDirect
Patient Education and Counseling journal homepage: www.elsevier.com/locate/pateducou
Information overload in patients with atrial fibrillation: Can the cancer information overload (CIO) scale be used? Kehinde Obamiroa,b , Kenneth Leeb,c,* a b c
Centre for Rural Health, School of Health Sciences, College of Health and Medicine, University of Tasmania, Tasmania, Australia Division of Pharmacy, School of Medicine, College of Health and Medicine, University of Tasmania, Tasmania, Australia Division of Pharmacy, School of Allied Health, Faculty of Health and Medical Sciences, University of Western Australia, Western Australia, Australia
A R T I C L E I N F O
A B S T R A C T
Article history: Received 28 May 2018 Received in revised form 5 September 2018 Accepted 3 October 2018
Objective: Information overload can negatively impact positive health behaviors such as cancer screening. The 8-item Cancer Information Overload (CIO) scale appears to be the only validated measure of healthrelated information overload. The present study assesses the validity of the CIO scale when modified for use in patients with atrial fibrillation (AF) residing in Australia. Methods: We conducted a secondary analysis of data from a study of adult Australian patients with AF (N = 386) in which a modified version of the CIO scale was used. In the present study, we examined the construct (convergent and divergent) validity and performed an exploratory factor analysis for the modified scale. Results: All items on the modified-CIO scale appear to load onto a single factor. As predicted, higher education levels (rs=-.24, p < .001) and higher oral anticoagulant knowledge (rs=-.17, p = .001) were significantly associated with lower modified-CIO scores; no other demographic characteristics were significantly associated with CIO scores. Conclusion: When adapted to the AF context, the modified-CIO scale appears to be a valid measure of information overload. Practice Implications: A valid scale is required to measure information overload accurately. Knowledge of the interplay between information overload and various health behaviors help focus future efforts to support patient empowerment. © 2018 Elsevier B.V. All rights reserved.
Keywords: Health information overload Atrial fibrillation Measurement CIO scale Health behavior Patient knowledge
1. Introduction Globally, the prevalence of chronic diseases continues to increase [1]. Chronic diseases often require daily self-management [2]. As such, patients need to be equipped to contribute to the management of their conditions. There is growing literature to suggest that many patients have inadequate levels of health literacy to be able to self-manage their health conditions [3–6]. Additionally, much of the health information available to patients are written at a level higher than the average consumer’s level of educational attainment [7]. A review in 2014 identified various approaches to supporting patients in their self-management, information-seeking journeys [8]. Such approaches include
* Corresponding author at: Division of Pharmacy, School of Allied Health, Faculty of Health and Medical Sciences, University of Western Australia, M315, 35 Stirling Highway, Crawley WA 6009, Australia. E-mail address: [email protected] (K. Lee).
educational interventions for patients to improve their information literacy, and interventions to support health care providers in educating patients about their health conditions and how to navigate the healthcare system. While assisting patients to understand and better utilize health information is important, little research appears to investigate the issue of information overload in a health context. Information overload can be defined as a situation whereby the volume of information supplied in a given time frame exceeds an individual’s capacity to process information [9]. In such situations, an individual may fail to promptly and carefully pay attention to information, incorrectly process information, or avoid information [10]. In a chronic disease self-management context, failure to use health information appropriately could have a negative impact on a patient’s health outcomes [11]. Furthermore, a multisite study conducted on university students and adults in South Korea and Israel found that online health information overload has potentially negative implications on people’s psychological health [12]. Given the abundance of health information available to patients,
https://doi.org/10.1016/j.pec.2018.10.005 0738-3991/© 2018 Elsevier B.V. All rights reserved.
Please cite this article in press as: K. Obamiro, K. Lee, Information overload in patients with atrial fibrillation: Can the cancer information overload (CIO) scale be used?, Patient Educ Couns (2018), https://doi.org/10.1016/j.pec.2018.10.005
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particularly from online sources, there appears to be a need to investigate ways to address the issue of health information overload. In order to address the issue of health information overload, we need to have accurate ways to measure health information overload. While various measures exist to measure cognitive load in education settings [13–15], there appears to be only one measure of information overload in a healthcare context: the cancer information overload (CIO) scale [16]. The 8-item CIO scale was developed and validated by Jensen et al. to evaluate the associations between cancer information overload and cancerrelated behaviors such as cancer screening [16]; the study found that a higher score on the CIO scale (that is, patients who reported feeling overloaded with cancer information) negatively predicted the likelihood of colon cancer screening within an 18-month period. A further study utilizing the CIO scale suggests that patients with lower educational levels and motivation to seek and utilize information were more likely to experience cancer information overload [17]. As there are no other scales available to measure information overload in other health conditions, little is known about health information overload in conditions other than cancer. Although cancer is a large group of conditions with sometimes high mortality and often high morbidity rates [18], a similar argument could be made with a variety of non-cancerous chronic diseases. For example, atrial fibrillation (AF) is the most common form of heart arrhythmia [19]; People living with AF have a significantly increased stroke risk [20], and a two-fold increase in mortality [21]. Like other chronic diseases, early detection and adherence to management strategies can improve the health burden of AF [20]. Thus, there appears to be a need to measure information overload in other chronic diseases such as AF, so as to assist in encouraging positive preventive behaviors such as screening. Given the wording of items within the 8-item CIO scale, we believe that, with slight modifications, the CIO scale can be used to assess information overload in other health conditions. In 2015, Costa et al. suggested that the CIO scale could be reduced to five items, with three of the five items benefitting from being rephrased [22]. However, Costa et al.’s claims have yet to be evaluated and Costa et al. did not provide explicit recommendations for rephrasing the three (of the five) items [22]. Nevertheless, we believe it is still worthwhile to evaluate the validity of the fiveitem version suggested by Costa et al. [22], even without rephrasing the items. This is because a shorter scale could mitigate response burden, thereby increasing response quality [23]. The present study therefore seeks to assess the validity of the 8item CIO scale when modified and administered to patients with
AF residing in Australia. We also want to assess the validity of the five-item version suggested by Costa et al. [22], hereafter referred to in this study as the ‘reduced 5-item CIO scale’, among the same sample of patients with AF. 2. Methods This study was a secondary analysis of data from an online survey examining the oral anticoagulant medication knowledge of Australian residents with AF (N = 386) [24]. Participants in the online survey were aged 18 years and over; in this survey study, a modified version of the 8-item CIO scale was included to assess health information overload as one of the potential predictors of oral anticoagulant medication knowledge. The original 8-item CIO scale contains four points per item (from strongly disagree with a value of 1, to strongly agree with a value of 4) [16]. Scoring of the original CIO scale is performed by summing the value of each item (scores range from 8 to 32), with higher scores indicating a greater degree of health information overload) [16]. The original CIO scale was validated to predict colonoscopy insurance claim among healthcare and manufacturing employees [16]. In the modified CIO scale used in the present study and the online survey [24], the four points per item were retained as well as the original method of scoring, but the word ‘cancer’ from all items within the original CIO scale was substituted with ‘atrial fibrillation’ to suit patients with AF (see Table 1). The online survey requested respondents to provide the following demographic information: age, gender, postal code, annual income, highest educational level and employment status [24]. Knowledge of oral anticoagulant medications was assessed via the validated Oral Anticoagulant Knowledge Tool [25]. The survey was active online between February and May 2017. Data analyses were conducted using SPSS version 23 (IBM, Armonk, New York, US). Item responses were described using means and standard deviations. From our modified 8-item CIO scale, further analyses were repeated on the five items suggested by Costa et al. [22] (items 1, 3, 5, 6, 8 from Table 1), to assess the validity of the ‘reduced 5-item CIO scale.’ Exploratory factor analysis with oblique rotation (direct oblimin method) was conducted for both scales to determine their respective factor structure. Sampling adequacy was determined using Kaiser-Meyer-Olkin (KMO) test, and the factorability of the correlation matrix was tested using the Bartlett's test of sphericity [26,27]. Factor extraction was conducted using principal axis factoring and the resulting model was evaluated using the scree test and parallel analysis (Monte Carlo simulation) [26,27].
Table 1 Univariate summary statistics for the modified 8-item CIO scale. Item
1 There are so many different recommendations about managing atrial fibrillation, it’s hard to know which ones to follow 2 There is not enough time to do all of the things recommended to manage atrial fibrillation 3 It has gotten to the point where I don’t even care to hear new information about atrial fibrillation 4 No one could actually do all of the atrial fibrillation management recommendations that are given 5 Information about atrial fibrillation all starts to sound the same after a while 6 I forget most of the information about atrial fibrillation right after I hear it 7 Most things I hear or read about atrial fibrillation seem pretty far-fetched 8 I feel overloaded by the amount of information about atrial fibrillation I am supposed to know
Response option frequencies (%)
M (SD)
Skewness Kurtosis
24 (6.2)
2.41 (0.76)
.02
70 (18.1)
7 (1.8)
2.03 (0.67)
.34*
185 (47.9)
57 (14.8)
6 (1.6)
1.82 (0.73)
.53*
.20
67 (17.4)
213 (55.2)
94 (24.4)
12 (3.1)
2.13 (0.73)
.29*
.07
49 (12.7)
160 (41.5)
163 (42.2)
14 (3.6)
2.37 (0.75)
70 (18.1) 82 (21.2) 71 (18.4)
218 (56.5) 256 (66.3) 234 (60.6)
90 (23.3) 45 (11.7) 76 (19.7)
8 (2.1) 3 (0.8) 5 (1.3)
2.09 (0.70) 1.92 (0.60) 2.04 (0.66)
Strongly Disagree
Disagree
Agree
41 (10.6)
171 (44.3)
150 (38.9)
74 (19.2)
235 (60.9)
138 (35.8)
Strongly Agree
.20 .24 .25* .24
.37 .31
.51* .09 .67* .10
* p<.05.
Please cite this article in press as: K. Obamiro, K. Lee, Information overload in patients with atrial fibrillation: Can the cancer information overload (CIO) scale be used?, Patient Educ Couns (2018), https://doi.org/10.1016/j.pec.2018.10.005
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Internal consistency was determined using the Cronbach α score [28,29]. Convergent and divergent validity (subsets of construct validity) were assessed by performing Spearman’s rank-order correlation between the modified CIO scale (and reduced 5-item scale) and respondents’ demographic characteristics. As lower education levels have been found to be significantly associated with greater cancer information overload [17], we predict that education levels and oral anticoagulant knowledge would be negatively correlated with our modified CIO scale scores. While there is limited studies examining the relationship between other demographic characteristics and health information overload, we predict that there will not be any significant correlations between our other demographic characteristics (age, gender, post code, annual income, and employment status). The Tasmanian Social Science Human Research Ethics Committee (reference number H0015972) approved the study. Consent was implied by participation in the survey. 3. Result No data were missing. Four items were significantly skewed, and two items were significantly kurtotic. Participants had the lowest means score of 1.82 on item 3, and reported the highest mean score on item 1 (Table 1). Additionally, we observed a potential ceiling effect for each item as very few respondents selected the ‘strongly agree’ option (Table 1). For the 8-item scale, a value of 0.87 was obtained for the KMO test and Bartlett’s test of sphericity was significant (χ2 = 1188.68, df = 28, p < .001), suggesting that structure detection is suitable. Similarly, for the reduced 5-item scale, a value of 0.81 was obtained for the KMO test and Bartlett’s test of sphericity was significant (χ2 = 473.83, df = 10, p < .001). The scree test and parallel analysis (Monte Carlo simulation) for both the eight and 5-item scales suggest a one-factor solution. In both scales, the total percentage of variance explained by the extracted factor was greater than 50%. The factor loadings for individual items are shown in Table 2. Bivariate correlations between various constructs and both modified CIO scales were conducted to evaluate the convergent/ divergent validity. As we predicted, apart from the highest level of education completed (rs=-.24, p<.001 for the 8-item scale; rs=-.21, p<.001 for the 5-item scale), no significant association was observed between the modified eight and reduced 5-item CIO scales and any other demographic characteristic. Additionally, as predicted, a higher oral anticoagulant knowledge score was significantly associated with a lower score on both the modified eight (rs=-.17, p = .001) and 5-item (rs=-.21, p<.001) CIO scales. The Cronbach α scores for the eight and 5-item scales were 0.86 and 0.76, respectively.
Table 2 Factor loadings for items in the modified 8-item and 5-item CIO scale.
Item 1 Item 2 Item 3 Item 4 Item 5 Item 6 Item 7 Item 8 % variance explained NA, not applicable.
Modified 8-item scale
Modified, reduced 5-item scale
.45 .58 .63 .74 .75 .66 .74 .69 50.68%
.37 NA .64 NA .75 .68 NA .71 52.31%
3
4. Discussion and conclusion 4.1. Discussion 4.1.1. Principal findings The present study was a secondary analysis of data from a previous study [24] that assessed oral anticoagulant medication knowledge in adult Australians with AF. We evaluated the construct validity of both the modified 8-item CIO scale and a modified version of the 5-items proposed by Costa et al. [22]. Both the eight and reduced 5-item scales appear to be valid and reliable scales for measuring health information overload among adult Australians living with AF. We discuss each scale in further detail in the following paragraphs. For the modified 8-item CIO scale, the scree test and parallel analysis suggested there was a single factor. Given the accuracy of parallel analysis in identifying the correct number of factors [30,31], we conclude that the modified 8-item CIO scale appeared to measure a single construct among our sample of adult Australians with AF. Despite the 8-item CIO scale being developed and validated to measure United States residents’ perceived cancer information overload [16], it appears that the 8-item CIO scale used in Jensen et al’s study [16] and the modified version of the 8-item CIO scale used our present study measure a common, single construct: health information overload. As the 8-item CIO scale by Jensen et al. [16] is the only validated measure of health information overload available, few correlates of health information overload have been identified using a validated tool. One of the key significant correlates of cancer information overload identified by Chae et al., using Jensen’s unmodified 8-item CIO scale, is education level [17]: people with a higher level of education are less likely to perceive experiencing cancer information overload, as measured by the 8-item CIO scale. Similarly, in our sample of adult Australians with AF, we found a significant negative correlation between education level and the modified 8-item CIO scale (see Section 3.3). In a study by Kim et al. on predictors of cancer information overload whereby the first item of the 8-item CIO scale was used as the sole measure of cancer information overload, a higher education level significantly predicted a lower likelihood of perceived cancer information overload [32]. While cancer knowledge was not measured in Chae et al.’s study [17], it was measured in Kim et al.’s study [32]; poorer cancer knowledge was significantly associated with greater perceived cancer information overload. Similarly, in the present study, there was a significant negative association between oral anticoagulant knowledge and the modified 8-item CIO scale scores (see Section 3.3). The significant associations between education and oral anticoagulant knowledge with AFrelated information overload identified in our present study, albeit small-to-medium in effect size, appear to align with similar factors (education and cancer knowledge) in studies about cancer information overload. Collectively, within the context of extant literature, the modified 8-item CIO scale appears to be a valid measure of AF-related information overload. For the 5-item CIO scale, the scree test and parallel analysis consistently demonstrated a one-factor structure. Similar with the modified 8-item CIO scale, education level and oral anticoagulant knowledge were significantly and negatively associated with the CIO scale score. Based on our results, the reduced 5-item scale, as suggested by Costa et al. [22], could be a comparable measure of AF-related information overload. Some potential advantages of using a shorter scale could be time efficiency in administering the scale, and time saved by the participants. A shorter scale could also be preferable to improve response rates and/or mitigate respondent fatigue [33]. In a review and meta-analysis by Rolstad et al. [34], questionnaire lengths were significantly and negatively
Please cite this article in press as: K. Obamiro, K. Lee, Information overload in patients with atrial fibrillation: Can the cancer information overload (CIO) scale be used?, Patient Educ Couns (2018), https://doi.org/10.1016/j.pec.2018.10.005
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associated with response rates; the authors, however, noted the difficulty in separating the impact of the content from the length of the questionnaires. While further studies are recommended, based on results from our present study, the reduced 5-item modified CIO scale appears to be a valid scale in the context of adult Australians with AF. 4.1.2. Limitations It is important to recognize that the presented study is a secondary analysis of data from a published study [24]. As such, we were not able to assess predictive validity. Our ability to assess convergent and divergent validity were also limited to the variables collected in the aforementioned published study [24]. However, it is important to acknowledge that, given the infancy of the field of health information overload, particularly AF-related information overload, there is a paucity of empirical evidence identifying constructs that are related and dissimilar to AF-related information overload; thus, convergent and divergent validity would be a challenge to definitively assess regardless of study design. In the present study, we have attempted to assess convergent and divergent validity using constructs identified by cancer information overload studies, within the limits of the variables collected in the published study by Obamiro et al. [24]. Similarly, there is a paucity of empirical evidence to definitively assess predictive validity in the context of AF-related information overload. As the present study is a secondary analysis of data, we were unable to establish test-retest reliability. However, we were able to evaluate internal consistency: both 8-item and the reduced 5-item CIO scales produced acceptable Cronbach’s α values (>0.7 and <0.9) [29], particularly given both scales contain few items [35]. While we acknowledge the limitations of the present study, we believe the present study offers an initial insight into the viability of using the 8-item and reduced 5-item CIO scales in a cohort of participants with vast differences from the original target population of Jensen et al.’s 8-item CIO scale, both geographically and in terms of the diagnosed medical condition. We ask that the findings from the present study be considered alongside its limitations, and recommend further studies to build on our findings. 4.1.3. Further research We believe that both the eight and reduced 5-item modified CIO scales could be readily adapted to other chronic diseases whereby the word ‘cancer’ and words relating to cancer could be replaced with the disease group of interest. This is because only minor modifications to the wording of the original CIO scale was needed to adapt the scale from a cancer context to an AF context. Given the abundance of available health information, particularly in an online context, patients, regardless of their medical condition, are likely to be faced with insurmountable quantities of information. Having a validated scale that can be applied across various chronic diseases can allow researchers to identify chronic diseases where information overload is particularly a problem. We believe both CIO scales could be valid for a variety of diseases, but recommend that chronic diseases such as diabetes and various heart diseases be key conditions to explore in future studies in terms of health information overload, as these conditions require complex management in terms multidisciplinary care requirements, and non-pharmacological and therapeutic management. As the field of health information overload is in its infancy, we recommend that further studies explore other correlates of information overload to allow for a better understanding of the demographic characteristics of information overload.
Additionally, we recommend further studies be conducted on a similar population of adult Australians with AF to provide further support for the validity and reliability of both the eight and reduced 5-item modified CIO scales. Specifically, we recommend that constructs such as information-seeking behavior and information avoidance be measured when assessing AF-related information overload via the modified CIO scales, so as to establish predictive validity; we recommend measuring these constructs because, as mentioned in Section 1 (Introduction), there is evidence to suggest that people may avoid information when in a state of information overload [10]. Based on the sample used in the present study, there appears to be a potential ceiling effect with the modified CIO scales because very few respondents selected the ‘strongly agree’ options for each item. As frequencies for each response option within each item of the CIO scale have not been reported in previous studies, it is unclear whether a potential ceiling effect has been observed in other studies. We therefore recommend further studies utilising the CIO scale (or a modified version of the scale) to report frequencies for each response option, and to investigate whether a ceiling effect truly exists. While we performed a principal axis factor analysis in the present study to explore the number of latent factors in an AF context, we recommend the use of a confirmatory factor analysis in a subsequent study to confirm the one-factor structure identified in the present study, using a separate sample [36]. We also recommend that future studies include a re-test so as to establish test-retest reliability of both the eight and reduced 5-item CIO scales. 4.2. Conclusion Both the eight and reduced 5-item modified CIO scales appear to be valid and internally consistent measures of AF-related information overload. The reduced 5-item modified CIO scale may be preferable for studies where there is limited time to collect responses from participants, or in attempts to mitigate respondent fatigue, and improve response quality. We believe that both eight and reduced 5-item modified CIO scales may be suitably adapted for health information overload in patients with other chronic diseases. 4.3. Practice implications The ability to measure an individual’s level of information overload has the potential to allow for appropriate, tailored, information delivery to patients by health professionals, and inform the design of consumer-facing written health information. Having a scale that can be readily adapted to other chronic diseases allows researchers to 1) identify conditions where there is an overabundance of available health information, and 2) provide direction for future efforts to consolidate available health information. The present study demonstrates that the existing 8-item CIO scale can be readily adapted for use in the AF context. Therefore, the present study provides a first step towards future efforts to reduce the information burden on patients, thereby enabling patients to better self-manage their chronic diseases. Funding sources This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Conflict of interests None.
Please cite this article in press as: K. Obamiro, K. Lee, Information overload in patients with atrial fibrillation: Can the cancer information overload (CIO) scale be used?, Patient Educ Couns (2018), https://doi.org/10.1016/j.pec.2018.10.005
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Acknowledgements The authors would like to acknowledge Professor Jakob Jensen and his colleagues for being the pioneers in the field of health information overload through their development and validation of the first health-related information overload scale. We hope that our attempts to modify the Cancer Information Overload scale encourages other researchers to explore the utility of the scale across other chronic diseases. References [1] World Health Organization, Global Status Report on Noncommunicable Diseases 2014 (Accessed 27 May 2018), http://www.who.int/nmh/ publications/ncd-status-report-2014/en/. [2] K. Lee, K. Hoti, J.D. Hughes, L.M. Emmerton, Consumer use of “Dr Google”: a survey on health information-seeking behaviors and navigational needs, J. Med. Internet Res. 17 (2015) e288, doi:http://dx.doi.org/10.2196/jmir.4345. [3] M.K. Paasche-Orlow, R.M. Parker, J.A. Gazmararian, L.T. Nielsen-Bohlman, R.R. Rudd, The prevalence of limited health literacy, J. Gen. Intern. Med. 20 (2005) 175–184, doi:http://dx.doi.org/10.1111/j.1525-1497.2005.40245.x. [4] K. Sørensen, J.M. Pelikan, F. Röthlin, K. Ganahl, Z. Slonska, G. Doyle, J. Fullam, B. Kondilis, D. Agrafiotis, E. Uiters, M. Falcon, M. Mensing, K. Tchamov, S. van den Broucke, Health literacy in Europe: comparative results of the European health literacy survey (HLS-EU), Eur. J. Public Health 25 (2015) 1053–1058, doi:http:// dx.doi.org/10.1093/eurpub/ckv043. [5] M.I. Cajita, T.R. Cajita, H.-R. Han, Health literacy and heart failure: a systematic review, J. Cardiovasc. Nurs. 31 (2016) 121–130, doi:http://dx.doi.org/10.1097/ JCN.0000000000000229. [6] D.M. Taylor, S.D.S. Fraser, J.A. Bradley, C. Bradley, H. Draper, W. Metcalfe, G.C. Oniscu, C.R.V. Tomson, R. Ravanan, P.J. Roderick, A systematic review of the prevalence and associations of limited health literacy in CKD, Clin. J. Am. Soc. Nephrol. 12 (2017) 1070–1084, doi:http://dx.doi.org/10.2215/CJN.12921216. [7] L.M. Stossel, N. Segar, P. Gliatto, R. Fallar, R. Karani, Readability of patient education materials available at the point of care, J. Gen. Intern. Med. 27 (2012) 1165–1170, doi:http://dx.doi.org/10.1007/s11606-012-2046-0. [8] K. Lee, K. Hoti, J.D. Hughes, L.M. Emmerton, Interventions to assist health consumers to find reliable online health information: a comprehensive review, PLoS One 9 (2014)e94186, doi:http://dx.doi.org/10.1371/journal. pone.0094186. [9] M.J. Eppler, J. Mengis, The concept of information overload: a review of literature from Organization Science, Accounting, Marketing, MIS, and related disciplines, Inform. Soc. 20 (2004) 325–344, doi:http://dx.doi.org/10.1080/ 01972240490507974. [10] J.G. Miller, Information input overload and psychopathology, Am. J. Psychiatry 116 (1960) 695–704, doi:http://dx.doi.org/10.1176/ajp.116.8.695. [11] R. El Sherif, P. Pluye, C. Thoër, C. Rodriguez, Reducing negative outcomes of online consumer health information: qualitative interpretive study with clinicians, librarians, and consumers, J. Med. Internet Res. 20 (2018) e169, doi: http://dx.doi.org/10.2196/jmir.9326. [12] B. Swar, T. Hameed, I. Reychav, Information overload, psychological ill-being, and behavioral intention to continue online health information search, Comput. Human Behav. 70 (2017) 416–425, doi:http://dx.doi.org/10.1016/j. chb.2016.12.068. [13] F.G.W.C. Paas, J.J.G. van Merriënboer, J.J. Adam, Measurement of cognitive load in instructional research, Percept. Mot. Skills 79 (1994) 419–430, doi:http://dx. doi.org/10.2466/pms.1994.79.1.419. [14] J. Leppink, F. Paas, C.P.M. van der Vleuten, T. van Gog, J.J.G. van Merriënboer, Development of an instrument for measuring different types of cognitive load, Behav. Res. Methods 45 (2013) 1058–1072, doi:http://dx.doi.org/10.3758/ s13428-013-0334-1.
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[15] A. Schmeck, M. Opfermann, T. van Gog, F. Paas, D. Leutner, Measuring cognitive load with subjective rating scales during problem solving: differences between immediate and delayed ratings, Instr. Sci. 43 (2015) 93–114, doi: http://dx.doi.org/10.1007/s11251-014-9328-3. [16] J.D. Jensen, N. Carcioppolo, A.J. King, C.L. Scherr, C.L. Jones, J. Niederdieppe, The cancer information overload (CIO) scale: establishing predictive and discriminant validity, Patient Educ. Couns. 94 (2014) 90–96, doi:http://dx.doi. org/10.1016/j.pec.2013.09.016. [17] J. Chae, C.-J. Lee, J.D. Jensen, Correlates of cancer information overload: Focusing on individual ability and motivation, Health Commun. 31 (2016) 626–634, doi:http://dx.doi.org/10.1080/10410236.2014.986026. [18] Cancer Research UK, Worldwide Cancer Mortality Statistics (accessed 27 May 2018), http://www.cancerresearchuk.org/health-professional/cancerstatistics/worldwide-cancer/mortality. [19] C.R. Wyndham, Atrial fibrillation: the most common arrhythmia, Tex. Heart Inst. J. 27 (2000) 257–267. https://www.ncbi.nlm.nih.gov/pubmed/11093410. [20] N. Lowres, L. Neubeck, J. Redfern, S.B. Freedman, Screening to identify unknown atrial fibrillation. A systematic review, Thromb. Haemost. 110 (2013) 213–222, doi:http://dx.doi.org/10.1160/TH13-02-0165. [21] E.J. Benjamin, P.A. Wolf, R.B. D’Agostino, H. Silbershatz, W.B. Kannel, D. Levy, Impact of atrial fibrillation on the risk of death: the Framingham Heart Study, Circulation 98 (1998) 946–952, doi:http://dx.doi.org/10.1161/01.CIR.98.10.946. [22] D.S.J. Costa, A.B. Smith, B.T. Lim, J.E. Fardell, Simplifying the assessment of cancer information overload: a comment on Jensen et al. (2014), Patient Educ. Couns. 98 (2015) 1450, doi:http://dx.doi.org/10.1016/J.PEC.2015.04.020. [23] M. Galesic, M. Bosnjak, Effects of questionnaire length on participation and indicators of response quality in a web survey, Public Opin. Q. 73 (2009) 349– 360, doi:http://dx.doi.org/10.1093/poq/nfp031. [24] K.O. Obamiro, L. Chalmers, K. Lee, B.J. Bereznicki, L.R.E. Bereznicki, Anticoagulation knowledge in patients with atrial fibrillation: an Australian survey, Int. J. Clin. Pract. [Internet] 72 (2018)e13072, doi:http://dx.doi.org/ 10.1111/ijcp.13072. [25] K.O. Obamiro, L. Chalmers, L.R.E. Bereznicki, Development and validation of an oral anticoagulation knowledge tool (AKT), PLoS One 11 (2016)e0158071, doi: http://dx.doi.org/10.1371/journal.pone.0158071. [26] I. Izquierdo, J. Olea, F. Abad, Exploratory factor analysis in validation studies: uses and recommendations, Psicothema. 26 (2014) 395–400, doi:http://dx. doi.org/10.7334/psicothema2013.349. [27] B. Williams, A. Onsman, T. Brown, Exploratory factor analysis: a five-step guide for novices, J. Emerg. Prim. Health. Care. 19 (1996) 42–50, doi:http://dx.doi. org/10.1080/09585190701763982. [28] M. Tavakol, R. Dennick, Making sense of Cronbach’s alpha, J. Int. Assoc. Med. Sci. Educ. 2 (2011) 53–55, doi:http://dx.doi.org/10.5116/ijme.4dfb.8dfd. [29] J.M. Bland, D.G. Altman, Cronbach’s alpha, Br. Med. J. 314 (1997) 572, doi:http:// dx.doi.org/10.1136/bmj.314.7080.572. [30] R.K. Henson, J.K. Roberts, Use of Exploratory Factor Analysis in published research: common errors and some comment on improved practice, Educ. Psychol. Meas. 66 (2006) 393–416, doi:http://dx.doi.org/10.1177/ 0013164405282485. [31] A. Field, Discovering Statistics Using SPSS, fourth ed., SAGE, London, 2013. [32] K. Kim, M.L. Lustria, D. Burke, Predictors of cancer information overload: findings from a national survey, Inform. Res. 12 (2007). http://www. informationr.net/ir/12-4/paper326.html. [33] P. Ben-Nun, Respondent Fatigue, in: P.J. Lavrakas (Ed.), Encyclopedia of Survey Research Methods, SAGE, Thousand Oaks, 2011, pp. 743, doi:http://dx.doi.org/ 10.4135/9781412963947.n480. [34] S. Rolstad, J. Adler, A. Rydén, Response burden and questionnaire length: is shorter better? A review and meta-analysis, Value Health 14 (2011) 1101–1108, doi:http://dx.doi.org/10.1016/j.jval.2011.06.003. [35] D.L. Streiner, Starting at the beginning: an introduction to coefficient alpha and internal consistency, J. Pers. Assess. 80 (2003) 99–103, doi:http://dx.doi.org/ 10.1207/S15327752JPA8001_18. [36] R.L. Worthington, T.A. Whittaker, Scale development research: a content analysis and recommendations for best practices, Couns. Psychol. 34 (2006) 806–838, doi:http://dx.doi.org/10.1177/0011000006288127.
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Contents lists available at ScienceDirect
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Information overload in patients with atrial fibrillation: Can the cancer information overload (CIO) scale be used? Kehinde Obamiroa,b , Kenneth Leeb,c,* a b c
Centre for Rural Health, School of Health Sciences, College of Health and Medicine, University of Tasmania, Tasmania, Australia Division of Pharmacy, School of Medicine, College of Health and Medicine, University of Tasmania, Tasmania, Australia Division of Pharmacy, School of Allied Health, Faculty of Health and Medical Sciences, University of Western Australia, Western Australia, Australia
A R T I C L E I N F O
A B S T R A C T
Article history: Received 28 May 2018 Received in revised form 5 September 2018 Accepted 3 October 2018
Objective: Information overload can negatively impact positive health behaviors such as cancer screening. The 8-item Cancer Information Overload (CIO) scale appears to be the only validated measure of healthrelated information overload. The present study assesses the validity of the CIO scale when modified for use in patients with atrial fibrillation (AF) residing in Australia. Methods: We conducted a secondary analysis of data from a study of adult Australian patients with AF (N = 386) in which a modified version of the CIO scale was used. In the present study, we examined the construct (convergent and divergent) validity and performed an exploratory factor analysis for the modified scale. Results: All items on the modified-CIO scale appear to load onto a single factor. As predicted, higher education levels (rs=-.24, p < .001) and higher oral anticoagulant knowledge (rs=-.17, p = .001) were significantly associated with lower modified-CIO scores; no other demographic characteristics were significantly associated with CIO scores. Conclusion: When adapted to the AF context, the modified-CIO scale appears to be a valid measure of information overload. Practice Implications: A valid scale is required to measure information overload accurately. Knowledge of the interplay between information overload and various health behaviors help focus future efforts to support patient empowerment. © 2018 Elsevier B.V. All rights reserved.
Keywords: Health information overload Atrial fibrillation Measurement CIO scale Health behavior Patient knowledge
1. Introduction Globally, the prevalence of chronic diseases continues to increase [1]. Chronic diseases often require daily self-management [2]. As such, patients need to be equipped to contribute to the management of their conditions. There is growing literature to suggest that many patients have inadequate levels of health literacy to be able to self-manage their health conditions [3–6]. Additionally, much of the health information available to patients are written at a level higher than the average consumer’s level of educational attainment [7]. A review in 2014 identified various approaches to supporting patients in their self-management, information-seeking journeys [8]. Such approaches include
* Corresponding author at: Division of Pharmacy, School of Allied Health, Faculty of Health and Medical Sciences, University of Western Australia, M315, 35 Stirling Highway, Crawley WA 6009, Australia. E-mail address: [email protected] (K. Lee).
educational interventions for patients to improve their information literacy, and interventions to support health care providers in educating patients about their health conditions and how to navigate the healthcare system. While assisting patients to understand and better utilize health information is important, little research appears to investigate the issue of information overload in a health context. Information overload can be defined as a situation whereby the volume of information supplied in a given time frame exceeds an individual’s capacity to process information [9]. In such situations, an individual may fail to promptly and carefully pay attention to information, incorrectly process information, or avoid information [10]. In a chronic disease self-management context, failure to use health information appropriately could have a negative impact on a patient’s health outcomes [11]. Furthermore, a multisite study conducted on university students and adults in South Korea and Israel found that online health information overload has potentially negative implications on people’s psychological health [12]. Given the abundance of health information available to patients,
https://doi.org/10.1016/j.pec.2018.10.005 0738-3991/© 2018 Elsevier B.V. All rights reserved.
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particularly from online sources, there appears to be a need to investigate ways to address the issue of health information overload. In order to address the issue of health information overload, we need to have accurate ways to measure health information overload. While various measures exist to measure cognitive load in education settings [13–15], there appears to be only one measure of information overload in a healthcare context: the cancer information overload (CIO) scale [16]. The 8-item CIO scale was developed and validated by Jensen et al. to evaluate the associations between cancer information overload and cancerrelated behaviors such as cancer screening [16]; the study found that a higher score on the CIO scale (that is, patients who reported feeling overloaded with cancer information) negatively predicted the likelihood of colon cancer screening within an 18-month period. A further study utilizing the CIO scale suggests that patients with lower educational levels and motivation to seek and utilize information were more likely to experience cancer information overload [17]. As there are no other scales available to measure information overload in other health conditions, little is known about health information overload in conditions other than cancer. Although cancer is a large group of conditions with sometimes high mortality and often high morbidity rates [18], a similar argument could be made with a variety of non-cancerous chronic diseases. For example, atrial fibrillation (AF) is the most common form of heart arrhythmia [19]; People living with AF have a significantly increased stroke risk [20], and a two-fold increase in mortality [21]. Like other chronic diseases, early detection and adherence to management strategies can improve the health burden of AF [20]. Thus, there appears to be a need to measure information overload in other chronic diseases such as AF, so as to assist in encouraging positive preventive behaviors such as screening. Given the wording of items within the 8-item CIO scale, we believe that, with slight modifications, the CIO scale can be used to assess information overload in other health conditions. In 2015, Costa et al. suggested that the CIO scale could be reduced to five items, with three of the five items benefitting from being rephrased [22]. However, Costa et al.’s claims have yet to be evaluated and Costa et al. did not provide explicit recommendations for rephrasing the three (of the five) items [22]. Nevertheless, we believe it is still worthwhile to evaluate the validity of the fiveitem version suggested by Costa et al. [22], even without rephrasing the items. This is because a shorter scale could mitigate response burden, thereby increasing response quality [23]. The present study therefore seeks to assess the validity of the 8item CIO scale when modified and administered to patients with
AF residing in Australia. We also want to assess the validity of the five-item version suggested by Costa et al. [22], hereafter referred to in this study as the ‘reduced 5-item CIO scale’, among the same sample of patients with AF. 2. Methods This study was a secondary analysis of data from an online survey examining the oral anticoagulant medication knowledge of Australian residents with AF (N = 386) [24]. Participants in the online survey were aged 18 years and over; in this survey study, a modified version of the 8-item CIO scale was included to assess health information overload as one of the potential predictors of oral anticoagulant medication knowledge. The original 8-item CIO scale contains four points per item (from strongly disagree with a value of 1, to strongly agree with a value of 4) [16]. Scoring of the original CIO scale is performed by summing the value of each item (scores range from 8 to 32), with higher scores indicating a greater degree of health information overload) [16]. The original CIO scale was validated to predict colonoscopy insurance claim among healthcare and manufacturing employees [16]. In the modified CIO scale used in the present study and the online survey [24], the four points per item were retained as well as the original method of scoring, but the word ‘cancer’ from all items within the original CIO scale was substituted with ‘atrial fibrillation’ to suit patients with AF (see Table 1). The online survey requested respondents to provide the following demographic information: age, gender, postal code, annual income, highest educational level and employment status [24]. Knowledge of oral anticoagulant medications was assessed via the validated Oral Anticoagulant Knowledge Tool [25]. The survey was active online between February and May 2017. Data analyses were conducted using SPSS version 23 (IBM, Armonk, New York, US). Item responses were described using means and standard deviations. From our modified 8-item CIO scale, further analyses were repeated on the five items suggested by Costa et al. [22] (items 1, 3, 5, 6, 8 from Table 1), to assess the validity of the ‘reduced 5-item CIO scale.’ Exploratory factor analysis with oblique rotation (direct oblimin method) was conducted for both scales to determine their respective factor structure. Sampling adequacy was determined using Kaiser-Meyer-Olkin (KMO) test, and the factorability of the correlation matrix was tested using the Bartlett's test of sphericity [26,27]. Factor extraction was conducted using principal axis factoring and the resulting model was evaluated using the scree test and parallel analysis (Monte Carlo simulation) [26,27].
Table 1 Univariate summary statistics for the modified 8-item CIO scale. Item
1 There are so many different recommendations about managing atrial fibrillation, it’s hard to know which ones to follow 2 There is not enough time to do all of the things recommended to manage atrial fibrillation 3 It has gotten to the point where I don’t even care to hear new information about atrial fibrillation 4 No one could actually do all of the atrial fibrillation management recommendations that are given 5 Information about atrial fibrillation all starts to sound the same after a while 6 I forget most of the information about atrial fibrillation right after I hear it 7 Most things I hear or read about atrial fibrillation seem pretty far-fetched 8 I feel overloaded by the amount of information about atrial fibrillation I am supposed to know
Response option frequencies (%)
M (SD)
Skewness Kurtosis
24 (6.2)
2.41 (0.76)
.02
70 (18.1)
7 (1.8)
2.03 (0.67)
.34*
185 (47.9)
57 (14.8)
6 (1.6)
1.82 (0.73)
.53*
.20
67 (17.4)
213 (55.2)
94 (24.4)
12 (3.1)
2.13 (0.73)
.29*
.07
49 (12.7)
160 (41.5)
163 (42.2)
14 (3.6)
2.37 (0.75)
70 (18.1) 82 (21.2) 71 (18.4)
218 (56.5) 256 (66.3) 234 (60.6)
90 (23.3) 45 (11.7) 76 (19.7)
8 (2.1) 3 (0.8) 5 (1.3)
2.09 (0.70) 1.92 (0.60) 2.04 (0.66)
Strongly Disagree
Disagree
Agree
41 (10.6)
171 (44.3)
150 (38.9)
74 (19.2)
235 (60.9)
138 (35.8)
Strongly Agree
.20 .24 .25* .24
.37 .31
.51* .09 .67* .10
* p<.05.
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Internal consistency was determined using the Cronbach α score [28,29]. Convergent and divergent validity (subsets of construct validity) were assessed by performing Spearman’s rank-order correlation between the modified CIO scale (and reduced 5-item scale) and respondents’ demographic characteristics. As lower education levels have been found to be significantly associated with greater cancer information overload [17], we predict that education levels and oral anticoagulant knowledge would be negatively correlated with our modified CIO scale scores. While there is limited studies examining the relationship between other demographic characteristics and health information overload, we predict that there will not be any significant correlations between our other demographic characteristics (age, gender, post code, annual income, and employment status). The Tasmanian Social Science Human Research Ethics Committee (reference number H0015972) approved the study. Consent was implied by participation in the survey. 3. Result No data were missing. Four items were significantly skewed, and two items were significantly kurtotic. Participants had the lowest means score of 1.82 on item 3, and reported the highest mean score on item 1 (Table 1). Additionally, we observed a potential ceiling effect for each item as very few respondents selected the ‘strongly agree’ option (Table 1). For the 8-item scale, a value of 0.87 was obtained for the KMO test and Bartlett’s test of sphericity was significant (χ2 = 1188.68, df = 28, p < .001), suggesting that structure detection is suitable. Similarly, for the reduced 5-item scale, a value of 0.81 was obtained for the KMO test and Bartlett’s test of sphericity was significant (χ2 = 473.83, df = 10, p < .001). The scree test and parallel analysis (Monte Carlo simulation) for both the eight and 5-item scales suggest a one-factor solution. In both scales, the total percentage of variance explained by the extracted factor was greater than 50%. The factor loadings for individual items are shown in Table 2. Bivariate correlations between various constructs and both modified CIO scales were conducted to evaluate the convergent/ divergent validity. As we predicted, apart from the highest level of education completed (rs=-.24, p<.001 for the 8-item scale; rs=-.21, p<.001 for the 5-item scale), no significant association was observed between the modified eight and reduced 5-item CIO scales and any other demographic characteristic. Additionally, as predicted, a higher oral anticoagulant knowledge score was significantly associated with a lower score on both the modified eight (rs=-.17, p = .001) and 5-item (rs=-.21, p<.001) CIO scales. The Cronbach α scores for the eight and 5-item scales were 0.86 and 0.76, respectively.
Table 2 Factor loadings for items in the modified 8-item and 5-item CIO scale.
Item 1 Item 2 Item 3 Item 4 Item 5 Item 6 Item 7 Item 8 % variance explained NA, not applicable.
Modified 8-item scale
Modified, reduced 5-item scale
.45 .58 .63 .74 .75 .66 .74 .69 50.68%
.37 NA .64 NA .75 .68 NA .71 52.31%
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4. Discussion and conclusion 4.1. Discussion 4.1.1. Principal findings The present study was a secondary analysis of data from a previous study [24] that assessed oral anticoagulant medication knowledge in adult Australians with AF. We evaluated the construct validity of both the modified 8-item CIO scale and a modified version of the 5-items proposed by Costa et al. [22]. Both the eight and reduced 5-item scales appear to be valid and reliable scales for measuring health information overload among adult Australians living with AF. We discuss each scale in further detail in the following paragraphs. For the modified 8-item CIO scale, the scree test and parallel analysis suggested there was a single factor. Given the accuracy of parallel analysis in identifying the correct number of factors [30,31], we conclude that the modified 8-item CIO scale appeared to measure a single construct among our sample of adult Australians with AF. Despite the 8-item CIO scale being developed and validated to measure United States residents’ perceived cancer information overload [16], it appears that the 8-item CIO scale used in Jensen et al’s study [16] and the modified version of the 8-item CIO scale used our present study measure a common, single construct: health information overload. As the 8-item CIO scale by Jensen et al. [16] is the only validated measure of health information overload available, few correlates of health information overload have been identified using a validated tool. One of the key significant correlates of cancer information overload identified by Chae et al., using Jensen’s unmodified 8-item CIO scale, is education level [17]: people with a higher level of education are less likely to perceive experiencing cancer information overload, as measured by the 8-item CIO scale. Similarly, in our sample of adult Australians with AF, we found a significant negative correlation between education level and the modified 8-item CIO scale (see Section 3.3). In a study by Kim et al. on predictors of cancer information overload whereby the first item of the 8-item CIO scale was used as the sole measure of cancer information overload, a higher education level significantly predicted a lower likelihood of perceived cancer information overload [32]. While cancer knowledge was not measured in Chae et al.’s study [17], it was measured in Kim et al.’s study [32]; poorer cancer knowledge was significantly associated with greater perceived cancer information overload. Similarly, in the present study, there was a significant negative association between oral anticoagulant knowledge and the modified 8-item CIO scale scores (see Section 3.3). The significant associations between education and oral anticoagulant knowledge with AFrelated information overload identified in our present study, albeit small-to-medium in effect size, appear to align with similar factors (education and cancer knowledge) in studies about cancer information overload. Collectively, within the context of extant literature, the modified 8-item CIO scale appears to be a valid measure of AF-related information overload. For the 5-item CIO scale, the scree test and parallel analysis consistently demonstrated a one-factor structure. Similar with the modified 8-item CIO scale, education level and oral anticoagulant knowledge were significantly and negatively associated with the CIO scale score. Based on our results, the reduced 5-item scale, as suggested by Costa et al. [22], could be a comparable measure of AF-related information overload. Some potential advantages of using a shorter scale could be time efficiency in administering the scale, and time saved by the participants. A shorter scale could also be preferable to improve response rates and/or mitigate respondent fatigue [33]. In a review and meta-analysis by Rolstad et al. [34], questionnaire lengths were significantly and negatively
Please cite this article in press as: K. Obamiro, K. Lee, Information overload in patients with atrial fibrillation: Can the cancer information overload (CIO) scale be used?, Patient Educ Couns (2018), https://doi.org/10.1016/j.pec.2018.10.005
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associated with response rates; the authors, however, noted the difficulty in separating the impact of the content from the length of the questionnaires. While further studies are recommended, based on results from our present study, the reduced 5-item modified CIO scale appears to be a valid scale in the context of adult Australians with AF. 4.1.2. Limitations It is important to recognize that the presented study is a secondary analysis of data from a published study [24]. As such, we were not able to assess predictive validity. Our ability to assess convergent and divergent validity were also limited to the variables collected in the aforementioned published study [24]. However, it is important to acknowledge that, given the infancy of the field of health information overload, particularly AF-related information overload, there is a paucity of empirical evidence identifying constructs that are related and dissimilar to AF-related information overload; thus, convergent and divergent validity would be a challenge to definitively assess regardless of study design. In the present study, we have attempted to assess convergent and divergent validity using constructs identified by cancer information overload studies, within the limits of the variables collected in the published study by Obamiro et al. [24]. Similarly, there is a paucity of empirical evidence to definitively assess predictive validity in the context of AF-related information overload. As the present study is a secondary analysis of data, we were unable to establish test-retest reliability. However, we were able to evaluate internal consistency: both 8-item and the reduced 5-item CIO scales produced acceptable Cronbach’s α values (>0.7 and <0.9) [29], particularly given both scales contain few items [35]. While we acknowledge the limitations of the present study, we believe the present study offers an initial insight into the viability of using the 8-item and reduced 5-item CIO scales in a cohort of participants with vast differences from the original target population of Jensen et al.’s 8-item CIO scale, both geographically and in terms of the diagnosed medical condition. We ask that the findings from the present study be considered alongside its limitations, and recommend further studies to build on our findings. 4.1.3. Further research We believe that both the eight and reduced 5-item modified CIO scales could be readily adapted to other chronic diseases whereby the word ‘cancer’ and words relating to cancer could be replaced with the disease group of interest. This is because only minor modifications to the wording of the original CIO scale was needed to adapt the scale from a cancer context to an AF context. Given the abundance of available health information, particularly in an online context, patients, regardless of their medical condition, are likely to be faced with insurmountable quantities of information. Having a validated scale that can be applied across various chronic diseases can allow researchers to identify chronic diseases where information overload is particularly a problem. We believe both CIO scales could be valid for a variety of diseases, but recommend that chronic diseases such as diabetes and various heart diseases be key conditions to explore in future studies in terms of health information overload, as these conditions require complex management in terms multidisciplinary care requirements, and non-pharmacological and therapeutic management. As the field of health information overload is in its infancy, we recommend that further studies explore other correlates of information overload to allow for a better understanding of the demographic characteristics of information overload.
Additionally, we recommend further studies be conducted on a similar population of adult Australians with AF to provide further support for the validity and reliability of both the eight and reduced 5-item modified CIO scales. Specifically, we recommend that constructs such as information-seeking behavior and information avoidance be measured when assessing AF-related information overload via the modified CIO scales, so as to establish predictive validity; we recommend measuring these constructs because, as mentioned in Section 1 (Introduction), there is evidence to suggest that people may avoid information when in a state of information overload [10]. Based on the sample used in the present study, there appears to be a potential ceiling effect with the modified CIO scales because very few respondents selected the ‘strongly agree’ options for each item. As frequencies for each response option within each item of the CIO scale have not been reported in previous studies, it is unclear whether a potential ceiling effect has been observed in other studies. We therefore recommend further studies utilising the CIO scale (or a modified version of the scale) to report frequencies for each response option, and to investigate whether a ceiling effect truly exists. While we performed a principal axis factor analysis in the present study to explore the number of latent factors in an AF context, we recommend the use of a confirmatory factor analysis in a subsequent study to confirm the one-factor structure identified in the present study, using a separate sample [36]. We also recommend that future studies include a re-test so as to establish test-retest reliability of both the eight and reduced 5-item CIO scales. 4.2. Conclusion Both the eight and reduced 5-item modified CIO scales appear to be valid and internally consistent measures of AF-related information overload. The reduced 5-item modified CIO scale may be preferable for studies where there is limited time to collect responses from participants, or in attempts to mitigate respondent fatigue, and improve response quality. We believe that both eight and reduced 5-item modified CIO scales may be suitably adapted for health information overload in patients with other chronic diseases. 4.3. Practice implications The ability to measure an individual’s level of information overload has the potential to allow for appropriate, tailored, information delivery to patients by health professionals, and inform the design of consumer-facing written health information. Having a scale that can be readily adapted to other chronic diseases allows researchers to 1) identify conditions where there is an overabundance of available health information, and 2) provide direction for future efforts to consolidate available health information. The present study demonstrates that the existing 8-item CIO scale can be readily adapted for use in the AF context. Therefore, the present study provides a first step towards future efforts to reduce the information burden on patients, thereby enabling patients to better self-manage their chronic diseases. Funding sources This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Conflict of interests None.
Please cite this article in press as: K. Obamiro, K. Lee, Information overload in patients with atrial fibrillation: Can the cancer information overload (CIO) scale be used?, Patient Educ Couns (2018), https://doi.org/10.1016/j.pec.2018.10.005
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Please cite this article in press as: K. Obamiro, K. Lee, Information overload in patients with atrial fibrillation: Can the cancer information overload (CIO) scale be used?, Patient Educ Couns (2018), https://doi.org/10.1016/j.pec.2018.10.005