Visualization approaches to support healthy aging: A systematic review

Uba Backonja

Department of Biomedical Informatics and Health Education, University of Washington School of Medicine, USA

Nai-Ching Chi

Department of Biobehavioral Nursing and Health Systems, University of Washington School of Nursing, USA

Yong Choi

Department of Biomedical Informatics and Health Education, University of Washington School of Medicine, USA

Amanda K. Hall

Department of Biomedical Informatics and Health Education, University of Washington School of Medicine, USA

Thai Le

Department of Biomedical Informatics and Health Education, University of Washington School of Medicine, USA

Youjeong Kang

Department of Biobehavioral Nursing and Health Systems, University of Washington School of Nursing, USA

George Demiris

Department of Biomedical Informatics and Health Education, University of Washington School of Medicine, and Department of Biobehavioral Nursing and Health Systems, University of Washington School of Nursing, USA

Author address for correspondence

Uba Backonja

Department of Biomedical Informatics and

Health Education, University of Washington

School of Medicine, USA


Cite this article: Backonja U, Chi N-C, Choi Y, Hall AK, Le T, Kang Y, Demiris G. Visualization approaches to support healthy aging: A systematic review. J Innov Health Inform. 2016;23(3):600–610.

Copyright © 2016 The Author(s). Published by BCS, The Chartered Institute for IT under Creative Commons license


Background Informatics tools have the potential to support the growing number of older adults who are aging in place. Many tools include visualizations (data visualizations and visualizations of physical representations). However, the role of visualizations in supporting aging in place remains largely unexplored.

Objective To synthesize and identify gaps in the literature evaluating visualizations (data visualizations and visualizations of physical representations) for informatics tools to support healthy aging.

Methods We conducted a search in CINAHL, Embase, Engineering Village, PsycINFO, PubMed, and Web of Science using a priori defined terms for publications in English describing community-based studies evaluating visualizations used by adults aged ≥ 65 years.

Results Six out of the identified 251 publications were eligible. Most studies described in the publications were user studies and all varied methodological quality. Three publications described visualizations of virtual representations supported performing at-home exercises. Participants found visual representations either (1) helpful, motivational, and supported their understanding of their health behaviours or (2) not an improvement over alternatives. Three publications described data visualizations that aimed to support understanding of one’s health. Participants were able to interpret data visualizations that used precise data and encodings that were more concrete better than those that did not provide precision or were abstract. Participants found data visualizations helpful in understanding their overall health and granular data.

Conclusions Few studies were identified that used and evaluated visualizations for older adults to promote engagement in exercises or understanding of their health. While visualizations demonstrated some promise to support older adult users in these activities, the studies had various methodological limitations. More research is needed, including research that overcomes methodological limitations of studies we identified, to develop visualizations that older adults could use with ease and accuracy to support their health behaviours and decision making.

Keywords: Aged, consumer health information, data display, informatics, visualization


By 2050, the older adult population (age ≥ 65 years) is estimated to double in the US and triple worldwide.1,2 Many older adults will likely live at home – in 2013, 26.8 million US households were headed by older adults3 and approximately 80% of US older adults receiving long-term care services resided at home.4 Informatics tools can address the needs of older adults aging in place,5 including telehealth68 and smart home systems.9,10 Research has focused on the technical feasibility of these systems rather than on the effectiveness of visualizations that such systems generate. Development of tools with visualizations, including visualizations of data and virtual representations (e.g. environments and people) and tools’ roles in supporting healthy aging in place, remain largely unexplored.

Data visualization is the visual representations of data, encoded using position, length, size and/or colour, among others, to reduce complexity and effectively communicate information to support discovery and understanding of patterns within data, decision making and memory.1114 In health informatics, data visualizations can display longitudinal health information (e.g. historical vital sign or symptom data) and support health-related decision making and behaviours (e.g. using icons to convey disease risk, medication side effects or treatment benefits).1523 Data visualization has been used to support clinical care24,25 and personal health tracking (e.g.

Visualizations of physical representations include virtual environments (e.g. landscapes) and people, among others. With advancements in graphics and movement capture technologies used in gaming consoles (e.g. Xbox Kinect), interaction with physical representation visualizations is increasingly prevalent. Technologies providing these visualizations using movement capture can support older adults’ health and wellness.2629

Unfortunately, few informatics tools with data or physical representation visualizations have been specifically developed to support older adults and the benefits of these visualizations have not been established. Also, it is unknown how data visualizations and visualizations of physical representations can be used to support community-dwelling older adults’ ability to understand and use information. The purpose of this systematic review was to synthesize and identify gaps in the literature regarding the evaluation of data visualizations and visualizations of physical representations included in informatics tools to support healthy aging in place.


Publications were eligible if they were published before 9 June 2015 and were full-text peer reviewed articles, described a study, took place in a community-based setting, included older adults aged ≥ 65 years, visualization users were older adults, included evaluation of visualizations, and were in English. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to guide our reporting.30

Using a predetermined list of terms developed with a health sciences librarian (Supplemental Table 1), two researchers (YC, NCC) conducted searches independently in CINAHL, Embase, Engineering Village, PsycINFO, PubMed, and Web of Science. The two researchers met to compare results, which were identical. Compiled citations were uploaded into, in which pairs of researchers used to review each abstract (UB and NCC; YC and JK) and full-text article (UB and GD; AKH and NCC) for eligibility. The following information was abstracted from eligible publications: design, sample, description of comparison group, criteria for evaluating visualizations, and methods that researchers used to improve internal validity in their study designs and study results. Researchers noted limitations that publication authors identified and limitations not discussed by the authors.


We identified 251 publications (Figure 1). Of those, 199 (79.3%) publications did not meet inclusion criteria and 52 (20.7%) were included for full-text review. Of the 52 full texts, 46 (88.5%) were excluded (e.g. older adults were not the visualization user). Six of the 52 (11.5%) met our inclusion criteria.3136

Study characteristics

Table 1 provides characteristics of the studies described in the six publications. Studies were observational user studies of visualization tools,32,33,35 quasi-experimental within-subject studies comparing the completion of exercises using a printed informational booklet or visualization31,36 or a heuristic evaluation of visualizations.34 Sample sizes ranged from two to 165. Among publications with demographic information, samples generally included older adults aged ≥ 65 years and participants were healthy or experiencing health problems (e.g. had a chronic disease). Studies were completed in Denmark, the United Kingdom, or the US and published in 2013–2015.

Figure 1 PRISMA flow diagram of the manuscript selection process

Visualizations, evaluations and findings

Table 2 provides information about visualizations, their evaluations, and study findings. Visualizations either supported performing exercises via virtual using three-dimensional representations (e.g. virtual outdoor environments)31,32,36 or understanding of one’s health via data visualizations (e.g. graphs, charts or icons to represent quantitative data).3335

Virtual representation visualizations to support exercises

Two virtual representation visualizations were developed that included mannequins and natural landscapes presented on screens with which participants interacted. Ayoade et al.31 and Uzor and Baillie36 developed animated visualizations to engage older adults in home exercises using human-like representations (mannequins). Participants wore sensors that collected information to provide visual feedback about their movements and proper posture using a real-time feedback mannequin and a guide mannequin, respectively. Weekly progress charts were provided to participants but not evaluated in the study. Mannequin visualizations were evaluated by comparing within-subject completion of exercises using an informational booklet followed by the mannequins. Ayoade et al.31 collected feedback via observation, semi-structured interviews and short questionnaires. Uzor and Baillie36 used a questionnaire and assessed differences in time to complete exercises when using the booklet and then the visualizations. Both studies demonstrated that the visualizations improved participants’ perceived confidence in performing exercises and ability to perform more controlled movements compared to when using the booklet. Participants found mannequins helpful to identify movement or position problems while completing exercises and motivated them to complete otherwise unexciting exercises. When timed, participants using the visualizations took longer to complete exercise repetitions compared to using the booklet.

Bruun-Pedersen et al.32 described a virtual outdoor environment projected on a monitor to support exercise engagement. Older adults rode exercise bicycles and viewed a virtual environment mimicking natural landscapes that changed while pedalling. No feedback about performance was given to participants. Researchers used open-ended interviews to assess participants’ experiences using the virtual environment. Most participants felt the environment enhanced their exercise experience and gave them energy and a sense of accomplishment. They felt the virtual environment could motivate them to exercise regularly or for a longer duration. Two of the ten participants with pain did not feel the virtual environment impacted their exercise engagement. Five of the ten participants stated the virtual environment did not match their interests or could become less engaging if novelty was lost.

Table 1 Study characteristics of studies to evaluate visualizations used in consumer health technologies to support older adults living in the community.

Data visualizations to support understanding of one’s health

Three publications described studies in which researchers evaluated visualizations of quantitative health information in the form of graphs and icons. Gronvall and Verdezoto33 developed data visualizations to support participants’ understanding of blood pressure (BP) measurements. They created (1) three data visualizations (icon-based, bar charts, line charts) to provide a one-week BP overview and (2) four data visualizations (icon based, text based, speedometer and slider) to show daily BP measurements. Data visualizations were evaluated by (1) older adults who participated in a workshop in which they measured their BP for one week and interpreted visualizations of their BP data and (2) adults who completed an online survey. It is unclear how researchers presented the visualizations to the workshop participants; participants in the survey study viewed the visualizations within the web-based survey. Participants felt the data visualizations enhanced their understanding of BP measurements; however, they were concerned with visualization precision. For the one-week overview, participants positively responded to the line chart. For the daily view, participants noted that icons were simple yet lacked precision; they used text representations for precise values. Participants had mixed reactions towards the speedometer visualization, noting that there might be problems with the precision of interpreting the visualization.

Table 2 Visualization intervention, evaluation, and results of observational studies to evaluate visualizations used in consumer health technologies to support older adults living in the community.

Le et al.34 created a streamgraph (variant of stacked linegraph) and a radial plot (a circle that represents a 24-hour clock) using motion data from sensors worn for six months by older adults in their apartments. Visualizations were developed based on interview data with older adults who wore the sensors, cognitive perceptual visualization guidelines, the emotional design principles of Norman37 and Shah and Hoeffner’s model of information visualization processing.38 For evaluation, researchers recruited gerontology experts to review the data visualizations presented digitally on a laptop and provide heuristic-based feedback. Participants mostly understood the spatial and temporal component of the stream graph and radial plot visualizations. They found the radial plot easier to understand than the streamgraph to compare components within the visualization and understand granular data.

Le et al.35 developed three interactive data visualizations to provide information about older adults’ overall wellness and social, physical, cognitive, and spiritual health. The data visualizations included a bar graph, a radial plot (area represented score; different from the radial plot described in the previous paragraph) and a light ball metaphor (a circle for which the size and brightness encoded data). Researchers designed the data visualizations based on findings from previous research, focus groups with gerontology experts and heuristic design guidelines. To evaluate the visualizations, they held a focus group with older adults who used then reported on their experiences with the visualizations, which were presented on paper. Participants used the data visualizations first for a holistic perspective and then looked at details. They felt there was too much information displayed in the visualization and were confused by data abstractions (e.g. light ball metaphor). It was difficult for participants to notice differences in sizes and brightness encodings. Participants appreciated separation of visualizations for different components of wellness. They felt there was potential for data visualizations to support assessments of their wellness and promote shared decision making with healthcare providers.

Methodological quality

Study design

Four publications described studies that assessed participants’ opinions about visualizations. These studies provide information about potential value of visualizations but do not compare visualizations to alternatives. Two publications described studies that used within-subject designs to compare the current standard of providing exercise information (a booklet) to their visualization tool, providing data comparing opinions and abilities after using the booklet and visualization tools. All participants first used the booklet and then the visualization tool; therefore, participants were aware of and had performed the exercises by the time they started exercises with the visualization tool. This ordering effect could have impacted participants’ opinions about and ability to perform subsequent exercises.


Most studies had sample sizes ≤ 10. While researchers can detect usability issues using five to eight participants,39 conclusions drawn from experimental studies with small sample sizes should be made with caution; it is possible that samples were not big enough to detect differences in performance (e.g. interpreting data). Most studies had incomplete information about participants’ gender, socioeconomic status, education, health status, and technology use, limiting assessments of the generalizability of findings.

Visualization development

Researchers varied in amounts of evidence they used to guide development of their visualizations. They varied from using one previously published paper to using a combination of sources (e.g. previous research, visualization guidelines and a theoretical model). It is possible that the number and types of evidence researchers used to develop the visualization could have impacted their efficacy.

Visualization evaluation

Most studies included interviews with or gathered feedback from users. A fewer studies included questionnaires; ­information was not provided in the publications about questionnaire reliability and validity, whether researchers developed the questionnaires, or if questionnaire ­development was guided by a theory or framework. Interview and questionnaire methods are adequate for providing qualitative and/or quantitative feedback; however, most studies using these methods did not describe providing a usual information or data presentation option (i.e. a control comparison) for ­participants to which to compare. Participants provided feedback on one or multiple visualizations designed by researchers. These publications do not provide insights on if and how the visualizations compare to usual data presentations. Two ­publications31, 36 described studies in which researchers compared exercise completion using a traditional method (information booklet; a control comparison) versus visualizations (real-time feedback and guide mannequins). However, these studies were of within-subject design and did not change the order in which participants received the booklet or visualization tool. It is difficult to determine why there were differences in time to complete the exercise repetition. Finally, all studies appeared to be short in duration making it difficult to determine if (1) learning curves for the health-related visualizations were overcome with prolonged use or (2) older adults engaged in sustained use of certain exercise and health-related visualizations.


We summarized the current published research ­evaluating visualizations of physical representations to support ­exercise engagement and data visualizations for understanding one’s health incorporated into tools for older adults in the ­community. Studies evaluating virtual environments or human ­representations (three publications31,32,36) showed the potential to promote exercise engagement. Older adults found them motivating, which may be important among older adults who find it difficult to engage in activities due to impaired physical abilities. These studies were limited methodologically in several ways, including study duration, making it is difficult to draw clear conclusions about the efficacy of the visual representations.

Studies of data visualizations to better understand one’s health (three publications3335) also showed promise, although they had several methodological limitations that should be taken into consideration when interpreting the findings. Among standard data visualizations, line and bar graphs were developed by study researchers to show quantitative health data. Previous quantitative data visualization research indicates that position and length – how line and bar graphs are represented, respectively – support more accurate data interpretation.40,41 Researchers of the studies we identified in this review (e.g. Gronvall and Verdezoto33) found that line and bar graphs (optimal encodings) were more understandable among their participants than alternatives such as abstract icons. Previous data visualization research also indicates that area and hue are harder to interpret than position and length. In research to understand graphical perception for older adults, Le et al.42 found that participants were not as quick or accurate in understanding stacked bar charts and pie graphs (encode area; less optimal) compared to bar charts (encodes length; optimal). During their studies, Le et al.35 found that older adults who viewed the light balls metaphor visualization (area and hue encodings; less optimal) had difficulty identifying differences between balls. It is possible to use area to represent something familiar. For example, Le et al.34 used circle radial plots representing a 24-hour clock to encode temporal data, which older adults preferred to the streamgraph. Later, Le et al.35 used radial plots more similar to pie charts in which areas and arc lengths are compared, which older adults found confusing. Thus, it is possible for researchers to investigate (1) the validity of previous data visualization research in the context of consumer health informatics tools for older adults and (2) new approaches to visualize quantitative data in ways that optimize older adults’ familiarity with certain objects.

The speedometer is another representation using arc length to encode quantitative data in a familiar way. However, this visualization could be difficult to interpret – speedometers visualize speed, which may not map to health and wellness characteristics. Gronvall and Verdezoto33 in their speedometer visualization provided (1) general BP categories (e.g. low and normal) across the speedometer arc encoded with colour and (2) BP values in a box that had colours identically to the category on which the needle was positioned. Although redundant encodings were included, participants felt the speedometer lacked precision, possibly because arc length is not as optimal in encoding quantitative data as position or linear length.40,41 While study authors did not provide information about whether participants preferred speedometers to a slider (similar to a stacked bar graph, a more optimal encoding than arc length), participants stated they found the slider useful.

Participants in the three health data visualizations appeared to have had different encoding preferences depending on data granularity. They preferred overviews, were overwhelmed if too much data were presented and wanted ways to access precise data.33,35 One solution is to provide static views of overviews and granular data, as in Gronvall and Verdezoto’s work,33 or interactive visualizations to allow viewing an overview, zooming and filtering of data and accessing to detailed information on demand,43 as in Le et al.’s work.35

Future research could build on the current literature by addressing methodological limitations of studies included in this review. This includes using multiple sources of evidence to inform the design of visualizations to guide researchers towards more understandable visual encodings; using designs that allow comparison between usual standards and visualizations; including larger, diverse samples; allowing for extended use of visualizations; and including validated measures and interviews to evaluate visualizations. Within-subject studies could randomly assign the order in which participants used current standards and novel visualizations. Also, researchers should be cognizant of how evaluate visualizations for older adults. Le et al.44 evaluated three approaches to assess interactive visualizations for older adults. They found the evaluation methods varied in differences with task completion time and accuracy. In addition, researchers could consider assessing graph literacy and numeracy in addition to comprehension when evaluating visualizations. Nayak et al.45 found that older adult prostate cancer patients who were highly educated and had high health literacy varied in their comprehension of a dashboard that included a table, line graph and bar graph depending on their graph literacy and numeracy. Researchers could further investigate evaluation techniques and consider using evaluation methodology when assessing their visualization tools.


We identified that few publications and studies were heterogeneous in design. Therefore, we were unable to aggregate data across studies. We consulted with a health informatics librarian to develop the search strategy; however, we may not have identified all relevant articles.


We identified six studies in which researchers evaluated visualizations of physical representations to promote engagement in exercises or data visualizations for understandings of one’s health. Visualizations show promise in supporting the health and wellness of community-dwelling older adults; however, because of the low number of publications we identified and the methodological limitations of studies described in these publications, caution should be made in interpreting and extending findings from these studies. Future research could build on this currently literature to develop informatics tools including visualizations that older adults could use with ease and accuracy. With the projected rise of older adults living at home in the coming decades, more home-based tools using data visualizations and visualizations of physical representations are needed. Informatics tools may provide that support; however, developers of informatics tools for older adults’ in the community could benefit from developing evidence-based visualizations that they then evaluate.


Many thanks to Diana N.K. Louden, Health Sciences Librarian at the University of Washington, for assistance in developing the search strategy and terms.


Financial support for this study was provided in part by grants from the National Institutes of Health, National Library of Medicine (NLM) Biomedical and Health Informatics Training Program at the University of Washington (Grant Nr. T15LM007442) (authors UB & MKH) and the NIH National Institute of Nursing Research (NINR) Aging and Informatics Training Program at the University of Washington School of Nursing (Grant Nr. T32NR014833) (author YK).


1. National Institute on Aging. Global Health and Aging. NIH Publication no. 11-7737. Bethesda, MD: National Institutes of Health, 2011.

2. Vincent GK and Velkoff VA. The Next Four Decades: The Older Population in the United States: 2010 to 2050. Report P25-1138. Washington, D.C.: US Census Bureau. 2010.

3. Administration on Aging. A Profile of Older Americans: 2014. Report, US Department of Health and Human Services, USA. 2014.

4. Congressional Budget Office. Rising Demand for Long-Term Services and Supports for Elderly People. Report, Congress of the United States, USA; 2014.

5. Hanson GJ, Takahashi PY and Pecina JL. Emerging technologies to support independent living of older adults at risk. Care Management Journals 2013;14:58–64. PMid:23721044.

6. Chi NC, Demiris G. A systematic review of telehealth tools and interventions to support family caregivers. Journal of Telemedicine and Telecare. 2015; 21:37-44.

7. Demiris G, Thompson H, Boquet J, Le T, Chaudhuri S and Chung J. Older adults’ acceptance of a community-based telehealth wellness system. Informatics for Health and Social Care 2013;38:27-36.

8. Gellis ZD, Kenaley B, McGinty J, Bardelli E, Davitt J and Ten Have T. Outcomes of a telehealth intervention for homebound older adults with heart or chronic respiratory failure: a randomized controlled trial. Gerontologist 2012; 52:541–52.

9. Demiris G and Hensel BK. Technologies for an aging society: a systematic review of “smart home” applications. Yearbook of Medical Informatics 2008:33–40. PMid:18660873.

10. Reeder B, Meyer E, Lazar A, Chaudhuri S, Thompson HJ and Demiris G. Framing the evidence for health smart homes and home-based consumer health technologies as a public health intervention for independent aging: a systematic review. International Journal of Medical Informatics 2013;82:565–79.

11. Bertin J. Semiology of Graphics: Diagrams, Networks, Maps. Madison, WI: University of Wisconsin Press, 1983. PMCid:PMC2714599.

12. Card SK, Mackinlay J and Shneiderman B. Readings in Information Visualization: Using Vision to Think. San Francisco, CA: Morgan Kaufmann,1999.

13. Few S. Now You See It: Simple Visualization Techniques for Quantitative Analysis. Oakland, CA: Analytics Press, 2009.

14. Heer J, Bostock M and Ogievetsky V.A. Tour through the Visualization Zoo: A survey of powerful visualization techniques, from the obvious to the obscure. Graphics. 2010; 8(5).

15. Elting LS, Martin CG, Cantor SB and Rubenstein EB. Influence of data display formats on physician investigators’ decisions to stop clinical trials: prospective trial with repeated measures. British Medical Journal 1999;318:1527–31. PMid:10356010 PMCid:PMC27896.

16. Feldman-Stewart D, Brundage MD and Zotov V. Further insight into the perception of quantitative information: judgments of gist in treatment decisions. Medical Decision Making 2007;27:34–43. PMid:17237451.

17. Feldman-Stewart D, Kocovski N, McConnell BA, Brundage MD and Mackillop WJ. Perception of quantitative information for treatment decisions. Medical Decision Making 2000;20:228–38. 0.1177/0272989X0002000208. PMid:10772360.

18. Gaissmaier W, Wegwarth O, Skopec D, Müller AS, Broschinski S and Politi MC. Numbers can be worth a thousand pictures: individual differences in understanding graphical and numerical representations of health-related information. Journal of Health Psychology 2012;31:286–96. PMid:21842998.

19. Garcia-Retamero R, Okan Y and Cokely ET. Using visual aids to improve communication of risks about health: a review. ScientificWorldJournal 2012;2012:562637. PMid:22629146 PMCid:PMC3354448.

20. Hoeke JO, Bonke B, van Strik R and Gelsema ES. Evaluation of techniques for the presentation of laboratory data: support of pattern recognition. Methods of Information in Medicine 2000;39:88–92. PMid:10786077.

21. Morrow DG, Hier CM, Menard WE and Leirer VO. Icons improve older and younger adults’ comprehension of medication information. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences 1998;53:P240–54.

22. Shneiderman B, Plaisant C and Hesse BW. Improving health and healthcare with interactive visualization methods.Computer.2013. Available from:

23. Zikmund-Fisher BJ, Witteman HO, Dickson M, Fuhrel-Forbis A, Kahn VC, Exe NL et al. Blocks, ovals, or people? icon type affects risk perceptions and recall of pictographs. Medical Decision Making 2014;34:443–53.

24. Duke JD, Li X and Grannis SJ. Data visualization speeds review of potential adverse drug events in patients on multiple medications. Journal of Biomedical Informatics 2010;43:326–31.

25. Hugine AL, Guerlain SA and Turrentine FE. Visualizing surgical quality data with treemaps. Journal of Surgical Research 2014;191:74–83.

26. Bieryla KA. Xbox Kinect training to improve clinical measures of balance in older adults: a pilot study. Aging Clinical and Experimental Research.2016;28(3):451–7. [Epub ahead of print]. PMid:26386865.

27. Chao YY, Scherer YK and Montgomery CA. Effects of using Nintendo Wii™ exergames in older adults: a review of the literature. Journal of Aging and Health 2015;27:379–402.

28. Laufer Y, Dar G, Kodesh E. Does a Wii-based exercise program enhance balance control of independently functioning older adults? A systematic review. Journal of Clinical Interventions in Aging 2014;9:1803–13.

29. Molina KI, Ricci NA, de Moraes SA and Perracini MR. Virtual reality using games for improving physical functioning in older adults: a systematic review. Journal of Neuro Engineering and Rehabilitation 2014;11:156.

30. Moher D, Liberati A, Tetzlaff J, Altman DG and PRISMA Group. Preferred reporting items for systematic reviews and metaanalyses: the prisma statement. PLoS Med. 2009;6:e1000097. doi:10.1371/journal.pmed1000097.

31. Ayoade M, Uzor S and Baillie L. The development and evaluation of an interactive system for age related musculoskeletal rehabilitation in the home. Kotzé P, Marsden G, Lindgaard G, Wesson J, Winckler M (Ed). 14th IFIP TC 13 International Conference on Human-Computer Interaction, INTERACT 2013,2–6 September 2013; Cape Town, South Africa. (8120):1–18.

32. Bruun-Pedersen JR, Pedersen KS, Serafin S and Kofoed LB. Augmented exercise biking with virtual environments for elderly users: a preliminary study for retirement home physical therapy. In: 2nd Workshop on Virtual and Augmented Assistive Technology, VAAT 2014 –Co-located with the 2014 Virtual Reality Conference,30 March 2014, Minneapolis, Minnesota, USA. 23–27.

33. Gronvall E and Verdezoto N. Understanding challenges and opportunities of preventive blood pressure self-monitoring at home. 31st European Conference on Cognitive Ergonomics: Work, Education, Society, ECCE 2013,26–28 August 2013, Toulouse, France. Paper no. 31, New York: ACM.

34. Le T, Reeder B, Chung J, Thompson H and Demiris G. Design of smart home sensor visualizations for older adults. Technol Health Care 2014;22:657–66.

35. Le T, Reeder B, Yoo D, Aziz R, Thompson HJ and Demiris G. An evaluation of wellness assessment visualizations for older adults. Telemedicine Journal and E-Health. 2015;21:9–15.

36. Uzor S and Baillie L. Exploring designing tools to enhance falls rehabilitation in the home. 31st Annual CHI Conference on Human Factors in Computing Systems: Changing Perspectives, CHI 2013,27 April 2013 to 2 May 2013; Paris, France.1233–1242;

37. Norman DA. Emotional design why we love (or hate) everyday things. New York: Basic Books,2004.

38. Shah P and Hoeffner J. Review of Graph Comprehension Research: Implications for Instruction. Educational Psychology Review 2002;14:47–69.

39. Lewis JR. Sample Sizes for Usability Studies: Additional Considerations Human Factors. Human Factors 1994;36:368–78,

40. Cleveland WS and McGill R. An experiment in graphical perception. International Journal of Man-Machine Studies 1986;25:491–500.

41. Cleveland WS and McGill R. Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. Journal of the American Statistical Association. 1984;79.387:531–54.

42. Le T, Aragon C, Thompson HJ and Demiris G. Elementary graphical perception for older adults: a comparison with the general population. Perception 2014;43:1249–60. PMid:25638940.

43. Shneiderman B. The eyes have it: a task by data type taxonomy for information visualizations. IEEE Symposium on Visual Languages,3–6 September 1996, Boulder, Colorado, USA; Los Alamitos, CA: IEEE Computer Society Press,1996:336–343.

44. Le T, Thompson H and Demiris G. A Comparison of Health Visualization Evaluation Techniques With Older Adults. IEEE Computer Graphics and Applications 2015. [Epub ahead of print].

45. Nayak JG, Hartzler AL, Macleod LC, Izard JP, Dalkin BM and Gore JL. Relevance of graph literacy in the development of patient-centered communication tools. Patient Education and Counseling 2015. pii: S0738-3991(15)30075–6. [Epub ahead of print].


Predetermined list of search terms used for this systematic review to identify publications in which researchers evaluated visualizations for health technologies to support the health and wellness of elders living in the community


  • There are currently no refbacks.

This is an open access journal, which means that all content is freely available without charge to the user or their institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal starting from Volume 21 without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of open accessFor permission regarding papers published in previous volumes, please contact us.

Privacy statement: The names and email addresses entered in this journal site will be used exclusively for the stated purposes of this journal and will not be made available for any other purpose or to any other party.

Online ISSN 2058-4563 - Print ISSN 2058-4555. Published by BCS, The Chartered Institute for IT