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Does the research suggest that there are differences in younger and older users? What can we do to enable older adults to interact with our Web sites at the same level as younger adults?

It has been clear for the past 20 years, that the normal effects of aging include a decline in computer-related sensing, cognitive and responding abilities (Welford, 1981; Salthouse, 1991). These declines in the ability to sense, process information and respond can negatively affect older users' ability to perform many tasks.

Sensing Limitations

A good example of aging influences on the senses is with hearing. As people age they require louder sounds to be able to hear adequately. Cohen (1994) had subjects listen to speech sounds and indicate the level they preferred for listening. The hearing comfort level by age was:

Age in Years Sound Level (dB)
15 54
25 57
35 61
45 65
55 69
65 74
75 79
85 85

Not only do older users need louder sounds, but they also require larger letters. Charness and Dijkstra (1999) reported that older adults were slowed more than younger adults by smaller type fonts when reading prose text. They suggest that the best reading speeds can be attained with:

  • 14-point type in columns that were 4 inches wide, or
  • 12-point type in columns that were 3 inches wide.

Ellis and Kurniawan (2000) proposed that the visual sensing limitations of older users could be better addressed if designers:
- Used only sans serif fonts (Arial, Helvetica, Verdana), and
- Used black type on a white background

Both Ellis and Kurniawan (2000) and Czaja (1997) recommend that designers should create links that:

  • Are distinct and easy to see,
  • Are fairly large (at least 180 x 22 pixels for a graphic button), and
  • Have plenty of open space around them.

Cognitive Processing

As users age, there seems to be a general overall slowing of brain processing speed. The largest impact seems to be with tasks that require the most cognitive processing, such as with working memory, overall attentional capacity, and visual search performance. Age effects are smallest for tasks where knowledge is an important aspect of the task, and largest for tasks where successful performance is primarily dependent on speed (Sharit and Czaja, 1994).

Mead, Spaulding, Sit, Meyer and Walker (1997) had young (ages 19 to 36) and older (64 to 81) adults with little computer experience conduct searches using different Web sites The older users had the most problems with tasks that required 3 or more clicks. Older users also searched less efficiently than younger users, requiring them to make 81% more moves. Most of the difficulties encountered by older users seemed to be directly related to memory limitations.

Research Problems

Unfortunately, the participants in the above study had different computer experiences. The younger group used computers about once a week, while the older group used computers only about once a month.

Differences in test participants other than age has been a major problem with much of the early research in this area. One study (Mead and Fisk, 1997) reported that their group of young adults differed substantially from their group of older adults. Their young adults reliably:

  • used ATMs and computers more often,
  • read faster,
  • had greater reading comprehension and working memory capacity,
  • had faster choice reaction times (there was no difference in simple reaction time),
  • had higher perceptual speed scores,
  • were less educated, and
  • had lower vocabulary scores.

Another major problem is the lack of consistency across studies when defining younger versus older users. For example, Charness and Dijkstra (1999) conducted three different studies where they defined older adults in three different ways (a) those over age 58, (b) those over 40, and (c) those over 50. Also, they reviewed the results of three other studies where older adults were defined as (a) those over 60, (b) those over age 50, and (c) a group "with an average age of 75."


As users age, their ability to make movements slows, and becomes less reliable. This causes them to type and mouse slower. Kalasky, (1999) attempted to determine if older users would be better off using highly practiced speaking for input. They and others (Morris and Brown, 1994) found that the time taken to read a text passage into the computer took reliably longer for older users than for younger users. In the study, the older users had an average speaking rate that was about 14% slower than younger users.

Age-Related Interventions

Designers need to find more ways to improve the performance of older adults without hindering the performance of younger adults. An excellent example was proposed by Aileen Worden, Neff Walker, Krishna Bharat and Scott Hudson at the Georgia Institute of Technology in 1997. They found a way to enhance cursor movement for older users (average age of 70.1 years) without degrading the performance of younger users (average age of 23.4).

These researchers created an "area cursor" and "sticky icons." Traditional cursors have a one-pixel "hot spot" which serves as the point of activation, whereas their "area cursor" was a 12x12 pixel square that had 144 hot spots. Their "sticky icons" enabled an automatic 30% reduction of the cursor’s gain ratio as the cursor neared a target, and then returned to normal after passing target. The area cursor and sticky icons had no effect on accuracy, but substantially improved the speed of performance over the traditional pointer for both young and old users:

Percent Improvement over Traditional Cursor

Younger Users Older Users  
Sticky icons only 2% 27%
Area cursor only 19% 14%
Both sticky icons and area cursor 28% 57%

Another interesting intervention was proposed by Intons-Peterson, (1998). They evaluated the effect of time-of-day preferences on user performance. They found that more younger people preferred the afternoon, and more older people preferred the morning. Using this preference data, they tested participants at optimal and non-optimal times. Both older and younger groups showed improved performance on a memory task when tested at their preferred time of day. Perhaps more importantly, when tested at preferred times, older adults showed memory effects similar to those of younger subjects.

Another intervention may be to train older users in ways that are most beneficial. Wiedenbeck and Zila (1997) evaluated different training methods with younger and older users. Older users benefited most from training that told them exactly how to accomplish an activity on certain tasks, and training that was more conceptual on other tasks. The researchers concluded that matching the training approach to specific tasks could allow older users to perform almost as well as younger users.


Charness, N. and Dijkstra, K. (1999), Age, luminance, and print legibility in homes, offices, and public places, Human Factors, 41(2), 173-193.

Coren, S. (1994), Most comfortable listening level as a function of age, Ergonomics, 37(7), 1269-1274.

Czaja, S.J. (1997), Computer technology and older adults, In M.E. Helander, T.K. Landauer and P. Prabhu (Eds.), Handbook of Human-Computer Interaction (2nd Edition), New York: Elsevier, 797-812.

Ellis, R.D. and Kurniawan, S.H. (2000), Increasing the usability of online information for older users: A case study in participatory design, International Journal of Human-Computer Interaction, 12(2), 263-276.

Intons-Peterson, M.J., Rocchi, P., West, T., McLellan, K. and Hackney, A. (1998), Aging, optimal testing times, and negative priming, Journal of Experimental Psychology: Learning, Memory, and Cognition, 24(2), 362-376 (1998).

Kalasky, M.A., Czaja, S.J., Sharit, J. and Nair S.N. (1999), Is speech recognition technology robust enough for the older population? Proceedings of the Human Factors and Ergonomics Society, 725-727.

Mead, S.E. and Fisk, A.D. (1997), Effects of matching cognitive and perceptual-motor training to task components on complex task performance by older and younger adults, Proceedings of the Human Factors and Ergonomics Society, 115-119.

Mead, SE, Spaulding, R.A., Sit, B.M. and Walker, N. (1997), Effects of age and training on World Wide Web navigation strategies, Proceedings of the Human Factors and Ergonomics Society, 152-156.

Morris, R.J. and Brown, W.S. (1994), Age-related differences in speech variability among women, Journal of Communication Disorders, 27, 49-64.

Salthouse, T.A. (1991), Theoretical Perspectives on Cognitive Aging, Hillsdale, NJ: Erlbaum.

Sharit, J. and Czaja, S.J. (1994), Ageing, computer-based task performance, and stress: Issues and challenges, Ergonomics, 37(4), 559-577.

Welford, A.T. (1981), Signal, noise, performance and age, Human Factors, 23, 97-109.

Wiedenbeck, S. and Zila, P.L. (1997), Hands-on Practice in Learning to Use Software: A Comparison of Exercise, Exploration, and Combined Formats, ACM Transactions on Computer-Human Interaction, 4(2), 169-196.

Worden, A., Walker, N., Bharat, K. and Hudson, S. (1997), Making computers easier for older adults to use: Area cursors and sticky icons, Proceedings of CHI '97, 266-271.

Message from the CEO, Dr. Eric Schaffer — The Pragmatic Ergonomist

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Reviewed: 18 Mar 2014

This Privacy Policy governs the manner in which Human Factors International, Inc., an Iowa corporation (“HFI”) collects, uses, maintains and discloses information collected from users (each, a “User”) of its website and any derivative or affiliated websites on which this Privacy Policy is posted (collectively, the “Website”). HFI reserves the right, at its discretion, to change, modify, add or remove portions of this Privacy Policy at any time by posting such changes to this page. You understand that you have the affirmative obligation to check this Privacy Policy periodically for changes, and you hereby agree to periodically review this Privacy Policy for such changes. The continued use of the Website following the posting of changes to this Privacy Policy constitutes an acceptance of those changes.


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Reviewed: 18 Mar 2014

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