Association between computer use speed and age, impairments in function, and touch typing training in people with rheumatoid arthritis




To explore the associations between impairments in hand function associated with rheumatoid arthritis (RA) and limitations in computer peripheral use.


A total of 45 computer users with RA were recruited from the Arthritis Network Research Registry. Impairments in hand function were measured using the Keitel Hand Function Index, and the Arthritis Hand Function Test, while general activity limitations were measured with the Health Assessment Questionnaire. Speed of computer peripheral device use was obtained at a laboratory work station using the Assessment of Computer Task Performance (ACTP).


Multiple regression models suggested that keyboarding speed was significantly associated with touch typing training and age, while mouse speed was significantly associated with age. Impairments in hand function were significantly associated with only 2 of 7 keyboarding tasks and no mouse tasks. General activity limitations were associated with 2 of 7 keyboarding tasks and 2 of 5 significant mouse tasks. A comparison of this study's sample with a normative sample reported for the ACTP suggested that this sample's keyboarding speed was similar to a nonimpaired sample, while mouse speed was much slower.


Reduced keyboarding speed is most strongly associated with touch typing skill. General activity limitations appear to be the strongest arthritis-related predictor of decreased computer use speeds. Many computer users with RA will not experience reduced productivity in typing speeds, although many may be slower than their nonimpaired counterparts for mouse use.


From 1993 to 2003, the number of workers using computers increased from 46% (1) to 56% (2), and this number is expected to continue to increase. Workers, therefore, must be efficient computer users to remain competitive in the work force. For workers with rheumatoid arthritis (RA), their capacity to use a computer can be limited. People with RA experience impairments in hand range of motion and strength due to deformities secondary to chronic, progressive synovitis of the joints (3). They report problems with hand-intensive tasks generally (3), and specifically with computer use (4, 5).

Workers with RA have high rates of work disability, including premature work cessation (6–9) and reduced hours (10). One way workers with RA reduce work disability is to switch to less physically demanding jobs (5, 11) such as administrative or managerial positions (5, 7, 12). Intensive computer use is required by 86% of management jobs and 80% of administrative jobs (13); therefore, workers with RA may remain at risk for work disability secondary to problems with computer use.

Computer users input data with 2 types of peripheral devices, the keyboard and mouse, both of which require coordinated hand function such as typing, holding down 2 or more keyboard keys simultaneously, gripping and moving the mouse, stabilizing the mouse while activating buttons, and holding down mouse buttons while dragging objects. There are 2 different methods of typing: visually-guided typing and touch typing. In visually-guided typing, typists scan the keyboard to locate keys, usually typing with only 1 or 2 fingers. In touch typing, keys are activated by specific, predesignated fingers, allowing the use of kinesthetic feedback to locate the keys. Kinesthetic feedback eliminates visual interactions with the keyboard (14) and allows integration of all fingers during typing (14, 15). Touch typing is a task-specific skill acquired through training and practice (14, 16), and is a more efficient method of typing than visually-guided typing (17). Motor skill, such as the ability to tap fingers quickly, is not consistently associated with speed in touch typing because speed in touch typing is most strongly associated with the ability to look ahead in the text (17), a perceptual ability rather than a motor skill (16, 17). Older expert typists can be as fast as younger typists, even when they demonstrate slowed reaction time, as they compensate for slowed reaction times by looking farther ahead in the text (18).

There is no similar method of training for mouse use. Mouse use is a complex motor task that requires a variety of skills (19) such as high precision (pointing), rapid movements (double clicking), coordinated hand/arm movements (dragging), and superior hand/eye coordination (pointing, dragging) (20). Studies have found that elderly mouse users tend to make more errors during mouse use than younger ones (20). In particular, elderly mouse users have problems with clicking, double clicking, and dragging due to slip errors (21, 22). The term slip error refers either to prematurely moving the mouse off a target while clicking, or prematurely releasing the mouse button while dragging.

Based on the requirements of computer use, it would seem that computer users with RA would need to have functional integrity of the hand, such as adequate hand range of motion (ROM), strength, and manipulative skills to be able to efficiently use computer peripherals; however, there is, to our knowledge, no information on how impairments in strength and ROM can affect the use of computer peripherals. In addition, although research has identified that people with RA have self-reported problems using computers (4), there has been no objective documentation of reduced productivity during computer use by people with RA. As many workers with arthritis use a computer on the job, it becomes vital to understand if there is reduced productivity during computer use and how the impairments in hand function caused by RA affect the productive use of computer peripherals.

This descriptive study explored the associations between impairments in hand function associated with RA and limitations in peripheral device use. We examined 2 questions: which variables, impairments in range of motion, impairments in hand function, general activity limitations, or task-specific training (i.e., touch typing training) explain the most variance in keyboard and mouse speeds in computer users with RA?, and how do keyboard and mouse speeds in a sample of computer users with RA compare with samples of computer users with and without impairments?



This correlational study was approved by the University of Pittsburgh Institutional Review Board (IRB). Participants were recruited from the University of Pittsburgh Medical Center (UPMC) Arthritis Network Research Registry. This confidential registry is an IRB-approved list of people with RA, fibromyalgia, osteoarthritis, osteoporosis, or gout, interested in participating in arthritis-related studies. People who attend a university-based or 1 of 2 community-based rheumatology practices in western Pennsylvania are approached to enroll in the Registry. Principal investigators interested in using the Registry submit an IRB-approved recruitment letter that is mailed, along with a Registry cover letter, to registrants who meet inclusion criteria. Registrants interested in participating in the study respond by contacting study personnel. The Registry is an honest broker system; therefore, study personnel are never provided with a list of the registrants. We were, therefore, unable to compare respondents and nonrespondents.

Respondents were included in this study if they were age 18–65 years, had RA, used a computer, and had some arthritis-related involvement of the upper extremity. Respondents were excluded if they had comorbidities that could prevent upper extremity use, if they had other rheumatic diseases, or if they did not speak English. Recruitment started in August 2007 and was completed by March 2008.


Hand function was assessed using the Keitel Hand Function Index (KHFI), which assesses active ROM (23), and the Arthritis Hand Function Test (AHFT), which assesses overall hand function (24). The KHFI is a subsection of the Keitel Functional Test (25, 26), and consists of 11 performance test items that measure active ROM of the thumb, fingers, wrists, forearms, and elbows. Total KHFI scores range from 4 to 52, with higher scores indicating greater impairment in active ROM. The KHFI has demonstrated good reliability, validity, and sensitivity (23, 27).

The AHFT measures pure and applied strength and dexterity in a variety of hand tasks. It consists of 11 test items: 3 pure strength (right/left 1 grip and 2 pinch), 1 pure dexterity (right and left 9-hole peg test), 5 bilateral applied dexterity (e.g., lacing a shoe and doing buttons), and 2 bilateral strength (e.g., lifting cans and pouring water). One bilateral dexterity task, opening and closing a safety pin, was omitted due to concerns about subjects pricking their fingers, leaving 10 test items. The AHFT has demonstrated good reliability and validity (24, 28, 29). The AHFT raw scores can be transformed into 4 categorical impairment scores of severe, moderate, mild, and effective (30). For this study, the impairment scores were summed to obtain a single hand function score, total AHFT. Total AHFT scores range from 14 to 56, with higher scores indicating less impairment in hand function.

General activity limitations were determined with the Health Assessment Questionnaire (HAQ) (31). The HAQ measures activity participation in 8 areas and has good reliability and validity (31). The raw data from each category are translated into an overall HAQ disability index (HAQ DI) scaled 0–3, where 3 indicates worse functioning (32).

Computer peripheral device use was assessed using the Assessment of Computer Task Performance (ACTP) (33). The ACTP is a timed test that assesses the ability to use a standard computer keyboard and a standard computer mouse. Each section consists of 7 subtests (Table 1). Outcomes are measured in seconds with higher scores indicating slower performances. The ACTP has adequate validity and good reliability and has normative data obtained on 30 individuals without impairment and 24 individuals with impairments (33). The impaired group consisted of people with upper extremity physical impairments or pain who might require assistive devices to use a computer. The sample was quite heterogeneous for diagnoses and included stroke, cerebral palsy, quadriplegia, quadriparesis, arthritis, and degenerative disease. The nonimpaired sample consisted of people without impairment. Inclusion criteria for both groups included: 1) experience using a computer; 2) English speaking; 3) cognitively intact; 4) age ≥14 years; and 5) education level greater than elementary school (33). Data from these 2 normative samples were used as the comparison sample to answer this study's research question: How do keyboard and mouse speeds in a sample of computer users with RA compare with samples of computer users with and without impairments?

Table 1. Description of the subtests of the Assessment of Computer Task Performance (33)*
  • *

    K = keyboard tasks; M = mouse tasks.

K1Type alphabetTyping the alphabet from memory
K2Type wordsTyping a series of words that as a whole contain all letters of the alphabet
K3Type sentencesTyping two sentences
K4Repeat keysTyping a sequence of keys that contain repeated strikes of the same letter or symbol (from 2 to 5 repeats)
K5Hold key downHolding down a single key to fill the space between two marks 2–3 inches apart
K6Move cursor w/keyRepeatedly striking the arrow keys to move the cursor along a path
K7Type paragraphTyping a paragraph consisting of almost all the keyboard keys
M1Point/clickPointing and clicking on a specific point, moving the cursor along a specified path, clicking again
M2Drag/drop (curved)Dragging an icon along a corkscrewed path
M3Drag/drop (angled)Dragging an icon along a path containing numerous right angles
M4Stop/2-clickPointing and double clicking, moving the cursor to another point, stopping and double clicking
M5Drag/drop (repeat)Dragging an icon and repeatedly releasing it at specified points
M6Change window size (edge)Moving a rectangle to a target, then resizing the rectangle to the target by dragging the edges (3 steps)
M7Change window size (corner)Moving a rectangle to a target, then resizing the rectangle to the target by dragging the corners (2 steps)


After obtaining informed consent, demographic information was obtained, including age, sex, race, RA duration, and computer use information, including whether the participants had ever received touch typing training. The HAQ, the KHFI, and the AHFT were administered. Participants were seated at a computer workstation adjusted to their preferred configuration. Participants used a standard flat keyboard (Dell model #SK-8115; Dell Computers, Roundrock, TX) for the typing tasks, and a standard mouse (Microsoft Comfort Optical Mouse 3000; Microsoft, Redmond, WA) for the mouse use tasks. The ACTP was administered, and per the ACTP protocol each subtest was administered twice.

Statistical analyses.

The KHFI 11 subscores were summed to create a total KHFI score. The AHFT raw scores were transformed into impairment scores (30) and these were summed to create a total AHFT score. The HAQ data were transformed into an overall HAQ DI score (32). Dumont et al (33) only reported the time score for the second administration of each test. We therefore only used the time score from the second administration of each of our tests for data analyses.

Initial data analyses were exploratory. Frequencies, means, ranges, SDs, and/or interquartile ranges were computed depending on the data type. All outcome data were examined and found to approximate a normal distribution. We used Pearson's correlations to explore the univariate associations between upper extremity impairments and limitations and computer use speed, and then regression modeling to further explore the associations for each independent variable while controlling for the other variables. Each of the 14 subtests on the ACTP was the outcome variable for a separate regression model. Predictor variables for the keyboarding tasks were 2 general variables, age (years), and touch typing training (yes, no), and 3 arthritis-related variables: total KHFI, total AHFT, and HAQ DI. We omitted touch typing training for the mouse tasks. Because this was an exploratory study, the alpha was set at 0.05 a priori, and no controls were placed for multiple testing. We calculated semipartial correlations (r2), which represent the relationship between each predictor variable while controlling for the effect of all other variables.

To explore the effect of RA impairment on productivity, we compared this study's mean scores obtained for each of the ACTP subtests with the normative data provided by the ACTP developers using effect size r. Effect size r is a point-biserial correlation that indicates the degree to which the independent variables (this study's data versus the ACTP normative data) are associated with the outcome score (keyboarding or mouse speed). An r <0.10 is considered a negligible effect size, an r ≥0.10 a small effect, an r ≥0.24 a moderate effect, an r ≥0.37 a large effect, and an r ≥0.71 a very large effect size (34). We calculated a 95% confidence interval (95% CI) around r.


We mailed 1,100 recruitment letters to registrants with RA in the UPMC Arthritis Network Research Registry. Ninety-five people responded (9% response rate); 29 were ineligible, 8 declined, and 13 contacted us after we had already obtained our sample of 45 participants. On average, the sample was in their mid-50s, had RA for 17 years, and was primarily female and white. Approximately 50% were employed in full- or part-time paid employment. All but one participant used a computer at home, and 100% of those who worked used a computer at work. The average hours of computer use were 18 hours per week. A majority of the sample had been trained in touch typing (73%). Most workers reported that using a computer was very important for work (82%), and many participants reported that using a computer was somewhat or very important at home (73%) (Table 2). On average, the sample demonstrated mild impairments for the total KHFI and HAQ DI, and moderate impairments for the total AHFT (Table 3), as the mean score for this sample for the total KHFI and HAQ DI was lower than the central point of the range of scores for these measures, while the total AHFT mean score was similar to the central point. Individually, some participants had severe impairments and activity limitations as indicated by the interquartile scores and the SDs.

Table 2. Comparison of the variables of the nonimpaired and impaired samples with those of the study of Dumont et al (33)*
VariableCurrent study RA (n = 45)Dumont et al
No impairment (n = 30)Impairment (n = 24)
  • *

    Values are the number (percentage) unless indicated otherwise. RA = rheumatoid arthritis; NP = information was not provided.

Age, mean ± SD years56.2 ± 8.534 ± NP46 ± NP
RA duration, mean ± SD years16.7 ± 10.3NPNP
Computer use, mean ± SD hours/week18.1 ± 15.6NPNP
Female sex38 (84)17 (57)9 (38)
White race39 (87)NPNP
Right hand dominance39 (87)29 (97)14 (58)
Have paid employment23 (51)14 (47)17 (71)
Computer use   
 Touch type, yes33 (73)12 (40)3 (13)
 Home use, yes44 (98)NPNP
 Work use, yes22 (49)NPNP
Most frequently used device   
 Keyboard10 (22)NPNP
 Mouse12 (27)NPNP
 Both equally22 (49)NPNP
Computer importance   
 Work (n = 22)   
  Very important18 (82)NPNP
  Somewhat important3 (14)NPNP
  Not important1 (5)NPNP
 Home (n = 44)   
  Very important15 (34)NPNP
  Somewhat important17 (39)NPNP
  Not important12 (27)NPNP
Table 3. Mean ± SDs, minimum and maximum, and mean quartile scores of the outcome variable ACTP and predictor variables*
 No.Mean ± SDMinimumMaximum25th50th75th
  • *

    K1–M7 are measured in seconds. ACTP = Assessment of Computer Task Performance (in seconds; lower score = faster peripheral use); KHFI = Keitel Hand Function Index (range 4–52; lower score = less impairment); AHFT = Arthritis Hand Function Test (range 14–56; higher score = less impairment); HAQ DI = Health Assessment Questionnaire disability index (range 0–3; lower score = less impairment); K1 = type alphabet; K2 = type words; K3 = type sentences; K4 = repeat keys; K5 = hold key down; K6 = move cursor w/key; K7 = type paragraph; M1 = point and click; M2 = drag and drop (curved); M3 = drag and drop (angled); M4 = stop and 2-click; M5 = drag and drop (repeat); M6 = change window size (edge); M7 = change window size (corner).

  • Some subjects were not included because they were unable to complete the test.

Total KHFI4321.8 ±
Total AHFT4535.5 ±
HAQ DI451.0 ±
K14514.6 ± 5.56.532.110.413.717.6
K24436.7 ± 18.412.588.021.334.249.1
K34533.6 ±
K44518.0 ± 6.87.536.112.916.221.3
K54528.7 ± 8.116.955.822.727.134.2
K64529.9 ± 7.318.348.025.127.634.2
K745210.1 ± 93.672.0418.0122.0195.0284.0
M14558.7 ± 19.724.4108.343.555.072.2
M24349.2 ± 15.030.488.837.845.154.4
M34324.1 ± 7.312.345.617.923.428.8
M4459.6 ±
M54511.7 ±
M64520.0 ± 13.97.381.011.715.524.5
M73911.5 ±

All keyboarding regression models were significant, explaining 27–45% of the variance in typing speed (Table 4). Touch typing training had large significant associations with faster typing speeds in 4 models (K2, K3, K5, K7; r2 = 0.40–0.56). A younger age had moderate to large significant associations (r2 = 0.32–0.51) with faster typing speeds in 4 models (K1, K4, K5, K6). Of the 3 arthritis-related variables, total AHFT (K1 r2 = −0.29, K6 r2 = −0.35) and HAQ DI (K2 r2 = 0.26, K4 r2 = 0.30) were significantly associated with typing speed in 2 models. For the total AHFT, those with less impairment in hand function had faster typing speeds, and for the HAQ DI, those with greater activity participation had faster typing speeds. The total KHFI was significantly associated in 1 model (K1 r2 = −0.34) and indicated that those with greater active ROM impairment had faster typing speeds.

Table 4. Multiple regression models of the associations between peripheral use speed and predictor variables*
 No.Age r2KHFI r2AHFT r2HAQ r2Touch r2Model
  • *

    K1–M7 are measured in seconds. KHFI = Keitel Hand Function Index (lower score = less impairment); AHFT = Arthritis Hand Function Test (higher score = less impairment); HAQ = Health Assessment Questionnaire Disability Index (lower score = less impairment); K1 = type alphabet; K2 = type words; K3 = type sentences; K4 = repeat keys; K5 = hold key down; K6 = move cursor with key; K7 = type paragraph; M1 = point and click; M2 = drag and drop (curved); M3 = drag and drop (angled); M4 = stop and 2-click; M5 = drag and drop (repeat); M6 = change window size (edge); M7 = change window size (corner).

  • P ≤ 0.05.

  • P ≤ 0.01.

K5430.32−0.11−< 0.001
M6430.530.< 0.001

Five of the 7 mouse regression models (M2, M4, M5, M6, M7) were significant, explaining from 26–43% of the variance in mouse speed (Table 4). A younger age had significant, large associations with faster mouse speeds in all the significant models (r2 = 0.43–0.54). The HAQ DI had a significant association with mouse speed in 2 of the significant models (M5 r2 = 0.31, M6 r2 = 0.27), suggesting that those with better activity participation had faster mouse speeds. No other variable was associated with mouse speed in any model (Table 4).

Table 3 provides the mean, minimum, maximum, and percentile scores for the ACTP subtests. The effect sizes provided in Figures 1 and 2 put the differences between this study sample's and the sample scores of Dumont et al (33) into perspective. The negligible to small effect sizes and the 95% CIs crossing zero suggest that this study sample's keyboarding speed scores were comparable to the nonimpaired sample of Dumont et al (Figure 1). Effect sizes for mouse speeds were generally moderate to large and favored the nonimpaired sample of Dumont et al. Conversely, the effect sizes comparing this sample with the impaired sample indicated that the effect sizes for keyboard and mouse speeds were generally large to very large and favored our study sample. The exceptions were point and click (M1) and drag and drop curved (M2) (Figure 2).

Figure 1.

Comparison of current study with nonimpaired sample from the study by Dumont et al (33). Effect sizes comparing the scores reported for the nonimpaired sample reported by Dumont et al and this study's sample for the Assessment of Computer Task Performance. Solid shapes represent the effect size (r) and horizontal lines define the 95% confidence intervals. M7 = change window size (corner); M6 = change window size (edge); M5 = drag and drop (repeat); M4 = stop and 2-click; M3 = drag and drop (angled); M2 = drag and drop (curved); M1 = point and click; K7 = type paragraph; K6 = move cursor w/key; K5 = hold key down; K4= repeat keys; K3 = type sentences; K2 = type words; K1 = type alphabet.

Figure 2.

Comparison of current study with impaired sample from the study by Dumont et al (33). Effect sizes comparing the scores reported for the impaired sample reported by Dumont et al and this study's sample for the Assessment of Computer Task Performance. The solid shapes represent the effect size (r) and the horizontal lines define the 95% confidence intervals. M7 = change window size (corner); M6 = change window size (edge); M5 = drag and drop (repeat); M4 = stop and 2-click; M3 = drag and drop (angled); M2 = drag and drop (curved); M1 = point and click; K7 = type paragraph; K6 = move cursor w/key; K5 = hold key down; K4= repeat keys; K3 = type sentences; K2 = type words; K1 = type alphabet.


Generally, the strongest predictor of typing speed was touch typing training. Tasks where touch typing training demonstrated the lowest association (K1, K4, K6) were memorized tasks or tasks that required accurate key strikes. The strong association of touch typing supports other studies that have suggested that the perceptual aspect of looking ahead while typing is more important to speed than the physical aspect of striking the keys (17). Only in models where touch typing was not important (K1, K6) was speed significantly related to the level of impairment in hand function, suggesting that the ability to rapidly strike keys rather than correctly perceive content was the more important skill for these tasks.

For mouse use, the most important variable explaining mouse speed was age, which was significant in all 5 models that reached statistical significance. For typing, age was significant in 4 of 7 significant models. Older age is associated with reduced speeds for reaction time and tapping rate (21). Although research on mouse use has indicated that older adults are significantly slower than younger adults (21, 22), research on typing suggests that older expert typists are not slower (18) because they develop compensation strategies. Our study supported these observations: for keyboard tasks that required repeated key strikes (K1, K4, K6), age was the strongest predictor of keyboarding speed, but for those that required touch typing, touch typing associations were stronger than age.

Generally, this study found few significant associations between peripheral device use speed and impairments in hand function, and it was those tasks that required accurate key strikes (K1, K6) that were significantly associated with impairments in hand function. There was a trend in the data that indicated that larger impairments in range of motion, as measured by the KHFI, were associated with faster typing and mouse use speeds, an unexpected result. Previous research on the association between impairments and task performance has suggested that there is not a consistently strong association (35–37). Instead of impairments in hand function, decreased capacity in context-specific activity ability, as measured by the HAQ DI, was the arthritis-specific variable most often significantly associated with speed in all 14 models. These significant associations suggest that individuals who experience general activity limitations in activities of daily living may also experience limitations in specific tasks such as peripheral device use.

On average, the keyboarding speed of this sample of computer users with RA was comparable to a sample of computer users without impairment (33). This suggests that, in general, this sample would be competitive in the job market for keyboarding skills. However, a closer examination of the most complex typing task, paragraph typing (K7), suggests that while the average computer user with RA had speeds similar to those without an impairment, a minority of computer users with RA were considerably slower (418 seconds) than the slowest keyboard user without an impairment reported in the study by Dumont et al (380.2 seconds) (33), and slower than the average computer user with an impairment (313.2 seconds). Since our sample's computer users with RA demonstrated significantly slower mouse speeds than the nonimpaired sample in the study by Dumont et al (Figure 1), they may demonstrate lower productivity and skill levels for overall computer use.

This sample of computer users with RA were less impaired during computer use than the average computer user with impairments reported by Dumont et al (33) for both keyboard and mouse use. The impaired sample in the study by Dumont et al consisted of clients with a variety of diagnoses, including stroke, traumatic brain injury, and cerebral palsy. These diagnoses often result in both physical and perceptual impairments. Both typing and mouse use require good perceptual and cognitive abilities (17, 38). The slower peripheral device use reported in the Dumont study may be due to cognitive and perceptual impairments as well physical ones.

There were several limitations to our study. The response rate for this study was small. Although this study's sample is similar in demographics to other studies that have examined workers with RA (39), the results may only represent a very specific group of people with RA. Due to the honest broker system in the Registry we were unable to compare respondents with nonrespondents to determine if they differed. The logistic regression models in this study were developed on a small sample, which may have made them unstable. We tried to examine overall trends rather than individual models to control for this. We could not control for the number of errors produced during the ACTP. Thus, participants might use the computer slowly to prevent errors rather than because they had an impairment. This may explain the low correlations between impairment scores and computer use speeds and may also explain the unexpected findings that those with greater active ROM impairment had faster typing speeds after controlling for all other variables. Future studies should identify methods to control for errors in analysis. Although we identified if participants had received touch typing training, we did not identify the extensiveness of that training, nor did we assess if they actually used touch typing techniques during the ACTP. The comparison sample was an historic control sample obtained from a study by Dumont et al (33). There is only limited information on their recruitment, testing, and results. Our study's subjects were older than those in the study by Dumont et al. Since older users are typically slower for mouse use speeds there might have been a smaller difference between the nonimpaired sample and our study's sample if similar ages had been used. To help reduce the biases of using an historic comparison group, this study should be replicated with a sample of both people with RA and people without RA so that the effect of personal factors, such as age, can be examined statistically. The small sample size, low response rate, and the use of an historic comparison sample limit the ability to generalize the results of the study.

This study is an exploratory study that should be used to develop questions to ask in more rigorous studies. However, this study provides some useful insights related to computer use by a motor-impaired sample, and supports the importance of task-specific training in increasing productivity during keyboard use. Individuals with touch typing training were faster keyboard users than those without training. This suggests that if people with motor impairments have the capacity to learn touch typing it might increase their overall speed. Clinicians may want to consider informing computer users with RA to take touch typing training to improve performance.

Unfortunately, there is no similar task-specific training for mouse use, and there was a larger effect of RA on mouse speed. Research has suggested that different mouse use tasks require different capacities (19), and therefore, mouse use training should consist of training in several skills, including accurate pointing and the ability to drag objects on a screen. Unlike keyboard use, all mouse use requires people to look at the screen while using the mouse, which may be problematic for individuals with sensory problems. Besides mouse use training, interventions that decrease the accuracy requirements of a task, such as increasing the size of icons, and methods to reduce slippage during pointing and dragging tasks may help increase mouse use speed in computer users with arthritis.

In conclusion, impairments in hand function have a limited association with reduced computer use speed; it is task-specific skill training that appears to have the strongest relationship. Many computer users with RA will not experience reduced productivity in typing speeds, although some may be slower than their nonimpaired counterparts for mouse use. This reduced productivity has the potential to place workers with RA at risk for work disability. Further research is needed to identify the most effective strategies to maintain productivity in computer users with RA.


All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be submitted for publication. Dr. Baker had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study conception and design. Baker, Rogers.

Acquisition of data. Baker.

Analysis and interpretation of data. Baker, Rogers.


The authors thank Abbey Sipp for her assistance with data collection.