Visual–Motor Integration (VMI) Is Also Relevant for Computer,Smartphone,and Tablet Use by Adults: Introducing the Brief Box Clicking Test

Abstract

Importance: Visual–motor integration (VMI) is typically examined in children to promote handwriting, but it may also be relevant for adults’ capacity for technology use.

Objective: To examine the reliability and validity of speed of completion of the box clicking test, a web-based test of VMI.

Design: Participants in the Understanding America Study completed online surveys on a regular basis, including a very brief (less than 30 s) self-administered box clicking test. For validity testing, we examined whether box clicking speed was associated with constructs relevant to visual–perceptual skills and motor coordination, the skills underlying VMI. Test–retest reliability was examined by computation of intraclass correlation coefficients.

Participants: A total of 11,114 adults.

Measures: Measures included the completion time for the box clicking task and measures relevant to visual perception (e.g., perceptual speed) and motor coordination (e.g., self-reported functional limitation).

Results: Results suggested that the box clicking test was a VMI task. Slower test performance was associated with lower visual–perceptual speed and a greater likelihood of reporting difficulties with dressing, a motor coordination relevant task. Box clicking tests taken within at least 2 yr of one another had moderate test–retest stability, but future studies are needed to examine test–retest reliabilities over brief (e.g., 2-wk) time intervals.

Conclusions and Relevance: The box clicking test may serve both as a tool for research and to clinically observe whether clients have VMI difficulties that interfere with computer, smartphone, or tablet use.

Plain-Language Summary: Use of devices such as smartphones and computers is increasingly becoming integral for daily functioning. Visual–motor integration (VMI) has often been addressed by occupational therapists to support handwriting of children, but it may also be important for technology use by adults. Prior literature supports the relevance of VMI to technology use, and adults with various chronic conditions have been found to have decrements in VMI. We tested the psychometric properties of a brief box clicking test of VMI that could be used to examine VMI underlying technology use among adults. Overall, results suggested that the box clicking test was a VMI task. Just as speed of gait has been used as an index of functional mobility, speed on the box clicking task seemed serviceable as an index of VMI ability. The box clicking test may also be used for clinical observation of whether VMI interferes with technology use.

The box clicking test can be used as a tool for research as well as to clinically observe whether occupational therapy clients have visual–motor integration difficulties that interfere with their use of a computer, smartphone, or tablet.

Visual–motor integration (VMI) is the ability to coordinate visual perception (the reception and interpretation of visual information; Bouska et al., 1990) and motor coordination (i.e., hand–eye coordination; Beery et al., 1997). For adults, VMI decrements can interfere with technology use, which is an increasingly important part of daily functioning (Muñoz-Neira et al., 2012) for tasks including health management (e.g., accessing personal medical records; Liu et al., 2019), making online purchases (Koch et al., 2020), and maintaining social connections (Williams, 2019). VMI is often addressed by occupational therapists in pediatric settings to support handwriting (Dankert et al., 2003; Kaiser et al., 2009), but it may also have importance for technology use among adult populations (Ahn, 2021; Perochon et al., 2023; Radovanovic, 2013).

Several formal VMI tests have been developed. They typically involve copying shapes (paper–pencil administration) with the assumption that the ability to copy more complicated shapes is indicative of greater VMI ability (Beery et al., 1997; Deitz et al., 2007). A more recently developed VMI test involved presenting people with multiple moving bubbles on a tablet screen and asking them to pop (tap) them (Perochon et al., 2023). Metrics such as the average amount of time needed to pop a bubble, number of screen touches, and tapping accuracy were used to infer VMI ability (Perochon et al., 2023).

We propose a box clicking test as a brief measure of VMI that can be administered online and completed on computers, tablets, and smartphones. A typical shape-copying task can be divided into the visual–perceptual step of looking at the shape to copy and the motor coordination step of moving the writing utensil to replicate the shape. The box clicking test can similarly be divided into visual–perceptual and motor coordination steps. In the visual–perceptual step, participants need to read instructions to click the four boxes underneath the text (Figure 1), after which they need to visually identify the boxes. Reading speed has been strongly associated with visual–perceptual skills (Çayir, 2017; Legge et al., 2007). Next, the motor coordination step involves moving a mouse or one’s finger to all the perceived boxes.

Figure 1.

Box clicking task screen.

Unlike other VMI tests that often emphasize accuracy of copying, we hypothesized that the speed of completing the box clicking test could serve as an indicator of VMI ability. That is, we anticipated that individuals with greater VMI ability would complete the box clicking task faster than those with lower VMI ability. Consistent with this, prior research has found an association between greater VMI ability and faster handwriting (Brown & Link, 2016; Tseng & Chow, 2000). Furthermore, there is precedence for inferring ability from speed. For instance, physical therapists often measure gait speed as an indicator of functional mobility (Kim et al., 2016).

Present Study

The purpose of this study was to evaluate the reliability and validity of the box clicking test as an indicator of VMI ability. We administered the box clicking test to more than 10,000 adults ages 18 yr and older in a nationally representative internet-based longitudinal panel. A subset of the sample completed the box clicking test twice, allowing for examination of test–retest reliability. We took advantage of the breadth of other variables measured as part of the ongoing internet panel study to examine the validity of the box clicking test as an indicator of VMI ability.

We hypothesized that faster box clicking test speed would be associated with higher scores on cognitive performance measures, particularly those that are closely related to visual perception. Visual perception is a key underlying component of VMI, and prior research had found associations between the two (Bellocchi et al., 2017; Brown, 2012). We anticipated significant but smaller associations between box clicking speed and cognitive measures less directly related to visual perception, including tests of executive functioning, working memory, and fluid intelligence (Deary et al., 2010).

We expected that slower box clicking test speed would be associated with greater difficulties with activities of daily living (ADLs) and instrumental activities of daily living (IADLs). The motor coordination involved in clicking or tapping boxes has fine motor requirements, so we also hypothesized that box clicking speed would have a higher association with ADLs and IADLs that have a greater fine motor component, including dressing, preparing meals, and making phone calls.

We hypothesized that slower box clicking speed would be associated with a greater chance of reporting a medical diagnosis, particularly for diagnoses with presentations that likely affect VMI. Such diagnoses include stroke (Barrett & Muzaffar, 2014; i.e., through visual perception and motor coordination), diabetes (i.e., through visual perception; Yun et al., 2013), arthritis (i.e., through motor coordination; Kauranen et al., 2000), and dementia (through visual perception; Wood et al., 2013).

Finally, we expected slower box clicking speed to be significantly associated with older age, poorer eyesight, and less confidence in computer use. Poorer eyesight should lower VMI through decreased visual perception and so is associated with slower box clicking. Computer use confidence may serve as an approximate indicator of the amount of practice a person has with the visual–motor aspect of computer use. Greater practice would be expected to result in faster box clicking.

Method

All data for the current study were collected in the Understanding America Study (UAS), a probability-based internet-based longitudinal survey panel of U.S. adult residents, instituted by the Center for Economic and Social Research at the University of Southern California, Los Angeles (Alattar et al., 2018). Invited respondents without prior internet access are provided with a tablet and broadband internet access. Panel members are 18 yr of age or older and complete one or two web-based assessments per month. Participants provide electronic informed consent to be part of the UAS and to be invited to complete surveys, including core surveys covering a wide variety of topics, which are administered every 2 yr. Because respondents join the UAS at different time points (the panel is still growing) and complete assessments at varying schedules, the timing and number of assessments available per person and measure differ across respondents. Only study measures completed within 1 yr of when a participant completed the box clicking test were included in our analyses. The study was approved by the institutional review board of the University of Southern California, Los Angeles. All active UAS members were invited to complete the survey that included the box clicking test, and no specific inclusion or exclusion criteria were applied.

Measures

Box Clicking Test

The box clicking test was adapted from the Health and Retirement Study (2018) and programmed for web administration in the UAS. The test presented participants with the page shown in Figure 1. Boxes changed color when clicked to provide a visual confirmation that they were clicked. The box clicking test was offered to all UAS respondents once. A smaller subset of UAS participants ages 50 yr and older completed the box clicking test for a second time as part of a study comparing online assessment with telephone cognitive assessment (Gatz et al., 2023). A demo of the box clicking test can be accessed at the following link: https://uas.usc.edu/survey/playground/boxclick/test/index.php.

Our primary measure for VMI was the box clicking time from when the box clicking test page was loaded to when the final box was clicked, log-transformed to normalize its distribution. This box clicking time encompasses both visual perception (i.e., reading the instructions and perceiving the boxes) and motor coordination (i.e., clicking), consistent with VMI’s operationalization as a construct encompassing both these components. For details on data cleaning steps followed, refer to the Supplemental Material (available online with this article at https://research.aota.org/ajot). We also examined subcomponents of the box clicking time; the corresponding results are presented in the Supplemental Material.

Cognitive Performance

Various cognitive domains were assessed in the UAS. One visual perception test that we anticipated would have a strong association with box clicking speed relative to other cognitive measures was the Figure Identification task (Liu et al., 2022). Another was the average response time to brief survey questions across all surveys that UAS participants completed within a year of the box clicking test. There is evidence that these response times can serve as approximate measures of perceptual speed (Hernandez et al., 2023; Junghaenel et al., 2022; Schneider et al., 2023). The choice reaction time subtest of the Stop and Go task (Liu et al., 2022) was one more assessment closely related to visual perception, because the score was dependent on the speed of reading single words (i.e., “stop” or “go”) and then pushing the correct corresponding key. Cognitive measures less directly related to visual perception were other subtests of the Stop and Go task (i.e., response inhibition and task switching; Liu et al., 2022), Word Recall (episodic memory; Runge et al., 2015), Serial 7s (attention and working memory; Bristow et al., 2016), Number Series (fluid intelligence; Mather & Jaffe, 2016), Verbal Analogies (fluid intelligence; Mather & Jaffe, 2016), and Picture Vocabulary (word knowledge; Mather & Jaffe, 2016).

Self-Reported Functional Limitations

Questions regarding difficulties with ADLs or IADLs were taken from the Health and Retirement Study survey question, “Because of a health or memory problem, do you have any difficulty with (ADL/IADL)? Exclude any difficulties that you expect to last less than three months.” The response options were yes, no, or can’t do. If a participant responded with yes or can’t do, it was coded as a 1 to indicate difficulty, whereas a no response was coded 0 to indicate no difficulty. ADLs assessed were dressing, eating, walking, getting in or out of bed, using the toilet, and bathing. IADLs assessed were preparing meals, making a phone call, shopping for groceries, taking medications, doing housework or yardwork, and money management.

Self-Reported Diagnoses

Questions regarding diagnoses had the following structure: “Has a doctor ever told you that you have (diagnosis).” Diagnoses asked about were diabetes, arthritis, stroke, dementia, heart condition, hypertension, cancer, lung disease, and emotional–psychiatric problems.

Other Measures

We assessed eyesight with the question, “Is your eyesight excellent, very good, good, fair, or poor—using glasses or corrective lens as needed?” Response options were on a Likert scale from 1 (excellent) to 5 (poor). We tested computer use confidence with the question, “In general, how confident are you in using a computer for writing tasks that involve typing on the computer keyboard such as answering email?” Response options were on a Likert scale from 1 (not confident) to 4 (completely confident).

Statistical Analyses

Reliability

We examined test–retest reliability of the box clicking test for a smaller subset of the UAS sample that completed the box clicking test twice. The average time between tests was 1.3 yr (range = 0.2–3.3 yr), meaning that true change in VMI (e.g., from aging) could decrease reliability relative to a situation in which the tests were administered closer together. We examined reliability via computation of the intraclass correlation coefficient (ICC). ICCs may be interpreted according to the following cutoffs: poor (0–.19), fair (.20–.39), moderate (.40–.59), substantial (.60–.79), and almost perfect (.80–1.0; Bushnell et al., 2001). We examined how ICCs differed by the amount of time that elapsed between two completions of the box clicking test.

Validity

We examined correlations between the box clicking speed metrics and other study measures. We also examined partial correlations between the study measures that were adjusted for age and device type. Correlations can be interpreted according to the following guidelines: .1 for small, .3 for medium, and .5 for large (Cohen, 1988). All analyses were conducted with Mplus (Version 8.8; Muthén & Muthén, 1998) via the R package MplusAutomation (Hallquist & Wiley, 2018) in the statistical software R (R Core Team, 2020).

To compare the sizes of different correlations (e.g., testing whether the correlation between total box clicking time and visual–perceptual tests was higher than the correlation between box clicking time and other cognitive measures), we computed the full correlation matrix between all measures and conducted significance tests of differences between dependent correlations (Steiger, 1980). These tests of differences were conducted by first using Fisher’s z to transform each correlation to account for the nonnormal distribution of correlation coefficients (Asuero et al., 2006). Next, we jointly computed differences between Fisher’s z-transformed correlations that were expected to differ, and for each difference we tested whether the value was statistically significantly different from zero using the δ method in Mplus (Version 8.10; Muthén & Muthén, 1998).

Results

Data from a total of 11,114 participants were included in the analyses. Table A.1 in the Supplemental Material shows the participants’ demographic information. In brief, the average age of the sample was 49.7 yr (SD = 16.3), and 61% (n = 6,795) were female. In terms of race, the largest categories were White (n = 8,072; 73%), Black (n = 1,225; 11%), and Asian (n = 783; 7%); 17% of the sample (n = 1,847) identified as Spanish or Latino.

The box clicking time weighted quantiles were as follows: 25th percentile 8.88 s, median 12.27 s, and 75th percentile 17.73 s. Box clicking time differed significantly by device type (p < .001); 53% of participants completed the box clicking test on a computer (n = 5,844), 44% on a mobile phone (n = 4,912), and 3% on a tablet (n = 356). The weighted median box clicking time was 12.21 s for computers, 12.15 s for mobile phones, and 16.77 s for tablets. Table A.2 shows the box clicking time quantiles by age group, by subcomponents of box clicking time, and for the smaller telephone cognitive assessment study.

Reliability

Table 1 shows the different ICCs for box clicking time with various amounts of time between tests. We examined reliability for 1,563 participants who completed the box clicking test twice. The ICC for box clicking tests completed 6 mo or less apart was .48 (moderate reliability), and it decreased to .42 (still moderate) for tests completed within 1.5 to 2 yr of one another. The ICC dropped below .40 if there were more than 2 yr between tests, indicating fair reliability. Reliabilities for subcomponents of the box clicking time were similar to or lower than those shown in Table 1 (Table A.3 in the Supplemental Material).

Table 1.

Intraclass Correlation Coefficients (ICCs) for Test–Retest Correlations Between Box Clicking Tests

Measure	n	ICC
All observations	1,563	.44
0 mo to ≤6 mo between tests	156	.48
6 mo to ≤1 yr between tests	262	.48
1 yr to ≤1.5 yr between tests	519	.46
1.5 yr to ≤2 yr between tests	333	.42
2 yr to ≤2.5 yr between tests	435	.39
2.5 yr to ≤3 yr between tests	68	.32

Validity

Correlations between the box clicking time and other measures of relevance to VMI are shown in Table 2. Table 2 also lists the prevalence rates for ADL and IADL difficulties and the various diagnoses. The correlations between subcomponents of the box clicking time and other measures were similar to those in Table 2 but lower in magnitude (Table A.4 in the Supplemental Material). Table A.5 shows correlations between box clicking time and other measures stratified by device type, and Table A.6 depicts box clicking time correlations with adjustment for age and device type. Table A.7 shows the results of testing of differences between correlations that were expected to differ.

Table 2.

Correlations Without Adjustment for Age or Device Type (N = 11,114)

Variable	n	Total Box Clicking Time
Variable	n	r	p
Cognition a
Figure ID (perceptual speed)	7,671	−.40*	<.001
Average response time to brief questions across several surveys (perceptual speed)	10,459	−.57*	<.001
Stop and Go test baseline (choice reaction time, baseline)	7,231	−.26*	<.001
Stop and Go reverse (response inhibition)		−.28*	<.001
Stop and Go nonswitch (task switching)		−.33*	<.001
Stop and Go switch (task switching)		−.26*	<.001
Serial 7s (attention and working memory)	9,720	−.07*	<.001
Number Series (fluid intelligence)	9,883	−.11*	<.001
Verbal Analogies (fluid intelligence)	9,039	−.17*	<.001
Picture Vocabulary (word knowledge)	9,473	.04*	<.001
Word Recall–immediate	9,714	−.12*	<.001
Word Recall–delayed	9,715	−.13*	<.001
ADLs (binary)
Difficulty dressing (422/10,283 = 4.1% yes)		.08*	<.001
Difficulty eating (113/10,282 = 1.1% yes)		.03*	<.003
Difficulty walking (257/10,281 = 2.5% yes)		.06*	<.001
Difficulty getting in or out of bed (360/10,282 = 3.5% yes)		.06*	<.001
Difficulty using toilet (267/10,282 = 2.6% yes)		.05*	<.001
Difficulty bathing (350/10,281 = 3.4% yes)		.07*	<.001
At least 1 ADL difficulty reported (822/10,280 = 8% yes)		.10*	<.001
IADLs (binary)
Difficulty with meal preparation (226/10,281 = 2.2% yes)		.06*	<.001
Difficulty making phone calls (134/10,285 = 1.3% yes)		−.03*	.011
Difficulty shopping for groceries (473/10,283 = 4.6% yes)		.07*	<.001
Difficulty taking medications (165/10,289 = 1.6% yes)		.00	.625
Difficulty with housework–yardwork (1,035/10,244 = 10.1% yes)		.11*	<.001
Difficulty with money management (247/10,284 = 2.4% yes)		.00	.899
At least 1 IADL difficulty reported (1,433/10,236 = 14% yes)		.10*	<.001
Self-reported diagnoses (binary)
Diabetes (1,816/11,074 = 16.4% yes)		.12*	<.001
Arthritis (2,912/11,074 = 26.3% yes)		.16*	<.001
Stroke (255/11,075 = 2.3% yes)		.06*	<.001
Dementia (79/11,074 = 0.7% yes)		.02	.128
Heart condition (1,008/11,077 = 9.1% yes)		.10*	<.001
Hypertension (3,976/11,075 = 35.9% yes)		.17*	<.001
Cancer (897/11,074 = 8.1% yes)		.09*	<.001
Lung disease (532/11,078 = 4.8% yes)		.04*	<.001
Emotional/psychiatric problem (2,403/11,075 = 21.7% yes)		−.09*	<.001
Other
Age	11,073	.36*	<.001
Poor eyesight	11,080	.11*	<.001
Computer use confidence	7,894	−.24*	<.001

Note. Variables in bold are those for which associations of greater magnitude with the box clicking times were expected. ADLs = activities of daily living; IADLs = instrumental activities of daily living.

For all cognitive tests, higher scores indicate better cognitive functioning.

*p < .05.

Cognition

Consistent with our hypotheses, except for the Picture Vocabulary test, higher time on the box clicking task was significantly associated with lower scores on all cognitive measures (rs range from –.07 to –.57). After adjustment for age and device type, the association between the Picture Vocabulary score and box clicking time was in the expected direction (r = –.12, p < .001). Also consistent with expectations, the correlation between box clicking time and the Figure Identification task (r = –.40, p < .001) and the correlation between box clicking time and survey item response time–based perceptual speed (r = –.57, p < .001) were significantly greater than the correlations between box clicking time and all other cognitive measures less directly related to visual perception. The correlation between choice reaction time and box clicking time (r = –.26, p < .001) was significantly greater than the correlations between box clicking time and all other cognitive measures less relevant to visual perception except for the other subtests of the Stop and Go task.

ADLs

As anticipated, box clicking time was significantly associated with difficulties in all ADLs, although the correlations were small, ranging from .03 to .08. The correlation between box clicking time and dressing (r = .08, p < .001) was significantly greater than the correlations between box clicking time and four of five other ADLs anticipated to have lower fine motor demands. Contrary to our hypotheses, the correlation was not significantly larger than the correlation between box clicking time and difficulty bathing.

IADLs

Box clicking time had small correlations with difficulties in all IADLs except taking medications and money management, although significant relationships with all IADLs were expected. The correlation between box clicking time and difficulty with meal preparation (r = .06, p < .001) was significantly greater than the correlations between box clicking time and difficulty with two of four other IADLs (taking medications and money management) anticipated to have lower fine motor demands. Contrary to expectations, the correlation between box clicking time and phone use was not significantly greater than the correlations between box clicking time and all other IADLs anticipated to have lower fine motor demands.

Self-Reported Diagnoses

Box clicking time had small but significant positive correlations with all self-reported diagnoses (rs range from .04 to .17) except for psychiatric problems (r = –.09, p < .001) and dementia (r = .02, p = .128). The correlation between box clicking time and diabetes (r = .12, p < .001) was significantly greater than the correlations between box clicking time and two of five other diagnoses (lung disease and emotional problem) anticipated to have less of an impact on VMI. For arthritis, the correlation between box clicking time and arthritis (r = .16, p < .001) was significantly greater than the correlations between box clicking time and all other diagnoses expected to be less relevant to VMI, except hypertension. Finally, the correlations between box clicking time and stroke (r = .06, p < .001) and dementia (r = .02, p = .128) were not significantly greater than the correlations between box clicking time and any other diagnoses.

Other Measures

Box clicking time was significantly correlated with age, eyesight, and computer use confidence. Specifically, consistent with our hypotheses, slower performance of the box clicking task was associated with older age, poorer eyesight, and lower confidence in computer use.

Discussion

Even though the box clicking test requires only seconds to complete, our results support its validity as a VMI task. Box clicking time on box clicking tests taken within at least 2 yr of one another had moderate test–retest stability, but we were not able to assess test–retest reliabilities over brief (e.g., 2-wk) time intervals. Most of our validity hypotheses were confirmed, suggesting that scores on the box clicking task generally had expected relationships with relevant visual–perceptual and motor coordination variables.

The correlations of total box clicking time with ADL–IADL problems and self-reported diagnoses were all small to very small. Because participants were members of an internet panel study, our sample may have relatively high levels of functioning and technological experience. Thus, many participants may have found ways to use electronic devices despite difficulties associated with their ADL–IADL status and diagnoses, which may have attenuated the observed correlations. Future studies are needed to examine the relationship between box clicking time and other measures using samples with a broader range of functional ability. Nevertheless, the small magnitude of the correlations was aligned with expectation, given many possible presentations of difficulties with ADLs–IADLs and diagnoses. For instance, although fine motor skills may often underlie difficulties with dressing and therefore be anticipated to have an association with visual–motor skills (and box clicking time), numerous other factors can cause dressing difficulties, such as difficulties with standing–sitting balance and apraxia. The nonvisual–motor causes of dressing difficulties may have decreased the magnitude of the correlation between dressing difficulty and box clicking time.

Reliability

The fact that reliability was moderate for speed on box clicking tests taken 2 yr or less apart implies some degree of temporal stability in VMI. Reliability lowered for tests taken more than 2 yr apart, likely because differences in VMI scores across assessment time points were due to true changes in VMI (e.g., aging effects) rather than measurement error. Future research is needed to examine test–retest reliabilities over brief (e.g., 2-wk) time intervals to assess reliability with less influence from change over time.

Validity

The score on the Picture Vocabulary test may have had a positive association with box clicking time because older individuals may have greater word knowledge (Salthouse, 2014) but also worse VMI (Kim et al., 2014). Consistent with this, after adjustment for age, the association between the Picture Vocabulary test and box clicking time was negative. Furthermore, follow-up analyses indicated that older age was associated with a higher Picture Vocabulary test score (r = .41, p < .001).

All subtests of the Stop and Go task had similar associations with box clicking time, although the choice reaction time subtest was anticipated to have the largest correlation. This may have been because all subtests had scores that considered the speed of reading single words, and they differed primarily in the requested action after reading.

Bathing, shopping for groceries, and doing housework or yardwork had a larger than anticipated association with box clicking time on the box clicking test. When reporting difficulty in bathing, perhaps respondents had greater consideration of fine motor elements of the task (e.g., in-hand manipulation of soap bars) as opposed to other parts (e.g., transferring to the tub), thereby resulting in a higher association with box clicking task speed compared with what was expected. This argument had some support in follow-up analyses, which showed a high correlation between difficulty with dressing (a fine motor heavy activity) and difficulty with bathing (r = .51, p < .001). In terms of reporting difficulty in shopping for groceries, respondents may have put greater weight on the visual–perceptual component of the task (e.g., reading food labels) instead of other aspects (e.g., functional mobility), resulting in a higher association with box clicking time than anticipated. A correlation of −.11 (p < .001) was found between difficulty grocery shopping and visual perception as measured by the Figure Identification task. The association between box clicking time and housework–yardwork is difficult to decipher because of the wide range of activities (e.g., sweeping, gardening) that can constitute housework and yardwork. Finally, slower box clicking time was unexpectedly associated with less difficulty in making phone calls, and this may be because people with lower VMI took advantage of adaptations to make phone calls easier (e.g., voice dialing).

In terms of diagnoses, stroke had a lower correlation with box clicking time than expected. This may be because many participants who reported stroke did not have it recently (median of 4 yr between box clicking test data and stroke date), meaning they had time to regain motor function. Hypertension had an unexpectedly large association with box clicking time. A systematic review found that high blood pressure was associated with greater risk of cognitive decline in areas such as visuospatial abilities (Forte et al., 2019), meaning hypertension’s association with VMI may be attributable to decreased visual–perceptual skills. Contrary to expectation, individuals who reported a psychiatric diagnosis had a faster box clicking time. This could in part have been due to an age effect, whereby younger individuals may be more likely to have psychiatric diagnoses compared with older individuals (Substance Abuse and Mental Health Services Administration, 2022), but are also more likely to have higher VMI compared with older individuals (Kim et al., 2014). Consistent with this, in our sample, for every 1 yr increase in age, the log odds of reporting an emotional or psychiatric problem decreased by 0.022 (p < .001). Finally, contrary to our hypothesis, dementia was not found to have an association with box clicking time. This may be because a very small percentage of the sample (0.7%) reported dementia.

Adjustment for age and device type decreased the magnitude of most of the correlations with box clicking time in Table 2. The decrease in magnitude was most pronounced, however, in the correlations with ADLs, IADLs, and diagnoses. Perhaps age was a large driver of the association between box clicking time and these variables.

Limitations

Our study had several limitations. Box clicking time on the box clicking speed test was not compared with traditional VMI measures such as the Beery–Buktenica Developmental Test of Visual–Motor Integration (Beery et al., 1997). Future studies are needed to examine whether these measures are correlated, as would be expected even with the different types of tasks used (i.e., copying shapes vs. clicking on boxes).

Reliability within a day for speed-based tests such as the gait speed test and Timed Up and Go test has often been computed from two test trials, one after the other (Kim et al., 2016; Sebastião et al., 2016). Future research is needed to examine this within-day form of reliability for the box clicking test.

The reading requirement of the box clicking test may make it less relevant for young children but also a more naturalistic VMI task for adults The reading requirement of the test means it may not be feasible to implement with young children without adaptation.

Implications for Occupational Therapy Practice and Research

The results of this study have several implications for occupational therapy research and practice:

The box clicking test may be seen as the VMI counterpart to established assessments in other functioning domains such as the gait speed test and the Timed Up and Go test. All share the commonality of inferring ability from speed in conducting tasks of importance to daily functioning.

Occupational therapists can potentially use the box clicking test to allow for clinical observation of whether VMI interferes with computer, smartphone, or tablet use.

In its current form, the box clicking test can be used by researchers to study VMI at the population level, such as in large panel studies like the UAS.

To capture more granular differences in VMI, researchers can develop other versions of the clicking test that include more trials and difficulty levels.

Conclusion

The results of our study suggested that the brief box clicking test was a VMI task. Box clicking time on the box clicking test generally had expected associations with relevant measures of visual perception and motor coordination. A major strength of the test was that it took seconds to complete, making it easier to administer to clients as well as to participants in online studies. With technology (e.g., computers, smartphones, tablets) becoming increasingly integral to daily functioning, the box clicking test provides a form of VMI assessment of greater relevance to technology use in everyday life.

Supplemental Material

Supplemental Information

Supplementary material for Visual–Motor Integration (VMI) Is Also Relevant for Computer, Smartphone, and Tablet Use by Adults: Introducing the Brief Box Clicking Test

Supplementary material, sj-pdf-1-aot-10.5014_ajot.2024.050680.pdf for Visual–Motor Integration (VMI) Is Also Relevant for Computer, Smartphone, and Tablet Use by Adults: Introducing the Brief Box Clicking Test by Raymond Hernandez, Margaret Gatz, Stefan Schneider, Deborah Finkel, Jill E. Darling, Bart Orriens, Ying Liu and Arie Kapteyn in The American Journal of Occupational Therapy

References

Ahn

S. N.

(2021). Combined effects of virtual reality and computer game-based cognitive therapy on the development of visual–motor integration in children with intellectual disabilities: A pilot study. Occupational Therapy International, 2021, 6696779. https://doi.org/10.1155/2021/6696779

Alattar

Messel

, & Rogofsky

(2018). An introduction to the Understanding America Study internet panel. Social Security Bulletin, 78, 13–28.

Asuero

A. G.

Sayago

, & González

A. G.

(2006). The correlation coefficient: An overview. Critical Reviews in Analytical Chemistry, 36, 41–59. https://doi.org/10.1080/10408340500526766

Barrett

A. M.

, & Muzaffar

(2014). Spatial cognitive rehabilitation and motor recovery after stroke. Current Opinion in Neurology, 27, 653–658. https://doi.org/10.1097/WCO.0000000000000148

Beery

K. E.

Beery

N. A.

, & Buktenica

N. A.

(1997). The Beery–Buktenica Developmental Test of Visual–Motor Integration: VMI, with supplemental developmental tests of visual perception and motor coordination: Administration, scoring and teaching manual. Modern Curriculum Press.

Bellocchi

Muneaux

Huau

Lévêque

Jover

, & Ducrot

(2017). Exploring the link between visual perception, visual–motor integration, and reading in normal developing and impaired children using DTVP–2. Dyslexia, 23, 296–315. https://doi.org/10.1002/dys.1561

Bouska

M. J.

Kauffman

N. A.

, & Marcus

S. E.

(1990). Disorders of the visual perceptual system. In D. A. Umpherd (Ed.), Neurological rehabilitation (pp. 705–740). Mosby

Bristow

Jih

C.-S.

Slabich

, & Gunn

(2016). Standardization and adult norms for the sequential subtracting tasks of serial 3’s and 7’s. Applied Neuropsychology: Adult, 23, 372–378. https://doi.org/10.1080/23279095.2016.1179504

Brown

(2012). Are motor-free visual perception skill constructs predictive of visual–motor integration skill constructs? Hong Kong Journal of Occupational Therapy, 22, 48–59. https://doi.org/10.1016/j.hkjot.2012.06.003

10.

Brown

, & Link

(2016). The association between measures of visual perception, visual–motor integration, and in-hand manipulation skills of school-age children and their manuscript handwriting speed. British Journal of Occupational Therapy, 79, 163–171. https://doi.org/10.1177/0308022615600179

11.

Bushnell

C. D.

Johnston

D. C. C.

, & Goldstein

L. B.

(2001). Retrospective assessment of initial stroke severity: Comparison of the NIH Stroke Scale and the Canadian Neurological Scale. Stroke, 32, 656–660. https://doi.org/10.1161/01.STR.32.3.656

12.

Çayir

(2017). Analyzing the reading skills and visual perception levels of first grade students. Universal Journal of Educational Research, 5, 1113–1116. https://doi.org/10.13189/ujer.2017.050704

13.

Cohen

(1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge. https://doi.org/10.4324/9780203771587

14.

Dankert

H. L.

Davies

P. L.

, & Gavin

W. J.

(2003). Occupational therapy effects on visual–motor skills in preschool children. American Journal of Occupational Therapy, 57, 542–549. https://doi.org/10.5014/ajot.57.5.542

15.

Deary

I. J.

Penke

, & Johnson

(2010). The neuroscience of human intelligence differences. Nature Reviews Neuroscience, 11, 201–211. https://doi.org/10.1038/nrn2793

16.

Deitz

J. C.

Kartin

, & Kopp

(2007). Review of the Bruininks–Oseretsky Test of Motor Proficiency, Second Edition (BOT–2). Physical and Occupational Therapy in Pediatrics, 27, 87–102. https://doi.org/10.1080/J006v27n04_06

17.

Forte

De Pascalis

Favieri

, & Casagrande

(2019). Effects of blood pressure on cognitive performance: A systematic review. Journal of Clinical Medicine, 9, 34. https://doi.org/10.3390/jcm9010034

18.

Gatz

Schneider

Meijer

Darling

J. E.

Orriens

Liu

, & Kapteyn

(2023). Identifying cognitive impairment among older participants in a nationally representative internet panel. Journals of Gerontology: Series B, 78, 201–209. https://doi.org/10.1093/geronb/gbac172

19.

Hallquist

M. N.

, & Wiley

J. F.

(2018). MplusAutomation: An R package for facilitating large-scale latent variable analyses in Mplus . Structural Equation Modeling, 25, 621–638. https://doi.org/10.1080/10705511.2017.1402334

20.

Health and Retirement Study. (2018). HRS 2018—Section D: Cognition. https://hrs.isr.umich.edu/sites/default/files/meta/2018/core/qnaire/online/04hr18D.pdf

21.

Hernandez

Schneider

Wagman

Håkansson

Spruijt-Metz

, & Pyatak

E. A.

(2023). Validity and reliability of the Occupational Balance Questionnaire (OBQ11) in a U.S. sample of adults with Type 1 diabetes. American Journal of Occupational Therapy, 77, 7704205120. https://doi.org/10.5014/ajot.2023.050173

22.

Junghaenel

D. U.

Schneider

Orriens

Jin

Lee

P.-J.

Kapteyn

, . . . Stone

A. A.

(2022). Inferring cognitive abilities from response times to web-administered survey items in a population-representative sample. Journal of Intelligence, 11, 3. https://doi.org/10.3390/jintelligence11010003

23.

Kaiser

M.-L.

Albaret

J.-M.

, & Doudin

P.-A.

(2009). Relationship between visual–motor integration, eye-hand coordination, and quality of handwriting. Journal of Occupational Therapy, Schools, and Early Intervention, 2, 87–95. https://doi.org/10.1080/19411240903146228

24.

Kauranen

Vuotikka

, & Hakala

(2000). Motor performance of the hand in patients with rheumatoid arthritis. Annals of the Rheumatic Diseases, 59, 812–816. https://doi.org/10.1136/ard.59.10.812

25.

Kim

Park

Y.-K.

Byun

Y.-H.

Park

M.-S.

, & Kim

(2014). Influence of aging on visual perception and visual motor integration in Korean adults. Journal of Exercise Rehabilitation, 10, 245–250. https://doi.org/10.12965/jer.140147

26.

Kim

H. J.

Park

Lee

H. J.

, & Lee

(2016). The reliability and validity of gait speed with different walking pace and distances against general health, physical function, and chronic disease in aged adults. Journal of Exercise Nutrition and Biochemistry, 20, 46–50. https://doi.org/10.20463/jenb.2016.09.20.3.7

27.

Koch

Frommeyer

, & Schewe

(2020). Online shopping motives during the COVID-19 pandemic—Lessons from the crisis. Sustainability, 12, 10247. https://doi.org/10.3390/su122410247

28.

Legge

G. E.

Cheung

S.-H.

Chung

S. T. L.

Lee

H.-W.

, & Owens

D. P.

(2007). The case for the visual span as a sensory bottleneck in reading. Journal of Vision, 7, 9. https://doi.org/10.1167/7.2.9

29.

Liu

Schneider

Orriens

Meijer

Darling

J. E.

Gutsche

, & Gatz

(2022). Self-administered web-based tests of executive functioning and perceptual speed: Measurement development study with a large probability-based survey panel. Journal of Medical Internet Research, 24, e34347. https://doi.org/10.2196/34347

30.

Liu

Zhang

Yang

Zhou

Ren

Wang

, . . . Deen

M. J.

(2019). A novel cloud-based framework for the elderly healthcare services using digital twin. IEEE Access: Practical Innovations, Open Solutions, 7, 49088–49101. https://doi.org/10.1109/ACCESS.2019.2909828

31.

Mather

, & Jaffe

L. E.

(2016). Woodcock–Johnson IV: Reports, recommendations, and strategies. Wiley. https://doi.org/10.1002/9781394258864

32.

Muñoz-Neira

López

O. L.

Riveros

Núñez-Huasaf

Flores

, & Slachevsky

(2012). The Technology–Activities of Daily Living Questionnaire: A version with a technology-related subscale. Dementia and Geriatric Cognitive Disorders, 33, 361–371. https://doi.org/10.1159/000338606

33.

Muthén

L. K.

, & Muthén

B. O.

(1998). Mplus user’s guide. Muthén & Muthén.

34.

Perochon

Matias Di Martino

Carpenter

K. L. H.

Compton

Davis

Espinosa

, . . . Dawson

(2023). A tablet-based game for the assessment of visual motor skills in autistic children. NPJ Digital Medicine, 6, 17. https://doi.org/10.1038/s41746-023-00762-6

35.

R Core Team. (2020). The R project for statistical computing. https://www.r-project.org

36.

Radovanovic

(2013). The influence of computer games on visual–motor integration in profoundly deaf children. British Journal of Special Education, 40, 182–188. https://doi.org/10.1111/1467-8578.12042

37.

Runge

S. K.

Craig

B. M.

, & Jim

H. S.

(2015). Word recall: Cognitive performance within internet surveys. JMIR Mental Health, 2, e20. https://doi.org/10.2196/mental.3969

38.

Salthouse

T. A.

(2014). Quantity and structure of word knowledge across adulthood. Intelligence, 46, 122–130. https://doi.org/10.1016/j.intell.2014.05.009

39.

Schneider

Junghaenel

D. U.

Meijer

Stone

A. A.

Orriens

Jin

, . . . Kapteyn

(2023). Using item response times in online questionnaires to detect mild cognitive impairment. The Journals of Gerontology: Series B, 78, 1278–1283. https://doi.org/10.1093/geronb/gbad043

40.

Sebastião

Sandroff

B. M.

Learmonth

Y. C.

, & Motl

R. W.

(2016). Validity of the Timed Up and Go test as a measure of functional mobility in persons with multiple sclerosis. Archives of Physical Medicine and Rehabilitation, 97, 1072–1077. https://doi.org/10.1016/j.apmr.2015.12.031

41.

Steiger

J. H.

(1980). Tests for comparing elements of a correlation matrix. Psychological Bulletin, 87, 245–251. https://doi.org/10.1037/0033-2909.87.2.245

42.

Substance Abuse and Mental Health Services Administration. (2022). Key substance use and mental health indicators in the United States: Results from the 2021 National Survey on Drug Use and Health (HHS Publication No. PEP22-07-01-005, NSDUH Series H-57). https://www.samhsa.gov/data/report/2021-nsduh-annual-national-report

43.

Tseng

M.-H.

, & Chow

S. M.

(2000). Perceptual–motor function of school-age children with slow handwriting speed. American Journal of Occupational Therapy, 54, 83–88. https://doi.org/10.5014/ajot.54.1.83

44.

Williams

J. R.

(2019). The use of online social networking sites to nurture and cultivate bonding social capital: A systematic review of the literature from 1997 to 2018. New Media and Society, 21, 2710–2729. https://doi.org/10.1177/1461444819858749

45.

Wood

J. S.

Firbank

M. J.

Mosimann

U. P.

Watson

Barber

Blamire

A. M.

, & O’Brien

J. T.

(2013). Testing visual perception in dementia with Lewy bodies and Alzheimer disease. American Journal of Geriatric Psychiatry, 21, 501–508. https://doi.org/10.1016/j.jagp.2012.11.015

46.

Yun

H.-S.

Kim

Suh

S.-R.

Kim

M.-H.

, & Kim

(2013). Diabetes reduces the cognitive function with the decrease of the visual perception and visual motor integration in male older adults. Journal of Exercise Rehabilitation, 9, 470–476. https://doi.org/10.12965/jer.130059

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.27 MB