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The decline of cognitive processing speed (CPS) is a common dysfunction in persons with multiple sclerosis (MS). The Symbol Digit Modalities Test (SDMT) is widely used to formally quantify CPS. We implemented a variant of the SDMT in MS sherpa, a smartphone app for persons with MS.
The aim of this study was to investigate the construct validity and test-retest reliability of the MS sherpa smartphone variant of the SDMT (sSDMT).
We performed a validation study with 25 persons with relapsing-remitting MS and 79 healthy control (HC) subjects. In the HC group, 21 subjects were matched to the persons with MS with regard to age, gender, and education and they followed the same assessment schedule as the persons with MS (the “HC matched” group) and 58 subjects had a less intense assessment schedule to determine reference values (the “HC normative” group). Intraclass correlation coefficients (ICCs) were determined between the paper-and-pencil SDMT and its smartphone variant (sSDMT) on 2 occasions, 4 weeks apart. Other ICCs were determined for test-retest reliability, which were derived from 10 smartphone tests per study participant, with 3 days in between each test. Seven study participants with MS were interviewed regarding their experiences with the sSDMT.
The SDMT scores were on average 12.06% higher than the sSDMT scores, with a standard deviation of 10.68%. An ICC of 0.838 was found for the construct validity of the sSDMT in the combined analysis of persons with MS and HC subjects. Average ICCs for test-retest reliability of the sSDMT for persons with MS, the HC matched group, and the HC normative group were 0.874, 0.857, and 0.867, respectively. The practice effect was significant between the first and the second test of the persons with MS and the HC matched group and trivial for all other test-retests. The interviewed study participants expressed a positive attitude toward the sSDMT, but they also discussed the importance of adapting a smartphone cognition test in accordance with the needs of the individual persons with MS.
The high correlation between sSDMT and the conventional SDMT scores indicates a very good construct validity. Similarly, high correlations underpin a very good test-retest reliability of the sSDMT. We conclude that the sSDMT has the potential to be used as a tool to monitor CPS in persons with MS, both in clinical studies and in clinical practice.
Multiple sclerosis (MS) is a chronic disease in which the body’s immune system mistakenly attacks the isolating sheath (myelin) that surrounds the nerve fibers in the central nervous system. MS may affect the brain, the spinal cord, and the optic nerves, and disrupt the information flow across the affected nerves. This may cause a variety of symptoms, including loss of vision (optic neuritis), muscle weakness, sensory symptoms, cognitive dysfunction, altered coordination, and fatigue. For most persons with MS, sudden relapses contribute to the unpredictability of the disease, which makes it difficult to devise a treatment plan specific to an individual patient.
The prevalence of cognitive dysfunction in persons with MS is estimated to be between 40% and 70%, depending on the research setting [
The Paced Auditory Serial Addition Test (PASAT) [
Although the SDMT has been proven to be clinically relevant, it is not regularly used in standard care. This is partly because patients often do not complain about cognitive changes during consultation with a health care professional. It is also because of the time factor and administrative burden, despite the relatively short duration of one assessment, that is, 90 seconds. Paper-and-pencil variants of the SDMT have been existing for quite some time [
MS sherpa (Orikami Digital Health Products) is a smartphone app intended to support the monitoring of persons with MS, in order to give patients and their health care professionals personalized insight into the presence and progress of MS-related symptoms and signs. MS sherpa contains a smartphone variant of the SDMT (sSDMT). This sSDMT works as follows: a large black symbol present in the middle of the screen has to be matched to the correct digit. Matching can be done at the bottom of the screen. Once an answer is given, a new symbol appears. The sSDMT score is the number of correct answers in this 90-second test. A small stopwatch counting down is shown above the numbers at the bottom of the screen. The key is shown at the top of the screen. The same key is used during 1 assessment, but it varies between assessments; the symbols are randomly matched to digits in the key each time a test is done. These symbols are different from those in the original SDMT since these are protected by copyrights.
Note that contrary to the original SDMT, in sSDMT, one cannot look ahead as to which symbol is to be matched next. However, also in the original SDMT, skipping a symbol in the presented sequence is not allowed. There are 2 other aspects in which the sSDMT differs from the SDMT. First, in the original SDMT, the first 26 items are selected from the first 6 symbols in the key. In the sSDMT, symbols are selected from the full key during the whole assessment. This was also the case in the first version of other alternative versions of the SDMT [
We aimed to study the construct validity and test-retest reliability of the sSDMT that is implemented in MS sherpa as an assessment for CPS and present our findings in this paper, along with respondents’ experiences with the sSDMT. The validation of the sSDMT was part of the MS Self study. The MS Self study was a validation study during which study participants performed self-monitoring assessments during 4 weeks with a precursor of MS sherpa, which was called the “Mijn Kwik” app and a Fitbit Charge 2 wearable. Besides investigating the validity of the sSDMT, another research objective of the MS Self study was to investigate first the experiences with digital self-monitoring through smartphone apps and activity trackers of persons with MS by interviewing 7 study participants with MS. Furthermore, the study aimed to validate a smartphone variant of the 2-minute walking test (s2MWT) and a smartphone walking balance test (sWBT). In particular, we investigated if the outcomes of the smartphone tests were in agreement with the outcomes of corresponding clinical tests, namely, SDMT, the 2-minute walking test (2MWT), and the Timed Up and Go test (TUG). The other results from the interviews as well as a description of the methodology have recently been published [
This study was performed in 25 persons with relapsing-remitting MS and 2 groups of healthy control (HC) subjects (n=79). The HC subjects in the first control group (HC matched, n=21) were matched to the persons with MS with regard to age, gender, and education. Five education categories were defined: “secondary general education,” “senior secondary general education and preuniversity education,” “secondary vocational education,” “higher professional education,” and “scientific education”. The second control group (HC normative, n=58) was set up to determine the normal distribution for the smartphone test results.
The inclusion criteria were as follows: (1) willing to participate and capable of doing all the tasks mentioned in the protocol, (2) able and willing to use own smartphone, which should be an iPhone 5 or a newer Apple device, or a phone with an Android operating system version 6 or higher, and (3) aged between 20 years and 50 years. For persons with MS, the following additional inclusion criteria were set: (1) diagnosis of relapsing-remitting MS for more than one year and (2) having an EDSS score between 1.5 and 6.5. A maximal EDSS score of 6.5 is needed to perform the 2MWT. A minimal EDSS score of 1.5 was chosen to find a difference in the 2MWT results for persons with MS and HC subjects.
On the first and the last day of the study, persons with MS and the HC matched group came to the premises of the Dutch National MS Foundation (Nationaal MS Fonds [NMSF]) to perform a paper-and-pencil SDMT, followed by a simultaneously performed 2MWT and a s2MWT in the open air. Later that day, and at most 24 hours later, they also performed the sSDMT in their home environment. During the 4-week follow-up, these study participants performed the s2MWT and sSDMT tests at home, once every 3 days, that is, 10 times in total. A schematic overview of the study design is given in
Overview of the study design and assessment scheme. SDMT: Symbol Digit Modalities Test; 2MWT: 2-minute walking test.
The HC subjects in the HC normative group were instructed to do the s2MWT and the sSDMT 3 times in total, with 1 week in between the assessments. From these tests and from the 10 home assessments of the other study participants, the test-retest reliability of the sSDMT was determined. The combined test results “SDMT-sSDMT” for the persons with MS and the HC matched group were used to determine the construct validity of the sSDMT. This combination will hereafter be referred to as the validation assessment. Note that there are 2 validation assessments per study participant: one in the beginning and one at the end of the study.
The persons with MS and HC matched group were recruited via the NMSF (Rotterdam, the Netherlands). The HC normative group was recruited via the social network of the app manufacturer.
This study was approved by the medical ethical committee METC Brabant (Tilburg, the Netherlands) under protocol number NL61291.028.17. All study participants agreed to the privacy statement of the Mijn Kwik app prior to first use. Persons with MS and the HC matched group signed an informed consent letter on paper.
Data were collected between May 2017 and May 2018. The ages of the HC subjects in the HC matched group were matched as closely as possible to that of the persons with MS, which led to at most 2 years difference for 86% (18/21) of the pairs. Approximately 90% (19/21) of the pairs matched in gender. An exact match in education category was obtained for 29% (6/21) of the pairs.
In the analyses performed in this study, the SDMT score on the first day of the study was compared with the first sSDMT score and the SDMT score on the last day of the study was compared with the last sSDMT score. In the data cleaning process of the sSDMT data, the home assessments in which the study participants had a score below 20 were removed because these were outliers. It was assumed that the users were distracted during these tests. A score below 20 occurred in less than 1% (3/423) of all the sSDMTs that were done.
Following the study protocol, the significance level α was set to 5%. Two-sided
To investigate the construct validity of the sSDMT, we calculated the (1) ICC between the SDMT and the sSDMT and the (2) Pearson correlation coefficient between the SDMT and the sSDMT (the distributions were checked for normality by applying Shapiro-Wilk tests).
For these statistical tests, we used the same acceptance criteria as for the test-retest reliability: values smaller than 0.59 were considered to be “moderate,” values between 0.60 and 0.79 were considered “good,” and values above 0.80 were considered “very good.”
We calculated different ICCs for the construct validity and the test-retest reliability, following the McGraw and Wong [
We checked if the distributions of the number of correct answers on the first sSDMT that was done for the 3 populations in this study were normally distributed via Shapiro-Wilk tests and if the 2 groups of the HC subjects had the same underlying distribution via a 2-sample Kolmogorov-Smirnov test. Finally, we did a two-sided
The mean, median, and standard deviation in the ages for the various groups are listed in
Participant demographics.
Characteristics | Persons with MSa, n=25 | HCb matched, n=21 | HC normative, n=58 | |
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Mean (SD) | 40 (8) | 37 (8) | 34 (8) |
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Median | 43 | 36 | 32 |
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Female, n (%) | 23 (92) | 17 (81) | 29 (50) |
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Male, n (%) | 2 (8) | 4 (19) | 29 (50) |
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Secondary general education, n (%) | 1 (4) | 3 (14) | —c |
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Senior secondary general education and preuniversity education, n (%) | 3 (12) | 3 (14) | — |
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Secondary vocational education, n (%) | 8 (32) | 3 (14) | — |
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Higher professional education, n (%) | 9 (36) | 6 (29) | — |
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Scientific education, n (%) | 4 (16) | 6 (29) | — |
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Mean (SD) | 3.1 (1.4) | N/Ae | N/A |
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Median | 3.0 | N/A | N/A |
aMS: multiple sclerosis.
bHC: healthy control.
cNot available.
dEDSS: Expanded Disability Status Scale.
eN/A: not applicable.
Besides these 104 study participants, 2 more persons were recruited, but 1 person with MS dropped out of the study because she was experiencing a relapse and 1 HC dropped out because she did not like wearing Fitbit.
Distributions in the number of correct answers on the first test done with the sSDMT for the 3 groups in this study. The thin solid line shows the distribution for persons with multiple sclerosis, the dotted line shows the distribution for the healthy control matched group, and the dashed line shows the distribution for the healthy control normative group. The thick solid lines represent Gaussian fits to the distributions, of which the means (SD) are shown in the legend. sSDMT: smartphone variant of Symbol Digit Modalities Test.
Two variants of the conventional SDMT were used in the validation assessments: the original paper version of the SDMT [
The construct validity was determined from 37 validation assessments with the SDMT. One of these assessments was the first validation assessments of a dropout. The vSDMT score of the first validation assessments of the other dropout is included in the
It was found that, on average, 6.62 less correct answers were given on the sSDMT than on the SDMT (
Distribution of the differences between the number of correct answers on the sSDMT and the SDMT. The dashed line represents a normal distribution. sSDMT: smartphone variant of Symbol Digit Modalities Test; SDMT: Symbol Digit Modalities Test.
Bland-Altman plot of differences between the number of correct answers on the sSDMT and SDMT, expressed as percentages of the mean value (∆/mean) versus the mean of the 2 measurements (raw data in Multimedia Appendix 1). The dashed line shows the mean percentage difference, and the dotted lines show the 95% confidence interval. sSDMT: smartphone variant of Symbol Digit Modalities Test; SDMT: Symbol Digit Modalities Test.
When investigating the agreement between the SDMT and the sSDMT using a
Scatter plot showing the ICCs(3,1) and the correlation (Pearson r, upper left corner) between the number of correct answers on the SDMT (horizontal axis, “x”) and the sSDMT (vertical axis, “y”). A linear fit through the data points is visualized as a black solid line, for which the formula is also given in the upper left corner. ICC: intraclass correlation coefficient; sSDMT: smartphone variant of Symbol Digit Modalities Test; SDMT: Symbol Digit Modalities Test.
For the persons with MS, we checked if the number of correct answers on the first sSDMT was dependent on the screen size of their smartphones or their operating systems. The resulting distributions are respectively shown in the left and right panels of
A: Distribution of the number of correct answers on the first sSDMT of 13 persons with multiple sclerosis who used a smartphone with a large screen size (dashed line) and 13 persons with multiple sclerosis who used a smartphone with a small screen size (dotted line). The thick solid lines represent Gaussian fits to the distributions. B: Distribution of the number of correct answers on the first sSDMT of 11 persons with multiple sclerosis who used a smartphone with an iPhone operating system (iOS) (dotted line) and 15 persons with multiple sclerosis who used a smartphone with an Android operating system (dashed line). The thick solid lines represent Gaussian fits to the distributions. In both panels, the dot-dashed line represents a Gaussian fit to the full distribution (both categories). MS: multiple sclerosis; sSDMT: smartphone variant of Symbol Digit Modalities Test.
The 2 categories of screen size as well as the 2 categories of operating systems had the same underlying distribution as confirmed using 2-sample Kolmogorov-Smirnov tests (Kolmogorov-Smirnov statistic for screen size=0.23,
One of the dropouts did 7 home assessments with the sSDMT. We consider this a sufficiently large number to calculate test-retest reliability; therefore, we included this dropout for the analyses.
A: Histogram showing the distribution of total number of tests done. B: Histogram showing the distribution of number of days in between tests, averaged over all tests of a study participant. C: Scatter plot that shows the relation between the number of tests done (horizontal axis) and the average number of days in between tests, averaged over all tests of a study participant (vertical axis) for all study participants. Filled circles correspond to participants that finished the study within 28 days, open circles correspond to those for which the study duration exceeded 28 days. The blue lines in panels A and B correspond to these filled circles, and the black lines in panels A and B correspond to the open circles.
The ICCs(A,1) (with their 95% CI), Cronbach α, and Cohen
Test-retest reliability scores ICC(A,1), Cronbach α, and Cohen d of the sSDMT for persons with multiple sclerosis and the healthy control matched group. The numbers in the parentheses after the ICCs(A,1) indicate lower and upper boundaries of the 95% confidence interval, respectively.
Test-retest | ICCa(A,1) persons with MSb | ICC(A,1), HCc subjects | Cronbach α of persons with MS | Cronbach α of HC subjects | Cohen |
Cohen |
1 | 0.747 (0.297-0.902) | 0.735 (0.401-0.888) | .903 | .875 | 0.477 | 0.384 |
2 | 0.937 (0.863-0.972) | 0.859 (0.688-0.940) | .967 | .926 | 0.062 | 0.153 |
3 | 0.899 (0.788-0.953) | 0.885 (0.740-0.952) | .945 | .943 | 0.030 | 0.160 |
4 | 0.849 (0.690-0.930) | 0.797 (0.554-0.916) | .922 | .889 | 0.160 | 0.169 |
5 | 0.805 (0.611-0.909) | 0.820 (0.567-0.931) | .893 | .895 | 0.138 | 0.000 |
6 | 0.880 (0.752-0.944) | 0.913 (0.764-0.970) | .934 | .952 | 0.041 | 0.072 |
7 | 0.939 (0.867-0.972) | 0.837 (0.586-0.942) | .968 | .908 | 0.059 | 0.095 |
8 | 0.915 (0.814-0.963) | 0.961 (0.871-0.989) | .956 | .978 | 0.094 | 0.000 |
9 | 0.898 (0.769-0.957) | 0.904 (0.644-0.977) | .946 | .946 | 0.107 | 0.098 |
mean | 0.874 (0.717-0.945) | 0.857 (0.646-0.945) | .937 | .924 | 0.130 | 0.126 |
aICC: intraclass correlation coefficient.
bMS: multiple sclerosis.
bHC: healthy control.
Test-retest reliability scores ICC(A,1), Cronbach α, and Cohen d of the sSDMT for the healthy control normative group. The numbers in the parentheses after the ICCs(A,1) indicate the lower and upper boundaries of the 95% CI, respectively.
Test-retest | ICCa(A,1) | Cronbach α | Cohen |
sSDMTb test-retest 1 | 0.877 (0.772-0.931) | .943 | 0.201 |
sSDMT test-retest 2 | 0.857 (0.769-0.913) | .925 | 0.116 |
mean | 0.867 (0.771-0.922) | .934 | 0.159 |
aICC: intraclass correlation coefficient.
bsSDMT: smartphone variant of Symbol Digit Modalities Test.
As was planned, we quantified the practice effect using the effect size Cohen
Study participants performed better on the SDMT on the last day of the study, on average, compared to their score on the first day of the study. The average increase between these 2 tests was 5.57%, with a standard deviation of 11.40%, as can be calculated for the 14 study participants that did the SDMT both at the first and at the last day of the study, and even differences as high as 34.92% are reached. Comparing the first and last sSDMT for these 14 study participants yielded, on average, an increase in the number of correct answers of 11.77%, with a standard deviation of 7.84%. The Cohen
The 7 participants with MS who were interviewed about their experiences with the smartphone app and Fitbit activity tracker in general expressed a positive attitude regarding sSDMT. They liked doing the test, often describing it as a “game” or a “puzzle.” Moreover, 5 respondents imagined that this test could provide valuable information to health care providers about the health status of their patients, as this is relevant information about how their patients are doing. Despite this general positive attitude, respondents also expressed some remarks regarding the sSDMT. Two respondents noticed that sometimes the digits they pressed did not seem to respond. They were unsure whether this was caused by the app or their phone. Furthermore, several respondents discussed that in order to use a smartphone cognition test in their daily life, such a test should become personalized. Three respondents mentioned that every person with MS has different difficulties regarding cognition and felt that these differences could not be captured by a single test. Rather, they envisioned a smartphone app with multiple cognition tests from which persons with MS can choose from, depending on their personal cognitive issues. Another point of personalization concerned the test frequency. Four respondents expected that during the stable periods of their MS, they would feel less need to perform the sSDMT than during periods of relapse. Therefore, they desired flexibility in the sSDMT test frequency when they would use the smartphone app in their daily life.
We found that the sSDMT can distinguish between persons with MS and HC subjects at the group level. The test scores on the sSDMT are, on average, 12.06% lower than that on the paper-and-pencil SDMT. Although there is no exact agreement between the sSDMT and the SDMT, the 2 tests are strongly correlated, with ICC(3,1) values of 0.784 and 0.852 for persons with MS and HC subjects, respectively. The sSDMT shows very good test-retest reliability, with average ICCs of 0.874, 0.857, and 0.867 for persons with MS, the HC matched group, and the HC normative group, respectively. The practice effect was significant between the first and the second test of the persons with MS and the HC matched group and trivial for all other test-retests. A positive attitude toward the sSDMT was found during the interviews with study participants with MS. Importantly, interview respondents expressed the desire to adapt smartphone cognition tests according to the individual needs of persons with MS, with regard to both the type and frequency of testing.
The SDMT is a relevant tool both for screening and monitoring not only for MS but for many other clinical diseases such as Huntington disease, Parkinson disease, and stroke [
A 10% difference between 2 SDMT scores of a patient is found to be an indication of a clinically meaningful change in the patients’ CPS [
One might wonder how often an sSDMT could be scheduled, to keep its value as an objective instrument for measuring CPS. Benedict et al [
This study was not set up to cross-device validate the sSDMT, but we have tried to give some sense of the validity over different screen sizes and the main software platforms, because we believed that these 2 factors could affect the results. In our study, we found an approximately equal number of study participants using the Android or iPhone operating system platform, which gave us the opportunity to significantly show that there is no structural bias in using any of the platforms. A more structural approach to cross-device validation and more detailed analysis of the performance over different devices could help to detect validity issues in different devices.
In our study, we did not explicitly give the instructions to self-monitor under the same conditions each time as much as possible (preferably in a quiet room). We expect that such instructions would improve the test-retest reliability. The fact that the ICCs and Cronbach α values of the first test-retest of the persons with MS and the HC matched group were lower than all following 8 can likely be explained by the fact that there is, in general, a large practice effect between the first and the second time a cognitive test is done, especially with only 3 days in between the tests. This is also reflected in the Cohen
The fact that 60% (28/47) of the persons with MS and HC subjects in the matched group continued to do home assessments after 4 weeks might be because the end day of the study could often not be planned 4 weeks after the first day. It could also be a sign of high acceptance of the sSDMT, as expressed by the interviewed study participants. These results, together with the qualitative findings that participants liked doing the sSDMT, often describing it as a “game” or a “puzzle,” support that the sSDMT was viewed as “game-like” and confirms that the sSDMT is well-designed for the user.
Since persons with MS were asked to write down their answers on the paper-and-pencil SDMT themselves, persons with MS who had limited hand dexterity might perform lower on the paper-and-pencil SDMT than those with the same CPS without hand dexterity problems. There was no Nine Hole Peg Test scheduled in this study to investigate this possible bias. During the sSDMT, persons with MS are asked to tap the correct answer. We expect hand dexterity problems to be less of a problem for tapping on a smartphone than for writing down a number on paper. An alternative solution for this bias would be an oral sSDMT, which should then be validated against an oral SDMT. However, persons with MS might not find this a natural way of self-monitoring.
The number of study participants for which the test-retest reliability of the paper-and-pencil SDMT could be studied was relatively low, because 2 different versions of the paper-and-pencil SDMT were used. Although the reliability of the paper-and-pencil SDMT is well known in literature, it would have been interesting to study the practice effect of frequently performing our sSDMT on the paper-and-pencil SDMT in more detail.
Finally, an important limitation is the omission of the 10 mandatory practice items on the paper-and-pencil SDMT. This could have introduced a bias toward a better correlation with the sSDMT, because in the digital variant, practicing is optional, and therefore it might affect our construct validity. The construct validity should therefore ideally be reevaluated in a follow-up study in which study participants do the standard 10 practice items on the paper-and-pencil SDMT.
As mentioned in the Introduction, originally our rationale for an optional practice assessment was that practicing should not be mandatory for persons with MS that do the sSDMT repeatedly from a usability perspective. Practicing was optional but could be done unlimitedly by pressing the corresponding button on the instruction screen. However, our results have let us to reconsider this perspective and we now believe that the sSDMT nor a practice session should be accessible unlimitedly, because of the accumulative nature of the practice effect. However, when the sSDMT is scheduled only once a month, users might want to practice before each digital assessment. Therefore, we currently suggest 10 mandatory practice items before each digital assessment, which is also more similar to the paper-and-pencil SDMT, and therefore might improve the construct validity.
A fair number of computerized neuropsychological assessment devices for monitoring cognitive impairment in MS has been developed in recent years. A recent systematic review of the literature on test batteries and single tests with good evidence for reliability and validity yielded 44 CPS tests, of which all computerized tests based on SDMT correlated with the conventional SDMT, with correlation coefficients ranging from 0.75 to 0.88 [
To our knowledge, there are 2 other smartphone-based SDMT apps for MS that should be mentioned as alternative solutions to monitor CPS. One is MSCopilot [
Well-validated digital assessment devices for CPS screening include the PST and the recently introduced Multiple Screener tool, which contains a digital SDMT for which an ICC of 0.79 between digital and paper-and-pencil-based assessment was reported [
To our knowledge, this is the first study in which a smartphone variant of the SDMT that can be done unsupervised was cross-platform validated. We obtained valuable novel insights into frequent home monitoring with the SDMT such as the observation that the practice effect was only nontrivial between the first and second sSDMT (with 10 assessments scheduled and approximately 3 days in between each assessment) but also about the cumulative practice effects that are involved. Furthermore, we believe the qualitative insights obtained from patient interviews on the needs and wishes of smartphone-based home monitoring using sSDMT can inspire developers, caregivers, and researchers for future developments.
This study shows the construct validity of the sSDMT since the ICCs(3,1) between the SDMT and the sSDMT for persons with MS and HC subjects were 0.78 and 0.85, respectively. The sSDMT does have very good test-retest reliability because only the first test-retest of the persons with MS and the HC matched group yielded an ICC(A,1) smaller than 0.80. All the other ICCs were higher than 0.80, both for persons with MS and for HC subjects. We conclude that the sSDMT has the potential to be used as a tool to monitor CPS in persons with MS, both in clinical studies and in clinical practice.
Number of correct answers on the SDMT and the sSDMT and the agreement between the two methods, for the 37 validation assessments that were done with the SDMT.
Number of correct answers on the vSDMT and the sSDMT and the agreement between the two methods, for the 54 validation assessments that were done with the vSDMT.
Number of correct answers on the sSDMT on 10 occasions (labeled 1 to 10) for the persons with multiple sclerosis and the healthy controls in the matched group.
Number of correct answers on the sSDMT on three occasions (labeled 1, 2 and 3) for the healthy control normative group.
2-minute walking test
Brief International Cognitive Assessment for Multiple Sclerosis
cognitive processing speed
Expanded Disability Status Scale
healthy control
intraclass correlation coefficient
multiple sclerosis
Multiple Sclerosis Functional Composite
Nationaal MS Fonds
Paced Auditory Serial Addition Test
smartphone 2-minute walking test
Symbol Digit Modalities Test
smartphone variant of Symbol Digit Modalities Test
smartphone walking balance test
Timed Up and Go
variant of the Symbol Digit Modalities Test
The study received financial support from the European Regional Development Fund (EFRO OP Oost Project 00217) and the NMSF, Rotterdam, the Netherlands. We thank the anonymous referees for valuable comments.
PvO is employed by Orikami Digital Health Products. BdT is a founder and owner of Orikami Digital Health Products. PJJ is advisor to Orikami Digital Health Products and has received honoraria from Bayer Netherlands for consultancy activities.