Available via license: CC BY 4.0
Content may be subject to copyright.
Citation: Papukchieva, S.; Kahn, M.;
Eberl, M.; Friedrich, B.; Joschko, N.;
Ziemssen, T. Data on Ocrelizumab
Treatment Collected by MS Patients in
Germany Using Brisa App. J. Pers.
Med. 2024,14, 409. https://doi.org/
10.3390/jpm14040409
Academic Editor: Alberto Barcelo
Received: 22 March 2024
Revised: 6 April 2024
Accepted: 10 April 2024
Published: 12 April 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Journal of
Personalized
Medicine
Article
Data on Ocrelizumab Treatment Collected by MS Patients in
Germany Using Brisa App
Steffeni Papukchieva 1, Maria Kahn 1, Markus Eberl 1, Benjamin Friedrich 1,*, Natalie Joschko 2
and Tjalf Ziemssen 3
1Temedica GmbH, 80687 Munich, Germany; steffeni.papukchieva@temedica.com (S.P.)
2Roche Pharma AG, 79639 Grenzach-Wyhlen, Germany
3Center of Clinical Neuroscience, Department of Neurology, University Clinic Carl Gustav Carus & Dresden
University of Technology, 01307 Dresden, Germany
*Correspondence: benjamin.friedrich@temedica.com
Abstract: Background: With a rising number of multiple sclerosis (MS) cases and increasing pressure
on health systems, digital companion apps like Brisa, designed specifically for people with MS,
can play an important role in the patient journey. These apps enable the collection of real-time
longitudinal data that are critical to our understanding of the pathophysiology and progression of
MS. Methods: This retrospective, descriptive analysis consists of data from Brisa users who registered
between 6 August 2021 and 8 September 2022. Of the unique users, 37.7% (n= 1593) fulfilled the
inclusion criteria including information about medication and demographics and tracked one or more
symptoms and/or patient-reported outcomes. Users were classified as moderate-efficacy treatment
users, high-efficacy treatment users and ocrelizumab users, and the reporting frequency and scores
of symptoms and patient-reported outcomes were analyzed. Results: The largest cohort of Brisa
users (405) reported treatment with ocrelizumab and were mostly diagnosed 2–5 years before the
survey. The most reported MS symptoms were similar between OUs (ocrelizumab users), HETUs
(high-efficacy treatment users) and METUs (moderate-efficacy treatment users). OUs on average
reported symptoms and answered questionnaires more frequently. Baseline scores between HETUs
and OUs were similar, whereas baseline scores of METUs were slightly lower in comparison. In a
further analysis of OUs, disability scores increased with age; users aged 26–45 years had higher pain
scores than 18–25-year-olds. No significant differences were found in quality of life, bowel control
and vision between age groups. Conclusion: These findings show that the characteristics of the
Brisa cohort are similar to the results of other studies and registries and can provide a representative
overview of everyday disease management. Thereby, these results can bridge the gap between
clinical research and real patient experience, but they also raise new questions, such as how often the
hard-and-early therapy approach is already used and whether baseline characteristics and reasons
for choosing a particular treatment contribute to the different outcomes over time. Answering these
questions requires further research and analysis.
Keywords: multiple sclerosis; Brisa; patient reported outcomes; digital health
1. Introduction
In recent years, more and more medical device applications have entered the health-
care landscape, therefore placing decision-making power directly into patients’ hands.
Especially in chronic diseases like multiple sclerosis (MS), the continuous collection of
patient-generated data is crucial to understanding the very individual path of a patient’s
disease and treatment journey.
Currently, it is not uncommon for people diagnosed with MS to see their doctor
only two to three times a year [
1
]. These long periods between medical check-ups can be
perceived as a gap in patient care. As the number of MS cases increases worldwide [
2
],
J. Pers. Med. 2024,14, 409. https://doi.org/10.3390/jpm14040409 https://www.mdpi.com/journal/jpm
J. Pers. Med. 2024,14, 409 2 of 12
digital companion apps may be an option for many patients [
3
]. Due to the individualistic
nature of MS, the collection of real-time data on a longitudinal basis—along with a variety
of digital biomarkers—afforded by such apps is becoming increasingly crucial to our
understanding of this disease and its pathophysiology and progression [
4
,
5
]. Brisa is an
example of an MS companion that allows the daily tracking of the range of symptoms
MS patients suffer from. Monitoring symptoms could be crucial for detecting relapses
and identifying progression, but it also empowers the patients to be part of the therapy
decision-making process and exchange their journey in a community. Also, Ziemssen
et al. have already shown the importance of a physician-completed tool for supporting
physician–patient interaction in assessing signs of disease progression and uncovered the
need for supporting tools [6].
The collection of patient-centered data in chronic diseases such as MS is becoming
increasingly important. Understanding how patients experience their symptoms and how
these symptoms affect their daily lives is essential to improve patient care and treatment
outcomes [
7
]. Additionally, the possibility of close symptom monitoring by patients
themselves could make a crucial contribution to knowledge about and identification of
signs of disease progression, as well as empowering patients to steer towards a personalized
therapy approach.
The optimal disease management for MS is being revised with an increasing emphasis
on personalized treatment approaches [
8
]. Over the past 25 years, the treatment approach
and disease management have changed significantly [
9
]. While patients have traditionally
been treated in an escalating manner starting from lower efficacy treatment and moving
to higher efficacy treatment only after ongoing treatment fails [
10
], increasing evidence
emphasizes the importance of an early intervention following diagnosis accompanied by
optimization of treatment for each patient individually [
11
,
12
]. Variations in symptoms
can occur from person to person depending on the severity of the neuronal damage, and
although high-efficacy therapies are available, some patients still suffer from relapses and
subclinical disease activity. Relapses and disease progression can take place in different
functional systems through a wide range of symptoms that include fatigue, impaired
motor function, spasticity, pain, gait disturbance, speech problems and cognitive impair-
ment
[4,13,14]
. These symptoms can have a negative impact on individuals both physically
and psychologically, with the progression of this disease leading to difficulties in perform-
ing everyday tasks due to impaired motor skills and affecting social life and the ability
to live independently [
15
]. Especially in a disease termed ‘disease of a thousand faces’, it
is crucial to gain an understanding of all relevant symptoms not only to assess signs of
disease activity and progression, but also to improve patients’ disease management.
While progress has been made in terms of developing new treatments due to a more
comprehensive understanding of the course and pathogenesis of MS [
14
], MS is still
considered to be an incurable disease [
4
]. A variety of medications, with moderate to high
efficacy, are available for the treatment of MS, particularly the relapsing–remitting form
(RRMS) [
16
]. For the primary progressive form of MS (PPMS), ocrelizumab is currently
the only approved medication [
9
,
10
]. Therefore, ocrelizumab, approved for both RMS
and PPMS, occupies a special position in the treatment landscape and has already been
proven to fulfill the safety profile, treatment persistence and adherence of clinical trials in a
real-world MS population [
17
]. The current study focused on characteristics, symptoms and
PRO reporting and the corresponding scores of Brisa users who are treated with medium-
and high-efficacy drugs, with an individual assessment of ocrelizumab users representing
the biggest treatment cohort of Brisa users.
The aim of this work is to better understand the Brisa users treated with high- and
medium-efficacy therapies and to assess differences reflected in symptom and PRO report-
ing and scores.
J. Pers. Med. 2024,14, 409 3 of 12
2. Methods
2.1. Ethical Approval
Since the data were aggregated and analyzed retrospectively and Brisa users consented
to the use of their data, ethical approval is not applicable.
2.2. Data Source
Brisa (version 2.1.0) is a smartphone application available for Android and iOS devices
intended to support MS patients in their day-to-day lives by offering guidance and advice
in a variety of areas. The data source and collection were described previously [18,19]
2.3. Inclusion Criteria of Study Cohort
This retrospective, descriptive analysis consists of data from Brisa users who registered
between 6 August 2021 and 8 September 2022. Of all registered Brisa users (n= 6092),
69.4% (n= 4228) gave consent to the use of their data for scientific purposes. In the
timeframe of analysis, on average, 65 users actively used the app (answered a PRO and/or
reported a symptom) on a daily basis, 164 weekly and 412 monthly. Of the unique users,
37.7% (n= 1593) fulfilled the inclusion criteria listed below and were therefore included
in our analysis. The inclusion criteria were age 18–80 years, gender reported, MS type
reported, year of diagnosis reported, medication reported, consent for health data usage for
scientific purposes, answered at least one questionnaire completely and reported at least
one symptom.
2.4. Data Processing and Analysis
Data processing and analysis were performed using Python (version 3.9). To study
the demographic characteristics of Brisa users/patients such as gender, age, type of MS,
time since MS diagnosis and medications, onboarding information was examined. For each
parameter analyzed, only those who responded and provided details for the corresponding
parameter were considered. Users with skipped (categorized as ‘unknown’) or invalid
entries were excluded. Additional inclusion criteria specific for each parameter under
investigation and their classification were as follows:
Age was calculated using the year of birth. For all age-related analyses, users between
the ages of 18 and 85 were considered. They were further classified into 5 subgroups based
on age: 18–25, 26–35, 36–45, 46–55, >55 years.
To study gender-based age distribution, users who reported both parameters were
included. This applies to all cases throughout the analysis where two or more parameters
were involved, unless mentioned otherwise.
Time since diagnosis was computed using the year of diagnosis. All entries up to
30 years since diagnosis were considered for analysis. Based on the years since diagnosis,
users were further grouped into 5 categories: 0–1 year, 2–5 years, 6–10 years, 11–20 years,
and 21–30 years.
All patients with known medication entries were included.
Based on recent publications [
20
], users were grouped into moderate-efficacy treat-
ment users (METUs), high-efficacy treatment users (HETUs) and ocrelizumab users (OUs)
(Table 1)
Table 1. Medication grouping.
Moderate-Efficacy Treatment Users
(METUs) High-Efficacy Treatment Users (HETUs) Ocrelizumab Users
(OUs)
Dimethylfumarate Alemtuzumab
Ocrelizumab
Diroximelfumarate Cladribine
Glatirameracetate Natalizumab
Teriflunomide Ofatumumab
Interferons S1P Modulators (Fingolimod, Ozanimod, Ponesimod)
J. Pers. Med. 2024,14, 409 4 of 12
To identify the symptoms that predominantly affected our study cohort, users who
tracked at least 1 symptom once after onboarding were considered.
2.5. Statistical Methodology
Statistical analysis was performed using Graphpad (version 10.0). To calculate signifi-
cant differences in base scores, we ran a multiple-comparison Kruskal–Wallis test correcting
for p-values using Dunn’s test. p-values < 0.05 were considered significant.
3. Results
We characterized our cohort by studying their demographic features such as gender,
age, MS type, year of diagnosis and medications used. In addition, we examined the
symptoms and PROs users on different medication classes are concerned about most and
evaluated their scores with an in-depth analysis of the ocrelizumab users (OUs) since they
represented the biggest cohort of patients using a high-efficacy medication.
3.1. Demographic Characteristics of Users
Data from 1593 Brisa users were available for analysis. As shown in Table 1, 405 Brisa
users entered the usage of ocrelizumab as their treatment, whereas 1188 Brisa users stated
a medication other than ocrelizumab. A detailed distribution of medications can be seen
in Supplementary Figure S1. The distribution regarding gender and MS type (Table 2) is
according to the normal gender and MS-type distribution. Tables 3and 4show specifically
the distribution of ocrelizumab users. While users diagnosed with RRMS/SPMS decrease
with age, users diagnosed with PPMS increase with age. Most ocrelizumab users (OUs)
were diagnosed 2–5 years before the survey.
Table 2. Demographic characteristics of Brisa users. Distribution of Brisa users (n= 1593) taking
ocrelizumab (n= 405) compared to non-ocrelizumab users (n= 1188) and distribution of Brisa users
regarding gender and MS type.
Brisa Users (n= 1593) Non-Ocrelizumab Users (n= 1188) Ocrelizumab Users (n= 405)
Gender Female Male Divers Female Male Divers
1021 167 0 306 98 1
MS Type SP PP RR SP PP RR
53 0 1135 42 108 255
Table 3. Age and time since diagnosis distribution of Brisa users as total number (n) and % of users
within medication group.
OUs (n= 405) HETUs (n= 884) METUs (n= 709)
Age group
18–25 24 (5.9%) 68 (7.7%) 57 (8.0%)
26–35 113 (27.9%) 275(31.1%) 241 (34.0%)
36–45 105 (25.9%) 251 (28.4%) 207 (39.2%)
46–55 105 (25.9%) 204 (23.1%) 143 (20.2%)
>55 58 (14.3%) 86 (9.7%) 61 (8.6%)
Time since diagnosis
0–1 9 (2.2%) 28 (3.2%) 45 (6.3%)
2–5 142 (35.1%) 280 (31.7%) 286 (40.3%)
6–10 108 (26.7%) 246 (27.8%) 176 (24.8%)
11–20 91 (22.5%) 225 (25.5%) 128 (18.1%)
21–30 55 (13.6%) 105 (11.9%) 74 (10.4%)
J. Pers. Med. 2024,14, 409 5 of 12
Table 4. Age and time since diagnosis distribution of ocrelizumab users as total number (n) and % of
users within MS Type.
RRMS PPMS
Age group
18–25 22 (7.4%) 2 (1.9%)
26–35 104 (35.0%) 9 (8.3%)
36–45 81(27.3%) 24 (22.2%)
46–55 64 (21.5%) 41(38.0%)
>55 26 (8.8%) 32 (29.6%)
Time since diagnosis
0–1 6 (2.0%) 3 (2.8%)
2–5 112 (37.7%) 56 (51.9%)
6–10 75 (25.3%) 24 (22.2)
11–20 64 (21.5%) 15 (13.9)
21–30 40 (13.5%) 10 (9.3)
3.2. Symptoms Reported by the Different Treatment Groups
To further understand the symptoms that ocrelizumab users (OUs) are mainly dealing
with and how they differ from non-ocrelizumab users, we examined the top five most
reported symptoms in each treatment category. While fatigue, tingling and concentration
disorder were reported by more than 20% of the users of the different treatment cohorts,
moderate-efficacy treatment users (METUs) were the only ones to report visual disturbances
and forgetfulness. Bladder disorders were only within the top five reported symptoms in
the high-efficacy treatment user (HETU) group, while leg foot lifting disorder was only
listed in the ocrelizumab user cohort. Pain was listed in both the HETU and OU cohorts
(Figure 1A). Figure 1B shows the average number of times users in the respective treatment
group answered the symptom of interest. Overall, OUs reported their top five symptoms
more often during the observation period than the HETU and METU cohorts. Leg foot
lifting disorder was tracked on average 5.7 times during the observation period, whereas
concentration disorder and tingling were tracked least with an average of 4 times. METUs
reported their top five symptoms at least with an average of around 2.4 times during the
observation period.
The deep dive into OUs shows that users diagnosed with PPMS tracked different top
five symptoms (bladder disorder, spasticity/cramps, leg foot lifting disorder and strong
sensitivity to heat) compared to users diagnosed with RMS (RRMS/SPMS) (concentration
disorder, tingling, pain, visual disturbances). Fatigue was the most tracked symptom in
both groups (Figure 1C). A focus on the age groups within the OU cohort revealed that
fatigue was mentioned in all age groups, whereas visual disturbances were mentioned
in the top five only in the age group 26–35. Age groups 46–55 and >55 tracked leg foot
lifting disorders, whereas sensitivity to heat was only represented in the age group 36–45.
Depression was one of the top five symptoms reported in the youngest age group (Table 5).
Table 5. Top 5 reported symptoms within OU group based on age group.
Age Group
Symptom 18–25 (n= 23) 26–35 (n= 103) 36–45 (n= 96) 46–55 (n= 98) >56 (n= 48)
n% of Age
Group n% of Age
Group n% of Age
Group n% of Age
Group n% of Age
Group
Fatigue 10 43.5 26 25.2 34 35.4 33 33.7 17 35.4
Numbness 7 30.4
Pain 7 30.4 21 20.4 23 24.0 26 26.5
Bladder disorder 5 21.7 12 25.0
Depression 5 21.4
Tingling 22 21.4 28 28.6 12 25.0
J. Pers. Med. 2024,14, 409 6 of 12
Table 5. Cont.
Age Group
Symptom 18–25 (n= 23) 26–35 (n= 103) 36–45 (n= 96) 46–55 (n= 98) >56 (n= 48)
n% of Age
Group n% of Age
Group n% of Age
Group n% of Age
Group n% of Age
Group
Concentration disorder 29 28.2 24 25.0
Visual disturbances 20 19.4
Strong sensitivity to heat 28 29.2
Spasticity/cramps 25 26.0 26 26.5 14 29.2
Leg foot lifting disorder 25 25.5 15 31.3
J. Pers. Med. 2024, 14, x FOR PEER REVIEW 6 of 13
Figure 1. Symptoms that concern Brisa users most. (A) Brisa users were grouped into three catego-
ries based on the medications they use. Top 5 most reported symptoms of each respective treatment
group are depicted for ‘moderate-efficacy treatment users’ (METUs), ‘high-efficacy treatment users’
(HETUs) and ‘ocrelizumab users’ (OUs). (B) Average reporting. Average number of reported symp-
toms based on the number of all users that reported the respective symptom at least once in the re-
spective treatment group during the observation period. (C) Most reported symptoms of OUs grouped
by MS type.
The deep dive into OUs shows that users diagnosed with PPMS tracked different top
five symptoms (bladder disorder, spasticity/cramps, leg foot lifting disorder and strong
sensitivity to heat) compared to users diagnosed with RMS (RRMS/SPMS) (concentration
disorder, tingling, pain, visual disturbances). Fatigue was the most tracked symptom in
both groups (Figure 1C). A focus on the age groups within the OU cohort revealed that
fatigue was mentioned in all age groups, whereas visual disturbances were mentioned in
the top five only in the age group 26–35. Age groups 46–55 and >55 tracked leg foot lifting
disorders, whereas sensitivity to heat was only represented in the age group 36–45. De-
pression was one of the top five symptoms reported in the youngest age group (Table 5).
Figure 1. Symptoms that concern Brisa users most. (A) Brisa users were grouped into three categories
based on the medications they use. Top 5 most reported symptoms of each respective treatment group
are depicted for ‘moderate-efficacy treatment users’ (METUs), ‘high-efficacy treatment users’ (HETUs)
and ‘ocrelizumab users’ (OUs). (B) Average reporting. Average number of reported symptoms based
on the number of all users that reported the respective symptom at least once in the respective
treatment group during the observation period. (C) Most reported symptoms of OUs grouped by
MS type.
J. Pers. Med. 2024,14, 409 7 of 12
3.3. Top Five Completed Patient Reported Outcomes (PROs)
Similarly to daily reported symptoms, we also analyzed the completion of PROs to
gain better insights into which PROs are mainly reported by METUs, HETUs and OUs.
As shown in Figure 2A, vision (IVIS5) was reported by more than 30% of the respective
treatment cohort. Pain (PES), (bowel control) BWCS and cognition (PDQ-5) were among
the top five reported PROs in all treatment cohorts, whereas MFIS5 (fatigue) was only
answered in the METU cohort. Disability (PDDS) was among the top five reported PROs in
the HETU and OU treatment cohorts. PDDS was not in the top five completed PROs in the
METU cohort. As shown in Figure 2B, OUs on average answered respective PROs around
2 times during the entire observation period, whereas the average reporting for HETUs
and METUs was lower, 1.7 times and 1.4 times, respectively. In the differentiation by PPMS
and RMS, IVIS5 and PDQ-5 were only reported in the RMS group, whereas bladder control
(BLCS) and fatigue (MFIS5) were reported only in the PPMS cohort (Figure 2C).
J. Pers. Med. 2024, 14, x FOR PEER REVIEW 8 of 13
Figure 2. PROs that concern Brisa users most. (A) Brisa users were grouped into three categories
based on the medications they use. Top 5 most completed PROs of each respective treatment group
are depicted for ‘moderate efficacy treatment users’ (METUs), ‘high efficacy treatment users’ (HE-
TUs) and ‘ocrelizumab users’ (OUs). (B) Average reporting. Average number of completed PROs
based on the number of all users that completed the respective PRO at least once in the respective
treatment group during the observation period. (C) Most completed PRO of OU grouped by MS
type.
Specifically, in the OU cohort, depression (BDI-FS) was only completed as one of the
top five questionnaires in the youngest age group (18–25), whereas fatigue (MFIS-5) was
one of the most answered PROs within the 26–35-year-old group. The age group 26–35
was the only age group to not report PES in the top five PROs. Disability (PDDS) was in
the top five reported PROs for age groups over 36 years (Table 6).
Table 6. Top 5 completed PROs within OU group based on age group.
Age Group
PRO 18–25 (n = 18) 26–35 (n = 79) 36–45 (n = 73) 46–55 (n = 76) >56 (n = 48)
n % of Age
Group n % of Age
Group n % of Age
Group n % of Age
Group n % of Age
Group
PES (pain) 8 44.4 16 20.3 26 35.6 30 39.5 12 25.0
IVIS (vision) 5 27.8 28 35.4 33 45.2 21 27.6 11 22.9
BDI-FS (depression) 4 22.2
PDQ-5 (Cognition) 4 22.2 17 21.5 21 28.8 20 26.3
BLWS (bladder control) 3 16.7
BWCS (bowel control 19 24.1 18 37.5
MF5I (fatigue) 16 20.3 20 27.4 17 22.4 10 20.8
PDDS (disability) 24 32.9 29 38.2 21 43.8
Figure 2. PROs that concern Brisa users most. (A) Brisa users were grouped into three categories
based on the medications they use. Top 5 most completed PROs of each respective treatment group
are depicted for ‘moderate efficacy treatment users’ (METUs), ‘high efficacy treatment users’ (HETUs)
and ‘ocrelizumab users’ (OUs). (B) Average reporting. Average number of completed PROs based on
the number of all users that completed the respective PRO at least once in the respective treatment
group during the observation period. (C) Most completed PRO of OU grouped by MS type.
Specifically, in the OU cohort, depression (BDI-FS) was only completed as one of the
top five questionnaires in the youngest age group (18–25), whereas fatigue (MFIS-5) was
one of the most answered PROs within the 26–35-year-old group. The age group 26–35 was
the only age group to not report PES in the top five PROs. Disability (PDDS) was in the top
five reported PROs for age groups over 36 years (Table 6).
J. Pers. Med. 2024,14, 409 8 of 12
Table 6. Top 5 completed PROs within OU group based on age group.
Age Group
PRO 18–25 (n= 18) 26–35 (n= 79) 36–45 (n= 73) 46–55 (n= 76) >56 (n= 48)
n% of Age
Group n% of Age
Group n% of Age
Group n% of Age
Group n% of Age
Group
PES (pain) 8 44.4 16 20.3 26 35.6 30 39.5 12 25.0
IVIS (vision) 5 27.8 28 35.4 33 45.2 21 27.6 11 22.9
BDI-FS (depression) 4 22.2
PDQ-5 (Cognition) 4 22.2 17 21.5 21 28.8 20 26.3
BLWS (bladder control) 3 16.7
BWCS (bowel control 19 24.1 18 37.5
MF5I (fatigue) 16 20.3 20 27.4 17 22.4 10 20.8
PDDS (disability) 24 32.9 29 38.2 21 43.8
3.4. Baseline PRO Scores—All Patients vs. Ocrelizumab Users
In addition, we assessed whether the baseline scores of the top five completed PROs
vary depending on the medication class the Brisa users are treated with. Overall, the
baseline scores between the treatment cohorts were similar (Figure 3A). Specifically, the
average scores for bowel control (BWCS) and vision (IVIS5) were low, whereas scores for
cognition (PDQ-5), fatigue (MFIS5), disability (PDDS) and pain (PES) were in the mid-range.
J. Pers. Med. 2024, 14, x FOR PEER REVIEW 9 of 13
3.4. Baseline PRO Scores—All Patients vs. Ocrelizumab Users
In addition, we assessed whether the baseline scores of the top five completed PROs
vary depending on the medication class the Brisa users are treated with. Overall, the base-
line scores between the treatment cohorts were similar (Figure 3A). Specifically, the aver-
age scores for bowel control (BWCS) and vision (IVIS5) were low, whereas scores for cog-
nition (PDQ-5), fatigue (MFIS5), disability (PDDS) and pain (PES) were in the mid-range.
When OUs were analyzed in more detail, there were no significant differences in re-
ported baseline scores between OUs diagnosed with PPMS and RRMS/SPMS users (Fig-
ure 3B), but the disability (PDDS score) increases with increasing age; specifically, age
groups 46–55 and over 55 have significantly higher scores than the age group 26–35 (Fig-
ure 3C).
Figure 3. Baseline scores of top 5 completed PROs in respective treatment groups. (A) Brisa users
were grouped into three categories based on the medications they use. Baseline scores of the top 5
most reported PROs in the respective medication group are depicted for ‘moderate-efficacy treat-
ment users’ (METUs), ‘high-efficacy treatment users’(HETUs) and ‘ocrelizumab users (OUs)’. (B)
Baseline scores of most reported PROs of ocrelizumab users grouped by MS type and (C) age group.
Significant differences in base scores were calculated using multiple-comparison Kruskal–Wallis
test correcting for p-values using Dunn’s test.* p < 0.05.
4. Discussion
While clinical trial data have delivered an indispensable understanding of the effi-
ciency and outcomes of disease-modifying treatments, data regarding day-to-day disease
management based on RWE are still lacking. Although there are many MS companion
apps on the market focusing on various use cases like medication tracking, symptom
tracking or lifestyle tracking, Brisa is a first-of-its-kind medical device smartphone appli-
cation in Germany intended not only to support MS patients in their day-to-day lives, but
also to collect patient-centered data and experiences.
Our retrospective analysis focused on the well-being of Brisa users on medium- and
high-efficacy drugs, with a focus on ocrelizumab patients, and aimed to generate insights
to narrow the gap between experimental research, clinical studies and real-world data of
MS patients.
Twenty-five percent of Brisa users declared the usage of ocrelizumab as their treat-
ment. The characteristics of the ocrelizumab Brisa cohort are comparable to the findings
of previous clinical trials [21,22] and non-interventional studies such as the CONFI-
DENCE study with more than 3000 participants [17]. With a mean age of 41.9 ± 11.2 years,
our cohort was around 10 years younger than the participants of the CONFIDENCE study
(51.3 ± 10.0 years) [23] and 5.6 years younger than the average age reported by the MS
registry (47.5 ± 12.5 years) [24]. Recent studies show that younger users have a signifi-
cantly higher affinity for using mobile technology in disease management [25] when
Figure 3. Baseline scores of top 5 completed PROs in respective treatment groups. (A) Brisa users were
grouped into three categories based on the medications they use. Baseline scores of the top 5 most
reported PROs in the respective medication group are depicted for ‘moderate-efficacy treatment users’
(METUs), ‘high-efficacy treatment users’(HETUs) and ‘ocrelizumab users (OUs)’. (B) Baseline scores
of most reported PROs of ocrelizumab users grouped by MS type and (C) age group. Significant
differences in base scores were calculated using multiple-comparison Kruskal–Wallis test correcting
for p-values using Dunn’s test. * p< 0.05.
When OUs were analyzed in more detail, there were no significant differences in
reported baseline scores between OUs diagnosed with PPMS and RRMS/SPMS users
(Figure 3B), but the disability (PDDS score) increases with increasing age; specifically,
age groups 46–55 and over 55 have significantly higher scores than the age group 26–35
(Figure 3C).
4. Discussion
While clinical trial data have delivered an indispensable understanding of the effi-
ciency and outcomes of disease-modifying treatments, data regarding day-to-day disease
management based on RWE are still lacking. Although there are many MS companion apps
on the market focusing on various use cases like medication tracking, symptom tracking
J. Pers. Med. 2024,14, 409 9 of 12
or lifestyle tracking, Brisa is a first-of-its-kind medical device smartphone application in
Germany intended not only to support MS patients in their day-to-day lives, but also to
collect patient-centered data and experiences.
Our retrospective analysis focused on the well-being of Brisa users on medium- and
high-efficacy drugs, with a focus on ocrelizumab patients, and aimed to generate insights
to narrow the gap between experimental research, clinical studies and real-world data of
MS patients.
Twenty-five percent of Brisa users declared the usage of ocrelizumab as their treatment.
The characteristics of the ocrelizumab Brisa cohort are comparable to the findings of
previous clinical trials [
21
,
22
] and non-interventional studies such as the CONFIDENCE
study with more than 3000 participants [
17
]. With a mean age of 41.9
±
11.2 years, our
cohort was around 10 years younger than the participants of the CONFIDENCE study
(51.3
±
10.0 years) [
23
] and 5.6 years younger than the average age reported by the MS
registry (47.5
±
12.5 years) [
24
]. Recent studies show that younger users have a significantly
higher affinity for using mobile technology in disease management [
25
] when compared
to older users, which could explain why the average age of users was lower than in the
registry and study data. The mean time since diagnosis in our cohort was
9.1 ±7.2 years
,
whereas the mean time since diagnosis of the CONFIDENCE study participants was
5.5 ±6.7 years
. Our cohort was representative of the gender distribution (75% female)
which is also reported by the MS registry in Germany, where 70.9% of MS patients are
female. In our cohort, the share of RRMS/SPMS users decreased with age, whereas PPMS
increased with age. This inverse relationship is also reflected in current data, where the
median age of patients diagnosed with PPMS was 50 years, whereas RRMS patients are
usually diagnosed in their 20s to 30s [
26
]. Overall, our cohort is representative of the
described MS population in Germany, which allows us to extrapolate our findings to the
overall MS population in Germany.
Our ocrelizumab cohort consisted of 73% users diagnosed with RRMS/SPMS and 27%
diagnosed with PPMS. Symptoms of concern depend on MS type, which becomes apparent
when analyzing the top five reported symptoms in the ocrelizumab cohort by MS type.
According to the National MS Society, patients diagnosed with PPMS tend to have more
lesions in the spinal cord than in the brain and therefore tend to experience more problems
walking [
27
]. PPMS-diagnosed users are concerned with symptoms that affect the disability
aspect, like spasticity cramps and leg foot lifting disorder, whereas RRMS/SPMS-diagnosed
users are more concerned about the sensory spectrum of symptoms like tingling and pain.
As ocrelizumab is the only highly effective treatment approved for PPMS, no PPMS-specific
symptoms are reported in the top five in the HETU or METU group.
PDDS was only in the top five answered PROs in the OU and HETU groups, but not
the METU cohort. Our patients in the OU and HETU groups were older than those in the
METU group, which could be one explanation as to why only these two groups answered
PDDS questionnaires under the most common PROs. Overall, we would have expected
PDDS to be under the most tracked PROs since it is an established way to determine the
patient’s disease progression regarding disability. The voluntary aspect of answering the
chosen PRO may be the reason other PROs were answered more frequently.
Within the OU cohort, the average disability (PDDS) score in our PPMS cohort using
ocrelizumab was 3.8, which is slightly lower than the score reported in the study on the real-
world safety and effectiveness of ocrelizumab in patients with PPMS [
23
] which showed
that the majority of patients had a significant disability at baseline (EDSS
≥
4.0). The
inclusion of PPMS patients, who experience more effects on mobility during their disease
course, may be one additional explanation for the higher disability (PDDS) scores with
increasing age in the OU cohort.
In the OU cohort, both the disability (PDDS) and pain (PES) scores increased also
with increasing age. Similarly, the symptom ‘leg foot lifting disorder’ concerns mainly the
older users in the ocrelizumab cohort. This is most likely linked to a potential progression
independent of relapses (PIRA), where the severity of disability increases over time [28].
J. Pers. Med. 2024,14, 409 10 of 12
Fatigue is one of the most common symptoms of MS, affecting about 80% of people [
29
].
In our cohort, across all treatment groups, the quick-check symptom fatigue is also tracked
by the largest proportion of users. Interestingly, when comparing to the most reported
PROs in the respective treatment cohort, MFIS-5 (fatigue) is only completed by around 22%
of METUs, whereas fatigue PRO (MFIS-5) is not in the top five completed PROs in the OU
and HETU treatment groups. This could be due to several reasons. Symptom tracking in
the Brisa app occurs daily, whereas the PROs are completed every 2 weeks. It is possible
that since fatigue is such a common symptom and affects most MS patients, users do not
see the need to track the PROs in addition to the daily symptom quick check. Instead, users
focus on the disease symptoms that are underrepresented in the daily symptom check.
Although depression in its various forms is one of the most common symptoms of
MS [
29
], it is striking that young users aged 18–25 years tracked this symptom more often
than older age groups. One explanation could be that younger people are more aware of
psychological issues, but more research is needed to understand the reasons behind this.
A follow-up analysis comparing the scores over time will give greater insight into the
ability to track disease development using a companion app. We could not identify any
significant differences in baseline PRO scores for disability (PDQ-5), bowel control (BWCS),
pain (PES) and vision (IVIS5) between the three treatment cohorts.
Overall, both the daily symptom quick check and the PROs are completed more often
in the OU treatment group compared to the other treatment groups. One hypothesis is
that the patients on a high-efficacy treatment are perceiving a measurable improvement
and want to document their therapy success, although this hypothesis would need to be
verified in a follow-up study to measure the perceived outcomes of patient treatments.
Analyses involving patient-reported data entail the probability of a small percentage
of false data inputs by users themselves, which cannot be overlooked. Also, the lack
of additional information, especially information regarding the frequency of flare-ups,
periods of remission, other types of medications used, etc., limited us from gathering
further insights and drawing associations between symptoms and medications. With the
continuation of the application development, these features could potentially contribute to
a fuller picture of patient-reported data.
5. Conclusions
In conclusion, the methodology of collecting patient-centered data in a chronic disease
such as MS is essential for improving patient care and treatment outcomes and provides an
angle that is fundamental for patient-centered treatment approaches. Digital companion
apps, such as Brisa, provide a means for the collection of real-time data on a longitudinal
basis, which is crucial to our understanding of MS pathophysiology and progression, and
serve as tools for communication between patients and physicians. Studies have shown
that patient involvement is crucial for the development of a meaningful patient companion
in an iterative way [30].
The current study’s findings could potentially lead to a better understanding of MS
and improved patient care for those on ocrelizumab treatment. However, these findings
raise new questions, such as what the increased reporting willingness is due to and what
the underlying reasons for patients’ reporting behavior are. Answering these questions
requires further research and analysis.
Supplementary Materials: The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/jpm14040409/s1, Ref. [
31
] is cited in Supplementary Materials
File. Figure S1: Smiley-face based rating system used in Brisa app. Figure S2: Distribution of
medications among Brisa users.
J. Pers. Med. 2024,14, 409 11 of 12
Author Contributions: Data analysis, S.P. and M.K.; literature research and manuscript prepa-
ration, S.P. and M.K.; discussed findings and shaped the manuscript, B.F., N.J., M.E. and T.Z.;
writing—original
draft preparation, S.P., M.K., M.E., B.F., N.J. and T.Z.; writing—review and editing,
S.P., M.K., M.E., B.F., N.J. and T.Z. All authors have read and agreed to the published version of
the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Informed consent for health data usage was obtained from all subjects
involved in the study.
Data Availability Statement: The data are not publicly available due to data protections regulations.
Acknowledgments: We would like to thank all Brisa users who gave consent to the analysis of their
data, thereby allowing us to gain valuable insights and contribute to the improvement of patient care.
Conflicts of Interest: S.P., M.K., M.E. and B.F. are employees of Temedica GmbH. N.J. is an employee
of Roche Pharma AG, Grenzach-Wyhlen. T.Z. reports grants and personal fees from Biogen, Roche,
Merck, TEVA and Almirall; grants, personal fees and non-financial support from Genzyme and
Novartis; and personal fees from Bayer, BAT, Celgene and Gilead.
Abbreviations
BDI-FS Beck Depression Inventory—Fast Screen
BLCS Bladder Control Scale
BWCS Bowel Control Scale
DMT disease-modifying therapy
EBV Epstein–Barr virus
GDPR General Data Protection Regulation
IVIS Impact of Visual Impairment Scale
MFIS-5 Modified Fatigue Impact Scale—5-Item Version
MS multiple sclerosis
PDDS Patient-Determined Disease Steps
PDQ-5 Perceived Deficits Questionnaire—5-Item Version
PRO patient-reported outcome
PES MOS Pain Effects Scale
PPMS primary progressive MS
RRMS relapsing–remitting MS
RWE real-world evidence
SPMS secondary progressive MS
SSS Sexual Satisfaction Scale
References
1.
MS-in-EU-Access.pdf. Available online: https://emsp.org/wp-content/uploads/2021/06/MS- in-EU-access.pdf (accessed on 5
March 2022).
2.
Walton, C.; King, R.; Rechtman, L.; Kaye, W.; Leray, E.; Marrie, R.A.; Robertson, N.; La Rocca, N.; Uitdehaag, B.; van der Mei, I.;
et al. Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS, third edition. Mult. Scler. J. 2020,26,
1816–1821. [CrossRef] [PubMed]
3.
Scholz, M.; Haase, R.; Schriefer, D.; Voigt, I.; Ziemssen, T. Electronic Health Interventions in the Case of Multiple Sclerosis: From
Theory to Practice. Brain Sci. 2021,11, 180. [CrossRef] [PubMed]
4.
Dillenseger, A.; Weidemann, M.L.; Trentzsch, K.; Inojosa, H.; Haase, R.; Schriefer, D.; Voigt, I.; Scholz, M.; Akgün, K.; Ziemssen, T.
Digital Biomarkers in Multiple Sclerosis. Brain Sci. 2021,11, 1519. [CrossRef] [PubMed]
5.
Voigt, I.; Inojosa, H.; Wenk, J.; Akgün, K.; Ziemssen, T. Building a monitoring matrix for the management of multiple sclerosis.
Autoimmun. Rev. 2023,22, 103358. [CrossRef] [PubMed]
6.
Ziemssen, T.; Giovannoni, G.; Alvarez, E.; Bhan, V.; Hersh, C.; Hoffmann, O.; Oreja-Guevara, C.; Robles-Cedeño, R.R.; Trojano, M.;
Vermersch, P.; et al. Multiple Sclerosis Progression Discussion Tool Usability and Usefulness in Clinical Practice: Cross-sectional,
Web-Based Survey. J. Med. Internet Res. 2021,23, e29558. [CrossRef] [PubMed]
7.
D’Amico, E.; Haase, R.; Ziemssen, T. Review: Patient-reported outcomes in multiple sclerosis care. Mult. Scler. Relat. Disord. 2019,
33, 61–66. [CrossRef] [PubMed]
J. Pers. Med. 2024,14, 409 12 of 12
8.
Rotstein, D.; Montalban, X. Reaching an evidence-based prognosis for personalized treatment of multiple sclerosis. Nat. Rev.
Neurol. 2019,15, 287–300. [CrossRef] [PubMed]
9.
Giovannoni, G. Disease-modifying treatments for early and advanced multiple sclerosis: A new treatment paradigm. Curr. Opin.
Neurol. 2018,31, 233–243. [CrossRef] [PubMed]
10.
Simpson, A.; Mowry, E.M.; Newsome, S.D. Early Aggressive Treatment Approaches for Multiple Sclerosis. Curr. Treat. Options
Neurol. 2021,23, 19. [CrossRef] [PubMed]
11. Tintoré, M. Early MS treatment. Int. MS J. 2007,14, 5–10. [PubMed]
12.
Ziemssen, T.; Derfuss, T.; de Stefano, N.; Giovannoni, G.; Palavra, F.; Tomic, D.; Vollmer, T.; Schippling, S. Optimizing treatment
success in multiple sclerosis. J. Neurol. 2016,263, 1053–1065. [CrossRef] [PubMed]
13.
Inojosa, H.; Schriefer, D.; Ziemssen, T. Clinical outcome measures in multiple sclerosis: A review. Autoimmun. Rev. 2020,19,
102512. [CrossRef] [PubMed]
14. Hauser, S.L.; Cree, B.A.C. Treatment of Multiple Sclerosis: A Review. Am. J. Med. 2020,133, 1380–1390. [CrossRef]
15.
Schriefer, D.; Haase, R.; Ettle, B.; Ziemssen, T. Patient- versus physician-reported relapses in multiple sclerosis: Insights from a
large observational study. Eur. J. Neurol. 2020,27, 2531–2538. [CrossRef]
16. DGN One|Leitlinien Details. Available online: https://dgn.org/leitlinie/176 (accessed on 21 November 2022).
17.
Weber, M.S.; Buttmann, M.; Meuth, S.G.; Dirks, P.; Muros-Le Rouzic, E.; Eggebrecht, J.C.; Hieke-Schulz, S.; Leemhuis, J.; Ziemssen,
T. Safety, Adherence and Persistence in a Real-World Cohort of German MS Patients Newly Treated With Ocrelizumab: First
Insights From the CONFIDENCE Study. Front. Neurol. 2022,13, 863105. [CrossRef]
18.
Balakrishnan, P.; Groenberg, J.; Jacyshyn-Owen, E.; Eberl, M.; Friedrich, B.; Joschko, N.; Ziemssen, T. Demographic Patterns of MS
Patients Using BRISA: An MS-Specific App in Germany. J. Pers. Med. 2022,12, 1100. [CrossRef] [PubMed]
19.
Mountford, S.; Kahn, M.; Balakrishnan, P.; Jacyshyn-Owen, E.; Eberl, M.; Friedrich, B.; Joschko, N.; Ziemssen, T. Correlation and
differences of patient-reported outcomes vs. Likert-Rating of MS symptoms in a real-world cohort using a digital patient app.
Digit. Health 2023,9, 20552076231173520. [CrossRef]
20.
Wiendl, H.; Gold, R.; Berger, T.; Derfuss, T.; Linker, R.; Mäurer, M.; Aktas, O.; Baum, K.; Berghoff, M.; Bittner, S.; et al. Multiple
Sclerosis Therapy Consensus Group (MSTCG): Position statement on disease-modifying therapies for multiple sclerosis (white
paper). Ther. Adv. Neurol. Disord. 2021,14, 17562864211039648. [CrossRef] [PubMed]
21.
Montalban, X.; Hauser, S.L.; Kappos, L.; Arnold, D.L.; Bar-Or, A.; Comi, G.; de Seze, J.; Giovannoni, G.; Hartung, H.-P.; Hemmer,
B.; et al. Ocrelizumab versus Placebo in Primary Progressive Multiple Sclerosis. N. Engl. J. Med. 2017,376, 209–220. [CrossRef]
[PubMed]
22.
Hauser, S.L.; Bar-Or, A.; Comi, G.; Giovannoni, G.; Hartung, H.-P.; Hemmer, B.; Lublin, F.; Montalban, X.; Rammohan, K.W.;
Selmaj, K.; et al. Ocrelizumab versus Interferon Beta-1a in Relapsing Multiple Sclerosis. N. Engl. J. Med. 2017,376, 221–234.
[CrossRef]
23.
Buttmann, M.; Meuth, S.; Weber, M.; Dirks, P.; Eggebrecht, J.; Hieke-Schulz, S.; Leemhuis, J.; Ziemssen, T. The Real World
Safety and Effectiveness of Ocrelizumab in Patients with Primary Progressive Multiple Sclerosis a CONFIDENCE Study Interim
Analysis. Available online: https://medically.gene.com/global/en/unrestricted/neuroscience/ECTRIMS-2022/ectrims-2022
-poster-buttmann-the-real-world-safety-and- .html (accessed on 28 February 2023).
24.
MS-Register Deutschland. Available online: https://www.msregister.de/ms-register/das-ms- register/ (accessed on 28 February
2023).
25.
Reber, S.; Scheel, J.; Stoessel, L.; Schieber, K.; Jank, S.; Lüker, C.; Vitinius, F.; Grundmann, F.; Eckardt, K.-U.; Prokosch, H.-U.; et al.
Mobile Technology Affinity in Renal Transplant Recipients. Transplant. Proc. 2018,50, 92–98. [CrossRef] [PubMed]
26.
Miclea, A.; Salmen, A.; Zoehner, G.; Diem, L.; Kamm, C.P.; Chaloulos-Iakovidis, P.; Miclea, M.; Briner, M.; Kilidireas, K.; Stefanis,
L.; et al. Age-dependent variation of female preponderance across different phenotypes of multiple sclerosis: A retrospective
cross-sectional study. CNS Neurosci. Ther. 2019,25, 527–531. [CrossRef] [PubMed]
27.
National MS Society. Available online: https://www.nationalmssociety.org/What-is-MS/Types-of-MS/Primary- progressive-MS
(accessed on 28 March 2023).
28. Ford, H. Clinical presentation and diagnosis of multiple sclerosis. Clin. Med. 2020,20, 380–383. [CrossRef] [PubMed]
29.
MS Symptoms. National Multiple Sclerosis Society. Available online: https://www.nationalmssociety.org/Symptoms-Diagnosis/
MS-Symptoms (accessed on 4 March 2022).
30.
Allen-Philbey, K.; Middleton, R.; Tuite-Dalton, K.; Baker, E.; Stennett, A.; Albor, C.; Schmierer, K. Can We Improve the Monitoring
of People With Multiple Sclerosis Using Simple Tools, Data Sharing, and Patient Engagement? Front. Neurol. 2020,11, 464.
[CrossRef] [PubMed]
31.
Learmonth, Y.C.; Motl, R.W.; Sandroff, B.M.; Pula, J.H.; Cadavid, D. Validation of patient determined disease steps (PDDS) scale
scores in persons with multiple sclerosis. BMC Neurol. 2013,13, 37. [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.
