JMIR FORMATIVE RESEARCH

Apolinário-Hagen et al

Original Paper

Acceptance of Mobile Health Apps for Disease Management
Among People With Multiple Sclerosis: Web-Based Survey Study
Jennifer Apolinário-Hagen1, Dipl-Psych, PhD; Mireille Menzel1, MSc; Severin Hennemann2, Dipl-Psych, PhD;
Christel Salewski1, Dipl-Psych, PhD
1

Department of Health Psychology, Faculty of Psychology, University of Hagen, Hagen, Germany

2

Department of Clinical Psychology, Psychotherapy and Experimental Psychopathology, Institute of Psychology, University of Mainz, Mainz, Germany

Corresponding Author:
Jennifer Apolinário-Hagen, Dipl-Psych, PhD
Department of Health Psychology
Faculty of Psychology
University of Hagen
Universitätsstraße 33
Hagen, 58097
Germany
Phone: 49 2331 987 ext 2272
Fax: 49 2331 987 1047
Email: jennifer.apolinario-hagen@fernuni-hagen.de

Abstract
Background: Mobile health (mHealth) apps might have the potential to promote self-management of people with multiple
sclerosis (MS) in everyday life. However, the uptake of MS apps remains poor, and little is known about the facilitators andbarriers for their efficient utilization, such as technology acceptance.
Objective: The aim of this study was to examine the acceptance of mHealth apps for disease management in the sense of
behavioral intentions to use and explore determinants of utilization among people with MS based on the Unified Theory ofAcceptance and Use of Technology (UTAUT).
Methods: Participants for this Web-based cross-sectional study were recruited throughout Germany with the support of regional
MS associations and self-help groups. To identify determinants of intention to use MS apps, a measure based on the UTAUT
was adapted with 4 key determinants (performance expectancy, effort expectancy, social influence, and facilitating conditions)and extended by Intolerance of Uncertainty (IU) and electronic health literacy. Potential influencing effects of both MS andcomputer self-efficacy (C-SE) as mediators and fatigue as a moderator were analyzed using Hayes’s PROCESS macro (SPSS
version 3.0) for IBM SPSS version 24.0.
Results: A total of 98 participants (mean age 47.03 years, SD 10.17; 66/98, 67% female) with moderate fatigue levels completed
the survey. Although most participants (91/98, 92%) were daily smartphone users, almost two-thirds (62/98, 63%) reported noexperience with MS apps. Overall, the acceptance was moderate on average (mean 3.11, SD 1.31, minimum=1 and maximum=5),with lower scores among persons with no experience (P=.04) and higher scores among current users (P<.001). In multiple
regression analysis (R2=63% variance explained), performance expectancy (beta=.41) and social influence (beta=.33) wereidentified as significant predictors of acceptance (all P<.001). C-SE was confirmed as a partial mediator in the relationshipbetween IU and acceptance (indirect effect: B=−.095, 95% CI −0.227 to −0.01). Furthermore, a moderated mediation by C-SE
was shown in the relationship between IU and behavioral intentions to use MS apps for low (95% CI −0.42 to −0.01) and moderatelevels (95% CI −0.27 to −0.01) of fatigue.
Conclusions: Overall, this exploratory pilot study indicates for the first time that positive expectations about the helpfulness
for self-management purposes and social support might be important factors to be considered for improving the acceptance ofMS apps among smartphone users with MS. However, given some inconsistent findings, especially regarding the role of effort
expectancy and IU and self-efficacy, the conceptual model needs replication with a larger sample of people with MS, varying
more in fatigue levels, and a longitudinal assessment of the actual usage of MS apps predicted by acceptance in the sense of
behavioral intentions to use.

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Apolinário-Hagen et al(JMIR Formativ Res 2018;2(2):e11977) doi: 10.2196/11977
KEYWORDS
multiple sclerosis; eHealth; mHealth, acceptability of health care; patient preference

Introduction
Multiple Sclerosis: Challenges for Self-Management
Multiple sclerosis (MS) is a chronic autoimmune disease of thecentral nervous system (CNS), which is characterized byexacerbations of neurological dysfunction [1]. Approximately
2.3 million people live with MS worldwide [2], of which halfof those affected live in Europe [3] and about 200,000 people
live in Germany [4]. Prevalence rates vary between region andregistry. The onset of MS is usually between the 20th and 40thyear of life, with women being affected more often than men
at a ratio between 2:1 [5,6] and 3:1 [3]. Thus, MS is one of themost prevalent neurological disorders in young adulthood,leading to permanent disability and early retirement [3,4].
Principally, 4 major MS forms related to different challenges
can be distinguished [6]: with 85% of cases, the most common
MS form is relapsing-remitting MS, which is characterized byrelapses and exacerbations as well as phases of remission. This
form can transit to the secondary progressive form with
continuing worsening. The primary progressive form of MS
with gradual, continuous worsening from the onset affects
approximately 10% of people with MS, whereas progressive
relapsing MS with symptom progression from the onset without
periods of remission represents less than 5% of cases [6].
Depending on the neuroanatomical localization of plaques ofdemyelination in the CNS, symptoms can be manifested as
various motor, visual, cognitive, and sensory disturbances aswell as fatigue [5], making it difficult to predict the individual
MS course [6]. Therefore, coping with uncertainties is a key
challenge of living with MS [7].
Because little is known about the etiology of MS [5] and there
is currently no cure, the long-term MS treatment also has tofocus on disease management or self-management and copingwith uncertainties [7]. Self-management of a chronic illness canbe defined as a dynamic process of actively coping [8]. Given
that research has shown that people with MS prefer to take anactive role in treatment decisions among most people with MS[9], self-help tools such as mobile health (mHealth) apps for
MS could be useful for supporting disease management [10].

Multiple Sclerosis Apps as Self-Management Tools
Given that people with MS often experience issues in accessing
health care services because of barriers such as mobility
restrictions, mHealth and electronic health (eHealth) services
represent a promising way to facilitate MS disease management[11]. In fact, the internet is the first source for health information
for many people with MS [12].
Research suggests that modern technologies and new media in
the health context (eHealth) may be helpful for people with MS
by promoting adherence, self-management skills [10], mental
health [13], physical activity [14-17], and fatigue management[18,19].
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Many MS patients in Germany are familiar with using mobile
phones for communication with health care providers [20].
Furthermore, a recent survey of the North American ResearchCommittee on Multiple Sclerosis registry showed that 28.6%
of participants with MS have used secure Web-based portals
for the exchange of medical information with health careproviders and that 46.2% of the smartphone and tablet users
used an mHealth app [21].
Moreover, mHealth apps appear especially suitable for people
with MS because of their location-independent and time-flexible
accessibility [11]. There is also a growing number of positive
economic evaluations on the cost-effectiveness of mHealth apps
for medical conditions [22]. Hence, high-quality mHealth appsfor MS can further help empower people with MS to be more
active in their disease management and informed decision
making [23].
The focus of existing apps and other stand-alone or blendedcare eHealth solutions lies in screening and assessment, disease
monitoring and self-management, advice and education, as wellas treatment and rehabilitation [11]. A narrative review of 28
eHealth and mHealth solutions for MS by Marziniak et al [11]showed that mHealth apps for MS patients such as Msdialog,
COGNI-TRACK,orMyBetaApp
usually
address
self-management and monitoring (eg, medication reminder andsymptom tracking), whereas Web-based interventions such as
Deprexis or MS Invigor8 focus on treatment and rehabilitation[11].
However, despite these advantages, the uptake of MS apps ispoor. A scoping review [24] indicated that most MS apps fordisease management failed to meet the needs and demands ofusers with MS. While advantages of eHealth and mHealthsolutions include the possibility to connect with others [25],improved health care access or greater independence [26],perceived disadvantages involve the potentially poor usability
for people with neurological impairments [25], and data security
concerns [26]. However, especially in countries such as
Germany with an early stage of eHealth adoption in routine care[27], little is known about the acceptance of mHealth services
for MS as another barrier.
To shed light on the determinants of MS apps’ uptake,technology acceptance models (TAM) [28] such as the Unified
Theory of Acceptance and Use of Technology (UTAUT) [29]provide guidance as a validated framework. In these models,
acceptance is operationalized as behavioral intention to use or
actual usage of a technological innovation as dependent
variables of a set of personal and interpersonal attitudinal
factors. In the context of the TAM, Davis et al [28] argued thatpeople form attitudes and intentions toward learning to use anovel technology, which are associated with uncertainties, before
starting efforts aimed at performing. As an early form of
acceptance, intention to use represents a well-establishedpredictor of behavior, for instance, in terms of health behavior
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JMIR FORMATIVE RESEARCH[30] and the use of psychological services [31] as well as
information technology [28]. In accordance with the Theory ofPlanned Behavior (TPB [30]), behavioral intention can beunderstood as an evaluation or attitude toward a behavior,affecting the likelihood of performing a particular behavior.
The UTAUT [29], which was evaluated in organizations inwhich learning to use a novel technology was either voluntaryor mandatory for employees, is a synthesis of 8 validated models
such as the TPB [30]; TAM [28,32]; the Diffusion of Innovation
theory [33]; and the Social Cognitive Theory [34], hypothesizing
a total of 32 constructs and up to 4 moderators (ie, gender, age,voluntariness, and experience) [29]. According to the UTAUT[29], performance expectancy (eg, perceived usefulness), effort
expectancy (eg, ease of use), and social influence (eg, subjectivenorm) are predictors of the intention to use a technology,
whereas facilitating conditions (eg, support and compatibility)and behavioral intention are direct determinants of usage
behavior in business organizations.
In recent years, the UTAUT and TAM have been implemented
in various medical settings [35-41]. However, to the knowledgeof the authors, no study to date has modified the UTAUT modelto the acceptance of MS apps.
On the basis of the theoretical considerations and empiricalfindings from other medical contexts [35], it can be hypothesizedthat the intention to use MS apps, as an early form of acceptance,is higher, in case of high degrees of the perceived usefulness of
MS apps for self-management (performance expectancy), theexpected ease of use (effort expectancy), the approval as being
helpful by significant others (social influence), and available
facilitating factors related to the use of MS apps such astechnical support (facilitating conditions).
For the specific context of MS apps, further MS-related andtechnology-related variables could be relevant to understandtheir acceptance. For instance, research indicates that Intoleranceof Uncertainty (IU) with respect to problem-focused coping [7]and eHealth literacy [42] might be additional predictors of healthbehavior and disease management in MS.
Moreover, self-efficacy, defined as the personal belief in one’s
capability to overcome challenges with respect to MS (multiple
sclerosis self-efficacy, MS-SE [43]) and using technology(computer self-efficacy, C-SE [44]), might influence theacceptance of MS apps.
Fatigue is a common disabling condition in MS [45], with about
three-thirds of people being affected by severe fatigue(compared to NARCOMS, 74% [46]). Hence, fatigue mightplay a moderating role in behavioral intention to use MS apps.

Goals of This Study
The purpose of this pilot study was to explore factors influencing
the acceptance of MS apps among smartphone users with MS
in Germany. We hypothesize that the expectations and beliefs
associated with the use of MS apps, IU, and eHealth literacy
will significantly predict the acceptance of MS apps. We
assumed a significant predictive contribution of the following
core UTAUT determinants in the behavioral intention to use
MS apps (in the sense of early acceptance): (a) performance
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Apolinário-Hagen et al
expectancy, (b) effort expectancy, (c) social influence, (d)facilitating conditions as well as extended predictors, (e) IU,
and (f) eHealth literacy. We expected significant positive
associations between the predictors of the UTAUT determinants
and eHealth literacy behavioral intention MS apps and asignificant negative association between IU and behavioral
intention MS apps. Our research questions are as follows: Does
self-efficacy explain and fatigue influence the hypothesized
relationships between the determinants proposed underhypothesis 1 and acceptance of MS apps? Consequently, anothergoal was to determine mediating effects of (a) MS-SE and (b)C-SE (research question 1a and b), and moderating effects offatigue in the relationship between the 6 predictors andbehavioral intentions to use MS apps (research question 2).

Methods
Study Design and Setting
We conducted a Web-based cross-sectional survey. Data werecollected anonymously between March 8, 2017, and April 15,
2017, using Unipark (Enterprise Feedback Suite survey, version
Spring 2017, Questback). No ethical approval was required by
the institution of the principal investigators because it was ananonymously conducted, self-selected, and voluntary Web-based
survey study that involved no intervention, no deception, andno potentially adverse or burdensome questions or tests.
Participants were required to give informed consent toparticipate in the study using Unipark (click-to-agree). No
monetary compensation was offered for participation. The
average completion time was 15 min.

Participants and Recruitment
In this Web-based pilot study using convenience sampling, wewere interested in the opinions of smartphone users with MS.
As this study aimed to include only people with diagnosed MS
over the age of 18 years, participants were recruited via a letterto the national associations of the German Multiple Sclerosis
Society (“Deutsche Multiple Sklerose Gesellschaft” [DMSG]).
Overall, 11 out of 16 regional associations accepted theinvitation to share the link to the study via their websites,
Facebook profiles, and newsletters. In the 5 other federal states,
the recruitment took place by inviting 25 local MS self-helpgroups via email. In addition, there was a call via the online
platform “MSlife” (Biogen, Germany).
A priori power analyses using G*Power, version 3.1 [47], (Ftests, multiple regression: Omnibus, R2 deviation from 0) yieldeda required sample size of at least 77 participants to determinea moderate-to-high effect of f2=0.3 (alpha=.05; power=0.95)
for the multiple regression model with 6 predictors (critical
F6,70=2.23, noncentrality parameter λ=23.10). The power of
0.95 was chosen based on the assumption of low risk of false
negatives with this study design. The effect size was chosen
based on a previous work on the UTAUT [48] and a study using
a UTAUT-questionnaire design we adapted [35], showing highexplained variance. Because there was no study using the samemeasure, we decided to use the squared correlation R2=.25 to

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calculate the effect size f2. This resulted in the effect size of
f2=0.33, which we rounded to f2=0.3.

Formulation of a Conceptual Model for the Acceptanceof Multiple Sclerosis Apps and Its Operationalization:
Adaptation of the Unified Theory of Acceptance andUse of Technology Framework
Because the UTAUT [29] originates in organizational contexts,
the constructs were adapted and extended to the context of MSapps (Figure 1). Items from the UTAUT model were adapted
to MS apps for smartphones based on the original questionnaire[29] as well as an adapted measure for Web-based aftercare in
Germany [35] and extended with the 2 additional predictors IU
and eHealth literacy. To minimize the risk of overload becauseof an excessive number of items, truncated scales were used.
For the items we adapted from the English version of scales,
forward translation was used, which was checked independently
by 3 professionals, of which 2 had a scientific or psychologicalbackground (PhD level and BSc level) and the other 1 was an
English teacher. Furthermore, various items of prior studies
using adaptations of the UTAUT measure were available inGerman through contacting the study’s authors [35]. The 8-item
German eHealth Literacy Scale (G-eHEALS) [49] was alsoavailable in German. Testing the transadaption was performed
using cognitive debriefing. To ensure the comprehensibility ofitems adapted from scales available in German or English, 5
students were asked to give their feedback independently fromeach other. Furthermore, the final Web-based survey waspretested by 13 external persons from the personal network of
the second author to avoid technical problems. All items weused to assess the conceptual model are presented in Multimedia
Appendix 1.

Apolinário-Hagen et al

Variables and Measures
Scales of the Adapted and Extended Unified Theory ofAcceptance and Use of Technology Model
The overall survey consisted of 60 items, of which 40 items
were used for the assessment of the model (16 items for UTAUT
variables, 20 items for additional variables, and 4 items for thecovariates).
In all adapted scales with numerical variables, participants wereasked to indicate their agreement to statements on a 5-point
Likert scale ranging from 1 (“fully disagree”) to 5 (“fully
agree”).

Acceptance: Behavioral Intention to Use Multiple
Sclerosis Apps as Criterion
As an indicator of early acceptance, behavioral intention was
operationalized as the plan to use MS apps for disease
management purposes. Behavioral intentions to use MS apps
in general and within the next 4 weeks were measured using 3
items from the original UTAUT [29] we adapted. The sample
was divided into 3 groups who received slightly modified items,
considering actual use of MS apps in relation to intentions touse: participants who currently use MS apps (group 1=users),
participants who have never used MS apps before (group2=nonusers), and those who used MS apps in the past (group3=past users). We asked current users if they would also use
MS apps in the future, whereas past users were asked if they
intend to use MS apps again. Nonusers received similar but
more generally formulated items (Multimedia Appendix 1). A
total scale score unifying the responses of the 3 groups was
generated for the regression analysis, in which the mean valueof the 3 items targeting acceptance for each participant wascalculated and transferred to the total scale.

Figure 1. Conceptual study model: adapted and extended Unified Theory of Acceptance and Use of Technology for the acceptance of multiple sclerosis
apps. UTAUT: Unified Theory of Acceptance and Use of Technology; MS: multiple sclerosis; eHealth: electronic health.

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Predictors of Acceptance of the Extended Unified Theoryof Acceptance and Use of Technology Model
Unified Theory of Acceptance and Use of Technology:
Performance Expectancy
Performance expectancy is defined as the extent to which aperson believes that using a technology could improve outcomes
and has the strongest predictive value for acceptance in the
UTAUT [29]. In this study, performance expectancy was
operationalized as the expectation of a person with MS thatusing MS apps would be helpful for disease management
purposes.
Performance expectancy was assessed with 4 items used byHennemann et al [35], which we adapted to assess expected
outcomes in connection to the perceived usefulness or
helpfulness of MS apps. Because social participation is essential
for successful adjustment to MS [50], a fifth item was added tothe survey (“Using MS apps could help me maintain social
contacts.”).
Unified Theory of Acceptance and Use of Technology: Effort
Expectancy
Effort expectancy is one’s belief about how easy it is to use atechnology, taking its complexity and difficulty into account[29]. In this study, effort expectancy is defined as the extent ofperceived ease with which MS apps can be used.
Effort expectancy was evaluated with 2 adapted items based onstudies in inpatient medical settings and the original UTAUT[29,35] (eg, “I suspect that using MS apps would be easy”).
Unified Theory of Acceptance and Use of Technology: Social
Influence
Social influence refers to the extent to which a person believes
that relevant others think one should perform the behavior inquestion and that the technical innovation could be useful inrelation to a particular goal [29]. In this study, social influencein the sense of subjective norm was measured by asking
participants to assess the extent to which (1) their close family
members, (2) primary care provider, and (3) friends would
consider the use of MS apps helpful for disease management.
An other response option was provided so that respondents
could add examples of other groups of people, but no additionalgroups were added.
Unified Theory of Acceptance and Use of Technology:
Facilitating Conditions
Facilitating conditions are defined as a person’s belief thatorganizational and technical support is available when using anovel technology. This construct also involves the extent towhich an innovation is experienced as compatible with personalvalues, needs, and experiences; perceived behavioral control;and objective factors [29]. Among people with MS, the plan tousing a new technology despite a disabling long-term condition
might be affected by perceived available resources, such as
knowledge and support options.
Facilitating conditions were assessed based on 3 items, of which
2 items were completely adopted and adapted from a study ininpatient medical settings [35]. The third additional item was
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Apolinário-Hagen et al
created based on the original UTAUT [29], which considerssocial support as a facilitating condition (“If I had problems
using MS apps, I would know where to get help.”).
Intolerance of Uncertainty
People with MS are confronted with numerous disease-related
uncertainties [51]. High levels of IU are associated withincreasing efforts to regain control of the uncontrollable
situation, which can result in dysfunctional coping strategies[7]. IU is often related to incapacitation, stress-inducing
perceptions, and a tendency to avoidance [52]. Under theassumption of using MS apps as a strategy for problem-focused
coping with respect to disease management strategies, IU could
have a negative influence on intentions to use.
Of the 27-item IU Scale (IUS [52]), this study used 4 items withthe highest factor loadings per subscale from the primary study
using a student sample (items 5, 12, 19, and 16). In total, 3 items
were only translated, for instance, “My mind can’t be relaxed
if I don’t know what will happen tomorrow” (item 12 of theIUS). We only adapted 1 item by adding a relationship to adisease (“When it’s time to act, uncertainty associated with mydisease rather paralyzes me”).
Electronic Health Literacy
To be able to take an active role in medical decision making
and self-management, people with MS require adequate
information [51], which they often seek online [20]. eHealthliteracy is defined as the ability to search, find, understand,evaluate, and use health information available via electronic
resources [53]. As eHealth and mHealth tools for MS can
improve self-management skills [10], it can be assumed thathigher eHealth literacy may increase the likelihood of effectively
using electronic resources such as MS apps [54].
eHealth literacy was measured using 4 slightly modified itemsof the 8-item G-eHEALS [49]. From the subscale information
search (6 items), 2 items with the highest factor loadings wereselected, whereas both items comprising the information
evaluation were included.

Mediator Effects of Self-Efficacy Beliefs
Multiple Sclerosis Self-Efficacy
MS-SE can be understood as one’s confidence in the ability tohandle challenges related to MS [43]. Self-efficacy has beenshown to predict health-related behavior in people with MS,
including physical mobility [55-57], psychosocial adjustment[43], or pain-related coping strategies [58], and to mediate
health-related relationships [59,60]. Thus, it can be assumed
that MS-SE can help explain problem-focused coping strategies
such as using MS apps.
MS-SE was assessed using the 11-item Liverpool Self-Efficacy
Scale [61]. For economic reasons, 2 items per subscale (control
and personal agency) were chosen based on the criterion of facevalidity. We added the term MS in the German translation, forinstance, “Despite my difficulties, I still manage to cope withdaily life with MS.”JMIR Formativ Res 2018 | vol. 2 | iss. 2 | e11977 | p. 5
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Apolinário-Hagen et al

Computer Self-Efficacy

Procedure and Scale Metrics

C-SE is defined as the personal belief regarding one’s ability
to use computer technology to perform specific tasks [62]. A
meta-analysis of 102 studies [44] confirmed that C-SE is
associated with behavioral intention and usage behavior as wellas with components of technology acceptance such as perceived
usefulness and ease of use. Therefore, we will examine theextent to which C-SE with respect to MS apps can explain the
proposed relationships.

The Web-based survey comprised a total of 60 items andoptional commentary fields. The first part of the survey
consisted of sociodemographic (6 items) and MS-related (4items) questions using nominal scales.

A measure of general computer self-efficacy (GCSE [63]) wasused, but computer was replaced by smartphone. For economic
reasons, the original 5-item scale was limited to the 3 items withthe highest factor loadings (items 2, 3, and 5), for instance, “I
believe I have the ability to remove apps from my smartphone
I no longer need” (adapted item 5 of the GCSE).

Moderator Effects of Fatigue
Fatigue can be described as a state of subjective physical ormental exhaustion, varying largely in intensity over the day
course [64,65]. Such fluctuations can hinder simple routines,
job performance, and social activities, making fatigue one of
the main reasons for incapacity to work [64,66]. On the one
hand, MS apps such as MoreStamina [67] could be used on acompensatory basis for the management of fatigue. On the other
hand, fatigue might also be one reason for the poor uptake ofMS apps, for instance, because of negative effort expectancies.
Hence, a moderating role of fatigue appears possible.
For the retrospective assessment of fatigue in the past 4 weeks,
the 5-item Modified Fatigue Impact Scale [68] was used.

Control Variables
In the original UTAUT, age, gender, voluntariness (of learning
to use a technology vs mandatory use in organizations), andexperience were confirmed as moderators for the key
relationships [29]. To control their influence in this study, age
and gender were included as covariates in the mediation andmoderation models. Gender as a categorical variable wasincluded with dummy coding (0=male and 1=female). Age andduration of MS were included as numerical variables. Because
using MS apps as a self-help tool is a voluntary choice,voluntariness was no applicable variable in this study.
Experience was operationalized as the duration of MS, not as
experience with MS apps. Due to the unclear proportion of MS
app users in the target population, we found that the durationof living with MS might be a more meaningful indicator forexperience with disease management. Furthermore, theeducation level was considered, as studies indicate a more likely
use of eHealth services among higher educated people with MS[69]. An MS Registry survey [21] found a higher likelihood ofsmartphone, tablet, and mHealth app use being associated withyounger age and higher education in people with MS. In linewith the Comparative Analysis of Social Mobility in Industrial
Nations (CASMIN-classification [70]), the educational
attainment was assessed as an ordinal scale in 3 levels ranging
from 1 (“low”) to 3 (“high”).

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Then, the MS-related constructs fatigue (5 items), IU (4 items),
and MS-SE (4 items) were assessed on 5-point Likert scales.
Smartphone use and frequency of use (2 items) as well as usage
preferences (4 items) were asked using nominal scales. eHealthliteracy (4 items) and C-SE (3 items) were evaluated on 5-point
Likert scales. Use of MS apps (5 items) was assessed on anominal scale. Overall, 3 optional questions (free text fields)
were asked about subjective benefits, challenges, andsuggestions for improvements regarding MS apps. To
summarize the responses, the UTAUT model was used to map
the responses to categories following the approach of aquantitative content analysis. Finally, 16 items on a 5-point
Likert scale were used to measure UTAUT variables: behavioral
intention to use (3 items), performance expectancy (5 items),
effort expectancy (2 items), social influence (3 items), andfacilitating conditions (3 items).

Statistical Analysis
Only completed surveys were considered for data analyses(listwise deletion). No imputation technique was used tocompensate missing values because the vast majority of dropouts
occurred after the first 3 demographic questions (missing not
at random). Descriptive analyses were performed to obtain
information on sociodemographics and usage of modern
technology for MS-related purposes. Both simple and multiple
linear regression analyses were conducted to determine
predictors for the acceptance of MS apps. Due to the exploratory
nature of this study and as the limited evidence base related tothe proposed relationships in this specific model was too scarce,the predictors were included simultaneously in the multiple
regression model. All analyses were performed using SPSS,
version 24 (IBM Analytics), in which the macro PROCESS byHayes [71] was implemented to test mediation (C-SE and
MS-SE) and moderation hypotheses (fatigue). Before theanalyses, the assumptions of multiple linear regression analysis
were confirmed being sufficiently fulfilled to perform parametrictests. Data analyses were performed independently andcross-checked by 2 researchers. The significance level for all
hypotheses was alpha <.05.

Results
Descriptive Analyses
The survey platform was accessed 496 times. In total, 175
people agreed to participate (informed consent), of which 113
people fully completed the survey (attrition rate of 35.4%). Most
participants (52/62, 84%) who dropped out of the survey did
so after the first 3 demographic questions (ie, year of birth,
gender, and postal code). The data of the other 15 participants
were eliminated because of incomplete data because they
indicated they did not possess a smartphone. Because the target
sample was smartphone users, data from these participants were
not included in the analysis.
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Sample Characteristics
As shown in Table 1, the final sample consisted of 98 people
aged between 22 and 67 years (median 48.0 years, interquartile
range 14.0 years). The CASMIN-based mean score (mean 2.23,
SD 0.61) indicated a moderately high education level [70].
Detailed sample characteristics are presented in Multimedia
Appendix 2.

Apolinário-Hagen et al
In terms of MS-specific use, the mobile phone or smartphone
was mostly used as internet access to search MS informationor connect with others (51/98, 52%), scheduling (eg, for medical
consultations, 45/98, 46%), making calls for medical purposes(44/98, 45%), MS-related emails or text messages (34/98, 35%),
and for music or games (eg, for relaxation and cognitive training,
19/98, 19%). Other use (8/98, 8%) included apps as medication
reminders. In total, 19% (19/98) did not answer this question.

Frequency and Purposes of Mobile Phone Use
Figure 2 shows the frequency of using the mobile phone or
smartphone in general and for MS-related purposes.
Table 1. Sample characteristics (N=98).
Variables

Statistics

Age (years)
All, mean (SD), range

47.03 (10.17), 22-67

Women, mean (SD), range

45.11 (10.09), 22-67

Men, mean (SD), range

51.0 (9.28), 22-66

20-35 years, n (%)

16 (16)36-50 years, n (%)

43 (44)51-67 years, n (%)

39 (40)Gender, n (%)
Female66 (67)Male

32 (33)

Secondary education, n (%)
Certificate of secondary educationa

8 (8)General certificate of secondary educationb

27 (28)Advanced technical college entrance qualificationc

19 (19)General qualification for university entranced

44 (45)

Vocational training and tertiary education, n (%)
No professional qualification
e

4 (4)

Training qualification

62 (63)

Polytechnic or college degree

9 (9)University degree

23 (24)

Duration of multiple sclerosis (years)aAll, mean (SD), range

13.92 (9.84), 1-45

1-10, n (%)

43 (44)11-21, n (%)

37 (38)>21, n (%)

18 (18)German “Hauptschulabschluss” as basic school qualification.

b

German secondary school level-I certificate (“Mittlere Reife”).

cGerman “Fachhochschulreife.”dGerman “Allgemeine Hochschulreife” (“Abitur” or A Level).

eGerman dual training model.

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Figure 2. Frequency of general and multiple sclerosis–related smartphone use, proportions in percent (N=98). MS: multiple sclerosis.

Use of Multiple Sclerosis Apps for Disease Management
Purposes
The majority of participants (62/98, 63%) reported no experience
with MS apps. Of the 36 participants (36/98, 37%) reporting
experience with MS apps, 18 people (18/36, 50%) were currently
using them.
Most of the 36 participants with experience with MS apps
indicated the use of the app MS Kognition (MS cognition) by
the DMSG (15/36, 42%) in the comment field. Further apps
reported by the participants are presented in Multimedia
Appendix 2. In addition, Multimedia Appendix 2 provides asummary of optional free text responses on perceived benefits
and challenges of MS apps as well as ideas for improvements.
Specific MS apps were used for purposes of cognitive training
to improve attention or concentration (12/18, 67%), informationand education about MS (9/18, 50%), reminders of appointments
or medication intake (8/18, 44%), documentation (6/18, 33%),
maintaining social contacts (6/18, 33%), and strengthening
physical skills (3/18, 17%).
In addition to MS apps, 48% (47/98) participants also used other(non-MS-specific) mHealth apps for disease management,
mostly for cognitive training (23/47, 49%); strengthening
physical well-being, including yoga and fitness (12/47, 26%);
orientation in public life (eg, finding barrier-free places, 10/47,21%); nutrition (8/47, 17%); stress management (3/47, 6%);
mood management (eg, anxiety and depression, 2/47, 4%); andother purposes (7/47, 14.9%). The awareness of the existenceof internet-based therapies was low (aware: 25/98, 26%; notaware: 67/98, 68%; and not sure: 6/98, 6%).

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Descriptive Analysis of the Scales Related to the
Acceptance of Multiple Sclerosis Apps
Table 2 summarizes the mean values, SDs, and Cronbach alphafor each numerical scale (N=98). As shown in Table 2, theoverall acceptance was moderate (mean 3.11, SD 1.31). When
compared with the overall mean score, participants reporting
no experience with using MS apps expressed significantly lower
acceptance (mean 2.76, SD 1.32; t61=–2.100; P=.04) and current
users had significantly higher acceptance scores (mean 4.33,
SD 0.79; t17=6.552; P<.001), whereas the difference with former
users was not significant (mean 3.11, SD 0.91; t17=0.005;
P=.996).

Principal Results of Regression Analyses
Multiple Regression Analysis
Correlation analyses and simple regression analyses (Multimedia
Appendix 3) showed significant correlations between thevariables and a significant contribution of the variables, except
for eHealth literacy, in behavioral intentions to use.
According to the F test (F6,91=25.702), the overall regression
model contributes 63% of explained variance (R2=.625; P<.001).
The additional inclusion of the 4 control variables would have
only yielded in a marginally increased explained variance of
1.3% (up to R2=64.3%). As shown in Table 3, with a regression
coefficient of B=.63 (beta=.41; P<.001), performance
expectancy proved to be a significant predictor of intention touse as well as social influence with B=.42 (beta=.33; P<.001).
Contrary to hypothesized, the other predictors had no meaningful
influence on acceptance in the multiple regression model (allP>.05).

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Table 2. Mean values, SDs, and internal consistency of the scales of the conceptual study model (N=98).
Variable or scalea

Mean (SD)b

Cronbach alphac

Behavioral intention to use overall (3 items per group, N=98)d3.11 (1.31)—eGroup 1: current users (n=18)

4.33 (0.79).83f

Group 2: nonusers (n=62)

2.76 (1.32).91g

Group 3: past users (n=18)

3.11 (0.91).73h

Performance expectancy (5 items)

2.81 (0.97).88f

Effort expectancy (2 items)

3.80 (0.80).60iSocial influence (3 items)

2.81 (1.02).90g

Facilitating conditions (3 items)

4.45 (0.78).85fIntolerance of uncertainty (4 items)

2.61 (0.99).78jElectronic health literacy (4 items)

4.22 (0.70).87f

Multiple sclerosis self-efficacy (4 items)

4.06 (0.80).85f

Computer self-efficacy (3 items)

4.18 (0.94).84f

Fatigue (5 items)

3.31 (1.17).89f

Behavioral intention to use per groupa

Items were adapted from previous research (Multimedia Appendix 1).

b

Scale range; minimum=1 to maximum=5. Item keying: higher scores mean a higher expression of the respective variable.

cInternal consistency; classification according to Cohen criteria [72].

dGroup 1=participants who are current users of MS apps, group 2=participants who never used MS apps, and group 3=participants who had used MS
apps in the past. All assessed 3 items on behavioral intention that were modified based on the experience with MS apps.
e

Not applicable.

f

Cronbach alpha: good.

g

Cronbach alpha: excellent.

hCronbach alpha: sufficient.

i

Cronbach alpha: questionable.

j

Cronbach alpha: acceptable.

Table 3. Coefficients in the multiple regression model of the adapted and extended Unified Theory of Acceptance and Use of Technology (N=98).

aPredictorsa

B

SE

Beta

t testP value

Tolerence

VIFb

Constant

−.56

0.77

—c

−0.73

.47

—

—

Performance expectancy

.63

0.12

.47

5.32

<.001

.52

1.92

Effort expectancy

.09

0.13

.06

0.70

.49

.62

1.6

Social influence

.42

0.13

.33

3.33

.001

.42

2.40

Facilitating conditions

.07

0.13

.04

0.51

.61

.64

1.56

Intolerance of uncertainty

.09

0.09

.06

0.90

.37

.79

1.27

Electronic health literacy

−.04

0.14

−.02

−.030

.77

.78

1.28

Criterion: behavioral intention to use MS apps. All predictors were included simultaneously, without covariates.

b

VIF: variance inflation factor.

c

Not applicable.

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Figure 3. Exploratory model for the assessment of the moderation hypotheses for fatigue. Criterion: behavioral intentions to use multiple sclerosis
apps. The numbering of the predictors corresponds to the numbering of the 6 reported models. eHealth: electronic health.

Mediator Effects of Self-Efficacy
Mediation hypotheses were examined individually for eachpredictor (Multimedia Appendix 3). Due to the correlation ofboth mediators (r=.289; P=.004), a multiple mediation analysis
was performed in which both C-SE and MS-SE weresuccessively tested.
However, the assumption of mediation could only be confirmed
for C-SE in the model specification with IU as a predictor of
behavioral intention (model 5). Both the effect of IU on C-SE
(a path, B=−.23; P=.01) and C-SE on behavioral intention (bpath, B=.42; P=.006) were significant. The significant indirect
effect of C-SE (B=−.095, 95% CI −0.227 to −0.01) suggests
the reduction of the direct effect (c path, B=.29; P=.046)
compared with the total effect (c path, B=.30; P=.02), indicatinga partial mediation.
Furthermore, the control variable age showed significant
influence on C-SE (B=−.03; P=.003), as did gender (B=−.45;
P=.02), meaning that younger and male participants had astronger expression of C-SE.

Moderator Effects of Fatigue
Simple regression analysis confirmed fatigue as a significant
positive predictor of behavioral intention (R2=.05; B=.24;
beta=.21; SE=.11; P=.04). As illustrated in Figure 3, eachpredictor was examined individually for the effect of fatigue.
Contrary to hypothesized, the interaction terms (predictor ×
moderator) as an indicator for moderation effects of fatigue
were not significant for performance expectancy (B=−.01;
P=.92), effort expectancy (B=.22; P=.08), social influence(B=−.07; P=.37), facilitating conditions (B=.05; P=.73), IU(B=−.06; P=.58), and eHealth literacy (B=.09; P=.57). Because
there was at least a marginal interaction effect (P=.08),
inferential statistics were applied, which showed a significant
fatigue-related effect of effort expectancy on behavioral
intention for average (95% CI 0.23 to 0.86) and high levels of
fatigue (95% CI 0.33 to 1.28; all P<.001), as shown in
Multimedia Appendix 3.
As mediation effects of C-SE could only be confirmed in therelationship between IU and intentions to use MS apps, only
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this model was tested for moderated mediation. For this purpose,the moderated mediation index was calculated for themoderator’s mean score (±1 SD). The results indicated asignificant conditional indirect effect for both low (95% CI−0.42 to −0.01) and moderate levels (95% CI −0.27 to −0.01)
of fatigue. Hence, the indirect effect of C-SE on the relationshipbetween IU and behavioral intention varies depending on thelevels of fatigue.

Discussion
Principal Findings and Comparison With Prior Work
The aim of this study was to assess determinants of acceptanceof MS apps for disease-related self-management purposes,
taking into account possible mediating effects of self-efficacy
and a moderating role of fatigue.

Predictors of the Acceptance of Multiple Sclerosis Apps
Within the multiple regression analysis of the adapted UTAUT
model, the explained variance of 63% proved to be relatively
high but was lower than in the original UTAUT (70% [29]) andprevious work (78% [35]). In line with previous research[29,48], performance expectancy was replicated as the strongest
predictor in this model. Furthermore, the significant predictive
contribution of social influence and the insignificant relationshipbetween facilitating conditions and behavioral intention werealso shown in a study investigating acceptance of medical
aftercare in inpatients by Hennemann et al [35].
Considering the significant role of social influence in mHealthacceptance, an extension to digital sources of support could beconsidered such as social media. People with MS are usuallywell informed about the disease, but, at the same time, appear
being vulnerable to scientifically not proven health informationand hopes of cure, especially on social media websites [73].
Although most people with MS use the internet as the first
choice for health information, their physician remains the mosttrusted source [12]. Furthermore, there is preliminary evidence
that the spread of misinformation about MS therapy options islower or less influential in social media networks for laypeople
with MS under the presence of medical experts [74] than in
open-access not moderated MS forums [75]. Social media
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JMIR FORMATIVE RESEARCH
appears to be a relevant information source for people with MS,
especially in terms of sharing opinions and experience with MS[75]. Hence, the beliefs and attitudes of other people with MS
within social networks (bottom-up process) and health careproviders (top-down process) should be considered in affecting
the acceptance of MS apps.
Contrary to the previous empirical findings [76], effort
expectancy proved to be an insignificant predictor in the multiple
regression analysis, whereas it was significant as a single
predictor. This is contrary to another study by Chua et al [77]that showed the relevant effect of effort expectancy besides
social influence and performance expectancy in the acceptanceof social media apps. One reason for this could be
methodological shortcomings of the 2-item scale, given thequestionable internal consistency (alpha=.60), as well as
common variance with other constructs. Another reason could
be the low experience with the usability of MS apps. In addition,
effort expectancy could be less relevant in this sample scoring
relatively high on eHealth literacy and C-SE. With reference toVenkatesh et al’s study [29], it can be further assumed that byincluding actual behavior, a stronger predictive weighting ofeffort expectancy could have been achieved.
Moreover, IU was not significant in the overall model anymore,which should be critically seen in view of methodological issues
with this construct [78] and inconsistent findings regarding its
effects on coping with MS [7].
Nonetheless, IU made at least a significant contribution topredicting acceptance in simple regression analysis. However,this relationship was positive and not negative as expected. This
unexpected finding suggests that IU seems to be associated not
only with incapacitation and avoidance [7,52,79] but may also
result in functional problem-focused coping strategies such asincreased willingness to use modern self-help tools. However,to identify which factors actually influence whether IU manifests
itself through functional or dysfunctional outcomes needs further
exploration [7]. It is also important to note that using mHealthapps could be emotion-focused, for instance, for stressmanagement purposes [80]. Potentially, this study had an overly
narrowed view of the problem-focused function of MS apps
such as cognitive training or medication reminders.
In contrast, perceived eHealth literacy was the only variablewithout significant predictive value. Potentially, this construct
is not suitable for the measurement of mHealth literacy in thisspecific context, as the construct validity has been debated [81].
Furthermore, the construct seems too restricted to Web-based
information and not related to other Web-based self-management
activities in long-term conditions. Another potential reason isceiling effects with respect to the identified high eHealth literacy
scores in this self-selected Web-based sample. A further
investigation of health literacy in a more diverse population
appears reasonable because research indicated that, for instance,
functional literacy is associated with higher comfort levels andperceived skills with using eHealth information [42] and thatmany people with MS are quite willing to using eHealth services[21].
Taken together, the theoretical and empirical validation of an
extended UTAUT model for MS apps and related innovative
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Apolinário-Hagen et al
tools can mainly rely on the classical determinants, when they
are adapted to the MS context. In contrast, the current evidencebase for further constructs appears too limited and inconsistent.

Mediating and Moderating Effects Involved in the
Acceptance of Multiple Sclerosis Apps
Another aim of this study was to investigate the role ofself-efficacy as mediator and fatigue as moderator. The findings
suggest that the hypothesis of mediation for MS-SE must be
rejected in all models. Evidence of partial mediation by C-SE
was found in the case of IU predicting intentions to use MS
apps. An explanation for this finding might be found in thesocial cognitive theory (SCT [34]). SCT proposes that theconcept of self-efficacy is based primarily on the convictionthat one is actually able to perform a certain behavior. It may
be possible that the influence of self-efficacy has been mitigatedby the decision to include only the intention to perform abehavior as a criterion in the model. William and Rhodes [82]argued that the self-efficacy construct is confounded andrepresents the motivation rather than one’s perceived capability
to perform a health-related behavior.
Although fatigue represents a common disabling symptom inMS [19], no moderation effect was identified. Only a marginallysignificant relationship between effort expectancy andbehavioral intention was found, indicating that the expectedease of use related to using MS apps could be higher under the
influence of average and higher levels of fatigue. In comparison
with clinical samples, the moderate fatigue levels in the
retrospective assessment observed in this study need to beconsidered. Interestingly, fatigue was a positive predictor of
accepting MS apps. Potentially, the current need to manage
subjective fatigue may have increased the general openness touse innovative self-help tools. Therefore, it would beconceivable that the intention to use MS apps may exist
regardless of fatigue, whereas fatigue may represent a barrier
for actually using MS apps. In this respect, the role of fatigue
should be explored in the context of an actual behavior with asample with more clinically relevant cases of fatigue.
Potentially, people suffering from severe fatigue might havenot participated.

Limitations
This study has different limitations. First, this observational
study does not allow for causal conclusions because of thecross-sectional nature. Therefore, the results should beinterpreted with caution. A next step would be to longitudinallyassess the actual usage as predicted by acceptance. In line withprior research, we evaluated behavioral intention to use as apredictor of usage, but the direct translation to actual behavior
is problematic (behavior-intention gap in technology use [83]).
A next step for an observational study in cooperation with MS
centers could be to include a follow-up assessment. Anotheroption could be to conduct a randomized controlled trial tosystematically assess the impact of acceptance interventions[84] on the actual uptake of MS apps in primary care (see the
Implications section).
Second, the data were collected via a Web-based survey. Hence,
disadvantages such as selection bias should be considered, as
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JMIR FORMATIVE RESEARCH
well as the high attrition rate of about one-third. This might
have also contributed to low variance of fatigue severity among
survey completers. Besides fatigue, cognitive difficulties could
be another reason for noncompletion. However, mostparticipants who dropped out did so after the first 3 demographic
questions (year of birth, gender, and postal code). This indicatesfurther reasons such as decline of interest or motivation.
Furthermore, the sample was quite small and not representative,
although the gender ratio, the mean age, and duration of MS
were comparable with the Bavarian data from the German MS
registry [85]. However, this anonymous survey provided no
option to verify such self-reported outcomes. We collected nodata (such as medical reports) to confirm the diagnosis of MS.
We only asked for the year of diagnosis, but not for the exact
diagnosis or subtype of MS. We tried to only reach people with
MS by using a selective recruitment strategy with the support
by a German MS society.
Third, the narrowed scope on MS apps in Germany restricts thegeneralizability to other eHealth contexts relevant for MS-related
self-help activities. Future studies should use an umbrella termor involve more eHealth and mHealth options. As prevalence
rates of MS are highest in Northern European countries [2,3],
we are confident that our findings can be applied to a broader
MS population.
Fourth, the majority of the assessed constructs were scoredusing translated and modified scales, which might havecompromised the reliability of the scale effort expectancy. In
this study, however, we avoided using longer scales to reduce
participant burden and to minimize the attrition rate. A next
step would be to assess the validity of the transadapted
UTAUT-related measure using a larger sample.
Fifth, the experience with MS apps was rather low. However,
we assume that the proportion of participants in this self-selected
Web-based sample who know about mHealth apps and havepreviously used them is higher than that in a primary caresetting. The study was conducted in Germany where themHealth or eHealth adoption in health care is at an early stage,
but as panel surveys show, there is already public awareness ofdigital self-help options to some extent [86]. Furthermore, on
average, this sample reported receiving an MS diagnosis adecade ago. Therefore, this and the connection to MS self-helporganizations indicate openness to (online) self-help.

Apolinário-Hagen et aluse of eHealth or mHealth services vary over time. For example,
a qualitative study by Colombo et al [88] indicated that MS
patients find the internet useful for disease management
purposes, but there can be a barrier at the beginning and laterstages of MS to actively search information online because itis perceived as stressful. Consequently, MS apps can not only
be a resource, but at the same time, such tools can also provide
a medium for illness representations. Vaughan et al [89] showedthat illness representations in people with MS reflect the medical
knowledge about MS, in which the consequences of impending
degeneration and lacking prospect of cure are salient. Such
representations are linked with the concept of pathogenesis, andlabels such as MS apps could underline this deficientperspective.
In contrast, the concept of salutogenesis [90] does not deny thechallenges of MS, but it raises awareness on how to use one’s
own resources to cope with them. In this sense, it can beassumed that a salutogenetic perspective could promote a greater
motivation for health promotion, for instance, supported byapps. Possibly, the relatively high proportion of people in thissample who used nonspecific mHealth services rather than MS
apps could indicate that they are avoiding stigmatization by thelabel MS. Hence, these considerations could be useful to improve
the awareness of suitable apps and for the development oftransdiagnostic apps for MS, with an emphasis on salutogenetic
aspects. Another factor that should be considered is that there
is not one MS but diverse manifestations that are related todifferent challenges and self-help preferences.
With performance expectancy being the strongest predictor of
acceptance of MS apps, it can be suggested that the benefits ofmHealth apps should be transparently communicated to potential
users with MS [10]. Given the significant role of social influencein this study, it can be further recommended that suchinformation should not just be provided via social media. In
particular, a next step would be to rethink the integration of thesocial environment in the treatment of people with MS using
eHealth and mHealth solutions.

Implications

In view of the limited experience with MS apps in this sample,
before assessing the acceptance, more detailed information
about digital self-management tools could be provided via
acceptance-facilitating interventions (AFI). Short, video-based
AFI have been shown to be effective in German primary caresettings, for instance, in improving acceptance of digitalinterventions among patients with depression [84] and pain [91].
However, the results on the efficacy of AFI in Web-based study
settings are inconsistent. For instance, a Web-based randomized
controlled trial by Lin et al [92] showed no significant effects
of a video-based AFI on the acceptance, adherence, and uptake
rate of a mobile phone–based and Web-based intervention forchronic pain among people with long-term conditions. The(baseline) acceptance was, however, found to be higher thanthat observed the in target populations in primary care.

Although mobile phones belong to the everyday life of people
with MS, their use in connection with MS seems relatively low[19]. In this Web-based study among smartphone users withMS, the vast majority (63.3%) reported no experience with MS
apps. In contrast, the use of smartphones for MS-related
purposes was higher. It can be assumed that the demand andAs there is a lack of such studies on the uptake of eHealthsolutions for people with MS, it appears reasonable to develop
AFIs with existing tools such as the commonly used MS
Kognition in a routine care setting to assess the actual use
predicted by acceptance. For this purpose, it is crucial to

Finally, it should be also mentioned that the retrospective
assessment of subjective fatigue is subject to methodologicallimitations. Furthermore, fatigue can be associated withcognitive deficits [87]. For a more accurate assessment of fatigue
in a target group with different manifestations of MS, the useof longer scales combined with neurological tests would be
worth considering.

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Apolinário-Hagen et alconsider quality standards of apps to identify appropriate tools
via app stores [93,94], especially with regard to disease
management for long-term conditions [95]. Moreover, it appears
likely that people with different forms of MS experience
different challenges that need to be considered when assessing
the adoption of MS apps and other self-help services. Hence,
the MS form should be considered in future studies. It is also
possible that the low adoption rate is also associated with thepoor quality of many mHealth apps available via app stores.
Hence, it is crucial to understand and systematically involve
the perspectives of users in the quality improvement of mHealthapps.

framework is only one option to evaluate subjective views ofpeople with MS on mHealth services. Attitudes may be moresuitable for the assessment of early adoption of eHealth ormHealth [35]. In addition, other types of eHealth tools could
be relevant for coping with MS in the target population,
including psychological services. User perspectives could also
be measured using validated measures such as the Attitudes
towards Psychological Online Interventions [97]. Potentially,
the evaluation of both acceptance and attitudes as well as related
constructs could provide a more complete picture of the needs
and preferences for different digital self-help tools among people
with MS.

Finally, there is a lack of suitable measures to determine theacceptance of eHealth solutions in specific populations andcontexts. In line with other research [35,36,84,91,96], we thus
chose to adapt the UTAUT framework to our target populationand service of interest. It is, thus, important to validate such
adapted UTAUT measures with large samples in upcoming
studies. This could be a first step to achieve methodologicalconsensus on the assessment of the acceptance and use ofmHealth apps for the disease management of MS and allow forcomparisons with other research. Moreover, the UTAUT

Conclusions
Taken together, this study suggests that the intention to use MS
apps is rather poor among the majority of participants without
usage experience and that positive expectations about the
helpfulness and social influence are important predictors forthe acceptance of MS apps. Moreover, noteworthy is the use ofMS-unspecific apps by almost half of the participants. Thisfinding makes the investigation of the acceptance and use of
MS-specific mHealth services in comparison with otherself-management options appear a logical next step.

Acknowledgments
For their support with recruitment, the authors would like to thank the German Multiple Sclerosis Society and, especially, thelocal DMSG associations in the states Baden-Wurttemberg, Bavaria, Brandenburg, Hamburg, Hesse, Lower Saxony, Mecklenburg
Western Pomerania, North-Rhine Westphalia, Rhineland Palatinate, Saxony, and Schleswig-Holstein as well as all supporting
self-help groups. Most importantly, the authors would like to thank all participants for contributing to this research.

Conflicts of Interest
None declared.

Multimedia Appendix 1
Questionnaire of the conceptual study model.
[PDF File (Adobe PDF File), 120KB-Multimedia Appendix 1]

Multimedia Appendix 2
Additional descriptive data.
[PDF File (Adobe PDF File), 1MB-Multimedia Appendix 2]

Multimedia Appendix 3
Additional regression analysis.
[PDF File (Adobe PDF File), 879KB-Multimedia Appendix 3]

References
1.
2.

3.

Zurawski J, Stankiewicz J. Multiple sclerosis re-examined: essential and emerging clinical concepts. Am J Med 2018
May;131(5):464-472. [doi: 10.1016/j.amjmed.2017.11.044] [Medline: 29274753]
Browne P, Chandraratna D, Angood C, Tremlett H, Baker C, Taylor BV, et al. Atlas of multiple sclerosis 2013: a growing
global problem with widespread inequity. Neurology 2014 Sep 09;83(11):1022-1024 [FREE Full text] [doi:10.1212/WNL.0000000000000768] [Medline: 25200713]
Kingwell E, Marriott JJ, Jetté N, Pringsheim T, Makhani N, Morrow SA, et al. Incidence and prevalence of multiple sclerosis
in Europe: a systematic review. BMC Neurol 2013 Sep 26;13:128 [FREE Full text] [doi: 10.1186/1471-2377-13-128][Medline: 24070256]

http://formative.jmir.org/2018/2/e11977/

XSL• FO
RenderX

JMIR Formativ Res 2018 | vol. 2 | iss. 2 | e11977 | p. 13(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
4.

5.
6.
7.
8.

9.

10.

11.

12.

13.

14.

15.

16.
17.
18.

19.

20.

21.
22.

23.

24.

Petersen G, Wittmann R, Arndt V, Göpffarth D. Epidemiology of multiple sclerosis in Germany: regional differences anddrug prescription in the claims data of the statutory health insurance. Nervenarzt 2014 Aug;85(8):990-998. [doi:10.1007/s00115-014-4097-4] [Medline: 25022894]
Huang WJ, Chen WW, Zhang X. Multiple sclerosis: pathology, diagnosis and treatments. Exp Ther Med 2017
Jun;13(6):3163-3166 [FREE Full text] [doi: 10.3892/etm.2017.4410] [Medline: 28588671]
Goldenberg MM. Multiple sclerosis review. P T 2012 Mar;37(3):175-184 [FREE Full text] [Medline: 22605909]
Alschuler KN, Beier ML. Intolerance of uncertainty: shaping an agenda for research on coping with multiple sclerosis. Int
J MS Care 2015;17(4):153-158 [FREE Full text] [doi: 10.7224/1537-2073.2014-044] [Medline: 26300700]
Schulman-Green D, Jaser S, Martin F, Alonzo A, Grey M, McCorkle R, et al. Processes of self-management in chronic
illness. J Nurs Scholarsh 2012 Jun;44(2):136-144 [FREE Full text] [doi: 10.1111/j.1547-5069.2012.01444.x] [Medline:
22551013]
Heesen C, Kasper J, Segal J, Köpke S, Mühlhauser I. Decisional role preferences, risk knowledge and information interestsin patients with multiple sclerosis. Mult Scler 2004 Dec;10(6):643-650. [doi: 10.1191/1352458504ms1112oa] [Medline:
15584489]
Fricke U, Apolinário-Hagen J. Fachzeitschrift für Onlineberatung und computervermittelte Kommunikation. 2016.
E-Health-Dienste und M-Health Apps zur Verbesserung des Selbstmanagements bei Multipler Sklerosein Review zur
aktuellen Forschungsliteratur zur Online-Selbsthilfe für MS-Betroffene URL: https://www.e-beratungsjournal.net/
ausgabe_02_16/fricke_apolinario.pdf [accessed 2018-12-06] [WebCite Cache ID 74SGrRBTY]
Marziniak M, Brichetto G, Feys P, Meyding-Lamadé U, Vernon K, Meuth SG. The use of digital and remote communication
technologies as a tool for multiple sclerosis management: narrative review. JMIR Rehabil Assist Technol 2018 Apr 24;5(1):e5[FREE Full text] [doi: 10.2196/rehab.7805] [Medline: 29691208]
Marrie RA, Salter AR, Tyry T, Fox RJ, Cutter GR. Preferred sources of health information in persons with multiple sclerosis:
degree of trust and information sought. J Med Internet Res 2013 Mar 17;15(4):e67 [FREE Full text] [doi: 10.2196/jmir.2466][Medline: 23635393]
Kidd T, Carey N, Mold F, Westwood S, Miklaucich M, Konstantara E, et al. A systematic review of the effectiveness of
self-management interventions in people with multiple sclerosis at improving depression, anxiety and quality of life. PLoS
One 2017;12(10):e0185931 [FREE Full text] [doi: 10.1371/journal.pone.0185931] [Medline: 29020113]
van Gemert-Pijnen JE, Kelders SM, Bohlmeijer ET. Understanding the usage of content in a mental health intervention for
depression: an analysis of log data. J Med Internet Res 2014;16(1):e27 [FREE Full text] [doi: 10.2196/jmir.2991] [Medline:
24486914]
Pilutti LA, Dlugonski D, Sandroff BM, Klaren R, Motl RW. Randomized controlled trial of a behavioral intervention
targeting symptoms and physical activity in multiple sclerosis. Mult Scler 2014 Apr;20(5):594-601. [doi:10.1177/1352458513503391] [Medline: 24009162]
Motl RW, Dlugonski D, Wójcicki TR, McAuley E, Mohr DC. Internet intervention for increasing physical activity inpersons with multiple sclerosis. Mult Scler 2011 Jan;17(1):116-128. [doi: 10.1177/1352458510383148] [Medline: 20921239]
Tallner A, Pfeifer K, Mäurer M. Web-based interventions in multiple sclerosis: the potential of tele-rehabilitation. Ther
Adv Neurol Disord 2016 Jul;9(4):327-335 [FREE Full text] [doi: 10.1177/1756285616640684] [Medline: 27366240]
van Kessel K, Wouldes T, Moss-Morris R. A New Zealand pilot randomized controlled trial of a web-based interactive
self-management programme (MSInvigor8) with and without email support for the treatment of multiple sclerosis fatigue.
Clin Rehabil 2016 May;30(5):454-462. [doi: 10.1177/0269215515584800] [Medline: 25952587]
Van Kessel K, Babbage DR, Reay N, Miner-Williams WM, Kersten P. Mobile technology use by people experiencing
multiple sclerosis fatigue: survey methodology. JMIR Mhealth Uhealth 2017 Feb 28;5(2):e6 [FREE Full text] [doi:10.2196/mhealth.6192] [Medline: 28246073]
Haase R, Schultheiss T, Kempcke R, Thomas K, Ziemssen T. Use and acceptance of electronic communication by patientswith multiple sclerosis: a multicenter questionnaire study. J Med Internet Res 2012 Oct 15;14(5):e135 [FREE Full text][doi: 10.2196/jmir.2133] [Medline: 23069209]
Marrie RA, Leung S, Tyry T, Cutter GR, Fox R, Salter A. Use of eHealth and mHealth technology by persons with multiple
sclerosis. Mult Scler Relat Disord 2018 Oct 02;27:13-19. [doi: 10.1016/j.msard.2018.09.036] [Medline: 30296732]
Iribarren SJ, Cato K, Falzon L, Stone PW. What is the economic evidence for mHealth? A systematic review of economic
evaluations of mHealth solutions. PLoS One 2017;12(2):e0170581 [FREE Full text] [doi: 10.1371/journal.pone.0170581][Medline: 28152012]
Giunti G, Guisado Fernández E, Dorronzoro Zubiete E, Rivera Romero O. Supply and demand in mHealth apps for persons
with multiple sclerosis: systematic search in app stores and scoping literature review. JMIR Mhealth Uhealth 2018 May
23;6(5):e10512 [FREE Full text] [doi: 10.2196/10512] [Medline: 29792295]
Giunti G, Kool J, Rivera Romero O, Dorronzoro Zubiete E. Exploring the specific needs of persons with multiple sclerosis
for mHealth solutions for physical activity: mixed-methods study. JMIR Mhealth Uhealth 2018 Feb 09;6(2):e37 [FREEFull text] [doi: 10.2196/mhealth.8996] [Medline: 29426814]

http://formative.jmir.org/2018/2/e11977/

XSL• FO
RenderX

Apolinário-Hagen et al

JMIR Formativ Res 2018 | vol. 2 | iss. 2 | e11977 | p. 14(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
25.

26.

27.

28.
29.
30.
31.
32.
33.
34.
35.

36.

37.

38.

39.

40.

41.

42.

43.

44.
45.
46.

47.

Winberg C, Kylberg M, Pettersson C, Harnett T, Hedvall PO, Mattsson T, et al. The use of apps for health in persons withmultiple sclerosis, Parkinson's disease and stroke - barriers and facilitators. Stud Health Technol Inform 2017;242:638-641.
[Medline: 28873864]
Griffin N, Kehoe M. A questionnaire study to explore the views of people with multiple sclerosis of using smartphone
technology for health care purposes. Disabil Rehabil 2018 Dec;40(12):1434-1442. [doi: 10.1080/09638288.2017.1300332][Medline: 28322588]
Topooco N, Riper H, Araya R, Berking M, Brunn M, Chevreul K, E-COMPARED consortium. Attitudes towards digitaltreatment for depression: a European stakeholder survey. Internet Interv 2017 Jun;8:1-9 [FREE Full text] [doi:10.1016/j.invent.2017.01.001] [Medline: 30135823]
Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 1989
Sep;13(3):319. [doi: 10.2307/249008]
Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. MIS
Q 2003;27(3):425. [doi: 10.2307/30036540]
Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process 1991 Dec;50(2):179-211. [doi:10.1016/0749-5978(91)90020-T]
Wilson CJ, Deane FP, Ciarrochi JV, Rickwood D. Measuring help-seeking intentions: properties of the General Help
Seeking Questionnaire. Canadian Journal of Counselling and Psychotherapy 2005;39(1) [FREE Full text]
Venkatesh V, Davis FD. A theoretical extension of the technology acceptance model: four longitudinal field studies. ManagSci 2000 Feb;46(2):186-204. [doi: 10.1287/mnsc.46.2.186.11926]
Rogers E. Diffusion of Innovations. New York: Free Press; 2003.
Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev 1977;84(2):191-215. [doi:10.1037/0033-295X.84.2.191] [Medline: 847061]
Hennemann S, Beutel ME, Zwerenz R. Drivers and barriers to acceptance of web-based aftercare of patients in inpatientroutine care: a cross-sectional survey. J Med Internet Res 2016 Dec 23;18(12):e337 [FREE Full text] [doi: 10.2196/jmir.6003][Medline: 28011445]
Liu L, Miguel Cruz A, Rios Rincon A, Buttar V, Ranson Q, Goertzen D. What factors determine therapists' acceptance of
new technologies for rehabilitation – a study using the Unified Theory of Acceptance and Use of Technology (UTAUT).
Disabil Rehabil 2015;37(5):447-455. [doi: 10.3109/09638288.2014.923529] [Medline: 24901351]
Benmessaoud C, Kharrazi H, MacDorman KF. Facilitators and barriers to adopting robotic-assisted surgery: contextualizing
the unified theory of acceptance and use of technology. PLoS One 2011 Jan 20;6(1):e16395 [FREE Full text] [doi:10.1371/journal.pone.0016395] [Medline: 21283719]
Dou K, Yu P, Deng N, Liu F, Guan Y, Li Z, et al. Patients' acceptance of smartphone health technology for chronic disease
management: a theoretical model and empirical test. JMIR Mhealth Uhealth 2017 Dec 06;5(12):e177 [FREE Full text][doi: 10.2196/mhealth.7886] [Medline: 29212629]
Wentink MM, Prieto E, de Kloet AJ, Vliet Vlieland TP, Meesters JJ. The patient perspective on the use of information andcommunication technologies and e-health in rehabilitation. Disabil Rehabil Assist Technol 2018 Oct;13(7):620-625. [doi:10.1080/17483107.2017.1358302] [Medline: 28758806]
Robinson J, Dixon J, Macsween A, van Schaik P, Martin D. The effects of exergaming on balance, gait, technology
acceptance and flow experience in people with multiple sclerosis: a randomized controlled trial. BMC Sports Sci Med
Rehabil 2015;7:8 [FREE Full text] [doi: 10.1186/s13102-015-0001-1] [Medline: 25969739]
Rost T, Stein J, Löbner M, Kersting A, Luck-Sikorski C, Riedel-Heller SG. User acceptance of computerized cognitive
behavioral therapy for depression: systematic review. J Med Internet Res 2017 Dec 13;19(9):e309 [FREE Full text] [doi:10.2196/jmir.7662] [Medline: 28903893]
Marrie RA, Salter A, Tyry T, Fox RJ, Cutter GR. Health literacy association with health behaviors and health care utilizationin multiple sclerosis: a cross-sectional study. Interact J Med Res 2014 Feb 10;3(1):e3 [FREE Full text] [doi:10.2196/ijmr.2993] [Medline: 24513479]
Schmitt MM, Goverover Y, Deluca J, Chiaravalloti N. Self-efficacy as a predictor of self-reported physical, cognitive, andsocial functioning in multiple sclerosis. Rehabil Psychol 2014 Feb;59(1):27-34 [FREE Full text] [doi: 10.1037/a0035288][Medline: 24320946]
Karsten R, Mitra A, Schmidt D. Computer self-efficacy. J Organ End User Comput 2012;24(4):54-80. [doi:10.4018/joeuc.2012100104]
Wijenberg ML, Stapert SZ, Köhler S, Bol Y. Explaining fatigue in multiple sclerosis: cross-validation of a biopsychosocial
model. J Behav Med 2016 Dec;39(5):815-822. [doi: 10.1007/s10865-016-9749-3] [Medline: 27236455]
Hadjimichael O, Vollmer T, Oleen-Burkey M, North American Research Committee on Multiple Sclerosis. Fatigue
characteristics in multiple sclerosis: the North American Research Committee on Multiple Sclerosis (NARCOMS) survey.
Health Qual Life Outcomes 2008 Nov 14;6:100 [FREE Full text] [doi: 10.1186/1477-7525-6-100] [Medline: 19014588]
Faul F, Erdfelder E, Lang A, Buchner A. G*Power 3: a flexible statistical power analysis program for the social, behavioral,
and biomedical sciences. Behav Res Methods 2007 May;39(2):175-191. [Medline: 17695343]

http://formative.jmir.org/2018/2/e11977/

XSL• FO
RenderX

Apolinário-Hagen et al

JMIR Formativ Res 2018 | vol. 2 | iss. 2 | e11977 | p. 15(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
48.
49.
50.

51.
52.
53.
54.
55.
56.
57.

58.

59.

60.

61.

62.
63.
64.
65.

66.
67.

68.
69.
70.

71.
72.

Taiwo AA, Downe AG. The theory of user acceptance and use of technology (UTAUT): a meta-analytic review of empiricalfindings. Journal of Theoretical & Applied Information Technology 2013;49(1):48-58 [FREE Full text]
Soellner R, Huber S, Reder M. The concept of eHealth literacy and its measurement. J Media Psychol 2014 Jan;26(1):29-38.
[doi: 10.1027/1864-1105/a000104]
van den Akker LE, Beckerman H, Collette EH, Bleijenberg G, Dekker J, Knoop H, TREFAMS-ACE study group. The roleof appraisal and coping style in relation with societal participation in fatigued patients with multiple sclerosis: a cross-sectional
multiple mediator analysis. J Behav Med 2016 Dec;39(5):855-865 [FREE Full text] [doi: 10.1007/s10865-016-9762-6][Medline: 27372714]
Köpke S, Solari A, Khan F, Heesen C, Giordano A. Information provision for people with multiple sclerosis. Cochrane
Database Syst Rev 2014 Apr 21(4):CD008757. [doi: 10.1002/14651858.CD008757.pub2] [Medline: 24752330]
Buhr K, Dugas MJ. The intolerance of uncertainty scale: psychometric properties of the English version. Behav Res Ther
2002 Aug;40(8):931-945. [Medline: 12186356]
Norman CD, Skinner HA. eHealth literacy: essential skills for consumer health in a networked world. J Med Internet Res
2006 Jun 16;8(2):e9 [FREE Full text] [doi: 10.2196/jmir.8.2.e9] [Medline: 16867972]
Kreps GL. The relevance of health literacy to mHealth. Stud Health Technol Inform 2017;240:347-355. [Medline: 28972527]
Suh Y, Joshi I, Olsen C, Motl RW. Social cognitive predictors of physical activity in relapsing-remitting multiple sclerosis.
Int J Behav Med 2014 Dec;21(6):891-898. [doi: 10.1007/s12529-013-9382-2] [Medline: 24407400]
Nickel D, Spink K, Andersen M, Knox K. Attributions and self-efficacy for physical activity in multiple sclerosis. Psychol
Health Med 2014;19(4):433-441. [doi: 10.1080/13548506.2013.832783] [Medline: 24007261]
Ferrier S, Dunlop N, Blanchard C. The role of outcome expectations and self-efficacy in explaining physical activity
behaviors of individuals with multiple sclerosis. Behav Med 2010;36(1):7-11. [doi: 10.1080/08964280903521354] [Medline:
20185396]
Kratz AL, Molton IR, Jensen MP, Ehde DM, Nielson WR. Further evaluation of the motivational model of pain
self-management: coping with chronic pain in multiple sclerosis. Ann Behav Med 2011 Jun;41(3):391-400 [FREE Fulltext] [doi: 10.1007/s12160-010-9249-6] [Medline: 21213092]
Bogosian A, Hughes A, Norton S, Silber E, Moss-Morris R. Potential treatment mechanisms in a mindfulness-based
intervention for people with progressive multiple sclerosis. Br J Health Psychol 2016 Nov;21(4):859-880. [doi:10.1111/bjhp.12201] [Medline: 27238732]
Hughes AJ, Beier M, Hartoonian N, Turner AP, Amtmann D, Ehde DM. Self-efficacy as a longitudinal predictor of perceived
cognitive impairment in individuals with multiple sclerosis. Arch Phys Med Rehabil 2015 May;96(5):913-919 [FREE Fulltext] [doi: 10.1016/j.apmr.2015.01.008] [Medline: 25597915]
Airlie J, Baker GA, Smith SJ, Young CA. Measuring the impact of multiple sclerosis on psychosocial functioning: thedevelopment of a new self-efficacy scale. Clin Rehabil 2001 Jun;15(3):259-265. [doi: 10.1191/026921501668362643][Medline: 11386395]
Compeau DR, Higgins CA. Computer self-efficacy: development of a measure and initial test. MIS Q 1995 Jun;19(2):189[FREE Full text] [doi: 10.2307/249688]
Wang D, Xu L, Chan HC. Understanding the continuance use of social network sites: a computer self-efficacy perspective.
Behav Inf Technol 2014 Sep 04;34(2):204-216. [doi: 10.1080/0144929X.2014.952778]
Khan F, Amatya B, Galea M. Management of fatigue in persons with multiple sclerosis. Front Neurol 2014;5:177 [FREEFull text] [doi: 10.3389/fneur.2014.00177] [Medline: 25309504]
Mulligan H, Wilkinson A, Barclay A, Whiting H, Heynike C, Snowdon J. Evaluation of a fatigue self-management programfor people with multiple sclerosis. Int J MS Care 2016;18(3):116-121 [FREE Full text] [doi: 10.7224/1537-2073.2015-019][Medline: 27252598]
MacAllister WS, Krupp LB. Multiple sclerosis-related fatigue. Phys Med Rehabil Clin N Am 2005 May;16(2):483-502.
[doi: 10.1016/j.pmr.2005.01.014] [Medline: 15893683]
Giunti G, Mylonopoulou V, Rivera Romero O. More stamina, a gamified mHealth Solution for persons with multiple
sclerosis: research through design. JMIR Mhealth Uhealth 2018 Mar 02;6(3):e51 [FREE Full text] [doi:10.2196/mhealth.9437] [Medline: 29500159]
D'Souza E. Modified Fatigue Impact Scale - 5-item version (MFIS-5). Occup Med (Lond) 2016 Apr;66(3):256-257. [doi:10.1093/occmed/kqv106] [Medline: 27016749]
Nielsen AS, Halamka JD, Kinkel RP. Internet portal use in an academic multiple sclerosis center. J Am Med Inform Assoc
2012;19(1):128-133 [FREE Full text] [doi: 10.1136/amiajnl-2011-000177] [Medline: 21571744]
König W. A comparative analysis of the development and structure of educational systems : methodological foundations
and the construction of a comparative educational scale. Mannheim: Universität Mannheim Institut für Sozialwissenschaften;
1988.
Hayes AF. Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New
York, NY: Guilford Press; 2013.
Cohen J. Statistical Power Analysis for the Behavioral Sciences (2nd edition). New York: Lawrence Erlbaum Associates;1988.

http://formative.jmir.org/2018/2/e11977/

XSL• FO
RenderX

Apolinário-Hagen et al

JMIR Formativ Res 2018 | vol. 2 | iss. 2 | e11977 | p. 16(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
73.

74.

75.

76.
77.
78.

79.
80.

81.

82.

83.
84.

85.

86.
87.
88.

89.
90.
91.

92.

93.

94.

Lavorgna L, Russo A, de Stefano M, Lanzillo R, Esposito S, Moshtari F, et al. Health-related coping and social interactionin people with multiple sclerosis supported by a social network: pilot study with a new methodological approach. Interact
J Med Res 2017 Jul 14;6(2):e10 [FREE Full text] [doi: 10.2196/ijmr.7402] [Medline: 28710056]
Lavorgna L, de Stefano M, Sparaco M, Moccia M, Abbadessa G, Montella P, et al. Fake news, influencers and health-related
professional participation on the web: a pilot study on a social-network of people with multiple sclerosis. Mult Scler Relat
Disord 2018 Oct;25:175-178 [FREE Full text] [doi: 10.1016/j.msard.2018.07.046] [Medline: 30096683]
Sudau F, Friede T, Grabowski J, Koschack J, Makedonski P, Himmel W. Sources of information and behavioral patterns
in online health forums: observational study. J Med Internet Res 2014 Jan 14;16(1):e10 [FREE Full text] [doi:10.2196/jmir.2875] [Medline: 24425598]
Khechine H, Lakhal S, Ndjambou P. A meta-analysis of the UTAUT model: eleven years later. Can J Adm Sci 2016 Jun
06;33(2):138-152. [doi: 10.1002/cjas.1381]
Chua PY, Rezaei S, Gu ML, Oh YM, Jambulingam M. Elucidating social networking apps decisions: performance expectancy,
effort expectancy and social influence. NBRI 2018 Jun 04;9(2):118-142. [doi: 10.1108/NBRI-01-2017-0003]
Strout TD, Hillen M, Gutheil C, Anderson E, Hutchinson R, Ward H, et al. Tolerance of uncertainty: a systematic review
of health and healthcare-related outcomes. Patient Educ Couns 2018 Sep;101(9):1518-1537. [doi: 10.1016/j.pec.2018.03.030][Medline: 29655876]
Wineman NM, Durand EJ, Steiner RP. A comparative analysis of coping behaviors in persons with multiple sclerosis or aspinal cord injury. Res Nurs Health 1994 Jun;17(3):185-194. [Medline: 8184130]
Christmann CA, Hoffmann A, Bleser G. Stress management apps with regard to emotion-focused coping and behavior
change techniques: a content analysis. JMIR Mhealth Uhealth 2017 Feb 23;5(2):e22 [FREE Full text] [doi:10.2196/mhealth.6471] [Medline: 28232299]
van der Vaart R, van Deursen AJ, Drossaert CH, Taal E, van Dijk JA, van de Laar MA. Does the eHealth Literacy Scale(eHEALS) measure what it intends to measure? Validation of a Dutch version of the eHEALS in two adult populations. J
Med Internet Res 2011 Nov 09;13(4):e86 [FREE Full text] [doi: 10.2196/jmir.1840] [Medline: 22071338]
Williams DM, Rhodes RE. The confounded self-efficacy construct: conceptual analysis and recommendations for future
research. Health Psychol Rev 2016 Dec;10(2):113-128 [FREE Full text] [doi: 10.1080/17437199.2014.941998] [Medline:
25117692]
Bhattacherjee A, Sanford C. The intention–behaviour gap in technology usage: the moderating role of attitude strength.
Behav Inf Technol 2009 Jul;28(4):389-401. [doi: 10.1080/01449290802121230]
Baumeister H, Nowoczin L, Lin J, Seifferth H, Seufert J, Laubner K, et al. Impact of an acceptance facilitating intervention
on diabetes patients' acceptance of internet-based interventions for depression: a randomized controlled trial. Diabetes Res
Clin Pract 2014 Jul;105(1):30-39. [doi: 10.1016/j.diabres.2014.04.031] [Medline: 24862240]
Höer A, Schiffhorst G, Zimmermann A, Fischaleck J, Gehrmann L, Ahrens H, et al. Multiple sclerosis in Germany: data
analysis of administrative prevalence and healthcare delivery in the statutory health system. BMC Health Serv Res 2014
Sep 10;14:381 [FREE Full text] [doi: 10.1186/1472-6963-14-381] [Medline: 25209585]
Eichenberg C, Wolters C, Brähler E. The internet as a mental health advisor in Germany--results of a national survey. PLoS
One 2013;8(11):e79206 [FREE Full text] [doi: 10.1371/journal.pone.0079206] [Medline: 24278121]
El-Tamawy M, Darwish M, Ahmed S, Abdelalim A, Moustafa ES. Primary fatigue contributes to cognitive dysfunction inpatients with multiple sclerosis. Egypt J Neurol Psychiatry Neurosurg 2016;53(2):74. [doi: 10.4103/1110-1083.183406]
Colombo C, Mosconi P, Confalonieri P, Baroni I, Traversa S, Hill SJ, et al. Web search behavior and information needsof people with multiple sclerosis: focus group study and analysis of online postings. Interact J Med Res 2014 Jul 24;3(3):e12[FREE Full text] [doi: 10.2196/ijmr.3034] [Medline: 25093374]
Vaughan R, Morrison L, Miller E. The illness representations of multiple sclerosis and their relations to outcome. Br J
Health Psychol 2003 Sep;8(Pt 3):287-301. [doi: 10.1348/135910703322370860] [Medline: 14606974]
Antonovsky A. The salutogenic model as a theory to guide health promotion. Health Promot Int 1996;11(1):11-18. [doi:10.1093/heapro/11.1.11]
Baumeister H, Seifferth H, Lin J, Nowoczin L, Lüking M, Ebert D. Impact of an acceptance facilitating intervention on
patients' acceptance of internet-based pain interventions: a randomized controlled trial. Clin J Pain 2015 Jun;31(6):528-535.
[doi: 10.1097/AJP.0000000000000118] [Medline: 24866854]
Lin J, Faust B, Ebert DD, Krämer L, Baumeister H. A web-based acceptance-facilitating intervention for identifying patients'
acceptance, uptake, and adherence of internet- and mobile-based pain interventions: randomized controlled trial. J MedInternet Res 2018 Aug 21;20(8):e244 [FREE Full text] [doi: 10.2196/jmir.9925] [Medline: 30131313]
Terhorst Y, Rathner EM, Baumeister H, Sander L. Verhaltenstherapie. 2018 May 8. «Hilfe aus dem App-Store?»: Einesystematische Übersichtsarbeit und Evaluation von Apps zur Anwendung bei Depressionen URL: https://www.karger.com/
Article/Pdf/481692 [accessed 2018-12-06] [WebCite Cache ID 74SIDPRFh]
Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a new tool forassessing the quality of health mobile apps. JMIR Mhealth Uhealth 2015 Mar 11;3(1):e27 [FREE Full text] [doi:10.2196/mhealth.3422] [Medline: 25760773]

http://formative.jmir.org/2018/2/e11977/

XSL• FO
RenderX

Apolinário-Hagen et al

JMIR Formativ Res 2018 | vol. 2 | iss. 2 | e11977 | p. 17(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
95.

96.

97.

Apolinário-Hagen et al

Anderson K, Burford O, Emmerton L. App chronic disease checklist: protocol to evaluate mobile apps for chronic disease
self-management. JMIR Res Protoc 2016 Nov 04;5(4):e204 [FREE Full text] [doi: 10.2196/resprot.6194] [Medline:
27815233]
Ebert DD, Berking M, Cuijpers P, Lehr D, Pörtner M, Baumeister H. Increasing the acceptance of internet-based mental
health interventions in primary care patients with depressive symptoms. A randomized controlled trial. J Affect Disord
2015 May 01;176:9-17. [doi: 10.1016/j.jad.2015.01.056] [Medline: 25682378]
Schröder J, Sautier L, Kriston L, Berger T, Meyer B, Späth C, et al. Development of a questionnaire measuring Attitudes
towards Psychological Online Interventions-the APOI. J Affect Disord 2015 Nov 15;187:136-141. [doi:10.1016/j.jad.2015.08.044] [Medline: 26331687]

Abbreviations
AFI: acceptance-facilitating interventions
CASMIN: Comparative Analysis of Social Mobility in Industrial Nations
CNS: central nervous system
C-SE: computer self-efficacy
DMSG: Deutsche Multiple Sklerose Gesellschaft
eHealth: electronic health
GCSE: general computer self-efficacy
G-eHEALS: German eHealth Literacy Scale
IU: Intolerance of Uncertainty
IUS: Intolerance of Uncertainty ScalemHealth: mobile health
MS: multiple sclerosis
MS-SE: multiple sclerosis self-efficacy
NARCOMS: North American Research Committee on Multiple Sclerosis
SCT: social cognitive theory
TAM: technology acceptance model
TPB: Theory of Planned Behavior
UTAUT: Unified Theory of Acceptance and Use of Technology

Edited by G Eysenbach; submitted 20.08.18; peer-reviewed by G Giunti, S Thomas, L Zhou, J Lin; comments to author 21.09.18;
revised version received 24.10.18; accepted 22.11.18; published 12.12.18
Please cite as:
Apolinário-Hagen J, Menzel M, Hennemann S, Salewski C
Acceptance of Mobile Health Apps for Disease Management Among People With Multiple Sclerosis: Web-Based Survey StudyJMIR Formativ Res 2018;2(2):e11977
URL: http://formative.jmir.org/2018/2/e11977/
doi: 10.2196/11977
PMID: 30684408©Jennifer Apolinário-Hagen, Mireille Menzel, Severin Hennemann, Christel Salewski. Originally published in JMIR Formative
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JMIR Formativ Res 2018 | vol. 2 | iss. 2 | e11977 | p. 18(page number not for citation purposes)