Perceived usefulness of digital self-tracking among people with multiple sclerosis

Abstract

Background and aim

Self-care technologies may support patients with multiple sclerosis (MS) in their everyday disease management by enabling self-monitoring of various health indicators, such as symptom levels and physical activity levels. The aim of this study was to assess the usefulness of tracking self-selected MS- and health-related measures via a digital self-tracking tool for people with MS (PwMS) over a period of six weeks.

Methods

An initial development phase was followed by a six-week testing phase with 58 test participants. The evaluation phase followed a sequential, exploratory mixed-methods design, consisting of 14 interviews with test participants during the testing phase, followed by a survey of all participants after the testing phase to confirm and elaborate on the interview findings. The interview data were analyzed through a five-step thematic analysis, and the survey data were analyzed descriptively.

Results

The results of the mixed-methods study can be summarized in the following findings: (1) Use of the self-tracking tool assisted users in clarifying patterns regarding their symptoms, physical activity, sleep quality and emotional well-being. (2) Tracking physical activity and, to some extent, sleep had a motivational effect on participants in relation to increasing activity and/or changing habits. (3) Data quality/accuracy constitutes an important criterion for considering the self-tracking tool relevant. (4) The self-tracking tool may support dialogue between patients and healthcare professionals, and/or it may potentially play a role in peer-to-peer support.

Conclusion

The results of the present study indicate that the self-tracking of symptoms, sleep, physical activity and other measures may contribute positively to everyday self-management among PwMS. Professional support in interpreting and acting upon the data should be considered.

Keywords

Multiple sclerosis self-tracking disease management data collection data measurements digital tools mixed methods research

Introduction

Multiple sclerosis (MS) is a chronic autoimmune neurological disease with no known cure.¹ The axonal damage caused by MS will eventually result in a loss of motor and/or cognitive function and also cause a number of debilitating symptoms, such as pain, spasticity/cramps, bladder and bowel problems, sensory disturbances such as tingling or numbness, and fatigue.² The disease course is often characterized by flareups and remissions (relapsing remitting MS) or by a steady decline in functioning (progressive MS).

Medical treatment may delay disease progression and reduce the symptom burden in MS, and targeted physical activity and exercise have been shown to be important factors in reducing symptoms and may even play a part in slowing disease progression.³ However, daily self-management and maintaining a healthy lifestyle have also been shown to improve the physical and psychological well-being of people with MS (PwMS).^4,5 In this study, we use the definition of self-management suggested by Barlow et al., who describe it as ‘the individual's ability to manage the symptoms, treatment, physical and psychosocial consequences and life style changes inherent in living with a chronic condition’.⁶

Self-care technologies may assist patients with chronic diseases to manage their disease and achieve healthier lifestyles by, for example, enabling the self-monitoring of health indicators, such as biomarkers, symptom levels or physical activity levels.⁷ Studies have shown that, with MS, self-tracking may be used to obtain a sense of control in a situation that is uncontrollable due to the fluctuating and unpredictable nature of the disease.^8,9

In the present study, we explored how self-tracking via a digital tool could assist self-management in MS. More specifically, we developed, tested and evaluated a prototype self-tracking tool for MS, used in combination with an activity and sleep-tracking wearable (Fitbit Charge 4).¹⁰

Other studies have tested or investigated different types of self-tracking devices, generic and/or MS-specific, and have found potential benefits as well as drawbacks related to their use among PwMS.^9,11–14 Digital self-monitoring may help PwMS to better understand their disease and to identify or confirm patterns, such as the associations between physical activity levels and the severity of symptoms.^11,12 On the one hand, activity and sleep trackers may motivate improvements in physical activity and sleep habits.^11,12 On the other hand, self-tracking may lead to unwanted attention to the disease and its consequences and a focus on limitations rather than possibilities.¹²

Wendrich et al. and Ayobi et al. report on PwMS’ experiences using MS-specific apps for tracking; the ‘Mijn Kwik’ app (in combination with Fitbit Charge 2)¹² and the ‘Trackly’ app.¹³ Trackly is a highly customizable self-tracking app, drawing on the concept of bullet journaling, where users define their health and well-being indicators.¹³ However, Ayobi et al. did not report whether PwMS had been involved in the development of the app.¹³ Likewise, Wendrich et al. did not report whether PwMS had been involved in the development of the ‘Mijn Kwik’ app, but recommended, based on their findings, that in order to reduce emotional and user burden, digital tools for PwMS should be flexible and customizable to individual needs, and that they should be developed in close collaboration with PwMS.¹²

With this in mind, we sought a user-centred approach to developing and testing an MS-specific digital tool for self-tracking that involved PwMS from the idea development phase to the testing and evaluation phases. In the following, we briefly describe the development of a self-tracking prototype tool for MS, but the data analyses presented in this article adhere to the participants’ experiences of using the tool (the evaluation phase), collected through an interview study and a survey study during and following the testing phase (see Figure 1 for a description of the project phases and Table 1 for participant characteristics).

Figure 1.

The research and development process.

Table 1.

Participant characteristics in the testing and evaluation phases.

	All participants, N = 58	Interviewed participants, N = 14	Participants who completed the survey, N = 50
Gender, N (%)
Female	45 (77.6%)	9 (64.3%)	38 (76%)
Male	13 (22.4%)	5 (35.7%)	12 (24%)
Age
< 40 years	17 (29.3%)	4 (28.6%)	15 (30%)
40–59 years	33 (56.9%)	5 (35.7%)	28 (56%)
60 + years	8 (13.8%)	5 (35.7%)	8 (14%)
MS subtype, N (%)
RRMS	44 (75.9%)	8 (57.1%)	37 (74%)
SPMS	5 (8.6%)	1 (7.2%)	5 (10%)
PPMS	8 (138%)	5 (35.7%)	7 (14%)
Do not know	1 (1.7%)	0	1 (2%)
Average years since MS diagnosis (SD)	11 (7.7)	11 (6)	11 (7,5)

RRMS: Relapsing-remitting multiple sclerosis; SPMS: Secondary progressive multiple sclerosis; PPMS: Primary progressive multiple sclerosis.

The aim of the present study is hence to investigate how PwMS perceived the usefulness of a digital self-tracking tool that was developed in collaboration with PwMS to track relevant health-related measures for self-management.

In the remainder of the study, the term ‘self-tracking tool’ refers to the app-based prototype MS self-tracking tool that was developed in the study and tested in combination with the wearable Fitbit Charge 4 and its associated app, from which data were entered into the self-tracking tool by the participants.

Methods

Study design

An initial development phase was followed by a six-week testing phase. The evaluation phase followed a sequential, exploratory mixed-methods design,¹⁵ including an interview study with test participants during the testing phase, followed by a survey study of all the participants after the testing phase to confirm and elaborate on the findings from the interview study. The participants in the development phase were not the same as the participants in the testing and evaluation phases.

Development of the self-tracking tool

The self-tracking tool was developed in close collaboration with PwMS as well as healthcare professionals (HCPs), specialized in MS.¹⁶ We conducted two preliminary workshops in two geographical areas with a total of 10 participants (five participants in each area) to explore how PwMS envision using a self-tracking tool and what such a tool should optimally contain. Twelve participants were initially recruited via the MS Society's website and social media platforms. Experiences/competencies in information technology (IT) or apps were neither an inclusion nor exclusion criteria. In total four men and six women (mean age = 50.6), who on average had had the MS diagnoses for 7.8 years, ended up participating in the workshop. The workshops took an open and exploratory approach. Based on previous research on self-tracking and self-tracking indicators,^14,17,18 the researchers presented participants with possible themes (physical activity, sleep, well-being, symptoms and nutrition) to include in a self-tracking tool. Participants were asked to reflect on the relevance of these themes, to suggest more specific indicators that they could picture themselves tracking in a digital tool and to reflect on practical aspects of tracking, such as preferences regarding frequency, time expenditure and customizability. The workshops resulted in a broad outline for a self-tracking tool that contains customizable items to be tracked daily or as often as desired. The overall themes for the tool (what the tool should be able to measure), defined in the workshops, included symptom levels, physical activity levels, sleep levels, dietary intake and mental health/stress indicators. No existing tool for MS management contained all the elements specified in the workshops. That is, no existing MS-specific self-tracking tool featured a combination of symptom tracking and tracking of physical activity, diet and sleep. Hence, it was decided to develop a prototype of a self-tracking tool that would include the measures requested by the involved PwMS.

A collaboration was initiated with the tech company Monsenso,¹⁹ which had already developed an app-based self-tracking tool for mental health that included the main features required for the prototype. All permissions for use of the self-tracking tool in the research project were provided by the company.

Based on the results of the workshops, a content description for the self-tracking tool was drafted, including an item list that included the measures defined in the workshops. Ten individual interviews were conducted with PwMS who had not previously been involved in the process. Participants were recruited via the Danish MS Society's social media channels. During the interviews, the item list and the overall design of the tool were presented. The items were adjusted based on the comments and input from the interview informants. Explanatory texts were added to the majority of the items, as this was a recurring suggestion. Subsequently, the item list was presented to and discussed with three HCPs who specialize in MS – a neurologist, a nurse, and a rehabilitation specialist – all employed at Danish MS hospitals that specialize in MS rehabilitation. The HCPs had no specific experiences with IT or self-tracking tool/apps. Finally, the items were included in an online survey program to simulate how they would appear in a self-tracking tool, and five cognitive interviews were conducted with PwMS who had not previously participated in the study to ensure that all items and response categories were understandable and meaningful.

The MS self-tracking tool was configured and simplified such that it contained only the relevant features, which consisted of a self-evaluation module, a visualization module and a library module that contained explanatory texts. The visualization module showed simple graphs of data over time (see Figure 2). All tracked items could be illustrated in the visualization module, and as many combined as chosen by the user. This allowed for comparison of the development of various symptoms and activities and thereby for possible associations to be clarified.

Figure 2.

Illustration of features in the self-tracking tool.

It was necessary to combine the self-tracking tool with an existing wearable, and its associated app, from which the tracked data on physical activity and sleep were entered into the self-tracking tool by the participants. Fitbit Charge 4 was chosen for practical and professional reasons as it combined a relatively small wristband with an acceptable level of accuracy (Table 2).¹⁰

Table 2.

Survey results related to the four themes.

	Disagree/strongly disagree N (%)	Neither agree nor disagree N (%)	Agree/strongly agree N (%)
Participating in the trial has made me more aware of how I am feeling	3 (6%)	14 (28%)	33 (66%)
Participating in the trial has given me greater insight into my sleep	5 (10%)	7 (14%)	38 (76%)
Participating in the trial has given me a greater overview of my physical activity	3 (6%)	14 (28%)	33 (66%)
Participating in the trial has given me a greater overview of my symptoms	5 (10%)	14 (28%)	31 (62%)
Participating in the trial has made me aware of patterns in symptoms, physical activity, sleep, or other things, that I was previously not aware of	6 (12%)	16 (32%)	28 (56%)
Participating in the trial has confirmed patterns in symptoms, physical activity, sleep, or other things that I was already aware of	5 (10%)	20 (40%)	25 (50%)
Participating in the trial has motivated/helped me to be more physically active	8 (16%)	17 (34%)	25 (50%)
Participating in the trial has motivated/helped me to change my sleeping habits	10 (20%)	23 (46%)	17 (34%)
Tracking took too much of my energy	33 (66%)	9 (18%)	8 (16%)
Tracking took too much of my time	30 (60%)	13 (26%)	7 (14%)
I experienced feelings of guilt related to tracking my physical activity	33 (66%)	7 (14%)	10 (20%)
Tracking made me too aware of my disease	37 (74%)	6 (12%)	7 (14%)
Tracking made me too aware of my sleep	41 (82%)	6 (12%)	3 (6%)
It was easy to use the visualization module in the MS self-tracking tool	9 (18%)	11 (22%)	27 (54%)
The visualization module in the MS self-tracking tool provided useful information	9 (18%)	15 (30%)	22 (44%)
I like being able to measure my physical activity via an activity watch	2 (4%)	3 (6%)	45 (90%)
I like being able to measure my sleep via an activity watch	3 (6%)	4 (8%)	43 (86%)
I think I receive better help from healthcare professionals when I use technologies	20 (40%)	-	30 (60%)

The final self-tracking tool featured 47 items in the self-evaluation module. These include the following:

Twenty-three items that measured symptom levels and one item that evaluates symptom management;

Eight physical activity items, of which three (step count, zone minutes and calories burnt) were measured via Fitbit and entered into the self-tracking tool by the participants;

Seven items that measured sleep and rest, of which two (sleep score and sleep length) were measured via Fitbit and entered into the self-tracking tool by the participants;

Items that measured dietary, fluid and alcohol intake;

Items that measured psychological stress levels and the levels of daily tasks;

Items that measured outdoor temperature and sensitivity to temperature;

A menstruation item (indicating whether the user is having her period); and

Finally, one item that measured the overall experience of the day.

Most items were answered on a 0–10 Likert scale, except those that indicate numbers (steps, minutes/hours, calories, etc).

The self-tracking tool was customizable in the sense that users could select the measures they wanted to track. There was no limit to how few or how many could be selected. However, to reduce the registration burden, participants were encouraged to track only the measures they found relevant in relation to their individual situation and health challenges. In addition, the measures shown in the visualization module could be selected and deselected as preferred, and the time period over which the graphs were shown was also adjustable. The participants were encouraged to perform the self-evaluations daily, and the registrations could be performed anywhere. Only the participants had access to the data registered in the tool.

Screenshot 1: Illustration of some of the 47 items to choose from.

Screenshot 2: Illustration of the data from the FitBit sleep score, entered into the self-tracking tool – ‘What was your sleep score for the night according to the FitBit watch?’ (scale: 0–100).

Screenshot 3: Illustration of the self-rated spasticity score – ‘To what extend have you been affected by your spasticity in the past 24 hours?’ (scale: 0–10).

Screenshot 4: Illustration of the visualization module showing the data from the FitBit sleep score, entered into the self-tracking tool and the self-rated spasticity data in two graphs.

The customizability to personal MS symptoms/challenges was unique to the self-tracking tool, and the role of Fitbit was merely to provide tracked data on physical activity and sleep to be entered into the self-tracking tool. However, no limitations were given to the participants’ use of Fitbit's features, which included various ways of presenting data on physical activity and sleep, for example, digital badges when certain numbers of steps per day were reached.

Recruitment and testing

Participants were recruited to the study through the Danish MS Society's respondent panel as well as through the society's Facebook page and website. The inclusion criteria included having been diagnosed with MS, having a smartphone and being able to use a non-specialized smartphone app. Experience with IT or apps was neither an inclusion nor exclusion criterion. A total of 143 PwMS responded to the invitation. Of these, 60 were selected to participate. The participants were selected to reflect the demographics of the general MS population in Denmark in terms of age, gender and years since MS diagnosis based on data on the total Danish MS population from the Danish MS Registry (non-published data). We were not able to obtain data on EDSS scores among the participants, and our best indicator for disease severity is hence the disease duration. Two participants withdrew before the start of the trial period due to undisclosed reasons. In total, 58 persons participated throughout the six-week testing period. The characteristics of the 58 study participants are shown in Table 1. Persons over 60 years of age are somewhat underrepresented in this study as this age group constituted 14% of the study population versus 35% of the total MS population in Denmark.

All participants received written instructions on how to onboard and use the MS self-tracking tool and the Fitbit wearable, and two online onboarding meetings were conducted prior to the testing period. At this meeting, the participants were presented practical details and the overall purpose of the research project. During the six-week testing period, telephone and e-mail support were available five days a week (Monday to Friday).

Data collection

Through interviews and a survey study, integrated in a mixed-methods design, the present study presents data regarding the perceived usefulness of the self-tracking tool as experienced by the participants in the testing phase. Approximately four weeks into the testing period, individual qualitative in-depth interviews were conducted with 14 participants, who were strategically selected to represent variety in terms of gender, age and geographical location. The interviews were semi-structured, which allowed for unexpected themes to arise and be developed. The interview guide was prepared by the authors who also conducted the interviews. All interviews were performed face-to-face in the participants’ home. On one occasion, the participant's spouse participated in part of the interview. The duration of the interviews varied from 28 to 67 min. All interviews were audio-recorded with the participant's consent. The participants were at the beginning of each interview informed about the interviewer's professional background, position at the Danish MS Society and previous experience with research on wearables and self-tracking.

Based on the preliminary analyses of the interview data, an online questionnaire was developed to allow for the main perspectives of the entire group of participants to be investigated (Supplemental material). This way of using results from the interview study to inform the construction of a survey study was guided by the sequential mixed-methods design. The questionnaire was distributed to all 58 participants 12 days after the final day of testing. The 12 days provided time to construct the questionnaire based on the interview data without compromising the participants’ recall abilities. A reminder was sent out 10 days later to participants who had not yet responded. Fifty participants (86%) responded to the survey. The characteristics of the interview and survey participants, respectively, are also shown in Table 1.

This study did not require ethical approval from the Danish National Committee of Health Research. The study adhered to the EU General Data Protection Regulations and the ethical principles for medical research, as given in the Declaration of Helsinki.

Data analysis

The interviews were transcribed verbatim, and all coding was completed using the NVivo R1.6 software package. Transcripts and analyses were not returned to the participants for comments. The thematic analysis, deriving themes from the interview data, was inspired by Nowell et al.²⁰ and was conducted in five steps, namely (1) familiarization with the data; (2) generation of initial codes; (3) searching for themes; (4) reviewing themes and (5) final definition and naming of themes and sub-themes. The first three steps were conducted by authors LS (male) and JS (female), the identified themes were discussed with author ML (female) in step 4, and consensus was found among all three authors in step 5. All three authors had more than five years of training in performing qualitative research.

The survey data were analyzed using Excel/Stata IC 16, and the descriptive analyses are presented as percentages. Open-text responses from the survey are presented in the text and in Table 3.

Table 3.

Concrete associations brought into awareness – survey results.

Insight into new associations	Confirmation of known associations
Positive associations: More energy as a result of more physical activity Better sleep and fewer symptoms if physical activity is done early in the day Fewer symptoms as a result of physical activity Better sleep as a result of more physical activity Better balance as a result of physical activity Better sleep as a result of light physical activity Negative associations: Dizziness as a result of too much physical activity the day before Tiredness as a result of too little sleep and/or low sleep quality Cognitive challenges as a result of fatigue Bad temper as a result of low sleep quality Poor sleep quality as a result of late intake of coffee More anxiety as a result of stress Worsening of neuropathic pain as a result of intense physical exercise Worsening of pain as a result of physical exercise Lower sleep quality as a result of physical exercise Neutral associations: A direct association between energy and level of symptoms Associations between diet and sleep, stress and sensory disturbances Associations between sleep and intake of medicine	Positive associations: Less fatigue as a result of physical exercise Better sleep as a result of physical activity and a healthy diet Better mood, balance, and walking ability as a result of good sleep quality Fewer symptoms as a result of more physical activity More energy and fewer symptoms as a result of good sleep Better sleep as a result of a healthy diet Good sleep quality as a result of physical activity Negative associations: Fatigue as a result of poor diet Fatigue as a result of menstruation Worsening of symptoms as a result of ordinary sickness (e.g., the flu) Worsening of sleep quality as a result of hard physical exercise Worsening of almost everything as a result of poor sleep quality Worsening of symptoms as a result of poor sleep Tiredness as a result of poor sleep Poor sleep quality as a result of pain Neutral associations: Associations between fatigue and muscle strength Less physical activity as a result of lack of sleep Less physical activity as a result of poor sleep

Insight into new associations

Confirmation of known associations

Positive associations:

More energy as a result of more physical activity

Better sleep and fewer symptoms if physical activity is done early in the day

Fewer symptoms as a result of physical activity

Better sleep as a result of more physical activity

Better balance as a result of physical activity

Better sleep as a result of light physical activity

Negative associations:

Dizziness as a result of too much physical activity the day before

Tiredness as a result of too little sleep and/or low sleep quality

Cognitive challenges as a result of fatigue

Bad temper as a result of low sleep quality

Poor sleep quality as a result of late intake of coffee

More anxiety as a result of stress

Worsening of neuropathic pain as a result of intense physical exercise

Worsening of pain as a result of physical exercise

Lower sleep quality as a result of physical exercise

Neutral associations:

A direct association between energy and level of symptoms

Associations between diet and sleep, stress and sensory disturbances

Associations between sleep and intake of medicine

Positive associations:

Less fatigue as a result of physical exercise

Better sleep as a result of physical activity and a healthy diet

Better mood, balance, and walking ability as a result of good sleep quality

Fewer symptoms as a result of more physical activity

More energy and fewer symptoms as a result of good sleep

Better sleep as a result of a healthy diet

Good sleep quality as a result of physical activity

Negative associations:

Fatigue as a result of poor diet

Fatigue as a result of menstruation

Worsening of symptoms as a result of ordinary sickness (e.g., the flu)

Worsening of sleep quality as a result of hard physical exercise

Worsening of almost everything as a result of poor sleep quality

Worsening of symptoms as a result of poor sleep

Tiredness as a result of poor sleep

Poor sleep quality as a result of pain

Neutral associations:

Associations between fatigue and muscle strength

Less physical activity as a result of lack of sleep

Less physical activity as a result of poor sleep

Following the mixed-methods design, results from the survey are presented in the following along with the relevant themes from the qualitative study. Results from the qualitative study are not presented with percentages or numbers but with indication of prevalence.

Results

We identified four main themes from the interview data that describe the usefulness and relevance of the self-tracking tool, namely (1) awareness and understanding, (2) behaviour and motivation, (3) data quality and possible adjustments and (4) further potential. In the following, the four themes are presented as main themes and divided into sub-themes. Illustrative quotes from the interview data are included. The names presented are pseudonyms. Following the sequential, exploratory mixed-methods design, we present the results from the survey as they relate to each theme.

Theme 1: Awareness and understanding

Using the self-tracking tool helped the participants become more aware of and reflect on patterns in their symptoms, physical activity, sleep quality and emotional well-being. Some participants became aware of patterns that they had not previously noticed, while most participants used the self-tracking tool to confirm or validate patterns they were already aware of or presumed to have. The increased awareness and attention to symptoms, physical activity, sleep and emotional well-being elicited by self-tracking were mainly experienced as helpful and not harmful. However, a few participants experienced the daily tracking as burdensome.

Confirmation/validation of existing knowledge. Several of the interview informants felt that the self-tracking tool could help them confirm patterns related to their MS that they already assumed or knew about. For example, the self-tracking tool helped some interview informants confirm that impaired sleep quality affected general well-being or the ability to be physically active on the following day(s). For others, it confirmed that physical activity positively affected balance and inactivity increased spasms. Peter said, ‘Again, it is more like a confirmation of the things I already know affect me and are related. But I think I am very visual. It has an effect on me, and I can see it. That I’m not just thinking it’. The self-tracking tool helped another participant, Susan, by confirming that she needed daily naps. Susan said that, based on data tracked by Fitbit, she could tell that she fell into a deep sleep during her daily afternoon nap: ‘Yes, well, you can see it on my app if you want to at some point. I take a nap, fall into a deep sleep, and am completely gone. And that’s a sign that I need it’. Although the informants had considerable knowledge of their bodies’ needs and how they reacted to different exposures and were, therefore, not surprised by what the self-tracking tool showed them, most still found it useful to have this knowledge confirmed. Sofie said, ‘Well, I just think it is very interesting to look at because I am aware of when it is going wrong [worsening of symptoms]. But I think it is interesting to see that it shows me why as well’. Susan expressed that she believed that the self-tracking tool might be especially useful for newly diagnosed persons or persons who are not so observant regarding their MS. She said, ‘I think that new [people newly diagnosed with MS] or people who are not dealing with it [the MS (symptoms)], they could gain a lot more’.

In addition to having patterns/associations between the MS and symptom triggers confirmed, tracking in general made some of the informants more aware of their level of physical activity or their sleeping patterns. Michelle said, ‘I have learned a lot about my sleep. Actually, I have had to research how sleep works. And this knowledge is new to me. I thought that it went through stages; I thought you had one stage of REM and one stage of deep sleep, and that's it. But the stages repeat, just like you can wake up a couple of times during the night. I have been very concerned about my own sleep and now I have seen that there is no need to be’.

Table 2 summarizes the results of the survey related to all four themes. In line with the interview data, several of the survey respondents (50%) stated that the self-tracking tool helped them to confirm patterns in their symptoms, physical activity, sleep or other matters that they had already been aware of. Based on the open-text responses in the survey (presented in Table 3), the survey respondents experienced confirmation of patterns related to, for example, less fatigue as a result of physical activity; better sleep as a result of physical activity and a healthy diet; and better mood, balance and walking ability as a result of good sleep quality. The majority (66%) of the survey respondents experienced increased awareness of their overall well-being and gained greater insight into sleep (76%), physical activity (66%) and symptoms (62%).

New insights

Most interview informants used the self-tracking tool to confirm or verify already known patterns related to their MS. However, some informants discovered new associations and gained new insights into their MS and symptom triggers through the self-tracking tool. For example, some interview informants experienced that, when a specific number of steps (tracked on Fitbit) was exceeded during the day, it would have a negative impact on the body the following day. Furthermore, tracking made some interview informants reflect on new potential associations and pay more attention to what might affect their MS. Louise said, ‘But I think about those things about the symptoms where I might not always think that it was connected to the MS. But there are some things I think a little about now. (…) I notice it a lot more because I sort of evaluate it in the evening or in the morning after, depending on how I actually felt. And I haven't done that before’.

The self-tracking tool encouraged some informants to explore their MS and test potential associations. Susan said, ‘I have started to try to mix some things, so, if I do this and that, what does it then affect? For instance, if I take Modiodal [fatigue medication], does it then affect my sleep?’ The self-tracking tool also made it easier for some informants to predict their level of energy. Susan said, ‘If I then have been really tired in the morning (…), I can check if there is an explanation as to why I feel like this today? (…) I have actually been awake for two hours this night, so it makes sense that I am tired today’.

In the survey, slightly more than half of the respondents (56%) stated that using the self-tracking tool had made them aware of patterns related to symptoms, physical activity, sleep or other matters that they had not previously been aware of. Hence, the results from the survey complement the findings from the interviews, by showing that a large percentage of the participants reported having experienced new associations when specifically asked about this. Based on the open-text survey responses, the respondents reported that they had discovered new associations related to, for example, better sleep, balance and energy, as a result of physical activity; more anxiety as a result of stress; and dizziness as a result of too much physical activity. More examples are provided in Table 3.

Emotional burden

The participants were encouraged to use the self-tracking tool on a daily basis. A few informants felt guilty if they had not managed to track the self-tracking measures every day. For some participants, the daily tracking was experienced as a great responsibility, which could feel stressful. Michelle said, ‘I can get kind of stressed out about trying to remember it [to track] … It’s the thing about remembering, well, and being accountable because I feel guilty if I do not remember it because that is not how I am usually. I keep track of things, right?’

Another issue that affected a few of the informants was that self-tracking made them more conscious of their symptoms in a negative way. Christian said, ‘I thought, well, if I am aware of my symptoms the whole time, what would it then do to my everyday life? And that has been a very negative experience’. While Christian initially endorsed the idea of self-tracking, he found that it made him think more negatively about how MS has affected his everyday life. For example, the Fitbit sleep score made him question his perception of how he had slept: ‘A specific example is that a question is being asked to how your self-evaluated sleep has been and there I thought that it has been fine, it has been okay, and then you look at the Fitbit [wearable] and see that it has actually been going really poorly for you (…) suddenly you start to get used to, that when I wake up and feel like this it is because I have slept poorly and then you start out the day negatively’. Similarly, Christian experienced that tracking his pain levels made managing his pain more difficult, ‘My mind forgets that it hurts, and that is fantastic. It is fantastic that I forget because otherwise it would hurt all the time. And when I get asked every day how my sensory disturbances are, I get aware of them, I get reminded of them, and I think, “Hey, it actually still hurts”.’

However, the majority of informants did not find it stressful or too time-consuming to self-track, nor did they find that it caused them increased concern. Louise said, ‘Well it has been—well I actually think it has been good. Because it is not like I use a lot of time on it. I fill out and write just that, but it is not like I sit and get depressed that now it has been a bad day. I don’t do that’.

The survey results confirm that a minority of participants experienced negative emotions related to self-tracking. Approximately 15% found it too time- and energy-consuming to track, and approximately the same proportion (14%) found that it made them too aware of the disease. About 20% experienced feelings of guilt as a consequence of tracking their physical activity. Only 6% thought that self-tracking made them too aware of their sleep.

In summary, based on interviews and survey results, the majority of the participants experienced that the self-tracking tool assisted them to clarify patterns regarding their symptoms, physical activity, sleep quality and emotional well-being. Some participants gained insight into new aspects of their MS. A few participants had negative emotional experiences and found that the focus on symptoms, physical activity and poor sleep overshadowed the usefulness of the tool. However, the majority of the participants did not report that they experienced negative emotions related to self-tracking.

Theme 2: Behaviour and motivation

Tracking physical activity and, to some extent, sleep had a motivational effect on the participants. When informants spoke about motivation and changing habits, they mainly referred to tracking via the Fitbit watch (and the associated Fitbit app). The MS self-tracking tool did not seem to play a significant role in relation to motivation.

Goal setting. The informants appreciated that the Fitbit wearable and the associated app allowed them to set goals, and motivational gimmicks such as receiving encouraging push notifications were emphasized as helpful in achieving goals. Louise said, ‘Yes, but the thing about it is that it makes me move a bit more than I would have. Because I can see it. And then you think more about it. The thing about when it beeps—you are missing 130 steps, then I will get up and walk the 130 steps until it beeps again. Then I can relax again’.

However, a few informants were more skeptical of the motivating aspect of tracking physical activity and worried that it could be a stressful reminder to exercise. Susan said, ‘So, I think it is very important to make it clear to those who are going to use it, that the purpose is to make you more aware about your disease; it is not to hit you over your head and have a trainer standing next to you, if you understand’.

Changing habits

A few informants shared how using the self-tracking tool had resulted in adjustments in habits regarding sleep and physical activity. For Peter, the Fitbit wearable provided insights into his sleep patterns and how they affected him negatively, which led to changes in his sleep behavior: ‘Now I go to bed at 11—11.30 pm., so I have moved it forward an hour. And that makes me get a higher score [on the Fitbit]. (…) I sleep better; it is more about the fact that I sleep better. Yes. (…) There are not that many periods where I am awake’.

For other interview informants, self-tracking affected changes in physical activity. Christian works as a teacher, and, since he was diagnosed with MS two years ago, he has performed all his teaching duties while sitting on a chair. However, Fitbit made him aware that he was quite inactive and that the number of steps he took during a day was minimal. Therefore, he tried to stand and walk around in the classroom while teaching to establish whether he could incorporate more physical activity during the day without being overburdened. Christian said, ‘I have been teaching sitting down for a very long time after I got it [the MS]. (…). And now I have stopped, because I tested myself and what effect it would have if I stood up and walked around when I helped people out [the students] or drove over to them on a chair. (…). And I found out that it did not do that much to take a few small steps between sitting down (…). I don’t have to drive back and forth on a chair to affect my everyday life, but it makes me move more’.

In this way, the tracking of steps taken during the day (via the Fitbit watch) allowed Christian to test small but important/meaningful adjustments to increase physical activity without also increasing the symptom burden.

In the survey, half of the respondents agreed that using the self-tracking tool had motivated or helped them to become more physically active, and a third (34%) of the respondents agreed that they had been motivated or that it helped to change their sleeping habits (see Table 1). About 20% of respondents in the survey agreed that tracking their physical activity caused them feelings of guilt, which was evident in the interview data to a slight degree. The majority of the respondents enjoyed using a wearable device to track sleep (86%) and physical activity (90%).

In summary, the Fitbit wearable played a motivational role for many of the participants. Daily tracking and elements of gamification (e.g. trophies and badges) appeared in the Fitbit app to encourage changes in behaviour, especially in terms of increasing physical activity. However, tracking their physical activity also made some participants feel guilty. Fitbit seemed to be the primary driver of motivation for physical activity, whereas there was little indication that the tracking of self-reported symptoms or other indicators of well-being in the self-tracking tool played a significant role in motivating behaviour change. However, the tracking of symptoms in the MS self-tracking tool seems to have played a more indirect role for some users in increasing their attention toward MS symptoms and the fluctuations, and, thereby, it supported behaviour change.

Theme 3: Data quality and possible adjustments

In general, the participants found that the self-tracking tool functioned well. However, some participants experienced data quality and functionality issues, and some had suggestions for improvements to the self-tracking tool.

Data quality

Two issues regarding the quality of the data were mentioned in the interviews. One issue was related to the quality of self-reported data given the setup of the self-tracking tool. MS symptoms may fluctuate significantly within a day. The app only allowed one self-evaluation per day, and participants had been advised to complete the self-evaluation in the evening to capture the entire day. However, this posed a challenge for some informants. Susan said, ‘Yes, well, now I go to bed and think “oh I have to register how my balance has been today?” And then it is just the last two hours because I always have a good feeling about my balance in the evening, there is no problems at all. Then I would write that there almost have not been any [problems]. So, I have actually forgotten about the morning where I used the walker’.

In addition, many people with MS experience cognitive challenges, and some informants mentioned problems with assessing symptoms correctly if they had to recall in the evening how they felt and what activities they had performed during the course of the day.

The other issue regarding data quality concerned what the Fitbit app reported versus what the informants experienced. Some informants experienced a discrepancy between their own assessments and the Fitbit scores, especially regarding the quality of sleep. In the majority of cases where a discrepancy was articulated in the interviews, the informant had experienced worse sleep quality and more awake time than what the Fitbit sleep score had indicated. Michael said, ‘Yes, several times where I have been writing, that it had not been a good sleep, dammit, I had been awake a lot of times and then I got 85? [sleep score on the watch which indicates a good night of sleep]’. This caused several informants to question whether the Fitbit measurements were correct. Louise said, ‘I have that age where I have entered menopause which does mean, especially this summer, that I have had so many problems with almost no sleep at all. And there it [the Fitbit] does not always tell the truth. Because it cannot feel if I am lying down, and my brain is about to explode because I am tired and cannot fall asleep’.

Visualizing data. The visualization module of the self-tracking tool was intended to aid users in the creation of an overview of the fluctuations of their symptoms over time, physical activity, sleep, etc., to reveal possible relationships. However, only a few of the informants used the visualization module and found it useful. Several informants mentioned that the visualization module was not sufficiently clear and not user-friendly.

Some informants found it confusing that both the self-tracking tool and the Fitbit app contained visualizations in the form of graphs and were not sure of which app they were supposed to use. Other informants were aware of the visualization module in the MS self-tracking tool but preferred the Fitbit app.

Diary function to note relevant events

Several of the informants suggested that a diary function should have been included in the self-tracking tool to note specific events or occurrences that could significantly affect some of the other measures. These could be events such as being sick, taking medicine, having vaccinations, attending social events, etc. Isabel said, ‘And maybe it should also be possible to note in the app, like for instance I was sick in the first week with a cold and felt dizzy, and I went almost nowhere. And then I just thought about that if you had such an app then it would be nice if you could go back and see: well why have you not moved that much that week? Then you could see that it was because you were sick that week’.

In the survey, there was a total of 18 comments regarding the content of the self-tracking tool (some respondents left more than one comment, and others did not comment at all). Only two respondents commented on the lack of a diary function. The most frequently commented-on app feature was the visualization module, which those who commented found difficult to use or read. However, also according to the survey, 54% of the respondents found the visualization module easy to use, and 44% found that the visualization module provided useful information (Table 1).

In summary, the participants found the functionality of the app acceptable and useful. Some participants questioned the quality of the data from the Fitbit app. Others questioned the quality of their self-reported data because daily fluctuations in symptoms could not be captured, and some expressed concerns related to recall bias. A note/diary function and an optimization of the visualization module in the MS self-tracking tool may be relevant features to incorporate or adjust for the future use of the app.

Theme 4: Further potential

The informants noted several possibilities for further use of the app, beyond what they had used it for in the testing period. Some informants regarded the app as a potential tool for communication with HCPs. They also suggested that the app could contain, or play a part in, peer-to-peer forums, where users of the app could share information and experiences. Finally, the informants suggested that using the app over a longer period of time than the six-week testing period may yield new and helpful insights.

A helpful tool in consultations with HCPs

Several informants stated that the self-tracking tool could be relevant in their dialogue with HCPs. Some informants explained how they often had difficulties in remembering how their MS had affected them since their previous consultation with an MS specialist. The self-tracking tool was considered helpful to remember and document fluctuations in symptoms and other relevant issues related to MS. Emma said, ‘So I actually felt that I was missing it [an app for tracking symptoms]. Just for my own sake. So you could remember when you have had symptoms and use them when you get to the doctor once every three months, so you could remember and tell what has happened and how you have been. (…). They [the health professionals] ask all the time about how you have been, and then I just think it would be easier to remember if you had been writing it down and tracking the symptoms in some sort of way.” Some informants found the self-tracking tool helpful in documenting how they experienced symptoms and everyday life with MS. They expressed that the documentation of their experiences would make them more confident in meetings with HCPs. Caroline said, “I have been confirmed in the thoughts I have had. And I would use it when I go talk to a neurologist because now I know that it is not only something in my own mind.”

Sharing information

Several of the informants expressed interest in sharing information with other people with MS as part of using the self-tracking tool. Some informants suggested that patient groups could be formed to share experiences and support each other. Michael said, “Basically, it would be smart if you could make such groups. Regardingmy MS, I basically don't know if there are other people who feel like this or if it is just me. To share, just like we sit here and share and talk about what I gained from this, it could be that it is just me who hasn't seen it and someone else has (…) we just agreed that my biggest problem is my spasms, I would really like to know what other people do about their spasms.”

Following trends over time

Some informants mentioned the potential for exploring trends over time if the self-tracking tool was made available beyond the testing period. This would, for example, make it possible to identify associations between activities and outcomes, such as sleep quality, that had not been evident during the six-week trial. Susan said, ‘If I could find out if I, for instance, did not drink soda in the evening [when having dinner guests], can I then fall asleep quicker? Can you do something not only to avoid a poor night's sleep, but also not to ruin the day after?’ Maria talked about how self-tracking throughout the entire course of the disease could contribute to a more thorough understanding of what was happening: ‘I often think about how it was in the beginning because it could be nice to have data about the progress [of the course of disease] to see the progress and how the food is now compared to how it was back then, walking distance and other data’. Another informant, Michael, saw the main potential in exploring the association between strength training and spasticity, and whether the training is worth it over time: ‘Well, if I have been working out and done heavy leg training, as I should do, it can sometimes make my spasms worse in the evening or in the night. It is a choice between two evils, so I have to look at it in the long run’.

In the survey, 70% of the respondents agreed that the use of technologies would improve their communication with HCPs, and 60% thought that they would receive more support from HCPs when using technologies. One survey respondent wrote in the open-text responses that sharing information with others when self-tracking would be useful to motivate each other and share experiences.

In summary, the participants generally saw further potential in the use of the self-tracking tool. Sharing knowledge with HCPs as well as with non-professionals constituted one important aspect. Further identification of trends over time was also emphasized as an important potential; however, this would require a longer tracking period.

Discussion

Main findings

The present study aimed at investigating the perceived usefulness of a digital self-tracking tool developed in collaboration with PwMS to track relevant health-related measures. The results of the study indicate that a self-tracking tool has the potential to increase awareness of patterns related to symptoms and activities among PwMS. The qualitative data indicate that confirmation of already known associations constituted an important gain, although insight into new associations was also emphasized by many respondents, mainly expressed in the survey data. Negative emotional experiences were rare.

Self-management through self-tracking

In the present study, we found that many of the participants used data from the digital tools to confirm existing knowledge or – to some degree – to obtain new knowledge regarding associations, for example, related to the level of physical activity vs. the level of sleep quality or the amount of sleep during the day vs. the level of sleep quality. Such findings are, to the best of our knowledge, only described to a limited extent in other studies on the use of digital tools for self-management among PwMS. In their study, Babbage et al.²¹ concluded that some people who had been living with MS fatigue for many years discovered new information or new ways of managing their fatigue through the use of an app developed specifically for the management of fatigue in MS.²¹ However, only a few studies describe the acquisition of new knowledge related to patterns between various conditions among PwMS. This difference may be explained by the fact that the daily registration of symptoms, which was an integrated part of the present study, is only included in very few similar studies.^11,12

The results of the present study may indicate that a learning aspect could be of importance in relation to self-tracking. While there was little indication that the self-tracking tool played a significant role in motivating behavioural change, the results of the study indicate that the increased concurrent awareness of activities, symptoms and other conditions noted in the daily registration of data may have encompassed a learning aspect and enhanced behavioural change or the initiation of such behavioural change, by maintaining a focus on possible associations. Whereas the tracking of physical activity through the Fitbit app provided a direct source of knowledge, the registration of symptoms in the MS self-tracking tool entailed a more indirect awareness of the manifestations of the disease and reflections regarding possible patterns. Hence, overall, the self-tracking tool supported individual self-management of the MS disease among the participants by combining the tracking of physical activity and the registration of symptoms and, thereby, providing information to be used by the individual user for the interpretation of possible patterns between the conditions registered. A few studies have emphasized the potential of gaining control^9,22 and providing lifestyle insights²³ through the use of digital self-tracking tools among PwMS, but the aspect of individual learning has a low prevalence in the literature. This may be linked to the fact that the learning aspect was specifically articulated from the beginning in the present study, to encourage participants to be aware of possible new or recognized knowledge during the process. In their studies, Wendrich et al. and Oirschoti et al. found that participants requested guidance to increase the value of the data^12,23 and noted that there is a learning potential if adequate support is provided. Likewise, the results of the present study indicate that the relevance of professional support in interpreting data and possible associations should be considered. While the initiation of a process that inspires a person with MS to reflect, and possibly act upon, new health-related information can be regarded as positive from a motivational perspective, it may also entail challenges if data is misinterpreted and/or overinterpreted. Brichetto et al. state that a lack of appropriate education in interpreting data from wearables may cause PwMS to pursue unsafe coping strategies.²⁴ Professional support or guidance could be a relevant supplement in self-tracking processes, with the aim of supporting correct interpretation of data as well as ensuring that only relevant behavioural changes are implemented.

The risk of entailing negative emotions

Several studies on self-tracking among PwMS mention the issue of self-tracking engendering negative emotions. Babbage et al. found that focusing on fatigue could prompt negative thoughts. Wendrich et al. found that being confronted with their MS could be a challenge for the participants, and Oirschoti et al. found that higher disease awareness could be confrontational.^12,21,23 In the present study, no more than 14% of the participants mentioned negative disease awareness as a consequence of the self-tracking. The same low prevalence of negative implications was found in another Danish study on the use of activity trackers among PwMS.^25,26 Possible explanations for this difference could be related to the fact that the participants in the present study were recruited because of their active interest as well as the fact that they had been diagnosed with MS for several years – entailing possibly a certain degree of acceptance of the disease.

Validity of data

In general, the participants found that the tracking devices functioned well. However, we found in the present study that some participants experienced discrepancies between the passively collected data and their own experience. Maddocks et al.²⁷ argue that measurements from wearables such as Fitbit may underestimate physical activity compared to validated accelerometers and may overestimate sleep duration compared to sleep monitors. Babbage et al. found that some participants reported that the information in an app for self-management of fatigue seemed to contradict or exclude their own strategy.²¹ This aspect was also noted in a study by Bergien et al.,²⁶ and this constitutes a point that should receive attention, as the validity of data – and thereby, trust in the data provided to the users – may be crucial to retain the users’ interest.

As has already been investigated in previous studies,^28–30 data from self-tracking may have the potential to strengthen the dialogue between patients with MS and HCPs. Several of the participants in the present study mentioned the possibility of promoting the sharing of health information. A recent Danish study that examined the use of patient-reported outcomes in MS treatment has found interest as well as skepticism among HCPs and emphasized the importance of securing high-quality data and high adherence if HCPs are to include such data in their clinical practice.³¹ This perspective emphasizes the importance of securing data with high validity during self-tracking. A study on digital patient care among PwMS has indicated that long-term monitoring of PREMs (patient-reported experience measures) related to walking assessment holds the potential to identify and thereby avoid problems early – seen from both a patient and an HCP perspective.³²

Motivational aspects

In the present study, the Fitbit wearable and app played a motivational role for many of the participants, especially in terms of increasing their physical activity. Likewise, several studies have emphasized the potential of digital tools to motivate PwMS to become more physically active. Wendrich et al. found that a smartphone app and an activity tracker increased the respondents’ awareness of their physical status and stimulated them to act on the data.¹² Likewise, Vandelanotte et al., Brickwood et al. and Oirschoti et al. found in their studies that the use of an activity tracker motivated PwMS to increase their physical activity.^23,33,34 The results of the present study broadly support these findings, although it was mainly the Fitbit tracker (and the associated Fitbit app) that was emphasized as being motivating by the participants in the present study, along with the registration of symptoms in the MS self-tracking tool. Ayobi et al. emphasize that some participants found that a self-tracking tool could impede their capacities and thereby have a demotivating effect, especially if it focused on predefined health indicators and the optimization of health behaviour.⁹ This concern was also mentioned by a few participants in the present study, which indicates the need to be aware of this potential barrier.

More real world-based knowledge is needed

Even though several studies over the past years, as mentioned above, have indicated that digital tools may hold a potential in strengthening self-management among PwMS, more solid knowledge is needed regarding the overall interest and potential adherence among PwMS regarding the integration of self-tracking solutions in everyday life. The results of the present study indicate that the self-tracking of symptoms and activities may contribute positively to everyday self-management among PwMS, by clarifying patterns between symptoms and activities and, thereby, initiating concrete behavioural changes. Marrie et al. have shown that the use of eHealth technologies is common in the MS population³⁵ and a survey study by Griffin et al. indicates that smartphone use is extensive, frequent and acceptable for healthcare purposes among PwMS.³⁶ However, the group of survey respondents in this last-mentioned study was self-selected, and even though a survey study among PwMS in Denmark has shown that 41% would find it relevant to use a self-tracking tool to provide an overview of lifestyle factors and MS-related symptoms,³⁷ we need more real world-based knowledge on the proportion of PwMS likely to actively use a self-tracking tool if offered, and in which contexts.

Methodological strengths and limitations

The present study was conducted as a sequential mixed-methods study, initiated by a thorough, in-depth qualitative study and followed by a survey with a high response rate. This research design allowed the participants to articulate a wide range of themes, experiences and reflections. The research design also allowed for the application of a quantitative perspective, which ensured that the various themes were included in the overall results. Furthermore, the interviews and the survey both provided important perspectives regarding the participants’ reflections and experiences. However, following the sequential mixed-methods design, we chose not to pilot-test the questionnaire before distributing it to the participants. The questionnaire was constructed on the basis of the preliminary results of the interview study, and by including a time-consuming testing phase, we would have risked compromising the recall abilities among the participants. Hence, the results of the survey showed that the experience of having discovered new patterns was more prevalent than initially indicated by the results of the qualitative study. The issue was investigated in both the interviews and the survey, but the survey may have provided a framework for a different reflection compared to the interviews. In the survey, half of the respondents agreed that using the self-tracking tool had motivated or helped them to become more physically active, and slightly more than one-third of the respondents agreed that they had been motivated or helped to change their sleeping habits. This was, however, not reflected in the interview data. In addition, 20% of respondents in the survey agreed that tracking their physical activity caused them to have a guilty conscience, which was not expressed in the interview data. From a mixed-methods perspective, the combination of the interviews and the survey provided a nuanced reflection of the participants’ experiences, although it also entailed a few incongruent areas. These incongruencies may be interpreted as unsuccessful data saturation in the interview study. However, we believe that data saturation was achieved in the interview study as no new themes occurred during the last 3–4 interviews. The incongruencies should rather be interpreted as the result of different types of reflection among the participants, evoked by the two different types of data generation integrated in the mixed-methods design.

The length of the testing period may be regarded as a limitation of the study. Six weeks may not be sufficient to allow complex patterns between symptoms and activities to emerge. The graphic shortcomings related to the visualization module must also be regarded as a limitation that reduced the cogency of the study regarding the usefulness of such visualization. In the present study, negative consequences of tracking were rare. However, the group of participants was limited to PwMS who had an interest in tracking as well as a certain level of technical skills. Previous usage habits with tracking technologies were not registered, which would have been relevant. Furthermore, participants over 60 years of age were under-represented in the study. Therefore, the results of the study may not be representative of an overall MS population with mixed interest in the use of digital tracking tools and varying IT competencies.

The app was developed for – and in cooperation with – a group of interested people with MS, which should be regarded as a significant strength. However, this also means that the app is suitable for a specific target group and is not necessarily optimal for all potential users. Further individual accommodations may be relevant.

In the present study, the digital tool was developed with the aim of being highly customizable to the individual user, for example, such as choosing which symptoms and activities to track. However, supplemental customizability could be relevant, for example, in relation to scales, individual annotations and the daily time for registration. The fact that the participants themselves had to enter data from the Fitbit app into the self-tracking tool was a limitation in the study. Future studies should prioritize a technical solution that allows for automatic transfer of data into the self-tracking tool.

Implications

Self-tracking of symptoms and activities may contribute positively to everyday self-management among PwMS. This is mainly obtained through clarification of patterns between symptoms and activities and a consequential initiation of concrete behavioural changes. Professional support in interpreting and acting upon data should be considered to avoid misinterpretation of data, support appropriate levels of behavioural change and prevent any negative consequences of increased disease awareness. Furthermore, self-tracking may provide useful data for communication between patients and HCPs. Digital solutions should preferably be simple to use and provide high-quality graphical solutions. The results of the present study indicate that complexity in the combination of digital tools should be avoided, not least due to the extensive prevalence of cognitive challenges among PwMS.

Conclusion

The results of the present study indicate that self-tracking of symptoms, sleep, physical activity and other measures may contribute positively to everyday self-management among PwMS. Professional support in interpreting and acting upon data should be considered.

Supplemental Material

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Supplemental material, sj-pdf-1-dhj-10.1177_20552076241264389 for Perceived usefulness of digital self-tracking among people with multiple sclerosis by Lasse Skovgaard, Josephine Lyngh Steenberg and Marie Lynning in DIGITAL HEALTH

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Supplemental material, sj-docx-2-dhj-10.1177_20552076241264389 for Perceived usefulness of digital self-tracking among people with multiple sclerosis by Lasse Skovgaard, Josephine Lyngh Steenberg and Marie Lynning in DIGITAL HEALTH

Footnotes

Acknowledgements

We would like to thank Astrid Karnøe Knudsen and Lars Kayser for their valuable input during the research process.

Contributorship

Lasse Skovgaard: drafting/revising the manuscript, study concept or design, analysis or interpretation of data, accepts responsibility for conduct of research and final approval, and study supervision. Josephine Lyngh Steenberg: revising the manuscript, analysis or interpretation of data, accepts responsibility for conduct of research and final approval. Marie Lynning: revising the manuscript, analysis or interpretation of data, accepts responsibility for conduct of research and final approval, and statistical analysis.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical approval and informed consent

This study did not require ethical approval from the Danish National Committee of Health Research. The study adhered to the EU General Data Protection Regulation and the ethical principles for medical research, as presented in the Declaration of Helsinki. Informed consent was obtained from all participants in all phases of the project. Written consent was obtained in relation to the development and testing phases as well as the survey study in the evaluation phase. Oral consent was obtained with regards to the interviews in the evaluation phase. Participants were informed about anonymity and their right to withdraw at any time.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Guarantor

LS.

ORCID iD

Lasse Skovgaard

Supplemental material

Supplemental material for this article is available online.

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