Chronic Conditions, Multiple, Fatigue, Multiple Sclerosis
Conditions
Keywords
eHealth, mHealth, gamification
Brief summary
The overall study aims are to explore the feasibility, acceptability, and usability of More Stamina, a mobile app for fatigue self-management for persons with MS.
Detailed description
Multiple sclerosis (MS) is one of the world's most common neurologic disorders of the young adults leading to severe disability. Fatigue is one of most common symptoms that persons with MS experience, having significant impact on their quality of life and limiting their activity levels. Self-management strategies are used to support them in the care of their health. Mobile health (mHealth) solutions are a way to offer persons with chronic conditions tools to successfully manage their symptoms and problems. User-centered design (UCD) is a design philosophy that proposes placing end-users' needs and characteristics in the center of software design and development and involving users early in the different phases of the software life cycle. More Stamina is an mHealth solution created following UCD principles to help persons with MS manage their fatigue. A mixed methods study was used to assess the feasibility, acceptability, and usability of More Stamina. The study took place between January and March of 2023 in Finland. A longitudinal cohort study took take place, along with think-aloud protocols, open-ended interviews and short answer questionnaires. The study enrolled 20 persons with MS that met the criteria who utilized the More Stamina solution for 60 days.
Interventions
OTHERMore Stamina
More Stamina is a gamified task organization tool designed to help persons with MS manage their energy, to minimize the impact of fatigue in their day to day life.
Sponsors
Oulu University Hospital
University of Oulu, Finland
Hospital Italiano de Buenos Aires
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
No
Inclusion criteria
* be over 18 years old; * have a confirmed MS diagnosis (including secondary, relapsing and progressive MS) for at least 1 year, * have none to moderate physical disability (EDSS \< 6.5) at the time of recruiting; * no major cognitive or haptile impairment influencing the ability to use the app; * own a compatible smartphone device;
Exclusion criteria
* Refusal to participate or the informed consent process; * Objective cognitive impairment that hinders the use of mobile applications; * Physical or cognitive impossibility to use the visual interface of mobile applications; * difficulties in access or availability for full attendance at the 3 encounters of the study.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| App Engagement Patterns | Over the 60-day study period. | Participants' engagement with the More Stamina app, categorized into power, regular, and light users based on daily session averages. Engagement analyzed through session frequency, timing, and activity types. |
| Qualitative Insights into Usability and Barriers | Day 1, Day 15, Day 30, and Day 60. | Insights into usability challenges and facilitators were gathered using think-aloud protocols and semi-structured interviews. Common themes, including cognitive load, technical issues, and emotional responses, were analyzed. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Association Between User Categories and Fatigue Severity Scores | Over the 60-day study period. | User categories (power, regular, and light users) were determined by average daily app sessions. Fatigue severity was measured using the Fatigue Severity Scale (FSS), with scores ranging from 1 (no fatigue) to 7 (severe fatigue). The relationship between user engagement categories and FSS scores was analyzed using Spearman's rank-order correlation and linear regression models. |
| Association Between User Categories and Health-Related Quality of Life Scores | Over the 60-day study period. | User categories defined by app engagement patterns (power, regular, and light users). HRQoL was assessed using the 15D instrument, with scores ranging from 0 (death) to 1 (full health). Scores were inverted (1 - HRQoL score) to align with worse QoL. Correlations and regression models explored associations between user categories and QoL. |
Countries
Finland
Outcome results
None listed