BMC neurology · 2025
Воздействие онлайновой программы поддержки пациентов под руководством медсестер на усталость, сон и качество жизни у пациентов с множественным склерозом: квазиэкспериментальное исследование.
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Yalçın Gizem Yağmur, Tulek Zeliha, Kürtüncü Murat
Аннотация
Множественный склероз (MS) становится все более распространенным. Физические расстройства и проблемы со сном, особенно усталость, ухудшают качество жизни этих пациентов. One Одним из наиболее успешных и долгосрочных стратегий борьбы с симптомами является обучение пациентов. Цель этого исследования заключалась в оценке воздействия онлайновой программы поддержки под руководством медсестер на усталость, сон и качество жизни пациентов с множественным склерозом (PwMS). МЕТОДЫ: Это квазиэкспериментальное исследование было проведено в амбулаторной клинике MS одной из университетских больниц в Стамбуле. Пациенты участвовали в five- недельная онлайновая программа поддержки под руководством медицинских сестер, оценка которой проводилась с использованием шкалы "Усталость" (FSS), индекса качества сна Питтсбурга (PSQI) и EQ-5D Качество жизни до, после и после 3 и 6 месяцев. Зарегистрировано также число пациентов на ступенях. Результаты: Thirty В исследование были включены пациенты, закончившие программу. По сравнению с базовыми показателями показатели СФС и число этапов улучшились (p = 0.008, p = 0.008). Улучшились также показатели PSQI (p = 0.026). Хотя разницы между ними не было. EQ-5D оценки симптомов, EQ-5D Визуальные аналоговые баллы, как правило, улучшаются (p = 0.085).ВЫВОДЫ: Программа онлайновой поддержки под руководством медицинских сестер может быть практически осуществимым и ресурсоэффективным подходом к решению проблем усталости и нарушения сна в PwMS, особенно в условиях ограниченного доступа к медицинскому обслуживанию. Для подтверждения этих выводов необходимы более крупные контролируемые исследования. ТРИАЛ РЕГИСТРАЦИЯ: Это исследование было зарегистрировано в Клиническом производстве. 24, 2023 (В тыс.NCT06166043).
Оригинал публикации
Effect of a nurse-led online patient support program on fatigue, sleep and quality of life in patients with multiple sclerosis: a quasi-experimental study.
BACKGROUND: Multiple sclerosis (MS) is becoming more prevalent. Physical impairments and sleep issues, particularly fatigue, diminish these patients' quality of life. One of the most successful and long-lasting strategies for managing symptoms is patient education. The aim of this study was to evaluate effect of a nurse-led online support program on fatigue, sleep, and quality of life in patients with multiple sclerosis (PwMS).
METHODS: This quasi-experimental study was conducted in the MS outpatient clinic of a university hospital in Istanbul. Patients participated in a five-week nurse-led online support program and were assessed using the Fatigue Severity Scale (FSS), Pittsburgh Sleep Quality Index (PSQI), and EQ-5D Quality of Life Scale before, after, and at 3 and 6 months. Patients' step counts were also recorded.
RESULTS: Thirty patients who completed the program were included in the study. Compared to baseline, FSS scores and number of steps improved (p = 0.008, p = 0.008). PSQI scores also improved (p = 0.026). Although there was no difference in EQ-5D symptom scores, the EQ-5D visual analogue scale scores tended to improve (p = 0.085).
CONCLUSIONS: A nurse-led online support program may be a feasible and resource-efficient approach to managing fatigue and sleep disturbances in PwMS, especially in settings with limited access to care. Larger controlled studies are needed to confirm these findings.
TRIAL REGISTRATION: This study was registered on ClinicalTrials.gov on August 24, 2023 (NCT06166043).
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Introduction
Multiple sclerosis (MS) is the most common progressive neurological disease in young adults worldwide [ 1 ]. According to a 2020 report, approximately 2.8 million people worldwide are living with the disease. Fatigue is one of the most common symptoms of MS, reported by between 35 and 96%. Fatigue is one of the most debilitating symptoms of MS and is associated with worse functional outcomes, poor quality of life, and sleep disturbances [ 2 ]. The treatment of fatigue is one of the treatment priorities for PwMS. Recent systematic reviews have shown that nurse-led programs, especially those conducted online, can lead to significant improvements in self-management and symptom relief in chronic neurological conditions, including multiple sclerosis [ 3 ]. These interventions often include components such as patient education, self-monitoring, lifestyle coaching, and stress management techniques.
Various instruments have been developed to measure fatigue in MS patients. While the Fatigue Severity Scale (FSS) remains widely used in Türkiye due to its validated Turkish version, newer tools such as the Fatigue Symptoms and Impact Questionnaire–Relapsing Multiple Sclerosis (FSIQ-RMS) have also been introduced for broader assessment, particularly in multinational studies [ 4 ]. However, local adaptation of these newer tools remains limited. Methods for treating MS-related fatigue include pharmacological treatments and non-pharmacological treatments such as physical rehabilitation, physical activity and exercise therapy, and non-invasive brain stimulation [ 5 ]. The treatment of MS-related fatigue also includes behavioral and psychological interventions [ 6 ].
Another problem reported by PwMS is sleep disturbance. Studies confirm poor sleep quality in PwMS [ 7 , 8 ]. The prevalence of sleep disorders in PwMS is between 25 and 54%. Poor sleep quality in MS is associated with negative outcomes such as reduced quality of life and increased fatigue[ 7 , 9 ]. It is known that improving sleep is an effective way to alleviate fatigue in these patients [ 10 ]. It has also been observed that fatigue and sleep quality improve after a fatigue management intervention [ 11 ]. There are studies showing that a person’s physical activity level, as well as sleep patterns, affect fatigue levels [ 12 – 15 ]
Nurses have a vital role in empowering patients to manage chronic illness [ 16 ]. They facilitate the management of complex diseases and improve lifestyle behaviours [ 17 ]. There is evidence that nurse-led patient support and follow-up programs have positive effects on fatigue, depression, constipation, sleep quality, quality of life and self-efficacy in various patient populations [ 18 – 20 ]. There are few studies examining the impact of nurse-led patient education programs on outcomes such as fatigue, sleep quality and quality of life in PwMS [ 21 , 22 ]. Recent systematic reviews have shown that nurse-led programs, especially those conducted online, can lead to significant improvements in self-management and symptom relief in chronic neurological conditions, including multiple sclerosis [ 23 ]. These interventions often include components such as patient education, self-monitoring, lifestyle coaching, and stress management techniques.
Non-pharmacological approach for MS-related fatigue usually include a range of different applications interventions such as fatigue management [ 24 , 25 ], energy conservation [ 26 ], endurance and resistance exercise [ 23 ], mindfulness [ 27 ], yoga [ 28 ], tai chi [ 29 ], and cognitive behavioral therapy [ 30 ]. Especially with the COVID pandemic, the implementation of these interventions via telehealth has become an issue in many countries. Although the effectiveness of many of these interventions has been demonstrated in an in-person setting, the effectiveness of telehealth has not yet been studied [ 24 ]. A meta-analysis found that telehealth applications for neurological conditions other than stroke are only possible to a very limited extent [ 31 ].
Physical activity, including step count, is increasingly recognized as a crucial indicator for monitoring these patients. For instance, the FITriMS project demonstrated that wearable technology can effectively measure daily step counts, providing valuable data on physical activity levels and linking them with clinical outcomes like the Expanded Disability Status Scale [ 32 , 33 ]. However, the integration of step count measurement into nurse-led telehealth programs for PwMS remains limited. This study aims to address this gap by evaluating the effect of a nurse-led online support program, incorporating step count tracking, on fatigue, sleep, and quality of life in PwMS.
Objectives
This study aimed to evaluate the effectiveness of a nurse-led online support program for people with multiple sclerosis (PwMS). The primary outcome was fatigue severity, assessed by the Fatigue Severity Scale (FSS). Secondary outcomes included sleep quality (PSQI), physical activity (step count), and quality of life (EQ-5D). A further objective was to explore the clinical significance of changes by comparing observed effects with minimal clinically important difference (MCID) thresholds. Feasibility outcomes (e.g., dropout rate, adherence) were also monitored to assess acceptability of the intervention.
The primary outcome was fatigue severity, assessed by the Fatigue Severity Scale (FSS).
Secondary outcomes included sleep quality (PSQI), physical activity (step count), and quality of life (EQ-5D).
A further objective was to explore the clinical significance of changes by comparing observed effects with minimal clinically important difference (MCID) thresholds.
Feasibility outcomes (e.g., dropout rate, adherence) were also monitored to assess acceptability of the intervention.
Methods
Sample and setting
This quasi-experimental study was conducted between October 2022 and November 2023 in an MS outpatient clinic at a university hospital in Istanbul. The study involved patients who were between 18 and 60 years old, literate, had been diagnosed with MS for at least six months (based on the McDonald criteria) and were willing to participate in the study. Patients who had a smartwatch that could record step count and had access to a tablet or computer with internet capability to engage with the online education intervention were included in the study. Patients with diseases other than MS that could cause fatigue (cancer, anemia, thyroid disease, orthopedic problems, other neurological diseases), patients with depressed mood (with a score above 18 on the Beck Depression Scale), severe disability (EDSS ≥ 5) or cognitive dysfunction (mini-cog ≤ 3) were excluded from the study. Patients who were experiencing a recent relapse were also excluded. Compensation sessions were organized for participants who failed to attend only one training session. However, patients who did not attend more than one training session or did not wish to continue were excluded from the study.
Sample size was calculated using G*Power 3.1.9.2. Assuming an effect size of d = 0.60, α = 0.05, and 1 − β = 0.80, a minimum of 28 participants was needed. To account for potential dropouts, 37 participants were recruited [ 20 ]. Considering that dropouts may occur, a total of 37 patients who met the study inclusion criteria and agreed to participate in the study were included. During the study, seven patients were excluded from the study because they were unable to attend the scheduled sessions. Of 112 patients screened, 50 did not meet inclusion criteria, and 25 declined to participate (lack of time, scheduling conflicts, and low perceived need for a fatigue intervention.). A total of 37 participants were enrolled; 7 dropped out due to missed sessions or personal reasons, resulting in a final sample of 30 (81.1% retention).The 7 dropouts had a mean BDI score of 11.85, EDSS of 3.0, FSS of 3.5, PSQI of 8.0, EQ-VAS of 80.0, and EQ-5D index of 0.796. These were not significantly different from those who completed the study.
Data from 30 patients who completed all processes and measurements were analyzed. The flowchart of the study is presented in Fig. 1 . Fig. 1 Study Flowchart
Study Flowchart
Intervention
In our study, a nurse-led fatigue management education program and a patient follow-up program were conducted. The training program consisted of 10 live sessions provided via the Zoom platform. The intervention was delivered by the first author, a registered nurse specialized in MS care. Patients were able to participate in the training program with a single click via a link sent to them before each session. The educational content was created considering similar studies [ 21 , 34 – 36 ] and the opinion of experts (a neurologist, two neurology nurses with PhDs and a neurology nurse with a BA degree) (Table 1 . Content of the Nurse-led fatigue management education program session.) The content included materials, photos and videos on each topic and was presented in the form of lectures and Q&A sessions. Although not formally trained in mindfulness or therapeutic relaxation, she developed the intervention content based on peer-reviewed literature and nursing practice guidelines. For the patient follow-up portion of the program, the researcher contacted participants one week after each training session to assess the implementation of the topic taught. During the conversation, the problems encountered in the implementation of the training content for the patient were discussed and suggestions were made to the patient. In addition, the researcher provided feedback during each phone call and encouraged the patient to adhere to the training. Each phone call lasted about 10–15 min. At the end of the study, the patient received the topics taught in the form of a booklet.
Table 1 Content of the Nurse-led fatigue management education program session Session Duration Content 1 40 min ✓ Meet ✓ Introduction of the education program (definition, benefits, stages, etc.) 2 40 min ✓ What is multiple sclerosis? ✓ Why do we get tired? 3 40 min ✓ What are the types of fatigue? ✓ What is the relationship between Multiple Sclerosis and fatigue? 4 40 min ✓ What are the causes of fatigue other than Multiple Sclerosis? 5 40 min ✓ What is MS-related fatigue? ✓ How do we recognize fatigue? ✓ How fatigue affects a person? 6 40 min ✓ How do we cope with fatigue? ✓ Strategies to handle fatigue in Daily life-1 7 40 min ✓ Strategies to handle fatigue in daily life-2 8 40 min ✓ Techniques for conserving energy ✓ Exercise 9 40 min ✓ Practicing a fatigue diary ✓ Breathing exercises ✓ Relaxation exercises 10 40 min ✓ Mindfulness practice ✓ Feedback on Program
Table 1 Content of the Nurse-led fatigue management education program session Session Duration Content 1 40 min ✓ Meet ✓ Introduction of the education program (definition, benefits, stages, etc.) 2 40 min ✓ What is multiple sclerosis? ✓ Why do we get tired? 3 40 min ✓ What are the types of fatigue? ✓ What is the relationship between Multiple Sclerosis and fatigue? 4 40 min ✓ What are the causes of fatigue other than Multiple Sclerosis? 5 40 min ✓ What is MS-related fatigue? ✓ How do we recognize fatigue? ✓ How fatigue affects a person? 6 40 min ✓ How do we cope with fatigue? ✓ Strategies to handle fatigue in Daily life-1 7 40 min ✓ Strategies to handle fatigue in daily life-2 8 40 min ✓ Techniques for conserving energy ✓ Exercise 9 40 min ✓ Practicing a fatigue diary ✓ Breathing exercises ✓ Relaxation exercises 10 40 min ✓ Mindfulness practice ✓ Feedback on Program
Content of the Nurse-led fatigue management education program session
✓ Meet
✓ Introduction of the education program (definition, benefits, stages, etc.)
✓ What is multiple sclerosis?
✓ Why do we get tired?
✓ What are the types of fatigue?
✓ What is the relationship between Multiple Sclerosis and fatigue?
✓ What is MS-related fatigue?
✓ How do we recognize fatigue?
✓ How fatigue affects a person?
✓ How do we cope with fatigue?
✓ Strategies to handle fatigue in Daily life-1
✓ Techniques for conserving energy
✓ Exercise
✓ Practicing a fatigue diary
✓ Breathing exercises
✓ Relaxation exercises
✓ Mindfulness practice
✓ Feedback on Program
Data collection
Data collection instruments included a sociodemographic questionnaire, the Fatigue Severity Scale (FSS), the Pittsburgh Sleep Quality Index (PSQI) and the EQ-5D quality of life scale. In addition, step counts were used as an objective indicator of fatigue, with patients asked to report their seven-day step count during each measurement period.
Fatigue severity scale (FSS)
The scale questions the fatigue status in the last month, including the day it was completed. The Turkish validity and reliability study of the scale was conducted by Armutlu. The Cronbach`s alpha value of the Turkish version was reported as 0.889 by Armutlu and 0.940 by Keser [ 37 ]. In this nine-item scale individuals can obtain a score between 0 and 9. A higher score indicates greater fatigue [ 38 ]. A change of 0.45 points on the scale is considered clinically significant [ 39 ].
Beck depression inventory (BDI)
This scale was developed by Beck et al. in 1961 to evaluate depressive mood [ 40 ]. A Turkish validity and reliability study of the scale was conducted. Scores can be obtained from the scale range between 0 and 63, with a high score indicating depressive mood. A score between 0 and 9 is interpreted as minimal depression, 10–16 points as mild depression, 17–29 points as moderate depression and 30–63 points as severe depression.
Pittsburgh sleep quality index (PSQI)
This is a 24-item scale developed by Buysse et al. (1989) that measures sleep quality and sleep-related disturbances over a one-month period [ 41 ]. The PSQI index consists of seven components: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, disturbances in sleep, use of sleeping medication, and day time dysfunction. The total score on the scale ranges from 0 to 21, with a total PSQI score of more than five indicating that the person’s sleep quality is inadequate. The Turkish study on the validity and reliability of the scale was conducted by Ağargün et al. [ 42 ].
EuroQol general quality of life scale (EQ-5D-3L)
EQ-5D is a scale consisting of five questions (mobility, self-care, usual activities, pain/discomfort and anxiety/depression). There is also a section where the patient is asked to mark their current health status on a 20 cm vertical ruler, where 0 indicating the “worst possible health status” and 100 the “best possible health status” using EQ VAS (Visual Analogue Scale). The 3-point Likert-type version was used in our study [ 43 ]
Expanded disability status scale (EDDS)
The Expanded Disability Status Scale (EDSS) was developed to assess the functional status of patients with MS [ 44 ]. The EDSS is scored between 0 and 10. A high score indicates worse disability.
Statistical analysis
Statistical analyses were performed using IBM SPSS Statistics for Windows, Version 22.0 (IBM Corp., Armonk, NY, USA). The conformity of the data to the normal distribution was tested using the Kolmogorov–Smirnov and Shapiro–Wilk tests. The descriptive statistics in the study were reported as mean, standard deviation, minimum, maximum, median and frequency. The Mann–Whitney U test and the Kruskal–Wallis test (Bonferroni as post-hoc analysis) were used to compare continuous variables. The Friedman test (post-hoc Wilcoxon sign test) was used to compare the scale scores before and after the intervention. Spearman correlation analysis was used to analyze the relationships between the variables. The Fisher exact and chi-square tests were used to compare the distributions of the categorical data. Significance was assessed at the level of p < 0.05.
Results
A total of 30 patients completed the study, 24 (80%) females, aged between 18 and 50 years. The mean age was 34.3 ± 8.1 years, the mean duration of diagnosis was 8.8 ± 6.3 years and the mean EDSS was 2.4 ± 1.8. The mean Beck depression inventory score was 12.0 ± 4.5 (Table 2 ). Fifteen of the patients were taking fingolimod, 4 ocrelizumab, 4 glatiramer acetate, 3 natalizumab, 1 dimethyl fumarate, 1 alemtuzumab, 1 teriflunomide and 1 cladribine (data not shown in the tables).
Table 2 Sociodemographic and clinical characteristics of the patients Variables n (%) Sex Female 24 (80.0) Male 6 (20.0) Education Primary/secondary school 3 (10.0) High school 6 (20.0) University 21 (70.0) Working status Yes 13 (43.3) No 17 (56.7) Income Less than expenses 2 (6.7) Equal to expenses 22 (73.3) More than expenses 6 (20.0) Marital status Married 20 (66.7) Single/divorced/widow 10 (33.3) Having a child Yes 17 (56.7) No 13 (43.3) Age (mean ± sd) (median) 34.3 ± 8.1 (33.5) Time since diagnosis (year) (mean ± sd) (median) 8.8 ± 6.3 (7.5) EDSS (mean ± sd) (median) 2.4 ± 1.8 (2) Functional status EDSS 0.5–3 23 (76.6) EDSS ≥ 3.5 7 (23.4) Type of MS RRMS 30 (100.0) Antidepressant use Yes 7 (23.3) No 23 (76.7) Smoking Yes 8 (26.7) No 22 (73.3) BDI (mean ± sd) (median) 12.0 ± 4.5 (11) Depressive mood Yes (BDI ≥ 9) 23 (76.7) No (BDI < 9) 7 (23.3) EDSS Expanded Disability Status Scale, MS Multiple sclerosis, RRMS Relapsing–remitting MS, BDI Beck Depresssion Inventory, sd Standard deviation
Table 2 Sociodemographic and clinical characteristics of the patients Variables n (%) Sex Female 24 (80.0) Male 6 (20.0) Education Primary/secondary school 3 (10.0) High school 6 (20.0) University 21 (70.0) Working status Yes 13 (43.3) No 17 (56.7) Income Less than expenses 2 (6.7) Equal to expenses 22 (73.3) More than expenses 6 (20.0) Marital status Married 20 (66.7) Single/divorced/widow 10 (33.3) Having a child Yes 17 (56.7) No 13 (43.3) Age (mean ± sd) (median) 34.3 ± 8.1 (33.5) Time since diagnosis (year) (mean ± sd) (median) 8.8 ± 6.3 (7.5) EDSS (mean ± sd) (median) 2.4 ± 1.8 (2) Functional status EDSS 0.5–3 23 (76.6) EDSS ≥ 3.5 7 (23.4) Type of MS RRMS 30 (100.0) Antidepressant use Yes 7 (23.3) No 23 (76.7) Smoking Yes 8 (26.7) No 22 (73.3) BDI (mean ± sd) (median) 12.0 ± 4.5 (11) Depressive mood Yes (BDI ≥ 9) 23 (76.7) No (BDI < 9) 7 (23.3) EDSS Expanded Disability Status Scale, MS Multiple sclerosis, RRMS Relapsing–remitting MS, BDI Beck Depresssion Inventory, sd Standard deviation
Sociodemographic and clinical characteristics of the patients
EDSS Expanded Disability Status Scale, MS Multiple sclerosis, RRMS Relapsing–remitting MS, BDI Beck Depresssion Inventory, sd Standard deviation
When the Fatigue Severity Scale (FSS) scores were analyzed before and after the intervention, a significant change was observed ( p = 0.002). No significant difference was found between baseline (T0) and the end of the control period (T1) (median 5.2 vs 5.2, p = 0.504). However, compared to T0, significant reductions in fatigue were observed at the end of the intervention (T2; median 4.9, p = 0.005), at 3 months (T3; median 4.1, p = 0.002), and at 6 months (T4; median 4.2, p = 0.004). Similarly, when compared to T1, FSS scores significantly decreased at T2 ( p = 0.040), T3 ( p = 0.002), and T4 ( p = 0.006). No significant differences were found between T2, T3, and T4, indicating stabilization of post-intervention effects (Fig. 2 ). Fig. 2 Comparison of Fatigue Severity Scale Scores before and after the intervention (Data are presented as medians with interquartile ranges (IQR))
Comparison of Fatigue Severity Scale Scores before and after the intervention (Data are presented as medians with interquartile ranges (IQR))
Based on the minimal clinically important difference (MCID) for FSS, 66.7% of participants experienced a meaningful reduction in fatigue following the intervention. A change of ≥ 0.45 points on the FSS is considered clinically meaningful. Our results showed a mean change of 0.82 points, thus exceeding this threshold and indicating a clinically significant improvement.
The change in the number of steps measured before and after the intervention was also significant ( p = 0.001). The measurements in the 3rd month and 6 th month after the intervention were significantly higher than the measurements before the intervention (5000 vs 7000, p = 0.001; 5000 vs 6650, p = 0.001) (Fig. 3 ). Fig. 3 Comparison of step count before and after the intervention (Data are presented as medians with interquartile ranges (IQR))
Comparison of step count before and after the intervention (Data are presented as medians with interquartile ranges (IQR))
A significant change was also found in the Pittsburgh sleep quality scale scores evaluated before and after the intervention ( p = 0.026). A significant difference was observed in 6 th month measurement compared to two control measurements ( p = 0.023; p = 0.023). When comparing the EQ-5D quality of life scores evaluated before and after the intervention, no significant change was found in the domain score ( p = 0.868), while the difference in the VAS score tended to be significant ( p = 0.085) (Table 3 ).
Table 3 Comparison of Quality of Life and Sleep Quality Scores Before and After the Intervention Baseline End of the control period End of the intervention At 3rd month At 6 th month T 0 T 1 T 2 T 3 T 4 p PSQI 7.7 ± 3.0 (7.5) 7.9 ± 2.7 (8) 6.9 ± 3.6 (6) 7.1 ± 3.8 (6.5) 6.9 ± 3.5 (6) 0.026 EQ-5D Score 0.7 ± 0.1 (0.7) 0.7 ± 0.1 (0.7) 0.7 ± 0.1 (0.8) 0.7 ± 0.1 (0.7) 0.7 ± 0.1 (0.7) 0.868 EQ-5D VAS 72.6 ± 15.3 (70) 69.6 ± 22.5 (70) 77.6 ± 15.4 (80) 74.3 ± 15.4 (80) 71.0–19.0 (70) 0.085 Numbers show mean ± standard deviation, numbers in parenthesis show median. Friedman test PSQI Pittsburgh Sleep Quality Index, EQ-5D Euroqol Quality of Life Questionnaire, VAS EQ-5D Visual Assessment Scale
Table 3 Comparison of Quality of Life and Sleep Quality Scores Before and After the Intervention Baseline End of the control period End of the intervention At 3rd month At 6 th month T 0 T 1 T 2 T 3 T 4 p PSQI 7.7 ± 3.0 (7.5) 7.9 ± 2.7 (8) 6.9 ± 3.6 (6) 7.1 ± 3.8 (6.5) 6.9 ± 3.5 (6) 0.026 EQ-5D Score 0.7 ± 0.1 (0.7) 0.7 ± 0.1 (0.7) 0.7 ± 0.1 (0.8) 0.7 ± 0.1 (0.7) 0.7 ± 0.1 (0.7) 0.868 EQ-5D VAS 72.6 ± 15.3 (70) 69.6 ± 22.5 (70) 77.6 ± 15.4 (80) 74.3 ± 15.4 (80) 71.0–19.0 (70) 0.085 Numbers show mean ± standard deviation, numbers in parenthesis show median. Friedman test PSQI Pittsburgh Sleep Quality Index, EQ-5D Euroqol Quality of Life Questionnaire, VAS EQ-5D Visual Assessment Scale
Comparison of Quality of Life and Sleep Quality Scores Before and After the Intervention
Numbers show mean ± standard deviation, numbers in parenthesis show median. Friedman test
PSQI Pittsburgh Sleep Quality Index, EQ-5D Euroqol Quality of Life Questionnaire, VAS EQ-5D Visual Assessment Scale
In our study, the improvements in scale scores compared to baseline (T0) were analyzed according to sociodemographic characteristics in order to determine which patients benefited more from the intervention. Improvements in Fatigue Severity Scale (FSS) scores and daily step counts were greater among women compared to men, with a median difference of 0.6 points in FSS ( p = 0.005) and 1,200 steps/day ( p = 0.001). Unemployed participants showed greater improvements than employed individuals, with a median FSS reduction of 0.7 points ( p = 0.008) and a median step count increase of 1050 steps/day ( p = 0.005). Non-smokers experienced greater improvements in both fatigue and activity compared to smokers, with median differences of 0.8 points in FSS ( p = 0.006) and 1100 steps/day ( p = 0.007). Antidepressant users demonstrated greater improvements than non-users, with a median FSS improvement of 0.9 points ( p = 0.006) and a step count increase of 1300 steps/day ( p = 0.004). For marital status, married participants showed a greater median reduction in FSS scores compared to single participants (0.5 points, p = 0.035). Conversely, improvements in sleep quality scores (PSQI) were greater in single participants than in married ones (median difference : 1.6 points, p = 0.002), and in unemployed versus employed individuals (median difference : 1.4 points, p = 0.010). (Data not shown in tables.)
Discussion
The primary outcome of this study, which was conducted to assess the impact of a nurse-led follow-up program supported by online education on fatigue, sleep and quality of life in people with MS, was fatigue. In our study, to assess whether a placebo effect occurred due to patient follow-up, two baseline assessments were conducted five weeks apart before the intervention, and the results were found to be similar. When patients were assessed after the intervention in terms of both subjective fatigue perception and step counts as an objective indicator of fatigue, our findings showed that patients’ fatigue was significantly lower than in the control period and their step count increased after participating in this program. The change in fatigue levels was not only statistically but also clinically significant. We observed that the improvements in fatigue and step count appeared to be maintained over time. Patient ratings at the 3rd and 6 th month follow-ups were comparable to those recorded immediately post-intervention, suggesting a potential for sustained effects. However, while these findings point to a promising trend, further studies with extended follow-up periods are needed to more conclusively establish the long-term impact of the intervention. Additionaly, peer-led expert patient programs have also been effective in improving quality of life and self-efficacy in PwMS [ 45 ]. We believe that adding peer education to these nurse-led programs could increase sustainability and participation.
Although there are few studies on nurse-led fatigue management in patients with MS [ 21 , 22 ], web-based programs have been reported to be effective in fatigue management [ 46 – 48 ]. Compared to prior nurse-led and telerehabilitation programs [ 49 , 50 ], our findings demonstrate similar benefits. On the other hand, there are also studies that report that energy conservation methods have no effect on fatigue in MS patients [ 51 ]. The reason for these differences could lie in the type or quality of the intervention. In our study, energy conservation combined with telephone follow-up may have motivated patients. When we review the literature, there are studies that suggest that follow-up programs for patients in parallel to our study reduce fatigue [ 13 , 52 , 53 ]. Another reason, why there was no difference in the other study could be that the mean age of the patients was older than in our sample.
In our study, the secondary endpoints were sleep and quality of life. Our results showed that the sleep quality of MS patients changed significantly. There are studies that show that telerehabilitation programs significantly improve patients’ sleep quality [ 52 ]. Similarly, there is a study showing that an intervention that includes sleep hygiene education, cognitive behavioral therapy, physical activity and occupational therapy reduces fatigue and improves sleep quality in patients with MS [ 54 – 56 ]. Regarding quality of life, only the change in the VAS score tended to be significant. The tendency for the VAS score to show significant change, while no such change was observed in the item-based quality of life scores, can be explained by the fact that the VAS score in the EQ-5D scale reflects the patient’s overall health perception. It is hypothesized that the item-based EQ-5D scores may be more appropriate for assessing quality of life in patients with more advanced disabilities.
There are some limitations to this study. Fatigue assessment relied solely on self-reports rather than objective parameters. One limitation of this study is the absence of a separate control group. Instead, participants served as their own controls using a repeated-measures design. While this approach reduced the risk of contamination between groups and minimized potential placebo effects associated with parallel follow-up, it limits the ability to infer causality and may introduce time-related confounders. Patients with moderate-to-severe depressive symptoms were excluded to minimize confounding effects on fatigue and sleep outcomes. However, this exclusion may limit generalizability. All outcome data were self-reported and measured by the same intervention nurse , which may introduce performance and detection bias. This potential bias should be considered when interpreting the results. The nurse delivering the intervention was not formally certified in mindfulness or relaxation therapies, which may limit standardization and replicability of these components. However long follow-up period, which adds credibility to the results, is a strenght of study. This extended assessment allowed for a deeper understanding of the impact of the education program on patients’ daily activities.Future studies should consider including larger sample sizes and an independent control group. Furthermore, in addition to self-reports, fatigue, sleep quality, and step counts could be evaluated using objective measurement tools to increase the validity of the findings. This would allow for a more comprehensive evaluation of the effectiveness of similar digital health programs.
This quasi-experimental study found that participation in a nurse-led online support program was associated with sustained improvements in fatigue and sleep quality in PwMS. While promising, these findings should be interpreted with caution due to the study design. Larger, controlled studies are needed to confirm the efficacy and generalizability of this approach.
Multiple sclerosis (MS) is the most common progressive neurological disease in young adults worldwide [ 1 ]. According to a 2020 report, approximately 2.8 million people worldwide are living with the disease. Fatigue is one of the most common symptoms of MS, reported by between 35 and 96%. Fatigue is one of the most debilitating symptoms of MS and is associated with worse functional outcomes, poor quality of life, and sleep disturbances [ 2 ]. The treatment of fatigue is one of the treatment priorities for PwMS. Recent systematic reviews have shown that nurse-led programs, especially those conducted online, can lead to significant improvements in self-management and symptom relief in chronic neurological conditions, including multiple sclerosis [ 3 ]. These interventions often include components such as patient education, self-monitoring, lifestyle coaching, and stress management techniques.
Various instruments have been developed to measure fatigue in MS patients. While the Fatigue Severity Scale (FSS) remains widely used in Türkiye due to its validated Turkish version, newer tools such as the Fatigue Symptoms and Impact Questionnaire–Relapsing Multiple Sclerosis (FSIQ-RMS) have also been introduced for broader assessment, particularly in multinational studies [ 4 ]. However, local adaptation of these newer tools remains limited. Methods for treating MS-related fatigue include pharmacological treatments and non-pharmacological treatments such as physical rehabilitation, physical activity and exercise therapy, and non-invasive brain stimulation [ 5 ]. The treatment of MS-related fatigue also includes behavioral and psychological interventions [ 6 ].
Another problem reported by PwMS is sleep disturbance. Studies confirm poor sleep quality in PwMS [ 7 , 8 ]. The prevalence of sleep disorders in PwMS is between 25 and 54%. Poor sleep quality in MS is associated with negative outcomes such as reduced quality of life and increased fatigue[ 7 , 9 ]. It is known that improving sleep is an effective way to alleviate fatigue in these patients [ 10 ]. It has also been observed that fatigue and sleep quality improve after a fatigue management intervention [ 11 ]. There are studies showing that a person’s physical activity level, as well as sleep patterns, affect fatigue levels [ 12 – 15 ]
Nurses have a vital role in empowering patients to manage chronic illness [ 16 ]. They facilitate the management of complex diseases and improve lifestyle behaviours [ 17 ]. There is evidence that nurse-led patient support and follow-up programs have positive effects on fatigue, depression, constipation, sleep quality, quality of life and self-efficacy in various patient populations [ 18 – 20 ]. There are few studies examining the impact of nurse-led patient education programs on outcomes such as fatigue, sleep quality and quality of life in PwMS [ 21 , 22 ]. Recent systematic reviews have shown that nurse-led programs, especially those conducted online, can lead to significant improvements in self-management and symptom relief in chronic neurological conditions, including multiple sclerosis [ 23 ]. These interventions often include components such as patient education, self-monitoring, lifestyle coaching, and stress management techniques.
Non-pharmacological approach for MS-related fatigue usually include a range of different applications interventions such as fatigue management [ 24 , 25 ], energy conservation [ 26 ], endurance and resistance exercise [ 23 ], mindfulness [ 27 ], yoga [ 28 ], tai chi [ 29 ], and cognitive behavioral therapy [ 30 ]. Especially with the COVID pandemic, the implementation of these interventions via telehealth has become an issue in many countries. Although the effectiveness of many of these interventions has been demonstrated in an in-person setting, the effectiveness of telehealth has not yet been studied [ 24 ]. A meta-analysis found that telehealth applications for neurological conditions other than stroke are only possible to a very limited extent [ 31 ].
Physical activity, including step count, is increasingly recognized as a crucial indicator for monitoring these patients. For instance, the FITriMS project demonstrated that wearable technology can effectively measure daily step counts, providing valuable data on physical activity levels and linking them with clinical outcomes like the Expanded Disability Status Scale [ 32 , 33 ]. However, the integration of step count measurement into nurse-led telehealth programs for PwMS remains limited. This study aims to address this gap by evaluating the effect of a nurse-led online support program, incorporating step count tracking, on fatigue, sleep, and quality of life in PwMS.
Objectives
This study aimed to evaluate the effectiveness of a nurse-led online support program for people with multiple sclerosis (PwMS). The primary outcome was fatigue severity, assessed by the Fatigue Severity Scale (FSS). Secondary outcomes included sleep quality (PSQI), physical activity (step count), and quality of life (EQ-5D). A further objective was to explore the clinical significance of changes by comparing observed effects with minimal clinically important difference (MCID) thresholds. Feasibility outcomes (e.g., dropout rate, adherence) were also monitored to assess acceptability of the intervention.
The primary outcome was fatigue severity, assessed by the Fatigue Severity Scale (FSS).
Secondary outcomes included sleep quality (PSQI), physical activity (step count), and quality of life (EQ-5D).
A further objective was to explore the clinical significance of changes by comparing observed effects with minimal clinically important difference (MCID) thresholds.
Feasibility outcomes (e.g., dropout rate, adherence) were also monitored to assess acceptability of the intervention.
Methods
Sample and setting
This quasi-experimental study was conducted between October 2022 and November 2023 in an MS outpatient clinic at a university hospital in Istanbul. The study involved patients who were between 18 and 60 years old, literate, had been diagnosed with MS for at least six months (based on the McDonald criteria) and were willing to participate in the study. Patients who had a smartwatch that could record step count and had access to a tablet or computer with internet capability to engage with the online education intervention were included in the study. Patients with diseases other than MS that could cause fatigue (cancer, anemia, thyroid disease, orthopedic problems, other neurological diseases), patients with depressed mood (with a score above 18 on the Beck Depression Scale), severe disability (EDSS ≥ 5) or cognitive dysfunction (mini-cog ≤ 3) were excluded from the study. Patients who were experiencing a recent relapse were also excluded. Compensation sessions were organized for participants who failed to attend only one training session. However, patients who did not attend more than one training session or did not wish to continue were excluded from the study.
Sample size was calculated using G*Power 3.1.9.2. Assuming an effect size of d = 0.60, α = 0.05, and 1 − β = 0.80, a minimum of 28 participants was needed. To account for potential dropouts, 37 participants were recruited [ 20 ]. Considering that dropouts may occur, a total of 37 patients who met the study inclusion criteria and agreed to participate in the study were included. During the study, seven patients were excluded from the study because they were unable to attend the scheduled sessions. Of 112 patients screened, 50 did not meet inclusion criteria, and 25 declined to participate (lack of time, scheduling conflicts, and low perceived need for a fatigue intervention.). A total of 37 participants were enrolled; 7 dropped out due to missed sessions or personal reasons, resulting in a final sample of 30 (81.1% retention).The 7 dropouts had a mean BDI score of 11.85, EDSS of 3.0, FSS of 3.5, PSQI of 8.0, EQ-VAS of 80.0, and EQ-5D index of 0.796. These were not significantly different from those who completed the study.
Data from 30 patients who completed all processes and measurements were analyzed. The flowchart of the study is presented in Fig. 1 . Fig. 1 Study Flowchart
Study Flowchart
Intervention
In our study, a nurse-led fatigue management education program and a patient follow-up program were conducted. The training program consisted of 10 live sessions provided via the Zoom platform. The intervention was delivered by the first author, a registered nurse specialized in MS care. Patients were able to participate in the training program with a single click via a link sent to them before each session. The educational content was created considering similar studies [ 21 , 34 – 36 ] and the opinion of experts (a neurologist, two neurology nurses with PhDs and a neurology nurse with a BA degree) (Table 1 . Content of the Nurse-led fatigue management education program session.) The content included materials, photos and videos on each topic and was presented in the form of lectures and Q&A sessions. Although not formally trained in mindfulness or therapeutic relaxation, she developed the intervention content based on peer-reviewed literature and nursing practice guidelines. For the patient follow-up portion of the program, the researcher contacted participants one week after each training session to assess the implementation of the topic taught. During the conversation, the problems encountered in the implementation of the training content for the patient were discussed and suggestions were made to the patient. In addition, the researcher provided feedback during each phone call and encouraged the patient to adhere to the training. Each phone call lasted about 10–15 min. At the end of the study, the patient received the topics taught in the form of a booklet.
Table 1 Content of the Nurse-led fatigue management education program session Session Duration Content 1 40 min ✓ Meet ✓ Introduction of the education program (definition, benefits, stages, etc.) 2 40 min ✓ What is multiple sclerosis? ✓ Why do we get tired? 3 40 min ✓ What are the types of fatigue? ✓ What is the relationship between Multiple Sclerosis and fatigue? 4 40 min ✓ What are the causes of fatigue other than Multiple Sclerosis? 5 40 min ✓ What is MS-related fatigue? ✓ How do we recognize fatigue? ✓ How fatigue affects a person? 6 40 min ✓ How do we cope with fatigue? ✓ Strategies to handle fatigue in Daily life-1 7 40 min ✓ Strategies to handle fatigue in daily life-2 8 40 min ✓ Techniques for conserving energy ✓ Exercise 9 40 min ✓ Practicing a fatigue diary ✓ Breathing exercises ✓ Relaxation exercises 10 40 min ✓ Mindfulness practice ✓ Feedback on Program
Table 1 Content of the Nurse-led fatigue management education program session Session Duration Content 1 40 min ✓ Meet ✓ Introduction of the education program (definition, benefits, stages, etc.) 2 40 min ✓ What is multiple sclerosis? ✓ Why do we get tired? 3 40 min ✓ What are the types of fatigue? ✓ What is the relationship between Multiple Sclerosis and fatigue? 4 40 min ✓ What are the causes of fatigue other than Multiple Sclerosis? 5 40 min ✓ What is MS-related fatigue? ✓ How do we recognize fatigue? ✓ How fatigue affects a person? 6 40 min ✓ How do we cope with fatigue? ✓ Strategies to handle fatigue in Daily life-1 7 40 min ✓ Strategies to handle fatigue in daily life-2 8 40 min ✓ Techniques for conserving energy ✓ Exercise 9 40 min ✓ Practicing a fatigue diary ✓ Breathing exercises ✓ Relaxation exercises 10 40 min ✓ Mindfulness practice ✓ Feedback on Program
Content of the Nurse-led fatigue management education program session
✓ Meet
✓ Introduction of the education program (definition, benefits, stages, etc.)
✓ What is multiple sclerosis?
✓ Why do we get tired?
✓ What are the types of fatigue?
✓ What is the relationship between Multiple Sclerosis and fatigue?
✓ What is MS-related fatigue?
✓ How do we recognize fatigue?
✓ How fatigue affects a person?
✓ How do we cope with fatigue?
✓ Strategies to handle fatigue in Daily life-1
✓ Techniques for conserving energy
✓ Exercise
✓ Practicing a fatigue diary
✓ Breathing exercises
✓ Relaxation exercises
✓ Mindfulness practice
✓ Feedback on Program
Data collection
Data collection instruments included a sociodemographic questionnaire, the Fatigue Severity Scale (FSS), the Pittsburgh Sleep Quality Index (PSQI) and the EQ-5D quality of life scale. In addition, step counts were used as an objective indicator of fatigue, with patients asked to report their seven-day step count during each measurement period.
Fatigue severity scale (FSS)
The scale questions the fatigue status in the last month, including the day it was completed. The Turkish validity and reliability study of the scale was conducted by Armutlu. The Cronbach`s alpha value of the Turkish version was reported as 0.889 by Armutlu and 0.940 by Keser [ 37 ]. In this nine-item scale individuals can obtain a score between 0 and 9. A higher score indicates greater fatigue [ 38 ]. A change of 0.45 points on the scale is considered clinically significant [ 39 ].
Beck depression inventory (BDI)
This scale was developed by Beck et al. in 1961 to evaluate depressive mood [ 40 ]. A Turkish validity and reliability study of the scale was conducted. Scores can be obtained from the scale range between 0 and 63, with a high score indicating depressive mood. A score between 0 and 9 is interpreted as minimal depression, 10–16 points as mild depression, 17–29 points as moderate depression and 30–63 points as severe depression.
Pittsburgh sleep quality index (PSQI)
This is a 24-item scale developed by Buysse et al. (1989) that measures sleep quality and sleep-related disturbances over a one-month period [ 41 ]. The PSQI index consists of seven components: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, disturbances in sleep, use of sleeping medication, and day time dysfunction. The total score on the scale ranges from 0 to 21, with a total PSQI score of more than five indicating that the person’s sleep quality is inadequate. The Turkish study on the validity and reliability of the scale was conducted by Ağargün et al. [ 42 ].
EuroQol general quality of life scale (EQ-5D-3L)
EQ-5D is a scale consisting of five questions (mobility, self-care, usual activities, pain/discomfort and anxiety/depression). There is also a section where the patient is asked to mark their current health status on a 20 cm vertical ruler, where 0 indicating the “worst possible health status” and 100 the “best possible health status” using EQ VAS (Visual Analogue Scale). The 3-point Likert-type version was used in our study [ 43 ]
Expanded disability status scale (EDDS)
The Expanded Disability Status Scale (EDSS) was developed to assess the functional status of patients with MS [ 44 ]. The EDSS is scored between 0 and 10. A high score indicates worse disability.
Statistical analysis
Statistical analyses were performed using IBM SPSS Statistics for Windows, Version 22.0 (IBM Corp., Armonk, NY, USA). The conformity of the data to the normal distribution was tested using the Kolmogorov–Smirnov and Shapiro–Wilk tests. The descriptive statistics in the study were reported as mean, standard deviation, minimum, maximum, median and frequency. The Mann–Whitney U test and the Kruskal–Wallis test (Bonferroni as post-hoc analysis) were used to compare continuous variables. The Friedman test (post-hoc Wilcoxon sign test) was used to compare the scale scores before and after the intervention. Spearman correlation analysis was used to analyze the relationships between the variables. The Fisher exact and chi-square tests were used to compare the distributions of the categorical data. Significance was assessed at the level of p < 0.05.
Results
A total of 30 patients completed the study, 24 (80%) females, aged between 18 and 50 years. The mean age was 34.3 ± 8.1 years, the mean duration of diagnosis was 8.8 ± 6.3 years and the mean EDSS was 2.4 ± 1.8. The mean Beck depression inventory score was 12.0 ± 4.5 (Table 2 ). Fifteen of the patients were taking fingolimod, 4 ocrelizumab, 4 glatiramer acetate, 3 natalizumab, 1 dimethyl fumarate, 1 alemtuzumab, 1 teriflunomide and 1 cladribine (data not shown in the tables).
Table 2 Sociodemographic and clinical characteristics of the patients Variables n (%) Sex Female 24 (80.0) Male 6 (20.0) Education Primary/secondary school 3 (10.0) High school 6 (20.0) University 21 (70.0) Working status Yes 13 (43.3) No 17 (56.7) Income Less than expenses 2 (6.7) Equal to expenses 22 (73.3) More than expenses 6 (20.0) Marital status Married 20 (66.7) Single/divorced/widow 10 (33.3) Having a child Yes 17 (56.7) No 13 (43.3) Age (mean ± sd) (median) 34.3 ± 8.1 (33.5) Time since diagnosis (year) (mean ± sd) (median) 8.8 ± 6.3 (7.5) EDSS (mean ± sd) (median) 2.4 ± 1.8 (2) Functional status EDSS 0.5–3 23 (76.6) EDSS ≥ 3.5 7 (23.4) Type of MS RRMS 30 (100.0) Antidepressant use Yes 7 (23.3) No 23 (76.7) Smoking Yes 8 (26.7) No 22 (73.3) BDI (mean ± sd) (median) 12.0 ± 4.5 (11) Depressive mood Yes (BDI ≥ 9) 23 (76.7) No (BDI < 9) 7 (23.3) EDSS Expanded Disability Status Scale, MS Multiple sclerosis, RRMS Relapsing–remitting MS, BDI Beck Depresssion Inventory, sd Standard deviation
Table 2 Sociodemographic and clinical characteristics of the patients Variables n (%) Sex Female 24 (80.0) Male 6 (20.0) Education Primary/secondary school 3 (10.0) High school 6 (20.0) University 21 (70.0) Working status Yes 13 (43.3) No 17 (56.7) Income Less than expenses 2 (6.7) Equal to expenses 22 (73.3) More than expenses 6 (20.0) Marital status Married 20 (66.7) Single/divorced/widow 10 (33.3) Having a child Yes 17 (56.7) No 13 (43.3) Age (mean ± sd) (median) 34.3 ± 8.1 (33.5) Time since diagnosis (year) (mean ± sd) (median) 8.8 ± 6.3 (7.5) EDSS (mean ± sd) (median) 2.4 ± 1.8 (2) Functional status EDSS 0.5–3 23 (76.6) EDSS ≥ 3.5 7 (23.4) Type of MS RRMS 30 (100.0) Antidepressant use Yes 7 (23.3) No 23 (76.7) Smoking Yes 8 (26.7) No 22 (73.3) BDI (mean ± sd) (median) 12.0 ± 4.5 (11) Depressive mood Yes (BDI ≥ 9) 23 (76.7) No (BDI < 9) 7 (23.3) EDSS Expanded Disability Status Scale, MS Multiple sclerosis, RRMS Relapsing–remitting MS, BDI Beck Depresssion Inventory, sd Standard deviation
Sociodemographic and clinical characteristics of the patients
EDSS Expanded Disability Status Scale, MS Multiple sclerosis, RRMS Relapsing–remitting MS, BDI Beck Depresssion Inventory, sd Standard deviation
When the Fatigue Severity Scale (FSS) scores were analyzed before and after the intervention, a significant change was observed ( p = 0.002). No significant difference was found between baseline (T0) and the end of the control period (T1) (median 5.2 vs 5.2, p = 0.504). However, compared to T0, significant reductions in fatigue were observed at the end of the intervention (T2; median 4.9, p = 0.005), at 3 months (T3; median 4.1, p = 0.002), and at 6 months (T4; median 4.2, p = 0.004). Similarly, when compared to T1, FSS scores significantly decreased at T2 ( p = 0.040), T3 ( p = 0.002), and T4 ( p = 0.006). No significant differences were found between T2, T3, and T4, indicating stabilization of post-intervention effects (Fig. 2 ). Fig. 2 Comparison of Fatigue Severity Scale Scores before and after the intervention (Data are presented as medians with interquartile ranges (IQR))
Comparison of Fatigue Severity Scale Scores before and after the intervention (Data are presented as medians with interquartile ranges (IQR))
Based on the minimal clinically important difference (MCID) for FSS, 66.7% of participants experienced a meaningful reduction in fatigue following the intervention. A change of ≥ 0.45 points on the FSS is considered clinically meaningful. Our results showed a mean change of 0.82 points, thus exceeding this threshold and indicating a clinically significant improvement.
The change in the number of steps measured before and after the intervention was also significant ( p = 0.001). The measurements in the 3rd month and 6 th month after the intervention were significantly higher than the measurements before the intervention (5000 vs 7000, p = 0.001; 5000 vs 6650, p = 0.001) (Fig. 3 ). Fig. 3 Comparison of step count before and after the intervention (Data are presented as medians with interquartile ranges (IQR))
Comparison of step count before and after the intervention (Data are presented as medians with interquartile ranges (IQR))
A significant change was also found in the Pittsburgh sleep quality scale scores evaluated before and after the intervention ( p = 0.026). A significant difference was observed in 6 th month measurement compared to two control measurements ( p = 0.023; p = 0.023). When comparing the EQ-5D quality of life scores evaluated before and after the intervention, no significant change was found in the domain score ( p = 0.868), while the difference in the VAS score tended to be significant ( p = 0.085) (Table 3 ).
Table 3 Comparison of Quality of Life and Sleep Quality Scores Before and After the Intervention Baseline End of the control period End of the intervention At 3rd month At 6 th month T 0 T 1 T 2 T 3 T 4 p PSQI 7.7 ± 3.0 (7.5) 7.9 ± 2.7 (8) 6.9 ± 3.6 (6) 7.1 ± 3.8 (6.5) 6.9 ± 3.5 (6) 0.026 EQ-5D Score 0.7 ± 0.1 (0.7) 0.7 ± 0.1 (0.7) 0.7 ± 0.1 (0.8) 0.7 ± 0.1 (0.7) 0.7 ± 0.1 (0.7) 0.868 EQ-5D VAS 72.6 ± 15.3 (70) 69.6 ± 22.5 (70) 77.6 ± 15.4 (80) 74.3 ± 15.4 (80) 71.0–19.0 (70) 0.085 Numbers show mean ± standard deviation, numbers in parenthesis show median. Friedman test PSQI Pittsburgh Sleep Quality Index, EQ-5D Euroqol Quality of Life Questionnaire, VAS EQ-5D Visual Assessment Scale
Table 3 Comparison of Quality of Life and Sleep Quality Scores Before and After the Intervention Baseline End of the control period End of the intervention At 3rd month At 6 th month T 0 T 1 T 2 T 3 T 4 p PSQI 7.7 ± 3.0 (7.5) 7.9 ± 2.7 (8) 6.9 ± 3.6 (6) 7.1 ± 3.8 (6.5) 6.9 ± 3.5 (6) 0.026 EQ-5D Score 0.7 ± 0.1 (0.7) 0.7 ± 0.1 (0.7) 0.7 ± 0.1 (0.8) 0.7 ± 0.1 (0.7) 0.7 ± 0.1 (0.7) 0.868 EQ-5D VAS 72.6 ± 15.3 (70) 69.6 ± 22.5 (70) 77.6 ± 15.4 (80) 74.3 ± 15.4 (80) 71.0–19.0 (70) 0.085 Numbers show mean ± standard deviation, numbers in parenthesis show median. Friedman test PSQI Pittsburgh Sleep Quality Index, EQ-5D Euroqol Quality of Life Questionnaire, VAS EQ-5D Visual Assessment Scale
Comparison of Quality of Life and Sleep Quality Scores Before and After the Intervention
Numbers show mean ± standard deviation, numbers in parenthesis show median. Friedman test
PSQI Pittsburgh Sleep Quality Index, EQ-5D Euroqol Quality of Life Questionnaire, VAS EQ-5D Visual Assessment Scale
In our study, the improvements in scale scores compared to baseline (T0) were analyzed according to sociodemographic characteristics in order to determine which patients benefited more from the intervention. Improvements in Fatigue Severity Scale (FSS) scores and daily step counts were greater among women compared to men, with a median difference of 0.6 points in FSS ( p = 0.005) and 1,200 steps/day ( p = 0.001). Unemployed participants showed greater improvements than employed individuals, with a median FSS reduction of 0.7 points ( p = 0.008) and a median step count increase of 1050 steps/day ( p = 0.005). Non-smokers experienced greater improvements in both fatigue and activity compared to smokers, with median differences of 0.8 points in FSS ( p = 0.006) and 1100 steps/day ( p = 0.007). Antidepressant users demonstrated greater improvements than non-users, with a median FSS improvement of 0.9 points ( p = 0.006) and a step count increase of 1300 steps/day ( p = 0.004). For marital status, married participants showed a greater median reduction in FSS scores compared to single participants (0.5 points, p = 0.035). Conversely, improvements in sleep quality scores (PSQI) were greater in single participants than in married ones (median difference : 1.6 points, p = 0.002), and in unemployed versus employed individuals (median difference : 1.4 points, p = 0.010). (Data not shown in tables.)
Discussion
The primary outcome of this study, which was conducted to assess the impact of a nurse-led follow-up program supported by online education on fatigue, sleep and quality of life in people with MS, was fatigue. In our study, to assess whether a placebo effect occurred due to patient follow-up, two baseline assessments were conducted five weeks apart before the intervention, and the results were found to be similar. When patients were assessed after the intervention in terms of both subjective fatigue perception and step counts as an objective indicator of fatigue, our findings showed that patients’ fatigue was significantly lower than in the control period and their step count increased after participating in this program. The change in fatigue levels was not only statistically but also clinically significant. We observed that the improvements in fatigue and step count appeared to be maintained over time. Patient ratings at the 3rd and 6 th month follow-ups were comparable to those recorded immediately post-intervention, suggesting a potential for sustained effects. However, while these findings point to a promising trend, further studies with extended follow-up periods are needed to more conclusively establish the long-term impact of the intervention. Additionaly, peer-led expert patient programs have also been effective in improving quality of life and self-efficacy in PwMS [ 45 ]. We believe that adding peer education to these nurse-led programs could increase sustainability and participation.
Although there are few studies on nurse-led fatigue management in patients with MS [ 21 , 22 ], web-based programs have been reported to be effective in fatigue management [ 46 – 48 ]. Compared to prior nurse-led and telerehabilitation programs [ 49 , 50 ], our findings demonstrate similar benefits. On the other hand, there are also studies that report that energy conservation methods have no effect on fatigue in MS patients [ 51 ]. The reason for these differences could lie in the type or quality of the intervention. In our study, energy conservation combined with telephone follow-up may have motivated patients. When we review the literature, there are studies that suggest that follow-up programs for patients in parallel to our study reduce fatigue [ 13 , 52 , 53 ]. Another reason, why there was no difference in the other study could be that the mean age of the patients was older than in our sample.
In our study, the secondary endpoints were sleep and quality of life. Our results showed that the sleep quality of MS patients changed significantly. There are studies that show that telerehabilitation programs significantly improve patients’ sleep quality [ 52 ]. Similarly, there is a study showing that an intervention that includes sleep hygiene education, cognitive behavioral therapy, physical activity and occupational therapy reduces fatigue and improves sleep quality in patients with MS [ 54 – 56 ]. Regarding quality of life, only the change in the VAS score tended to be significant. The tendency for the VAS score to show significant change, while no such change was observed in the item-based quality of life scores, can be explained by the fact that the VAS score in the EQ-5D scale reflects the patient’s overall health perception. It is hypothesized that the item-based EQ-5D scores may be more appropriate for assessing quality of life in patients with more advanced disabilities.
There are some limitations to this study. Fatigue assessment relied solely on self-reports rather than objective parameters. One limitation of this study is the absence of a separate control group. Instead, participants served as their own controls using a repeated-measures design. While this approach reduced the risk of contamination between groups and minimized potential placebo effects associated with parallel follow-up, it limits the ability to infer causality and may introduce time-related confounders. Patients with moderate-to-severe depressive symptoms were excluded to minimize confounding effects on fatigue and sleep outcomes. However, this exclusion may limit generalizability. All outcome data were self-reported and measured by the same intervention nurse , which may introduce performance and detection bias. This potential bias should be considered when interpreting the results. The nurse delivering the intervention was not formally certified in mindfulness or relaxation therapies, which may limit standardization and replicability of these components. However long follow-up period, which adds credibility to the results, is a strenght of study. This extended assessment allowed for a deeper understanding of the impact of the education program on patients’ daily activities.Future studies should consider including larger sample sizes and an independent control group. Furthermore, in addition to self-reports, fatigue, sleep quality, and step counts could be evaluated using objective measurement tools to increase the validity of the findings. This would allow for a more comprehensive evaluation of the effectiveness of similar digital health programs.
This quasi-experimental study found that participation in a nurse-led online support program was associated with sustained improvements in fatigue and sleep quality in PwMS. While promising, these findings should be interpreted with caution due to the study design. Larger, controlled studies are needed to confirm the efficacy and generalizability of this approach.