Introduction
Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative immune-mediated disease affecting the central nervous system (CNS), characterized by varying levels of damage to the myelin and axons [ 1 ]. It is one of the most common non-traumatic disabling CNS diseases among young adults [ 2 ].
To date, the exact etiology of MS remains elusive, but the widely accepted pathophysiological model involves a complex interplay between various genetic factors and environmental influences [ 2 , 3 ]. MS typically begins in early adulthood, most often between the ages of 20 and 40, and is more common in women [ 4 ]. Patients with MS suffer from a multifaceted array of disabling symptoms, such as fatigue, cognitive impairment affecting memory and concentration, mood changes such as anxiety and depression, pain, spasticity, urinary incontinence, and sleep disturbances [ 5 , 6 ]. These symptoms do not occur independently of one another; rather, they act synergistically, and their cumulative effects on quality of life affect virtually all aspects of existence. There is also the psychological burden of an inherently unstable disease, with relapses and remissions or steady progression, and one must constantly accommodate to one’s changing level of function [ 7 ].
Moreover, the severe psychosocial effects of MS, such as social isolation, unemployment rates of 22–80%, financial instability, and relationship problems because of role and emotional overload, emphasize the fact that the effects of the disease go well beyond the physical aspects [ 8 ]. The complex nature of the disease has made it increasingly evident that the management of MS goes beyond pharmacological treatment and that there is an increasing need for self-management to become an integral part of MS management, especially in patients with physical, cognitive, and emotional disturbances [ 7 , 9 ].
Self-management is recognized as a key strategy for enhancing perceived control over chronic illness and improving QOL [ 1 ]. It is generally defined as the process of acquiring and applying the skills needed to maintain an active and emotionally fulfilling life despite living with a chronic condition [ 2 ]. It involves the individual’s capacity, often in collaboration with family, community, and healthcare professionals, to address symptoms, treatments, lifestyle adjustments, and the psychosocial, cultural, and spiritual impacts of their condition [ 10 ].
MS-specific self-management abilities can be categorized into five key dimensions: adhering to treatment and overcoming barriers, gaining knowledge and actively learning about MS, managing daily health routines, actively participating in decision-making with HCP, and addressing the physical, emotional, and social impacts of MS [ 11 , 12 ]. Research on self-management in MS is still an emerging area of study; however, findings from the past decade suggest that self-management is linked to favorable clinical and rehabilitation outcomes [ 13 , 14 ]. A systematic review and meta-analysis of self-management education in chronic conditions such as diabetes, hypertension, and heart diseases have shown that structured self-management education improves control of these conditions, improves psychological well-being and mental health, and has a positive effect on QoL [ 15 ]. Within the more specific context of MS, there has been an increasing body of research associating self-management with positive clinical and rehabilitation outcomes [ 16 , 17 ].
A systematic review revealed that multicomponent interventions, such as those that employed integrated coping strategies, stress and depression management, emotional regulation techniques, multimodal delivery formats, and principles derived from cognitive behavioral therapy, were commonly employed and associated with significant improvements in QoL [ 18 ].
Although significant progress has been made in MS care, including rehabilitation, many individuals with MS face barriers to accessing these advancements due to challenges such as limited mobility, fatigue, and the expenses related to travel, so telehealth is an alternative modern therapeutic option in the management of MS [ 19 ]. Therefore, we conducted the current study to assess the effectiveness of a telehealth self-management educational program for PwMS at a single tertiary MS center in Egypt.
Methods
Study design and settings
We conducted a one-group pre-test post-test intervention study on PwMS in the MS outpatient clinic at Alexandria Main University Hospital during the period between April 2023 and February 2024.
Inclusion and exclusion criteria
PwMS aged from 18 to 50 years with a definitive MS diagnosis by a neurologist based on the revised 2017 McDonald’s criteria [ 20 ] with no evidence of activity or relapses in the six weeks before recruitment to the research, and their expanded disability status scale (EDSS) score of ≤ 5.0. Educated patients, at least primarily educated, with access to mobile phones and WhatsApp, were involved in the study. Patients with severe sensory or motor impairment or serious psychiatric or mental disorders were excluded. Before enrollment, patients were screened by the study neurologist. Severe sensory or motor impairment was assessed using EDSS, and patients with a sensory subscore > 3 or a motor subscore > 2 were excluded. A rapid screening for depression was also conducted, and patients fulfilling DSM-5 diagnostic criteria for depressive disorders were excluded.
Sample size calculation
Assuming that the effect size of an interventional program on self-management among patients with MS is 0.2, the number of repetitions is 3 (pre-test, immediate post-test, and after 3 months) for one group, the minimum required sample size was 42 individuals, using an alpha error of 5% and a study power of 80%. The sample size was calculated using G.Power software.
Type of sample and method of selection
A convenience sample was recruited from the MS outpatient clinic at Alexandria Main University Hospital. Eligible patients attending the clinic during data collection days were approached consecutively, with one eligible patient recruited approximately every 30 min, corresponding to the average time required to complete the interview questionnaire. The process of participant recruitment, allocation, follow-up, and analysis is summarized in Fig. 1 . A total of 53 eligible participants were initially enrolled, of whom 42 completed the study (response rate = 79.2%) and were included in the analysis, with losses occurring during follow-up and before analysis as illustrated in the flowchart.
Fig. 1 Flowchart of the participants through the 3 phases of the intervention study
Data collection tools
A pre-coded interviewing questionnaire was designed to collect data from PwMS regarding the following items before the intervention. It was composed of four parts:
a. Socio-demographic data: Age, sex, educational level, marital status, occupation, income, and residence. b. MS-related data: Age at disease onset, disease duration, current symptoms of the disease, frequency of relapses, duration of remission, and PDDS to capture the patient’s perspective on functional impairment while maintaining objective eligibility criteria [ 21 ]. c. Multiple sclerosis self-management scale revised (MSSM-R) questionnaire: This questionnaire is used to evaluate patients’ MS knowledge and self-management skills [ 22 ]. The MSSM-R consists of 24 questions. The questions included in the MSSM-R address issues regarding healthcare provider (HCP) relationship and communication (six items), treatment adherence and barriers (seven items), social and family support (three items), MS knowledge and information (four items), and health maintenance behavior (four items). The responses were rated on a five-point Likert scale of 1 through 5, where 1 means “1 completely disagree, and 5 means " I completely agree”. The negatively worded items’ scores were reversed [ 22 ]. For the Arabic version, validity was tested by 3 experts, and Cronbach’s alpha was used to test reliability; Cronbach’s alpha for each domain was above 0.7. The items of each domain were added together, and the mean and standard deviation were calculated for each domain before the program, immediately after the program, and after three months. d. Multiple sclerosis quality of life (MusiQoL) questionnaire: The MusiQoL questionnaire comprises 31 items that describe nine dimensions. Each dimension is named according to its constitutive items, as follows: activities of daily living (eight items); psychological wellbeing (four items); symptoms (four items); relationships with friends (three items); relationships with family (three items); relationship with the healthcare system (three items); sentimental and sexual life (two items); coping (two items); and rejection (two items). Each item is rated on a five-point Likert scale, where 1 = never, 2 = rarely, 3 = sometimes, 4 = often/a lot, 5 = always, and 0 = not applicable. The negatively worded item scores were reversed. The Arabic version was used [ 23 ]. The items of each domain were added together, and the mean and standard deviation were calculated for each domain before the program, immediately after the program, and after three months.
The scores of each subscale and total score were transformed into a 0-100 scale by the following formula: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned}\text{Transformed scale}=&\left[\left(\text{actual raw score}\right.\right.\\&\left.\left.-\text{lowest possible score}\;\right.\right.\\&\left.\left.\div\;\text{possible raw score range}\right)\right]{\ast}100\end{aligned}$$\end{document}
Where the possible raw score range is the difference between the maximum possible score of the scale and the lowest possible score of the scale.
Before beginning the full-scale field study, we conducted preliminary pilot research on ten patients and asked the participants to complete the questionnaire in the Arabic language. The questions were clear, and the questionnaire was ready to collect the required information.
Program description
We carried out the program over four weeks as shown in Table 1 . The sample was divided into five small groups (8–12 patients each). To facilitate their interaction with the provider, a WhatsApp group was created for each group, through which educational material was delivered. An open discussion was held at the end of each week for each group through a call on Telegram. Patients who couldn’t attend the meeting were contacted privately.
Table 1 MS self-management education program content Week Objectives Videos Week 1 • Recognize the nature of MS. • Determine the benefits and barriers of being physically active. • Identify the proper diet in MS and its effect on symptoms. Video 1: Nature of MS, causes, types, and treatment. Video 2: Importance of physical activity to MS patients, and how to overcome barriers. Video 3: Proper diet for MS patients. Week 2 • Describe self-management. • Practice stress and anxiety management. • Use tricks to overcome memory problems. Video 1: Definition of self-management. Video 2: Stress and anxiety management. Video 3: Memory troubles and how to overcome them. Week 3 • Communicate effectively with their physicians. • Practice problem-solving skills. • Practice decision-making skills. Video 1: Problem-solving and decision-making skills. Video2: Effective communication with healthcare provider Week 4 • Identify the meaning of social support and its types. • Identify some tricks to help them during their day. Video 1: Role of social support and how it helps patients. Video 2: Important advice to MS patients during their day.
Table 1
MS self-management education program content
| Week | Objectives | Videos |
| Week 1 | • Recognize the nature of MS. • Determine the benefits and barriers of being physically active. • Identify the proper diet in MS and its effect on symptoms. | Video 1: Nature of MS, causes, types, and treatment. Video 2: Importance of physical activity to MS patients, and how to overcome barriers. Video 3: Proper diet for MS patients. |
| Week 2 | • Describe self-management. • Practice stress and anxiety management. • Use tricks to overcome memory problems. | Video 1: Definition of self-management. Video 2: Stress and anxiety management. Video 3: Memory troubles and how to overcome them. |
| Week 3 | • Communicate effectively with their physicians. • Practice problem-solving skills. • Practice decision-making skills. | Video 1: Problem-solving and decision-making skills. Video2: Effective communication with healthcare provider |
| Week 4 | • Identify the meaning of social support and its types. • Identify some tricks to help them during their day. | Video 1: Role of social support and how it helps patients. Video 2: Important advice to MS patients during their day. |
Follow-up phone calls with each patient were held at week 4 and week 8 after the intervention to remind the patients of self-management and ensure that they practice these skills. The educational methods included short, recorded videos by the researcher, audiovisual materials, and videos to facilitate the learning process.
The educational program was designed after a structured review of MS and self-management skills [ 14 , 24 – 27 ]. The preliminary content framework was prepared and subsequently evaluated for content validity by a multidisciplinary expert panel comprising one consultant neurologist, one professor of mental health, and one professor of health education and behavioral sciences. The panel assessed the program for scientific accuracy, clinical relevance, clarity, and cultural appropriateness. Modifications were made based on their feedback until consensus on the final version of the program was achieved. The educational materials were delivered through pre-recorded videos presented by the first author. Each video ranged from approximately [8–10] minutes. The recorded materials were reviewed and approved by the expert panel before dissemination to participants.
Data analysis
After the data were collected, it was revised, coded, and fed to IBM SPSS version 21, and graphs were drawn using Excel software. The content validity of the tool was reviewed by a panel of three experts in the field of health education and mental health, and Cronbach’s alpha was used to assess the internal consistency of the different scales. All statistical analyses were done using an alpha error of 0.05. A p-value less than or equal to 0.05 was considered statistically significant.
The following statistical tests were used: descriptive statistics included mean with standard deviation for quantitative data and frequency and percentage for qualitative data. The Shapiro–Wilk test was conducted to assess the normality assumption of the data distribution; the data did not significantly deviate from normality ( p = 0.279. Independent sample t-test and ANOVA were used to compare means within categorical variables. Repeated measures ANOVA was used to assess changes across the study time points. The assumption of sphericity was assessed using Mauchly’s test of sphericity; when this assumption was violated ( p < 0.05), the Greenhouse–Geisser correction was applied. Effect sizes were estimated using partial eta squared (partial η²). Statistical significance was considered at p < 0.0.
Results
The sample consisted mainly of young adult females, most of whom were married (64.3%), highly educated (85%), and not working (59.5%), with generally sufficient income (73.8%). Clinically, over half had been diagnosed after 1–5 years and showed moderate disability levels, while about half reported no recent relapses. The most common symptoms were cognitive difficulties and fatigue, whereas muscle cramps were the least reported (Table 2 ).
Table 2 Socio-demographic and clinical data of the recruited sample ( n = 42) at baseline assessment, Alexandrai, Egypt, 2023–2024 Socio-demographic characteristic N = 42 % Age ▪ 20–29 ▪ 30–39 ▪ 40–49 18 17 7 42.9 40.5 16.6 Range Mean 20–49 32.32 ± 7.9 Gender ▪ Male ▪ Female 8 34 19 81 Marital status ▪ Single ▪ Married ▪ Divorced 12 27 3 28.6 64.3 7.1 Educational level ▪ Secondary ▪ University 6 36 14.3 85.7 Working ▪ No ▪ Yes 25 17 59.5 40.5 Income ▪ Insufficient ▪ Sufficient 11 31 26.2 73.8 Duration since the start of symptoms ▪ 1–5 ▪ 6–10 ▪ 11–19 11 20 11 26.2 47.6 26.2 Range Mean (years) 2–19 8.6 ± 4.2 Duration since the diagnosis ▪ 1–5 ▪ 6–10 ▪ 11–16 24 13 5 57.1 31 11.9 Range Mean (years) 1–16 5.83 ± 3.7 Symptoms (multiple responses) ▪ Fatigue ▪ Memory and/or concentration difficulties ▪ Numbness ▪ Balance ▪ Visual problems ▪ Urinary ▪ Mobility ▪ Muscle cramps 34 34 22 21 18 16 13 7 80.9 80.9 52.3 50 42.8 38 30.9 16.6 Number of attacks in the last year ▪ No ▪ 1–2 ▪ 3–4 22 17 3 52.3 40.4 7.3 Duration of attack ▪ No attacks ▪ 3–5 days ▪ 7–10 days 22 10 10 52.3 23.8 23.8
Table 2
Socio-demographic and clinical data of the recruited sample ( n = 42) at baseline assessment, Alexandrai, Egypt, 2023–2024
| Socio-demographic characteristic | N = 42 | % |
| Age | ||
| ▪ 20–29 ▪ 30–39 ▪ 40–49 | 18 17 7 | 42.9 40.5 16.6 |
| Range Mean | 20–49 32.32 ± 7.9 | |
| Gender | ||
| ▪ Male ▪ Female | 8 34 | 19 81 |
| Marital status | ||
| ▪ Single ▪ Married ▪ Divorced | 12 27 3 | 28.6 64.3 7.1 |
| Educational level | ||
| ▪ Secondary ▪ University | 6 36 | 14.3 85.7 |
| Working | ||
| ▪ No ▪ Yes | 25 17 | 59.5 40.5 |
| Income | ||
| ▪ Insufficient ▪ Sufficient | 11 31 | 26.2 73.8 |
| Duration since the start of symptoms | ||
| ▪ 1–5 ▪ 6–10 ▪ 11–19 | 11 20 11 | 26.2 47.6 26.2 |
| Range Mean (years) | 2–19 8.6 ± 4.2 | |
| Duration since the diagnosis | ||
| ▪ 1–5 ▪ 6–10 ▪ 11–16 | 24 13 5 | 57.1 31 11.9 |
| Range Mean (years) | 1–16 5.83 ± 3.7 | |
| Symptoms (multiple responses) | ||
| ▪ Fatigue ▪ Memory and/or concentration difficulties ▪ Numbness ▪ Balance ▪ Visual problems ▪ Urinary ▪ Mobility ▪ Muscle cramps | 34 34 22 21 18 16 13 7 | 80.9 80.9 52.3 50 42.8 38 30.9 16.6 |
| Number of attacks in the last year | ||
| ▪ No ▪ 1–2 ▪ 3–4 | 22 17 3 | 52.3 40.4 7.3 |
| Duration of attack | ||
| ▪ No attacks ▪ 3–5 days ▪ 7–10 days | 22 10 10 | 52.3 23.8 23.8 |
Table 3 presents the distribution of PWMS according to factors associated with multiple sclerosis self-management. Patients who suffered from fewer than four symptoms had significantly higher scores on the MSSM-R scale compared to those with ≥ four symptoms. Better quality of life was significantly associated with higher education, being employed, having fewer symptoms, and lower disability levels ( p < 0.05).
Table 3 Factors related to MSSM-R scores and MusiQoL mean scores of the recruited sample at baseline assessment Factors MSSM- R Mean ± SD ANOVA\ T-test P value MusiQoL Mean ± SD ANOVA\ T-test P value Age ▪ 20–29 ▪ 30–39 ▪ 40–49 83.3 ± 15.6 82.1 ± 13.5 87.6 ± 15.2 0.633 55.3 ± 18.8 58.7 ± 14.7 57.7 ± 12.3 0.78 Gender ▪ Male ▪ Female 82.7 ± 19 83.7 ± 13.5 0.837 64.4 ± 13.7 54.7 ± 16.3 0.088 Marital status ▪ Single ▪ Married ▪ Divorced 78.6 ± 17.9 85.3 ± 12.9 86.8 ± 12 0.168 52.2 ± 15.4 58.9 ± 16.9 53.7 ± 12 0.382 Educational level ▪ Secondary ▪ University 75.9 ± 13.3 85.8 ± 14.2 0.940 48.2 ± 14.8 58.9 ± 15.9 0.042* Work ▪ Not working ▪ Working 83.2 ± 13.7 84.2 ± 16.4 0.801 52.8 ± 17.2 63.8 ± 11.2 0.017* Income ▪ Insufficient ▪ Sufficient 82.8 ± 13.9 84.1 ± 15 0.851 51.4 ± 15.3 59.6 ± 16.1 0.072 Duration since diagnosis ▪ 1–5 ▪ 6–10 ▪ 11–16 83.7 ± 15.3 83.4 ± 13.8 83.5 ± 14.2 0.997 57.9 ± 18 56.1 ± 15.1 52.2 ± 10.7 0.68 No of symptoms ▪ < 4 ▪ ≥ 4 90 ± 15.1 79.9 ± 13 0.015* 68.2 ± 14.5 50 ± 13.2 <0.001* PDDS ▪ 0–2 ▪ 3 88 ± 14.5 80.6 ± 13.9 0.766 66.4 ± 15.6 51.2 ± 13.2 0.001* Attacks in the last year ▪ No ▪ Yes 85.6 ± 15.9 81.7 ± 13.1 0.173 60.1 ± 14.3 53.3 ± 17.3 0.13 * P < 0.05
Table 3
Factors related to MSSM-R scores and MusiQoL mean scores of the recruited sample at baseline assessment
* P < 0.05
| Factors | MSSM- R Mean ± SD | ANOVA\ T-test P value | MusiQoL Mean ± SD | ANOVA\ T-test P value |
| Age | ||||
| ▪ 20–29 ▪ 30–39 ▪ 40–49 | 83.3 ± 15.6 82.1 ± 13.5 87.6 ± 15.2 | 0.633 | 55.3 ± 18.8 58.7 ± 14.7 57.7 ± 12.3 | 0.78 |
| Gender | ||||
| ▪ Male ▪ Female | 82.7 ± 19 83.7 ± 13.5 | 0.837 | 64.4 ± 13.7 54.7 ± 16.3 | 0.088 |
| Marital status | ||||
| ▪ Single ▪ Married ▪ Divorced | 78.6 ± 17.9 85.3 ± 12.9 86.8 ± 12 | 0.168 | 52.2 ± 15.4 58.9 ± 16.9 53.7 ± 12 | 0.382 |
| Educational level | ||||
| ▪ Secondary ▪ University | 75.9 ± 13.3 85.8 ± 14.2 | 0.940 | 48.2 ± 14.8 58.9 ± 15.9 | 0.042* |
| Work | ||||
| ▪ Not working ▪ Working | 83.2 ± 13.7 84.2 ± 16.4 | 0.801 | 52.8 ± 17.2 63.8 ± 11.2 | 0.017* |
| Income | ||||
| ▪ Insufficient ▪ Sufficient | 82.8 ± 13.9 84.1 ± 15 | 0.851 | 51.4 ± 15.3 59.6 ± 16.1 | 0.072 |
| Duration since diagnosis | ||||
| ▪ 1–5 ▪ 6–10 ▪ 11–16 | 83.7 ± 15.3 83.4 ± 13.8 83.5 ± 14.2 | 0.997 | 57.9 ± 18 56.1 ± 15.1 52.2 ± 10.7 | 0.68 |
| No of symptoms | ||||
| ▪ < 4 ▪ ≥ 4 | 90 ± 15.1 79.9 ± 13 | 0.015* | 68.2 ± 14.5 50 ± 13.2 | <0.001* |
| PDDS | ||||
| ▪ 0–2 ▪ 3 | 88 ± 14.5 80.6 ± 13.9 | 0.766 | 66.4 ± 15.6 51.2 ± 13.2 | 0.001* |
| Attacks in the last year | ||||
| ▪ No ▪ Yes | 85.6 ± 15.9 81.7 ± 13.1 | 0.173 | 60.1 ± 14.3 53.3 ± 17.3 | 0.13 |
Self-management scores showed significant and progressive improvement following the intervention. Knowledge, health maintenance behaviors, and treatment adherence all increased from baseline to immediately post-intervention and continued to improve at three-month follow-up with moderate to large effect size. Overall, the total MSSM-R score also demonstrated a sustained and significant increase over time ( p < 0.001), as shown in Table 4 .
Table 4 Mean scores of the MSSM-R reported by recruited sample before and after the intervention MSSM- R Phase of the intervention program Repeated measure ANOVA (F) P -value Partial Eta 2 Before ( n = 42) Mean ± SD 95% CI Immediately after ( n = 42) Mean ± SD 95% CI After 3 months ( n = 42) Mean ± SD 95% CI Knowledge 13.6 ± 3.7 (12.5–14.8) 16.2 ± 2.3 a (15.5–17) 17.1 ± 2.4 bc (16.3–17.9) 26.7 < 0.001* 0.507 Health maintenance behavior 12.5 ± 3.8 (11.3–13.8) 13.7 ± 3.3 a (12.6–14.8) 15.7 ± 3 bc (14.7–16.6) 23.7 < 0.001* 0.398 HCP relationship and communication 21.4 ± 5.1 (19.8–22.9) 22.1 ± 4.9 (20.6–23.7) 22.2 ± 8.6 (21.4–24.9) 2.6 0.06 0.04 Treatment adherence 26.4 ± 7.4 (24.1–28.7) 27.7 ± 6.7 a (25.5–29.9) 29.2 ± 6.4 bc (27.2–31.2) 7.7 0.001* 0.228 Social\ family support 10.5 ± 3.6 (9.4–11.7) 11 ± 3.3 (9.9–12.1) 10.6 ± 3.6 (9.5–11.7) 1.7 0.086 0.032 Total score 84.7 ± 14.4 (80.2–89.2) 90.9 ± 13.8 a (86.5–95.9) 96.1 ± 14.3 bc (91.3- 100.6) 35.6 < 0.001* 0.64 * P < 0.05 In the paired t-test: a significant difference between before and immediately after b significant difference between before and after 3 months c significant difference between immediately after and after 3 months.
Table 4
Mean scores of the MSSM-R reported by recruited sample before and after the intervention
* P < 0.05 In the paired t-test: a significant difference between before and immediately after b significant difference between before and after 3 months c significant difference between immediately after and after 3 months.
| MSSM- R | Phase of the intervention program | Repeated measure ANOVA (F) | P -value | Partial Eta 2 | ||
| Before ( n = 42) Mean ± SD 95% CI | Immediately after ( n = 42) Mean ± SD 95% CI | After 3 months ( n = 42) Mean ± SD 95% CI | ||||
| Knowledge | 13.6 ± 3.7 (12.5–14.8) | 16.2 ± 2.3 a (15.5–17) | 17.1 ± 2.4 bc (16.3–17.9) | 26.7 | < 0.001* | 0.507 |
| Health maintenance behavior | 12.5 ± 3.8 (11.3–13.8) | 13.7 ± 3.3 a (12.6–14.8) | 15.7 ± 3 bc (14.7–16.6) | 23.7 | < 0.001* | 0.398 |
| HCP relationship and communication | 21.4 ± 5.1 (19.8–22.9) | 22.1 ± 4.9 (20.6–23.7) | 22.2 ± 8.6 (21.4–24.9) | 2.6 | 0.06 | 0.04 |
| Treatment adherence | 26.4 ± 7.4 (24.1–28.7) | 27.7 ± 6.7 a (25.5–29.9) | 29.2 ± 6.4 bc (27.2–31.2) | 7.7 | 0.001* | 0.228 |
| Social\ family support | 10.5 ± 3.6 (9.4–11.7) | 11 ± 3.3 (9.9–12.1) | 10.6 ± 3.6 (9.5–11.7) | 1.7 | 0.086 | 0.032 |
| Total score | 84.7 ± 14.4 (80.2–89.2) | 90.9 ± 13.8 a (86.5–95.9) | 96.1 ± 14.3 bc (91.3- 100.6) | 35.6 | < 0.001* | 0.64 |
QoL showed overall improvement across the program, with significant gains observed in psychological well-being, symptoms, feelings of rejection, and total MusiQoL score over time ( p < 0.05). While some domains, such as activities of daily living, did not improve immediately after the intervention, they demonstrated significant improvement by the three-month follow-up, indicating a delayed but sustained positive effect, as shown in Table 5 .
Table 5 Mean scores of the Musi-QoL reported by the recruited sample before and after the intervention Musi-QoL Phase of the intervention program Repeated measure ANOVA (F) P -value Partial Eta 2 Before ( n = 42) Mean ± SD 95% CI Immediately After ( n = 42) Mean ± SD 95% CI After 3 months ( n = 42) Mean ± SD 95% CI Activities of daily living 65.6 ± 22.3 (58.7–72.6) 65 ± 22.2 (58- 71.1) 70.8 ± 19 b (64.9–76.7) 4.6 0.015* 0.18 Psychological wellbeing 44 ± 32.6 (33.8–54.2) 47.3 ± 31.7 (37.4–57.2) 54.3 ± 26.5 bc (46-62.5) 7.7 0.001* 0.26 Symptoms 58.3 ± 19.4 (52.2–64.3) 64.8 ± 19.1 a (58.9–70.8) 69.4 ± 17.6 b (63.9–75) 15.8 < 0.001* 0.22 Relation with friends 42.8 ± 6.4 (43.8–64.7) 48.2 ± 6.3 (48.2–68.8) 47.2 ± 6.2 (47.7–67.8) 1.6 0.205 0.02 Relation with family 61.7 ± 36.9 (50.1–73.2) 58.2 ± 41.2 (49.4–73.1) 65.4 ± 34.3 (54.7–76.1) 1.3 0.264 0.04 Sentimental and sexual life 49.5 ± 35.6 (38.4–60.6) 48.8 ± 36 (37.5–60) 48.5 ± 38.1 (36.6–60.4) 0.06 0.934 0.003 Coping 80.6 ± 26 81.2 ± 25.6 87.7 ± 21.9 2.6 0.084 0.08 Rejection 77.6 ± 32.1 78.8 ± 31.6 84.2 ± 25.1 c 3.9 0.028* 0.16 Relation to healthcare system 65.4 ± 32 (55.4–75.4) 66.2 ± 31.9 (56.3–76.2) 68.4 ± 32.7 (58.2–78.6) 0.48 0.619 0.02 Total score 59.8 ± 15.2 (56- 65.6) 61.5 ± 14.8 (57.8–67.2) 65.7 ± 14.8 bc (62.1–71.5) 14.4 < 0.001* 0.32 * P < 0.05 a significant difference between before and immediately after b significant difference between before and after 3 months c significant difference between immediately after and after 3 months
Table 5
Mean scores of the Musi-QoL reported by the recruited sample before and after the intervention
* P < 0.05 a significant difference between before and immediately after b significant difference between before and after 3 months c significant difference between immediately after and after 3 months
| Musi-QoL | Phase of the intervention program | Repeated measure ANOVA (F) | P -value | Partial Eta 2 | ||
| Before ( n = 42) Mean ± SD 95% CI | Immediately After ( n = 42) Mean ± SD 95% CI | After 3 months ( n = 42) Mean ± SD 95% CI | ||||
| Activities of daily living | 65.6 ± 22.3 (58.7–72.6) | 65 ± 22.2 (58- 71.1) | 70.8 ± 19 b (64.9–76.7) | 4.6 | 0.015* | 0.18 |
| Psychological wellbeing | 44 ± 32.6 (33.8–54.2) | 47.3 ± 31.7 (37.4–57.2) | 54.3 ± 26.5 bc (46-62.5) | 7.7 | 0.001* | 0.26 |
| Symptoms | 58.3 ± 19.4 (52.2–64.3) | 64.8 ± 19.1 a (58.9–70.8) | 69.4 ± 17.6 b (63.9–75) | 15.8 | < 0.001* | 0.22 |
| Relation with friends | 42.8 ± 6.4 (43.8–64.7) | 48.2 ± 6.3 (48.2–68.8) | 47.2 ± 6.2 (47.7–67.8) | 1.6 | 0.205 | 0.02 |
| Relation with family | 61.7 ± 36.9 (50.1–73.2) | 58.2 ± 41.2 (49.4–73.1) | 65.4 ± 34.3 (54.7–76.1) | 1.3 | 0.264 | 0.04 |
| Sentimental and sexual life | 49.5 ± 35.6 (38.4–60.6) | 48.8 ± 36 (37.5–60) | 48.5 ± 38.1 (36.6–60.4) | 0.06 | 0.934 | 0.003 |
| Coping | 80.6 ± 26 | 81.2 ± 25.6 | 87.7 ± 21.9 | 2.6 | 0.084 | 0.08 |
| Rejection | 77.6 ± 32.1 | 78.8 ± 31.6 | 84.2 ± 25.1 c | 3.9 | 0.028* | 0.16 |
| Relation to healthcare system | 65.4 ± 32 (55.4–75.4) | 66.2 ± 31.9 (56.3–76.2) | 68.4 ± 32.7 (58.2–78.6) | 0.48 | 0.619 | 0.02 |
| Total score | 59.8 ± 15.2 (56- 65.6) | 61.5 ± 14.8 (57.8–67.2) | 65.7 ± 14.8 bc (62.1–71.5) | 14.4 | < 0.001* | 0.32 |
Discussion
In the present study, implementation of a telehealth self-management educational program for people with MS was associated with significant improvements in self-management capacity and overall quality of life. This highlights the importance of tailored educational interventions in enhancing the well-being of PWMS.
The multidimensional nature of the telehealth program used in the current study is nearly like that of a study by Bayat et al., who used a randomized controlled trial to deliver five sessions of telenursing, which resulted in significant improvements in all dimensions of self-management in the intervention group compared to the control group [ 14 ]. Similarly, face-to-face self-management educational programs have shown positive findings. Maslakpak et al. [ 28 ] noted that there were improvements in self-efficacy and emotional regulation domains after a four-week face-to-face educational program. In the same way, Feicke et al. [ 14 ] noted that all self-management domains were significantly enhanced after a face-to-face educational program, which was conducted for a total of 450 min, spread out over five sessions.
A significant improvement in total MSSM-R scores was observed across the three assessment phases ( p < 0.001, η² p = 0.6), indicating a large effect size. This may be explained by equipping participants with knowledge and practical skills to manage their condition effectively, resulting in empowered participants taking an active role in their healthcare, leading to better self-management scores.
Similarities were also found in the structure and duration of previously effective education interventions; however, no significant improvement was seen regarding social support and communication with healthcare providers. Though a specific session was given on communication skills, no significant improvement was seen in this domain, which may be due to the fact that the development of communication competency is better done through experiential learning rather than through pre-recorded sessions [ 29 ]. Similarly, no improvement was seen regarding social support; however, the significant improvement seen with other domains of self-management education supports the evidence that telehealth education is comparable with face-to-face education concerning the improvement seen with knowledge, behavioral skills, and psychological well-being among patients with MS.
Significant post-intervention improvements were also noted in total MusiQoL scores and specific domains, such as activities of daily living, psychological well-being, symptom management, and acceptance. The program mainly focuses on teaching necessary self-management skills as well as improving patients’ knowledge about their disease condition. In this study, problem-solving, decision-making, stress management techniques, and effective communication with healthcare providers were perceived as valuable self-management tools. Nemours studies reported that individuals who acquire these self-management skills can take an active role in managing their condition and adopting a healthy lifestyle, and this may explain the noted improvement in QoL [ 18 – 31 ].
However, the absence of improvement in domains related to social/sexual relationships and healthcare system interaction suggests the need for partner involvement and systemic healthcare changes to achieve meaningful benefits. Evidence from comparative research indicates that while patient-centered self-care education improves outcomes in multiple sclerosis, family-centered interventions may yield superior benefits, particularly in quality of life. The greater improvement observed in the family-centered group highlights the potential added value of partner and family involvement in optimizing self-management outcomes [ 32 ]. Our findings align with the systematic reviews indicating that most self-management education interventions in MS lead to improvements in QoL, with effects persisting for up to 12 months, particularly for programs delivered remotely [ 33 ]. As well, the significant improvement with high effect size (0.6) in fatigue after delivering mobile health training, indicating that tele-coaching and tele-monitoring capabilities through a smartphone reduce fatigue in PWMS that may be presented as improvement in quality of life [ 34 ].
Aside from multiple sclerosis, the benefits of self-management education have also been noted among other chronic health conditions. For diabetes mellitus, for example, a randomized controlled study has noted the positive effects of self-management education, particularly when conducting the intervention using behavioral goals and follow-up [ 35 ]. Similarly, for patients with chronic heart failure, self-management education has also been noted to have a positive impact, particularly when delivering the intervention face-to-face or even using a telehealth intervention [ 36 ].
Likewise, a quasi-experimental study conducted in Saudi Arabia to assess the effectiveness of self-management educational program among patients with common chronic diseases as diabetes, hypertension, asthma, heart disease, chronic obstructive pulmonary disease, or hyperlipidemia showed statistically significant improvements in terms of the mean scores of self-efficacy, self-management behaviors, and health outcomes 6 months after completing the program with F-value ranged from 8.8 to 26.5 indicating that self-management educational program was effective in helping participants adopt necessary healthy behaviors [ 37 ]. These findings are also consistent with the systematic review conducted by Huang et al. [ 15 ] which concluded that self-management interventions have positive effects on patients’ quality of life, self-efficacy, and mental health, providing strong support for the care and management of chronic disease patients.
Strengths and limitations of the study
One of the strengths of this study is the use of a structured telehealth intervention targeting multiple MS-related domains, and the application of validated disease-specific instruments (MSSM-R and MusiQoL), and domain-level analysis that provides a nuanced understanding of intervention effects beyond total scores. In addition, the study supports the feasibility of remotely delivered behavioral programs for patients with mobility limitations, enhancing the scalability and accessibility of care.
This study has some limitations that should be considered when interpreting the findings. The one-group pre–post design without a control group limits causal inference, as the observed improvements may be influenced by the Hawthorne effect, regression to the mean, or time-related factors. The use of convenience sampling from a single center may introduce selection bias and limit generalizability, particularly as participation required access to internet-enabled devices. The absence of randomization and blinding increases the risk of performance and measurement bias, especially given the reliance on self-reported outcomes. In addition, adherence to the intervention was not comprehensively quantified, limiting assessment of intervention fidelity. Potential confounding factors, such as the use of disease-modifying therapies, were not assessed. Finally, the relatively small sample size and short follow-up period may affect the precision of estimates and limit conclusions regarding the long-term sustainability of the observed effects.
Conclusion
Self-management skill educational program leads to statistically significant improvement in MSSM-R scores and all subscales except for the social support and healthcare provider communication. Moreover, self-management education has a significant positive effect on the Musi-Qol scale. Also, daily living activities, psychological state, symptoms, and feelings of rejection showed significant improvement between before and after the program.
Based on these findings, it is recommended that telehealth tools should be incorporated for continuous education, remote consultations, and monitoring progress. Also, healthcare providers should provide tailored educational sessions focusing on understanding MS, its symptoms, and progression, especially for newly diagnosed patients. Moreover, psychosocial interventions should be designed to improve invisible symptoms of MS that negatively affect their daily function, social life, and close relations with families and friends.
Abbreviations
Disease-modifying therapies
Healthcare provider
Multiple Sclerosis Self-Management Scale–Revised
Multiple Sclerosis International Quality of Life questionnaire
Patient-determined disease step scale
People with multiple sclerosis
Quality of life
Publisher’s note
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Acknowledgements
The authors are grateful to patients who participated in the program and to directors of health authorities and health facilities who approved conducting the research.
Authors’ contributions
This work was carried out in collaboration between all authors. EMF and ZNS were responsible for choosing the tool of the study. FE and ZNS designed the study and created the program.EH helped choosing the appropriately diagnosed MS patients and ensured the accuracy of educational material given to the patients. FE was responsible for data collection, delivering the program and statistical analysis. All authors read and approved the final manuscript.
Funding
No source of funding.
Data availability
Data is available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
The study protocol was approved by the research ethics committee of the High Institute of Public Health, Alexandria University (Institutional Review Board IRB, 00013692) on 28 March 2023. Written informed consent was obtained from the patients to participate in the study. Anonymity and confidentiality were assured and maintained.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
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