Health-Related Quality of Life in People with Relapsing-Remitting Multiple Sclerosis: A Prospective Observational Study

1. Background

Multiple sclerosis (MS) is a chronic, demyelinating, autoimmune, inflammatory, and degenerative neurological disease that affects the central nervous system, particularly the brain and spinal cord. Its etiology remains undetermined, although it is attributed to multifactorial causes, including genetic, immunological, and environmental factors [1,2,3,4].

The clinical manifestations of MS include motor, sensory, visual, urinary, and cognitive impairments, as well as neuropsychiatric symptoms such as anxiety and depression, which pose a significant burden on affected individuals. Anxiety and depression, prevalent in a high percentage of people with MS, interfere with treatment adherence and limit the ability to cope with daily challenges. Additionally, social isolation and the stigma associated with chronic diseases can reduce community support and exacerbate emotional impact. Social support has been demonstrated as a protective factor in the management of chronic diseases. In the case of MS, strong support networks can improve health perception and the ability to manage relapses and physical limitations. [2,5,6].

It is estimated that MS affects over 2.8 million people worldwide, with a growing prevalence in Spain, reaching over 55,000 individuals (80 to 180 cases per 100,000 inhabitants). In Galicia, prevalence rates range between 140 and 183 cases per 100,000 inhabitants, with the average age of disease onset being 29 years [7].

Its prevalence is higher in regions at elevated latitudes and among women aged 20 to 40 years, with an approximate ratio of 2:1 compared to men [3,4,8,9]. Clinically, MS is classified into relapsing-remitting MS (RRMS), secondary progressive MS, primary progressive MS, and progressive-relapsing MS. Among these, RRMS is the most prevalent form, affecting approximately 85% of diagnosed individuals [2,10].

RRMS is characterized by episodes of neurological dysfunction or relapses, followed by remission periods during which symptoms stabilize. These episodes may involve partial or complete recovery, allowing for distinctions between active, inactive, and stable RRMS [10]. During periods of neurological deficit, manifestations such as optic neuritis, sensory, visual, or motor disturbances, sphincter dysfunction, spasticity, and cognitive impairment may be observed. These are primarily caused by the migration of immune cells to the central nervous system, leading to inflammation and thus the manifestations typical of the relapsing-remitting course [2,5,6].

This impact translates into a significant economic burden, with direct costs related to treatment and follow-up ranging between €10,486 and €27,217 per person per year, depending on the degree of disability. These expenses are compounded by non-healthcare costs, such as transportation, home adaptations, informal care, or work absences, which can amount to between €454 and €25,850 annually. Individuals with lower incomes or precarious employment situations may face greater barriers to accessing mental health services, potentially exacerbating the emotional and social symptoms associated with MS [9,11].

Health-related quality of life (HRQoL) is a key indicator in the comprehensive care of the MS population. It encompasses physical, emotional, social, and functional aspects such as fatigue, physical disability, sleep disorders, and depressive mood. These factors limit autonomy, interpersonal relationships, and work capacity in affected individuals [7]. The variable course of the disease and the emergence of psychological comorbidities further increase the direct and indirect costs for both users and healthcare systems [9,11].

HRQoL enables the evaluation of physical health, mental health, social relationships, and adaptive capacity among individuals with MS, offering a more comprehensive understanding of their situation and facilitating a person-centered approach. Key factors such as fatigue, sleep disorders, physical disability, and depressive mood deteriorate HRQoL, making its measurement essential for assessing treatment effectiveness and early detection of psychosocial factors requiring intervention [12].

This study stems from the need to analyze the evolution of quality of life in a sample of individuals diagnosed with RRMS who have not undergone any specific intervention. The main objective is to analyze the quality of life in individuals diagnosed with relapsing-remitting multiple sclerosis based on sociodemographic and clinical variables. Specific objectives include: (1) Describing sociodemographic, clinical, and quality of life characteristics; (2) Determining the relationship between sociodemographic and clinical variables and quality of life; (3) Identifying predictive factors for quality of life.

2. Methods

2.1. Study Type

This is an observational, analytical, and prospective study conducted on users treated at the Neurology and Neurosurgery Unit or the neurology nursing clinic of the Lucus Augusti University Hospital (HULA), diagnosed with RRMS between January 2023 and March 2025.

2.2. Population, Sample, and Inclusion Criteria

The target population for this study consists of individuals diagnosed for the first time with RRMS and attended by physicians from the Neurology and Neurosurgery Service at HULA. HULA is located in the city of Lugo, within the same province, which includes 332,100 inhabitants registered with a healthcare card as of February 2017.

Quality of life questionnaires were administered at the time of initial diagnosis and at 3 and 6 months of follow-up. The sample consisted of a convenience group of individuals with RRMS selected consecutively for this study.

The inclusion criteria were individuals aged 18 years or older and diagnosed with RRMS. Exclusion criteria included users who refused to participate in the study or withdrew from the original study before data collection was completed.

2.3. Justification and Sample Size Calculation

RRMS diagnoses account for approximately 80% of all individuals with MS. MS affects approximately 0.1% of the population; therefore, in the Lugo healthcare area, there would be a total of 332 individuals with MS, of whom 265 would have RRMS.

To ensure the study results were representative of the Lugo population, a minimum sample size of 31 users was calculated, considering a 90% confidence interval and a 14% margin of error. The formula used for the calculation was: Sample Size =${\displaystyle \frac{ \frac{z^2*P(1-P)}{e^2}}{1+( \frac{z^2*P\left(1-P\right)}{e^{2}N})}}$.

The study employed convenience sampling, selecting accessible and available individuals from the Neurology and Neurosurgery Unit and the Neurology Clinic at HULA, which explains the high participation rate (>98%). However, the final sample included 35 users due to logistical and temporal constraints. This reduction is justified by the duration of the inclusion period, the availability of individuals, and operational limitations of the service during this pilot phase.

2.4. Variables

2.4.1. Independent Variables

• Socio-epidemiological factors: sex, age, ethnicity, education level, marital status, employment status, annual income.
• Clinical factors: presence of family history, autoimmune diseases, previous mononucleosis, pregnancy planning, tobacco and alcohol consumption, ongoing treatment, initial symptoms.

2.4.2. Dependent Variables

• Quality of Life: physical health, role limitations related to physical or psychological problems, pain, mood, energy, health perception, social functioning, cognitive functioning, health-related concerns, and overall perception of quality of life.

2.5. Instrument

The Multiple Sclerosis Quality of Life-54 (MSQOL-54) is a specific tool designed to assess quality of life in individuals with MS. This questionnaire combines 18 MS-specific items, developed through expert opinions and a literature review, with 36 items from the Short-Form 36-item (SF-36), a widely used generic instrument for evaluating quality of life. In total, the MSQOL-54 consists of 54 items organized into two main scales: Physical Health and Mental Health, which are further divided into 12 multi-item subscales (Physical Function; Physical Limitations; Emotional Limitations; Pain; Mental Health; Energy; Health Perception; Social Function; Cognitive Function; Stress; Sexual Function; and Overall Perceived Quality of Life) and two single-item subscales (Health Changes and Satisfaction with Sexual Life).

Each subscale has a scoring range from 0 to 100, where values closer to 0 indicate poorer perceived quality of life, and higher values reflect better perceptions of quality of life. Subscale scores are calculated by summing the corresponding item values and transforming the result into a 0-100 scale using a proportional conversion formula. To facilitate interpretation, quantitative scores are categorized qualitatively into five levels: Extreme impairment (0-24), Quite impaired (25-49), Moderately impaired (50-74), Slightly impaired (75-99), and not impaired (100).

The MSQOL-54 has demonstrated strong validity in terms of content, constructs, reliability, discrimination, and responsiveness. Additionally, it exhibits high internal consistency, with Cronbach’s alpha values ranging from 0.75 to 0.96, supporting its reliability as a tool for assessing quality of life in individuals with MS. [7,13].

The instrument was validated in Spanish, with dimensions showing high internal consistency (Cronbach’s alpha: 0.70 to 0.92) across all areas except for two domains. Regarding external validity, a significant correlation was observed between the global index and all dimensions (Pearson coefficients: 0.46 to 0.76). The instrument’s reproducibility was satisfactory (intraclass correlation coefficients: 0.60 to 0.91). The questionnaire’s acceptability was high, and the average time required for completion was 9.8 ± 11.8 minutes. [14].

This instrument provides a comprehensive view of perceived quality of life, encompassing physical, emotional, and social aspects, and facilitates the identification of specific areas requiring intervention to improve the well-being of individuals.

2.6. Data Collection

The data gathered for this study were completed by the principal investigator and were only accessible to them and the project’s collaborative team. No external individuals outside the research team were permitted to make changes to the data. Data were collected at three specific points: at the initial diagnosis of users (in the Neurology Unit or Clinic), at three months, and at six months (in the Neurology Nursing Clinic).

For the evaluation, the MSQOL-54 was used to assess quality of life and its dimensions. The instrument was administered by trained nurses, and the data were recorded in a specifically designed collection notebook for this study. The information was stored anonymously and reviewed to ensure its quality and consistency.

2.7. Data Confidentiality and Ethical Considerations

This study received approval from the Santiago-Lugo Research Ethics Committee (Registration Code: 2022_388).

The project was conducted in strict adherence to the ethical principles outlined in the Declaration of Helsinki by the World Medical Association (2024), as well as the current legal regulations in Spain (Organic Law 3/2018 on Personal Data Protection and Guarantee of Digital Rights, Law 41/2002 on Patient Autonomy, and Law 3/2005 regarding access to electronic medical records).

To ensure participant privacy protection, clinical data were encoded and dissociated, guaranteeing that no identifiable information was included in the database. Only the principal investigator had the ability to associate data with specific individuals, and the information collected was managed to maintain anonymity. Upon study completion, the data will either be destroyed or retained in anonymized format, as stipulated in the informed consent signed by participants. HULA is the center responsible for data processing.

2.8. Data Analysis

A descriptive analysis was performed using measures of central tendency, such as the mean (M), and measures of dispersion, represented by the standard deviation (SD), for quantitative variables. For qualitative variables, absolute frequencies and percentages were used.

The Shapiro-Wilk test was applied to evaluate normality. Results indicated that all quantitative variables, except income, had a non-parametric distribution. Consequently, non-parametric tests were employed for all analyses, including cases where a variable with normal distribution was correlated with others exhibiting non-parametric distributions.

The statistical tests utilized included Kruskal-Wallis test for comparisons across more than two groups, Mann-Whitney U test for pairwise difference analysis, and Spearman’s correlation coefficient to examine the strength and direction of associations between quantitative variables.

All analyses were conducted using PASW statistical software (version 23.0; SPSS Inc., Chicago, Illinois), and a bilateral significance level of p < 0.05 was considered.

3. Results

3.1. Sociodemographic Characteristics

3.1.1. Age

The sample consisted of 35 individuals, with a mean age of 38.29±10.38 years, ranging from 20 to 59 years (Table 1). After categorizing this variable, the age groups were distributed as follows: the group of individuals under 28 years represented 20% of the sample, the group aged 29–38 years accounted for 34.3%, and the group over 39 years comprised 45.7%. The predominant group was participants aged over 39 years.

A significant relationship was found between age and quality of life at diagnosis and three months (p=0.018 and p=0.024, respectively), but no significant association was observed at six months (Table 2). Differences were evident when comparing age groups in terms of quality of life at diagnosis and at three months (χ²=8.789;df=2;p=0.012 and χ²=8.415;df=2;p=0.015, respectively). The mean ranks for quality of life at diagnosis were 26 for the ≤28 years group, 13.33 for the 29–38 years group, and 18 for the ≥39 years group. At three months, the mean ranks were 26.50 for ≤28 years, 14.88 for 29–38 years, and 16.63 for ≥39 years.

3.1.2. Sex

Regarding sex, 57.1% of participants were women, while 42.9% were men. No significant differences were observed between groups.

Income, Marital Status, Education Level, and Employment.

Annual income distribution revealed that 40% of participants had incomes below €12,450, 31.4% between €20,200 and €35,200, 5.7% between €35,200 and €60,000, and 2.9% over €60,000. No individuals reported incomes exceeding €300,000.

Marital status distribution showed that 54.3% of participants were married, 40% were single, and 5.7% were in a domestic partnership. Regarding education level, 40% of participants had university studies, 22.9% completed secondary education (ESO), 14.3% had higher vocational training, 11.4% completed high school, and 11.4% had intermediate vocational training.

Marital status showed interactions with the stress dimension at diagnosis (p=0.034).

Education level correlated with the cognitive function dimension at diagnosis (p=0.04) and at three months (p=0.025).

Employment status showed significant correlations with the energy dimension at diagnosis (p=0.024), at three months (p=0.039), and at six months (p=0.001). It also interacted with the social function dimension at three months (p=0.012) and six months (p=0.001), as well as with the stress dimension at three months and six months (p=0.001 for both). Finally, employment status revealed relationships with the emotional limitation dimension (p=0.007) and health changes dimension (p=0.012) at six months.

3.2. Clinical Characteristics

3.2.1. Family History and Previous Diseases

Eighty percent of participants reported no family history, while 20% did. Regarding autoimmune diseases, 82.9% did not have such conditions, while 17.1% reported them. Additionally, 94.3% had no history of previous mononucleosis. Most participants were employed (42.9%), followed by self-employed individuals (31.4%) and students (22.9%).

A relationship was identified between family history and the mental health dimension at diagnosis (p = 0.028), as well as with the health changes dimension at three months (p=0.025). Finally, family history was associated with the energy dimension at six months (p=0.008).

A significant relationship was found between autoimmune diseases and physical function at diagnosis (p=0.035). Associations were also observed with the stress dimension (p=0.031) and health changes dimension (p=0.002) at three months, and with the pain dimension at six months (p=0.038).

Previous mononucleosis was related to the social function dimension at diagnosis (p=0.043) and the sexual function dimension (p=0.037).

Pregnancy planning was related to the sexual satisfaction dimension (p=0.02) and the sexual function dimension (p=0.002) at three months.

3.2.2. Lifestyle Habits and treatment.

Regarding lifestyle habits, 77.1% of participants did not use tobacco, alcohol consumption was reported by 8.6% of participants, 97.1% did not report cannabis use, while 2.9% indicated cannabis consumption.

Cannabis use showed interactions with social function, sexual function, and sexual satisfaction at diagnosis; however, since only one individual in the sample reported cannabis use, these results should be interpreted with caution.

Tobacco use was associated with the physical limitation dimension at three months (p=0.045).

Participants received a variety of pharmacological treatments. The most common treatments were Ocrelizumab (20%) and Mavenclad (14.3%). Other treatments included Tecfidera, Tysabri, Vumerity, and Ofatumumab, each accounting for 2.9%, while Alemtuzumab, Cladribine, Corticosteroids, and Diroximel fumarate were administered in 5.7% of cases.

3.3. Clinical Results: Quality of Life

3.3.1. Physical Function

At diagnosis, physical function had a mean score of 80.67±17.67 points, which increased to 83.14±13.13 points at three months and slightly decreased to 81.52±12.71 points at six months.

3.3.2. Physical Limitations

The mean score for physical limitations was 59.57±25.79 at diagnosis, slightly decreased to 57.43±22.17 at three months, and remained stable at 57.43±21.50 at six months.

3.3.3. Emotional Limitations

Emotional limitations showed a mean score of 64.38±23.03 at diagnosis, 62.84±12.72 at three months, and worsened further to 58.86±20.48 at six months.

3.3.4. Pain Perception

Pain perception showed a slight improvement, with a mean score of 78.93±23.59 at diagnosis, 80.71±22.46 at three months, and 81.79±20.52 at six months.

3.3.5. Mental Health

Mental health had a mean score of 44.34±14.49 at diagnosis, which increased to 57.03±17.17 at three months but slightly decreased to 55.09±15.16 at six months. A significant relationship was revealed between mental health at diagnosis and family history (p=0.028); however, no significant differences were observed in mean ranks.

3.3.6. Energy

Energy levels showed a mean score of 54.86±20.27 at diagnosis, 55.26±18.63 at three months, and remained stable at six months.

3.3.7. Health Perception

Health perception had a mean score of 48.11±18.99 at diagnosis, 47.54±16.77 at three months, and showed a slight improvement to 48.91±17.16 at six months.

3.3.8. Social Function

Social function had a mean score of 70.48±16.67 at diagnosis, while cognitive function scored 70.12±21.40. At three months, these variables slightly decreased, reaching mean scores of 66.86±15.55 and 69.04±19.78, respectively. At six months, social function worsened further compared to previous periods, reaching a mean score of 65.52±12.78. Cognitive function at six months declined to a mean score of 67.50±19.30.

3.3.9. Stress

Stress had a mean score of 44.29±22.51 at diagnosis, which increased to 47.38±18.40 at three months and further to 48.95±17.84 at six months, showing a trend toward improvement.

3.3.10. Sexual Function

Sexual function showed a slight decline, dropping from 79.46±17.12 at diagnosis to 78.21±18.96 at three months and 78.93±18.25 at six months.

3.3.11. Health Changes Dimension

The health changes dimension had a mean score of 37.71±18 at diagnosis, showing an improvement trend at three months (45.71±24.05) and at six months (49.71±23.95).

3.3.12. Sexual Satisfaction

Sexual satisfaction was scored at 65.14±23.93 at diagnosis, 65.71±20.33 at three months, and slightly worsened to 64.15±20.20 at six months.

3.3.12. Perceived Quality of Life

Perceived quality of life had a mean score of 62.39±14.15 points at diagnosis, 62.85±12.71 at three months, and 62.39±11.77 at six months, remaining stable throughout the analyzed period.

3.3.13. Overall Quality of Life

Perceived quality of life at diagnosis showed a mean score of 62.39±14.15 points, slightly increasing to 62.84±12.72 points at three months and remaining stable at six months with a mean score of 62.40±11.80 points (Figure 1).

The paired sample analysis revealed no statistically significant differences in quality of life scores across the three time points measured (baseline, 3 months, and 6 months). Effect sizes were minimal across all comparisons, ranging from 0.00 to 0.10, indicating negligible clinical relevance of the observed differences. In contrast, the paired sample correlations demonstrated very strong and statistically significant relationships between the quality of life scores at the three time points. These findings suggest a high degree of consistency in quality of life measurements over time, despite the lack of significant changes between the time points. (Table 3 and Table 4).

4. Discussion

4.1. Relationships Between Sociodemographic Variables and Quality of Life

The results revealed a significant relationship between age and quality of life at diagnosis and at three months. However, this relationship was not sustained at six months. Younger participants might perceive a better initial quality of life, possibly due to fewer comorbidities and greater physical and psychological recovery capacity in the early stages of the disease. Gil-González et al. [15] identified age as a relevant factor in the perception of quality of life, especially during the initial phases of the disease, where younger individuals tend to report better physical and emotional well-being. Moreover, the cumulative burden of the disease in older individuals negatively impacts their perception of well-being [16].

Although women represented 57.1% of the sample, no significant differences were observed between men and women in relation to quality of life. Research indicates that, despite the higher prevalence of MS among women, differences in quality of life between sexes are more influenced by factors such as social support and symptom severity rather than sex itself. [16,17]. This aligns with the study by Sabanagic-Hajric et al. [18], which highlights that gender does not have a direct impact on quality of life but does affect specific domains, such as sexual function, influenced by clinical and social factors.

Married participants showed a significant interaction with stress at diagnosis. Marital status could influence stress perception due to the availability of emotional support, as suggested by previous research. Uhr et al. [19] identified marital status and social support as protective factors against anxiety and depression in individuals with MS, directly influencing their perception of quality of life.

Similarly, educational level showed a correlation with cognitive function at diagnosis and at three months, supporting studies indicating that higher educational attainment may be associated with better cognitive strategies for coping with the disease [17,18]. Reece et al. [20] noted that higher education levels facilitate the adoption of health education programs and coping strategies, thereby improving perceived quality of life.

4.2. Impact of Clinical Characteristics

A significant relationship was observed between family history and mental health at diagnosis, as well as with health changes at three months. This may reflect the psychological impact of having a family history of severe illnesses, as documented in studies analyzing the emotional effects of chronic diseases within family environments. [16,17]. Uhr et al. [19] emphasized that family stress associated with chronic diseases can negatively influence mental health perception.

The significant relationship between family history and mental health at diagnosis, as well as health changes at three months, highlights the psychological impact that a history of severe illnesses within the family environment may have on individuals with RRMS. This finding underscores the importance of considering the family context in the comprehensive care of these individuals, as stress associated with chronic diseases within the family unit can negatively affect mental health perception. [15,21].

It would be pertinent to design specific interventions aimed at providing psychological support to both patients and their families, with the goal of mitigating emotional impact and improving perceived quality of life. For example, implementing family support groups or psychoeducation sessions could help reduce associated stress and foster more effective coping strategies. [15,19,21].

Autoimmune diseases showed significant interaction with physical function at diagnosis, as well as with stress and health changes at three months, and were further associated with pain at six months. Recent research has indicated that autoimmune comorbidities can exacerbate symptoms and negatively affect quality of life, particularly in physical and psychological dimensions. [18]. Gil-González et al. [15] emphasized that autoimmune comorbidities have a cumulative impact on quality of life, especially in physical and emotional domains.

Prior mononucleosis was associated with social function at diagnosis and sexual function. This aligns studies suggesting that previous infections, such as mononucleosis, may have residual impacts on social and sexual health in individuals with MS. [17]. Sabanagic-Hajric et al. [18] highlighted that clinical factors, such as prior infections, can influence sexual and social function in individuals with MS.

Although cannabis use showed interaction with several dimensions, the low frequency of consumption within the sample limits the interpretation of these results. On the other hand, tobacco use was associated with physical limitations at three months, highlighting its negative impact on physical health, as also noted in studies on lifestyle habits and quality of life in individuals with neurological diseases. [16]. Reece et al. [20] identified that habits such as tobacco use negatively affect the perception of quality of life, particularly in physical dimensions.

Lifestyle habits, such as tobacco, alcohol, and cannabis use, have a significant impact on various dimensions of quality of life. In the case of tobacco, the results show that its consumption is associated with physical limitations at three months, possibly reflecting its negative effects on respiratory and cardiovascular capacity, exacerbating fatigue and reducing energy available for daily activities. On the other hand, although alcohol consumption was low in the sample, previous research has indicated that even moderate consumption can negatively influence cognitive and social function, affecting quality of life perception. Cannabis use, though limited to a single participant in this study, showed interaction with social function, sexual function, and sexual satisfaction, which may be related to its effects on mood and perceived well-being. [20,22,23].

From a preventive approach, it would be useful to implement educational programs aimed at promoting healthy lifestyle habits in individuals with RRMS, emphasizing the risks associated with substance use and its impact on quality of life. Additionally, interventions focused on promoting physical exercise, a balanced diet, and stress management techniques could counteract the negative effects of these habits and improve physical, emotional, and social dimensions of quality of life. [20,22,23].

4.3. Lifestyle Habits and Quality of Life

A significant relationship was found between pregnancy planning and sexual satisfaction and function at three months, which may reflect greater concern for reproductive aspects among individuals considering pregnancy. This has also been reported in studies on quality of life and reproductive health in women with MS [17]. Sabanagic-Hajric et al. [18] confirmed that reproductive planning has a significant impact on sexual function and the perception of quality of life.

Energy levels showed a significant correlation with employment status at diagnosis, three months, and six months. Social function and stress also interacted with employment status at different time points, suggesting that the type of work may influence overall quality of life perception. Gil-González et al. [15] highlighted that job stability acts as a protective factor for quality of life by providing structure and social support. This aligns with studies indicating that employment can serve as a protective factor in quality-of-life perception, offering structure and social support [18].

4.4. Evolution of Quality of Life

The impact of MS on quality of life has been extensively documented in the literature. The study by Hernández et al. [11], focused exclusively on quality-of-life assessment, observed how this is influenced by the type of MS, the degree of disability, and the time elapsed since diagnosis.

Perceived quality of life remained relatively stable during the period analyzed, with an average score of approximately 62 points. However, significant positive correlations between quality of life at diagnosis, three months, and six months suggest that individuals with better initial quality of life tend to maintain this perception over time. Previous studies have reported similar patterns, where initial quality of life strongly predicts future evolution [16,18].

The evolution of results in the present study aligns with those described by Martínez-Espejo et al. [13], who, after applying the MSQOL-54 to a sample of MS patients, reported a global mean score of 61.1 points (SD=20.06), with a wide range of scores between 13.9 and 97.2. This data not only indicates significant inter-individual variability but also reflects a generalized and significant impact on quality of life from early stages.

The similarity between the results of Martínez-Espejo et al. [13] and those observed in this study reinforces the reliability of the identified deterioration pattern and provides external consistency to the phenomenon described herein.

Additionally, Kobelt et al. [23] conducted an analysis of quality of life and associated costs of MS in Spain, revealing that quality of life decreases significantly as disability progresses. This underscores the importance of early interventions and continuous care to mitigate cumulative deterioration.

Although some dimensions, such as physical function and pain perception, showed slight improvement over time, others, such as emotional limitations and social function, worsened progressively. This may reflect the cumulative psychological and social burden of the disease, as also reported in longitudinal studies on quality of life in MS patients [17]. While the mentioned studies consistently highlight moderate to severe impairment of quality of life from early stages, it is also important to analyze research where interventions targeting physical and emotional distress have been implemented.

In this regard, a study conducted by Fidao et al. [24] reinforces the need for early intervention by demonstrating that the adoption of several healthy lifestyle habits is associated with improvements in both physical and mental quality of life in MS patients. This comparison also suggests that improving quality of life does not necessarily require complex or costly interventions but can be promoted through simple and sustained recommendations over time.

Moreover, a literature review by Montañés-Masias et al. [25] provides a complementary perspective, focusing on psychological approaches through the evaluation of the effectiveness of online interventions targeting MS patients. Their findings concluded that programs such as mindfulness, cognitive-behavioral therapy, or psychoeducational resources have a positive impact on emotional symptoms and perceived quality of life. In contrast to the present study, which highlights negative evolution in the absence of interventions, the findings of Montañés-Masias et al. [25] showed that quality of life can improve significantly when emotional and cognitive dimensions are addressed.

Similarly, Broche-Pérez et al. [21] demonstrated that psychological resilience acts as a modulating factor, mitigating the impact of cognitive worry on quality of life, particularly in its physical and mental dimensions. This highlights the importance of strengthening personal coping resources from the moment of diagnosis.

Further evidence from recent studies identifies various factors that significantly affect quality of life in MS patients. Fatigue, for instance, has been described as one of the most disabling symptoms, with a direct impact on autonomy and emotional well-being. A recent study published by Piñar-Morales et al. [22] confirms that fatigue, anxiety, sleep disorders, depression, and cognitive impairment negatively affect the quality of life of MS patients.

4.5. Clinical Implications and Future Research Directions

The findings highlight the importance of addressing comorbidities, family history, and lifestyle habits in the comprehensive care of individuals with RRMS. Specific interventions, such as cognitive-behavioral therapy programs, mindfulness sessions, and psychosocial support groups, could significantly improve mental health and social functioning in these patients. For instance, cognitive-behavioral therapy can help identify and modify negative thought patterns that impact quality-of-life perception, while mindfulness can reduce stress and enhance emotional regulation. Psychosocial support groups, on the other hand, provide a safe space for sharing experiences and fostering a sense of belonging and community support. These recommendations align with recent studies emphasizing a multidimensional approach to MS management [17].

The stability of perceived quality of life suggests that current treatments may be effective in maintaining overall well-being; however, a more targeted approach is needed to address dimensions that tend to deteriorate, such as emotional limitations and social functioning. Specific intervention designs, such as health education programs focused on self-care and coping strategies, could be implemented to address these areas. For example, educational workshops that include stress management techniques, interpersonal communication skills, and resilience training can empower individuals to face the emotional and social challenges associated with the disease. Additionally, technology-based interventions, such as mobile applications offering psychoeducational resources and personalized monitoring, could facilitate access to emotional and social support tools from the early stages of the disease.

Future studies should explore the influence of specific factors, such as social support and psychological coping strategies, on the evolution of quality of life in these patients.

5. Limitations

This study presents several limitations that must be considered when interpreting the results. First, the small sample size (35 patients) limits the generalizability of the conclusions to a broader population, especially given the heterogeneity of RRMS patients. Second, the absence of long-term follow-up prevents the evaluation of the evolution of the studied variables beyond the subacute phase, which could provide crucial information on functional and emotional stabilization in later stages. Additionally, although multiple clinical and emotional variables were included, other potentially relevant factors, such as social support or access to rehabilitation resources, were not assessed, which may influence the outcomes. Finally, the observational design of the study does not allow for definitive causal relationships to be established between the analyzed variables.

6. Conclusions

Perceived quality of life in individuals with relapsing-remitting multiple sclerosis (RRMS) remained relatively stable during the first six months following diagnosis, although certain dimensions, such as emotional limitations and social functioning, showed progressive deterioration. Factors such as family history, autoimmune diseases, and lifestyle habits had a significant impact on various dimensions of quality of life. The relationship between initial quality of life and its evolution suggests that early interventions could mitigate cumulative deterioration and improve overall well-being. This study underscores the need to implement multidimensional strategies that address physical, emotional, and social aspects of quality of life in individuals with relapsing-remitting multiple sclerosis.