Multidimensional Predictors of Fatigue in Amyotrophic Lateral Sclerosis: A Cross-Sectional Study in China

Multidimensional Predictors of Fatigue in Amyotrophic Lateral Sclerosis: A Cross-Sectional Study in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Multidimensional Predictors of Fatigue in Amyotrophic Lateral Sclerosis: A Cross-Sectional Study in China Fangfang Cai, Dandan Xu, Dan Yang, Fei Huang, Xiaoyu Song, Qianping Jiang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7668825/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Aim: This study aims to identify key predictors of fatigue in patients with amyotrophic lateral sclerosis (ALS) and examine the interaction of physiological, psychological, and social factors to inform management strategies. Design: A descriptive cross-sectional study. Methods: Between June 2023 and June 2024, a cohort of 239 patients diagnosed with ALS enrolled at the Integrated Traditional Chinese and Western Medicine Center, Hubei Provincial Hospital of Traditional Chinese Medicine. The participants were evaluated using validated instruments that assessed motor function, depressive symptoms, sleep quality, and social determinants. A multiple regression analysis was conducted to identify independent predictors of fatigue. Results: The ALS Functional Rating Scale-Revised (ALSFRS-R), depression (PHQ-9), sleep quality (PSQI), and living situation were significant predictors of fatigue, accounting for 28.6% of the variance (R 2 =0.286). The use of traditional Chinese medicine was associated with a reduction in fatigue, whereas muscle strength exhibited an inverse correlation with the severity of fatigue. Conclusions: Fatigue in ALS is influenced by multiple factors, extending beyond motor decline to include psychological well-being, sleep quality, and social environment. These findings highlight the need for multidisciplinary, personalized strategies that integrate both biomedical and supportive care approaches to enhance the quality of life for ALS patients. Health sciences/Diseases Health sciences/Health care Health sciences/Neurology Biological sciences/Neuroscience Amyotrophic lateral sclerosis Fatigue Predictors Depression Sleep Social determinants Figures Figure 1 Figure 2 Figure 3 Background Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder marked by the degeneration of motor neurons, resulting in muscle weakness, paralysis, and compromised movement, speech, and respiration [ 1 – 2 ]. The global incidence of ALS is estimated to be 1.5–2.5 cases per 100,000 individuals annually [ 3 ]. Although pharmacological interventions such as riluzole and edaravone offer modest delays in disease progression, they do not significantly alter the overall prognosis, leaving patients with substantial physical and psychological burdens [ 2 , 20 ]. Fatigue, a prevalent and debilitating non-motor symptom, affects 44%–86% of individuals with ALS and is closely associated with diminished quality of life, accelerated disease progression, and poor adherence to treatment regimens [ 10 , 12 , 13 ]. Previous research has linked fatigue in ALS to motor decline, psychological distress, nutritional imbalances, and metabolic dysfunction [ 4 – 5 ]. However, the focus has mainly been on physiological and psychological dimensions, with limited exploration of social and environmental factors such as living conditions, caregiver support, and access to healthcare [ 8 ]. This lack of integration impedes a comprehensive understanding of fatigue, particularly concerning the interaction between social determinants and clinical factors [ 23 ]. Furthermore, the fragmented nature of existing evidence and the scarcity of targeted interventions underscore the urgent need for more inclusive research. In clinical practice, the absence of multidimensional management strategies further complicates effective fatigue control [ 13 ]. The present study aims to systematically investigate the multidimensional determinants of fatigue in ALS, encompassing physiological, psychological, and social factors, and to explore their combined effects. By establishing a structured analytical framework, this research aims to provide new insights into how these factors interact to shape the perception of fatigue. The findings are anticipated to guide the development of personalized, multidimensional management strategies that enhance care and quality of life in ALS. Additionally, the results may have broader implications for fatigue management in other neurodegenerative diseases, contributing to both clinical practice and future research. Methods ALS is a progressive neurodegenerative disorder characterized by the degeneration of motor neurons, leading to muscle weakness, paralysis, and impaired movement, speech, and respiration. The global incidence of ALS is estimated to be 1.5–2.5 cases per 100,000 individuals annually [ 3 ]. This cross-sectional study was conducted at a tertiary hospital specializing in ALS and other neurodegenerative disorders. Patients were recruited through convenience sampling between June 2023 and June 2024. The inclusion criteria were: (1) a confirmed ALS diagnosis based on clinical and electrophysiological assessment, (2) the ability to provide informed consent, and (3) the absence of comorbidities likely to independently affect fatigue (e.g., cardiovascular or metabolic disease). A total of 239 patients were enrolled (139 men and 100 women; mean age 52 ± 4.8 years). The study received approval from the institutional ethics committee, and all participants provided written informed consent. The sample size required for this study was determined using the formula for multiple regression, specifically\(N\geq50 + 8m\), where\(m\) represents the number of predictors. Given the inclusion of nine independent variables, the minimum sample size was calculated to be 122. To accommodate potential attrition and enhance the study's robustness, a total of 239 patients were included. Study variables and instruments Validated instruments were employed to evaluate the demographic, physiological, psychological, and social determinants of fatigue[ 7 ]. The following tools were used: a General Information Questionnaire to collect demographic and socioeconomic data; a Disease Information Form to record disease duration, muscle strength score (MSS), and the use of traditional Chinese medicine (TCM); the Fatigue Severity Scale (FSS), consisting of nine items on a 7-point Likert scale, serving as the primary outcome measure [ 11 , 14 ]; the ALS Functional Rating Scale-Revised (ALSFRS-R), which assesses motor function through 12 items, with a score range of 0–48 [ 12 ]; the ALS Assessment Questionnaire (ALSAQ-40), comprising 40 items related to quality of life [ 13 ]; the Patient Health Questionnaire-9 (PHQ-9) for depression screening [ 17 ]; the Pittsburgh Sleep Quality Index (PSQI) for evaluating sleep quality [ 15 ]; the Dyspnea and Quality of Life Scale (DALS-15) to assess the impact of dyspnea [ 16 ]; and the Swallowing Function Assessment Scale (SSA) for evaluating swallowing impairment [ 21 ]. Statistical analysis Statistical analyses were conducted with continuous variables expressed as mean ± standard deviation (SD) and categorical variables as frequencies and percentages. For data exhibiting normal distribution, independent t-tests or analysis of variance (ANOVA) were employed, while the Kruskal–Wallis test was utilized for non-normally distributed data. Pearson correlation coefficients were calculated to assess the relationships between fatigue and other variables. Stepwise multiple linear regression analysis was performed to identify independent predictors of fatigue. Statistical significance was determined at p < 0.05. All analyses were conducted using SPSS version 29.0 (IBM, Armonk, NY). Results Sociodemographic and clinical factors No statistically significant associations were identified between the severity of fatigue and variables such as gender, age, BMI, education, employment, occupation, marital status, income, health insurance, disease duration, smoking, alcohol, or tea consumption (all p > 0.05). However, significant associations were observed with caregiver availability, residential status, the use of traditional Chinese medicine (TCM), and muscle strength score (MSS) ( p < 0.05; Table 1 , Fig. 1 ). Table 1 Analysis of Factors Influencing Fatigue Item Category Sample FSS scores (mean ± SD) t/F p Gender male female 139 100 32.21 ± 12.862 34.22 ± 12.000 -1.226 0.221 Age 0–39 40–49 50–59 ≥ 60 31 69 96 43 34.13 ± 11.589 32.43 ± 12.191 34.05 ± 12.489 31.02 ± 13.828 0.710 0.547 BMI <18.5 18.5–24.9 25-29.9 ≥ 30 20 153 58 8 35.30 ± 11.886 32.96 ± 12.753 32.19 ± 11.926 35.38 ± 15.222 0.398 0.755 Education level Primary school or below Middle school High school/vocational school Associate degree Bachelor's degree or higher 21 66 47 42 63 27.86 ± 11.355 33.52 ± 13.151 32.34 ± 12.326 34.26 ± 11.945 34.02 ± 12.660 1.158 0.330 Employment status Full-time employment Unemployed Retired Part-time employment Laid-off 99 81 48 6 5 32.40 ± 12.385 34.10 ± 12.344 32.52 ± 13.590 28.33 ± 7.394 39.60 ± 12.798 0.781 0.538 Occupation type Other General company employee business owner Skilled worker Farmer Public institution employee Civil servant 64 44 38 30 29 24 10 33.63 ± 11.933 29.59 ± 10.998 35.03 ± 13.118 34.57 ± 14.457 34.83 ± 11.711 31.67 ± 14.116 30.70 ± 12.202 1.019 0.414 Marital status Married Divorced Single 231 4 4 33.03 ± 12.611 34.25 ± 12.712 33.25 ± 9.179 0.019 0.981 Monthly income <1000 1000–2999 3000–4999 5000–10000 Above 10,000 13 31 29 76 90 36.38 ± 14.402 36.68 ± 10.609 30.41 ± 12.588 32.07 ± 12.678 33.00 ± 12.603 1.327 0.261 Caregiver status Yes No 181 58 33.51 ± 12.353 30.62 ± 13.042 2.017 <0.05 residential status Urban Rural 191 48 31.47 ± 12.717 35.35 ± 11.555 -2.876 <0.05 Health insurance status Employee insurance NRCMS Urban and Rural Resident Insurance Self-paying 149 50 32 8 32.31 ± 12.376 33.96 ± 13.160 35.69 ± 11.055 30.63 ± 16.843 0.833 0.477 Disease duration(m) 0–12 13–24 Over 24 months 89 102 46 33.81 ± 11.723 32.57 ± 11.955 0.072 0.931 Use of traditional Chinese medicine Yes No 168 71 34.12 ± 12.542 30.52 ± 12.192 2.043 <0.05 Smoking status Yes No 76 163 31.78 ± 14.300 33.64 ± 11.601 -1.074 0.284 Alcohol use history Yes No 79 160 31.58 ± 13.245 33.78 ± 12.127 -1.275 0.204 Tea drinking history Yes No 55 184 32.16 ± 12.639 33.32 ± 12.510 -0.598 0.551 Total muscle strength score 400 12 94 127 3 3 32.33 ± 15.488 33.01 ± 12.303 32.54 ± 12.357 49.33 ± 11.060 42.33 ± 3.786 4.238 <0.05 Correlation analysis Pearson correlation analyses revealed that fatigue exhibited significant associations with ALSFRS-R, ALSAQ-40, PSQI, PHQ-9, and DALS-15 scores (all p < 0.05). However, no significant correlation was identified with SSA scores (Table 2 , Fig. 2). Table 2 Correlation analysis of fatigue and various factors Variable Pearson correlation(r) p ALSFRS-R -0.415 <0.05 ALSAQ-40 0.363 <0.05 PSQI 0.347 <0.05 PHQ-9 0.355 <0.05 SSA 0.054 0.410 DALS-15 0.358 <0.05 Description of the legend: Blue denotes a significant correlation, whereas black signifies a non-significant correlation. Figure 2: Correlation Analysis of fatigue and Associated Factors Regression analysis Through multiple regression analysis, ALSFRS-R, PHQ-9, PSQI, and residential status were identified as independent predictors of fatigue severity, collectively accounting for 28.6% of the variance (R² = 0.286). Among these, ALSFRS-R emerged as the most significant predictor (β= − 0.483, p < 0.05), followed by PHQ-9 (β = 0.562), PSQI (β = 0.440), and residential status (β = 3.973) (Table 3, Fig. 3 ). Table 3. Stepwise Multiple Regression Analysis of Fatigue Variable B SE Standardized B t P Constant 37.873 4.063 9.321 ＜0.05 ALSFRS-R (X 5 ) ﹣0.483 0.084 -0.325 5.766 ＜0.05 PHQ-9 (X 8 ) 0.562 0.147 0.231 3.819 ＜0.05 PSQI (X 7 ) 0.440 0.127 0.206 3.465 ＜0.05 Residential status (X 2 ) 3.973 1.731 0.127 2.296 ＜0.05 Discussion The pathogenesis of amyotrophic lateral sclerosis (ALS) is characterized by a multifactorial nature, involving genetic predisposition, excitotoxicity, mitochondrial dysfunction, oxidative stress, deficits in neurotrophic signaling, and impairments in axonal transport [ 18 – 19 ]. As a result of this complexity, curative treatments remain elusive, and current therapeutic strategies primarily focus on symptom alleviation and enhancing quality of life [ 20 ]. Among the non-motor manifestations, fatigue is both highly prevalent and profoundly disabling, due to neuromuscular dysfunction, metabolic disturbances, and psychological factors [ 21 – 22 ]. Understanding its underlying determinants is therefore essential to inform targeted and comprehensive management strategies. This study confirmed the high prevalence and severity of fatigue in ALS, with a mean score of 33.01 ± 12.49. Multivariate regression analysis identified the ALS Functional Rating Scale-Revised (ALSFRS-R), depressive symptoms (PHQ-9), sleep quality (PSQI), and living situation as independent predictors, collectively accounting for 28.6% of the variance in fatigue. Among these, ALSFRS-R was the most significant predictor, highlighting the central role of neurological decline and functional impairment. Depression contributed 8% of the variance, highlighting the influence of mental health, while sleep disturbances accounted for 3.7%, reflecting the impact of nocturnal respiratory insufficiency and inadequate recovery [ 27 ]. Additionally, reduced muscle strength and compromised respiratory function exacerbated fatigue by increasing compensatory energy demands and limiting oxygen availability [ 24 – 26 ]. Collectively, these findings indicate that fatigue in ALS arises from the interplay of physiological decline and psychological burden. Our findings indicate that patients residing in rural areas and those without caregiver support experience significantly greater fatigue, highlighting the critical influence of social determinants. Barriers such as limited access to healthcare, insufficient professional care, and restricted rehabilitation opportunities in rural regions exacerbate both physical and psychological burdens. In contrast, urban residence and the availability of caregiver support are associated with reduced fatigue, aligning with previous evidence that underscores the protective role of social support in managing chronic illness [ 5 , 23 ]. These results suggest that equitable allocation of healthcare resources and the enhancement of caregiver involvement are essential strategies for mitigating fatigue in ALS. Frequent utilization of traditional Chinese medicine (TCM) has been correlated with reduced fatigue levels, consistent with prior research attributing these benefits to its antioxidant and anti-inflammatory properties [ 24 – 25 ]. Specific herbs, such as Astragalus membranaceus and ginseng, have demonstrated efficacy in reducing oxidative stress, modulating cytokine activity, and safeguarding against mitochondrial dysfunction, thereby contributing to the alleviation of fatigue. Notably, the impact of TCM was more pronounced among rural patients, indicating its potential to address health disparities in resource-constrained environments. These findings endorse the integration of TCM as an adjunctive element in multidisciplinary care; however, validation through rigorously designed randomized controlled trials remains imperative. This study underscores the necessity for a comprehensive and individualized approach to managing fatigue in patients with ALS. Given that fatigue results from the combined effects of neurological decline, depression, sleep disturbances, and social disadvantage, interventions should address these domains in an integrated manner. Rehabilitation strategies aimed at maintaining motor and respiratory function, supplemented by nutritional support, can help mitigate physical energy demands. Psychological therapies and sleep-focused interventions are equally crucial, while enhancing caregiver involvement and improving healthcare access, particularly in rural areas, may alleviate social contributors to fatigue. Furthermore, evidence-based Traditional Chinese Medicine may offer adjunctive benefits through its antioxidant and anti-inflammatory properties. Integrating these measures into coordinated care plans provides a practical framework for reducing fatigue, improving treatment adherence, and enhancing the quality of life in ALS patients. Limitations Several limitations should be noted. First, the use of convenience sampling and a single-center, cross-sectional design may limit the generalizability of the findings and preclude the assessment of fatigue over time. Larger, multicenter, longitudinal studies would be helpful to explore fatigue trajectories across different stages of ALS. Second, although the actual sample size exceeded the minimum requirement calculated for multiple regression analysis, it remains relatively limited, typical in rare disease research, and may restrict statistical power for detecting smaller effects or conducting subgroup analyses.Third, while this study suggests potential benefits of Traditional Chinese Medicine (TCM), further randomized controlled trials are needed to validate its clinical value, establish treatment protocols, and clarify mechanisms, especially regarding mitochondrial dysfunction and oxidative stress. Finally, unmeasured factors like medication side effects, nutritional status, or comorbidities could also contribute to fatigue but were not addressed in this study. Future research should impact fatigue and should be considered in future studies. Conclusion This study identified ALSFRS-R, depressive symptoms (PHQ-9), sleep quality (PSQI), and residential status as significant predictors of fatigue in patients with ALS. Muscle strength showed a negative correlation with fatigue, and the use of traditional Chinese medicine was associated with lower fatigue levels. These results highlight the multifactorial nature of fatigue and the need for comprehensive management strategies that address physiological, psychological, and social factors. Personalized approaches may help improve quality of life in ALS, although further studies are required to validate these findings and explore targeted interventions. Declarations Ethical approval and consent to participate This study was approved by the Ethics Committee of Hubei Provincial Hospital of Traditional Chinese Medicine, China (Approval No. HBZY2022-C41-01). Written informed consent was obtained from all participants in accordance with the Declaration of Helsinki. All data were anonymized and stored in asecure, password-protected database accessible only to the research team. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author Contribution CF and XD contributed equally to this work and are recognized as co-first authors. CF designed the study, performed data analysis, and drafted the initial manuscript. XD conducted the literature review and contributed to manuscript revision.ZY and ZJ supervised the project and provided critical feedback.SX and XP collected clinical data. JQ and WS assisted with data collection and analysis. YD and HF prepared figures and tables. All authors reviewed and approved the final manuscript. Acknowledgement We sincerely thank all patients and their families for their participation in this study. We are also grateful to the clinical staff of Hubei Provincial Hospital of Traditional Chinese Medicine for their valuable assistance with patient care and data collection. Data Availability The datasets generated and/or analyzed during the current study are not publicly available due to patient privacy and ethical restrictions but are available from the corresponding author upon reasonable request. References Janl, J., Jackd, S. & Samij, T. A C-terminal frameshift variant of TDP-43 with enhanced aggregation propensity leads to rimmed vacuole myopathy, not ALS or FTD. Acta Neuropathol. 145 (6), 793–814 (2023). Wang, J. & Feng, M. Research progress on exosomes and amyotrophic lateral sclerosis. Practical Geriatr. 37 (4), 326–330 (2023). Wolfson, C., Gauvin, D. E., Ishola, F. & Oskoui, M. Global variation in the incidence and prevalence of amyotrophic lateral sclerosis: a systematic review. Neurology ; 10 . (2023). Prell, T., Hartung, V. & Penzlin, S. Relationships between disease severity, social support, and health-related quality of life in patients with ALS. Soc. Indic. Res. 120 (4), 871–882 (2015). Aust, E. et al. 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02:05:52","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87029,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7668825/v1/e8677b4b675d51dcf0a32089.html"},{"id":93727013,"identity":"cf8c28fc-29a4-4768-be4f-be868f8d8a34","added_by":"auto","created_at":"2025-10-17 02:05:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":169861,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of Factors Influencing Fatigue\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7668825/v1/5ef30726371ed26722f6444e.png"},{"id":93727008,"identity":"f2b40590-6cc8-4ba6-aa40-96f6cf3fa4cd","added_by":"auto","created_at":"2025-10-17 02:05:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50711,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Analysis of fatigue and Associated Factors\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7668825/v1/5c1bf3890bd526284cd38c02.png"},{"id":93727014,"identity":"9c1f34b9-b2c4-4e09-9aab-fec27fd818d2","added_by":"auto","created_at":"2025-10-17 02:05:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":69448,"visible":true,"origin":"","legend":"\u003cp\u003eRegression Coefficients of Fatigue Predictors\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7668825/v1/243c0e27ee3958691a81925d.png"},{"id":99545357,"identity":"7f6698c8-3881-4a44-86be-78c59338ecb4","added_by":"auto","created_at":"2026-01-05 16:06:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1081075,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7668825/v1/7657f023-7601-46f1-a28f-7023fd5f613d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multidimensional Predictors of Fatigue in Amyotrophic Lateral Sclerosis: A Cross-Sectional Study in China","fulltext":[{"header":"Background","content":"\u003cp\u003eAmyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder marked by the degeneration of motor neurons, resulting in muscle weakness, paralysis, and compromised movement, speech, and respiration [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The global incidence of ALS is estimated to be 1.5\u0026ndash;2.5 cases per 100,000 individuals annually [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although pharmacological interventions such as riluzole and edaravone offer modest delays in disease progression, they do not significantly alter the overall prognosis, leaving patients with substantial physical and psychological burdens [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Fatigue, a prevalent and debilitating non-motor symptom, affects 44%\u0026ndash;86% of individuals with ALS and is closely associated with diminished quality of life, accelerated disease progression, and poor adherence to treatment regimens [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious research has linked fatigue in ALS to motor decline, psychological distress, nutritional imbalances, and metabolic dysfunction [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the focus has mainly been on physiological and psychological dimensions, with limited exploration of social and environmental factors such as living conditions, caregiver support, and access to healthcare [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This lack of integration impedes a comprehensive understanding of fatigue, particularly concerning the interaction between social determinants and clinical factors [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, the fragmented nature of existing evidence and the scarcity of targeted interventions underscore the urgent need for more inclusive research. In clinical practice, the absence of multidimensional management strategies further complicates effective fatigue control [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe present study aims to systematically investigate the multidimensional determinants of fatigue in ALS, encompassing physiological, psychological, and social factors, and to explore their combined effects. By establishing a structured analytical framework, this research aims to provide new insights into how these factors interact to shape the perception of fatigue. The findings are anticipated to guide the development of personalized, multidimensional management strategies that enhance care and quality of life in ALS. Additionally, the results may have broader implications for fatigue management in other neurodegenerative diseases, contributing to both clinical practice and future research.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eALS is a progressive neurodegenerative disorder characterized by the degeneration of motor neurons, leading to muscle weakness, paralysis, and impaired movement, speech, and respiration. The global incidence of ALS is estimated to be 1.5\u0026ndash;2.5 cases per 100,000 individuals annually [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This cross-sectional study was conducted at a tertiary hospital specializing in ALS and other neurodegenerative disorders. Patients were recruited through convenience sampling between June 2023 and June 2024. The inclusion criteria were: (1) a confirmed ALS diagnosis based on clinical and electrophysiological assessment, (2) the ability to provide informed consent, and (3) the absence of comorbidities likely to independently affect fatigue (e.g., cardiovascular or metabolic disease). A total of 239 patients were enrolled (139 men and 100 women; mean age 52\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8 years). The study received approval from the institutional ethics committee, and all participants provided written informed consent.\u003c/p\u003e\u003cp\u003eThe sample size required for this study was determined using the formula for multiple regression, specifically\\(N\\geq50\u0026thinsp;+\u0026thinsp;8m\\), where\\(m\\) represents the number of predictors. Given the inclusion of nine independent variables, the minimum sample size was calculated to be 122. To accommodate potential attrition and enhance the study's robustness, a total of 239 patients were included.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy variables and instruments\u003c/h2\u003e\u003cp\u003eValidated instruments were employed to evaluate the demographic, physiological, psychological, and social determinants of fatigue[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The following tools were used: a General Information Questionnaire to collect demographic and socioeconomic data; a Disease Information Form to record disease duration, muscle strength score (MSS), and the use of traditional Chinese medicine (TCM); the Fatigue Severity Scale (FSS), consisting of nine items on a 7-point Likert scale, serving as the primary outcome measure [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]; the ALS Functional Rating Scale-Revised (ALSFRS-R), which assesses motor function through 12 items, with a score range of 0\u0026ndash;48 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]; the ALS Assessment Questionnaire (ALSAQ-40), comprising 40 items related to quality of life [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]; the Patient Health Questionnaire-9 (PHQ-9) for depression screening [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]; the Pittsburgh Sleep Quality Index (PSQI) for evaluating sleep quality [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]; the Dyspnea and Quality of Life Scale (DALS-15) to assess the impact of dyspnea [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]; and the Swallowing Function Assessment Scale (SSA) for evaluating swallowing impairment [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were conducted with continuous variables expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and categorical variables as frequencies and percentages. For data exhibiting normal distribution, independent t-tests or analysis of variance (ANOVA) were employed, while the Kruskal\u0026ndash;Wallis test was utilized for non-normally distributed data. Pearson correlation coefficients were calculated to assess the relationships between fatigue and other variables. Stepwise multiple linear regression analysis was performed to identify independent predictors of fatigue. Statistical significance was determined at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All analyses were conducted using SPSS version 29.0 (IBM, Armonk, NY).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eSociodemographic and clinical factors\u003c/h2\u003e\u003cp\u003eNo statistically significant associations were identified between the severity of fatigue and variables such as gender, age, BMI, education, employment, occupation, marital status, income, health insurance, disease duration, smoking, alcohol, or tea consumption (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, significant associations were observed with caregiver availability, residential status, the use of traditional Chinese medicine (TCM), and muscle strength score (MSS) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis of Factors Influencing Fatigue\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSample\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFSS scores\u003c/p\u003e\u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003et/F\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emale\u003c/p\u003e\u003cp\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e139\u003c/p\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.21\u0026thinsp;\u0026plusmn;\u0026thinsp;12.862\u003c/p\u003e\u003cp\u003e34.22\u0026thinsp;\u0026plusmn;\u0026thinsp;12.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.221\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;39\u003c/p\u003e\u003cp\u003e40\u0026ndash;49\u003c/p\u003e\u003cp\u003e50\u0026ndash;59\u003c/p\u003e\u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003cp\u003e69\u003c/p\u003e\u003cp\u003e96\u003c/p\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.13\u0026thinsp;\u0026plusmn;\u0026thinsp;11.589\u003c/p\u003e\u003cp\u003e32.43\u0026thinsp;\u0026plusmn;\u0026thinsp;12.191\u003c/p\u003e\u003cp\u003e34.05\u0026thinsp;\u0026plusmn;\u0026thinsp;12.489\u003c/p\u003e\u003cp\u003e31.02\u0026thinsp;\u0026plusmn;\u0026thinsp;13.828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.547\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;18.5\u003c/p\u003e\u003cp\u003e18.5\u0026ndash;24.9\u003c/p\u003e\u003cp\u003e25-29.9\u003c/p\u003e\u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003cp\u003e153\u003c/p\u003e\u003cp\u003e58\u003c/p\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.30\u0026thinsp;\u0026plusmn;\u0026thinsp;11.886\u003c/p\u003e\u003cp\u003e32.96\u0026thinsp;\u0026plusmn;\u0026thinsp;12.753\u003c/p\u003e\u003cp\u003e32.19\u0026thinsp;\u0026plusmn;\u0026thinsp;11.926\u003c/p\u003e\u003cp\u003e35.38\u0026thinsp;\u0026plusmn;\u0026thinsp;15.222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.755\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary school or below\u003c/p\u003e\u003cp\u003eMiddle school\u003c/p\u003e\u003cp\u003eHigh school/vocational school\u003c/p\u003e\u003cp\u003eAssociate degree\u003c/p\u003e\u003cp\u003eBachelor's degree or higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003cp\u003e66\u003c/p\u003e\u003cp\u003e47\u003c/p\u003e\u003cp\u003e42\u003c/p\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.86\u0026thinsp;\u0026plusmn;\u0026thinsp;11.355\u003c/p\u003e\u003cp\u003e33.52\u0026thinsp;\u0026plusmn;\u0026thinsp;13.151\u003c/p\u003e\u003cp\u003e32.34\u0026thinsp;\u0026plusmn;\u0026thinsp;12.326\u003c/p\u003e\u003cp\u003e34.26\u0026thinsp;\u0026plusmn;\u0026thinsp;11.945\u003c/p\u003e\u003cp\u003e34.02\u0026thinsp;\u0026plusmn;\u0026thinsp;12.660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.330\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFull-time employment\u003c/p\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003cp\u003eRetired\u003c/p\u003e\u003cp\u003ePart-time employment\u003c/p\u003e\u003cp\u003eLaid-off\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99\u003c/p\u003e\u003cp\u003e81\u003c/p\u003e\u003cp\u003e48\u003c/p\u003e\u003cp\u003e6\u003c/p\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.40\u0026thinsp;\u0026plusmn;\u0026thinsp;12.385\u003c/p\u003e\u003cp\u003e34.10\u0026thinsp;\u0026plusmn;\u0026thinsp;12.344\u003c/p\u003e\u003cp\u003e32.52\u0026thinsp;\u0026plusmn;\u0026thinsp;13.590\u003c/p\u003e\u003cp\u003e28.33\u0026thinsp;\u0026plusmn;\u0026thinsp;7.394\u003c/p\u003e\u003cp\u003e39.60\u0026thinsp;\u0026plusmn;\u0026thinsp;12.798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.781\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.538\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupation type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003cp\u003eGeneral company employee\u003c/p\u003e\u003cp\u003ebusiness owner\u003c/p\u003e\u003cp\u003eSkilled worker\u003c/p\u003e\u003cp\u003eFarmer\u003c/p\u003e\u003cp\u003ePublic institution employee\u003c/p\u003e\u003cp\u003eCivil servant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003cp\u003e44\u003c/p\u003e\u003cp\u003e38\u003c/p\u003e\u003cp\u003e30\u003c/p\u003e\u003cp\u003e29\u003c/p\u003e\u003cp\u003e24\u003c/p\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.63\u0026thinsp;\u0026plusmn;\u0026thinsp;11.933\u003c/p\u003e\u003cp\u003e29.59\u0026thinsp;\u0026plusmn;\u0026thinsp;10.998\u003c/p\u003e\u003cp\u003e35.03\u0026thinsp;\u0026plusmn;\u0026thinsp;13.118\u003c/p\u003e\u003cp\u003e34.57\u0026thinsp;\u0026plusmn;\u0026thinsp;14.457\u003c/p\u003e\u003cp\u003e34.83\u0026thinsp;\u0026plusmn;\u0026thinsp;11.711\u003c/p\u003e\u003cp\u003e31.67\u0026thinsp;\u0026plusmn;\u0026thinsp;14.116\u003c/p\u003e\u003cp\u003e30.70\u0026thinsp;\u0026plusmn;\u0026thinsp;12.202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.414\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e231\u003c/p\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.03\u0026thinsp;\u0026plusmn;\u0026thinsp;12.611\u003c/p\u003e\u003cp\u003e34.25\u0026thinsp;\u0026plusmn;\u0026thinsp;12.712\u003c/p\u003e\u003cp\u003e33.25\u0026thinsp;\u0026plusmn;\u0026thinsp;9.179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.981\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonthly income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;1000\u003c/p\u003e\u003cp\u003e1000\u0026ndash;2999\u003c/p\u003e\u003cp\u003e3000\u0026ndash;4999\u003c/p\u003e\u003cp\u003e5000\u0026ndash;10000\u003c/p\u003e\u003cp\u003eAbove 10,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003cp\u003e31\u003c/p\u003e\u003cp\u003e29\u003c/p\u003e\u003cp\u003e76\u003c/p\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.38\u0026thinsp;\u0026plusmn;\u0026thinsp;14.402\u003c/p\u003e\u003cp\u003e36.68\u0026thinsp;\u0026plusmn;\u0026thinsp;10.609\u003c/p\u003e\u003cp\u003e30.41\u0026thinsp;\u0026plusmn;\u0026thinsp;12.588\u003c/p\u003e\u003cp\u003e32.07\u0026thinsp;\u0026plusmn;\u0026thinsp;12.678\u003c/p\u003e\u003cp\u003e33.00\u0026thinsp;\u0026plusmn;\u0026thinsp;12.603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaregiver status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e181\u003c/p\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.51\u0026thinsp;\u0026plusmn;\u0026thinsp;12.353\u003c/p\u003e\u003cp\u003e30.62\u0026thinsp;\u0026plusmn;\u0026thinsp;13.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eresidential status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e191\u003c/p\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.47\u0026thinsp;\u0026plusmn;\u0026thinsp;12.717\u003c/p\u003e\u003cp\u003e35.35\u0026thinsp;\u0026plusmn;\u0026thinsp;11.555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.876\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth insurance status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployee insurance\u003c/p\u003e\u003cp\u003eNRCMS\u003c/p\u003e\u003cp\u003eUrban and Rural Resident Insurance\u003c/p\u003e\u003cp\u003eSelf-paying\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e149\u003c/p\u003e\u003cp\u003e50\u003c/p\u003e\u003cp\u003e32\u003c/p\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.31\u0026thinsp;\u0026plusmn;\u0026thinsp;12.376\u003c/p\u003e\u003cp\u003e33.96\u0026thinsp;\u0026plusmn;\u0026thinsp;13.160\u003c/p\u003e\u003cp\u003e35.69\u0026thinsp;\u0026plusmn;\u0026thinsp;11.055\u003c/p\u003e\u003cp\u003e30.63\u0026thinsp;\u0026plusmn;\u0026thinsp;16.843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.477\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisease duration(m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;12\u003c/p\u003e\u003cp\u003e13\u0026ndash;24\u003c/p\u003e\u003cp\u003eOver 24 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89\u003c/p\u003e\u003cp\u003e102\u003c/p\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.81\u0026thinsp;\u0026plusmn;\u0026thinsp;11.723\u003c/p\u003e\u003cp\u003e32.57\u0026thinsp;\u0026plusmn;\u0026thinsp;11.955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.931\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUse of traditional Chinese medicine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e168\u003c/p\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.12\u0026thinsp;\u0026plusmn;\u0026thinsp;12.542\u003c/p\u003e\u003cp\u003e30.52\u0026thinsp;\u0026plusmn;\u0026thinsp;12.192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76\u003c/p\u003e\u003cp\u003e163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.78\u0026thinsp;\u0026plusmn;\u0026thinsp;14.300\u003c/p\u003e\u003cp\u003e33.64\u0026thinsp;\u0026plusmn;\u0026thinsp;11.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.284\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol use history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79\u003c/p\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.58\u0026thinsp;\u0026plusmn;\u0026thinsp;13.245\u003c/p\u003e\u003cp\u003e33.78\u0026thinsp;\u0026plusmn;\u0026thinsp;12.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTea drinking history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55\u003c/p\u003e\u003cp\u003e184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.16\u0026thinsp;\u0026plusmn;\u0026thinsp;12.639\u003c/p\u003e\u003cp\u003e33.32\u0026thinsp;\u0026plusmn;\u0026thinsp;12.510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal muscle strength score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;100\u003c/p\u003e\u003cp\u003e101\u0026ndash;200\u003c/p\u003e\u003cp\u003e201\u0026ndash;300\u003c/p\u003e\u003cp\u003e301\u0026ndash;400\u003c/p\u003e\u003cp\u003e\u0026gt;400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003cp\u003e94\u003c/p\u003e\u003cp\u003e127\u003c/p\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.33\u0026thinsp;\u0026plusmn;\u0026thinsp;15.488\u003c/p\u003e\u003cp\u003e33.01\u0026thinsp;\u0026plusmn;\u0026thinsp;12.303\u003c/p\u003e\u003cp\u003e32.54\u0026thinsp;\u0026plusmn;\u0026thinsp;12.357\u003c/p\u003e\u003cp\u003e49.33\u0026thinsp;\u0026plusmn;\u0026thinsp;11.060\u003c/p\u003e\u003cp\u003e42.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCorrelation analysis\u003c/h3\u003e\n\u003cp\u003ePearson correlation analyses revealed that fatigue exhibited significant associations with ALSFRS-R, ALSAQ-40, PSQI, PHQ-9, and DALS-15 scores (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, no significant correlation was identified with SSA scores (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;2).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation analysis of fatigue and various factors\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePearson correlation(r)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALSFRS-R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALSAQ-40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePSQI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHQ-9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.410\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDALS-15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDescription of the legend: Blue denotes a significant correlation, whereas black signifies a non-significant correlation.\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;2: Correlation Analysis of fatigue and Associated Factors\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eRegression analysis\u003c/h2\u003e\u003cp\u003eThrough multiple regression analysis, ALSFRS-R, PHQ-9, PSQI, and residential status were identified as independent predictors of fatigue severity, collectively accounting for 28.6% of the variance (R\u0026sup2; = 0.286). Among these, ALSFRS-R emerged as the most significant predictor (β= \u0026minus;\u0026thinsp;0.483, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), followed by PHQ-9 (β\u0026thinsp;=\u0026thinsp;0.562), PSQI (β\u0026thinsp;=\u0026thinsp;0.440), and residential status (β\u0026thinsp;=\u0026thinsp;3.973) (Table\u0026nbsp;3, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"588\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 588px;\"\u003e\n \u003cp\u003eTable 3.\u0026nbsp;Stepwise Multiple Regression Analysis of Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eStandardized B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e37.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e4.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e9.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eALSFRS-R\u0026nbsp;(X\u003csub\u003e5\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e﹣0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e-0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e5.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003ePHQ-9\u0026nbsp;(X\u003csub\u003e8\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003ePSQI\u0026nbsp;(X\u003csub\u003e7\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eResidential status\u0026nbsp;(X\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e3.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe pathogenesis of amyotrophic lateral sclerosis (ALS) is characterized by a multifactorial nature, involving genetic predisposition, excitotoxicity, mitochondrial dysfunction, oxidative stress, deficits in neurotrophic signaling, and impairments in axonal transport [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. As a result of this complexity, curative treatments remain elusive, and current therapeutic strategies primarily focus on symptom alleviation and enhancing quality of life [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Among the non-motor manifestations, fatigue is both highly prevalent and profoundly disabling, due to neuromuscular dysfunction, metabolic disturbances, and psychological factors [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Understanding its underlying determinants is therefore essential to inform targeted and comprehensive management strategies.\u003c/p\u003e\u003cp\u003eThis study confirmed the high prevalence and severity of fatigue in ALS, with a mean score of 33.01\u0026thinsp;\u0026plusmn;\u0026thinsp;12.49. Multivariate regression analysis identified the ALS Functional Rating Scale-Revised (ALSFRS-R), depressive symptoms (PHQ-9), sleep quality (PSQI), and living situation as independent predictors, collectively accounting for 28.6% of the variance in fatigue. Among these, ALSFRS-R was the most significant predictor, highlighting the central role of neurological decline and functional impairment. Depression contributed 8% of the variance, highlighting the influence of mental health, while sleep disturbances accounted for 3.7%, reflecting the impact of nocturnal respiratory insufficiency and inadequate recovery [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, reduced muscle strength and compromised respiratory function exacerbated fatigue by increasing compensatory energy demands and limiting oxygen availability [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Collectively, these findings indicate that fatigue in ALS arises from the interplay of physiological decline and psychological burden.\u003c/p\u003e\u003cp\u003eOur findings indicate that patients residing in rural areas and those without caregiver support experience significantly greater fatigue, highlighting the critical influence of social determinants. Barriers such as limited access to healthcare, insufficient professional care, and restricted rehabilitation opportunities in rural regions exacerbate both physical and psychological burdens. In contrast, urban residence and the availability of caregiver support are associated with reduced fatigue, aligning with previous evidence that underscores the protective role of social support in managing chronic illness [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These results suggest that equitable allocation of healthcare resources and the enhancement of caregiver involvement are essential strategies for mitigating fatigue in ALS.\u003c/p\u003e\u003cp\u003eFrequent utilization of traditional Chinese medicine (TCM) has been correlated with reduced fatigue levels, consistent with prior research attributing these benefits to its antioxidant and anti-inflammatory properties [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Specific herbs, such as Astragalus membranaceus and ginseng, have demonstrated efficacy in reducing oxidative stress, modulating cytokine activity, and safeguarding against mitochondrial dysfunction, thereby contributing to the alleviation of fatigue. Notably, the impact of TCM was more pronounced among rural patients, indicating its potential to address health disparities in resource-constrained environments. These findings endorse the integration of TCM as an adjunctive element in multidisciplinary care; however, validation through rigorously designed randomized controlled trials remains imperative.\u003c/p\u003e\u003cp\u003eThis study underscores the necessity for a comprehensive and individualized approach to managing fatigue in patients with ALS. Given that fatigue results from the combined effects of neurological decline, depression, sleep disturbances, and social disadvantage, interventions should address these domains in an integrated manner. Rehabilitation strategies aimed at maintaining motor and respiratory function, supplemented by nutritional support, can help mitigate physical energy demands. Psychological therapies and sleep-focused interventions are equally crucial, while enhancing caregiver involvement and improving healthcare access, particularly in rural areas, may alleviate social contributors to fatigue. Furthermore, evidence-based Traditional Chinese Medicine may offer adjunctive benefits through its antioxidant and anti-inflammatory properties. Integrating these measures into coordinated care plans provides a practical framework for reducing fatigue, improving treatment adherence, and enhancing the quality of life in ALS patients.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eSeveral limitations should be noted. First, the use of convenience sampling and a single-center, cross-sectional design may limit the generalizability of the findings and preclude the assessment of fatigue over time. Larger, multicenter, longitudinal studies would be helpful to explore fatigue trajectories across different stages of ALS. Second, although the actual sample size exceeded the minimum requirement calculated for multiple regression analysis, it remains relatively limited, typical in rare disease research, and may restrict statistical power for detecting smaller effects or conducting subgroup analyses.Third, while this study suggests potential benefits of Traditional Chinese Medicine (TCM), further randomized controlled trials are needed to validate its clinical value, establish treatment protocols, and clarify mechanisms, especially regarding mitochondrial dysfunction and oxidative stress. Finally, unmeasured factors like medication side effects, nutritional status, or comorbidities could also contribute to fatigue but were not addressed in this study. Future research should impact fatigue and should be considered in future studies.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identified ALSFRS-R, depressive symptoms (PHQ-9), sleep quality (PSQI), and residential status as significant predictors of fatigue in patients with ALS. Muscle strength showed a negative correlation with fatigue, and the use of traditional Chinese medicine was associated with lower fatigue levels. These results highlight the multifactorial nature of fatigue and the need for comprehensive management strategies that address physiological, psychological, and social factors. Personalized approaches may help improve quality of life in ALS, although further studies are required to validate these findings and explore targeted interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Hubei Provincial Hospital of Traditional Chinese Medicine, China (Approval No. HBZY2022-C41-01). Written informed consent was obtained from all participants in accordance with the Declaration of Helsinki. All data were anonymized and stored in asecure, password-protected database accessible only to the research team.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eCF and XD contributed equally to this work and are recognized as co-first authors. CF designed the study, performed data analysis, and drafted the initial manuscript. XD conducted the literature review and contributed to manuscript revision.ZY and ZJ supervised the project and provided critical feedback.SX and XP collected clinical data. JQ and WS assisted with data collection and analysis. YD and HF prepared figures and tables. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe sincerely thank all patients and their families for their participation in this study. We are also grateful to the clinical staff of Hubei Provincial Hospital of Traditional Chinese Medicine for their valuable assistance with patient care and data collection.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to patient privacy and ethical restrictions but are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJanl, J., Jackd, S. \u0026amp; Samij, T. 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Neural Transm (Vienna)\u003c/em\u003e. \u003cb\u003e132\u003c/b\u003e (2), 217\u0026ndash;236 (2025).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Amyotrophic lateral sclerosis, Fatigue, Predictors, Depression, Sleep, Social determinants","lastPublishedDoi":"10.21203/rs.3.rs-7668825/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7668825/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eAim: \u003c/strong\u003eThis study aims\u003cstrong\u003e \u003c/strong\u003eto identify key predictors of fatigue in patients with amyotrophic lateral sclerosis (ALS) and examine the interaction of physiological, psychological, and social factors to inform management strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign:\u003c/strong\u003e A descriptive cross-sectional study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Between June 2023 and June 2024, a cohort of 239 patients diagnosed with ALS enrolled at the Integrated Traditional Chinese and Western Medicine Center, Hubei Provincial Hospital of Traditional Chinese Medicine. The participants were evaluated using validated instruments that assessed motor function, depressive symptoms, sleep quality, and social determinants. A multiple regression analysis was conducted to identify independent predictors of fatigue.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The ALS Functional Rating Scale-Revised (ALSFRS-R), depression (PHQ-9), sleep quality (PSQI), and living situation were significant predictors of fatigue, accounting for 28.6% of the variance (R\u003csup\u003e2\u003c/sup\u003e=0.286). The use of traditional Chinese medicine was associated with a reduction in fatigue, whereas muscle strength exhibited an inverse correlation with the severity of fatigue.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eFatigue in ALS is influenced by multiple factors, extending beyond motor decline to include psychological well-being, sleep quality, and social environment. These findings highlight the need for multidisciplinary, personalized strategies that integrate both biomedical and supportive care approaches to enhance the quality of life for ALS patients.\u003c/p\u003e","manuscriptTitle":"Multidimensional Predictors of Fatigue in Amyotrophic Lateral Sclerosis: A Cross-Sectional Study in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 02:05:47","doi":"10.21203/rs.3.rs-7668825/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-30T09:02:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-28T20:59:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-21T11:40:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203937671547969600670517552236396827077","date":"2025-10-06T07:48:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"132864369461236228758476600483082423511","date":"2025-10-03T09:17:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95135772443373047735574633914473176425","date":"2025-10-03T07:55:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-03T07:44:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-03T07:39:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-25T10:26:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-24T14:36:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-24T14:33:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8838d0fa-504c-4cd8-a712-e6c29a9daedd","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":55810792,"name":"Health sciences/Diseases"},{"id":55810794,"name":"Health sciences/Health care"},{"id":55810795,"name":"Health sciences/Neurology"},{"id":55810796,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-01-05T16:02:09+00:00","versionOfRecord":{"articleIdentity":"rs-7668825","link":"https://doi.org/10.1038/s41598-025-33192-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-01 15:58:26","publishedOnDateReadable":"January 1st, 2026"},"versionCreatedAt":"2025-10-17 02:05:47","video":"","vorDoi":"10.1038/s41598-025-33192-3","vorDoiUrl":"https://doi.org/10.1038/s41598-025-33192-3","workflowStages":[]},"version":"v1","identity":"rs-7668825","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7668825","identity":"rs-7668825","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}