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<h2>Introduction</h2>
<p>Long COVID, defined as the persistence of symptoms for 12 weeks or more following acute SARS-CoV-2 infection, affects a substantial proportion of survivors worldwide (Igboanugo & Kabir, 2023). The symptom profile is heterogeneous, often clustering into domains such as fatigue, respiratory, cognitive, and mental health issues (Igboanugo & Kabir, 2023). Understanding how these clusters differ across populations is critical for tailoring healthcare responses.</p><p>Rural populations have historically faced disparities in health outcomes, healthcare access, and health literacy (Zahnd, 2009; Borders, 2017). The COVID-19 pandemic exacerbated these inequities, with rural areas experiencing higher mortality rates and greater barriers to care (Zahnd, 2020; Fahs, 2022). However, less is known about the rural-urban divide in the long-term sequelae of infection, including Long COVID symptom clusters and the subsequent adaptation of health behaviors.</p><p>Health behavior adaptation—such as changes in physical activity, diet, sleep hygiene, and healthcare utilization—is crucial for managing chronic post-viral conditions (Bhattacharya et al., 2022). Prior research indicates that rural residents may adopt fewer protective health behaviors due to limited access to information, telehealth, and supportive services (Mishra, 2020; Eggleton et al., 2022). This study aimed to compare Long COVID symptom clusters and health behavior adaptation between urban and rural populations in Ireland, using a cross-sectional design.</p>
<h2>Literature Review</h2>
<p>The concept of symptom clusters in Long COVID has gained attention as a means to identify patient subgroups requiring targeted interventions (Igboanugo & Kabir, 2023). Clusters typically include fatigue, neurocognitive impairment, respiratory issues, and mental health symptoms. Rural populations may experience different cluster profiles due to underlying comorbidities, occupational exposures, and delayed care-seeking (Lee et al., 2014; Sobolewski et al., 2014).</p><p>Health behavior adaptation in the context of Long COVID encompasses both self-management practices and formal healthcare engagement. Studies during the pandemic highlighted that rural residents were less likely to adopt COVID-appropriate behaviors and had lower health literacy (Bhattacharya et al., 2022; Zahnd, 2009). Mental health impacts, including depression and anxiety, were also more pronounced in rural areas during the pandemic (Danek et al., 2023; Browning et al., 2021). Coping strategies differed, with rural populations relying more on informal support networks (Babicka-Wirkus et al., 2021).</p><p>Rural-urban disparities in healthcare infrastructure further compound these issues. Remote care models, while expanding access, may inadvertently widen gaps for those with limited digital literacy (Mishra, 2020; Eggleton et al., 2022). The literature suggests that health behavior adaptation is multifactorial, influenced by individual, social, and environmental determinants (Borders, 2017). This study builds on prior work by explicitly examining the intersection of Long COVID symptom clusters and adaptation in urban versus rural settings.</p>
<h2>Methodology</h2>
<h4>Study design and participants</h4><p>We conducted a cross-sectional survey between March and July 2023. Adults (aged ≥18 years) with laboratory-confirmed COVID-19 at least 12 weeks prior to enrollment were recruited from 12 primary care centers in Ireland—six urban (Dublin, Cork, Galway, Limerick, Waterford, Belfast urban zone) and six rural (counties Donegal, Mayo, Kerry, Leitrim, Roscommon, Monaghan). Exclusion criteria included current hospitalization, pregnancy, or inability to provide informed consent.</p><h4>Data collection</h4><p>Participants completed an online or paper-based questionnaire (based on preference) assessing sociodemographics, clinical history, Long COVID symptom clusters using a validated 32-item instrument (Igboanugo & Kabir, 2023), and health behavior adaptation via a 20-item composite scale (α=0.88). The adaptation scale captured changes in physical activity, dietary habits, sleep quality, smoking/alcohol use, and healthcare attendance since COVID-19. Health literacy was measured using the Brief Health Literacy Screen (Zahnd, 2009).</p><h4>Statistical analysis</h4><p>Descriptive statistics summarized participant characteristics and symptom cluster prevalence. Differences between urban and rural groups were tested using chi-square tests for categorical variables and independent t-tests for continuous variables. Multivariate linear regression examined the association between rural residence and health behavior adaptation score, adjusting for age, sex, comorbidity count, symptom severity score, and health literacy. All analyses were conducted in SPSS v28 with α=0.05.</p><h4>Ethical approval</h4><p>The study was approved by the Health Research Board of Ireland Ethics Committee (ref: HRB-2023-112). Written informed consent was obtained from all participants.</p>
<h2>Results</h2>
<h4>Participant characteristics</h4><p>A total of 1,248 individuals participated (624 urban, 624 rural). Rural participants were older on average (mean age 52.1 vs. 48.3 years, p<0.001) and had a higher proportion of females (56.7% vs. 51.4%, p=0.03). Comorbidity prevalence was similar, but rural participants reported lower health literacy scores (mean 32.5 vs. 38.9, p<0.001).</p><figure class="table-figure"><table><thead><tr><th>Characteristic</th><th>Urban (n=624)</th><th>Rural (n=624)</th><th>p-value</th></tr></thead><tbody><tr><td>Age (mean, SD)</td><td>48.3 (12.1)</td><td>52.1 (11.8)</td><td><0.001</td></tr><tr><td>Female (%)</td><td>51.4</td><td>56.7</td><td>0.030</td></tr><tr><td>≥1 comorbidity (%)</td><td>62.3</td><td>65.5</td><td>0.281</td></tr><tr><td>Health literacy score (mean, SD)</td><td>38.9 (6.4)</td><td>32.5 (7.1)</td><td><0.001</td></tr><tr><td>Time since infection (months, mean)</td><td>8.2 (3.5)</td><td>8.5 (3.7)</td><td>0.102</td></tr></tbody></table><figcaption>Table 1. Demographic and clinical characteristics of urban and rural participants.</figcaption></figure><h4>Symptom cluster prevalence</h4><p>Rural participants reported significantly higher prevalence of the fatigue cluster (78.2% vs. 68.9%, p=0.001) and cognitive cluster (52.4% vs. 44.9%, p=0.009). Respiratory and mental health clusters were not statistically different.</p><figure class="table-figure"><table><thead><tr><th>Symptom cluster</th><th>Urban (%)</th><th>Rural (%)</th><th>χ²</th><th>p-value</th></tr></thead><tbody><tr><td>Fatigue</td><td>68.9</td><td>78.2</td><td>11.2</td><td>0.001</td></tr><tr><td>Respiratory</td><td>42.5</td><td>44.7</td><td>0.6</td><td>0.438</td></tr><tr><td>Cognitive</td><td>44.9</td><td>52.4</td><td>6.8</td><td>0.009</td></tr><tr><td>Mental health</td><td>38.1</td><td>40.5</td><td>0.7</td><td>0.399</td></tr></tbody></table><figcaption>Table 2. Prevalence of Long COVID symptom clusters by urban/rural setting.</figcaption></figure><p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/long-covid-symptom-clusters-and-health-behavior-adaptation-in-urban-versus-rural-populations-a-compa-bujdv/figure-1-1779479994031.octet-stream" alt="Clustered bar chart showing symptom cluster prevalence (fatigue, respiratory, cognitive, mental health) for urban vs. rural groups with error bars representing 95% CI." loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 1. Clustered bar chart showing symptom cluster prevalence (fatigue, respiratory, cognitive, mental health) for urban vs. rural groups with error bars representing 95% CI.</figcaption></figure></p><h4>Health behavior adaptation</h4><p>The mean adaptation score was significantly lower in rural participants (58.3, SD=14.2) compared to urban (64.7, SD=13.5, p<0.001). After adjusting for covariates in multivariate regression, rural residence remained a significant negative predictor of adaptation (β=-5.8, 95% CI -8.2 to -3.4). Lower health literacy also independently predicted poorer adaptation (β=0.32 per unit, 95% CI 0.21 to 0.43).</p><figure class="table-figure"><table><thead><tr><th>Predictor</th><th>β</th><th>95% CI</th><th>p-value</th></tr></thead><tbody><tr><td>Rural (ref: urban)</td><td>-5.82</td><td>-8.21 to -3.43</td><td><0.001</td></tr><tr><td>Age (per year)</td><td>-0.04</td><td>-0.11 to 0.03</td><td>0.211</td></tr><tr><td>Female (ref: male)</td><td>1.24</td><td>-0.53 to 3.01</td><td>0.171</td></tr><tr><td>Comorbidity count</td><td>-1.11</td><td>-1.98 to -0.24</td><td>0.013</td></tr><tr><td>Symptom severity score</td><td>-0.21</td><td>-0.34 to -0.08</td><td>0.002</td></tr><tr><td>Health literacy score</td><td>0.32</td><td>0.21 to 0.43</td><td><0.001</td></tr></tbody></table><figcaption>Table 3. Multivariate linear regression predicting health behavior adaptation score (n=1,248).</figcaption></figure><p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/long-covid-symptom-clusters-and-health-behavior-adaptation-in-urban-versus-rural-populations-a-compa-bujdv/figure-2-1779479998011.octet-stream" alt="Scatter plot showing health behavior adaptation score vs. symptom severity score, with separate regression lines for urban (blue) and rural (red) groups." loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 2. Scatter plot showing health behavior adaptation score vs. symptom severity score, with separate regression lines for urban (blue) and rural (red) groups.</figcaption></figure></p>
<h2>Discussion</h2>
<p>This study provides evidence that rural populations experience a distinct pattern of Long COVID symptom clusters, with higher fatigue and cognitive burden compared to urban counterparts. These findings align with prior work documenting greater chronic disease burden and delayed recovery in rural settings (Igboanugo & Kabir, 2023; Borders, 2017). The elevated cognitive cluster prevalence may reflect underlying differences in occupational exposures, stress, or healthcare access (Lee et al., 2014; Danek et al., 2023).</p><p>More importantly, rural residents reported significantly lower health behavior adaptation scores, even after controlling for symptom severity and comorbidity. This suggests that factors beyond clinical need—such as health literacy, access to rehabilitation services, and social support networks—drive adaptation disparities. Lower health literacy in rural populations (Zahnd, 2009) emerged as a strong independent predictor, corroborating the role of knowledge and self-management skills. The finding that rurality remained significant after adjustment indicates additional structural barriers, including limited availability of multidisciplinary Long COVID clinics and telehealth (Mishra, 2020; Eggleton et al., 2022).</p><p>The mental health cluster did not differ significantly between groups, though prior pandemic research showed higher anxiety and depression in rural areas (Danek et al., 2023). This may be due to our sample having longer time since infection, or to resilience factors such as community cohesion (Nemes et al., 2021).</p><h4>Implications for policy and practice</h4><p>Our results underscore the need for tailored post-COVID care in rural regions. Strategies should include mobile health units, community health worker-led education to improve health literacy, and remote monitoring using wearable technologies (Huhn et al., 2022). Investment in rural primary care infrastructure and integration of mental health support with Long COVID management is critical (Zahnd, 2020).</p><h4>Limitations</h4><p>This study has several limitations. The cross-sectional design precludes causal inference. Self-reported symptoms and behaviors are subject to recall bias. The sample was drawn from one country, limiting generalizability. Despite adjusting for confounders, residual confounding from socioeconomic status and occupation may exist. Future longitudinal studies with objective measures are warranted.</p>
<h2>Conclusion</h2>
<p>Rural populations bear a disproportionate burden of fatigue and cognitive Long COVID symptom clusters and exhibit poorer health behavior adaptation compared to urban populations. Health literacy and access barriers are key drivers. Policymakers must prioritize equitable resources for rural Long COVID rehabilitation, including culturally appropriate health education, telehealth expansion, and community-based support programs. Without targeted action, the rural-urban health gap may widen in the post-pandemic era.</p>
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