Effectiveness of a Community-Based Mobile Rehabilitation Program for Older Adults in Underserved Areas: Functional and Pain Outcomes

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Using real-world data from 10,369 older adults, the analysis examined functional performance (Short Physical Performance Battery, SPPB) and pain outcomes (Visual Analogue Scale, VAS). Hierarchical logistic regression models revealed that repeated rehabilitation sessions and greater exposure to health education significantly predicted both functional and pain improvement. Functional gains were particularly associated with higher service frequency and lower baseline performance, while pain relief was strongly linked to functional recovery. Regional disparities emerged, with residents in suburban, coastal, and mountainous regions showing lower odds of improvement compared to indigenous communities, underscoring equity challenges in rural rehabilitation delivery. The findings demonstrate that service intensity and structured engagement are critical for effective community-based rehabilitation. Policy implications highlight the need for sustained, culturally responsive, and mobile interventions to reduce health inequities, promote independence, and improve quality of life for aging populations. Public Health Mobile Health Units Rehabilitation Aged Rural Health Community Health Introduction Population aging is a global phenomenon, with profound implications for healthcare systems, particularly in the delivery of rehabilitation services for older adults. Older individuals commonly experience chronic pain, frailty, and functional limitations, which demand sustained and accessible rehabilitative care ( 1 , 2 ) However, health systems often fail to address the complex and evolving needs of older adults due to fragmented services and limited community integration ( 3 , 4 ). Rural and underserved communities face even greater challenges. Older adults in these regions experience pronounced disparities in health service access and rehabilitation availability, often due to geographical isolation and workforce shortages ( 5 , 6 ). Studies in Asia have highlighted large segments of the older population with unmet rehabilitation needs and limited formal service access ( 5 , 7 ). Similar structural inequalities exist in Taiwan, where rural populations encounter significant barriers to long-term and community-based care ( 8 , 9 ). In response, community-based of public health prevention and mobile rehabilitation models have emerged globally as scalable and context-sensitive solutions. These services extend therapeutic support to patients’ homes or local centers, reducing logistical barriers and improving participation among frail and low-mobility populations ( 10 , 11 ). A recent meta-analysis further supports the efficacy of mobile rehabilitation tools, including app-based programs, in improving outcomes for older adults following surgical or functional decline ( 12 ). Evidence from cardiac, musculoskeletal, and osteoarthritis rehabilitation also suggests that mobile or home-based interventions are feasible and effective in reducing pain and improving function ( 13 – 16 ). Despite growing global evidence, few studies have examined the effectiveness of mobile rehabilitation services in real-world rural settings from both physical function and pain perspectives. In Taiwan, the expansion of mobile geriatric services presents a valuable opportunity to evaluate the equity and impact of such interventions in underserved populations. Since early 2023, Pingtung County has piloted a community-based mobile rehabilitation program across multiple rural townships. The initiative has gained strong community support, demonstrated high satisfaction, and enabled timely referrals—highlighting its potential as a scalable, community-based care model. This study evaluates the outcomes of this public health policy and mobile rehabilitation program in terms of both functional improvement and pain relief among rural older adults. By analyzing standardized indicators (SPPB and VAS) and identifying geographic and service-related predictors, the findings aim to inform future policy development in aging societies facing similar challenges. Methodology Study design and participants This study adopted a retrospective observational design using administrative data from a county-level mobile rehabilitation program implemented in Pingtung County, Taiwan. The program aimed to improve physical functioning and alleviate pain among older adults residing in rural, coastal, mountainous, and indigenous areas. Multidisciplinary teams—including licensed physical therapists, physicians and nurses—delivered rehabilitation services directly to public health centers, village halls, or participants’ homes began Feb 21, 2023. Eligible participants were community-dwelling adults aged 65 years and older who had received at least two service visits during the study period. For inclusion in the final analysis, participants were required to have completed both pre- and post-intervention assessments for the Short Physical Performance Battery (SPPB) and self-reported pain using the Visual Analogue Scale (VAS). Individuals with missing key variables, such as geographic region, BMI classification, or intervention exposure data, were excluded. Of the 10,369 registered service users, 9,378 had valid data for at least one of the outcome measures (SPPB or VAS) and were included in the analysis. A subset of 6,539 participants had complete SPPB data, while 7,190 had valid VAS scores. All data were anonymized prior to analysis. The study protocol was reviewed and approved by the Institutional Review Board in accordance with the ethical standards of human research. The project has certified for exemption from Human Research Ethics Committee at National Cheng Kung University (Approval HREC No. [114–0586]). Measures This study included two primary outcome measures to assess the effectiveness of the mobile rehabilitation program. Functional improvement was evaluated using the Short Physical Performance Battery (SPPB), a validated instrument that comprises three subtests: balance, gait speed, and chair-stand performance. Total scores range from 0 to 12, with higher scores indicating better physical function. Improvement in SPPB was operationalized as a binary variable, defined by an increase in the total score from baseline to follow-up (i.e., post-test score > pre-test score). Pain improvement was assessed using the Visual Analogue Scale (VAS), which measures pain intensity on a scale from 0 (no pain) to 10 (worst imaginable pain). A binary variable for pain improvement was created, with improvement defined as a reduction in pain score from the initial to the most recent assessment (i.e., final VAS < initial VAS). Independent variables included demographic, clinical, functional, and service-related characteristics. Demographic variables were age (in years), gender (male or female), and body mass index (BMI), calculated from recorded height and weight and categorized according to standard BMI classification. Clinical indicators included the number of chronic conditions reported by participants (e.g., hypertension, diabetes, heart disease) and baseline SPPB and VAS scores. Geographic location was classified into five categories based on administrative and cultural features of residence: urban, suburban, coastal, mountainous, and indigenous areas. These classifications reflect regional variations in healthcare access and social determinants of health within Pingtung County. Service exposure variables included the total number of rehabilitation sessions received, the number of health education topics provided during the intervention, and physician instruction adherence, recorded as a binary variable (1 = followed, 0 = did not follow). All data were extracted from standardized service records maintained by the mobile rehabilitation team and reviewed for completeness and consistency prior to analysis. Participant data were anonymized at the time of extraction to ensure confidentiality. Statistical analysis Descriptive statistics were first computed to summarize participants’ demographic, clinical, and service-related characteristics. Means and standard deviations were reported for continuous variables, while frequencies and percentages were calculated for categorical variables. To assess preliminary associations between potential predictors and the two primary outcomes (SPPB improvement and VAS improvement), bivariate comparisons were conducted. Chi-square tests were used to examine associations between categorical variables and the outcome variables, whereas independent-samples t-tests or Mann–Whitney U tests were applied for continuous predictors, depending on the results of normality assessments. These initial comparisons supported the identification and selection of variables for inclusion in the hierarchical logistic regression models, and helped to interpret the directionality of effects. Two separate hierarchical binary logistic regression models were conducted to identify predictors of ( 1 ) functional improvement, defined as a positive change in SPPB score from baseline to post-intervention (1 = improved, 0 = not improved), and ( 2 ) pain improvement, defined as a reduction in self-reported pain score on the Visual Analogue Scale (VAS). In each model, predictors were entered sequentially in three blocks to assess their incremental explanatory power. Block 1 included demographic and geographic variables (age, gender, BMI classification, and geographic region of residence). Block 2 introduced clinical and baseline functional variables, including the number of chronic conditions, pre-intervention scores (SPPB or VAS), and functional improvement (used as a predictor in the VAS model). Block 3 added intervention exposure variables: number of rehabilitation sessions, number of health education topics received, and physician instruction adherence. Model performance was evaluated using the − 2 log likelihood, Nagelkerke’s R², and omnibus χ² statistics. Changes in R² across blocks were used to assess incremental explanatory power. Variance inflation factors (VIFs) were examined to detect multicollinearity among predictors. All analyses were conducted using SPSS version 27.0, and statistical significance was set at p < .05. Results Participant characteristics A total of 6,534 older adults were included in the final analysis after excluding those with pre- or post-test SPPB scores equal to zero. The average participant age was 71.70 years (SD = 12.00), and the mean body mass index (BMI) was 25.31 kg/m² (SD = 4.25). The sample was predominantly female, with 22.7% identifying as male. The mean Short Physical Performance Battery (SPPB) score at baseline was 9.52 (SD = 2.65), increasing to 10.10 (SD = 2.39) at post-test, indicating overall functional improvement following the intervention. Regarding chronic health conditions, 52.1% of participants had hypertension, 25.3% had diabetes, and 12.5% had heart disease. Participants were distributed across five regional categories based on township classification: 18.3% resided in urban areas, 42.0% in suburban areas, 22.1% in coastal regions, 10.7% in mountainous areas, and 6.9% in indigenous townships. This regional diversity provides a strong basis for evaluating the contextual effects of mobile rehabilitation delivery (see Table 1 ). Table 1 Participants’ Characteristics (N = 6,534) Variable Mean / N SD / Percentage (%) Age (years) 71.70 12.00 BMI (kg/m²) 25.31 4.25 Gender Male 1,483 22.7 Female 5,051 77.3 Hypertension Yes 3,404 52.1 No 3,130 47.9 Diabetes Yes 1,653 25.3 No 4,881 74.7 Heart Disease Yes 817 12.5 No 5,717 87.5 Urban Region Yes 1,196 18.3 No 5,338 81.7 Suburban Region Yes 2,744 42 No 3,790 58.0 Coastal Region Yes 1,444 22.1 No 5,090 77.9 Mountain Region Yes 699 10.7 No 5,835 89.3 Indigenous Region Yes 451 6.9 No 6,083 93.1 Note. Mean and standard deviation (SD) are presented for continuous variables. Percentages are shown for categorical variables. SPPB data with pre- or post-test score of 0 were excluded from the analysis. Bivariate analysis SPPB improvement Bivariate analyses revealed several significant associations between participant characteristics and functional improvement, as measured by the Short Physical Performance Battery (SPPB). Age was significantly higher among participants who showed improvement (M = 75.5, SD = 8.4) compared to those who did not (M = 69.1, SD = 13.4), t (6536) = − 22.03, p < .001. Participants with more chronic conditions were also more likely to improve ( M = 1.40 vs. 1.11; p < .001). In addition, those with functional improvement had received significantly more service sessions (M = 3.54 vs. 2.94; p < .001) and reported higher initial pain levels on the VAS scale (M = 6.43 vs. 6.11; p < .001). Conversely, their baseline physical function (pre_SPPB) was lower ( M = 8.15 vs. 10.47; p < .001), suggesting a greater potential for measurable improvement. Chi-square tests indicated that geographic region was strongly associated with SPPB improvement (χ² = 128.68, df = 4, p < .001). Urban participants were more likely to improve (49.9%) compared to those in mountainous (30.8%) and indigenous (39.7%) areas. Physician instruction adherence was also associated with greater improvement (χ² = 13.86, p < .001). However, no significant associations were observed with gender (χ² = 3.21, p = .073) or BMI classification (χ² = 4.01, p = .261). VAS improvement For pain improvement (VAS), participants who reported improvement had significantly lower baseline pain (VAS1: M = 6.22) than those who did not improve ( M = 4.97), t (10354) = − 23.48, p < .001. Participants who improved were also younger ( M = 69.7 vs. 72.2; p < .001) and had received more health education ( M = 1.24 vs. 1.19; p = .004). Although service frequency appeared slightly higher in the improved group ( M = 2.64 vs. 2.54), this difference was only marginal ( p = .065). No significant differences were observed in the number of comorbidities ( p = .780) or pre_SPPB scores ( p = .659). Chi-square analysis showed that geographic region was again a significant predictor (χ² = 51.57, df = 4, p < .001), with participants from indigenous areas showing higher rates of pain improvement (97.1%) compared to those from suburban (92.2%) and mountainous (92.0%) areas. However, gender (χ² = 2.64, p = .104), BMI classification (χ² = 6.70, p = .082), and physician instruction adherence (χ² = .846, p = .358) were not significantly related to VAS improvement. Notably, functional improvement (SPPB) was significantly associated with pain improvement (χ² = 47.48, p < .001), suggesting a linked outcome trajectory. Predictors of SPPB improvement To assess potential multicollinearity among predictors, variance inflation factors (VIFs) were calculated using a linear regression model that included the same set of independent variables. All VIF values were below 5 (ranging from 1.01 to 1.49), indicating no substantial multicollinearity ( 17 ). This study employed hierarchical binary logistic regression to examine the predictors of functional improvement, defined as a binary outcome variable "Improved SPPB" (1 = improved, 0 = not improved). Three blocks of independent variables were entered sequentially to explore their incremental explanatory power. The first block included four covariates: geographic region classification, age, gender, and BMI classification. This model yielded a Nagelkerke R² of 0.142 and − 2 Log Likelihood of 7415.11, indicating a modest level of explanatory power. The overall model was statistically significant (χ² = 666.305, df = 9, p < .001). Among the predictors, geographic region was significant: residents in mountainous areas were significantly less likely to experience SPPB improvement compared to those in indigenous areas (OR = 0.539, p < .001). Age was positively associated with improvement (OR = 1.018, p .05, respectively). The Hosmer-Lemeshow goodness-of-fit test yielded a χ² = 29.250, df = 8, p < .001, suggesting poor fit, possibly due to large sample size (N = 5973), as this test tends to detect trivial deviations in large datasets. In Block 2, we added illness count, pre-intervention SPPB scores (pre_SPPB), baseline pain scores (VAS1), and physician advice (medical instruction adherence). The model's Nagelkerke R² increased substantially to 0.299 and − 2LL dropped to 6582.71. The Hosmer-Lemeshow test showed χ² = 665.807, df = 8, p < .001. Among the added predictors, both pre_SPPB (OR = 0.695, p < .001) and VAS1 (OR = 1.123, p < .001) were significant. Lower pre_SPPB scores were associated with greater likelihood of improvement (ceiling effect), and higher baseline pain also predicted improvement. Illness count and physician instruction adherence were not statistically significant (p > .05), indicating these health status factors did not independently predict improvement in the context of this model. In the final block of the hierarchical logistic regression model predicting SPPB improvement, three variables related to participants’ engagement with the mobile rehabilitation program were introduced: number of health education topics received, total service frequency, and adherence to physician instruction. Inclusion of these intervention-related variables substantially improved model performance, increasing the Nagelkerke R² to 0.350 and reducing the − 2 Log Likelihood to 6285.14. The classification accuracy of the model also rose to 74.2%, with a sensitivity of 60.8% for identifying cases with functional improvement. Although the Hosmer–Lemeshow test remained statistically significant (χ² = 385.278, df = 8, p < .001), this may reflect the known sensitivity of the test in large sample sizes rather than poor model fit. Among the three predictors introduced in this block, two were statistically significant. The number of health education topics received was positively associated with the likelihood of improvement (OR = 1.200, p = .017), suggesting that greater exposure to educational content may enhance rehabilitation outcomes. Total service frequency also emerged as a strong predictor (OR = 1.676, p < .001), indicating that each additional service visit increased the odds of functional improvement by approximately 68%. In contrast, physician instruction adherence was not significantly associated with improvement ( p > .05), implying that passive receipt of medical advice may not translate into meaningful functional gains unless accompanied by active service participation. Overall, this final model underscores the importance of intervention intensity and content in predicting rehabilitation outcomes. While demographic and baseline clinical variables offered moderate predictive value, active engagement with mobile rehabilitation services—particularly repeated visits and targeted education—was essential to achieving functional improvement among older adults living in rural and underserved communities (see Table 2 and Table 3 ). Table 2 Summary of Hierarchical Binary Logistic Regression Results (N = 5973) Block -2 Log Likelihood Nagelkerke R² Classification Accuracy (%) Improvement Accuracy (%) Hosmer-Lemeshow χ² (df = 8) p-value Block 1 7415.11 0.142 63.3 42.1 29.25 < .001 Block 2 6582.71 0.299 71.5 55.8 665.807 < .001 Block 3 6285.14 0.35 74.2 60.8 385.278 < .001 Note: - Nagelkerke R² indicates the explained variance in improvement status. Classification Accuracy = overall correct prediction rate. Hosmer-Lemeshow test is sensitive to large sample sizes; p < .05 does not necessarily imply poor fit. Table 3 Summary of All Predictors in Final Model (N = 5973) Variable Odds Ratio (Exp(B)) p-value Interpretation Region (Mountainous vs. Indigenous) 0.539 < .001 Lower odds in mountainous areas Age 1.018 < .001 Slight increase in odds with age pre_SPPB 0.695 < .001 Higher pre_SPPB reduces odds (ceiling effect) VAS1 1.123 < .001 Higher baseline pain predicts improvement Number of Health Education Topics 1.2 .017 More education topics increase odds Service Frequency 1.676 .05 Not statistically significant Illness Count n.s. > .05 Not statistically significant Physician Instruction Adherence n.s. > .05 Not statistically significant Note: 'n.s.' indicates not significant at p .05 did not reach statistical significance in the final model. Predictors of VAS improvement A hierarchical binary logistic regression was conducted to identify predictors of pain improvement, operationalized as a binary dependent variable indicating whether participants experienced a reduction in self-reported pain intensity based on the Visual Analogue Scale (VAS) (1 = improvement, 0 = no improvement or worsening). The analysis included 5,976 participants with valid pre- and post-intervention pain data. Predictors were introduced sequentially in three blocks: demographic and geographic characteristics (Block 1), health and functional status (Block 2), and intervention exposure (Block 3). In Block 1, the model included participants' geographic region, age, and BMI classification. This initial model was statistically significant (Omnibus χ² = 58.269, df = 8, p < .001), though its explanatory power remained modest, as reflected in a Nagelkerke R² of 0.026 and a − 2 Log Likelihood of 2760.324. The Hosmer–Lemeshow test yielded a non-significant result (χ² = 8.805, df = 8, p = .359), indicating acceptable model fit. Geographic region emerged as a significant predictor of VAS improvement. Compared to participants living in indigenous areas, those residing in suburban (OR = 0.494, p = .002), mountainous (OR = 0.389, p < .001), and coastal regions (OR = 0.404, p < .001) had significantly lower odds of reporting pain relief. BMI classification also demonstrated a significant effect, with obese participants being less likely to experience improvement (OR = 0.707, p = .013). In contrast, age did not significantly predict VAS improvement ( p = .176). In the second block, variables related to participants' health status and functional capacity were added to the model, including the number of chronic conditions (illness count), baseline SPPB score, and whether participants experienced functional improvement during the intervention period. The inclusion of these variables improved overall model fit, with the Omnibus χ² increasing to 115.034 ( df = 11, p < .001). The Nagelkerke R² rose to 0.051, and the − 2 Log Likelihood decreased to 2703.558, indicating a better-fitting model. The Hosmer–Lemeshow test showed excellent calibration (χ² = 3.605, df = 8, p = .891), supporting the model’s adequacy. Among the new variables introduced, functional improvement based on the SPPB was a strong and statistically significant predictor of pain relief (OR = 0.403, p < .001), suggesting that participants who did not experience physical function gains were also less likely to report reductions in pain. Interestingly, age—which had not been significant in Block 1—became a significant negative predictor in this block (OR = 0.984, p = .004), indicating that older individuals were slightly less likely to benefit from pain reduction. In contrast, illness count and pre-intervention SPPB scores did not demonstrate significant associations with VAS improvement ( p > .05). In the final block of the regression model predicting pain improvement, three variables related to participants’ engagement with the mobile rehabilitation program were introduced: number of health education topics received, total number of service visits, and adherence to physician instruction. The inclusion of these intervention-related factors significantly enhanced model performance (Omnibus χ² = 144.709, df = 14, p < .001), with the Nagelkerke R² increasing to 0.064 and the − 2 Log Likelihood decreasing to 2673.884. The Hosmer–Lemeshow test remained non-significant (χ² = 13.881, df = 8, p = .085), indicating an acceptable model fit (see Table 4 and Table 5 ). Table 4 Summary of Hierarchical Binary Logistic Regression Results (N = 5976) Block -2 Log Likelihood Nagelkerke R² Classification Accuracy (%) Hosmer-Lemeshow χ² (df = 8) p-value Block 1 2760.324 0.026 93.7 8.805 .359 Block 2 2703.558 0.051 93.7 3.605 .891 Block 3 2673.884 0.064 93.7 13.881 .085 Note: Nagelkerke R² indicates the explained variance in VAS improvement;'n.s.' indicates not statistically significant at p < .05༛Region comparisons are relative to Indigenous areas. Table 5 Summary of All Predictors in Final Model (N = 5976) Variable Odds Ratio (Exp(B)) p-value Interpretation Region (Suburban vs. Indigenous) 0.494 .002 Lower odds of improvement in suburban areas Region (Mountainous vs. Indigenous) 0.389 < .001 Lower odds in mountainous areas Region (Coastal vs. Indigenous) 0.404 .05 Not significant pre_SPPB n.s. > .05 Not significant SPPB Improvement (Yes vs. No) 0.452 < .001 Strong predictor of VAS improvement Number of Health Education Topics 0.664 .001 More topics linked to greater pain relief Service Frequency 1.258 .05 Not significant Note: Hosmer-Lemeshow test p-values > .05 indicate acceptable model fit, though the test may be overly sensitive in large samples;Odds Ratios reflect likelihood of pain improvement associated with each predictor.༛All region-based ORs compare against Indigenous areas as the reference group༛'n.s.' denotes predictors that were not statistically significant (p > .05) in the final model. Among the intervention-related predictors, both the number of health education topics and total service frequency were statistically significant. Participants who received more health education topics had significantly higher odds of experiencing pain reduction (OR = 0.664, p = .001), indicating the additive effect of structured education on pain self-management. Similarly, each additional service session increased the likelihood of improvement by 25.8% (OR = 1.258, p < .001), reinforcing the importance of repeated rehabilitation contact. Notably, functional improvement (as measured by SPPB) remained a robust and independent predictor of VAS improvement (OR = 0.452, p .05), suggesting that direct service engagement may be more influential than passive medical advice or baseline clinical status in this context. Across the three-block model, predictive power improved from Nagelkerke R² = .026 → .051 → .064. While the overall effect size was modest, the classification accuracy remained at 93.7%, driven by the large proportion of participants with reported improvement. Notably, residents in suburban, mountainous, and coastal areas consistently showed lower odds of pain improvement compared to those in indigenous regions. This geographic disparity suggests potential inequities in access, engagement, or responsiveness to rehabilitation services. The finding has important implications for health policy, highlighting the need to enhance service coverage and culturally tailored support in underserved regions. Intervention variables, particularly repeated service participation and educational engagement, significantly enhanced the likelihood of pain improvement. These findings underscore the role of structured mobile rehabilitation services in reducing self-reported pain among older adults. Discussion Regional disparities in functional and pain outcomes The present study identified significant disparities in rehabilitation outcomes across different geographic regions of Pingtung County. Participants living in suburban, coastal, and mountainous regions showed significantly lower odds of functional (SPPB) and pain (VAS) improvement compared to those residing in indigenous townships. These results suggest that even within a localized mobile rehabilitation initiative, regional inequality in health outcomes persists—likely reflecting deeper structural gaps in health service access, engagement, and trust. Such findings align with international evidence showing that older adults in rural or underserved areas often face barriers to equitable rehabilitation services, including transportation challenges, reduced healthcare infrastructure, and limited health literacy ( 18 ). A study conducted by Zhang et al. found that rural older adults had significantly worse physical health outcomes due to reduced access to timely care, supporting the notion that geographic location plays a critical role in shaping rehabilitation success. Interestingly, our findings revealed that indigenous township residents had comparatively better outcomes, which may relate to the program's stronger integration with local community systems and cultural adaptation. This supports previous work by Das et al. (2023), who emphasized that culturally congruent, community-based outreach programs can enhance intervention acceptance and effectiveness, especially among historically marginalized populations( 19 ). These findings reinforce calls for a shift away from one-size-fits-all interventions and toward more locally responsive, equity-oriented rehabilitation policies ( 20 ). In sum, the regional variation observed in this study highlights the necessity of tailoring service delivery not only by age or health status, but by geography and cultural context. To promote equitable aging, future policy must address logistical and contextual constraints across remote regions and actively invest in community-based mobile rehabilitation as a scalable solution for reaching the underserved. Functional improvement: the importance of service intensity This study found that the number of rehabilitation sessions and the breadth of health education topics received were significant predictors of functional improvement among older adults. Specifically, higher service frequency and greater engagement with educational content were associated with increased likelihood of Short Physical Performance Battery (SPPB) score gains. These findings reinforce the view that rehabilitation “dose” matters—both in quantity and content—and that continuity and repetition are vital for measurable gains in function among frail older individuals. International literature increasingly supports this dose-response effect in geriatric rehabilitation. Cieślik et al. (2023), through a network meta-analysis of technology-assisted interventions, found that structured and repeated rehabilitation sessions yield superior mobility outcomes compared to minimal or one-time services( 21 ). Similarly, Wakabayashi (2024) emphasized the necessity of combining regular rehabilitation with nutritional and oral health support to counteract sarcopenia and functional decline, especially in older adults with complex care needs( 22 ). These findings parallel the results of our study, in which multi-faceted and repeated contact—not simply service access—was key to meaningful improvement. Moreover, policy-oriented research has highlighted that the acceptability and effectiveness of community-based rehabilitation hinges on sustained interaction with multidisciplinary teams ( 23 ). In our program context, the ability of the mobile units to deliver recurring, face-to-face therapy across townships likely contributed to the observed improvements. This echoes the conceptual framework proposed by Sibley et al. (2024), which places continuity, local accessibility, and care coordination at the center of adult community rehabilitation( 24 ). In East Asian settings, studies have repeatedly noted a mismatch between older adults’ rehabilitation demands and the actual delivery of community-based services ( 5 , 25 ). The present findings demonstrate that a high-volume, mobile rehabilitation model can begin to address this service gap, particularly when coupled with health education and consistent follow-up. Altogether, the results suggest that designing aging policy around low-intensity, sporadic intervention is unlikely to yield enough functional gains. Instead, scalable and sustained community-based rehabilitation—delivered with appropriate frequency and educational reinforcement—should be prioritized in rural and aging regions. Pain relief and its linkage to functional recovery The relationship between pain reduction and functional recovery has long been recognized as bi-directional, particularly in older adults with musculoskeletal limitations. In this study, improvement in SPPB scores significantly predicted VAS-based pain relief, suggesting that as individuals regained mobility and balance, they experienced less perceived pain. This aligns with existing evidence emphasizing the inseparability of physical and pain-related health outcomes in geriatric rehabilitation. Multiple recent reviews and trials support this interdependence. Sinatti et al. (2022) showed that patient education as part of conservative treatment for hip and knee osteoarthritis led to significant improvements in both pain and function( 26 ). This indicates that empowering patients with knowledge and self-management strategies can simultaneously influence pain perception and physical activity. Similarly, Wang et al. (2021) demonstrated through a meta-analysis that pain coping skills training had direct positive effects on pain intensity and functional mobility, underscoring the centrality of psychosocial interventions within physical rehabilitation programs( 27 ). More broadly, McDonough et al. (2021), in clinical practice guidelines, advocated for early, progressive, and personalized physical therapy following hip fractures, citing its dual benefits on functional capacity and pain mitigation( 28 ). This dual effect is also evident in technologically enhanced rehabilitation models. Tay et al. (2025) highlighted that combining photobiomodulation with exercise in knee osteoarthritis improved pain and physical function more than standard care alone( 29 ). Meanwhile, mHealth applications targeting chronic pain have also shown functional gains alongside pain reduction, as evidenced by Moreno-Ligero et al. (2023)( 30 ). Moreover, functional outcome tools such as the Patient-Specific Functional Scale (PSFS) have proven sensitive in detecting changes tied to pain-modulating interdisciplinary programs ( 31 ). These findings support the notion that pain and function must be considered as integrated outcomes when evaluating rehabilitation effectiveness, especially in mobile or community-based contexts. In the context of this study, the co-occurrence of SPPB and VAS improvement strengthens the argument that reducing functional limitations not only restores physical independence but also alleviates pain perception through enhanced musculoskeletal efficiency, reduced compensatory stress, and improved confidence in movement. Effective rehabilitation programs should thus maintain a dual focus—functional and sensory—when targeting older populations, particularly those in resource-constrained or rural settings. Non-significant predictors and contextual interpretation The relationship between pain reduction and functional recovery has long been recognized as bi-directional, particularly in older adults with musculoskeletal limitations. In this study, improvement in SPPB scores significantly predicted VAS-based pain relief, suggesting that as individuals regained mobility and balance, they experienced less perceived pain. This aligns with existing evidence emphasizing the inseparability of physical and pain-related health outcomes in geriatric rehabilitation. Multiple recent reviews and trials support this interdependence. Sinatti et al. (2022) showed that patient education as part of conservative treatment for hip and knee osteoarthritis led to significant improvements in both pain and function( 26 ). This indicates that empowering patients with knowledge and self-management strategies can simultaneously influence pain perception and physical activity. Similarly, Wang et al. (2021) demonstrated through a meta-analysis that pain coping skills training had direct positive effects on pain intensity and functional mobility, underscoring the centrality of psychosocial interventions within physical rehabilitation programs( 27 ). More broadly, McDonough et al. (2021), in clinical practice guidelines, advocated for early, progressive, and personalized physical therapy following hip fractures, citing its dual benefits on functional capacity and pain mitigation( 28 ). This dual effect is also evident in technologically enhanced rehabilitation models. Tay et al. (2025) highlighted that combining photobiomodulation with exercise in knee osteoarthritis improved pain and physical function more than standard care alone( 29 ). Meanwhile, mHealth applications targeting chronic pain have also shown functional gains alongside pain reduction, as evidenced by Moreno-Ligero et al. (2023)( 30 ). Moreover, functional outcome tools such as the Patient-Specific Functional Scale (PSFS) have proven sensitive in detecting changes tied to pain-modulating interdisciplinary programs ( 31 ). These findings support the notion that pain and function must be considered as integrated outcomes when evaluating rehabilitation effectiveness, especially in mobile or community-based contexts. In the context of this study, the co-occurrence of SPPB and VAS improvement strengthens the argument that reducing functional limitations not only restores physical independence but also alleviates pain perception through enhanced musculoskeletal efficiency, reduced compensatory stress, and improved confidence in movement. Effective rehabilitation programs should thus maintain a dual focus—functional and sensory—when targeting older populations, particularly those in resource-constrained or rural settings. Implications for rural rehabilitation policy and equity This study provides empirical evidence supporting the effectiveness of Pingtung County’s mobile rehabilitation program in improving both physical function and self-reported pain among older adults. Through hierarchical logistic regression analysis, we identified that service frequency and health education intensity were key predictors of positive outcomes, reinforcing the notion that sustained, community-delivered intervention is critical for functional and sensory improvement in aging populations. These findings directly align with the goals of the original program, which aimed to reduce urban-rural health disparities, enhance the accessibility of rehabilitation services, and support early intervention to prevent long-term disability. As outlined in the county’s 2023–2024 action plan, the mobile rehabilitation initiative was specifically designed to overcome geographic barriers, particularly in indigenous and mountainous regions where formal rehabilitation resources are limited. By deploying mobile units equipped with physical therapists and educational tools, the program reached over 14,000 residents and demonstrated high satisfaction, functional improvement, and pain relief outcomes. The policy implications are threefold. First, health authorities should prioritize integrated community-based care models that combine preventive education, early screening, and mobility-focused rehabilitation. Second, mobile services should be viewed as a complement to existing long-term care and public health strategies, offering scalable, flexible responses to emerging needs in rural populations. Third, future program iterations should incorporate behavioral adherence supports and digital follow-up mechanisms to further enhance continuity and cost-effectiveness. Moreover, the program’s structure demonstrates a viable template for cross-sectoral collaboration—engaging local governments, health institutions, community organizations, and private donors. This public-private partnership model not only expands service coverage but also fosters local workforce development, as evidenced by the employment of 38 physical therapists under the initiative. Overall, the evidence presented here supports the institutionalization and expansion of mobile rehabilitation as a core strategy in aging policy—especially in rural settings. As Taiwan and other nations face the dual challenges of demographic aging and health service inequity, the lessons from this model can inform broader national efforts to deliver equitable, community-anchored, and function-centered care. Limitations and future directions Despite the large sample size and real-world setting, this study has several limitations that warrant consideration. First, the observational nature of the design restricts causal inference. While associations between intervention intensity and outcomes were observed, it is not possible to definitively conclude that the rehabilitation services caused the improvements in pain or physical function. Future research should incorporate quasi-experimental or randomized controlled designs to strengthen causal interpretation. Second, although key predictors such as service frequency and health education were analyzed, other relevant psychosocial or environmental variables were not captured. Factors such as caregiver involvement, motivation, depression, or social support—known to influence adherence and rehabilitation response—were not available in the dataset. The inclusion of these variables in future analyses may enhance model precision and explanatory power. Third, the study relied on routinely collected administrative data, which may be subject to reporting errors, missing values, and inconsistencies in service documentation across teams. Although data quality controls were implemented, future studies should consider triangulating findings with qualitative data or independent clinical assessments to ensure robustness. Fourth, the generalizability of the findings may be context-dependent. Pingtung County’s unique geographic and demographic profile, as well as the specific design of its mobile rehabilitation infrastructure, may not fully reflect the conditions in other regions or countries. Future research should explore how this model can be adapted and scaled in diverse settings with different resource constraints and health system structures. Finally, while functional and pain outcomes were evaluated using validated tools (SPPB and VAS), the study did not assess long-term sustainability of gains or health system cost-effectiveness. Longitudinal follow-up is necessary to determine whether the improvements persist over time and whether the mobile model offers advantages in reducing institutional care demand and economic burden. Taken together, these limitations highlight the need for more comprehensive, longitudinal, and multi-level evaluations. Future research should also explore digital integration, behavioral supports, and multi-disciplinary coordination as means to strengthen mobile rehabilitation strategies in aging societies. Conclusion This study evaluated the effectiveness of a government-led mobile rehabilitation program implemented in rural and underserved regions of Pingtung County, Taiwan. By analyzing real-world service data from over 10,000 older adults, we identified that repeated rehabilitation sessions and exposure to health education were significant predictors of functional improvement and pain reduction. These findings demonstrate that service intensity and structured engagement are critical components of successful rehabilitation delivery in aging communities. Importantly, the results reinforce the value of mobile rehabilitation as a scalable strategy to bridge health service gaps in geographically and socially marginalized populations. Despite contextual and behavioral challenges—including variability in treatment adherence and the limited predictive value of baseline clinical indicators—the program achieved measurable benefits for older adults with chronic pain and mobility limitations. This study provides policy-relevant evidence in support of integrated, community-anchored rehabilitation models that combine physical therapy, preventive education, and accessible service delivery. It further highlights the need for long-term planning, cross-sector collaboration, and adaptive intervention models to ensure sustainability. As populations age and healthcare inequities persist, mobile rehabilitation represents a promising public health approach to promote functional independence, relieve pain, and improve quality of life for older adults. Declarations Conflicts of interest The authors declare no conflicts of interest Ethics approval and consent to participate This study adopted a retrospective observational design using administrative data from a county-level mobile rehabilitation program implemented in Pingtung County, Taiwan. All data were anonymized prior to analysis. The study protocol was reviewed and approved by the Institutional Review Board in accordance with the ethical standards of human research. The project has certified for exemption from Human Research Ethics Committee at National Cheng Kung University (Approval HREC No. [114–0586]). Consent for publication Not Applicable. Competing interests The authors declare that they have no competing interests Funding This research was not funded. Author Contribution Jin-Hung Lin: Conceptualization, Collection and Curation of Data, and Writing.Hsiu-Chun Chang: Conceptualization, Collection of Data.Wen-Hung Tou: Collection and Curation of Data, involved in design of the research.Chun Hao Liu: Collection and Curation of Data, involved in design of the research.Shang-Min Ma: Conceptualization, Formal Analysis, Writing and revised the text.All authors read and approved the final version of the manuscript. Acknowledgements Not applicable. Data Availability The datasets generated and/or analyzed during the current study are not publicly available due to confidentiality reasons. References Cowley A, Goldberg SE, Gordon AL, Logan PA. Rehabilitation potential in older people living with frailty: a systematic mapping review. BMC Geriatr. 2021;21(1):533. Falvey JR, Ye JZ, Parker EA, Beamer BA, Addison O. Rehabilitation Outcomes among Frail Older Adults in the United States. Int J Environ Res Public Health. 2022;19(17). Charumbira MY, Conradie T, Berner K, Louw QA. Bridging the chasm between patients' needs and current rehabilitation care: perceptions of adults presenting for primary care in the Eastern Cape. BMC Health Serv Res. 2024;24(1):166. Fenton L, McDaid E. 156 Capturing older adults’ rehabilitation needs and complexity in a rehabilitation hospital. Age Ageing. 2023;52(Supplement3):afad156. Xu L, Xue C, Yang K, Chen L, Chen X, Xie X, et al. A latent class analysis of community-based rehabilitation needs among Chinese older adults: a mixed study protocol. Front Public Health. 2024;11:1301752. Li X, Shi Y, Zhao D, Jin K, Zhu J, Wang Y. Unmet needs for rehabilitation service of middle-aged and older adult residents in Chengdu, Sichuan, China: A cross-sectional study. Sci Rep. 2023;13(1):11989. Tongsiri S. Scaling-up community-based rehabilitation programs in rural Thailand: the development of a capacity building program. BMC Health Serv Res. 2022;22(1):1070. Wu SC, Peng MC, Hsueh JY, Chiang TL, Tu YK, Tung YC, et al. Impact of a New Home Care Payment Mechanism on Growth of the Home Care Workforce in Taiwan. Gerontologist. 2021;61(4):505–16. Chen JJ, Liu LF, Chang SM. Approaching person-centered long-term care: The trajectories of intrinsic capacity and functional decline in Taiwan. Geriatr Gerontol Int. 2022;22(7):516–22. Treger I, Kosto A, Vadas D, Friedman A, Lutsky L, Kalichman L. Crafting the Future of Community-Based Medical Rehabilitation: Exploring Optimal Models for Non-Inpatient Rehabilitation Services through a Narrative Review. Int J Environ Res Public Health. 2024;21(10). Snoek JA, Prescott EI, van der Velde AE, Eijsvogels TMH, Mikkelsen N, Prins LF, et al. Effectiveness of Home-Based Mobile Guided Cardiac Rehabilitation as Alternative Strategy for Nonparticipation in Clinic-Based Cardiac Rehabilitation Among Elderly Patients in Europe: A Randomized Clinical Trial. JAMA Cardiol. 2021;6(4):463–8. Özden F, Sarı Z. The effect of mobile application-based rehabilitation in patients with total knee arthroplasty: A systematic review and meta-analysis. Arch Gerontol Geriatr. 2023;113:105058. Ades PA, Khadanga S, Savage PD, Gaalema DE. Enhancing participation in cardiac rehabilitation: Focus on underserved populations. Prog Cardiovasc Dis. 2022;70:102–10. Hattori T, Shimo K, Niwa Y, Katsura Y, Tokiwa Y, Ohga S, et al. Pain Sensitization and Neuropathic Pain-like Symptoms Associated with Effectiveness of Exercise Therapy in Patients with Hip and Knee Osteoarthritis. Pain Res Manag. 2022;2022:4323045. Tian Y, Liu ZY, Wang JH, Qian JH. The efficacy and safety of Baduanjin exercise as complementary therapy for pain reduction and functional improvement in knee osteoarthritis: A meta-analysis of randomized controlled trials. Complement Ther Med. 2025;88:103127. Hansford HJ, Jones MD, Cashin AG, Ostelo RW, Chiarotto A, Williams SA, et al. The smallest worthwhile effect on pain intensity of exercise therapy for people with chronic low back pain: a discrete choice experiment study. J Orthop Sports Phys Ther. 2024;54(7):477–85. Hair JF, William C, Black, Barry J, Babin, Rolph E. Anderson Multivariate data analysis (7th ed.). 2010. Zhang X, Dupre ME, Qiu L, Zhou W, Zhao Y, Gu D. Urban-rural differences in the association between access to healthcare and health outcomes among older adults in China. BMC Geriatr. 2017;17(1):151. Das J, Kundu S, Hossain B. Rural-urban difference in meeting the need for healthcare and food among older adults: evidence from India. BMC Public Health. 2023;23(1):1231. Zhao Y, Xu X, Dupre ME, Xie Q, Qiu L, Gu D. Individual-level factors attributable to urban-rural disparity in mortality among older adults in China. BMC Public Health. 2020;20(1):1472. Cieślik B, Mazurek J, Wrzeciono A, Maistrello L, Szczepańska-Gieracha J, Conte P, et al. Examining technology-assisted rehabilitation for older adults' functional mobility: a network meta-analysis on efficacy and acceptability. NPJ Digit Med. 2023;6(1):159. Wakabayashi H. Triad of rehabilitation, nutrition, and oral management for sarcopenic dysphagia in older people. Geriatr Gerontol Int. 2024;24(Suppl 1):397–9. McDonnell M, Bell M, Lawler F, Duffy A, Connolly M. Multidisciplinary Inpatient Community Rehabilitation Programmes for Frail Older People: A Scoping Review. Nurs Open. 2024;11(11):e70088. Sibley KM, Barclay R, Cooper J, Edwards J, Guse LW, Leclair L et al. Development of a Conceptual Framework for Adult Community Rehabilitation Policy, Planning, Care, and Research: A Multimethod Qualitative Approach. Health & Social Care in the Community. 2024;2024(1):3504396. Shi J, Liu X. Demands and determinants of community rehabilitation services for older adults. Chin J Rehabil Theory Pract. 2021;27(27):334–40. Sinatti P, Sánchez Romero EA, Martínez-Pozas O, Villafañe JH. Effects of Patient Education on Pain and Function and Its Impact on Conservative Treatment in Elderly Patients with Pain Related to Hip and Knee Osteoarthritis: A Systematic Review. Int J Environ Res Public Health. 2022;19(10). Wang L, Zhang L, Yang L, Cheng-Qi H. Effectiveness of pain coping skills training on pain, physical function, and psychological outcomes in patients with osteoarthritis: A systemic review and meta-analysis. Clin Rehabil. 2021;35(3):342–55. McDonough CM, Harris-Hayes M, Kristensen MT, Overgaard JA, Herring TB, Kenny AM, et al. Physical Therapy Management of Older Adults With Hip Fracture. J Orthop Sports Phys Ther. 2021;51(2):Cpg1–81. Tay YL, Ahmad MA, Mohamad Yahaya NH, Ajit Singh DK. Effects of photobiomodulation combined with rehabilitation exercise on pain, physical function, and radiographic changes in mild to moderate knee osteoarthritis: A randomized controlled trial protocol. PLoS ONE. 2025;20(1):e0314869. Moreno-Ligero M, Moral-Munoz JA, Salazar A, Failde I. mHealth Intervention for Improving Pain, Quality of Life, and Functional Disability in Patients With Chronic Pain: Systematic Review. JMIR Mhealth Uhealth. 2023;11:e40844. Gagnon CM, Yuen M, Palmer K. An Exploration of Physical Therapy Outcomes and Psychometric Properties of the Patient-Specific Functional Scale After an Interdisciplinary Pain Management Program. Clin J Pain. 2023;39(12):663–71. Additional Declarations No competing interests reported. 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Older individuals commonly experience chronic pain, frailty, and functional limitations, which demand sustained and accessible rehabilitative care (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) However, health systems often fail to address the complex and evolving needs of older adults due to fragmented services and limited community integration (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRural and underserved communities face even greater challenges. Older adults in these regions experience pronounced disparities in health service access and rehabilitation availability, often due to geographical isolation and workforce shortages (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Studies in Asia have highlighted large segments of the older population with unmet rehabilitation needs and limited formal service access (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Similar structural inequalities exist in Taiwan, where rural populations encounter significant barriers to long-term and community-based care (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn response, community-based of public health prevention and mobile rehabilitation models have emerged globally as scalable and context-sensitive solutions. These services extend therapeutic support to patients\u0026rsquo; homes or local centers, reducing logistical barriers and improving participation among frail and low-mobility populations (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). A recent meta-analysis further supports the efficacy of mobile rehabilitation tools, including app-based programs, in improving outcomes for older adults following surgical or functional decline (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Evidence from cardiac, musculoskeletal, and osteoarthritis rehabilitation also suggests that mobile or home-based interventions are feasible and effective in reducing pain and improving function (\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite growing global evidence, few studies have examined the effectiveness of mobile rehabilitation services in real-world rural settings from both physical function and pain perspectives. In Taiwan, the expansion of mobile geriatric services presents a valuable opportunity to evaluate the equity and impact of such interventions in underserved populations. Since early 2023, Pingtung County has piloted a community-based mobile rehabilitation program across multiple rural townships. The initiative has gained strong community support, demonstrated high satisfaction, and enabled timely referrals\u0026mdash;highlighting its potential as a scalable, community-based care model.\u003c/p\u003e \u003cp\u003eThis study evaluates the outcomes of this public health policy and mobile rehabilitation program in terms of both functional improvement and pain relief among rural older adults. By analyzing standardized indicators (SPPB and VAS) and identifying geographic and service-related predictors, the findings aim to inform future policy development in aging societies facing similar challenges.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis study adopted a retrospective observational design using administrative data from a county-level mobile rehabilitation program implemented in Pingtung County, Taiwan. The program aimed to improve physical functioning and alleviate pain among older adults residing in rural, coastal, mountainous, and indigenous areas. Multidisciplinary teams\u0026mdash;including licensed physical therapists, physicians and nurses\u0026mdash;delivered rehabilitation services directly to public health centers, village halls, or participants\u0026rsquo; homes began Feb 21, 2023.\u003c/p\u003e \u003cp\u003eEligible participants were community-dwelling adults aged 65 years and older who had received at least two service visits during the study period. For inclusion in the final analysis, participants were required to have completed both pre- and post-intervention assessments for the Short Physical Performance Battery (SPPB) and self-reported pain using the Visual Analogue Scale (VAS). Individuals with missing key variables, such as geographic region, BMI classification, or intervention exposure data, were excluded.\u003c/p\u003e \u003cp\u003eOf the 10,369 registered service users, 9,378 had valid data for at least one of the outcome measures (SPPB or VAS) and were included in the analysis. A subset of 6,539 participants had complete SPPB data, while 7,190 had valid VAS scores. All data were anonymized prior to analysis. The study protocol was reviewed and approved by the Institutional Review Board in accordance with the ethical standards of human research. The project has certified for exemption from Human Research Ethics Committee at National Cheng Kung University (Approval HREC No. [114\u0026ndash;0586]).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eThis study included two primary outcome measures to assess the effectiveness of the mobile rehabilitation program. Functional improvement was evaluated using the Short Physical Performance Battery (SPPB), a validated instrument that comprises three subtests: balance, gait speed, and chair-stand performance. Total scores range from 0 to 12, with higher scores indicating better physical function. Improvement in SPPB was operationalized as a binary variable, defined by an increase in the total score from baseline to follow-up (i.e., post-test score\u0026thinsp;\u0026gt;\u0026thinsp;pre-test score). Pain improvement was assessed using the Visual Analogue Scale (VAS), which measures pain intensity on a scale from 0 (no pain) to 10 (worst imaginable pain). A binary variable for pain improvement was created, with improvement defined as a reduction in pain score from the initial to the most recent assessment (i.e., final VAS\u0026thinsp;\u0026lt;\u0026thinsp;initial VAS).\u003c/p\u003e \u003cp\u003eIndependent variables included demographic, clinical, functional, and service-related characteristics. Demographic variables were age (in years), gender (male or female), and body mass index (BMI), calculated from recorded height and weight and categorized according to standard BMI classification. Clinical indicators included the number of chronic conditions reported by participants (e.g., hypertension, diabetes, heart disease) and baseline SPPB and VAS scores. Geographic location was classified into five categories based on administrative and cultural features of residence: urban, suburban, coastal, mountainous, and indigenous areas. These classifications reflect regional variations in healthcare access and social determinants of health within Pingtung County. Service exposure variables included the total number of rehabilitation sessions received, the number of health education topics provided during the intervention, and physician instruction adherence, recorded as a binary variable (1\u0026thinsp;=\u0026thinsp;followed, 0\u0026thinsp;=\u0026thinsp;did not follow). All data were extracted from standardized service records maintained by the mobile rehabilitation team and reviewed for completeness and consistency prior to analysis. Participant data were anonymized at the time of extraction to ensure confidentiality.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were first computed to summarize participants\u0026rsquo; demographic, clinical, and service-related characteristics. Means and standard deviations were reported for continuous variables, while frequencies and percentages were calculated for categorical variables. To assess preliminary associations between potential predictors and the two primary outcomes (SPPB improvement and VAS improvement), bivariate comparisons were conducted. Chi-square tests were used to examine associations between categorical variables and the outcome variables, whereas independent-samples t-tests or Mann\u0026ndash;Whitney U tests were applied for continuous predictors, depending on the results of normality assessments. These initial comparisons supported the identification and selection of variables for inclusion in the hierarchical logistic regression models, and helped to interpret the directionality of effects.\u003c/p\u003e \u003cp\u003eTwo separate hierarchical binary logistic regression models were conducted to identify predictors of (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) functional improvement, defined as a positive change in SPPB score from baseline to post-intervention (1\u0026thinsp;=\u0026thinsp;improved, 0\u0026thinsp;=\u0026thinsp;not improved), and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) pain improvement, defined as a reduction in self-reported pain score on the Visual Analogue Scale (VAS). In each model, predictors were entered sequentially in three blocks to assess their incremental explanatory power. Block 1 included demographic and geographic variables (age, gender, BMI classification, and geographic region of residence). Block 2 introduced clinical and baseline functional variables, including the number of chronic conditions, pre-intervention scores (SPPB or VAS), and functional improvement (used as a predictor in the VAS model). Block 3 added intervention exposure variables: number of rehabilitation sessions, number of health education topics received, and physician instruction adherence. Model performance was evaluated using the \u0026minus;\u0026thinsp;2 log likelihood, Nagelkerke\u0026rsquo;s R\u0026sup2;, and omnibus χ\u0026sup2; statistics. Changes in R\u0026sup2; across blocks were used to assess incremental explanatory power. Variance inflation factors (VIFs) were examined to detect multicollinearity among predictors. All analyses were conducted using SPSS version 27.0, and statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eParticipant characteristics\u003c/h2\u003e \u003cp\u003eA total of 6,534 older adults were included in the final analysis after excluding those with pre- or post-test SPPB scores equal to zero. The average participant age was 71.70 years (SD\u0026thinsp;=\u0026thinsp;12.00), and the mean body mass index (BMI) was 25.31 kg/m\u0026sup2; (SD\u0026thinsp;=\u0026thinsp;4.25). The sample was predominantly female, with 22.7% identifying as male. The mean Short Physical Performance Battery (SPPB) score at baseline was 9.52 (SD\u0026thinsp;=\u0026thinsp;2.65), increasing to 10.10 (SD\u0026thinsp;=\u0026thinsp;2.39) at post-test, indicating overall functional improvement following the intervention. Regarding chronic health conditions, 52.1% of participants had hypertension, 25.3% had diabetes, and 12.5% had heart disease. Participants were distributed across five regional categories based on township classification: 18.3% resided in urban areas, 42.0% in suburban areas, 22.1% in coastal regions, 10.7% in mountainous areas, and 6.9% in indigenous townships. This regional diversity provides a strong basis for evaluating the contextual effects of mobile rehabilitation delivery (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" 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\u003eParticipants\u0026rsquo; Characteristics (N\u0026thinsp;=\u0026thinsp;6,534)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean / N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD / Percentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHeart Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUrban Region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSuburban Region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCoastal Region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMountain Region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndigenous Region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote. Mean and standard deviation (SD) are presented for continuous variables. Percentages are shown for categorical variables. SPPB data with pre- or post-test score of 0 were excluded from the analysis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBivariate analysis\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eSPPB improvement\u003c/h2\u003e \u003cp\u003eBivariate analyses revealed several significant associations between participant characteristics and functional improvement, as measured by the Short Physical Performance Battery (SPPB). Age was significantly higher among participants who showed improvement (M\u0026thinsp;=\u0026thinsp;75.5, SD\u0026thinsp;=\u0026thinsp;8.4) compared to those who did not (M\u0026thinsp;=\u0026thinsp;69.1, SD\u0026thinsp;=\u0026thinsp;13.4), \u003cem\u003et\u003c/em\u003e (6536) = \u0026minus;\u0026thinsp;22.03, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001. Participants with more chronic conditions were also more likely to improve (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.40 vs. 1.11; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). In addition, those with functional improvement had received significantly more service sessions (M\u0026thinsp;=\u0026thinsp;3.54 vs. 2.94; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and reported higher initial pain levels on the VAS scale (M\u0026thinsp;=\u0026thinsp;6.43 vs. 6.11; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Conversely, their baseline physical function (pre_SPPB) was lower (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.15 vs. 10.47; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting a greater potential for measurable improvement.\u003c/p\u003e \u003cp\u003eChi-square tests indicated that geographic region was strongly associated with SPPB improvement (χ\u0026sup2; = 128.68, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Urban participants were more likely to improve (49.9%) compared to those in mountainous (30.8%) and indigenous (39.7%) areas. Physician instruction adherence was also associated with greater improvement (χ\u0026sup2; = 13.86, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). However, no significant associations were observed with gender (χ\u0026sup2; = 3.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.073) or BMI classification (χ\u0026sup2; = 4.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.261).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eVAS improvement\u003c/h3\u003e\n\u003cp\u003eFor pain improvement (VAS), participants who reported improvement had significantly lower baseline pain (VAS1: \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.22) than those who did not improve (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.97), \u003cem\u003et\u003c/em\u003e (10354) = \u0026minus;\u0026thinsp;23.48, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001. Participants who improved were also younger (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;69.7 vs. 72.2; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and had received more health education (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.24 vs. 1.19; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.004). Although service frequency appeared slightly higher in the improved group (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.64 vs. 2.54), this difference was only marginal (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.065). No significant differences were observed in the number of comorbidities (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.780) or pre_SPPB scores (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.659). Chi-square analysis showed that geographic region was again a significant predictor (χ\u0026sup2; = 51.57, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), with participants from indigenous areas showing higher rates of pain improvement (97.1%) compared to those from suburban (92.2%) and mountainous (92.0%) areas. However, gender (χ\u0026sup2; = 2.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.104), BMI classification (χ\u0026sup2; = 6.70, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.082), and physician instruction adherence (χ\u0026sup2; = .846, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.358) were not significantly related to VAS improvement. Notably, functional improvement (SPPB) was significantly associated with pain improvement (χ\u0026sup2; = 47.48, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting a linked outcome trajectory.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of SPPB improvement\u003c/h2\u003e \u003cp\u003eTo assess potential multicollinearity among predictors, variance inflation factors (VIFs) were calculated using a linear regression model that included the same set of independent variables. All VIF values were below 5 (ranging from 1.01 to 1.49), indicating no substantial multicollinearity (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). This study employed hierarchical binary logistic regression to examine the predictors of functional improvement, defined as a binary outcome variable \"Improved SPPB\" (1\u0026thinsp;=\u0026thinsp;improved, 0\u0026thinsp;=\u0026thinsp;not improved). Three blocks of independent variables were entered sequentially to explore their incremental explanatory power.\u003c/p\u003e \u003cp\u003eThe first block included four covariates: geographic region classification, age, gender, and BMI classification. This model yielded a Nagelkerke R\u0026sup2; of 0.142 and \u0026minus;\u0026thinsp;2 Log Likelihood of 7415.11, indicating a modest level of explanatory power. The overall model was statistically significant (χ\u0026sup2; = 666.305, df\u0026thinsp;=\u0026thinsp;9, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Among the predictors, geographic region was significant: residents in mountainous areas were significantly less likely to experience SPPB improvement compared to those in indigenous areas (OR\u0026thinsp;=\u0026thinsp;0.539, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Age was positively associated with improvement (OR\u0026thinsp;=\u0026thinsp;1.018, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Gender and BMI classification, however, were not statistically significant predictors of improvement (p\u0026thinsp;=\u0026thinsp;.058 and p\u0026thinsp;\u0026gt;\u0026thinsp;.05, respectively). The Hosmer-Lemeshow goodness-of-fit test yielded a χ\u0026sup2; = 29.250, df\u0026thinsp;=\u0026thinsp;8, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, suggesting poor fit, possibly due to large sample size (N\u0026thinsp;=\u0026thinsp;5973), as this test tends to detect trivial deviations in large datasets.\u003c/p\u003e \u003cp\u003eIn Block 2, we added illness count, pre-intervention SPPB scores (pre_SPPB), baseline pain scores (VAS1), and physician advice (medical instruction adherence). The model's Nagelkerke R\u0026sup2; increased substantially to 0.299 and \u0026minus;\u0026thinsp;2LL dropped to 6582.71. The Hosmer-Lemeshow test showed χ\u0026sup2; = 665.807, df\u0026thinsp;=\u0026thinsp;8, p\u0026thinsp;\u0026lt;\u0026thinsp;.001. Among the added predictors, both pre_SPPB (OR\u0026thinsp;=\u0026thinsp;0.695, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and VAS1 (OR\u0026thinsp;=\u0026thinsp;1.123, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) were significant. Lower pre_SPPB scores were associated with greater likelihood of improvement (ceiling effect), and higher baseline pain also predicted improvement. Illness count and physician instruction adherence were not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;.05), indicating these health status factors did not independently predict improvement in the context of this model.\u003c/p\u003e \u003cp\u003e In the final block of the hierarchical logistic regression model predicting SPPB improvement, three variables related to participants\u0026rsquo; engagement with the mobile rehabilitation program were introduced: number of health education topics received, total service frequency, and adherence to physician instruction. Inclusion of these intervention-related variables substantially improved model performance, increasing the Nagelkerke R\u0026sup2; to 0.350 and reducing the \u0026minus;\u0026thinsp;2 Log Likelihood to 6285.14. The classification accuracy of the model also rose to 74.2%, with a sensitivity of 60.8% for identifying cases with functional improvement. Although the Hosmer\u0026ndash;Lemeshow test remained statistically significant (χ\u0026sup2; = 385.278, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), this may reflect the known sensitivity of the test in large sample sizes rather than poor model fit.\u003c/p\u003e \u003cp\u003eAmong the three predictors introduced in this block, two were statistically significant. The number of health education topics received was positively associated with the likelihood of improvement (OR\u0026thinsp;=\u0026thinsp;1.200, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.017), suggesting that greater exposure to educational content may enhance rehabilitation outcomes. Total service frequency also emerged as a strong predictor (OR\u0026thinsp;=\u0026thinsp;1.676, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating that each additional service visit increased the odds of functional improvement by approximately 68%. In contrast, physician instruction adherence was not significantly associated with improvement (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05), implying that passive receipt of medical advice may not translate into meaningful functional gains unless accompanied by active service participation. Overall, this final model underscores the importance of intervention intensity and content in predicting rehabilitation outcomes. While demographic and baseline clinical variables offered moderate predictive value, active engagement with mobile rehabilitation services\u0026mdash;particularly repeated visits and targeted education\u0026mdash;was essential to achieving functional improvement among older adults living in rural and underserved communities (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eSummary of Hierarchical Binary Logistic Regression Results (N\u0026thinsp;=\u0026thinsp;5973)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlock\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2 Log Likelihood\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNagelkerke R\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClassification Accuracy (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eImprovement Accuracy (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHosmer-Lemeshow χ\u0026sup2; (df\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlock 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7415.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlock 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6582.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e665.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlock 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6285.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e385.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: - Nagelkerke R\u0026sup2; indicates the explained variance in improvement status.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eClassification Accuracy\u0026thinsp;=\u0026thinsp;overall correct prediction rate.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eHosmer-Lemeshow test is sensitive to large sample sizes; p\u0026thinsp;\u0026lt;\u0026thinsp;.05 does not necessarily imply poor fit.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of All Predictors in Final Model (N\u0026thinsp;=\u0026thinsp;5973)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\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\u003eOdds Ratio (Exp(B))\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion (Mountainous vs. Indigenous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower odds in mountainous areas\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\u003e1.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSlight increase in odds with age\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epre_SPPB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigher pre_SPPB reduces odds (ceiling effect)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigher baseline pain predicts improvement\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Health Education Topics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMore education topics increase odds\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eService Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEach additional visit increases odds by 67.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Female vs. Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot statistically significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI Classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot statistically significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIllness Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot statistically significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysician Instruction Adherence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot statistically significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: 'n.s.' indicates not significant at p\u0026thinsp;\u0026lt;\u0026thinsp;.05;Odds Ratios (Exp(B)) reflect the direction and strength of association with SPPB improvement༛Variables with p\u0026thinsp;\u0026gt;\u0026thinsp;.05 did not reach statistical significance in the final model.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of VAS improvement\u003c/h2\u003e \u003cp\u003eA hierarchical binary logistic regression was conducted to identify predictors of pain improvement, operationalized as a binary dependent variable indicating whether participants experienced a reduction in self-reported pain intensity based on the Visual Analogue Scale (VAS) (1\u0026thinsp;=\u0026thinsp;improvement, 0\u0026thinsp;=\u0026thinsp;no improvement or worsening). The analysis included 5,976 participants with valid pre- and post-intervention pain data. Predictors were introduced sequentially in three blocks: demographic and geographic characteristics (Block 1), health and functional status (Block 2), and intervention exposure (Block 3).\u003c/p\u003e \u003cp\u003eIn Block 1, the model included participants' geographic region, age, and BMI classification. This initial model was statistically significant (Omnibus χ\u0026sup2; = 58.269, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), though its explanatory power remained modest, as reflected in a Nagelkerke R\u0026sup2; of 0.026 and a \u0026minus;\u0026thinsp;2 Log Likelihood of 2760.324. The Hosmer\u0026ndash;Lemeshow test yielded a non-significant result (χ\u0026sup2; = 8.805, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.359), indicating acceptable model fit. Geographic region emerged as a significant predictor of VAS improvement. Compared to participants living in indigenous areas, those residing in suburban (OR\u0026thinsp;=\u0026thinsp;0.494, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002), mountainous (OR\u0026thinsp;=\u0026thinsp;0.389, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and coastal regions (OR\u0026thinsp;=\u0026thinsp;0.404, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) had significantly lower odds of reporting pain relief. BMI classification also demonstrated a significant effect, with obese participants being less likely to experience improvement (OR\u0026thinsp;=\u0026thinsp;0.707, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.013). In contrast, age did not significantly predict VAS improvement (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.176).\u003c/p\u003e \u003cp\u003eIn the second block, variables related to participants' health status and functional capacity were added to the model, including the number of chronic conditions (illness count), baseline SPPB score, and whether participants experienced functional improvement during the intervention period. The inclusion of these variables improved overall model fit, with the Omnibus χ\u0026sup2; increasing to 115.034 (\u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The Nagelkerke R\u0026sup2; rose to 0.051, and the \u0026minus;\u0026thinsp;2 Log Likelihood decreased to 2703.558, indicating a better-fitting model. The Hosmer\u0026ndash;Lemeshow test showed excellent calibration (χ\u0026sup2; = 3.605, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.891), supporting the model\u0026rsquo;s adequacy. Among the new variables introduced, functional improvement based on the SPPB was a strong and statistically significant predictor of pain relief (OR\u0026thinsp;=\u0026thinsp;0.403, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting that participants who did not experience physical function gains were also less likely to report reductions in pain. Interestingly, age\u0026mdash;which had not been significant in Block 1\u0026mdash;became a significant negative predictor in this block (OR\u0026thinsp;=\u0026thinsp;0.984, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.004), indicating that older individuals were slightly less likely to benefit from pain reduction. In contrast, illness count and pre-intervention SPPB scores did not demonstrate significant associations with VAS improvement (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05).\u003c/p\u003e \u003cp\u003eIn the final block of the regression model predicting pain improvement, three variables related to participants\u0026rsquo; engagement with the mobile rehabilitation program were introduced: number of health education topics received, total number of service visits, and adherence to physician instruction. The inclusion of these intervention-related factors significantly enhanced model performance (Omnibus χ\u0026sup2; = 144.709, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), with the Nagelkerke R\u0026sup2; increasing to 0.064 and the \u0026minus;\u0026thinsp;2 Log Likelihood decreasing to 2673.884. The Hosmer\u0026ndash;Lemeshow test remained non-significant (χ\u0026sup2; = 13.881, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.085), indicating an acceptable model fit (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Hierarchical Binary Logistic Regression Results (N\u0026thinsp;=\u0026thinsp;5976)\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=\"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 \u003cdiv align=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlock\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2 Log Likelihood\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNagelkerke R\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClassification Accuracy (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHosmer-Lemeshow χ\u0026sup2; (df\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlock 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2760.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.359\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlock 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2703.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlock 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2673.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Nagelkerke R\u0026sup2; indicates the explained variance in VAS improvement;'n.s.' indicates not statistically significant at p\u0026thinsp;\u0026lt;\u0026thinsp;.05༛Region comparisons are relative to Indigenous areas.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of All Predictors in Final Model (N\u0026thinsp;=\u0026thinsp;5976)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\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\u003eOdds Ratio (Exp(B))\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion (Suburban vs. Indigenous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower odds of improvement in suburban areas\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion (Mountainous vs. Indigenous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower odds in mountainous areas\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion (Coastal vs. Indigenous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower odds in coastal areas\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\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSlightly reduced odds with increasing age\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI Classification (Obese vs. Normal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObesity reduces odds of improvement\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIllness Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epre_SPPB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPPB Improvement (Yes vs. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong predictor of VAS improvement\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Health Education Topics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMore topics linked to greater pain relief\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eService Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMore sessions linked to better outcome\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical Instruction Adherence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Hosmer-Lemeshow test p-values\u0026thinsp;\u0026gt;\u0026thinsp;.05 indicate acceptable model fit, though the test may be overly sensitive in large samples;Odds Ratios reflect likelihood of pain improvement associated with each predictor.༛All region-based ORs compare against Indigenous areas as the reference group༛'n.s.' denotes predictors that were not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;.05) in the final model.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the intervention-related predictors, both the number of health education topics and total service frequency were statistically significant. Participants who received more health education topics had significantly higher odds of experiencing pain reduction (OR\u0026thinsp;=\u0026thinsp;0.664, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001), indicating the additive effect of structured education on pain self-management. Similarly, each additional service session increased the likelihood of improvement by 25.8% (OR\u0026thinsp;=\u0026thinsp;1.258, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), reinforcing the importance of repeated rehabilitation contact. Notably, functional improvement (as measured by SPPB) remained a robust and independent predictor of VAS improvement (OR\u0026thinsp;=\u0026thinsp;0.452, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Conversely, physician instruction adherence, baseline physical performance (pre_SPPB), and illness burden (illness count) were not significantly associated with pain improvement (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05), suggesting that direct service engagement may be more influential than passive medical advice or baseline clinical status in this context.\u003c/p\u003e \u003cp\u003eAcross the three-block model, predictive power improved from Nagelkerke R\u0026sup2; = .026 \u0026rarr; .051 \u0026rarr; .064. While the overall effect size was modest, the classification accuracy remained at 93.7%, driven by the large proportion of participants with reported improvement. Notably, residents in suburban, mountainous, and coastal areas consistently showed lower odds of pain improvement compared to those in indigenous regions. This geographic disparity suggests potential inequities in access, engagement, or responsiveness to rehabilitation services. The finding has important implications for health policy, highlighting the need to enhance service coverage and culturally tailored support in underserved regions. Intervention variables, particularly repeated service participation and educational engagement, significantly enhanced the likelihood of pain improvement. These findings underscore the role of structured mobile rehabilitation services in reducing self-reported pain among older adults.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRegional disparities in functional and pain outcomes\u003c/h2\u003e \u003cp\u003eThe present study identified significant disparities in rehabilitation outcomes across different geographic regions of Pingtung County. Participants living in suburban, coastal, and mountainous regions showed significantly lower odds of functional (SPPB) and pain (VAS) improvement compared to those residing in indigenous townships. These results suggest that even within a localized mobile rehabilitation initiative, regional inequality in health outcomes persists\u0026mdash;likely reflecting deeper structural gaps in health service access, engagement, and trust.\u003c/p\u003e \u003cp\u003eSuch findings align with international evidence showing that older adults in rural or underserved areas often face barriers to equitable rehabilitation services, including transportation challenges, reduced healthcare infrastructure, and limited health literacy (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). A study conducted by Zhang et al. found that rural older adults had significantly worse physical health outcomes due to reduced access to timely care, supporting the notion that geographic location plays a critical role in shaping rehabilitation success.\u003c/p\u003e \u003cp\u003eInterestingly, our findings revealed that indigenous township residents had comparatively better outcomes, which may relate to the program's stronger integration with local community systems and cultural adaptation. This supports previous work by Das et al. (2023), who emphasized that culturally congruent, community-based outreach programs can enhance intervention acceptance and effectiveness, especially among historically marginalized populations(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). These findings reinforce calls for a shift away from one-size-fits-all interventions and toward more locally responsive, equity-oriented rehabilitation policies (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn sum, the regional variation observed in this study highlights the necessity of tailoring service delivery not only by age or health status, but by geography and cultural context. To promote equitable aging, future policy must address logistical and contextual constraints across remote regions and actively invest in community-based mobile rehabilitation as a scalable solution for reaching the underserved.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFunctional improvement: the importance of service intensity\u003c/h2\u003e \u003cp\u003eThis study found that the number of rehabilitation sessions and the breadth of health education topics received were significant predictors of functional improvement among older adults. Specifically, higher service frequency and greater engagement with educational content were associated with increased likelihood of Short Physical Performance Battery (SPPB) score gains. These findings reinforce the view that rehabilitation \u0026ldquo;dose\u0026rdquo; matters\u0026mdash;both in quantity and content\u0026mdash;and that continuity and repetition are vital for measurable gains in function among frail older individuals.\u003c/p\u003e \u003cp\u003eInternational literature increasingly supports this dose-response effect in geriatric rehabilitation. Cieślik et al. (2023), through a network meta-analysis of technology-assisted interventions, found that structured and repeated rehabilitation sessions yield superior mobility outcomes compared to minimal or one-time services(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Similarly, Wakabayashi (2024) emphasized the necessity of combining regular rehabilitation with nutritional and oral health support to counteract sarcopenia and functional decline, especially in older adults with complex care needs(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). These findings parallel the results of our study, in which multi-faceted and repeated contact\u0026mdash;not simply service access\u0026mdash;was key to meaningful improvement. Moreover, policy-oriented research has highlighted that the acceptability and effectiveness of community-based rehabilitation hinges on sustained interaction with multidisciplinary teams (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In our program context, the ability of the mobile units to deliver recurring, face-to-face therapy across townships likely contributed to the observed improvements. This echoes the conceptual framework proposed by Sibley et al. (2024), which places continuity, local accessibility, and care coordination at the center of adult community rehabilitation(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn East Asian settings, studies have repeatedly noted a mismatch between older adults\u0026rsquo; rehabilitation demands and the actual delivery of community-based services (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The present findings demonstrate that a high-volume, mobile rehabilitation model can begin to address this service gap, particularly when coupled with health education and consistent follow-up.\u003c/p\u003e \u003cp\u003eAltogether, the results suggest that designing aging policy around low-intensity, sporadic intervention is unlikely to yield enough functional gains. Instead, scalable and sustained community-based rehabilitation\u0026mdash;delivered with appropriate frequency and educational reinforcement\u0026mdash;should be prioritized in rural and aging regions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePain relief and its linkage to functional recovery\u003c/h2\u003e \u003cp\u003eThe relationship between pain reduction and functional recovery has long been recognized as bi-directional, particularly in older adults with musculoskeletal limitations. In this study, improvement in SPPB scores significantly predicted VAS-based pain relief, suggesting that as individuals regained mobility and balance, they experienced less perceived pain. This aligns with existing evidence emphasizing the inseparability of physical and pain-related health outcomes in geriatric rehabilitation.\u003c/p\u003e \u003cp\u003eMultiple recent reviews and trials support this interdependence. Sinatti et al. (2022) showed that patient education as part of conservative treatment for hip and knee osteoarthritis led to significant improvements in both pain and function(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). This indicates that empowering patients with knowledge and self-management strategies can simultaneously influence pain perception and physical activity. Similarly, Wang et al. (2021) demonstrated through a meta-analysis that pain coping skills training had direct positive effects on pain intensity and functional mobility, underscoring the centrality of psychosocial interventions within physical rehabilitation programs(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMore broadly, McDonough et al. (2021), in clinical practice guidelines, advocated for early, progressive, and personalized physical therapy following hip fractures, citing its dual benefits on functional capacity and pain mitigation(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This dual effect is also evident in technologically enhanced rehabilitation models. Tay et al. (2025) highlighted that combining photobiomodulation with exercise in knee osteoarthritis improved pain and physical function more than standard care alone(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Meanwhile, mHealth applications targeting chronic pain have also shown functional gains alongside pain reduction, as evidenced by Moreno-Ligero et al. (2023)(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, functional outcome tools such as the Patient-Specific Functional Scale (PSFS) have proven sensitive in detecting changes tied to pain-modulating interdisciplinary programs (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). These findings support the notion that pain and function must be considered as integrated outcomes when evaluating rehabilitation effectiveness, especially in mobile or community-based contexts.\u003c/p\u003e \u003cp\u003eIn the context of this study, the co-occurrence of SPPB and VAS improvement strengthens the argument that reducing functional limitations not only restores physical independence but also alleviates pain perception through enhanced musculoskeletal efficiency, reduced compensatory stress, and improved confidence in movement. Effective rehabilitation programs should thus maintain a dual focus\u0026mdash;functional and sensory\u0026mdash;when targeting older populations, particularly those in resource-constrained or rural settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eNon-significant predictors and contextual interpretation\u003c/h2\u003e \u003cp\u003eThe relationship between pain reduction and functional recovery has long been recognized as bi-directional, particularly in older adults with musculoskeletal limitations. In this study, improvement in SPPB scores significantly predicted VAS-based pain relief, suggesting that as individuals regained mobility and balance, they experienced less perceived pain. This aligns with existing evidence emphasizing the inseparability of physical and pain-related health outcomes in geriatric rehabilitation.\u003c/p\u003e \u003cp\u003eMultiple recent reviews and trials support this interdependence. Sinatti et al. (2022) showed that patient education as part of conservative treatment for hip and knee osteoarthritis led to significant improvements in both pain and function(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). This indicates that empowering patients with knowledge and self-management strategies can simultaneously influence pain perception and physical activity. Similarly, Wang et al. (2021) demonstrated through a meta-analysis that pain coping skills training had direct positive effects on pain intensity and functional mobility, underscoring the centrality of psychosocial interventions within physical rehabilitation programs(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMore broadly, McDonough et al. (2021), in clinical practice guidelines, advocated for early, progressive, and personalized physical therapy following hip fractures, citing its dual benefits on functional capacity and pain mitigation(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This dual effect is also evident in technologically enhanced rehabilitation models. Tay et al. (2025) highlighted that combining photobiomodulation with exercise in knee osteoarthritis improved pain and physical function more than standard care alone(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Meanwhile, mHealth applications targeting chronic pain have also shown functional gains alongside pain reduction, as evidenced by Moreno-Ligero et al. (2023)(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Moreover, functional outcome tools such as the Patient-Specific Functional Scale (PSFS) have proven sensitive in detecting changes tied to pain-modulating interdisciplinary programs (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). These findings support the notion that pain and function must be considered as integrated outcomes when evaluating rehabilitation effectiveness, especially in mobile or community-based contexts.\u003c/p\u003e \u003cp\u003eIn the context of this study, the co-occurrence of SPPB and VAS improvement strengthens the argument that reducing functional limitations not only restores physical independence but also alleviates pain perception through enhanced musculoskeletal efficiency, reduced compensatory stress, and improved confidence in movement. Effective rehabilitation programs should thus maintain a dual focus\u0026mdash;functional and sensory\u0026mdash;when targeting older populations, particularly those in resource-constrained or rural settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eImplications for rural rehabilitation policy and equity\u003c/h2\u003e \u003cp\u003eThis study provides empirical evidence supporting the effectiveness of Pingtung County\u0026rsquo;s mobile rehabilitation program in improving both physical function and self-reported pain among older adults. Through hierarchical logistic regression analysis, we identified that service frequency and health education intensity were key predictors of positive outcomes, reinforcing the notion that sustained, community-delivered intervention is critical for functional and sensory improvement in aging populations. These findings directly align with the goals of the original program, which aimed to reduce urban-rural health disparities, enhance the accessibility of rehabilitation services, and support early intervention to prevent long-term disability. As outlined in the county\u0026rsquo;s 2023\u0026ndash;2024 action plan, the mobile rehabilitation initiative was specifically designed to overcome geographic barriers, particularly in indigenous and mountainous regions where formal rehabilitation resources are limited. By deploying mobile units equipped with physical therapists and educational tools, the program reached over 14,000 residents and demonstrated high satisfaction, functional improvement, and pain relief outcomes.\u003c/p\u003e \u003cp\u003eThe policy implications are threefold. First, health authorities should prioritize integrated community-based care models that combine preventive education, early screening, and mobility-focused rehabilitation. Second, mobile services should be viewed as a complement to existing long-term care and public health strategies, offering scalable, flexible responses to emerging needs in rural populations. Third, future program iterations should incorporate behavioral adherence supports and digital follow-up mechanisms to further enhance continuity and cost-effectiveness. Moreover, the program\u0026rsquo;s structure demonstrates a viable template for cross-sectoral collaboration\u0026mdash;engaging local governments, health institutions, community organizations, and private donors. This public-private partnership model not only expands service coverage but also fosters local workforce development, as evidenced by the employment of 38 physical therapists under the initiative. Overall, the evidence presented here supports the institutionalization and expansion of mobile rehabilitation as a core strategy in aging policy\u0026mdash;especially in rural settings. As Taiwan and other nations face the dual challenges of demographic aging and health service inequity, the lessons from this model can inform broader national efforts to deliver equitable, community-anchored, and function-centered care.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and future directions\u003c/h2\u003e \u003cp\u003eDespite the large sample size and real-world setting, this study has several limitations that warrant consideration. First, the observational nature of the design restricts causal inference. While associations between intervention intensity and outcomes were observed, it is not possible to definitively conclude that the rehabilitation services caused the improvements in pain or physical function. Future research should incorporate quasi-experimental or randomized controlled designs to strengthen causal interpretation. Second, although key predictors such as service frequency and health education were analyzed, other relevant psychosocial or environmental variables were not captured. Factors such as caregiver involvement, motivation, depression, or social support\u0026mdash;known to influence adherence and rehabilitation response\u0026mdash;were not available in the dataset. The inclusion of these variables in future analyses may enhance model precision and explanatory power. Third, the study relied on routinely collected administrative data, which may be subject to reporting errors, missing values, and inconsistencies in service documentation across teams. Although data quality controls were implemented, future studies should consider triangulating findings with qualitative data or independent clinical assessments to ensure robustness. Fourth, the generalizability of the findings may be context-dependent. Pingtung County\u0026rsquo;s unique geographic and demographic profile, as well as the specific design of its mobile rehabilitation infrastructure, may not fully reflect the conditions in other regions or countries. Future research should explore how this model can be adapted and scaled in diverse settings with different resource constraints and health system structures. Finally, while functional and pain outcomes were evaluated using validated tools (SPPB and VAS), the study did not assess long-term sustainability of gains or health system cost-effectiveness. Longitudinal follow-up is necessary to determine whether the improvements persist over time and whether the mobile model offers advantages in reducing institutional care demand and economic burden. Taken together, these limitations highlight the need for more comprehensive, longitudinal, and multi-level evaluations. Future research should also explore digital integration, behavioral supports, and multi-disciplinary coordination as means to strengthen mobile rehabilitation strategies in aging societies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study evaluated the effectiveness of a government-led mobile rehabilitation program implemented in rural and underserved regions of Pingtung County, Taiwan. By analyzing real-world service data from over 10,000 older adults, we identified that repeated rehabilitation sessions and exposure to health education were significant predictors of functional improvement and pain reduction. These findings demonstrate that service intensity and structured engagement are critical components of successful rehabilitation delivery in aging communities. Importantly, the results reinforce the value of mobile rehabilitation as a scalable strategy to bridge health service gaps in geographically and socially marginalized populations. Despite contextual and behavioral challenges\u0026mdash;including variability in treatment adherence and the limited predictive value of baseline clinical indicators\u0026mdash;the program achieved measurable benefits for older adults with chronic pain and mobility limitations. This study provides policy-relevant evidence in support of integrated, community-anchored rehabilitation models that combine physical therapy, preventive education, and accessible service delivery. It further highlights the need for long-term planning, cross-sector collaboration, and adaptive intervention models to ensure sustainability. As populations age and healthcare inequities persist, mobile rehabilitation represents a promising public health approach to promote functional independence, relieve pain, and improve quality of life for older adults.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThis study adopted a retrospective observational design using administrative data from a county-level mobile rehabilitation program implemented in Pingtung County, Taiwan. All data were anonymized prior to analysis. The study protocol was reviewed and approved by the Institutional Review Board in accordance with the ethical standards of human research. The project has certified for exemption from Human Research Ethics Committee at National Cheng Kung University (Approval HREC No. [114\u0026ndash;0586]).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot Applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was not funded.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJin-Hung Lin: Conceptualization, Collection and Curation of Data, and Writing.Hsiu-Chun Chang: Conceptualization, Collection of Data.Wen-Hung Tou: Collection and Curation of Data, involved in design of the research.Chun Hao Liu: Collection and Curation of Data, involved in design of the research.Shang-Min Ma: Conceptualization, Formal Analysis, Writing and revised the text.All authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to confidentiality reasons.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCowley A, Goldberg SE, Gordon AL, Logan PA. 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J Orthop Sports Phys Ther. 2021;51(2):Cpg1\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTay YL, Ahmad MA, Mohamad Yahaya NH, Ajit Singh DK. Effects of photobiomodulation combined with rehabilitation exercise on pain, physical function, and radiographic changes in mild to moderate knee osteoarthritis: A randomized controlled trial protocol. PLoS ONE. 2025;20(1):e0314869.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoreno-Ligero M, Moral-Munoz JA, Salazar A, Failde I. mHealth Intervention for Improving Pain, Quality of Life, and Functional Disability in Patients With Chronic Pain: Systematic Review. JMIR Mhealth Uhealth. 2023;11:e40844.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGagnon CM, Yuen M, Palmer K. An Exploration of Physical Therapy Outcomes and Psychometric Properties of the Patient-Specific Functional Scale After an Interdisciplinary Pain Management Program. Clin J Pain. 2023;39(12):663\u0026ndash;71.\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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Public Health, Mobile Health Units, Rehabilitation, Aged, Rural Health, Community Health","lastPublishedDoi":"10.21203/rs.3.rs-8208834/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8208834/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study evaluated the effectiveness of a government-led public health prevention and mobile rehabilitation program implemented in rural and underserved areas of Pingtung County, Taiwan. Using real-world data from 10,369 older adults, the analysis examined functional performance (Short Physical Performance Battery, SPPB) and pain outcomes (Visual Analogue Scale, VAS). Hierarchical logistic regression models revealed that repeated rehabilitation sessions and greater exposure to health education significantly predicted both functional and pain improvement. Functional gains were particularly associated with higher service frequency and lower baseline performance, while pain relief was strongly linked to functional recovery. Regional disparities emerged, with residents in suburban, coastal, and mountainous regions showing lower odds of improvement compared to indigenous communities, underscoring equity challenges in rural rehabilitation delivery. The findings demonstrate that service intensity and structured engagement are critical for effective community-based rehabilitation. 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