Validation of SAUDE: A Citizen Science App for Functional Balance Assessment in Older Adults

Validation of SAUDE: A Citizen Science App for Functional Balance Assessment in Older Adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Validation of SAUDE: A Citizen Science App for Functional Balance Assessment in Older Adults Alexandre Gomes Sancho, Arthur Sá Ferreira This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8627904/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Citizen science (CS) can broaden participation and support knowledge co-production in health research. However, its application in rehabilitation remains limited, particularly with respect to the validation of tools for functional assessment and fall-risk monitoring. Objective: To validate SAUDE, a mobile application designed to support citizen-led assessment of balance and mobility, and to examine its usability among older adults. Methods: A cross-sectional mixed-methods study was conducted with 47 community-dwelling older adults. Participants independently performed three standardized functional tests—Timed Up and Go (TUG), 30-second Sit-to-Stand (STS), and Single-Leg Stance (SLS)—using the SAUDE app, while simultaneous in-person assessments were conducted by a trained evaluator. Criterion validity was examined using concordance correlation coefficients (CCC). Convergent validity was assessed through inter-test correlations, and construct validity through associations with self-reported fall history. Usability observations were collected during testing. Results: Strong agreement was observed between app-based and assessor-based measurements for all tests (CCC [95% CI]: TUG = 0.893 [0.836, 0.932], STS = 0.977 [0.960, 0.987], SLS = 0.955 [0.922; 0.974]). Inter-test correlations supported convergent validity, and moderate associations between TUG performance and fall history provided evidence of construct validity. Most participants completed the assessments independently, although visual, cognitive, and motor limitations affected interaction with the app in some cases. Conclusions: SAUDE demonstrated validity and usability as a citizen science platform for self-assessment of functional mobility in older adults. Its open-access design and integration of fall-related outcomes support its potential for community-based health monitoring. Further development should focus on accessibility enhancements, data handling optimization, and evaluation in larger, real-world settings to advance inclusive rehabilitation strategies. Citizen Participation in Science and Technology Falls Management of Science Public Perception of Science Rehabilitation Technology and Innovation in Health Figures Figure 1 INTRODUCTION Rehabilitation sciences face growing challenges in achieving clinical outcomes. At the same time, they must address access, equity, and meaningful patient engagement. Individuals living with chronic conditions, disabilities, or recovering from acute health events frequently experience fragmented care and are seldom involved in planning or evaluating services that directly affect their well-being. Falls, for example, represent a leading cause of injury, hospitalization, and loss of independence among older adults, contributing substantially to healthcare costs and reduced quality of life [ 1 – 4 ]. Consequently, functional mobility, balance, and postural control have become central outcomes in rehabilitation practice and research [ 5 – 7 ], underscoring the need for accessible tools that support monitoring and intervention beyond clinical settings. Concurrently, advances in digital health technologies have created new opportunities for participatory and decentralized approaches to health research [ 8 – 10 ]. Citizen Science (CS), which actively engages non-expert individuals in scientific processes [ 11 ], has emerged as a promising framework to enhance community involvement, democratize knowledge production, and inform health interventions [ 12 – 14 ]. Although CS is well established in environmental sciences [ 15 , 16 ], its application in healthcare (and particularly in rehabilitation) remains limited and insufficiently explored [ 17 – 20 ], despite its potential to empower marginalized populations and support the co-production of socially relevant evidence [ 21 ]. Despite growing international interest in public participation in health research, the application of CS in rehabilitation remains scarce, heterogeneous, and largely confined to exploratory or pilot studies. A recent review identified only ten studies published between 2015 and 2024 that employed CS approaches in rehabilitation sciences, addressing topics such as robotics, virtual reality, and participatory design in geriatric care [ 20 ]. Although these studies demonstrate motivational benefits and technical feasibility, they also reveal persistent challenges related to data reliability, inclusivity, and methodological consistency. Moreover, while successful initiatives such as Our Voice [ 22 ] and the Dignity Framework [ 23 ] have advanced participatory practices in health research, few studies have rigorously validated mobile platforms specifically designed to support CS in rehabilitation contexts. In Brazil, CS remains an emerging field, characterized by limited academic output and a predominance of ecological monitoring initiatives rather than health-oriented applications [ 24 ]. This gap, however, represents a significant opportunity to expand participatory approaches in rehabilitation through digital tools tailored to local contexts. Such development is particularly critical in areas like fall prevention, where citizen engagement via scalable, community-based tools for monitoring mobility and balance may facilitate earlier detection of risk and promote individual empowerment. Taken together, these factors underscore a pressing need for platforms that not only foster broad participation but also enable valid and scalable assessments of functional outcomes through ethical, inclusive, and user-friendly mobile technologies. This study aims to validate SAUDE (Sistema de Avaliação e Utilização de Dados sobre o Envelhecimento / System for the Assessment and Utilization of Data on Aging), a mobile application developed to support CS initiatives in rehabilitation research. The platform enables individuals to self-assess functional mobility using standardized tests and to contribute anonymized data to a shared research repository. Specifically, we evaluated multiple dimensions of (criterion, convergent, and construct) validity by comparing app-based self-assessments with evaluator-administered measurements, examining inter-test correlations, and analyzing associations with self-reported fall history. In addition, we assessed the usability and perceived clarity of the platform based on participant feedback. METHODS Ethical Considerations This study was approved by the Research Ethics Committee of Centro Universitário Augusto Motta (CAAE No. 85025624.2.0000.5235; Report No. 7.252.377). The research protocol complied with national and institutional ethical standards for studies involving human participants, in accordance with Resolution No. 466/2012 of the Brazilian National Health Council. All participants provided written informed consent prior to study enrollment. Study Design This study employed a mixed-methods, cross-sectional validation design to evaluate the performance and acceptability of the SAUDE app as a CS tool in rehabilitation research. Quantitative data were collected concurrently in a controlled laboratory environment. Each participant performed three standardized functional mobility tests (TUG, STS, and SLS) using the SAUDE app, while being simultaneously evaluated by a trained assessor (A.G.S.). This design enabled real-time comparison between self-assessed and assessor-recorded performance, thereby minimizing recall bias and enhancing measurement reliability. The assessor was blinded to participants’ self-reported results to further reduce potential bias in the comparative analyses. In parallel, qualitative and usability-focused measures, including user experience feedback and engagement data, were collected to contextualize the app’s acceptability and practical use. The design and reporting adhered to the mobile health (mHealth) evidence reporting and assessment (mERA) checklist [ 25 ]. Sample size Sample size considerations were informed by recommendations for both quantitative [ 26 ] and qualitative research sufficiency [ 27 ]. For correlational analyses, an a priori power calculation was conducted using G*Power version 3.1 [ 28 ], assuming a two-tailed significance level of 5% and statistical power of 80%. Under these assumptions, detecting a correlation coefficient of r = 0.44 (representing at least a weak-to-moderate association [ 29 ]) would require a minimum sample size of 44 participants. Given the exploratory and validation-oriented nature of the present study, the primary aim was to assess feasibility, usability, and preliminary validity rather than to achieve definitive hypothesis testing. From a mixed-methods perspective, a sample size of approximately 20–30 participants has been shown to be adequate for early-stage methodological validation studies, particularly when complemented by qualitative usability assessments [ 27 ]. Participants and Recruitment Participants were community-dwelling older adults recruited through local outreach initiatives, including health clinics, community centers, and digital announcements disseminated via institutional mailing lists and social media platforms. Eligibility criteria included age 60 years or older; the ability to ambulate independently, with or without assistive devices; and sufficient cognitive and physical capacity to follow instructions and complete the required functional mobility tests. Access to a smartphone or the availability of in-person assistance to use the SAUDE app was also required. Exclusion criteria comprised acute illness; recent orthopedic or neurological surgery within the preceding six months; uncontrolled cardiovascular or metabolic conditions; and cognitive impairments that could interfere with task comprehension or the provision of informed consent. Assessment: The SAUDE app The SAUDE application (Sistema de Avaliação e Utilização de Dados sobre o Equilíbrio) is a web-based platform developed using R version 4.5.2 ( https://www.r-project.org ) and RStudio Desktop version 2026.01.0 + 392 ( https://posit.co ). The application was written in RMarkdown ( https://rmarkdown.rstudio.com ) and optimized for mobile-responsive use. The project is openly hosted in a GitHub repository ( https://github.com/ppgcr-unisuam/SAUDE ), and the application is publicly accessible at https://ppgcr-unisuam.github.io/SAUDE . It is distributed under a Creative Commons Attribution–NonCommercial (CC BY-NC) license, permitting non-commercial use, adaptation, and dissemination. SAUDE was designed to enable citizens to self-administer standardized functional mobility tests while contributing anonymized data to support public health monitoring and citizen science initiatives. The platform includes three core assessment modules: TUG [ 28 ], STS [ 29 ], and SLS [ 30 ]. Each test is implemented as an interactive module with embedded instructions, integrated timers, and self-report input fields. The app guides users through each procedure using clear on-screen instructions and visual cues, supported by simplified page layouts and accessibility-oriented formatting. After completing each test, participants submitted their results via embedded email links within the app. This mechanism facilitates data submission while enabling the optional reporting of contextual metadata (e.g., observer identity or environmental conditions) that may influence measurement outcomes. Recording such information is important for addressing variability in test execution and mitigating potential issues related to sampling heterogeneity or pseudo-replication [ 30 ]. With explicit user permission, the app can also collect geolocation data (latitude and longitude) through the device’s browser to support analyses of spatial distribution and environmental context. The SAUDE app does not require user accounts and does not collect personally identifiable information. Data Collection Procedures Quantitative data were collected concurrently in a controlled laboratory environment. Each participant performed three standardized functional mobility tests using the SAUDE app while being simultaneously evaluated by a trained assessor (A.G.S.). Participants followed the app’s on-screen instructions to self-administer each test and manually entered their results using embedded input fields. Concurrently, the assessor conducted standardized clinical assessments of the same tests in accordance with established protocols. Briefly: Timed Up and Go (TUG) : This test measures the time (in seconds) required for a participant to rise from a chair, walk three meters, turn around, return to the chair, and sit down [ 28 ]. Longer completion times (e.g., > 20 seconds) may indicate impaired functional mobility and an increased risk of falls [ 31 , 32 ]. 30-second Sit-to-Stand (STS) : This test assesses lower-limb strength and endurance by recording the number of complete sit-to-stand repetitions performed from a chair within 30 seconds, without the use of the arms [ 29 ]. Performance of fewer than approximately eight repetitions in 30 seconds has been associated with early functional decline in older adults [ 33 – 35 ]. Single-Leg Stance (SLS) : This test evaluates static balance by measuring the duration (in seconds) a participant can maintain a single-leg stance with arms at the sides and eyes open. Single-leg stance times of less than 21 seconds have been associated with reduced postural control and an increased risk of falls [ 30 ]. Usability Assessment Usability feedback was collected through open-ended observations conducted during the testing sessions. Upon completion of all assessments, participants responded to a brief, standardized question designed to evaluate perceived task difficulty: “How easy or difficult was it for you to complete the testing portion of this study (including reading the instructions and performing the tests)?” Statistical analysis All statistical analyses were conducted using R version 4.5.2 ( https://www.r-project.org ) and RStudio Desktop version 2026.01.0 + 392 ( https://posit.co ). Statistical significance was set at p < 0.05 (two-tailed). Descriptive statistics were used to summarize participant characteristics and test performance, with continuous variables reported as medians and interquartile ranges (IQR) and categorical variables reported as absolute and relative frequencies. To evaluate criterion (concurrent) validity, agreement between self-reported test performance collected via the SAUDE app and concurrent assessor-based measurements was examined using Concordance Correlation Coefficients (CCC) with 95% confidence intervals [ 36 ] and Bland-Altman analyses [ 37 ]. For each functional test, mean bias and limits of agreement (LoA) were calculated. Bias was calculated as app-based measurement minus assessor-based measurement. To further characterize agreement, calibration analyses were performed by fitting linear regression models with assessor-based measurements as observed values and app-based measurements as predictors. Calibration plots were generated for each test, including the identity line (perfect calibration), fitted regression line, intercept, slope, and coefficient of determination (R 2 ). Convergent validity was assessed using Pearson’s correlation coefficients to examine associations among the three functional mobility tests (TUG, STS, and SLS) when measured using the same method (i.e., SAUDE app or assessor-based). Construct validity was evaluated by examining the relationship between functional test performance and self-reported history of falls within the previous six months. Fall history was dichotomized (yes/no), and point-biserial correlation coefficients with 95% confidence intervals were calculated for each test and measurement method. Correlation coefficients derived from SAUDE app and assessor-based measurements were statistically compared using Fisher’s r-to-z transformation for independent correlations [ 38 ]. For qualitative usability analysis, open-ended responses were grouped by thematic content and summarized descriptively in tabular form. No formal coding or inferential qualitative analysis was performed, as the aim was to provide illustrative insights into user experience and identify potential areas for platform improvement. RESULTS Participants Demographic, socioeconomic, and clinical characteristics of the participants are presented in Table 1 . The median age of the sample was 73 years (interquartile range: 66–79 years), and most participants were women (74%). The majority were married (60%) and retired (87%). According to older adult-specific body mass index classifications, 9% of participants were underweight, while nearly half were classified as obese (49%). Household income was heterogeneous, with 30% reporting a monthly income of one minimum wage and 38% reporting income of up to one minimum wage. Most participants reported regular use of medications (89%), and 21% reported at least one fall in the previous six months. Table 1 Demographic and clinical characteristics of the study sample. Variable N = 47 1 Sex, n (%) Male 12 (26%) Female 35 (74%) Age, years 73 (66, 79) Body mass, kg 70.0 (61.0, 81.0) Body height, m 1.59 (1.54, 1.64) Body mass index, kg/m² 26.8 (25.6, 30.9) BMI classification, n (%) Underweight 4 (9%) Eutrophy 20 (43%) Obesity 23 (49%) Marital status, n (%) Single 3 (6%) Married 28 (60%) Widowed 12 (26%) Divorced 4 (9%) Living situation, n (%) Alone 11 (23%) With spouse 23 (49%) With relatives 13 (28%) Education level, n (%) Elementary incomplete 6 (13%) Elementary complete 13 (28%) High school incomplete 5 (11%) High school complete 10 (21%) Technical level 1 (2%) Higher education complete 12 (26%) Retired, n (%) 41 (87%) Family income range, n (%) No income 4 (9%) Up to 1/2 minimum wage 4 (9%) More than 1/2 to 1 minimum wage 4 (9%) 1 minimum wage 14 (30%) More than 1 to 2 minimum wages 4 (9%) More than 2 to 5 minimum wages 10 (21%) More than 5 to 10 minimum wages 7 (15%) Use of medications, n (%) 42 (89%) History of falls (6 months), n (%) 10 (21%) Hearing, n (%) Normal 27 (57%) Reduced 20 (43%) Vision, n (%) Normal 17 (36%) Reduced 30 (64%) 1 n (%); Median (Q1, Q3) Table 2 presents descriptive performance data for the three functional mobility tests obtained via the SAUDE app and assessor-based measurements. Mean and median values for the 30-second Sit-to-Stand test were similar between methods (assessor: 10.3 ± 3.5 repetitions; median = 10.0; SAUDE app: 10.5 ± 3.6 repetitions; median = 11.0). Single-Leg Stance times were slightly higher in app-based assessments (13.6 ± 10.4 s; median = 9.0) than in assessor measurements (12.1 ± 10.7 s; median = 8.0). Timed Up and Go performance showed modestly longer completion times using the SAUDE app (15.5 ± 5.6 s; median = 15.0) compared with assessor-based evaluation (14.7 ± 4.2 s; median = 14.0). Overall, distributions and central tendency measures were comparable across methods. Table 2 Results obtained using the SAUDE app and by the assessor. Group Assessor N = 47 1 SAUDE app N = 47 1 30-Second Sit-to-Stand Test (n) 10.3 (± 3.5) 10.0 [8.0, 12.0] 10.5 (± 3.6) 11.0 [8.0, 13.0] Single-Leg Stance Test (s) 12.1 (± 10.7) 8.0 [3.0, 22.0] 13.6 (± 10.4) 9.0 [5.0, 23.0] Timed Up and Go Test (s) 14.7 (± 4.2) 14.0 [11.0, 17.0] 15.5 (± 5.6) 15.0 [11.0, 20.0] 1 Mean (± SD) Median [Q1, Q3] Criterion validity Agreement between functional mobility scores obtained via the SAUDE app and concurrent assessor-based measurements is summarized in Table 3 . Strong agreement was observed across all tests, with CCC values of 0.893 (95% CI: 0.836–0.932) for the TUG, 0.977 (95% CI: 0.960–0.987) for the STS, and 0.955 (95% CI: 0.922–0.974) for the SLS. Bland–Altman analyses (Table 3 ; Fig. 1 ) demonstrated small mean biases (TUG: 0.85 s; STS: 0.17 repetitions; SLS: 1.55 s) and narrow limits of agreement between measurement methods. Table 3 Concurrent validity between the SAUDE app and assessor measurements. Test CCC 95% CI Bias Lower LOA Upper LOA Timed Up and Go Test (TUG) 0.893 (0.836, 0.932) 0.85 -3.34 5.04 30-Second Sit-to-Stand Test (STS) 0.977 (0.96, 0.987) 0.17 -1.26 1.60 Single-Leg Stance Test (SLS) 0.955 (0.922, 0.974) 1.55 -3.88 6.99 Bias and limits of agreement were derived from Bland–Altman analyses. Convergent validity Among assessor-administered measurements (Table 4 ), moderate inverse correlations were observed between TUG and both the STS (r = − 0.645; 95% CI: −0.786 to − 0.439; p < 0.001) and the SLS (r = − 0.588; 95% CI: −0.749 to − 0.363; p = 0.020). A moderate positive association was identified between STS and SLS performance (r = 0.411; 95% CI: 0.140 to 0.624; p = 0.004). Comparable correlation patterns were observed in SAUDE app-based assessments (Table 4 ). TUG scores were moderately and inversely correlated with STS (r = − 0.632; 95% CI: −0.778 to − 0.422; p < 0.001) and SLS performance (r = − 0.532; 95% CI: −0.710 to − 0.289; p < 0.001). The correlation between STS and SLS remained moderate and positive (r = 0.420; 95% CI: 0.151 to 0.631; p = 0.003). Direct statistical comparisons of correlation coefficients between SAUDE app–based and assessor-administered measurements revealed no significant differences across any pairwise associations (all p ≥ 0.64; Table 4 ). Table 4 Comparison of correlation coefficients obtained using the SAUDE app and by the assessor. SAUDE app Assessor Comparison Test r 95% CI P-value r 95% CI P-value P (Δr) TUG vs STS -0.632 (-0.778, -0.422) < 0.001 -0.645 (-0.786, -0.439) < 0.001 0.922 TUG vs SLS -0.532 (-0.71, -0.289) < 0.001 -0.588 (-0.749, -0.363) < 0.001 0.698 STS vs SLS 0.420 (0.151, 0.631) 0.003 0.411 (0.14, 0.624) 0.004 0.957 TUG vs Falls 0.488 (0.233, 0.68) < 0.001 0.560 (0.325, 0.73) < 0.001 0.642 STS vs Falls -0.319 (-0.555, -0.035) 0.029 -0.318 (-0.554, -0.034) 0.03 0.996 SLS vs Falls -0.294 (-0.536, -0.008) 0.045 -0.323 (-0.558, -0.039) 0.027 0.882 Pearson correlation coefficients with 95% confidence intervals. Correlations involving falls are point-biserial correlations. Construct validity In assessor-administered evaluations (Table 4 ), TUG performance was moderately and positively correlated with fall history (r = 0.488; 95% CI: 0.233–0.680; p < 0.001). Associations between fall history and both the STS test (r = − 0.319; 95% CI: −0.555 to 0.035; p = 0.029) and the SLS test (r = − 0.294; 95% CI: −0.536 to -0.008; p = 0.045) were weak and statistically significant. A similar pattern was observed in SAUDE app-based assessments (Table 4 ). TUG scores demonstrated a moderate positive correlation with fall history (r = 0.560; 95% CI: 0.325–0.730; p < 0.001), whereas associations involving STS (r = − 0.318; 95% CI: −0.554 to 0.034; p = 0.030) and SLS performance (r = − 0.323; 95% CI: −0.558 to -0.039; p = 0.027) remained weak and significant. Usability Usability observations revealed several recurring interaction challenges during app-based functional testing (Table 5 ). Three participants reported no usability-related comments or observed issues during testing. The most frequently observed issue involved button interaction difficulties , including problems locating or activating start/stop controls, delayed screen response, or reduced touchscreen sensitivity. Visual accessibility barriers were also common and primarily related to difficulty seeing buttons or text elements, often associated with reduced vision or small interface components. Physical or motor constraints that interfered with test execution or app interaction were frequently observed and included balance limitations, tremor, neuropathy, or orthopedic conditions. A closely related and prominent theme was difficulty holding or stabilizing the smartphone during testing , particularly during standing or balance tasks, which affected safe and independent task performance. Cognitive or literacy-related challenges , such as difficulty understanding instructions, test sequence, or app logic and the need for clarification, were observed in a smaller subset of participants. In contrast, a limited number of participants demonstrated good comprehension and motor readiness , completing all tasks independently without observed usability issues. Isolated difficulties related to data entry or result recording within the app interface were noted infrequently. Table 5 Thematic Analysis of Participant Observations During App-Based Functional Testing (n = 44). Theme Participants (IDs) Description Button interaction difficulties 4, 8, 9, 12, 13, 14, 15, 16, 17, 21, 24, 26, 27, 31, 32, 33, 34, 35, 37–47 Difficulty locating or activating start/stop buttons, delayed screen response, or reduced touchscreen sensitivity Visual accessibility barriers 4, 5, 9, 12, 15, 29, 30, 32, 34, 36, 41, 43 Difficulty seeing screen elements (buttons or text), often related to reduced vision or small interface components Physical or motor constraints 4, 10, 11, 12, 16, 20, 22, 25, 28, 35, 38, 40 Balance limitations, tremor, neuropathy, orthopedic conditions, or difficulty performing tests safely Difficulty holding or stabilizing the smartphone 9, 12, 15, 20, 22, 25, 28, 35, 38, 40, 41, 44 Inability to safely hold or stabilize the device during standing or balance tasks Cognitive or literacy-related challenges 2, 3, 6, 11, 13, 16 Difficulty understanding instructions, test sequence, or app logic; need for clarification Good comprehension and motor readiness 2, 3, 6, 16 Clear understanding of instructions and ability to complete all tasks independently Data entry or result recording difficulty 11 Difficulty recording or submitting test results via the interface No comments 1, 18, 19, 23, 27 No usability issues reported or observed DISCUSSION This study sought to validate SAUDE, a mobile application designed to foster CS participation in rehabilitation by enabling older adults to self-administer standardized balance and mobility tests. The findings demonstrated strong criterion validity, evidenced by high concordance correlation coefficients and minimal mean bias between app-based and assessor-administered measurements across all functional tests (TUG, STS, and SLS). Moderate to strong inter-test correlations supported convergent validity, while the association between poorer TUG performance and recent fall history provided preliminary evidence of construct validity. In addition, usability observations indicated that most participants were able to complete the assessments independently, although visual, cognitive, and motor-related challenges were noted in a subset of users. Within the Brazilian context – where CC remains underrepresented in rehabilitation research [ 20 ] – these findings position SAUDE as a novel, clinically grounded digital platform capable of supporting self-assessment while contributing valid functional data to participatory research initiatives. These findings are consistent with a growing body of evidence indicating that citizen science (CS) approaches in rehabilitation can support both valid data collection and meaningful community engagement. Prior initiatives, including the Our Voice framework in geriatric rehabilitation [ 22 ] and the Dignity Project in occupational therapy [ 23 , 39 ], have demonstrated the feasibility and value of integrating citizen perspectives into health service design. Likewise, studies employing virtual reality and other participatory technology platforms [ 40 , 41 ] have reported positive effects on user engagement and motor-related outcomes. Nevertheless, most existing mobile applications in this field have emphasized surveillance functions [ 9 ], environmental gerontology and urban or landscape planning [ 42 ], or the collection of environmental and personal health data [ 43 ], rather than incorporating clinically validated constructs relevant to rehabilitation practice, such as fall risk. Against this backdrop, SAUDE’s integration of standardized functional mobility tests—combined with moderate to strong inter-test correlations and meaningful associations with fall history—represents an important contribution. The platform extends beyond self-assessment by generating clinically interpretable outputs and by operationalizing validated rehabilitation metrics within an open, citizen-facing environment. In doing so, SAUDE bridges structured clinical assessment protocols with decentralized, community-based health monitoring, offering a first step toward scalable screening of mobility and balance impairments among older adults [ 1 – 4 ]. Despite these encouraging findings, several limitations should be acknowledged. First, the relatively small sample size – although sufficient for early-stage validation and usability assessment [ 26 , 27 ] – limits the generalizability of the results and precludes more detailed subgroup analyses. Second, participants were predominantly community-dwelling older adults with at least minimal digital literacy or access to support, which may have introduced selection bias favoring healthier and more digitally engaged individuals. This limitation is consistent with challenges reported in prior CS studies, in which digital exclusion, variable motivation, and differences in comprehension can influence both participation and data quality [ 8 , 44 , 45 ]. Observational data further identified specific usability barriers, including difficulties with button interaction, visual accessibility, and cognitive or literacy-related demands, underscoring the importance of inclusive and adaptive interface design. Although most participants were able to complete the assessments independently, those with visual impairment, mobility limitations, or lower health literacy often required assistance. Together, these findings highlight the need for iterative platform refinement and targeted accessibility enhancements to promote equitable participation across diverse aging populations while maintaining the methodological robustness of citizen-generated health data. As with many CS initiatives, the analysis of user-generated data poses distinct statistical and methodological challenges [ 46 – 48 ]. Variability in individual performance, inconsistent adherence to testing protocols, and the potential for data entry errors may increase heterogeneity and reduce comparability across users and settings [ 49 , 50 ]. To preserve the validity and interpretability of large-scale datasets, future implementations will require robust analytical frameworks capable of managing outliers, accommodating protocol deviations, and enabling meaningful subgroup analyses. At the same time, the platform’s capacity to collect geolocation data offers a strategic opportunity to examine functional mobility at the population level by linking performance metrics to regional infrastructure, urban characteristics, and environmental inequities. Integrating spatial patterns with demographic profiles could allow future versions of SAUDE to inform public health planning, support targeted resource allocation, and strengthen data-driven decision-making by policymakers and community stakeholders. Ultimately, embedding citizen-contributed functional assessment data within broader health system infrastructures has the potential to transform participation into actionable knowledge, advancing both rehabilitation equity and public health responsiveness [ 51 ]. CONCLUSIONS This study provides evidence supporting the validity and usability of SAUDE, a citizen science–based mobile application for functional assessment in rehabilitation. Self-administered evaluations can generate clinically meaningful data, posing SAUDE as a promising platform for scalable, community-level screening of mobility and balance impairments in older adults. Although usability constraints and statistical challenges inherent to citizen-generated data remain, the platform’s open and accessible design supports broad participation and offers a foundation for decentralized and timely public health monitoring. Future development should focus on interface optimization, extended real-world deployment, and the integration of spatial and demographic information to inform equitable rehabilitation strategies and advance participatory innovation within health systems. Declarations Conflicts of Interest Statement The authors have no conflicts of interest to declare. Declaration of generative AI and AI-assisted technologies in the manuscript preparation process During the preparation of this work the author(s) used ChatGPT (OpenAI) to revise the translated version from Portuguese. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article. Funding This study was supported by the Fundação Carlos Chagas Filho de Apoio à Pesquisa do Estado do Rio de Janeiro (FAPERJ, No. E-26/211.104/2021, E-26/204.369/2024, E-26/200.830/2024), Coordenação de Aperfeiçoamento de Pessoal (CAPES, Finance Code 001; No. 88881.708719/2022-01, and No. 88887.708718/2022-00), and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, No. 315453/2021-4, 311827/2025-0). Author Contribution Conceptualization: ASF; Data curation: ASG, ASF; Formal Analysis: ASF; Funding acquisition: ASF; Investigation: AGS; Methodology: AGS, ASF; Project administration: ASF; Resources: ASF; Software: ASF; Supervision: ASF; Validation: ASG, ASF; Visualization: ASF; Writing – original draft: AGS, ASF; Writing – review & editing: AGS, ASF Data Availability The data that support the findings of this study are available on request from the corresponding author, ASF. The data are not publicly available due to ethical restrictions. References Heinrich S, Rapp K, Rissmann U, et al. Cost of falls in old age: A systematic review. Osteoporos Int. 2010;21:891–902. https://doi.org/10.1007/s00198-009-1100-1 . Stevens JA, Corso PS, Finkelstein EA, Miller TR. The costs of fatal and non-fatal falls among older adults. Inj Prev. 2006;12:290–5. https://doi.org/10.1136/ip.2005.011015 . Montero-odasso M, Velde N, Van Der, Martin FC et al. (2022) World guidelines for falls prevention and management for older adults: a global initiative. 1–36. Lima JdaS, de Quadros DV, da Silva SLC, et al. Costs of hospital admission authorizations due to falls among older people in the Brazilian National Health System, Brazil, 2000–2020: a descriptive study. Epidemiologia e Servicos de Saude. 2022;31:1–13. https://doi.org/10.1590/S1679-49742022000100012 . Silva SDO, Barbosa JB, Lemos T, et al. Agreement and predictive performance of fall risk assessment methods and factors associated with falls in hospitalized older adults: A longitudinal study. Geriatr Nurs (Minneap). 2023;49:109–14. https://doi.org/10.1016/j.gerinurse.2022.11.016 . Menezes M, Meziat-Filho NAM, Araújo CS, et al. Agreement and predictive power of six fall risk assessment methods in community-dwelling older adults. Arch Gerontol Geriatr. 2020;87:103975. https://doi.org/10.1016/j.archger.2019.103975 . Menezes M, Meziat-Filho NAM, Lemos T, Ferreira AS. Believe the positive’ aggregation of fall risk assessment methods reduces the detection of risk of falling in older adults. Arch Gerontol Geriatr. 2020;91:104228. https://doi.org/10.1016/j.archger.2020.104228 . Nov O, Arazy O, Anderson D. (2011) Dusting for science: Motivation and participation of digital citizen science volunteers. ACM International Conference Proceeding Series 68–74. https://doi.org/10.1145/1940761.1940771 Katapally TR, Bhawra J, Leatherdale ST, et al. The SMART Study, a Mobile Health and Citizen Science Methodological Platform for Active Living Surveillance, Integrated Knowledge Translation, and Policy Interventions: Longitudinal Study. JMIR Public Health Surveill. 2018;4:e31. https://doi.org/10.2196/publichealth.8953 . Newman G, Wiggins A, Crall A, et al. The future of citizen science: emerging technologies and shifting paradigms. Front Ecol Environ. 2012;10:298–304. https://doi.org/10.1890/110294 . Eitzel MV, Cappadonna JL, Santos-Lang C, et al. Citizen Science Terminology Matters: Exploring Key Terms. Citiz Sci. 2017;2:1. https://doi.org/10.5334/cstp.96 . Bonney R, Phillips TB, Ballard HL, Enck JW. Can citizen science enhance public understanding of science? Public Underst Sci. 2016;25:2–16. https://doi.org/10.1177/0963662515607406 . Hajibayova L. (Un)theorizing citizen science: Investigation of theories applied to citizen science studies. J Assoc Inf Sci Technol. 2020;71:916–26. https://doi.org/10.1002/asi.24308 . Rowbotham S, McKinnon M, Leach J, et al. Does citizen science have the capacity to transform population health science? Crit Public Health. 2017;29:118–28. https://doi.org/10.1080/09581596.2017.1395393 . Cunha DGF, Marques JF, de Resende JC, et al. Citizen science participation in research in the environmental sciences: Key factors related to projects’ success and longevity. Acad Bras Cienc. 2017;89:2229–45. https://doi.org/10.1590/0001-3765201720160548 . Crain R, Cooper C, Dickinson JL. Citizen Science: A Tool for Integrating Studies of Human and Natural Systems. Annu Rev Environ Resour. 2014;39:641–65. https://doi.org/10.1146/annurev-environ-030713-154609 . Kullenberg C, Kasperowski D. What Is Citizen Science? – A Scientometric Meta-Analysis. PLoS ONE. 2016;11:e0147152. https://doi.org/10.1371/journal.pone.0147152 . Wiggins A, Wilbanks J. The Rise of Citizen Science in Health and Biomedical Research. Am J Bioeth. 2019;19:3–14. https://doi.org/10.1080/15265161.2019.1619859 . Follett R, Strezov V. An analysis of citizen science based research: Usage and publication patterns. PLoS ONE. 2015;10. https://doi.org/10.1371/journal.pone.0143687 . Sancho AG, de Ferreira A S. Engaging Citizen Science: Exploring Popular Participation in Scientific Projects in Rehabilitation. Aging Medicine and Healthcare in; 2026. Bela G, Peltola T, Young JC, et al. Learning and the transformative potential of citizen science. Conserv Biol. 2016;30:990–9. https://doi.org/10.1111/cobi.12762 . Gardiner P, Sam L, Tan V, et al. Patients used the Our voice Citizen Science Framework to improve a geriatric rehabilitation unit. Innov Aging. 2018;2:986–986. https://doi.org/10.1093/geroni/igy031.3646 . Chapman K, Dixon A, Cocks K, et al. The Dignity Project Framework: An extreme citizen science framework in occupational therapy and rehabilitation research. Aust Occup Ther J. 2022;69:742–52. https://doi.org/10.1111/1440-1630.12847 . Paoli T, Rumenos NN, Lucas J et al. (2021) O Estado da Arte das pesquisas sobre Ciência Cidadã no Brasil The State of the Art of Science-Citizen Research in Brazil. 1–8. Agarwal S, Lefevre AE, Lee J et al. (2016) Guidelines for reporting of health interventions using mobile phones: Mobile health (mHealth) Evidence reporting and assessment (mERA) checklist. BMJ (Online) 352:. https://doi.org/10.1136/bmj.i1174 Looney W S. Practical Issues in Sample Size Determination for Correlation Coefficient Inference. SM J Biometrics Biostatistics. 2018;3:1–4. https://doi.org/10.36876/smjbb.1027 . Hennink M, Kaiser BN. Sample sizes for saturation in qualitative research: A systematic review of empirical tests. Soc Sci Med. 2022;292:114523. https://doi.org/10.1016/j.socscimed.2021.114523 . Podsiadlo D, Richardson S. The Timed Up & Go: A test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. 1991;39:142–8. https://doi.org/10.1111/j.1532-5415.1991.tb01616.x . Bohannon RW, Barreca SR, Shove ME, et al. Documentation of daily sit-to-stands performed by community-dwelling adults. Physiother Theory Pract. 2008;24:437–42. https://doi.org/10.1080/09593980802511813 . Jonsson E, Seiger Å, Hirschfeld H. One-leg stance in healthy young and elderly adults: A measure of postural steadiness? Clin Biomech Elsevier Ltd. 2004;19:688–94. https://doi.org/10.1016/j.clinbiomech.2004.04.002 . Bretan O, Elias Silva J, Ribeiro OR, Eduardo Corrente J. Risk of falling among elderly persons living in the community: Assessment by the timed up and go test. Braz J Otorhinolaryngol. 2013;79:18–21. https://doi.org/10.5935/1808-8694.20130004 . Browne W, Nair B, Kichu) R. The Timed Up and Go test. Med J Aust. 2019;210:13. https://doi.org/10.5694/mja2.12045 . Rikli RE, Jones CJ. Development and Validation of Criterion-Referenced Clinically Relevant Fitness Standards for Maintaining Physical Independence in Later Years. Gerontologist. 2013;53:255–67. https://doi.org/10.1093/geront/gns071 . Lord SR, Murray SM, Chapman K, et al. Sit-to-Stand Performance Depends on Sensation, Speed, Balance, and Psychological Status in Addition to Strength in Older People. J Gerontol Biol Sci Med Sci. 2002;57:M539–43. https://doi.org/10.1093/gerona/57.8.M539 . Bruun IH, Mogensen CB, Nørgaard B, et al. Validity and Responsiveness to Change of the 30-Second Chair-Stand Test in Older Adults Admitted to an Emergency Department. J Geriatr Phys Ther. 2019;42:265–74. https://doi.org/10.1519/JPT.0000000000000166 . Lin LI-K. A Concordance Correlation Coefficient to Evaluate Reproducibility. Biometrics. 1989;45:255–68. Bland JM, Altman DG. Applying the right statistics: analyses of measurement studies. Ultrasound Obstet Gynecol. 2003;22:85–93. https://doi.org/10.1002/uog.122 . Fisher RA. Frequency Distribution of the Values of the Correlation Coefficient in Samples from an Indefinitely Large Population. Biometrika. 1915;10:507. https://doi.org/10.2307/2331838 . Chapman K, Dixon A, Ehrlich C, Kendall E. Dignity and the Importance of Acknowledgement of Personhood for People With Disability. Qual Health Res. 2024;34:141–53. https://doi.org/10.1177/10497323231204562 . Ventura RB, Hughes KS, Nov O et al. (2022) Data-Driven Classification of Human Movements in Virtual Reality-Based Serious Games: Preclinical Rehabilitation Study in Citizen Science. JMIR Serious Games 10:. https://doi.org/10.2196/27597 Ventura RB, Nakayama S, Raghavan P, et al. The role of social interactions in motor performance: Feasibility study toward enhanced motivation in telerehabilitation. J Med Internet Res. 2019;21. https://doi.org/10.2196/12708 . Barrie H, Soebarto V, Lange J, et al. Using Citizen Science to Explore Neighbourhood Influences on Ageing Well: Pilot Project. Healthcare. 2019;7:126. https://doi.org/10.3390/healthcare7040126 . Ottaviano M, Beltrán-Jaunsarás ME, Teriús-Padrón JG, et al. Empowering citizens through perceptual sensing of urban environmental and health data following a participative citizen science approach. Sens (Switzerland). 2019;19. https://doi.org/10.3390/s19132940 . Rotman D, Preece J, Hammock J et al. (2012) Dynamic changes in motivation in collaborative citizen-science projects. Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW 217–226. https://doi.org/10.1145/2145204.2145238 Strasser BJ, Tancoigne E, Baudry J, et al. Quantifying online citizen science: Dynamics and demographics of public participation in science. PLoS ONE. 2023;18:e0293289. https://doi.org/10.1371/journal.pone.0293289 . Bird TJ, Bates AE, Lefcheck JS, et al. Statistical solutions for error and bias in global citizen science datasets. Biol Conserv. 2014;173:144–54. https://doi.org/10.1016/j.biocon.2013.07.037 . Downs RR, Ramapriyan HK, Peng G, Wei Y. Perspectives on Citizen Science Data Quality. Front Clim. 2021;3:1–7. https://doi.org/10.3389/fclim.2021.615032 . Lukyanenko R, Parsons J, Wiersma YF. Emerging problems of data quality in citizen science. Conserv Biol. 2016;30:447–9. https://doi.org/10.1111/cobi.12706 . Hochachka WM, Fink D, Hutchinson RA, et al. Data-intensive science applied to broad-scale citizen science. Trends Ecol Evol. 2012;27:130–7. https://doi.org/10.1016/j.tree.2011.11.006 . Aceves-Bueno E, Adeleye AS, Feraud M, et al. The Accuracy of Citizen Science Data: A Quantitative Review. Bull Ecol Soc Am. 2017;98:278–90. https://doi.org/10.1002/bes2.1336 . Den Broeder L, Devilee J, Van Oers H, et al. Citizen Science for public health. Health Promot Int daw. 2016;086. https://doi.org/10.1093/heapro/daw086 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8627904","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587456917,"identity":"f0adc67f-c63e-4ee2-8304-b389330ebbe4","order_by":0,"name":"Alexandre Gomes Sancho","email":"","orcid":"","institution":"University Center Augusto Motta","correspondingAuthor":false,"prefix":"","firstName":"Alexandre","middleName":"Gomes","lastName":"Sancho","suffix":""},{"id":587456919,"identity":"a14576f8-008a-416d-a09d-9360c069c96c","order_by":1,"name":"Arthur Sá Ferreira","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYHACNiBmZuCHcCQQ4gY4dTBDtEg2kKzF4AC6BC4t/LPPH3vMu8dazvj84WPSvDss7A2On05g+FGxjcFcGsMUiEPOJbMb8zxLNza7kZYmzXtGInHDmdwNjD1nbjNY9iVgt+YMM5s0z4HDidtu8JhJ87ZJJBgcyN3AzNh2m8HgDHYd8lAt9Zv7z4C12Bucf4tfiwFUS4IBQw5YC+OGGwRsMTzDbCY550C64YwbacmWc9skEmfeeLvhINAvPJY92LXInWF8JvHmgLU8f//hgzfettXZ853P3fjgR8VtOXMe7FqwgwNATJKGUTAKRsEoGAWoAABMq1kp65IMFgAAAABJRU5ErkJggg==","orcid":"","institution":"University Center Augusto Motta","correspondingAuthor":true,"prefix":"","firstName":"Arthur","middleName":"Sá","lastName":"Ferreira","suffix":""}],"badges":[],"createdAt":"2026-01-17 19:08:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8627904/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8627904/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102211673,"identity":"76ce8459-aa2e-4b6f-8001-70d8f4ab5d97","added_by":"auto","created_at":"2026-02-09 12:28:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":227406,"visible":true,"origin":"","legend":"\u003cp\u003eLimits-of-Agreement (top panel) and Line-of-Identity (bottom panel) Plots for the Timed Up and Go (left panel), Sit-to-Stand (center panel), and One-Leg Stance (right panel) Tests.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8627904/v1/873dd5a3bcac6910c6e03874.png"},{"id":103904364,"identity":"e7703b54-21c7-45fa-943a-f98b1b29ec86","added_by":"auto","created_at":"2026-03-04 10:28:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1166329,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8627904/v1/946bda16-0ecc-458b-8990-875025f06bd8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Validation of SAUDE: A Citizen Science App for Functional Balance Assessment in Older Adults","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eRehabilitation sciences face growing challenges in achieving clinical outcomes. At the same time, they must address access, equity, and meaningful patient engagement. Individuals living with chronic conditions, disabilities, or recovering from acute health events frequently experience fragmented care and are seldom involved in planning or evaluating services that directly affect their well-being. Falls, for example, represent a leading cause of injury, hospitalization, and loss of independence among older adults, contributing substantially to healthcare costs and reduced quality of life [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Consequently, functional mobility, balance, and postural control have become central outcomes in rehabilitation practice and research [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], underscoring the need for accessible tools that support monitoring and intervention beyond clinical settings. Concurrently, advances in digital health technologies have created new opportunities for participatory and decentralized approaches to health research [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Citizen Science (CS), which actively engages non-expert individuals in scientific processes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], has emerged as a promising framework to enhance community involvement, democratize knowledge production, and inform health interventions [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Although CS is well established in environmental sciences [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], its application in healthcare (and particularly in rehabilitation) remains limited and insufficiently explored [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], despite its potential to empower marginalized populations and support the co-production of socially relevant evidence [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite growing international interest in public participation in health research, the application of CS in rehabilitation remains scarce, heterogeneous, and largely confined to exploratory or pilot studies. A recent review identified only ten studies published between 2015 and 2024 that employed CS approaches in rehabilitation sciences, addressing topics such as robotics, virtual reality, and participatory design in geriatric care [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Although these studies demonstrate motivational benefits and technical feasibility, they also reveal persistent challenges related to data reliability, inclusivity, and methodological consistency. Moreover, while successful initiatives such as Our Voice [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and the Dignity Framework [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] have advanced participatory practices in health research, few studies have rigorously validated mobile platforms specifically designed to support CS in rehabilitation contexts. In Brazil, CS remains an emerging field, characterized by limited academic output and a predominance of ecological monitoring initiatives rather than health-oriented applications [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This gap, however, represents a significant opportunity to expand participatory approaches in rehabilitation through digital tools tailored to local contexts. Such development is particularly critical in areas like fall prevention, where citizen engagement via scalable, community-based tools for monitoring mobility and balance may facilitate earlier detection of risk and promote individual empowerment. Taken together, these factors underscore a pressing need for platforms that not only foster broad participation but also enable valid and scalable assessments of functional outcomes through ethical, inclusive, and user-friendly mobile technologies.\u003c/p\u003e \u003cp\u003eThis study aims to validate SAUDE (Sistema de Avalia\u0026ccedil;\u0026atilde;o e Utiliza\u0026ccedil;\u0026atilde;o de Dados sobre o Envelhecimento / System for the Assessment and Utilization of Data on Aging), a mobile application developed to support CS initiatives in rehabilitation research. The platform enables individuals to self-assess functional mobility using standardized tests and to contribute anonymized data to a shared research repository. Specifically, we evaluated multiple dimensions of (criterion, convergent, and construct) validity by comparing app-based self-assessments with evaluator-administered measurements, examining inter-test correlations, and analyzing associations with self-reported fall history. In addition, we assessed the usability and perceived clarity of the platform based on participant feedback.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003e This study was approved by the Research Ethics Committee of Centro Universit\u0026aacute;rio Augusto Motta (CAAE No. 85025624.2.0000.5235; Report No. 7.252.377). The research protocol complied with national and institutional ethical standards for studies involving human participants, in accordance with Resolution No. 466/2012 of the Brazilian National Health Council. All participants provided written informed consent prior to study enrollment.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Design\u003c/h3\u003e\n\u003cp\u003eThis study employed a mixed-methods, cross-sectional validation design to evaluate the performance and acceptability of the SAUDE app as a CS tool in rehabilitation research. Quantitative data were collected concurrently in a controlled laboratory environment. Each participant performed three standardized functional mobility tests (TUG, STS, and SLS) using the SAUDE app, while being simultaneously evaluated by a trained assessor (A.G.S.). This design enabled real-time comparison between self-assessed and assessor-recorded performance, thereby minimizing recall bias and enhancing measurement reliability. The assessor was blinded to participants\u0026rsquo; self-reported results to further reduce potential bias in the comparative analyses.\u003c/p\u003e \u003cp\u003eIn parallel, qualitative and usability-focused measures, including user experience feedback and engagement data, were collected to contextualize the app\u0026rsquo;s acceptability and practical use. The design and reporting adhered to the mobile health (mHealth) evidence reporting and assessment (mERA) checklist [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eSample size\u003c/h3\u003e\n\u003cp\u003eSample size considerations were informed by recommendations for both quantitative [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and qualitative research sufficiency [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. For correlational analyses, an a priori power calculation was conducted using G*Power version 3.1 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], assuming a two-tailed significance level of 5% and statistical power of 80%. Under these assumptions, detecting a correlation coefficient of r\u0026thinsp;=\u0026thinsp;0.44 (representing at least a weak-to-moderate association [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]) would require a minimum sample size of 44 participants.\u003c/p\u003e \u003cp\u003eGiven the exploratory and validation-oriented nature of the present study, the primary aim was to assess feasibility, usability, and preliminary validity rather than to achieve definitive hypothesis testing. From a mixed-methods perspective, a sample size of approximately 20\u0026ndash;30 participants has been shown to be adequate for early-stage methodological validation studies, particularly when complemented by qualitative usability assessments [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eParticipants and Recruitment\u003c/h3\u003e\n\u003cp\u003eParticipants were community-dwelling older adults recruited through local outreach initiatives, including health clinics, community centers, and digital announcements disseminated via institutional mailing lists and social media platforms. Eligibility criteria included age 60 years or older; the ability to ambulate independently, with or without assistive devices; and sufficient cognitive and physical capacity to follow instructions and complete the required functional mobility tests. Access to a smartphone or the availability of in-person assistance to use the SAUDE app was also required. Exclusion criteria comprised acute illness; recent orthopedic or neurological surgery within the preceding six months; uncontrolled cardiovascular or metabolic conditions; and cognitive impairments that could interfere with task comprehension or the provision of informed consent.\u003c/p\u003e\n\u003ch3\u003eAssessment: The SAUDE app\u003c/h3\u003e\n\u003cp\u003eThe SAUDE application (Sistema de Avalia\u0026ccedil;\u0026atilde;o e Utiliza\u0026ccedil;\u0026atilde;o de Dados sobre o Equil\u0026iacute;brio) is a web-based platform developed using R version 4.5.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org\u003c/span\u003e\u003cspan address=\"https://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and RStudio Desktop version 2026.01.0\u0026thinsp;+\u0026thinsp;392 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://posit.co\u003c/span\u003e\u003cspan address=\"https://posit.co\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The application was written in RMarkdown (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rmarkdown.rstudio.com\u003c/span\u003e\u003cspan address=\"https://rmarkdown.rstudio.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and optimized for mobile-responsive use. The project is openly hosted in a GitHub repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/ppgcr-unisuam/SAUDE\u003c/span\u003e\u003cspan address=\"https://github.com/ppgcr-unisuam/SAUDE\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the application is publicly accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ppgcr-unisuam.github.io/SAUDE\u003c/span\u003e\u003cspan address=\"https://ppgcr-unisuam.github.io/SAUDE\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. It is distributed under a Creative Commons Attribution\u0026ndash;NonCommercial (CC BY-NC) license, permitting non-commercial use, adaptation, and dissemination.\u003c/p\u003e \u003cp\u003eSAUDE was designed to enable citizens to self-administer standardized functional mobility tests while contributing anonymized data to support public health monitoring and citizen science initiatives. The platform includes three core assessment modules: TUG [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], STS [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and SLS [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Each test is implemented as an interactive module with embedded instructions, integrated timers, and self-report input fields.\u003c/p\u003e \u003cp\u003eThe app guides users through each procedure using clear on-screen instructions and visual cues, supported by simplified page layouts and accessibility-oriented formatting. After completing each test, participants submitted their results via embedded email links within the app. This mechanism facilitates data submission while enabling the optional reporting of contextual metadata (e.g., observer identity or environmental conditions) that may influence measurement outcomes. Recording such information is important for addressing variability in test execution and mitigating potential issues related to sampling heterogeneity or pseudo-replication [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. With explicit user permission, the app can also collect geolocation data (latitude and longitude) through the device\u0026rsquo;s browser to support analyses of spatial distribution and environmental context. The SAUDE app does not require user accounts and does not collect personally identifiable information.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Collection Procedures\u003c/h2\u003e \u003cp\u003eQuantitative data were collected concurrently in a controlled laboratory environment. Each participant performed three standardized functional mobility tests using the SAUDE app while being simultaneously evaluated by a trained assessor (A.G.S.).\u003c/p\u003e \u003cp\u003eParticipants followed the app\u0026rsquo;s on-screen instructions to self-administer each test and manually entered their results using embedded input fields. Concurrently, the assessor conducted standardized clinical assessments of the same tests in accordance with established protocols. Briefly:\u003c/p\u003e \u003cp\u003e \u003cem\u003eTimed Up and Go (TUG)\u003c/em\u003e: This test measures the time (in seconds) required for a participant to rise from a chair, walk three meters, turn around, return to the chair, and sit down [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Longer completion times (e.g., \u0026gt;\u0026thinsp;20 seconds) may indicate impaired functional mobility and an increased risk of falls [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003e30-second Sit-to-Stand (STS)\u003c/em\u003e: This test assesses lower-limb strength and endurance by recording the number of complete sit-to-stand repetitions performed from a chair within 30 seconds, without the use of the arms [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Performance of fewer than approximately eight repetitions in 30 seconds has been associated with early functional decline in older adults [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eSingle-Leg Stance (SLS)\u003c/em\u003e: This test evaluates static balance by measuring the duration (in seconds) a participant can maintain a single-leg stance with arms at the sides and eyes open. Single-leg stance times of less than 21 seconds have been associated with reduced postural control and an increased risk of falls [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eUsability Assessment\u003c/h3\u003e\n\u003cp\u003eUsability feedback was collected through open-ended observations conducted during the testing sessions. Upon completion of all assessments, participants responded to a brief, standardized question designed to evaluate perceived task difficulty: \u0026ldquo;How easy or difficult was it for you to complete the testing portion of this study (including reading the instructions and performing the tests)?\u0026rdquo;\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using R version 4.5.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org\u003c/span\u003e\u003cspan address=\"https://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and RStudio Desktop version 2026.01.0\u0026thinsp;+\u0026thinsp;392 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://posit.co\u003c/span\u003e\u003cspan address=\"https://posit.co\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed). Descriptive statistics were used to summarize participant characteristics and test performance, with continuous variables reported as medians and interquartile ranges (IQR) and categorical variables reported as absolute and relative frequencies.\u003c/p\u003e \u003cp\u003eTo evaluate criterion (concurrent) validity, agreement between self-reported test performance collected via the SAUDE app and concurrent assessor-based measurements was examined using Concordance Correlation Coefficients (CCC) with 95% confidence intervals [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and Bland-Altman analyses [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. For each functional test, mean bias and limits of agreement (LoA) were calculated. Bias was calculated as app-based measurement minus assessor-based measurement.\u003c/p\u003e \u003cp\u003eTo further characterize agreement, calibration analyses were performed by fitting linear regression models with assessor-based measurements as observed values and app-based measurements as predictors. Calibration plots were generated for each test, including the identity line (perfect calibration), fitted regression line, intercept, slope, and coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eConvergent validity was assessed using Pearson\u0026rsquo;s correlation coefficients to examine associations among the three functional mobility tests (TUG, STS, and SLS) when measured using the same method (i.e., SAUDE app or assessor-based).\u003c/p\u003e \u003cp\u003eConstruct validity was evaluated by examining the relationship between functional test performance and self-reported history of falls within the previous six months. Fall history was dichotomized (yes/no), and point-biserial correlation coefficients with 95% confidence intervals were calculated for each test and measurement method.\u003c/p\u003e \u003cp\u003eCorrelation coefficients derived from SAUDE app and assessor-based measurements were statistically compared using Fisher\u0026rsquo;s r-to-z transformation for independent correlations [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor qualitative usability analysis, open-ended responses were grouped by thematic content and summarized descriptively in tabular form. No formal coding or inferential qualitative analysis was performed, as the aim was to provide illustrative insights into user experience and identify potential areas for platform improvement.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eDemographic, socioeconomic, and clinical characteristics of the participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age of the sample was 73 years (interquartile range: 66\u0026ndash;79 years), and most participants were women (74%). The majority were married (60%) and retired (87%). According to older adult-specific body mass index classifications, 9% of participants were underweight, while nearly half were classified as obese (49%). Household income was heterogeneous, with 30% reporting a monthly income of one minimum wage and 38% reporting income of up to one minimum wage. Most participants reported regular use of medications (89%), and 21% reported at least one fall in the previous six months.\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\u003eDemographic and clinical characteristics of the study sample.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \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\u003eN\u0026thinsp;=\u0026thinsp;47\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (26%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (74%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (66, 79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.0 (61.0, 81.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody height, m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59 (1.54, 1.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.8 (25.6, 30.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI classification, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEutrophy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (43%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (49%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (60%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (26%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving situation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (23%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith spouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (49%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (28%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary incomplete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary complete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (28%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school incomplete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (11%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school complete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (21%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnical level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher education complete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (26%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetired, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (87%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily income range, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUp to 1/2 minimum wage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 1/2 to 1 minimum wage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 minimum wage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 1 to 2 minimum wages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 2 to 5 minimum wages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (21%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 5 to 10 minimum wages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (15%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of medications, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (89%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of falls (6 months), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (21%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHearing, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (57%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReduced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (43%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVision, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (36%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReduced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (64%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003csup\u003e1\u003c/sup\u003en (%); Median (Q1, Q3)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents descriptive performance data for the three functional mobility tests obtained via the SAUDE app and assessor-based measurements. Mean and median values for the 30-second Sit-to-Stand test were similar between methods (assessor: 10.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5 repetitions; median\u0026thinsp;=\u0026thinsp;10.0; SAUDE app: 10.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6 repetitions; median\u0026thinsp;=\u0026thinsp;11.0). Single-Leg Stance times were slightly higher in app-based assessments (13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4 s; median\u0026thinsp;=\u0026thinsp;9.0) than in assessor measurements (12.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7 s; median\u0026thinsp;=\u0026thinsp;8.0). Timed Up and Go performance showed modestly longer completion times using the SAUDE app (15.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6 s; median\u0026thinsp;=\u0026thinsp;15.0) compared with assessor-based evaluation (14.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2 s; median\u0026thinsp;=\u0026thinsp;14.0). Overall, distributions and central tendency measures were comparable across methods.\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\u003eResults obtained using the SAUDE app and by the assessor.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssessor \u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;47\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSAUDE app \u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;47\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-Second Sit-to-Stand Test (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.3 (\u0026plusmn;\u0026thinsp;3.5)\u003c/p\u003e \u003cp\u003e10.0 [8.0, 12.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.5 (\u0026plusmn;\u0026thinsp;3.6)\u003c/p\u003e \u003cp\u003e11.0 [8.0, 13.0]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle-Leg Stance Test (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.1 (\u0026plusmn;\u0026thinsp;10.7)\u003c/p\u003e \u003cp\u003e8.0 [3.0, 22.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.6 (\u0026plusmn;\u0026thinsp;10.4)\u003c/p\u003e \u003cp\u003e9.0 [5.0, 23.0]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTimed Up and Go Test (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7 (\u0026plusmn;\u0026thinsp;4.2)\u003c/p\u003e \u003cp\u003e14.0 [11.0, 17.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.5 (\u0026plusmn;\u0026thinsp;5.6)\u003c/p\u003e \u003cp\u003e15.0 [11.0, 20.0]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e1\u003c/sup\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eMedian [Q1, Q3]\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCriterion validity\u003c/h2\u003e \u003cp\u003eAgreement between functional mobility scores obtained via the SAUDE app and concurrent assessor-based measurements is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Strong agreement was observed across all tests, with CCC values of 0.893 (95% CI: 0.836\u0026ndash;0.932) for the TUG, 0.977 (95% CI: 0.960\u0026ndash;0.987) for the STS, and 0.955 (95% CI: 0.922\u0026ndash;0.974) for the SLS.\u003c/p\u003e \u003cp\u003eBland\u0026ndash;Altman analyses (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) demonstrated small mean biases (TUG: 0.85 s; STS: 0.17 repetitions; SLS: 1.55 s) and narrow limits of agreement between measurement methods.\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\u003eConcurrent validity between the SAUDE app and assessor measurements.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBias\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower LOA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUpper LOA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTimed Up and Go Test (TUG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.836, 0.932)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-Second Sit-to-Stand Test (STS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.96, 0.987)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle-Leg Stance Test (SLS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.922, 0.974)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBias and limits of agreement were derived from Bland\u0026ndash;Altman analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eConvergent validity\u003c/h2\u003e \u003cp\u003eAmong assessor-administered measurements (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), moderate inverse correlations were observed between TUG and both the STS (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.645; 95% CI: \u0026minus;0.786 to \u0026minus;\u0026thinsp;0.439; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the SLS (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.588; 95% CI: \u0026minus;0.749 to \u0026minus;\u0026thinsp;0.363; p\u0026thinsp;=\u0026thinsp;0.020). A moderate positive association was identified between STS and SLS performance (r\u0026thinsp;=\u0026thinsp;0.411; 95% CI: 0.140 to 0.624; p\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e \u003cp\u003eComparable correlation patterns were observed in SAUDE app-based assessments (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). TUG scores were moderately and inversely correlated with STS (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.632; 95% CI: \u0026minus;0.778 to \u0026minus;\u0026thinsp;0.422; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and SLS performance (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.532; 95% CI: \u0026minus;0.710 to \u0026minus;\u0026thinsp;0.289; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The correlation between STS and SLS remained moderate and positive (r\u0026thinsp;=\u0026thinsp;0.420; 95% CI: 0.151 to 0.631; p\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e \u003cp\u003eDirect statistical comparisons of correlation coefficients between SAUDE app\u0026ndash;based and assessor-administered measurements revealed no significant differences across any pairwise associations (all p\u0026thinsp;\u0026ge;\u0026thinsp;0.64; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\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\u003eComparison of correlation coefficients obtained using the SAUDE app and by the assessor.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSAUDE app\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAssessor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP (Δr)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTUG vs STS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.778, -0.422)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.786, -0.439)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTUG vs SLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.71, -0.289)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.749, -0.363)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTS vs SLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.151, 0.631)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.14, 0.624)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTUG vs Falls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.233, 0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.325, 0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTS vs Falls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.555, -0.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.554, -0.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLS vs Falls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-0.536, -0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.558, -0.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePearson correlation coefficients with 95% confidence intervals. Correlations involving falls are point-biserial correlations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eConstruct validity\u003c/h2\u003e \u003cp\u003eIn assessor-administered evaluations (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), TUG performance was moderately and positively correlated with fall history (r\u0026thinsp;=\u0026thinsp;0.488; 95% CI: 0.233\u0026ndash;0.680; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Associations between fall history and both the STS test (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.319; 95% CI: \u0026minus;0.555 to 0.035; p\u0026thinsp;=\u0026thinsp;0.029) and the SLS test (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.294; 95% CI: \u0026minus;0.536 to -0.008; p\u0026thinsp;=\u0026thinsp;0.045) were weak and statistically significant.\u003c/p\u003e \u003cp\u003eA similar pattern was observed in SAUDE app-based assessments (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). TUG scores demonstrated a moderate positive correlation with fall history (r\u0026thinsp;=\u0026thinsp;0.560; 95% CI: 0.325\u0026ndash;0.730; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas associations involving STS (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.318; 95% CI: \u0026minus;0.554 to 0.034; p\u0026thinsp;=\u0026thinsp;0.030) and SLS performance (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.323; 95% CI: \u0026minus;0.558 to -0.039; p\u0026thinsp;=\u0026thinsp;0.027) remained weak and significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eUsability\u003c/h2\u003e \u003cp\u003eUsability observations revealed several recurring interaction challenges during app-based functional testing (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Three participants reported no usability-related comments or observed issues during testing.\u003c/p\u003e \u003cp\u003eThe most frequently observed issue involved \u003cem\u003ebutton interaction difficulties\u003c/em\u003e, including problems locating or activating start/stop controls, delayed screen response, or reduced touchscreen sensitivity. \u003cem\u003eVisual accessibility barriers\u003c/em\u003e were also common and primarily related to difficulty seeing buttons or text elements, often associated with reduced vision or small interface components.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePhysical or motor constraints\u003c/em\u003e that interfered with test execution or app interaction were frequently observed and included balance limitations, tremor, neuropathy, or orthopedic conditions. A closely related and prominent theme was difficulty \u003cem\u003eholding or stabilizing the smartphone during testing\u003c/em\u003e, particularly during standing or balance tasks, which affected safe and independent task performance.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCognitive or literacy-related challenges\u003c/em\u003e, such as difficulty understanding instructions, test sequence, or app logic and the need for clarification, were observed in a smaller subset of participants. In contrast, a limited number of participants demonstrated good \u003cem\u003ecomprehension and motor readiness\u003c/em\u003e, completing all tasks independently without observed usability issues.\u003c/p\u003e \u003cp\u003eIsolated difficulties related to \u003cem\u003edata entry\u003c/em\u003e or \u003cem\u003eresult recording\u003c/em\u003e within the app interface were noted infrequently.\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\u003eThematic Analysis of Participant Observations During App-Based Functional Testing (n\u0026thinsp;=\u0026thinsp;44).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticipants (IDs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eButton interaction difficulties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4, 8, 9, 12, 13, 14, 15, 16, 17, 21, 24, 26, 27, 31, 32, 33, 34, 35, 37\u0026ndash;47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDifficulty locating or activating start/stop buttons, delayed screen response, or reduced touchscreen sensitivity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisual accessibility barriers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4, 5, 9, 12, 15, 29, 30, 32, 34, 36, 41, 43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDifficulty seeing screen elements (buttons or text), often related to reduced vision or small interface components\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical or motor constraints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4, 10, 11, 12, 16, 20, 22, 25, 28, 35, 38, 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBalance limitations, tremor, neuropathy, orthopedic conditions, or difficulty performing tests safely\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifficulty holding or stabilizing the smartphone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9, 12, 15, 20, 22, 25, 28, 35, 38, 40, 41, 44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInability to safely hold or stabilize the device during standing or balance tasks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive or literacy-related challenges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2, 3, 6, 11, 13, 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDifficulty understanding instructions, test sequence, or app logic; need for clarification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood comprehension and motor readiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2, 3, 6, 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClear understanding of instructions and ability to complete all tasks independently\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData entry or result recording difficulty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDifficulty recording or submitting test results via the interface\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo comments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1, 18, 19, 23, 27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo usability issues reported or observed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study sought to validate SAUDE, a mobile application designed to foster CS participation in rehabilitation by enabling older adults to self-administer standardized balance and mobility tests. The findings demonstrated strong criterion validity, evidenced by high concordance correlation coefficients and minimal mean bias between app-based and assessor-administered measurements across all functional tests (TUG, STS, and SLS). Moderate to strong inter-test correlations supported convergent validity, while the association between poorer TUG performance and recent fall history provided preliminary evidence of construct validity. In addition, usability observations indicated that most participants were able to complete the assessments independently, although visual, cognitive, and motor-related challenges were noted in a subset of users. Within the Brazilian context \u0026ndash; where CC remains underrepresented in rehabilitation research [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] \u0026ndash; these findings position SAUDE as a novel, clinically grounded digital platform capable of supporting self-assessment while contributing valid functional data to participatory research initiatives.\u003c/p\u003e \u003cp\u003eThese findings are consistent with a growing body of evidence indicating that citizen science (CS) approaches in rehabilitation can support both valid data collection and meaningful community engagement. Prior initiatives, including the Our Voice framework in geriatric rehabilitation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and the Dignity Project in occupational therapy [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], have demonstrated the feasibility and value of integrating citizen perspectives into health service design. Likewise, studies employing virtual reality and other participatory technology platforms [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] have reported positive effects on user engagement and motor-related outcomes. Nevertheless, most existing mobile applications in this field have emphasized surveillance functions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], environmental gerontology and urban or landscape planning [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], or the collection of environmental and personal health data [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], rather than incorporating clinically validated constructs relevant to rehabilitation practice, such as fall risk. Against this backdrop, SAUDE\u0026rsquo;s integration of standardized functional mobility tests\u0026mdash;combined with moderate to strong inter-test correlations and meaningful associations with fall history\u0026mdash;represents an important contribution. The platform extends beyond self-assessment by generating clinically interpretable outputs and by operationalizing validated rehabilitation metrics within an open, citizen-facing environment. In doing so, SAUDE bridges structured clinical assessment protocols with decentralized, community-based health monitoring, offering a first step toward scalable screening of mobility and balance impairments among older adults [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite these encouraging findings, several limitations should be acknowledged. First, the relatively small sample size \u0026ndash; although sufficient for early-stage validation and usability assessment [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] \u0026ndash; limits the generalizability of the results and precludes more detailed subgroup analyses. Second, participants were predominantly community-dwelling older adults with at least minimal digital literacy or access to support, which may have introduced selection bias favoring healthier and more digitally engaged individuals. This limitation is consistent with challenges reported in prior CS studies, in which digital exclusion, variable motivation, and differences in comprehension can influence both participation and data quality [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Observational data further identified specific usability barriers, including difficulties with button interaction, visual accessibility, and cognitive or literacy-related demands, underscoring the importance of inclusive and adaptive interface design. Although most participants were able to complete the assessments independently, those with visual impairment, mobility limitations, or lower health literacy often required assistance. Together, these findings highlight the need for iterative platform refinement and targeted accessibility enhancements to promote equitable participation across diverse aging populations while maintaining the methodological robustness of citizen-generated health data.\u003c/p\u003e \u003cp\u003eAs with many CS initiatives, the analysis of user-generated data poses distinct statistical and methodological challenges [\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Variability in individual performance, inconsistent adherence to testing protocols, and the potential for data entry errors may increase heterogeneity and reduce comparability across users and settings [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. To preserve the validity and interpretability of large-scale datasets, future implementations will require robust analytical frameworks capable of managing outliers, accommodating protocol deviations, and enabling meaningful subgroup analyses. At the same time, the platform\u0026rsquo;s capacity to collect geolocation data offers a strategic opportunity to examine functional mobility at the population level by linking performance metrics to regional infrastructure, urban characteristics, and environmental inequities. Integrating spatial patterns with demographic profiles could allow future versions of \u003cem\u003eSAUDE\u003c/em\u003e to inform public health planning, support targeted resource allocation, and strengthen data-driven decision-making by policymakers and community stakeholders. Ultimately, embedding citizen-contributed functional assessment data within broader health system infrastructures has the potential to transform participation into actionable knowledge, advancing both rehabilitation equity and public health responsiveness [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study provides evidence supporting the validity and usability of SAUDE, a citizen science\u0026ndash;based mobile application for functional assessment in rehabilitation. Self-administered evaluations can generate clinically meaningful data, posing SAUDE as a promising platform for scalable, community-level screening of mobility and balance impairments in older adults. Although usability constraints and statistical challenges inherent to citizen-generated data remain, the platform\u0026rsquo;s open and accessible design supports broad participation and offers a foundation for decentralized and timely public health monitoring. Future development should focus on interface optimization, extended real-world deployment, and the integration of spatial and demographic information to inform equitable rehabilitation strategies and advance participatory innovation within health systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the manuscript preparation process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the author(s) used ChatGPT (OpenAI) to revise the translated version from Portuguese. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by the Funda\u0026ccedil;\u0026atilde;o Carlos Chagas Filho de Apoio \u0026agrave; Pesquisa do Estado do Rio de Janeiro (FAPERJ, No. E-26/211.104/2021, E-26/204.369/2024, E-26/200.830/2024), Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal (CAPES, Finance Code 001; No. 88881.708719/2022-01, and No. 88887.708718/2022-00), and Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico (CNPq, No. 315453/2021-4, 311827/2025-0).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: ASF; Data curation: ASG, ASF; Formal Analysis: ASF; Funding acquisition: ASF; Investigation: AGS; Methodology: AGS, ASF; Project administration: ASF; Resources: ASF; Software: ASF; Supervision: ASF; Validation: ASG, ASF; Visualization: ASF; Writing \u0026ndash; original draft: AGS, ASF; Writing \u0026ndash; review \u0026amp; editing: AGS, ASF\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author, ASF. 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Health Promot Int daw. 2016;086. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/heapro/daw086\u003c/span\u003e\u003cspan address=\"10.1093/heapro/daw086\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Citizen Participation in Science and Technology, Falls, Management of Science, Public Perception of Science, Rehabilitation, Technology and Innovation in Health","lastPublishedDoi":"10.21203/rs.3.rs-8627904/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8627904/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Citizen science (CS) can broaden participation and support knowledge co-production in health research. However, its application in rehabilitation remains limited, particularly with respect to the validation of tools for functional assessment and fall-risk monitoring.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eObjective: To validate SAUDE, a mobile application designed to support citizen-led assessment of balance and mobility, and to examine its usability among older adults.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: A cross-sectional mixed-methods study was conducted with 47 community-dwelling older adults. Participants independently performed three standardized functional tests—Timed Up and Go (TUG), 30-second Sit-to-Stand (STS), and Single-Leg Stance (SLS)—using the SAUDE app, while simultaneous in-person assessments were conducted by a trained evaluator. Criterion validity was examined using concordance correlation coefficients (CCC). Convergent validity was assessed through inter-test correlations, and construct validity through associations with self-reported fall history. Usability observations were collected during testing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Strong agreement was observed between app-based and assessor-based measurements for all tests (CCC [95% CI]: TUG = 0.893 [0.836, 0.932], STS = 0.977 [0.960, 0.987], SLS = 0.955 [0.922; 0.974]). Inter-test correlations supported convergent validity, and moderate associations between TUG performance and fall history provided evidence of construct validity. Most participants completed the assessments independently, although visual, cognitive, and motor limitations affected interaction with the app in some cases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusions: SAUDE demonstrated validity and usability as a citizen science platform for self-assessment of functional mobility in older adults. Its open-access design and integration of fall-related outcomes support its potential for community-based health monitoring. Further development should focus on accessibility enhancements, data handling optimization, and evaluation in larger, real-world settings to advance inclusive rehabilitation strategies.\u003c/p\u003e","manuscriptTitle":"Validation of SAUDE: A Citizen Science App for Functional Balance Assessment in Older Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 12:25:54","doi":"10.21203/rs.3.rs-8627904/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"22c0e96a-2954-403e-9968-04d4c6d092d1","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-04T10:27:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 12:25:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8627904","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8627904","identity":"rs-8627904","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}