The Physical Activity Behavior Scale for Older Adults<\/strong> is a specialized psychometric assessment developed to operationalize and evaluate the psychosocial, motivational, and cognitive determinants governing physical exercise among geriatric individuals. While conventional epidemiological instruments, such as the Global Physical Activity Questionnaire<\/a> (GPAQ) and the Physical Activity Scale for the Elderly (PASE), predominantly capture behavioral frequency, duration, metabolic expenditure, or physical exertion, they systematically overlook the psychological architecture that instigates and sustains such behaviors. Rooted in Icek Ajzen\u2019s Theory of Planned Behavior<\/a> (TPB), the instrument comprises 19 refined self-report items organized across four distinct yet interconnected subscales: Behavioral Attitude (6 items), Subjective Norm (3 items), Perceived Behavioral Control (5 items), and Behavioral Intention (5 items). Psychometric validation was conducted in a sample of 451 community-dwelling older adults aged 60 years and older in China (effective response rate: 96.57%; 45.5% male, 54.5% female). Content validity was established via a multidisciplinary expert panel yielding a Scale-Level Content Validity Index (S-CVI\/Ave) of 0.915. Exploratory factor analysis (EFA, n<\/em> = 152) using principal component analysis with promax rotation confirmed a four-factor structure explaining 69.26% of the total cumulative variance, with all factor loadings exceeding 0.70. Confirmatory factor analysis (CFA, n<\/em> = 299) demonstrated robust model fit indices: Comparative Fit Index (CFI) = 0.968, Tucker-Lewis Index (TLI) = 0.963, Root Mean Square Error of Approximation (RMSEA) = 0.049, and Standardized Root Mean Square Residual (SRMR) = 0.039, with standardized loadings ranging from 0.705 to 0.855. Multi-group confirmatory factor analyses verified configural, metric, scalar, and strict measurement invariance across gender and age strata (60\u201369 vs. 70+ years). By establishing a valid diagnostic tool, this scale empowers researchers and healthcare clinicians to isolate psychosocial impediments to active aging and construct targeted, tailored physical activity interventions<\/span>.<\/p>\n Physical activity, Older adults, Theory of Planned Behavior, Psychometrics, Gerontology, Health behavior, Measurement invariance, Behavioral intention, Self-efficacy, Active aging, Exercise determinants<\/p>\n The Physical Activity Behavior Scale for Older Adults was developed and psychometrically validated by a multidisciplinary team of nursing science and clinical health researchers in China:<\/p>\n In global public health and geriatric medicine, regular physical activity<\/a> represents a foundational non-pharmacological strategy for attenuating functional decline, preventing chronic diseases, mitigating cognitive impairment, and fostering active aging<\/a>. Despite rigorous evidence demonstrating the extensive physiological and neurocognitive benefits of habitual movement, global surveillance reports indicate that a substantial proportion of older adults fail to achieve the aerobic and muscle-strengthening benchmarks recommended by the World Health Organization and the Physical Activity Guidelines for Americans. A primary limitation within both clinical practice and behavioral research has been the overreliance on tools that measure behavioral endpoints rather than the cognitive-affective antecedents that produce those endpoints.<\/p>\n Instruments such as actigraphy accelerometers or retrospective physical recall surveys document the “what”\u2014namely, the total minutes of moderate-to-vigorous physical activity or daily step counts. However, they are inherently incapable of explaining the “why.” Older adults encounter a distinct, highly complex constellation of obstacles that are uncommon in younger cohorts, including degenerative joint diseases, fear of falling, bereavement, shrinking social circles, reduced environmental accessibility, and pervasive internalizations of ageist tropes regarding physical frailty. When an older person ceases to exercise, traditional activity logs cannot elucidate whether this avoidance stems from a belief that exertion is hazardous (attitude), the absent encouragement of surviving peers and adult children (subjective norm), financial or mobility constraints (perceived control), or the complete absence of deliberate goal formation (behavioral intention).<\/p>\n The Physical Activity Behavior Scale for Older Adults was specifically constructed to address this methodological divide. By translating Ajzen\u2019s sociocognitive model into an age-attuned psychometric inventory, the scale provides clinicians, physical therapists, community gerontologists, and public health strategists with an actionable diagnostic blueprint. Rather than issuing broad, generic admonitions to “move more,” practitioners can administer this scale to determine the precise locus of motivational attrition. For instance, if an older individual demonstrates elevated positive attitudes and social norms but critically low perceived behavioral control, clinical interventions can prioritize environmental modifications, assisted transport, or balance training rather than didactic education regarding health benefits. Furthermore, in clinical trials evaluating behavioral lifestyle modifications, this instrument serves as a sensitive measure of intermediate cognitive changes, allowing researchers to evaluate whether an intervention successfully modified the targeted psychological mechanisms prior to observing changes in physical activity biomarkers.<\/p>\n The psychometric architecture of the scale operationalizes four correlated yet theoretically divergent latent dimensions derived from cognitive-behavioral psychology. Each dimension addresses a distinct phase within the deliberation and preparation stages of health action:<\/p>\n Behavioral attitude reflects the older adult\u2019s overall favorable or unfavorable cognitive evaluation and affective appraisal of regular physical activity. Within the framework of expectancy-value conceptualizations, attitude is formulated through the integration of behavioral beliefs\u2014the subjective probability that exercise will yield specific positive or negative outcomes\u2014and outcome evaluations. In older cohorts, this construct does not merely evaluate whether exercise is perceived as “fun,” but rather examines functional instrumental beliefs (e.g., that physical movement preserves joint mobility, improves cardiovascular stability, manages chronic pain, or maintains independence in activities of daily living) alongside experiential evaluations (e.g., that exercise is emotionally refreshing, invigorating, and purposeful). A depressed score on this dimension reflects skepticism regarding the tangible utility of exercise in old age, fatalistic views toward physical senescence, or the fear that physical exertion precipitates acute cardiovascular or musculoskeletal events.<\/p>\n Subjective norm reflects the perceived social pressure, expectations, and normative influence exerted by significant social actors regarding whether the older adult should participate in regular physical activity. For geriatric populations, the social ecosystem undergoes profound transitions, often narrowing to proximate familial units, long-term companions, healthcare providers, and community peer groups. This construct measures normative beliefs\u2014the perception of what key individuals (e.g., spouse, adult children, personal physician, neighborhood peers) desire or expect\u2014weighted by the older adult\u2019s motivation to comply with these expectations. Positive subjective norms manifest when family members actively encourage morning walks, when physicians explicitly endorse tailored exercise, or when community groups validate collective movement (such as Tai Chi or group walking). Low scores indicate social isolation, perceived familial overprotection that discourages physical autonomy, or an absence of social networks that normalize active habits.<\/p>\n Perceived behavioral control (PBC) denotes an individual’s subjective appraisal of how simple or difficult it is to execute regular physical activity, accounting for perceived facilitators and impediments. This construct aligns closely with Albert Bandura\u2019s concept of self-efficacy<\/a>, yet expands to incorporate external structural resources. For older individuals, PBC involves an assessment of internal resources\u2014such as energy reserves, physical stamina, motor balance, and the management of episodic symptom flare-ups (e.g., osteoarthritis stiffness or dyspnea)\u2014in conjunction with external factors, including safe neighborhood infrastructure, proximity to recreation centers, financial means, specialized footwear, inclement weather adaptability, and time availability relative to caregiving responsibilities (e.g., looking after grandchildren). Low scores on this subscale reveal profound feelings of helplessness, fear of falling, or perceived inability to navigate physical surroundings safely.<\/p>\n Behavioral intention represents the proximal cognitive determinant of actual physical activity execution. It quantifies the degree of conscious effort, explicit planning, willingness, and deliberate personal readiness an older adult invests to engage in physical exercise over the coming days, weeks, or months. Intention functions as the motivational bridge translating positive attitudes, supportive social norms, and adequate perceived control into structured, goal-directed action. In psychometric terms, intention captures the formulation of subjective commitment (e.g., “I plan to engage in moderate exercise three times a week”). While external barriers can introduce an intention-behavior gap, a high behavioral intention score signifies that cognitive ambivalence has been resolved and that the individual possesses the psychological readiness required to initiate or maintain behavioral routines.<\/p>\n The development and structural configuration of the Physical Activity Behavior Scale for Older Adults is anchored in the Theory of Planned Behavior<\/strong> (TPB), originally formulated by social psychologist Icek Ajzen<\/a> as an extension of the Theory of Reasoned Action. The core axiom of the TPB is that human behavior is goal-directed, reasoned, and governed by deliberate cognitive processing, where the most proximal predictor of any voluntary action is the individual’s intention to perform that action.<\/p>\n Under the standard TPB architecture, behavioral intention is jointly determined by three exogenous cognitive components:<\/p>\n A critical theoretical contribution of Ajzen\u2019s model\u2014and one uniquely pertinent to older adults\u2014is the dual pathway through which perceived behavioral control operates. As illustrated in extensive health psychology literature, PBC not only influences behavior indirectly via its contribution to behavioral intention, but can also exert a direct predictive effect on behavior when perceived control accurately mirrors actual behavioral control. For older adults, biological constraints and structural barriers (e.g., neuromuscular frailty, sensory deficits, lack of accessible ramps) often impose genuine limitations. Consequently, capturing PBC alongside intention provides a more comprehensive framework for understanding health behaviors in this population than frameworks focused exclusively on motivation.<\/p>\n In designing this instrument, the researchers purposefully isolated the four cognitive constructs of the TPB from objective behavioral frequency metrics. Historically, several scales attempted to merge motivational items with recall metrics measuring minutes of walking or sport participation. This methodological conflation introduced shared method variance and confounded psychological predictors with behavioral outcomes. By restricting the 19 items exclusively to psychological and cognitive determinants, the authors preserved theoretical fidelity to the TPB. This structural independence allows investigators to utilize the scale in structural equation modeling<\/a> (SEM) to predict subsequent objective activity data\u2014derived from accelerometers or longitudinal activity logs\u2014without violating statistical assumptions of predictor-criterion independence.<\/p>\n Validation of the Physical Activity Behavior Scale for Older Adults adhered to the scientific recommendations outlined in the COSMIN guidelines (Consensus-based Standards for the selection of health Measurement Instruments) and established scale development methodologies (DeVellis & Thorpe, 2021; Boateng et al., 2018). The psychometric validation process encompassed content, face, construct, and measurement invariance analyses:<\/p>\n The initial item pool was evaluated by an expert consultation panel consisting of geriatricians, nursing professors, exercise physiologists, and psychometricians. Panel members evaluated each candidate item for clarity, relevance, and theoretical fidelity using a 4-point rating system. The quantitative assessment yielded a Scale-Level Content Validity Index averaging across all items (S-CVI\/Ave) of 0.915, surpassing the conventional 0.90 psychometric benchmark for excellent content relevance. Following expert review, cognitive debriefing interviews were conducted with a diverse cohort of community-dwelling older adults across various literacy levels. Ambiguous phrasing, abstract gerontological jargon, and linguistically convoluted statements were revised to ensure optimal comprehensibility, cognitive ease, and cultural resonance.<\/p>\n Construct validity was demonstrated through rigorous structural factor validation across two independent participant samples. Exploratory factor analysis confirmed theoretical dimensionality, which was subsequently verified via confirmatory factor analysis. Standardized factor loadings across all 19 retained items were uniformly high, ranging between 0.705 and 0.855, demonstrating that each item accounted for substantial variance in its intended latent dimension. Covariances among the four latent factors aligned with theoretical expectations: Behavioral Attitude, Subjective Norm, and Perceived Behavioral Control were positively and significantly correlated with Behavioral Intention, supporting the structural network posited by the Theory of Planned Behavior.<\/p>\n A critical methodological strength of this instrument is its comprehensive evaluation of measurement invariance<\/a>. Without verifying invariance, researchers cannot discern whether observed score differences between demographics represent genuine differences in psychological constructs or measurement bias. Using multi-group confirmatory factor analysis (MGCFA), the researchers evaluated four hierarchically nested models across gender (males vs. females) and age groups (young-old: 60\u201369 years vs. old-old: 70+ years):<\/p>\n The reliability and precision of the Physical Activity Behavior Scale for Older Adults were examined through classical test theory metrics, corrected item-total correlations, and internal consistency evaluations.<\/p>\n During the initial item-analysis phase, the discriminatory power of candidate items was tested using the Critical Ratio (CR) technique. Participants were stratified into high-scoring (top 27%) and low-scoring (bottom 27%) groups based on their cumulative scores. Independent-samples t<\/em>-tests confirmed that every retained item exhibited a statistically significant critical ratio (p<\/em> < 0.001), indicating strong capacity to differentiate between older adults with high versus low motivational profiles. Furthermore, Corrected Item-Total Correlations (CITC) were calculated for each item within its respective subscale. Items failing to achieve a CITC threshold of 0.30 were systematically purged. All 19 items in the final scale demonstrated robust CITC values well above 0.50, demonstrating high item homogeneity within latent dimensions<\/span>.<\/p>\n The final 19-item version exhibited robust internal consistency across the complete instrument and within each independent subscale. The elimination of ambiguous and psychometrically redundant candidate items minimized error variance. The subscales\u2014Behavioral Attitude (6 items), Subjective Norm (3 items), Perceived Behavioral Control (5 items), and Behavioral Intention (5 items)\u2014exhibited high internal coherence, with composite reliability and internal consistency estimates conforming to rigorous standards for clinical and empirical deployment. The psychometric properties verify that the instrument produces dependable, reproducible measurements of the psychological determinants of physical exercise in geriatric populations.<\/p>\n The factorial validity of the Physical Activity Behavior Scale for Older Adults was established using a two-step cross-validation methodology utilizing two distinct subsamples derived from the 451 participants.<\/p>\n An exploratory factor analysis was performed on the calibration subsample (n<\/em> = 152). Sample adequacy for factor extraction was demonstrated by the Kaiser-Meyer-Olkin (KMO) measure, which exceeded 0.85, and Bartlett\u2019s Test of Sphericity, which achieved statistical significance (p<\/em> < 0.001), confirming substantial inter-item correlations. Principal component analysis paired with an oblique promax rotation was selected to accommodate anticipated theoretical correlations among the latent constructs<\/span>.<\/p>\n Factor extraction guided by Kaiser\u2019s eigenvalue criterion (eigenvalues > 1.0) and scree plot inspection unambiguously identified four distinct factors. These four empirical factors corresponded directly to the theoretical dimensions of the Theory of Planned Behavior. Cumulatively, the four factors accounted for 69.26%<\/strong> of the total variance, well exceeding the 60% minimum standard for social science instruments. Crucially, all 19 retained items displayed primary factor loadings exceeding 0.70 on their designated construct, with no meaningful cross-loadings (> 0.30) onto secondary factors<\/span>.<\/p>\n To cross-validate the four-factor measurement model derived from the EFA, a confirmatory factor analysis was conducted on an independent validation subsample (n<\/em> = 299) using maximum likelihood estimation. The hypothesized four-factor model exhibited strong goodness-of-fit indices, comfortably satisfying the stringent criteria established by Hu and Bentler (1999)<\/span>:<\/p>\n All standardized factor loadings in the confirmatory model were statistically significant (p<\/em> < 0.001) and ranged from 0.705 to 0.855. Specifically, loadings for the Behavioral Attitude factor ranged from 0.721 to 0.842; for Subjective Norm, from 0.750 to 0.855; for Perceived Behavioral Control, from 0.708 to 0.812; and for Behavioral Intention, from 0.705 to 0.838. These findings confirm the structural stability of the 19-item, four-dimensional model<\/span>.<\/p>\n The Physical Activity Behavior Scale for Older Adults is configured as follows:<\/p>\n The Physical Activity Behavior Scale for Older Adults was published in 2026<\/strong> in BMC Geriatrics<\/em>. The publication is an open-access journal article distributed under the terms of the Creative Commons Attribution 4.0 International License<\/a> (CC BY 4.0). Academic researchers, clinicians, and healthcare organizations may review the scale’s methodology without fee.<\/p>\n However, the specific item wording and operational survey materials are held under author copyright and are not published in full in the open-access article text. Researchers and clinicians wishing to utilize, translate, or adapt the complete questionnaire items must obtain permission by contacting the corresponding author directly:<\/p>\n Ajzen, I. (2011). The theory of planned behaviour: Reactions and reflections. Psychology & Health<\/em>, 26(9), 1113\u20131127. https:\/\/doi.org\/10.1080\/08870446.2011.613995<\/a><\/p>\n An, H. Y., Chen, W., Wang, C. W., Yang, H. F., Huang, W. T., & Fan, S. Y. (2020). The relationships between physical activity and life satisfaction and happiness among young, middle-aged, and older adults. International Journal of Environmental Research and Public Health<\/em>, 17(13), 4817. https:\/\/doi.org\/10.3390\/ijerph17134817<\/a><\/p>\n Boateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Qui\u00f1onez, H. R., & Young, S. L. (2018). Best practices for developing and validating scales for health, social, and behavioral research: A primer. Frontiers in Public Health<\/em>, 6, 149. https:\/\/doi.org\/10.3389\/fpubh.2018.00149<\/a><\/p>\n Bosnjak, M., Ajzen, I., & Schmidt, P. (2020). The theory of planned behavior: Selected recent advances and applications. Europe’s Journal of Psychology<\/em>, 16(3), 352\u2013356. https:\/\/doi.org\/10.5964\/ejop.v16i3.3107<\/a><\/p>\n Bull, F. C., Maslin, T. S., & Armstrong, T. (2009). Global physical activity questionnaire (GPAQ): Nine country reliability and validity study. Journal of Physical Activity and Health<\/em>, 6(6), 790\u2013804. https:\/\/doi.org\/10.1123\/jpah.6.6.790<\/a><\/p>\n Caspersen, C. J., Powell, K. E., & Christenson, G. M. (1985). Physical activity, exercise, and physical fitness: Definitions and distinctions for health-related research. Public Health Reports<\/em>, 100(2), 126\u2013131. https:\/\/doi.org\/10.2307\/20056429<\/a><\/p>\n Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal<\/em>, 14(3), 464\u2013504. https:\/\/doi.org\/10.1080\/10705510701301834<\/a><\/p>\n Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal<\/em>, 9(2), 233\u2013255. https:\/\/doi.org\/10.1207\/S15328007SEM0902-5<\/a><\/p>\n DeVellis, R. F., & Thorpe, C. T. (2021). Scale development: Theory and applications<\/em> (5th ed.). SAGE Publications. https:\/\/us.sagepub.com\/en-us\/nam\/scale-development\/book269114<\/a><\/p>\n Eckstrom, E., Neukam, S., Kalin, L., & Wright, J. (2020). Physical activity and healthy aging. Clinics in Geriatric Medicine<\/em>, 36(4), 671\u2013683. https:\/\/doi.org\/10.1016\/j.cger.2020.06.009<\/a><\/p>\n Francis, J. J., Eccles, M. P., Johnston, M., Walker, A., Grimshaw, J., Foy, R., Kaner, E. F. S., Smith, L., & Bonetti, D. (2004). Constructing questionnaires based on the theory of planned behaviour: A manual for health services researchers<\/em>. Centre for Health Services Research, University of Newcastle upon Tyne. https:\/\/openaccess.city.ac.uk\/id\/eprint\/1735\/<\/a><\/p>\n Galle, S. A., Deijen, J. B., Milders, M. V., De Greef, M. H., Scherder, E. J., van der Flier, W. M., & Drent, M. L. (2023). The effects of a moderate physical activity intervention on physical fitness and cognition in healthy elderly with low levels of physical activity: A randomized controlled trial. Alzheimer’s Research & Therapy<\/em>, 15(1), 12. https:\/\/doi.org\/10.1186\/s13195-022-01123-3<\/a><\/p>\n Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal<\/em>, 6(1), 1\u201355. https:\/\/doi.org\/10.1080\/10705519909540118<\/a><\/p>\n Izquierdo, M., Duque, G., & Morley, J. E. (2021). Physical activity guidelines for older people: Knowledge gaps and future directions. The Lancet Healthy Longevity<\/em>, 2(6), e380\u2013e383. https:\/\/doi.org\/10.1016\/S2666-7568(21)00079-9<\/a><\/p>\n Li, Y., Wang, R., Zhang, X., Hu, X., Liu, Z., & Zhang, J. (2026). Physical Activity Behavior Scale for Older Adults. BMC Geriatrics<\/em>. https:\/\/doi.org\/10.1186\/s12877-026-07002-y<\/a><\/p>\n Piercy, K. L., Troiano, R. P., Ballard, R. M., Carlson, S. A., Fulton, J. E., Galuska, D. A., & Olson, R. D. (2018). The physical activity guidelines for Americans. JAMA<\/em>, 320(19), 2020\u20132028. https:\/\/doi.org\/10.1001\/jama.2018.14854<\/a><\/p>\n Prinsen, C. A. C., Mokkink, L. B., Bouter, L. M., Alonso, J., Patrick, D. L., De Vet, H. C. W., & Terwee, C. B. (2018). COSMIN guideline for systematic reviews of patient-reported outcome measures. Quality of Life Research<\/em>, 27(5), 1147\u20131157. https:\/\/doi.org\/10.1007\/s11136-018-1798-3<\/a><\/p>\n Qiu, Y., Fern\u00e1ndez-Garc\u00eda, B., Lehmann, H. I., Li, G., Kroemer, G., & L\u00f3pez-Ot\u00edn, C. (2023). Exercise sustains the hallmarks of health. Journal of Sport and Health Science<\/em>, 12(1), 8\u201335. https:\/\/doi.org\/10.1016\/j.jshs.2022.10.003<\/a><\/p>\n Sherrington, C., Fairhall, N. J., Wallbank, G. K., Tiedemann, A., Michaleff, Z. A., Howard, K., Clemson, L., Hopewell, S., & Lamb, S. E. (2019). Exercise for preventing falls in older people living in the community. Cochrane Database of Systematic Reviews<\/em>, 2019(1), CD012424. https:\/\/doi.org\/10.1002\/14651858.CD012424.pub2<\/a><\/p>\n Spiteri, K., Broom, D., Bekhet, A. H., de Caro, J. X., Laventure, B., & Grafton, K. (2019). Barriers and motivators of physical activity participation in middle-aged and older-adults: A systematic review. Journal of Aging and Physical Activity<\/em>, 27(4), 929\u2013944. https:\/\/doi.org\/10.1123\/japa.2018-0343<\/a><\/p>\n Strain, T., Flaxman, S., Guthold, R., Semenova, E., Cowan, M., Riley, L. M., Bull, F. C., & Stevens, G. A. (2024). National, regional, and global trends in insufficient physical activity among adults from 2000 to 2022: A pooled analysis of 507 population-based surveys with 5.7 million participants. The Lancet Global Health<\/em>, 12(8), e1232\u2013e1243. https:\/\/doi.org\/10.1016\/S2214-109X(24)00150-5<\/a><\/p>\n2. Keywords<\/h2>\n
3. Authors<\/h2>\n
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4. Purpose<\/h2>\n
5. Psychological Construct<\/h2>\n
Behavioral Attitude (Items 1, 2, 5, 6, 7, 8)<\/h3>\n
Subjective Norm (Items 14, 15, 16)<\/h3>\n
Perceived Behavioral Control (Items 19, 20, 21, 22, 23)<\/h3>\n
Behavioral Intention (Items 3, 18, 24, 25, 26)<\/h3>\n
6. Theoretical Framework<\/h2>\n
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7. Validity<\/h2>\n
Content and Face Validity<\/h3>\n
Construct Validity<\/h3>\n
Measurement Invariance Across Subgroups<\/h3>\n
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8. Reliability<\/h2>\n
Item Purification and Discrimination<\/h3>\n
Internal Consistency<\/h3>\n
9. Factor Analysis<\/h2>\n
Exploratory Factor Analysis (EFA)<\/h3>\n
Confirmatory Factor Analysis (CFA)<\/h3>\n
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10. Instrument \/ Measurement Tool<\/h2>\n
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11. Permissions & Fee and Test Year<\/h2>\n
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12. References<\/h2>\n