{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,11]],"date-time":"2026-11-11T05:28:43Z","timestamp":1794374923918,"version":"4.9.0"},"reference-count":42,"publisher":"Human Kinetics","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:p>\n                    <jats:italic>\n                      <jats:bold>Background\/Objectives<\/jats:bold>\n                      :\n                    <\/jats:italic>\n                    Walking speed is a functional biomarker, but clinical thresholds often miss community demands. This study quantified mechanical reserve (walking speed functional reserve, %FRWS) and physiological reserve (heart rate functional reserve, %FRHR) in Near- and Far-ecokinesiological groups and examined their alignment with ecological thresholds.\n                    <jats:italic>\n                      <jats:bold>Methods<\/jats:bold>\n                      :\n                    <\/jats:italic>\n                    Observational cross-sectional secondary analysis of a deidentified cohort. STROBE-compliant. Seventy older adults (\u226560\u00a0years) completed a 40-m elliptical-circuit test.\n                    <jats:italic>\n                      <jats:bold>Exclusions:<\/jats:bold>\n                    <\/jats:italic>\n                    Neurological disease, contraindication to exertion, lower-limb pain, or Mini-Mental State Examination\u2009&lt;\u200913.\n                    <jats:italic>\n                      <jats:bold>Measures:<\/jats:bold>\n                    <\/jats:italic>\n                    Self-selected and maximal walking speeds (MWS); %FRWS and %FRHR derived from heart rate work. Heart rate reserve was computed to monitor effort during testing.\n                    <jats:italic>\n                      <jats:bold>Ecokinesiology (EK) status:<\/jats:bold>\n                    <\/jats:italic>\n                    Near-EK (self-selected walking speed 0.80\u20131.19\u00a0m\/s) or Far-EK (\u22651.20\u00a0m\/s). Quadrant profiling combined %FRWS (\n                    <jats:italic>x<\/jats:italic>\n                    ) and %FRHR (\n                    <jats:italic>y<\/jats:italic>\n                    ).\n                    <jats:italic>\n                      <jats:bold>Results<\/jats:bold>\n                      :\n                    <\/jats:italic>\n                    Near-EK participants were older with higher body mass\/body mass index (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u2009.006;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u2009.009;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u2009.003). Far-EK showed higher self-selected walking speed and MWS (both\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u2009.001).\n                    <jats:italic>\n                      <jats:bold>Reserves diverged:<\/jats:bold>\n                    <\/jats:italic>\n                    %FRWS was higher in Near-EK (18.4\u2009\u00b1\u20096.3% vs. 14.0\u2009\u00b1\u20096.8%,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u2009.008), whereas %FRHR and its increment were higher in Far-EK (56.5\u2009\u00b1\u200921.6% vs. 45.9\u2009\u00b1\u200918.1%,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u2009.027; 18.7\u2009\u00b1\u20097.6% vs. 15.1\u2009\u00b1\u20099.9%,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u2009.016). Quadrant allocation was heterogeneous but nonsignificant (Near-EK \u03c7\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u20095.40,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u2009.15; Far-EK \u03c7\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u20092.04,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u2009.62), revealing capacity\u2013context mismatches.\n                    <jats:italic>\n                      <jats:bold>Conclusion<\/jats:bold>\n                      :\n                    <\/jats:italic>\n                    Ecological mobility cannot be inferred from reserve metrics alone. Integrating indicators with ecological thresholds enables stratification and exposes mismatches in Near- and Far-EK.\n                    <jats:italic>\n                      <jats:bold>Significance\/Implications<\/jats:bold>\n                      :\n                    <\/jats:italic>\n                    This approach supports personalized assessment, exercise prescription, and environment-informed configurations for healthy aging.\n                  <\/jats:p>","DOI":"10.1123\/japa.2025-0356","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T16:03:35Z","timestamp":1784045015000},"page":"1-12","source":"Crossref","is-referenced-by-count":0,"title":["Walking Speed Reserve in Community-Dwelling Older Adults: An Integrative Biokinesiology and Ecokinesiology Approach"],"prefix":"10.1123","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6608-4054","authenticated-orcid":true,"given":"Paul","family":"Medina-Gonz\u00e1lez","sequence":"first","affiliation":[{"name":"Departamento de Kinesiolog\u00eda, Facultad de Ciencias de la Salud, Universidad Cat\u00f3lica del Maule, Talca, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"100","reference":[{"issue":"1","key":"r1","series-title":"Journal of Novel Physiotherapy and Rehabilitation,","doi-asserted-by":"publisher","first-page":"5","DOI":"10.29328\/journal.jnpr.1001039","article-title":"FITT-CORRECT: Updated dynamic and evidence-based principle of exercise prescription","volume":"5","author":"Adhikari, S.P.","year":"2021","unstructured":"Adhikari, S.P., Tretriluxana, J., Dev, R., Eglitis, E., Shrestha, N., & Kerfeld, C. 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