T-Score - Health metric data from World Health Organization
About this page: This page combines data from authoritative sources with AI-assisted analysis to provide comprehensive metric benchmarks. The content has been written and reviewed by our team to ensure accuracy, relevance, and quality.
Our content creation process combines multiple approaches to ensure quality and accuracy:
Our commitment: We prioritize accuracy and transparency. If you find any errors or have concerns about content accuracy, please contact us.
How does your this metric compare to others in your demographic group? For All population of All in the Adult age range, understanding normal variation is crucial for meaningful health assessment. This guide provides the data-driven insights you need to interpret your measurements accurately and make informed decisions about your health.
A measurement of this metric The measurement of this metric in standard units provides objective health data that supports clinical decisions and personal health monitoring. Population reference values contextualize individual measurements within expected ranges.
Standardized measurement protocols ensure this metric data remains comparable across studies and populations. Accurate this metric measurement requires attention to standardized conditions: appropriate equipment calibration, consistent measurement technique, proper subject preparation, and trained personnel. These factors minimize variability and ensure reliable results.
The chart below shows how T-Score is distributed across the population. The percentile values help you understand where you fall relative to others in your demographic group.
Insufficient data for visualization
This metric does not have enough statistical parameters for generating a visualization.
From a clinical perspective, this metric percentiles guide interpretation and decision-making. Similarly, the 95th percentile (1.6) represents the upper threshold, with only 5% exceeding this value. Clinicians use these benchmarks alongside other factors to assess individual health status.
-
5% of the population falls below this value. This represents the lower range of typical variation.
-0.67
25% of the population falls below this value. This represents the lower-middle range.
-
This is the middle value. 50% of the population falls below and 50% falls above this value.
0.68
75% of the population falls below this value. This represents the upper-middle range.
1.65
95% of the population falls below this value. This represents the upper range of typical variation.
-
The arithmetic average of all values. This may differ from the median if the distribution is skewed.
Age significantly influences this metric through biological processes that vary across the lifespan. For All All individuals, age-specific benchmarks account for these developmental patterns. Age-appropriate reference data ensures accurate interpretation regardless of life stage.
The factors influencing this metric span genetic inheritance, lifestyle behaviors, environmental conditions, and overall health status. This complexity means that individual values reflect numerous influences working together. While genetic factors set certain parameters, lifestyle modifications may still influence where values fall within those limits. Understanding these determinants supports meaningful interpretation of individual measurements.
What can your this metric measurement tell you about potential health actions? Values within normal ranges generally require continued monitoring rather than intervention. Values at extremes may suggest opportunities for lifestyle modification or the need for further evaluation. Key questions to consider: Has your this metric changed significantly over time? Do you have symptoms related to this metric? Do other health indicators suggest concern? Are lifestyle modifications possible? Many factors influencing this metric respond to lifestyle modifications, making proactive health management potentially impactful.
Clinical utility of this metric extends beyond simple comparison to population norms. Healthcare providers consider: how values compare to demographic-matched benchmarks, whether significant changes have occurred, presence of associated symptoms, and relationship to other clinical findings. individual clinical significance depends on broader context. this metric contributes specific information to musculoskeletal evaluation. This nuanced approach enables meaningful clinical decision-making.
Scientific understanding of this metric continues to evolve through ongoing research. Current research explores how this metric relates to health outcomes, what factors influence it, and how benchmarks should be updated as populations change. This evolving science ensures that reference values remain relevant and useful.
this metric data serves practical purposes across multiple contexts. For individuals: understanding your values relative to benchmarks, tracking changes over time, and informing health discussions with providers. For healthcare: screening, diagnosis, treatment monitoring, and outcome assessment. For researchers: studying population health trends, evaluating interventions, and identifying health disparities. For public health: surveillance, policy development, and health promotion. This multi-level utility makes this metric benchmarks valuable across the health ecosystem.
This data is from the CDC NHANES survey, one of the most comprehensive national health examination surveys worldwide, with rigorous measurement protocols.
The US healthcare system emphasizes preventive screening and regular monitoring of key health metrics through primary care visits.
Official data from CDC (Centers for Disease Control and Prevention) ↗
Note: Primary data is from CDC NHANES (USA). Local statistics shown for comparison are from official national health surveys. (2024-01)
Normal this metric encompasses a range of values that varies by demographic group. For individuals aged Adult, All, All population, reference values are available. Values between the 5th and 95th percentiles (lower range to 1.6) represent normal variation. Using demographic-matched benchmarks ensures appropriate comparison.
Percentiles show where your this metric falls relative to others in your demographic group. At the 50th percentile (median), half the population is above and half below. Between the 25th (-0.7) and 75th (0.7) percentiles represents the middle half of the distribution—where most healthy values fall. Percentiles at extreme ends (below 5th or above 95th) are less common but not necessarily abnormal. Context matters for interpretation.
this metric can change over time due to age-related processes, lifestyle modifications, health conditions, and interventions. Some factors are relatively fixed (like genetics), while others respond to deliberate changes (like exercise or diet). In the Adult age range, age-related changes may be occurring. Tracking your this metric over time reveals personal trends that provide valuable health information. Consistent measurement conditions enable meaningful comparison of values over time.
Consider discussing your this metric with a healthcare provider if: values fall significantly outside normal range (below 5th or above 95th percentile), you've noticed substantial changes over time, values are associated with symptoms, or you have questions about health implications. Being at a percentile extreme doesn't automatically indicate problems—many healthy individuals naturally fall at distribution tails. Clinical significance depends on context, symptoms, and other health factors. Healthcare providers can offer personalized interpretation.
this metric values differ across ethnic groups due to genetic, environmental, and lifestyle factors. All populations show characteristic patterns that reflect population-specific genetics, dietary traditions, activity patterns, and environmental influences. These differences are normal and expected—not indicators of better or worse health. Using All-specific reference data ensures your comparison reflects meaningful variation rather than expected population differences. This demographic specificity improves the accuracy and relevance of health assessment.
