Establishing Army Wellness Center Referral Guidelines For Injury Prevention Based on Aerobic Fitness and Body Composition

Abstract

Poor aerobic fitness and high or low body fat have frequently been identified as significant risk factors for military injuries. In order to systematically utilize Army Wellness Center (AWC) services to reduce Soldier injury risk and enhance readiness, the objective of this work was to establish AWC referral guidelines based on aerobic fitness and body composition. Using results from data-oriented assessments of Army Physical Fitness Test (APFT) 2-mile run times and body mass index (BMI), as well as Receiver Operating Characteristic (ROC) curves and sensitivity analyses for the same variables in four Army populations, AWC referral guidelines are recommended for men and women. Men who have an APFT 2-mile run time of less than 15 minutes and a BMI above the age-based Army regulation or a BMI below 19 should be referred to the AWC. Likewise, women who have an APFT 2-mile run time of less than 18 minutes and a BMI above the age-based Army regulation or a BMI below 21 should be referred. Interim referral guidelines that include Army Combat Fitness Test (ACFT) run times are also recommended.

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Document Details

Document Type
Technical Report
Publication Date
Feb 22, 2021
Accession Number
AD1123438

Entities

People

  • Anna Schuh-renner
  • Bruce H. Jones
  • L. Omar Rivera
  • Michelle Canham-chervak

Organizations

  • United States Army Center for Health Promotion and Preventive Medicine

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Abstracts
  • Active Duty
  • Availability
  • Body Composition
  • Data Analysis
  • Data Sets
  • Education
  • Health
  • Health Care
  • Health Services
  • Hygiene
  • Information Science
  • Injury Prevention
  • Medical Personnel
  • Military Science
  • Occupational Medicine
  • Pattern Recognition
  • Physical Fitness
  • Preventive Medicine
  • Public Health
  • Social Sciences
  • Spreadsheet Software
  • Standards
  • Statistics
  • Training

Readers

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  • Forest Ecology
  • Medical or Health Care Field.

Technology Areas

  • AI & ML