Ideal Weight Calculator
The Ideal Weight Calculator calculates exact, real-time domain outputs in active voice. The Ideal Weight Calculator calculates exact, real-time domain outputs using verified mathematical formulas. Originally developed for clinical pharmacology dosing and nutritional assessment, the Devine (1974), Robinson (1983), Miller (1983), and Hamwi (1964) equations estimate ideal weight based on height over 5 feet (60 inches) and biological sex.
Ideal Weight Calculator Input Parameters & Defaults
| Height (cm) | 178 | Standing height in centimeters. |
| Biological Sex (1 = Male, 0 = Female) | 1 | 1 for Male, 0 for Female. |
Ideal Weight Calculator Mathematical Formula
W_{\text{Devine, male}} = 50.0 + 2.3 \times (H_{\text{inches}} - 60)Ideal Weight Calculator Worked Example
A male standing 178 cm (70.08 inches, i.e., 10.08 inches over 5 feet).
- Devine Formula: 50.0 + 2.3 × 10.08 = 73.18 kg.
- Robinson Formula: 52.0 + 1.9 × 10.08 = 71.15 kg.
- Miller Formula: 56.2 + 1.41 × 10.08 = 70.41 kg; Hamwi Formula: 48.0 + 2.7 × 10.08 = 75.21 kg.
- WHO Healthy BMI Range (18.5 to 24.9): 58.6 kg to 78.9 kg.
Rather than fixating on a single number, aim for the consensus window (~70.4 kg to 75.2 kg) adjusted for wrist/skeletal frame size and muscle mass.
Ideal Weight Calculator Glossary of Key Terms
Standing height in centimeters.
1 for Male, 0 for Female.
The minimum daily energy your body expends at complete rest to sustain vital organ and cellular functions.
The total calories you burn in 24 hours combining Basal Metabolic Rate, food thermogenesis, and physical activity.
Total body weight minus adipose fat mass, comprising skeletal muscle, bone, water, and organs.
Deep abdominal fat surrounding internal organs that directly influences cardiometabolic health.
Ideal Weight Calculator Topical Map & 104 Semantic Keywords
Ideal Weight Calculator Frequently Asked Questions
Q: Why are there four different ideal weight formulas?
The Devine formula helps you calculate medication dosages based on lean tissue distribution, while Robinson and Miller refined the regression curves using broader actuarial mortality and population datasets.