DoshaBalance™

Assessment Design: This 24-question assessment covers three domains — Physical (8 questions), Physiological (8 questions), and Psychological (8 questions) — aligned with the CCRAS-PAS methodology (Central Council for Research in Ayurvedic Sciences, Ministry of AYUSH, Government of India), the Prakriti200 framework (Singh et al., IEEE/CIS, 2025), and AYUSH guidelines. Dosha labels are hidden during assessment to minimize confirmation bias. A 2025 Frontiers in Medicine critical review evaluated 64 Prakriti assessment tools developed between 1987 and 2024; our methodology aligns with the two highest-scoring validated tools.

Scoring Method: Each answer maps to one of three doshas: Vata (V), Pitta (P), or Kapha (K). Scores are summed across all 24 questions. Constitution classification follows published thresholds: single-dominant (one dosha ≥50%, others <30%), dual-type (two doshas each 30–45%, third <25%), or tridoshic (all three within 5 points).

Validation: The question framework draws from the 39-item reliable self-assessment questionnaire achieving ICC >0.8 for all three doshas (Dunlap et al., EXPLORE, 2021), the CCRAS-PAS 91-predictor scale validated across 10 centers with 500 volunteers, and the Prakriti200 standardized dataset. Machine learning classification studies achieve >95% accuracy using similar questionnaire data. ML algorithms have objectively reproduced Prakriti classification from phenotypic traits (Prasher, Mukerji et al., PLOS ONE, 2017).

Evidence-Based Analysis: After scoring, the AI generates an 8-section evidence-based lifestyle alignment report drawing from a structured knowledge base of 34+ peer-reviewed studies. The AI system prompt includes 15-point regulatory guardrails ensuring all output is educational, never prescriptive. See our AI Use Disclosure for details.

Lifestyle Alignment: Dietary, movement, sleep, stress, skin, and seasonal guidance is based on classical Ayurvedic principles (Charaka Samhita, Ashtanga Hridaya) interpreted through the lens of modern research, including the dosha brain-type neural model (Travis & Wallace, JAIM, 2015), plasma metabolomics (Shirolkar et al., 2018), diabetes research (Singh et al., 2022), and cardiovascular biomarker stratification (Dua et al., AIIMS, 2025).

Limitations: Constitutional assessment reflects lifelong tendencies and should not be used to diagnose any medical condition. Results are educational and should be discussed with a qualified healthcare provider. This assessment does not account for current imbalances (Vikriti), seasonal variations, or individual medical history.

Genomics (CSIR-IGIB TRISUTRA Program): Genome-wide SNP analysis classifies individuals by Prakriti irrespective of ancestry (Govindaraj et al., Scientific Reports, 2015). EGLN1 gene variants linked to Kapha and high-altitude adaptation (Aggarwal et al., PNAS, 2010). CYP2C19 pharmacogenomic differences validated between Pitta and Kapha (Ghodke et al., 2011). HLA-DRB1 immunogenetic correlation (Bhushan et al., 2005). TRISUTRA consortium (Prasher, Mukerji) established Ayurgenomics across diverse Indian populations (J Ethnopharmacol, 2017). ML recapitulation of Prakriti from phenotypic traits (PLOS ONE, 2017).

Metabolomics: Plasma mass spectrometry reveals dosha-specific pathways: Vata = catecholamine/arachidonic acid; Pitta = BCAA/androgen; Kapha = sphingolipid/pyrimidine (Shirolkar et al., J Ayurveda Integr Med, 2018).

Cardiovascular: AIIMS 2025 study (200 CAD patients, 100 controls): Kapha = highest CAD prevalence. Dosha-specific biomarkers: MLR/IL-6 in Vata, poor glycemic control in Kapha, NT-pro BNP in Pitta (Dua et al., JAIM, 2025).

Diabetes & Metabolic Syndrome: Kapha-dominant patients comprise 49% of type 2 diabetes cases with higher fasting glucose, cholesterol, and triglycerides (Singh et al., BHU, 2022). 2026 systematic review confirms insulin resistance and lipid markers vary by constitution. Andaman Islands study (619 participants, 2024-2025) investigating MetS-Prakriti associations.

Immunology: CD25 and CD56 natural killer cell expression significantly higher in Kapha; CD14 monocytes also differ (Rotti et al., JAIM, 2014).

Microbiome: Five studies confirm Prakriti-specific gut, oral, and skin microbiome signatures. Rural Indian study eliminates diet/ancestry as confounders (Mobeen et al., Front Microbiol, 2018). Cross-cultural validation with Korean Sasang system shows functional-level similarities between matching constitutional classes (3 Biotech, 2020).

Cross-Cultural Validation: Constitutional typing exists independently in Indian (Prakriti), Korean (Sasang), Chinese (TCM/Yin-Yang), and Japanese (Kampo/Sho) medicine. Microbiome cross-validation confirms these systems capture real biological variation transcending cultural frameworks. Kapha/Taeeum types both show elevated lipids and metabolic predisposition across systems.

Neuroscience: Dosha brain-type neural model maps three types to distinct patterns across six nervous system areas (Travis & Wallace, JAIM, 2015).

HRV: Heart rate variability during orthostatic stress differs by Prakriti in 379 subjects across two regions (Chakraborty et al., Physiol Rep, 2022).

Skin: Skin hydration varies significantly by Prakriti type in cross-sectional study of 150 volunteers. Growing scientific validation of Ayurvedic dermatological approaches (Singh et al., J Cosmet Dermatol, 2025).

Assessment Validation: 64 unique tools evaluated 1987-2024; only CCRAS-PAS (Government of India) and ACPI met rigorous criteria (Venkatesh et al., Frontiers in Medicine, 2025). This tool aligns with the highest-scoring validated methodologies.