Die Gesundheitsdatenquellen und die Wissenschaft hinter Wellness Project

Kalorien- und Makronährstoffschätzung (Lebensmittelprotokollierung)

When you log food — by typing a description, chatting with an AI coach, or scanning a photo — the app estimates calories, protein, carbohydrates, fat, and alcohol for the foods and portions described. These are estimates, not measurements, and you can edit every value before or after saving.

• Nutrient values: Per-food calorie and macronutrient estimates are grounded in the USDA FoodData Central database (https://fdc.nal.usda.gov), the U.S. Department of Agriculture's reference database of food composition, supplemented by manufacturer-published nutrition labels for branded and restaurant foods.
• Energy conversion: Calories are related to macronutrients using the Atwater general factors — 4 kcal/g for protein, 4 kcal/g for carbohydrate, 9 kcal/g for fat, and 7 kcal/g for alcohol. Reference: Merrill AL & Watt BK, "Energy Value of Foods: Basis and Derivation," USDA Agriculture Handbook No. 74, 1973.
• Daily targets and context: Calorie and macro targets shown alongside your log follow the USDA Dietary Guidelines for Americans (https://www.dietaryguidelines.gov) and the Institute of Medicine's Dietary Reference Intakes for macronutrients. Reference: Institute of Medicine, "Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids," National Academies Press, 2005.
• Portion estimation from photos and descriptions: AI-estimated portion sizes are approximations. Accuracy is inherently limited by what is visible or described; values carry a confidence indicator where applicable and are always editable.

TDEE (täglicher Gesamtenergieverbrauch)

The app estimates daily calorie burn using one of three methods, selected automatically based on available data:

• Mifflin-St Jeor equation (primary): BMR derived from weight, height, age, and sex, scaled by an activity multiplier from step count and logged workout intensity. Reference: Mifflin MD et al., "A new predictive equation for resting energy expenditure in healthy individuals," American Journal of Clinical Nutrition, 1990.
• Katch-McArdle equation (lean-mass path): Used when recent body composition data is available. BMR derived from lean mass rather than total weight. Reference: McArdle WD, Katch FI, Katch VL, Exercise Physiology: Nutrition, Energy, and Human Performance.
• Adaptive TDEE (back-calculation): When sufficient logging history exists, TDEE is back-calculated from the relationship between observed caloric intake and smoothed weight trend using exponentially weighted moving average and regression analysis. Conceptual basis: Hall KD et al., "Quantification of the effect of energy imbalance on bodyweight," The Lancet, 2011; Thomas DM et al., "Time to correctly predict the amount of weight loss with dieting," Journal of the Academy of Nutrition and Dietetics, 2014.

NSI (normalisierter Kraftindex)

NSI scores a strength training set relative to population standards for the user's bodyweight, age, and sex.

• 1RM estimation: Derived from validated predictive equations using lifted weight and repetitions performed, with repetitions capped at 20 because higher-rep sets reflect muscular endurance rather than maximal strength. A single-repetition set is taken as measured rather than predicted, since the load lifted is itself the one-repetition maximum. Default equation: Epley. Reference: Epley B, "Poundage Chart," Boyd Epley Workout, 1985. Selectable alternatives include Lander and Brzycki. References: Lander J, "Maximums based on reps," National Strength and Conditioning Association Journal, 1985; Brzycki M, "Strength testing: predicting a one-rep max from reps-to-fatigue," Journal of Physical Education, Recreation & Dance, 1993.
• Population strength standards: Per-exercise benchmarks interpolated by bodyweight and adjusted by age factor, derived from published normative strength data and allometric scaling research. References: Haff GG & Triplett NT (eds.), Essentials of Strength Training and Conditioning, 4th ed., NSCA; Jaric S, "Muscle strength testing: use of normalisation for body size," Sports Medicine, 2002.
• Age adjustment: Strength standards are scaled by age to reflect the documented decline in peak force production across the lifespan. Reference: Pearson SJ et al., "Muscle function and ageing," Scandinavian Journal of Medicine & Science in Sports, 2002.

Fit Score (täglicher Gesamtwert 0–100)

The daily Fit Score combines training performance, sleep, nutrition, recovery, activity, and subjective wellbeing into a single 0-100 wellness index. Components re-normalize when data is missing for a given day. Underlying methodologies for each domain are documented in the individual metric sections on this page.

Schlaf-Score (0-100)

When stage data is available from a connected wearable, the Punktzahl uses four components. When only duration is available, the duration component scales to 100%.

• Duration: Scored against recommended nightly sleep duration for adults. Reference: Hirshkowitz M et al., "National Sleep Foundation's sleep time duration recommendations," Sleep Health, 2015.
• Deep sleep: Scored against published normative deep sleep values for adults. Reference: Ohayon M et al., "Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals," Sleep, 2004.
• REM sleep: Scored against published normative REM values for adults. Reference: Ohayon M et al., "Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals," Sleep, 2004; Carskadon MA & Dement WC in Principles and Practice of Sleep Medicine.
• Awakenings: Scored based on clinical sleep continuity guidelines. Reference: American Academy of Sleep Medicine, International Classification of Sleep Disorders.

Erholungswert (0-100)

Recovery Score combines physiological and subjective signals into a single readiness index.

• HRV: RMSSD values compared to the user's personal rolling baseline. Because HRV is highly individual, personal baseline is weighted more heavily than population norms. Reference: Plews DJ et al., "Training adaptation and heart rate variability in elite endurance athletes," Sports Medicine, 2013; Shaffer F & Ginsberg JP, "An overview of heart rate variability metrics and norms," Frontiers in Public Health, 2017.
• Resting heart rate: Compared to the user's personal baseline and population health norms. Lower RHR correlates with greater cardiovascular fitness and recovery. Reference: American Heart Association guidelines on resting heart rate as a cardiovascular risk marker.
• Sleep: Hours logged from wearable or manual entry. Reference: AASM 7-9 hour recommendation for adults.
• Wellbeing: Self-reported energy, mood, soreness, and stress. Soreness and stress are inverted (higher soreness/stress = lower recovery).

Herzfrequenzvariabilität (HRV)

The app records and displays RMSSD (root mean square of successive RR interval differences) in milliseconds, the most validated short-term HRV metric for athletic monitoring.

• Measurement: Sourced from Apple Health, Fitbit, Oura, or Google Health Connect. Each provider uses their own validated capture method (overnight average or morning reading).
• Interpretation: Compared to the user's own rolling baseline, not population averages, because absolute HRV values vary dramatically between individuals. Reference: Plews DJ et al., "Comparison of heart-rate-variability recording with smartphone photoplethysmography, Polar H7 chest strap, and electrocardiography," International Journal of Sports Physiology and Performance, 2014.
• Readiness signal: A meaningful drop below personal baseline (especially combined with elevated RHR) is used as a recovery flag. Reference: Buchheit M, "Monitoring training status with HR measures: do all roads lead to Rome?" Frontiers in Physiology, 2014.