A medical HealthCheck® designed to detect 25+ diseases
“The current system offers no space for continuous health insights that empower individuals to truly understand where they stand. I founded HealthCaters to prevent diseases like chronic heart disease, diabetes, asthma, from taking lives before they reach the operating table. Saving lives shouldn't start in surgery.”
Be your own doctor
Turn your annual health check into a fun, effortless experience — with our self-serve HealthCheck® powered by medically validated devices measuring the most important markers.
Blood pressure
ECG
Oxygen levels
Urine (10 markers)
Lung function
BMI and Body fat
Waist circumference
Blood Tests
Get deeper health insights with the most relevant blood tests, administered safely by our trained on site health assistants.
Blood cholesterol
Blood glucose
Mental Health
Get a holistic picture of your wellbeing by assessing your mental health through our clinically validated digital tests.
Depression
Burnout
Anxiety
What makes our HealthCheck® different?
Take charge of your health with a modern, science-backed screening
Be Your Own Doctor
Get access to early screening without going to a clinic through a fully guided on site biometric self-screening.
Personalized Risk Assessment
Get a personalised health assessment to understand what your data means in the context of your lifestyle, family history, and other factors that affect only you.
The HealthCaters App
Track your health, connect wearables, and get clear next steps - be it retesting, seeing a doctor, or pursuing a lifestyle change - all in one easy-to-use app.
Cancer Screening
Cancer is one of the most devastating diseases. The only way to combat it is to catch things early. Our Cancer Risk assessment help you understand when you need to act.
Testicular risk assessment
Testicular self exam
Breast risk assessment
Breast self exam
Lung cancer
Lifestyle & Musculoskeletal Assessments
Track how your lifestyle affects your health forecast.
Stress and HRV wearable analysis
Sleep
Musculoskeletal
Nutrition and Exercise
Risk Assessment
Advanced analytics combines 80+ data points to create a personalised health forecast for 9 common health areas.
Mental Health
Muscle and Joint Health
Lung Health
Heart Health
Urinary Health
Liver & bile ducts health
Kidney Health
Metabolic Health
Cancer Prevention
Personalised Prevention plan
Personalized advice and resources to help you take action, build better habits, and stay healthy long term.
Personalized health challenges
Weekly wellness goals
Rest and recovery reminders
Nutrition and activity guidance
Clear next steps
Whether it's retesting, seeing a doctor, or following a lifestyle change program we help you navigate your next steps for a healthier you.
Retest recommendations
Book a doctor’s appointment
Access to teleclinic or in-person care
Follow-up planning
Lifestyle change programs
Science-backed disease detection
Our HealthCheck strategically measures vital markers to detect early signs of 25+ critical conditions, from diabetes and heart disease to mental health challenges.
Obesity
Diabetes
Pre-diabetes
Hyperlipidemia
Hypertension
Cardiovascular Disease
Anxiety
Depression
Burnout
Sleep Disorders
Metabolic Syndrome
Lung Disease
Cancer Risk
Mental Health Issues
Every parameter backed by peer-reviewed studies
BMI & Body Composition
Effects of body fatness and physical activity on cardiovascular risk: Risk prediction using the bioelectrical impedance method
American Heart Association (2010) ‘Effects of body fatness and physical activity on cardiovascular risk: Risk prediction using the bioelectrical impedance method’, Circulation, 122(4), pp. 472–491.
The significance of measuring body fat percentage determined by bioelectrical impedance analysis for detecting subjects with cardiovascular disease risk factors
Kuwabara, M., Kuwabara, R., Hisatome, I., Roncal-Jimenez, C.A., Niwa, K., Andres-Hernando, A., Jensen, T., Bjornstad, P., Sato, Y., Milagres, T., Garcia, G., Ohno, M., Lanaspa, M.A. and Johnson, R.J. (2012) ‘The significance of measuring body fat percentage determined by bioelectrical impedance analysis for detecting subjects with cardiovascular disease risk factors’, Hypertension Research, 35(9), pp. 875–881.
Body fat estimates from bioelectrical impedance equations in cardiovascular risk assessment: The PREVEND cohort study
Byambasukh, O., Eisenga, M.F., Gansevoort, R.T., Bakker, S.J.L. and Corpeleijn, E. (2019) ‘Body fat estimates from bioelectrical impedance equations in cardiovascular risk assessment: The PREVEND cohort study’, European Journal of Preventive Cardiology, 26(9), pp. 905–916.
Excess Weight and Body Fat Percentage Associated with Waist Circumference as a Cardiometabolic Risk Factor in University Students
Vargas-Ruiz, A.G., Sosa-Zavaleta, L.D., Ponce-García, M., et al. (2022) ‘Excess Weight and Body Fat Percentage Associated with Waist Circumference as a Cardiometabolic Risk Factor in University Students’, Journal of Nutrition and Metabolism, 2022, Article ID 8439890, pp. 1–8.
Home Blood Pressure Monitoring: Current Status and New Developments
Stergiou, G.S., Palatini, P., Asmar, R., Ioannidis, J.P.A., Kollias, A., Lacy, P., McManus, R., Myers, M., Parati, G., Shennan, A., Wang, J., O’Brien, E. and Mancia, G. (2021) ‘Home Blood Pressure Monitoring: Current Status and New Developments’, Journal of Clinical Hypertension, 23(3), pp. 479–487.
The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: The JNC 7 Report
Chobanian, A.V., Bakris, G.L., Black, H.R., Cushman, W.C., Green, L.A., Izzo, J.L., Jones, D.W., Materson, B.J., Oparil, S., Wright, J.T. and Roccella, E.J. (2003) ‘The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: The JNC 7 Report’, JAMA, 289(19), pp. 2560–2572.
Self-blood pressure monitoring as a tool to increase hypertension control
Banegas, J.R. and Segura, J. (2019) ‘Self-blood pressure monitoring as a tool to increase hypertension control’, Journal of Clinical Hypertension, 21(8), pp. 1186–1188.
Screening for asymptomatic atrial fibrillation while monitoring the blood pressure at home: Trial of regular versus irregular pulse for prevention of stroke
Wiesel, J., Kamel, H., Haim, M., et al. (2013) ‘Screening for asymptomatic atrial fibrillation while monitoring the blood pressure at home: Trial of regular versus irregular pulse for prevention of stroke’, International Journal of Cardiology, 168(3), pp. 2457–2461.
Prospective blinded evaluation of the smartphone-based AliveCor Kardia ECG monitor for atrial fibrillation detection: The PEAK-AF study
Wegner, F.K., Kochhäuser, S., Ellermann, C., Lange, P.S., Frommeyer, G., Leitz, P., Eckardt, L. and Dechering, D.G. (2019) ‘Prospective blinded evaluation of the smartphone-based AliveCor Kardia ECG monitor for atrial fibrillation detection: The PEAK-AF study’, European Journal of Internal Medicine, 65, pp. 73–79.
Prevalence of type 2 diabetes in Germany in 2040: estimates from an epidemiological model
Tönnies, T., Röckl, S., Hoyer, A., Heidemann, C., Baumert, J., Du, Y., Paprott, R., Brinks, R. and Scheidt-Nave, C. (2012) ‘Prevalence of type 2 diabetes in Germany in 2040: estimates from an epidemiological model’, Deutsches Ärzteblatt International, 109(44), pp. 781–787.
Temporal changes in the prevalence of diagnosed diabetes, undiagnosed diabetes and prediabetes: findings from the German Health Interview and Examination Surveys in 1997–1999 and 2008–2011
Heidemann, C., Du, Y., Paprott, R., Haftenberger, M., Rathmann, W. and Scheidt-Nave, C. (2016) ‘Temporal changes in the prevalence of diagnosed diabetes, undiagnosed diabetes and prediabetes: findings from the German Health Interview and Examination Surveys in 1997–1999 and 2008–2011’, Diabetic Medicine, 33(10), pp. 1406–1414.
Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin
Diabetes Prevention Program Research Group (2002) ‘Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin’, New England Journal of Medicine, 346(6), pp. 393–403.
Total cholesterol as a risk factor for coronary heart disease and stroke in women compared with men: A systematic review and meta-analysis
Peters, S.A.E., Singhateh, Y., Mackay, D., Huxley, R.R. and Woodward, M. (2016) ‘Total cholesterol as a risk factor for coronary heart disease and stroke in women compared with men: A systematic review and meta-analysis’, Atherosclerosis, 248, pp. 123–131.
A prospective study of cholesterol, apolipoproteins, and the risk of myocardial infarction
Stampfer, M.J., Sacks, F.M., Salvini, S., Willett, W.C. and Hennekens, C.H. (1991) ‘A prospective study of cholesterol, apolipoproteins, and the risk of myocardial infarction’, New England Journal of Medicine, 325(6), pp. 373–381.
Airway resistance and peak expiratory flow-rate in smokers and non-smokers
Woolcock, A.J. and Colman, M.H. (1963) ‘Airway resistance and peak expiratory flow-rate in smokers and non-smokers’, The Lancet, 1(7293), pp. 1237–1238. doi: 10.1016/s0140-6736(63)91866-x.
Jackson, H. and Hubbard, R. (2003) ‘Detecting chronic obstructive pulmonary disease using peak flow rate: cross sectional survey’, BMJ, 327(7416), pp. 653–654. doi: 10.1136/bmj.327.7416.653.
Peak expiratory flow as a screening tool to detect airflow obstruction in a primary health care setting
Li, J., Wang, Z., Li, Y., Wang, Y., Gao, Y., Zhang, X. and Wang, X. (2012) ‘Peak expiratory flow as a screening tool to detect airflow obstruction in a primary health care setting’, Respiratory Care, 57(11), pp. 1893–1898. doi: 10.4187/respcare.01529.
Development and initial cohort validation of the Arthritis Research UK Musculoskeletal Health Questionnaire (MSK-HQ) for use across musculoskeletal care pathways
Hill, J.C., Kang, S., Benedetto, E., Myers, H., Blackburn, S., Smith, S., Dunn, K.M., Foster, N.E., Hay, E.M. and van der Windt, D.A. (2016) ‘Development and initial cohort validation of the Arthritis Research UK Musculoskeletal Health Questionnaire (MSK-HQ) for use across musculoskeletal care pathways’, BMJ Open, 6(8), e012331.
Diagnostic efficacy of urine dipstick in detecting chronic kidney disease
Kim, Y., Jeong, J.C., Kim, H., Kim, Y., Kim, S., Kim, H.J., Kim, Y. and Kim, S. (2017) ‘Diagnostic efficacy of urine dipstick in detecting chronic kidney disease’, PLoS ONE, 12(2), e0171106.
Trace albumin in the urine dipstick test is associated with coronary artery calcification in Korean adults
Song, J.J., Lee, K.B., Hyun, Y.Y. and Kim, H. (2018) ‘Trace albumin in the urine dipstick test is associated with coronary artery calcification in Korean adults’, Nephron, 140(2), pp. 112–119.
Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: individual participant data meta-analysis
Levis, B., Benedetti, A. and Thombs, B.D. (2021) ‘Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: individual participant data meta-analysis’, BMJ, 375, n2183.
The PHQ-9: Validity of a brief depression severity measure
Kroenke, K., Spitzer, R.L. and Williams, J.B.W. (2001) ‘The PHQ-9: Validity of a brief depression severity measure’, Journal of General Internal Medicine, 16(9), pp. 606–613.
Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: individual participant data meta-analysis
Levis, B., Benedetti, A. and Thombs, B.D. (2021) ‘Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: individual participant data meta-analysis’, BMJ, 375, n2183.
The Copenhagen Burnout Inventory: A new tool for the assessment of burnout
Kristensen, T.S., Borritz, M., Villadsen, E. and Christensen, K.B. (2005) ‘The Copenhagen Burnout Inventory: A new tool for the assessment of burnout’, Work & Stress, 19(3), pp. 192–207.
