Product - HealthCaters

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.

Available at: https://pubmed.ncbi.nlm.nih.gov/17132589/

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.

Available at: https://pubmed.ncbi.nlm.nih.gov/22784998/

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.

Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC6545622/

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.

Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC8754593/

Blood pressure monitoring

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.

Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC8385573/

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.

Available at: https://www.nhlbi.nih.gov/files/docs/guidelines/jnc7full.pdf

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.

Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC8030277/

Heart health

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.

Available at: https://pubmed.ncbi.nlm.nih.gov/23499278/

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.

Available at: https://www.sciencedirect.com/science/article/pii/S0953620519304212

Blood glucose

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.

Available at: https://pubmed.ncbi.nlm.nih.gov/22878939/

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.

Available at: https://pubmed.ncbi.nlm.nih.gov/26498983/

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.

Available at: https://pubmed.ncbi.nlm.nih.gov/11832527/

Cholesterol testing

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.

Available at: https://pubmed.ncbi.nlm.nih.gov/27016614/

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.

Available at: https://pubmed.ncbi.nlm.nih.gov/2062328/

Lung testing

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.

Available at: https://pubmed.ncbi.nlm.nih.gov/14003160/

Peak expiratory flow in symptomless elderly smokers and ex-smokers

Sherrill, D.L., Camilli, A.E. and Lebowitz, M.D. (1989) ‘Peak expiratory flow in symptomless elderly smokers and ex-smokers’, Thorax, 44(5), pp. 396–402.

Available at: https://pubmed.ncbi.nlm.nih.gov/2497893/

Detecting chronic obstructive pulmonary disease using peak flow rate: cross sectional survey

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.

Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC196392/

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.

Available at: https://pubmed.ncbi.nlm.nih.gov/22409956/

Musculoskeletal function

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.

Available at: https://bmjopen.bmj.com/content/6/8/e012331

Urine Analysis

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.

Available at: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0171106

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.

Available at: https://pubmed.ncbi.nlm.nih.gov/29991020/

Mental Health

Depression:

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.

Available at: https://pubmed.ncbi.nlm.nih.gov/34610915/

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.

Available at: https://pubmed.ncbi.nlm.nih.gov/11556941/

Sleep Quality:

Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: individual participant data meta-analysis

Burnout:

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.

Available at: https://doi.org/10.1080/02678370500297720

A clinical grade HealthCheck® for early health risk detection