Medical Research On-Demand Model

BitDoctor enables quick identification of specific individuals based on cardiovascular activity, health level, and age, allowing for precise recruitment for clinical trials or campaigns within seconds. With BitDoctor easily recognizing such individuals, it reduces both clinical trial and medical research costs. Ultimately, the end consumer benefits from significantly reduced top-process costs, creating a win-win-win situation for all parties involved.

Additionally, our dataset tracks health data over weeks or months, identifying trends and patterns. Data can be filtered by groups such as sex, age, or high-risk disease categories. This enables targeted user engagement with actions like diet or lifestyle changes, while recording their progress. The structured data is valuable for further research and analysis in health institutions. With a simple ease of accessing such resources at a fraction of the cost compared to the usual practice. This will bring down the cost of medicine research from top down, ultimately benefiting end consumer with a more affordable healthcare ecosystem

Available Medical Datasets From BitDoctor:

Vitals Heart Rate Irregular Heartbeat Count Breathing Rate Systolic Blood Pressure Diastolic Blood Pressure Mental BitDoctor Mental Stress Index Physical Body Mass Index (BMI) Facial Skin Age Waist to Height Ratio Body Shape Index Estimated Height Estimated Weight Waist Circumference Physiological Heart Rate Variability Cardiac Workload Vascular Capacity

General Risks Cardiovascular Disease Risk Heart Attack Risk Stroke Risk Metabolic Risks Hypertension Risk Type 2 Diabetes Risk Hypercholesterolemia Risk Hypertriglyceridemia Risk Fatty Liver Disease Risk Overall Metabolic Health Risk Blood Biomarkers Hemoglobin A1C Risk Fasting Blood Glucose Risk BitDoctor Scores BitDoctor Mental Score BitDoctor Physical Score BitDoctor Physiological Score BitDoctor Risks Score BitDoctor Vitals Score

Further research with user activation

Example 1: A low-carb, high-fiber diet is implemented for middle-aged adults with Type 2 Diabetes. Daily logs should be maintained to record blood sugar levels, dietary intake, and exercise activities. Weekly summaries can help track progress and note any deviations from the recommended plan. Researchers can then analyze the collected data to determine the effectiveness of specific dietary and lifestyle changes on blood sugar management. Example 2: Incorporating certain mindfulness practice and setting them up to engage in sharing an emotion with a person a day for young adults aged 18-30 years dealing with anxiety and depression. Individuals to provide daily logs of their mood and emotion shared. This dataset enables mental health professionals to optimize treatment effectiveness and promote mental wellness in this demographic.