A system for generating a longevity element and an instruction set for a longevity element plan, the system including at least a computing device, wherein the computing device is designed and configured to receive, from a user, at least an element of user-reported data, determine, using the at least an element of user-reported data and a first machine-learning process, a longevity element, calculate, using a longevity element and at least a second element of data, a compensatory supplement, and generate, using the at least an element of user-reported data and at least a longevity element, an instruction set for a longevity element plan.

The present disclosure proposes an apparatus for determining an intestinal microbiome index. The apparatus according to the present disclosure may: obtain test information about a biological sample of a subject from a test apparatus; determine, based on the test information, first information about the similarity of intestinal microflora, second information about a proportion of harmful intestinal microflora, third information about a proportion of beneficial intestinal microflora, and/or fourth information about the diversity of intestinal microflora; and determine an intestinal microbiome index indicating a state of intestinal microbiome of the subject based on the determined information.

A method includes applying, via a respiratory therapy system, initial therapy settings for a user during a first sleep session in which the user uses the respiratory therapy system. First physiological data, which is received from one or more sensors, is generated during the first sleep session. Modified therapy settings are applied, via the respiratory therapy system, during a second sleep session of the user. Second physiological data is received from the one or more sensors. The second physiological data is generated by the one or more sensors during the second sleep session. A set of sleep-related parameters is determined based on changes between the first physiological data and the second physiological data. One or more of a recommended therapy or recommended therapy settings is determined based on the set of sleep-related parameters.

A CPU of a health management apparatus functions as an acquisition unit, a first derivation unit, and a screen output control unit. The acquisition unit acquires a body-fat percentage which is an obesity parameter indicating the degree of obesity of a target pet and bone density which is a bone parameter indicating the degree of bone strength of the target pet. The first derivation unit derives health conditions of the target pet on the basis of a correlation between the body-fat percentage and the bone density. A screen output control unit performs control to output a medical examination result display screen on which the health conditions are displayed.

A CPU of a health management apparatus functions as an acquisition unit, a first derivation unit, and a screen output control unit. The acquisition unit acquires a body-fat percentage which is an obesity parameter indicating the degree of obesity of a target pet and bone density which is a bone parameter indicating the degree of bone strength of the target pet. The first derivation unit derives health conditions of the target pet on the basis of a correlation between the body-fat percentage and the bone density. A screen output control unit performs control to output a medical examination result display screen on which the health conditions are displayed.

A non-transitory computer-readable recording medium with an information processing program stored thereon, wherein the program instructs a computer to execute an acquisition step of acquiring current body information that is information on a body of a target user who is a processing target user at a present time and future body information that is information on a body that the target user wants to have after a lapse of a predetermined time since the present time, an estimation step of estimating recommended food information on a food recommended to be taken by the target user among foods that are captured in a meal image obtained by imaging a meal, on the basis of the current body information and the future body information acquired at the acquiring, and a providing step of providing the recommended food information estimated at the estimating to the target user.

A non-transitory computer-readable recording medium with an information processing program stored thereon, wherein the program instructs a computer to execute an acquisition step of acquiring current body information that is information on a body of a target user who is a processing target user at a present time and future body information that is information on a body that the target user wants to have after a lapse of a predetermined time since the present time, an estimation step of estimating recommended food information on a food recommended to be taken by the target user among foods that are captured in a meal image obtained by imaging a meal, on the basis of the current body information and the future body information acquired at the acquiring, and a providing step of providing the recommended food information estimated at the estimating to the target user.

A system for nutritional recommendation using artificial intelligence analysis of immune impacts includes a computing device designed and configured to receive a test result detecting an effect of at least an aliment on at least a biomarker, determine an immune system impact of the at least an aliment as a function of the at least a biomarker using a machine-learning process, the machine-learning process trained using a first training set relating biomarker levels to immune system function, generate a nutritional recommendation using the determined immune system impact, and provide the nutritional recommendation to the user.

A system for nutritional recommendation using artificial intelligence analysis of immune impacts includes a computing device designed and configured to receive a test result detecting an effect of at least an aliment on at least a biomarker, determine an immune system impact of the at least an aliment as a function of the at least a biomarker using a machine-learning process, the machine-learning process trained using a first training set relating biomarker levels to immune system function, generate a nutritional recommendation using the determined immune system impact, and provide the nutritional recommendation to the user.

Methods, computer-readable media, and systems for providing disease management. In one implementation, a system develops a treatment recommendation for a patient based on patient data. The system may receive patient data, execute a basic model of the physiological system of the patient to generate a modified model for the patient based on the patient data, perform a statistical analysis of the patient data to detect data excursions of the parameter values; use the modified model to determine factors causing the data excursions; and use the model to develop a treatment recommendation to ameliorate negative effects of the disease.