Method for optimizing a health plan

Abstract

The present invention relates generally to health and fitness. In particular, the present invention relates to a computer-implemented method for optimising a health plan to promote health and fitness and / or weight loss in a subject. The health plan involves monitoring the level of glucose and ketone of the subject over a multi-day time period to provide an indication on whether metabolic switch and / or ketosis is achieved in each day of the multi-day period. Also provided are devices and computer program products as defined herein.

Description

[0001] Method for Optimizing a Health Plan

[0002] Technical field

[0003] The present invention relates generally to health and fitness. In particular, the present invention relates to a computer-implemented method for optimizing a health plan to promote health and fitness and / or weight loss in a subject. Also provided are devices and computer program products as defined herein.

[0004] Background

[0005] The digital health field is experiencing substantial growth due to its potential for sustained and longitudinal deployment. In turn, this may drive improved monitoring and intervention as catalysts for behavioural change compared to traditional point-of-care practices. In particular, the increase in incidence of population health challenges such as diabetes, heart disease, fatty liver disease, and other disorders coupled with rising healthcare costs have emphasized the importance of exploring technical, economics, and implementation considerations, among others, in the decentralization of health and healthcare innovations. Both healthy individuals and patients stand to benefit from continued technical advances and studies in these domains.

[0006] The role of digital health towards enabling and supporting personalized, community and population health is being widely studied. For example, emerging platforms are investigating how to decentralize and democratize the monitoring and management of conditions such as heart disease, diabetes, frailty, and other conditions that are increasing in global incidence due to rapidly aging societies and factors which include but are not limited to cardiopulmonary disease and diabetes mellitus. Solutions being developed range from mobile applications to wear able devices, and the data obtained from these solutions are being applied to artificial intelligence (Al) and machine learning- based predictive modelling for clinical decision support and other uses. Barriers to sustainably implementing digital health solutions have included waning user adherence towards wearables and mobile applications, challenges with integrating predictive modelling into real-world clinical workflows, interoperability issues with electronic health records (EHRs) and other considerations. Collectively, these factors are important to consider as evidence generation will be essential towards consistently incorporating digital health into widespread health and medical use.

[0007] It would be desirable to overcome or alleviate at least one of the above-described problems, or at least to provide a useful alternative.

[0008] Summary

[0009] Disclosed herein is a computer-implemented method for optimizing a health plan to promote health and fitness and / or weight loss in a subject, the method comprising: a) monitoring the level of glucose and the level of a ketone of the subject comprising receiving glucose and ketone measurements from one or more devices over a multi-day time period; b) assigning a metabolism classification to the subject based on i) the level of glucose and the level of the ketone at a pre-defined time point following recommended daily exercise by the health plan over the multi-day period, wherein the level of glucose and the level of ketone at the predefined time point provide an indication on whether metabolic switching and / or ketosis is achieved in each day of the multi-day period; and c) generating and presenting to the subject recommendations that are dependent on the assigned metabolism classification so as to optimize the health plan to promote health and fitness and / or weight loss in the subject.

[0010] Disclosed herein is a computer-implemented method for optimizing a health plan to promote health and fitness and / or weight loss in a subject, the method comprising: a) monitoring the level of glucose and the level of a ketone of the subject comprising receiving glucose and ketone measurements from one or more devices over a multi-day time period; b) assigning a metabolism classification to the subject based on i) the level of glucose and the level of the ketone at a pre-defined time point following recommended daily exercise by the health plan over the multi-day period, wherein the level of glucose and the level of ketone at the predefined time point provide an indication on whether metabolic switching and / or ketosis is achieved in each day of the multi-day period, and ii) the number of days in which metabolic switching and / or ketosis is achieved over the multi-day period; and c) generating and presenting to the subject recommendations that are dependent on the assigned metabolism classification so as to optimize the health plan to promote health and fitness and / or weight loss in the subject.

[0011] Disclosed herein is a device for optimizing a health plan to promote health and fitness and / or weight loss in a subject, wherein the device comprises one or more processors and a memory, the memory storing instructions that, when executed by the one or more processors, perform a method of: a) monitoring the level of glucose and the level of a ketone of the subject comprising receiving glucose and ketone measurements from one or more devices over a multi-day time period; b) assigning a metabolism classification to the subject based on i) the level of glucose and the level of the ketone at a pre-defined time point following recommended daily exercise in the health plan over the multi-day period, wherein the level of glucose and the level of ketone provide an indication on whether metabolic switching and / or ketosis is achieved in each day of the multi-day period; and c) generating, and presenting to the subject, modifications to the health plan that are dependent on the assigned metabolism classification so as to optimize the health plan to promote health and fitness and / or weight loss in the subject.

[0012] Disclosed herein is a computer program product, embodied in one or more non-transitory computer-readable storage medium, comprising instructions executable by at least one processor to perform the method for optimizing a health plan as defined herein.

[0013] Brief description of the drawings

[0014] Embodiments of the present invention will now be described, by way of non-limiting example, with reference to the drawings in which:

[0015] Figure 1 Overview of N-of-1 health optimization. The optimization workflow includes strictly adhered intermittent fasting on a 20 / 4 schedule, biomarker readings from a suite of platforms, consistent strength and cardiovascular training, and ketogenic dietary regimen.

[0016] Figure 2 Intermittent fasting timeline. Subject N001 followed a 20 / 4 intermittent fast (20- 22 hours fasted, 4 hour eating window) with morning fasted fitness regimen daily. Biomarker readings pre- and post-workout as well as pre-feeding were recorded.

[0017] Figure 3 Continuous monitoring of biomarkers with intermittent fasting and fitness regimen, a Four-day continuous monitoring of blood glucose and ketone trajectories, b The comparison of recorded blood glucose and ketone levels with different fitness regimen (running and weights) in a two-week period (Glucose: N = 34-96; Ketone: N = 2-4; daily). Wilcoxon rank-sum test determined statistically significant difference between glucose levels as a result of running and weights training (Running: N = 600; Weights: N = 564; P<0.001). However, no statistical significance was detected for ketone levels as a result of the interventions (Running: N = 16; Weights: N = 18). c, d Blood glucose and ketone profiles with interventions consisting of fitness regimen (running or weights) and intermittent fasting (42 h or 22 h) in a two-day period, f, g, h Subject NOOl’s baseline glucose.

[0018] Figure 4 Longitudinal monitoring of ketone and glucose ketone index (GK1) trajectories for 33 days. The subject N0001 was in pre-ketosis stage (yellow) on SEP 6 (2023) and began ketosis the following day for 31 days. During ketosis, NOOl’s ketone and GK1 profiles were high-low-high and low-high-low, respectively. Note that some days may have been affected by regional and international travels.

[0019] Figure 5 Biomarker monitoring of N001 during a 72 h fast, a Ketosis was achieved on NOV 1 and high blood ketone levels were observed towards the end of the 72 h fast, b Daily blood pressure measurements (N = 3) indicated no significant changes to cardiovascular health as a result of a prolonged fast.

[0020] Figure 6 Blood pressure monitoring for subject N001. Measurements were taken pre- and post-workout in the morning (AM) and in the afternoon (PM) and night time (NT) (N = 3- 6). The desired blood pressure benchmarks 135 / 85 and 120 / 80 are indicated in blue and red zones. The darker colours represent 135 / 85 while the lighter colours represent 120 / 80.

[0021] Figure 7 Subject N001 body weight in SEP 2023. The sudden drop in body weight at beginning of SEP was a result of a 72 h fast (SEP 5-8) done by subject N001 (N = 3-6).

[0022] Figure 8 shows the ketone and glucose levels of a subject (N001) in (a) September 2024 (September 20) and (b) late October 2024 (October 27 / 28). (a) The dynamics of subject NOOl ’s glucose and ketone levels (measured via finger stick monitor) with intermittent fasting (48 hrs) and fitness regimen (90 mins). Subject NOOl’s glucose levels increased during workout and dropped post workout. In contrast, the subject’s ketone levels dropped during workout and substantially increased afterwards (Ketosis: > 0.5 mmol / L). (b) Adhering to interventions consisting of intermittent fasting, fitness regimen, and clean diet, subject N001 was able to achieve ketosis in less than 24 hrs. Detailed description

[0023] The present specification teaches a computer-implemented method for optimizing a health plan to promote health and fitness and / or weight loss in a subject. The method may comprise: a) monitoring the level of glucose and the level of a ketone of the subject comprising receiving glucose and ketone measurements from one or more devices over a multi-day time period; b) assigning a metabolism classification to the subject based on the level of glucose and the level of the ketone at a pre-defined time point following recommended daily exercise by the health plan over the multi-day period; and c) generating and presenting to the subject recommendations that arc dependent on the assigned metabolism classification so as to optimize the health plan to promote health and fitness and / or weight loss in the subject.

[0024] Disclosed herein is a computer-implemented method for optimizing a health plan to promote health and fitness and / or weight loss in a subject, the method comprising: a) monitoring the level of glucose and the level of a ketone of the subject comprising receiving glucose and ketone measurements from one or more devices over a multi-day time period; b) assigning a metabolism classification to the subject based on i) the level of glucose and the level of the ketone at a pre-defined time point following recommended daily exercise by the health plan over the multi-day period, wherein the level of glucose and the level of ketone at the predefined time point provide an indication on whether metabolic switching and / or ketosis is achieved in each day of the multi-day period; and c) generating and presenting to the subject recommendations that are dependent on the assigned metabolism classification so as to optimize the health plan to promote health and fitness and / or weight loss in the subject.

[0025] Disclose herein is a computer-implemented method for optimizing a health plan to promote health and fitness and / or weight loss in a subject, the method comprising: a) monitoring the level of glucose and the level of a ketone of the subject comprising receiving glucose and ketone measurements from one or more devices over a multi-day time period; b) assigning a metabolism classification to the subject based on i) the level of glucose and the level of the ketone at a pre-defined time point following recommended daily exercise by the health plan over the multi-day period, wherein the level of glucose and the level of ketone at the predefined time point provide an indication on whether metabolic switching and / or ketosis is achieved in each day of the multi-day period, and ii) the number of days in which metabolic switching and / or ketosis is achieved over the multi-day period; and c) generating and presenting to the subject recommendations that are dependent on the assigned metabolism classification so as to optimize the health plan to promote health and fitness and / or weight loss in the subject.

[0026] In one embodiment, the method involves monitoring the level of glucose and the level of a ketone of the subject comprising receiving glucose and ketone measurements from one or more devices over a multi-day time period.

[0027] The multi-day period may be at least 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 or 30 days.

[0028] The DELTA workflow may involve multi-device monitoring (e.g. continuous glucose monitoring (CGM), finger stick gluco sc / kctonc devices) and may also include other platforms, such as the grip strength dynamometer and cognitive performance tests, that provide insightful measurements into health management. Longitudinal monitoring with multiple digital platforms, devices or wearables may lead to a more comprehensive examination of one’s health dynamics and management over time. Wearable devices, which encompass digital devices to the analysis of biological fluid (e.g. blood, urine, sweat, interstitial, and others), as well as emerging digital therapeutics (DTx) may also be used.

[0029] The expression “receiving glucose and ketone measurements” may refer to receiving wireless transmissions of glucose and ketone measurements from one or more devices (such as CGM or stick glucose / ketone devices or breath devices).

[0030] The health plan as defined herein may comprise recommended daily exercise, personalized diet and / or intermittent fasting. The health plan may comprise daily exercise (i.e. fitness) and intermittent fasting as shown in the schedule in Fig. 2.

[0031] The present invention may involve gamification of adherence to intermittent fasting, fitness, and food regimen using a user-specific biomarker profile and resulting metabolic switching can potentially link a broad spectrum of factors to achieve desired health outcomes. These include but are not limited to user data, user personality, nudging, incentives, behavioural economics, and other factors towards achieving sustained behaviour change. In one embodiment, the method in step c) generates and presents to the subject recommendations that are dependent on the assigned metabolism classification to promote metabolic switching and / or ketosis following recommended daily exercise. The method may promote metabolic switching from a non-ketosis state to a ketosis state following recommended daily exercise. The method may promote ketosis for at least 1 hour (or at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 or 12 h) following recommended daily exercise.

[0032] The method as defined herein may comprise assigning a metabolism classification to the subject based on the level of glucose and the level of the ketone at a pre-defined time point following recommended daily exercise by the health plan. The pre-defined time point may be at least 1 hour (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 hours) following recommended daily exercise. The prc-dcfincd time point may be at least 6 hours (preferably 6 to 10 hours, such as 7, 8, 9 or 10 hours) following recommended daily exercise.

[0033] The method may comprise assigning a metabolism classification. This may comprise selecting the metabolism classification from a plurality of predefined metabolism classifications, each of the predefined metabolism classifications corresponding to a glucose ketone index (GKI) or ketone level at a pre-defined time point. The GKI or ketone level at the prc-dcfincd time point may be compared to GKI or ketone level at one or more reference time points. The metabolism classification may be assigned based on the GKI or ketone level at the pre-defined time point and the one or more reference time points.

[0034] Both ketone and GKI (and other biomarkers) may be used as a promoter of adherence to the plan. A first measurement (pre-workout measurement) can be taken in the morning which is the baseline data. This may be followed by a workout (i.e. a fitness regimen) which leads to a decrease in ketone level and increase in GKI. In the afternoon, before the feeding window, ketone and GKI can return to the baseline level. The evolving dynamics of these biomarkers drive a subject to retain the same trajectories, which require the subject to adhere to dietary, fitness, and intermittent fasting regimens.

[0035] The maintenance of a dynamic biomarker trajectory unexpectedly served as a promoter of adherence to the fasting, fitness, and dietary regimen. Specifically, maintaining the high- low-high ketone profile and low-high-low GKI profile was driven by the avoidance of a high-low-low ketone profile or low-high-high GKI profile. Moreover, evolving visualization of biomarker dynamics (e.g. high-low-high ketone profile) also served as driver for adherence and to modulate fitness, diet, and fasting interventions (e.g. duration, magnitude (calories / macro), or composition). An example of an action that would result in the latter profiles include breaking the fasting with food items that are non-compliant with the clean ketogenic regimen that was undertaken (e.g. consumption of carbohydrates that exceed ketogenic diet limits).

[0036] The drive to adhere to the fitness, dietary and fasting regimes can be hinged upon a measurement that reflects changes in the body quickly (i.e. within the day). This allows the participant to observe the impacts of their work quasi-immediately when compared to alternate measurements like body weight and hip-waist ratios which require a longer period of time to exhibit change.

[0037] It was found that the glucose ketone index (GKI) or ketone level is surprisingly dynamic following recommended daily exercise by the health plan and can be measured at the predefined time point (i.e. at least 6 hours) following recommended exercise.

[0038] In one embodiment, the assignment of the metabolism classification is based on a glucose ketone index (GKI) derived from the glucose and ketone measurements. In one embodiment, a decrease in GKI as compared to a reference (which can be a pre-defined threshold) predicts or indicates that the subject is undergoing ketosis and is adherent to the health plan. The predefined threshold may be a GKI of 3. The GKI can be determined with measured glucose and ketone levels. Low GKI (e.g. <3) may indicate ketosis state while high GKI (e.g. >3) may indicate non-ketosis state. GKI may be calculated using the standard method of dividing the glucose level by the ketone level.

[0039] In one embodiment, the assignment of the metabolism classification is based on a glucose ketone index (GKI) derived from the glucose and ketone measurements at the pre-defined time point, wherein a decrease in GKI as compared to a pre-defined threshold predicts or indicates that the subject is undergoing ketosis and is adherent to the health plan.

[0040] In one embodiment, the assignment of the metabolism classification is based on a glucose ketone index (GKI) derived from the glucose and ketone measurements. In one embodiment, an increase in GKI as compared to a reference (which can be a pre-defined threshold) predicts or indicates that the subject is not undergoing ketosis and is non-adherent to the health plan. The pre-defined threshold may be a GKI of 3.

[0041] In one embodiment, the assignment of the metabolism classification is based on a glucose ketone index (GKI) derived from the glucose and ketone measurements at the pre-defined time point, wherein an increase in GKI as compared to a pre-defined threshold predicts or indicates that the subject is not undergoing ketosis and is non-adherent to the health plan. The pre-defined threshold may be a GKI of 3.

[0042] The assignment of the metabolism classification may be based the GKIs one or more time points (including one, two, three or more time points). The assignment of the metabolism classification may be based on comparing the GKI at the pre-defined time point to GKIs from one or more reference time points. The one or more reference time points may be a time point selected from a) a time point prior to the recommended daily exercise and b) a time point during the recommended daily exercise.

[0043] In one embodiment, the assignment of the metabolism classification is based on the ketone level. In one embodiment, an increase in ketone level as compared to a reference (which can be a pre-defined threshold) predicts or indicates that the subject is undergoing ketosis and is adherent to the health-plan.

[0044] In one embodiment, the assignment of the metabolism classification is based on the ketone level, wherein an increase in ketone level as compared to a pre-defined threshold predicts or indicates that the subject is undergoing ketosis and is adherent to the health-plan.

[0045] In one embodiment, the assignment of the metabolism classification is based on the ketone level. In one embodiment, a decrease in ketone level as compared to a reference (which can be a pre-defined threshold) predicts or indicates that the subject is not undergoing ketosis and is non-adherent to the health-plan.

[0046] In one embodiment, the assignment of the metabolism classification is based on the ketone level at the pre-defined time point, wherein a decrease in ketone level as compared to a predefined threshold predicts or indicates that the subject is not undergoing ketosis and is non- adherent to the health-plan. The assignment of the metabolism classification may be based on comparing ketone levels at one or more time points (including one, two, three or more time points). The assignment of the metabolism classification may be based on comparing the ketone level at the predefined time point to the ketone level at one or more reference time points. The one or more reference time points may be a time point selected from a) a time point prior to the recommended daily exercise and b) a time point during the recommended daily exercise.

[0047] In one embodiment, the assignment of the metabolism classification is based on the GKI or ketone level at a) the pre-defined time point, b) a time point prior to the recommended daily exercise and c) a time point during the recommended daily exercise.

[0048] As used herein, the term “decrease”, or “decreased” refers to a statistically significant and measurable decrease as compared to a reference. The decrease may be a decrease of at least about 10%, at least about 20%, at least about 30%, at least about 40%, at least about 50%, at least about 60%, at least about 70%, at least about 80%, or at least about 90%.

[0049] The terms “increased”, and “increase” are used herein to mean an increase by a statistically significant amount. In some embodiments, the terms “increased” and “increase” can mean an increase of at least about 2% as compared to a reference level, for example an increase of at least about 3%, at least about 4%, at least about 5%, at least about 6%, at least about 7%, at least about 8%, at least about 9%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 45%, at least about 50%. at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, or up to and including a 100% increase or any increase between 2- 100% as compared to a reference level, or at least about a 2-fold, at least about a 3-fold, at least about a 4-fold, at least about a 5-fold, at least about a 6-fold, at least about a 7-fold, at least about a 8-fold, at least about a 9-fold, or at least about a 10-fold increase, or any increase between 2-fold and 10-fold or greater as compared to a reference level.

[0050] The terms “decreased”, and “decrease” are used herein to mean an decrease by a statistically significant amount. In some embodiments, the terms “decreased” and “decrease” can mean a decrease of at least about 2%> as compared to a reference level, for example a decrease of at least about 3%, at least about 4%, at least about 5%, at least about 6%, at least about 7%, at least about 8%, at least about 9%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 45%, at least about 50%. at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, or up to and including a 100% decrease or any decrease between 2-100% as compared to a reference level.

[0051] DELTA’S interventions may involve multiple levels, which include, for example, intermittent fasting, fitness regimens (cardio and strength training), and a ketogenic diet. These interventions are multi-faceted such that all aspects of healthy lifestyle are carefully considered during the course of the longitudinal monitoring.

[0052] In one embodiment, the recommendations in step c) include nudging and / or incentives to the subject. In one embodiment, the recommendations in step c) include a modified health plan. This may comprise a modification to the fitness regimen, diet or intermittent fasting schedule.

[0053] In one embodiment, the computer-implemented method is performed over a long period, c.g. at least 3 days, at least 7 days, at least 14 days, at least 21 days or at least 28 days or more. The computer-implemented method may be performed sometime (e.g. after at least 3 days, 7 days, 14 days, 21 days, 28 days or more) after the subject has commenced the health-plan.

[0054] Additional factors such as demographic-specific parameters for safe fasting, fitness, and food regimens may also be used. As the user feedback and comments were also obtained from a N-of-1 study, this work solely provides single subject insights following the specific regimen as noted. It would also serve beneficial to build upon the current study by expanding the variety of biomarkers monitored, allowing the study to take on a more holistic approach. An example would be to further investigate cardiovascular risk through observing apolipoprotein B and A-I (apoB and apoA-I) ratios or conducting lipid panels to longitudinally monitor subject cholesterol and triglyceride levels to serially assess coronary risk considerations. Additional biomarkers pertaining to healthy aging that may be ideal to examine in future studies also include, but arc not hmited to, estimated globular filtration rate (eGFR) through cystatin C measurements alone or in combination with creatinine (predictive of kidney function), total bilirubin or the combination of gamma-glutamyl transferase (GGT) and alkaline phosphatase (ALP) (predictive of general liver function) as well as pro-inflammatory markers such as plasma interleukin-6 and tumour necrosis factor a (predictive of natural ageing progress) as well as other longevity markers. Additional digital readouts may include strength, cognitive performance, and other physical performance assessments. While every effort was made to ensure consistency of biomarker monitoring timeframes and frequency, a properly powered human trial with adequate trial support and monitoring frequency and parameters (e.g. blood pressure, weight) may ensure that population scale datasets are fully populated. Importantly, safe fasting, fitness and dietary intervention is essential. Therefore, it should be noted that the aforementioned intermittent fasting, fitness, and dietary regimens should only be undertaken under the approval and guidance of a licensed medical profcssional / physician.

[0055] Disclosed herein is a device for optimizing a health plan to promote health and fitness and / or weight loss in a subject, wherein the device comprises one or more processors and a memory, the memory storing instructions that, when executed by the one or more processors, perform a method of: a) monitoring the level of glucose and the level of a ketone of the subject comprising receiving glucose and ketone measurements from one or more devices over a multi-day time period; b) assigning a metabolism classification to the subject based on i) the level of glucose and the level of the ketone at a pre-defined time point following recommended daily exercise in the health plan over the multi-day time period, wherein the level of glucose and the level of ketone provide an indication on whether metabolic switching and / or ketosis is achieved in each day of the multi-day period; and c) generating, and presenting to the subject, modifications to the health plan that are dependent on the assigned metabolism classification so as to optimize the health plan to promote health and fitness and / or weight loss in the subject.

[0056] Disclosed herein is a computer program product, embodied in one or more non-transitory computer-readable storage medium, comprising instructions executable by at least one processor to perform the method for optimizing a health plan as defined herein.

[0057] As used herein, “and / or” refers to and encompasses any and all possible combinations of one or more of the associated listed items, as well as the lack of combinations when interpreted in the alternative (or).

[0058] As used in this application, the singular form “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. For example, the term “an agent” includes a plurality of agents, including mixtures thereof.

[0059] Throughout this specification and the statements which follow, unless the context requires otherwise, the word “comprise”, and variations such as “comprises” and “comprising”, will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps.

[0060] Throughout this specification and the statements which follow, unless the context requires otherwise, the phrase "consisting essentially of", and variations such as "consists essentially of' will be understood to indicate that the recited element(s) is / are essential i.e. necessary elements of the invention. The phrase allows for the presence of other non-recited elements which do not materially affect the characteristics of the invention but excludes additional unspecified elements which would affect the basic and novel characteristics of the method defined.

[0061] The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an acknowledgment or admission or any form of suggestion that that prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates.

[0062] Those skilled in the art will appreciate that the invention described herein is susceptible to variations and modifications other than those specifically described. It is to be understood that the invention includes all such variations and modifications, which fall within the spirit and scope. The invention also includes all of the steps, features, compositions and compounds referred to or indicated in this specification, individually or collectively, and any and all combinations of any two or more of said steps or features.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0064] Certain embodiments of the invention will now be described with reference to the following examples which are intended for the purpose of illustration only and are not intended to limit the scope of the generality hereinbefore described.

[0065] EXAMPLES

[0066] Intermittent Fasting (IF)

[0067] Except where noted, intermittent fasting regimens consisted of a 20 / 4 schedule (20 hours fasted, 4 hour eating window). Every effort was made to maintain a consistent fasting window, and adherence was generally positive as reflected by the blood glucose and ketone readouts. Where necessary and possible due to international and / or regional travel, the feeding window was adjusted to lunchtime to account for time zone changes. When on international flights, both meals were served simultaneously to maintain fidelity to eating windows.

[0068] Dietary Regimen

[0069] As ketone measurement was a core output of this study, a clean ketogenic diet regimen was followed. Protein was obtained primarily from chicken. Fats were primarily derived from olive oil, avocados, pecans, chia seeds and pumpkin seeds. Fiber was obtained from leafy green vegetables (e.g. Primarily kale, spinach, and arugula). A multivitamin was taken daily (Centrum). During fasting periods, electrolytes, water, tea, and coffee were allowed. In a local context, the coffee and tea were only taken with no sugar and no milk (Kopi O Kosong and Teh O Kosong). The dietary regimen was consistently photographically recorded for documentation purposes. However, caloric intake was not recorded.

[0070] Fitness Regimen

[0071] For fitness regimens during continuous glucose monitoring (CGM, Abbott Freestyle Libre), the first week consisted of daily cardiovascular training (~ 1 hour) via the same limning route. The second week consisted of daily strength training (~90 minutes, each day focused on a specific muscle group). For regimens corresponding to finger stick measurements, weekly regimens were generally comprised of 5 days of strength training and 2 days of cardiovascular training. Every effort was made to commence fitness training at 7:00AM daily. For both cardiovascular and strength training regimens, in the event of time restrictions / scheduling considerations for N001, both regimens were shortened to a minimum of 30 minutes to ensure regimen alignment. Nonetheless, despite the running regimens being comparatively shorter in duration, they consistently reached Zone 4 for heart rate, and strength training regimens reached Zone 2.

[0072] Blood Glucose and Ketone Monitoring

[0073] Blood glucose monitoring was conducted using both continuous glucose monitoring (CGM, Freestyle Libre, Abbott Laboratories, Ltd.) and finger sticking (Optium Neo, Abbott Laboratories, Ltd.). Manufacturer specifications were followed for Libre and Optium Neo usage. Ketone measurements were also conducted using the Optium Neo. With regards to Optium Neo-based ketone and glucose monitoring, unless specified, readings were taken in the morning prior to fitness training, after fitness training, and immediately before starting the eating window. The glucose and ketone finger stick regimen were consistently photographically recorded for documentation purposes.

[0074] Blood Pressure Monitoring

[0075] Blood pressure was monitored at multiple time points, consisting of morning, mid-day, and evening. For each timepoint, 3 readings were taken. It should be noted that where stated, the monitoring device was changed to enable confirmation of the accuracy in arm position. Models used were the Microlife Gentle+ arm cuff monitor and the Omron 7 series wrist cuff Intclliscnsc monitor.

[0076] Weight Monitoring

[0077] Bodyweight measurements were taken daily during the initiation of a 72-hour fast, with follow-on measurements taken at subsequent timepoints (Omron KaradaScan). These readings served as readouts for regimen compliance.

[0078] Statistical Analysis

[0079] Selected biomarker and weight measurements were taken at least three times at a given time point, and the standard deviations (SD) were determined from the average of all three measurements either at a given time point or in a day. The distribution of measured glucose and ketone levels was tested using the Shapiro-Wilk normality test. The statistical significance of glucosc / kctoncs from different fitness regimens was determined using Wilcoxon rank-sum test.

[0080] Example 1

[0081] The present invention harnesses digital platforms to enhance long term adherence via dynamic energetics as a result of sustained regimens of intermittent fasting, fitness (strength and cardiovascular training), and high protein, low carbohydrate diet and parallel monitoring. These regimens were paired with serial blood ketone, blood glucose (wearable and finger stick) and blood pressure readings, as well as body weight measurements using a collection of devices. Collectively this suite of platforms and approaches were used to monitor metabolic switching from glucose to ketones as energy sources - a process associated with potential cardio- and neuroprotective functions. In addition to longitudinal biomarker dynamics, this invention discusses user perspectives on the potential role of harnessing digital devices to these dynamics as potential gamification factors, as well as considerations for the role of biomarker monitoring in health regimen development, user stratification, and potentially informing downstream population- scale studies to address metabolic disease, healthy aging and longevity, among other indications.

[0082] As the prevalence of super-aged societies continues to increase, addressing aging-related disorders such as metabolic disease and frailty will become increasingly important. This is evident given the rapid increase in national programs being initiated to prioritize preventive and population health. These include innovative platforms such as Healthier SG (Singapore), National Preventive Health Strategy (Australia), Better Health, Better Lives (Norway), and National Prevention Strategy (United States). Given the diversity of behavioral responses to the broad spectrum of interventions available, combinatorial approaches may be needed. Classes of potential interventions currently being evaluated include conventional pharmacologic therapies, dietary supplements, diverse diet strategics, fitness regimens, as well as intermittent fasting (IF), among others. Of note, IF has been increasingly explored as an intervention to address obesity, health and performance optimization, and healthy longevity due to its accessibility and simplicity relative to other approaches as well as its core capability in helping subjects achieve ketosis - the process whereby a subject’s primary energy source switches from glucose to fat breakdown. There is a vast range of biomarkers and endpoints that can be monitored against these interventions. They include standard endpoints such as blood pressure (BP), weight, and a multitude of scrum panels to assess heart, kidney and liver health for example. Other biomarkers that can be assessed include blood glucose (BG), which can be monitored using wearable devices (e.g. continuous glucose monitoring (CGM)) or handheld devices (e.g. blood / finger stick), and ketones, which can be measured using handheld devices (blood / finger stick), breath, or urine (Fig. 1). Importantly, the dynamics of these markers, as well as other indices such as the glucoseketone index can potentially also shed further insights on health profiles as well as user behavior and personality to drive gamification strategics for behavior change. To assess how these factors can impact biomarker behavior and potential surrogate indicators of health status from an individualized perspective, this work reports a N-of-1 study that harnesses a suite of digital platforms to monitor biomarker dynamics in response to a regimen combining a 20 / 4 intermittent fast (20 hours fasted, 4 hour eating window), morning fasted fitness regimen daily (strength and cardiovascular), and consistent high protein (>100g / day) / low carbohydrate (<30g / day) diet. Biomarkers consisting of glucose and ketone were recorded prior to the fitness regimen, after the fitness regimen, and prior to opening the feeding window (Fig. 2). In addition to the longitudinal monitoring of biomarker dynamics, this work also considered the unforeseen emergence of data-enabled gamification to drive user adherence to sustain biomarker profiles. User engagement insights pertaining to device usage, biomarker monitoring, and regimen sustainability are also provided. In sum, based on the suite of available biomarkcr monitoring devices, capacity for longitudinal monitoring of biomarker dynamics, and resulting adherence gamification, metabolic switching served as a study endpoint. Metabolic switch refers to the process of the body’s shift from glucose to ketones as a core energy source. Metabolic switching is also being explored as a method for physiological and performance optimization, addressing obesity and other risk factors for chronic illness, as well as cardio- and neuroprotective outcomes.

[0083] In addition to understanding N-of-1 dynamics during the metabolic switching process, preliminary findings from this work can potentially be expanded towards the development of large-scale, prospective, and interventional trials to determine if user personality can be harnessed based on individual data and biomarker profiles to drive behavioral change, with subsequent assessment of behavioral and healthcare economics outcomes at a population health level. These findings may also be applicable towards broader indications including healthy aging and longevity, sports science, and enhanced data collection protocols for Al and data science that arc applicable to a wide range of prevention and treatment needs.

[0084] Example 2

[0085] RESULTS

[0086] Exploring Metabolic Switching with IF and Fitness Interventions Initially, a suite of devices was harnessed to explore the dynamics of biomarkers with the interventions of fitness regimen and IF. A CGM device was employed to measure subject NOOl’s blood glucose levels. In parallel, the blood ketone levels were measured pre- and post-workout as well as in the aftemoon / evening immediately prior to closing the daily IF window. The fitness regimen varied daily with either cardiovascular or strength training. In Fig. 3a, the glucose and ketone trajectories were plotted against a 96-hour window. Notably, the ketone trajectories demonstrated high-low-high profiles, specifically low ketone levels post-workout. However, in the afternoon or evening, blood ketone levels rebounded back to higher levels. Further examining the profiles, when ketone reached lower levels, transiently elevated blood glucose levels were observed during workouts (Fig. 3a). This may be the result of glycogen depletion during workouts.

[0087] To assess the interventions of the fitness regimen, the measured blood glucose and ketone levels were comprehensively compared following both cardiovascular (e.g. running) and strength (e.g. weights) training (Fig. 3b). The average continuously measured blood glucose levels for cardiovascular training interventions (running) was 4.75+0.44 mmol / L (N = 600), while strength training resulted in blood glucose levels of 5.05±0.70 mmol / L (N = 564). Wilcoxon rank-sum test determined that there is statistically significant difference between the glucose levels resulting from both fitness interventions (P<0.001), while no statistically significant difference was detected for blood ketone levels as a result of fitness regimens (Running: N = 16; Weights: N = 18). Heavy weightlifting workouts during the second week of CGM usage may have stimulated the release of stress hormones, such as adrenaline, which may lead to increased release of glucose from the liver. Furthermore, fitness interventions accompanied with a 22+ h fast were also explored. In Fig. 3c and 3d, trajectories of either running with a 42-hour fast or weights with a 22-hour fast both similarly illustrated the high- low-high ketone trajectories and notably, pointed to elevated ketone levels towards the end of fasting. The elevated ketone levels may be attributed to metabolic switching from glucose depletion during 42- and 22-hour fasts. These preliminary findings further confirmed metabolic switching in subject N001 . The estimated A1C during the noted CGM timeframe was 4.8% (29 mmol / mol), indicating normal average blood glucose levels.

[0088] Longitudinal Monitoring of Biomarkers and Fitness Interventions Subsequently, the monitoring of biomarkers was further expanded to long-term observation. Instead of CGM, NOOl’s blood glucose levels were measured using a finger sticking device pre- and post-workout as well as in the aftemoon / evening immediately prior to closing the daily IF window. The blood ketone levels, which were measured using the same finger sticking device, followed the same schedule. Normalizing the blood glucose levels to the blood ketone levels gives rise to the glucose ketone index (GKI), which can reveal the state of ketosis and overall metabolic health of an individual. In Fig. 4, the GKI and ketone trajectories were plotted against time for 33 consecutive days (SEP 6 to OCT 8). Subject N001 was in a pre-ketosis state on SEP 6 and subsequently achieved ketosis for 31 days. On OCT 6, the subject reached the end of ketosis state. Similar to previous observations, the ketone profiles demonstrated high-low-high trajectories (Fig. 4). In contrast, the GKI profiles displayed low-high-low trajectories, where a GKI of 3 or less indicates high level of ketosis.

[0089] No Negative Effects Observed in 72 h IF

[0090] To monitor GKI and ketone dynamics during a long fast, subject N001 underwent a 72-hour fast. Blood glucose and ketone levels and blood pressures were consistently measured according to the schedule in Fig. 2. Over the four-day period, NOOl’s GKI gradually decreased during the 72-hour fast. In the first two days, the GKI readings were >10, indicating that N001 was not in ketosis (Fig. 5a). However, on day 3, the subject’s ketone levels substantially increased and the GKI level was observed to be below 3, suggesting a state of high ketosis. Aside from biomarker trajectories, the subject’s blood pressures were closely monitored thrice daily. Subject NOOl’s blood pressures were consistent throughout the 72-hour fast. These subject-specific data suggested that a 72-hour fast had no apparent adverse or negative effects on subject N001.

[0091] Blood Pressure and Body Weight Monitoring

[0092] Aside from biomarker monitoring, subject NOOl’s blood pressure trajectory over time was also closely monitored and recorded at least three times daily, with pre- and post-workout as well as evening measurements. (Fig. 6). Overall, the subject’s blood pressures were mostly within the two selected benchmarks which both serve as upper limits that define elevated blood pressure by multiple health authorities. These included systolic / diastolic limits of 135 / 85 and 120 / 80. Importantly, they were consistent in the duration of a month and a half. In addition, the differences between NOOl’s blood pressure measurements and the two respective benchmarks were determined (AmmHg = benchmark - measured blood pressures) to analyze potential improvement in cardiovascular health. The general trend demonstrated that both systolic and diastolic measurements lowered (increased AmmHg) towards the end of IF along with fitness and dietary regimens. With the stricter 120 / 80 benchmark, NOOl’s systolic measurements were well within range in the second half of IF while measurements in the first half were mostly out of 120 mmHg. It is important to note that there is an overall improved trend; however, this observation may be attributed by other factors including IF, fitness plans, and / or dietary regimens.

[0093] In the beginning of September, subject N001 underwent a 72-hour fast from SEP 5-8, 2023. During this time and for the rest of the month, the subject’s weights were recorded intermittently (Fig. 7). Fitness and dietary regimens were also regularly scheduled. As shown in Fig. 7, a 72-hour fast resulted in a ~4 kg decrease in weight. Even after the 72-hour fast, the subject consistently continued with the IF, fitness, and dietary regimens, in which the subject’s weight continued to decrease over time. In the beginning of SEP, the subject’s weight was slightly above 82 kg and towards the end of SEP, the subject’s weight was approximately 75 kg. Within one month, IF and the aforementioned interventions resulted in a ~7 kg decrease in weight for subject N001. At the time of reporting, due to overall regimen adherence, the subject’s weight has stabilized at 75kg.

[0094] DISCUSSION

[0095] Initial Biomarker Monitoring

[0096] This N-of-1 study made use of a collection of publicly accessible devices to monitor biomarker dynamics as a function of a consistent regimen of intermittent fasting, fitness, and high protein / low carbohydrate nutrition. The maintenance of these dynamics, particularly with regards to ketone trajectories, unexpectedly served as a data-driven incentive, resulting in the gamification of regimen adherence. Initial CGM and correlated ketone measurements were taken to understand the dynamics associated with pre- and post-workout glucose and ketone levels alongside the ketone measurements prior to feeding. These initial readings confirmed metabolic switching and illuminated the early potential of harnessing data and ketone dynamics towards adherence gamification (Fig. 3, 4, and 5). They also provided userspecific comparisons of the effect of cardiovascular training versus strength training on glycogen depletion towards the achievement of metabolic switching (Fig. 3). Of note, these biomarker profiles served as clear indicators that the human response to the various interventions is highly dynamic.

[0097] These observations emphasize the importance of serial health monitoring in order to sufficiently characterize a subject’s health status prior to the recommendation of interventions. These biomarker dynamics also indicate the importance of understanding how an individual’s biomarker levels change in response to specific interventions (e.g. fitness regimens, fasting, etc.) at fixed as well as modulated doses and intensity. These datasets would provide important information pertaining to inter- and intra-individual variation at the interface of health interventions and treatment response. In turn, this information may be helpful towards harnessing digital platforms for hyper-personalized gamification and health optimization.

[0098] Blood Pressure and Weight Monitoring

[0099] An assessment of blood pressure trajectory over time revealed a modest decrease in systolic and diastolic readings. While it cannot be determined if this specific outcome is attributable to the IF, fitness, and / or dietary components of the regimen, it is evident that this regimen did not have a negative effect on the blood pressure trajectory. Previous studies have shown that IF can potentially drive beneficial blood pressure outcomes. Furthermore, other preclinical and clinical studies have shown that adherence to set fitness and dietary regimens can have beneficial influences on both blood pressure and weight. Thus, the improvements observed in this N-of-1 study arc likely a result of composite influences within the adhered regimen. An assessment of the blood pressure during a 72-hour fast (SEP 5-8, 2023) also does not reveal a negative effect on blood pressure trajectory, aligning with prior studies correlating acute fasts with blood pressure outcomes. With regards to weight monitoring, a 72-hour fast revealed a weight decrease of ~4kg, aligning with previously reported values (Fig. 7). The weight loss trajectory tapered off over time as a steady IF regimen was implemented. It is unclear if reduced weight loss trajectory is a result of metabolic adaptation to IF regimen or reaching the lower ranges of the individual’s healthy weight. Additional longitudinal studies comparing altered IF, diet and fitness regimens to further promote metabolic switching as well as comparing this N-of-1 study to other individuals with different baseline blood pressure and weight measurements would help address this study limitation.

[0100] Biomarker Profiles and Gamification

[0101] A notable outcome of this study pertained to the role of biomarker dynamics, specifically ketone trajectories, towards driving adherence to the IF, fitness, and dietary interventions. Specifically, during the initial monitoring period using the CGM and Optium Neo, ketone levels were observed to be elevated in the morning prior to fitness training, lower following fitness training, and elevated prior to feeding (high-low-high) (Fig. 4). This corresponded with observed spikes in glucose levels and depletion in glycogen stores as a result of fitness training. Mechanisms of ketone level reduction as a result of fitness training with corresponding glycogen usage have also been noted. A further assessment of the glucoseketone index (GKI) dynamics in this study also revealed a low-high-low trajectory. Of note, these findings with subject N001 reflect the potential of gamifying adherence to a fasting, fitness, and food regimen to maintain biomarker profiles that are indicative of metabolic switching. A number of important studies have explored the role of gamification and nudge theory to drive increased physical activity and improved outcomes for indications such as cardiovascular health and type 2 diabetes, among others. These studies have shown that user behaviour insights and data from wearables can be harnessed to develop support / collaboration- or competition-based approaches to encourage sustained user engagement and adherence.

[0102] More specifically, for N001, the maintenance of a dynamic biomarker trajectory unexpectedly served as a promoter of adherence to the fasting, fitness, and dietary regimen. Specifically, maintaining the high-low-high ketone profile and low-high-low GKI profile was driven by the avoidance of a high-low-low ketone profile or low-high-high GKI profile (Fig. 4). An example of an action that would result in the latter profiles include breaking the fasting with food items that are non-compliant with the clean ketogenic regimen that was undertaken (e.g. consumption of carbohydrates that exceed ketogenic diet limits). Implementation of this data- and biomarker-driven adherence approach was specifically supported by the continuous visualization and user awareness of the daily profile. These observations may support the potential exploration of achieving biomarker trajectories that reflect sustained metabolic switching as a nudging strategy. In parallel, recent studies are exploring the role of multiple classes of incentives to drive user adherence to a broad range of interventions that span medication through fitness. These incentives include financial through digital means. In the case of this reported study, both the data (which can itself serve as a potential incentive), and the trajectory of data points (which can serve as a potential nudge) can collectively provide as a potential path forward for driving user adherence. Taken together, data-driven nudging paired with sustainable incentives may also impact health optimization at-scale. A properly-powered trial that properly stratifies data-responsive users may be effective in evaluating this approach towards community and population health.

[0103] Potential Health Benefits

[0104] Gamification of adherence to the aforementioned fasting, fitness, and food regimen using a user-specific biomarker profile and resulting metabolic switching can potentially link a broad spectrum of factors to achieve desired health outcomes. These include but are not limited to user data, user personality, nudging, incentives, behavioural economics, and other factors towards achieving sustained behaviour change. As the process of metabolic switching may have additional impact on healthy aging and physiological fitness parameters, harnessing scalable and sustainable data-guidcd strategics to achieve metabolic switch as part of a healthy aging regimen may warrant further study.

[0105] Following feeding, glucose serves as a core energy source, and fats are subsequently deposited in the form of triglycerides. Fasting can break down triglycerides into fatty acids that are subsequently metabolized to ketone bodies in the liver via ketogenesis. Metabolic switching refers to the process of converting from glycogen depletion and fat storage to fat mobilization and liver-based conversion of fatty acids into kctonc-drivcn energy sources. Preserving muscle mass is one of the potential outcomes of metabolic switching. In addition, studies have sought to examine whether interventions such as IF drive health benefits by addressing obesity, or due to metabolic switching. Findings have suggested that harnessing ketones as an energy source may have protective functions against oxidative stress and can potentially be cardioprotective. Additional studies have also explored the neuroprotective function of ketones as well. Importantly, this study showed that due to dynamic changes in glucose and ketone levels at different time points, assessing the respective biomarker levels or GKI at single time points may not provide a comprehensive view of a person’s health status.

[0106] As continued studies illuminate the potential benefits of metabolic switching, developing strategies that help individuals achieve the metabolic switch may be instrumental in advancing preventive healthcare, addressing age-related diseases and improving general health outcomes.

Claims

CLAIMS1. A computer-implemented method for optimizing a health plan to promote health and fitness and / or weight loss in a subject, the method comprising: a) monitoring the level of glucose and the level of a ketone of the subject comprising receiving glucose and ketone measurements from one or more devices over a multi-day time period; b) assigning a metabolism classification to the subject based on i) the level of glucose and the level of the ketone at a pre-defined time point following recommended daily exercise by the health plan over the multi-day time period, wherein the level of glucose and the level of ketone provide an indication on whether metabolic switching and / or ketosis is achieved in each day of the multi-day period; and c) generating and presenting to the subject recommendations that are dependent on the assigned metabolism classification so as to optimize the health plan to promote health and fitness and / or weight loss in the subject.

2. The method of claim 1 , wherein step c) generates and presents to the subject recommendations that are dependent on the assigned metabolism classification to promote metabolic switching and / or ketosis following recommended daily exercise.

3. The method of claim 1 or claim 2, wherein assigning the metabolism classification in step b) is further based on ii) the number of days in which metabolic switching and / or ketosis is achieved over the multi-day period.

4. The method of any one of claims 1 to 3, wherein the health plan comprises recommended daily exercise, personalized diet and intermittent fasting.

5. The method of any one of claims 1 to 4, wherein the pre-defined time point is at least 6 hours (preferably 7 to 10 hours) following recommended daily exercise.

6. The method of any one of claims 1 to 5, wherein recommendations in step c) include nudging and / or incentives to the subject.

7. The method of any one of claims 1 to 5, wherein recommendations in step c) include a modified health plan comprising modifications to the recommended daily exercise, personalized diet and intermittent fasting.

8. The method of any one of claims 1 to 7, wherein the assignment of the metabolism classification is based on a glucose ketone index (GKI) derived from the glucose and ketone measurements at the pre-defined time point, wherein a decrease in GKI as compared to a pre-defined threshold predicts or indicates that the subject is undergoing ketosis and is adherent to the health plan.

9. The method of any one of claims 1 to 7, wherein the assignment of the metabolism classification is based on a glucose ketone index (GKI) derived from the glucose and ketone measurements at the pre-defined time point, wherein an increase in GKI as compared to a pre-defined threshold predicts or indicates that the subject is not undergoing ketosis and is non-adherent to the health plan.

10. The method of claim 8 or 9, wherein the assignment of the metabolism classification is based on comparing the GKI at the pre-defined time point to GKIs from one or more reference time points.

11. The method of claim 10, wherein the one or more reference time points is a time point selected from a) a time point prior to the recommended daily exercise and b) a time point during the recommended daily exercise.

12. The method of any one of claims 1 to 7, wherein the assignment of the metabolism classification is based on the ketone level, wherein an increase in ketone level as compared to a pre-defined threshold predicts or indicates that the subject is undergoing ketosis and is adherent to the health-plan.

13. The method of any one of claims 1 to 7, wherein the assignment of the metabolism classification is based on the ketone level at the pre-defined time point, wherein a decrease in ketone level as compared to a pre-defined threshold predicts or indicates that the subject is not undergoing ketosis and is non-adherent to the health-plan.

14. The method of claim 12 or 13, wherein the assignment of the metabolism classification is based on comparing the ketone level at the pre-defined time point to the ketone level at one or more reference time points.

15. The method of claim 14, wherein the one or more reference time points is a time point selected from a) a time point prior to the recommended daily exercise and b) a time point during the recommended daily exercise.

16. The method of any one of claims 1 to 7, wherein the assignment of the metabolism classification is based on the GKT or ketone level at a) the pre-defined time point, b) a time point prior to the recommended daily exercise and c) a time point during the recommended daily exercise.

17. The method of any one of claims 1 to 16, wherein the health plan recommendations arc based additionally on health plan non-compliance events logged by the subject.

18. A computer-implemented method for optimizing a health plan to promote health and fitness and / or weight loss in a subject, the method comprising: a) monitoring the level of glucose and the level of a ketone of the subject comprising receiving glucose and ketone measurements from one or more devices over a multiday time period; b) assigning a metabolism classification to the subject based on i) the level of glucose and the level of the ketone at a pre-defined time point following recommended daily exercise by the health plan over the multi-day period, wherein the level of glucose and the level of ketone at the pre-defined time point provide an indication on whether metabolic switching and / or ketosis is achieved in each day of the multi-day period, and ii) the number of days in which metabolic switching and / or ketosis is achieved over the multi-day period; and c) generating and presenting to the subject recommendations that are dependent on the assigned metabolism classification so as to optimize the health plan to promote health and fitness and / or weight loss in the subject.

19. A device for optimizing a health plan to promote health and fitness and / or weight loss in a subject, wherein the device comprises one or more processors and amemory, the memory storing instructions that, when executed by the one or more processors, perform a method of: a) monitoring the level of glucose and the level of a ketone of the subject comprising receiving glucose and ketone measurements from one or more devices over a multi-day time period; b) assigning a metabolism classification to the subject based on i) the level of glucose and the level of the ketone at a pre-defined time point following recommended daily exercise in the health plan over the multi-day time period, wherein the level of glucose and the level of ketone provide an indication on whether metabolic switching and / or ketosis is achieved in each day of the multi-day period; and c) generating, and presenting to the subject, modifications to the health plan that are dependent on the assigned metabolism classification so as to optimize the health plan to promote health and fitness and / or weight loss in the subject.

20. The device of claim 19, wherein the device is a wearable device.

21. The device of claim 20 or 21, w herein the method of step c) generates and presents to the subject recommendations that are dependent on the assigned metabolism classification to promote ketosis and / or metabolic switching following recommended daily exercise.

22. A computer program product, embodied in one or more non-transitory computer- readable storage medium, comprising instructions executable by at least one processor to perform the method for optimizing a health plan according to any one of claims 1 to 18.

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