Patent Application
20140172314
The present invention relates to systems and methods for determining an individual's caloric intake using a personal correlation factor.
An increased public interest in healthy living has resulted in a growing market for personal fitness aids. For example, so-called pocket pedometers are increasingly popular among fitness enthusiasts, and approximate calories and fat grams burned over the course of a workout. Pedometers are also available as a download to a smart phone or tablet computer. These fitness aids can additionally provide workout logs and can suggest aerobic exercises and varied workout routines.
Current fitness aids also provide an approximation of an individual's caloric intake based on a manual food log. However, non-entries or incorrect entries to the food log can often lead to an under-approximated caloric intake. As a result, an individual can experience a weight gain when a weight loss is expected, despite logging a caloric intake less than the total amount of calories burned.
Accordingly, there remains a continued need for an improved determination of an individual's caloric intake. In particular, there remains a continued need for an improved determination of an individual's caloric intake that can be used in conjunction with a measured energy expenditure for weight loss programs, weight management programs, and general health and fitness programs.
Systems and methods for determining caloric intake are provided. The systems and methods include determining an individual's personal correlation factor and, using the personal correlation factor, determining the individual's caloric intake. The caloric intake can be used in conjunction with a weight loss or weight management program and for other purposes.
In one embodiment, a method for determining a personal correlation factor for an individual is provided. The method includes determining a body composition change over a calibration period, converting the body composition change to an equivalent energy value, and dividing the equivalent energy value by a net caloric value for the same calibration period, wherein the net caloric value includes the individual's caloric expenditure less the individual's caloric intake.
In another embodiment, the body composition change is determined using a bio-impedance sensor, and the caloric expenditure is determined using a pedometer. The caloric intake can be measured based on a food log for only the calibration period. Thereafter, the personal correlation factor can be used to indirectly measure caloric intake, without requiring use of the food log.
In still another embodiment, a wearable device is provided. The wearable device uses the individual's personal correlation factor to determine caloric intake, and to suggest an activity adjustment and/or a dietary adjustment. The wearable device includes a first sensor configured to measure the wearer's caloric expenditure, a second sensor configured to measure the wearer's body composition, a memory adapted to store the wearer's personal correlation factor, and a processor electrically coupled to the first and second sensors and adapted to perform a computer operation to determine the individual's caloric intake. The first sensor includes a pedometer or an accelerometer, and the second sensor includes a bio-impedance sensor. The wearable device is self-contained within a housing and worn on the wearer's wrist, ankles, or hips, for example.
In even another embodiment, a method for determining an individual's caloric intake using that individual's personal correlation factor is provided. The method includes converting a body composition change to an equivalent energy value, dividing the equivalent energy value by the personal correlation value, and adding to this quotient the individual's caloric expenditure, wherein each step is performed using a processor. The method can additionally include reporting the caloric intake, optionally with reference to a target value. Still further optionally, the method can include recommending a dietary modification and/or recommending an exercise regimen in response to the determined caloric intake.
These and other features and advantages of the present invention will become apparent from the following description of the invention in accordance with the accompanying drawings and appended claims.
The invention as contemplated and disclosed herein includes systems and methods for determining an individual's personal correlation factor and, using the personal correlation factor, determining the individual's caloric intake. Part I includes an overview of the relationship between caloric intake, caloric expenditure, stored body mass, and a personal correlation factor. Part II includes systems and methods for determining an individual's personal correlation factor. Part III includes systems and methods for determining the individual's caloric intake using the personal correlation factor to assist the individual in meeting his or her weight management goals.
The management of energy in the human body can be modeled by equation (1) below, where I(t) is the total caloric intake, E(t) is the total caloric expenditure, and U(t) is the stored caloric value:
I(t)−E(t)=U(t) (1)
According to the above equation, the caloric intake less the caloric expenditure is equal to the stored caloric value. Where the caloric intake is greater than the caloric expenditure, the stored caloric value is positive. Where the caloric intake is less than the caloric expenditure, the stored caloric value is negative.
The caloric expenditure E(t) in equation (1) can be further defined by equation (2) below, where BMR is the basal metabolic rate, AIE is the activity induced expenditure, TEF is the thermal effect of food, and NEAT is the non-exercise activity thermogenesis:
E(t)=BMR+AIE+TEF+NEAT (2)
BMR is a clinical measurement that can be measured while the individual is completely stationary, and is typically performed in a clinical setting. The individual's resting metabolic rate RMR is an approximation for BMR, and gives more leeway to small movements while measuring. Equations (3) and (4) below provide a predictive value for an individual's RMR, which again is used in place of BMR in equation (2) above:
Men: RMR=9.99·weight+6.25·height·4.92−age+5 (3)
Women: RMR=9.99·weight+6.25·height·4.92·age−161 (4)
Referring again to equation (2) for the energy expenditure E(t), the individual's activity induced expenditure AIE is determined based on certain physical characteristics and data collected by a 3-axis accelerometer. For example, speed may be calculated using equation (5):
Speed(m/min)=26.82×(SMA−1.34)×(249−7.86×H+0.0614×(H)2+3.05×H×A−0.00907×W×NC−0.0671×A×NC)+2.81 (5)
From equation (5) above, H is height, NC is the number of times the individual performs cardio, A is age, and W is the individual's weight in pounds.
Another component of AIE is VO2, which is a measure of the rate at which a person's body uses or transports oxygen. Equation (6) below from the American College of Sports Medicine (ACSM) can be used to estimate VO2:
VO
2=α1·S+β1·S·G
VO2 can be expressed in liters per minute, or as a rate per unit mass of the person such as milliliters per kilogram per minute. In equation (6) there are three parts, horizontal, vertical, and resting. Resting is left out because it is addressed above. The horizontal portion is the first part of equation (6). The α1 term is constant, and S is the speed the person is moving in meters per minute from equation (5) above. The second portion is the vertical piece where β1 is a constant S is speed, and G is the gradient of the hill.
Another way to estimate VO2 is identified below in Equation (7) below:
VO
2=αnS+βn·S·G+F(GP,A,S) (7)
The first part of equation (7) is similar to equation (6), however, the coefficients change depending on what segment of speed the individual is moving at. If the individual is walking, these coefficients are different from when the individual is running. These coefficients can be expressed as a function of speed as shown in equation (8) and (9),
αn=a·S+b, (8)
βn=c·S+d, (9)
where a, b, c, and d are constants. Substituting these equations into the first portion of equation (7) results in a multivariable polynomial equation (10):
VO
2
=a·S
2
+b·S+c·S
2
·G+d·S·G+F(GP,A,S)+ε (10)
From equation (10) above, ε is an error term, and F(GP, A,S) is a function of genetic profile, age, and sex. This function can make the calculations specific to the individual. Each individual takes in a different amount of oxygen when working out, and according to the ACSM equation's two people weighing the same will have the same VO2 levels. However, this is typically not the case. For example, an out of shape 130 lb. male child will burn energy at a different rate than a 130 lb. female marathon runner.
Equation (10) uses the following conversion equation (11) to calculate AIE. It is based on the premise that the average person burns 5 kcal per liter of O2.
The thermic effect of food (TEF) portion of equation (2) for calculating E(t) is based on the number of calories consumed in a day. An accepted approximation for TEF is given below in equation (12):
TEF=0.075·I(t) (12)
Referring again to equation (2) for energy expenditure, NEAT is a fixed value based on a person's lifestyle. Whatever is not quantified from the AIE in equation (11) can be rolled into NEAT using activity codes and Metabolic Equivalent Task (MET) intensities. If I(t) is unknown, NEAT may be ignored from equation (2).
Referring again to equation (1), U(t) is the change in energy stored (positive) or used (negative) by the body. This energy is stored either as fat mass or fat free mass. One method for determining the individual's body composition (i.e., the component fat mass and the component fat free mass) includes underwater weighing and water displacement tests. This measurement technique requires the individual to be fully submersed in a tank of water and measuring both the underwater weight and the change in water volume change upon submersion. These two measurements are then used to calculate body fat percentage. This method requires trained personnel and is not easily performed, however.
Other methods for determining body fat percentage include bio-impedance analysis (BIA) and bio-impedance spectroscopy (BIS). Bio-impedance analysis is performed by applying a low alternating current (˜800 μA) across two points on the body and measuring the complex impedance to the flow of current. Complex impedance is composed of a resistance, R (Ohms) and a reactance, Xc (Ohms). This type of analysis can be performed at single or multiple frequencies. Single frequency BIA is performed at 50 kHz and multi-frequency BIA is typically performed at seven discrete frequencies between a 0 kHz and 500 kHz (up to 1000 kHz).
BIS is similar to multi-frequency BIA, except BIS measures up to 256 discrete frequencies between 0 kHz and 1000 kHz. For example,
ECW=kecf(Wt1/2Ht2/Recf)2/3 (13)
The method for determining ICW utilizes equation (14) and equation (15). In these empirical equations, ECW is determined by equation (13) and rIE is determined using equation (15). In equation (15), rLH is the ration of Recf to Ricf, which are the estimated resistances of ECW and ICW respectively. The resistance of ECW, Recf, is described above and the resistance of the intracellular fluid is assumed to be the linear combination of R0 and R∞ and is defined as Ricf. The constant, kp is empirically determined.
ICW=rIEECW (14)
(1+rIE)5/2=rLH[1+(rIEkp)] (15)
Combined, ECW and ICW are an individual's TBW. TBW is converted to FFM using the empirical determined conversion of FFM=TBW/0.73. Fat mass is determined by subtracting FFM from total body mass.
Equation (1) is modified below to include a summation of I(t)-E(t) and U(t) over a statistically significant time period tsc:
The time component tsc can be described as follows: 1) the time an individual starts monitoring caloric intake I(t), caloric expenditure E(t) and stored caloric value U(t) is described by t0, and 2) the time required to observe a statistical change in body composition during a monitoring or calibration cycle is tsc. This timescale is typically on the order of several days, but can be within a period of hours or weeks, if desired. For example, t0=1 day on the first day that an individual begins monitoring changes in body composition. If 5 days are required to observe a statistical change in body composition, then tsc=5 days. When changes in body composition are monitored beyond this 5 day period a new t0 will be defined. In this example, the new t0=6 days and the new tsc=10 days (assuming the same time required to determine a statistical change in body composition).
According to equation (16), the difference between caloric intake I(t) and caloric expenditure E(t) over a statistically significant period is equal to a change in the stored caloric value U(t) for that period. The left side of equation (16) is termed “net caloric value” herein, and its component variables are discussed in Part I above. The right side of equation (16) relates to a body composition change. Where the stored caloric value U(t) is positive, an increase in body composition is expected. Where the stored caloric value U(t) is negative, a decrease in body composition is expected.
As discussed in Part I above, body composition includes both fat mass FM and fat free mass FFM. The relationship between the caloric value U(t) and fat mass FM and fat free mass FFM is set forth in equation (17) below:
From equation (17) above, the change in fat mass FM and fat free mass FFM is related to the stored caloric value U(t) modified by a personal correlation factor α. That is, not all of the stored caloric value U(t) will be converted to a change in body composition. Instead, a percentage of the stored caloric value U(t) is converted to a change in body composition, with that percentage being represented by the personal correlation factor α. absorbed by the body, converted to glucose and other energy sources, and eventually stored to, or drawn from, fat mass FM and fat free mass FFM.
Referring again to equation (17), the change in body composition is converted to an equivalent energy value by multiplying fat mass FM and fat free mass FFM by the respective energy densities p (kcal/g). On the right side of equation (17), the personal correlation factor α is a dimensionless coefficient that is personal to the individual, and is itself a function of a number of variables, represented by x1, x2, . . . xn, and tsc. Examples of the independent variables for a personal correlation factor α include any of the following: i) age, ii) gender, iii) genetics, iv) insulin sensitivity, v) weight and vi) activity level.
To determine an individual's specific personal correlation factor α, the independent variables, x can be fixed at scalar values for a specific period of time and tsc, is set to the time required to observe a change in FM and FFM. Some of the independent variables, x1, x2, . . . , xn, may be reset when a dramatic change occurs individual's life. Other independent variables may fixed indefinitely. For example, the scalar value associated with activity level can be reset if a person started to exercise more during the monitoring cycle, whereas the scalar values associated with genetics, age, and race can be fixed indefinitely. Taking these factors into consideration, a specific personal correlation factor α(tsc) is found by rearranging equation (17), resulting in equation (18) below:
Each of equations (16), (17) and (18) are additionally depicted in
Referring now to the flow chart of
As the term is used herein, measuring can include any direct or indirect determination or observation of a value, whether the value is estimated, approximated or actual. For example, measuring a caloric intake can include manually tracking a caloric intake over a predefined period of time, and subsequently summing the caloric intake. Also by example, measuring a caloric intake can include providing a meal plan having a plurality of pre-planned meals defining a known number of calories, and quantifying the caloric intake based on the number of meals consumed. More specifically, measuring a caloric intake I(t) at step 10 can be performed in a number of ways. Examples include i) having the individual enter meals into a computer or a device, ii) taking photos of the individual's meals and having a software engine determine or approximate caloric content, iii) scanning a barcode or NFC tag associated with a meal, iv) providing the individual with a pre-package meal plan having a known caloric content and v) combinations of the above. Still other ways for measuring caloric intake I(t) may be used as desired.
Again as noted above, the step of measuring caloric expenditure and body composition can include any direct or indirect determination or observation of an estimated, approximated or actual value. For example, measuring a caloric expenditure E(t) at step 12 can be performed in a number of ways. Examples include i) wearing a device including a three-axis accelerometer to track NEAT AIE, ii) wearing a temperature sensor to track TEF, and iii) taking periodic VO2/CO2 measurements to measure BMR. More invasive methods for determining caloric expenditure E(t) include nitrogen balance methods and heavy water techniques. Still other ways for measuring caloric expenditure E(t) may be used as desired. Measuring a change in body composition at step 14 can also be performed in a number of ways. Examples include i) bio-impedance spectroscopy, ii) a mobile scale that can provide weight information and/or bio-impedance measurements and iii) underwater weighing and water displacement tests. Still other ways for measuring a change in body composition may be used as desired.
Once the caloric intake I(t), caloric expenditure E(t) and change in body composition are measured in steps 10 through 14, the individual's actual or approximated personal correlation factor α(tsc) can be determined by computer operation using equation (18) above. In particular, the computer operation can include converting the body composition change into an equivalent energy value at step 16, and dividing this value by the caloric intake I(t) less the caloric expenditure E(t) at step 18. The resulting quotient provides the individual's actual or approximated personal correlation factor α(tsc), which can be used for a number of purposes as set forth more fully in Part III below, including to determine the individual's caloric intake.
Referring now to
Another method for determining a personal correlation factor α(tsc) includes the collection of clinical data relating to the effects of the independent variables factor α(tsc) for an individual or group of individuals sharing the same physiological or behavioral patterns or characteristics. According to this method for determining a personal correlation factor α(tsc), an individual can input characteristics into a processing engine, including for example a smartphone, a tablet computer, a laptop computer, or other computing device. The processing engine can then determine an actual or approximated personal correlation factor α(tsc) using a lookup table stored to computer readable memory. For example,
To reiterate, the present invention provides systems and methods for determining an actual or approximated personal correlation factor α(tsc). One such method includes determining a body composition change over a calibration period, converting the body composition change to an equivalent energy value, and dividing the equivalent energy value by a net caloric value for the same calibration period, wherein the net caloric value includes the individual's caloric expenditure less the individual's caloric intake. Another such method includes aggregating physiological data pertaining to the individual, and determining an actual or approximated personal factor with reference to a lookup table and/or a numerical computer operation.
Because the personal correlation factor α(tsc) can be a function of a number of independent variables, the personal correlation factor α(tsc) can periodically be ‘recalibrated,’ for example as the individual experiences significant changes in health, weight, age, stress level, diet, sleep patterns and other conditions. Further by example, the personal correlation factor α(tsc) can be recalibrated at regular intervals in a weight loss or weight management program, or upon reaching certain weight loss milestones. Further by example, the personal correlation factor α(tsc) can be recalibrated on a monthly basis, a semi-annual basis, or an annual basis as part of regular progress checks in a weight loss or weight management program. Still other recalibration intervals can be used as desired.
Once the individual's actual or approximated personal correlation factor α(tsc) is determined, the personal correlation factor α(tsc) can be used to indirectly measure an individual's actual or approximated caloric intake I(t). Referring now to the flow chart of
More specifically, measuring a caloric expenditure E(t) at step 20 and measuring a body composition change at step 22 can be performed using a portable device. Referring now to
In the present embodiment, the first sensor 38 includes a three-axis accelerometer adapted to detect an input relating to the three-dimensional motion of the host individual. In other embodiments, the first sensor 38 can alternatively include other motion or orientation sensors to determine an actual or approximated energy expenditure E(t). The second sensor 40 includes bio-impedance circuitry adapted to detect an input relating to the fat mass FM and fat free mass FFM of the host individual. The bio-impedance circuitry can include an interior sensor configured to engage the user's skin beneath the device and an exposed sensor that can be placed in contact with the user's skin at a location remote from the interior sensor. For example, if the personal device is a wristband, one sensor may be located on the inside of the wristband to engage the user's wrist on one arm and the other sensor may be exposed on the outside of the wristband so that it can be placed in contact with the skin on the user's other wrist to provide an arm-to-arm bio-impedance measurement. Other body composition measurement sensors can be used in other embodiments as desired. Additional sensors can also be utilized, including for example a temperature sensor or a moisture sensor.
As noted above, the first and second sensors 38, 40 are electrically coupled to the processor 42. The processor can be any processor adapted to perform a program set, including an integrated circuit, a microcontroller, or a field-programmable gate array. For example, the processor 42 can be configured to determine a prior caloric intake based on at least one sensor input and a personal correlation factor. Further by example, the processor 42 can be configured to determine a prior caloric intake based on the first and second sensor inputs and based on a personal correlation factor by implementing method steps 24, 26 and 28 noted above in connection with
As noted above, the portable device 34 includes an on-board memory 44 electrically coupled to the processor 42. The onboard memory 44 can be utilized to store one or more values, including for example the values used in the performance of method steps 24, 26 and 28. These values can include, but are not limited to, energy expenditure, body composition, personal correlation factor, and caloric input. The memory includes non-volatile memory in the present embodiment, including for example flash memory or EEPROM, but can include volatile or other categories of memory in other embodiments.
The portable device 34 further optionally includes a communications unit 48 electrically coupled to the processor 42. The communications unit 48 can be any unit adapted to transmit and/or receive wireless communications to or from a receive station 50 over a communications network. Exemplary networks include a Bluetooth network, a WiFi network, and a ZigBee network. Still other networks may be used in other embodiments as desired.
As further optionally shown in
Directional terms, such as “vertical,” “horizontal,” “top,” “bottom,” “upper,” “lower,” “inner,” “inwardly,” “outer” and “outwardly,” are used to assist in describing the invention based on the orientation of the embodiments shown in the illustrations. The use of directional terms should not be interpreted to limit the invention to any specific orientation(s).
The above description is that of current embodiments of the invention. Various alterations and changes can be made without departing from the spirit and broader aspects of the invention as defined in the appended claims, which are to be interpreted in accordance with the principles of patent law including the doctrine of equivalents. This disclosure is presented for illustrative purposes and should not be interpreted as an exhaustive description of all embodiments of the invention or to limit the scope of the claims to the specific elements illustrated or described in connection with these embodiments. For example, and without limitation, any individual element(s) of the described invention may be replaced by alternative elements that provide substantially similar functionality or otherwise provide adequate operation. This includes, for example, presently known alternative elements, such as those that might be currently known to one skilled in the art, and alternative elements that may be developed in the future, such as those that one skilled in the art might, upon development, recognize as an alternative. Further, the disclosed embodiments include a plurality of features that are described in concert and that might cooperatively provide a collection of benefits.
The present invention is not limited to only those embodiments that include all of these features or that provide all of the stated benefits, except to the extent otherwise expressly set forth in the issued claims. Any reference to claim elements in the singular, for example, using the articles “a,” “an,” “the” or “said,” is not to be construed as limiting the element to the singular.
| Number | Date | Country | |
|---|---|---|---|
| 61739384 | Dec 2012 | US |
