Title:
Fitness assessment
Kind Code:
A1


Abstract:
Methods and apparatus, including computer program products, for fitness assessment. A method includes, in a server system, receiving base data from a user, generating a fitness score in each of a plurality of categories, the categories selected from the group consisting of: flexibility, cardiovascular endurance, muscle strength, body composition, and overall fitness, and generating a report in conjunction with the fitness score in each of the plurality of categories.



Inventors:
Kagan, David (So. Hamilton, MA, US)
Freedson, Patty (South Deerfield, MA, US)
Staudenmeyer, John (Amherst, MA, US)
Porter, Jennifer L. (Belmont, MA, US)
Application Number:
11/704637
Publication Date:
10/04/2007
Filing Date:
02/09/2007
Primary Class:
International Classes:
A63B71/00
View Patent Images:
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Primary Examiner:
GANESAN, SUNDHARA M
Attorney, Agent or Firm:
GREENBERG TRAURIG, LLP (BOS) (BOSTON, MA, US)
Claims:
What is claimed is:

1. A computer-implemented method comprising: in a server system, receiving base data from a user; generating a fitness score in each of a plurality of categories from the base data, the categories selected from the group consisting of: flexibility, cardiovascular endurance, muscle strength, body composition, and overall fitness; and generating a report in conjunction with the fitness score in each of the plurality of categories.

2. The computer-implemented method of claim 1 further comprising generating suggestions to the user on how to improve the fitness score in each of the plurality of categories.

3. The computer-implemented method of claim 1 wherein the user is an adult.

4. The computer-implemented method of claim 1 wherein the user is a senior citizen.

5. The computer-implemented method of claim 1 wherein the user is a child.

Description:

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser. No. 60/786,939, filed on Mar. 28, 2006, and U.S. Provisional Application Ser. No. 60/857,588, filed on Nov. 8, 2006. The disclosures of U.S. Provisional Application Ser. No. 60/786,939 and U.S. Provisional Application Ser. No. 60/857,588 are incorporated herein by reference in their entirety.

BACKGROUND

The present invention relates to data processing by digital computer to health, and more particularly to fitness assessment.

The field of physical fitness assessment and testing has seen an increasing demand with rising public interest in physical fitness and the relevance of performance to soldiers, firefighters, athletes, and the like. For instance, with the high demand and financial stakes of professional and collegiate athletics, there is strong interest in accurately predicting and testing the physical fitness of the athletes. In addition, the increasingly health conscious public is interested in assessing their physical fitness.

Health clubs and physiological laboratories typically conduct elaborate fitness and performance assessments. Such assessments often test the aerobic and anaerobic fitness of participants. Several different tests have been used to make such aerobic and anaerobic assessments.

Drawbacks of the typical anaerobic and aerobic tests include the length of time and expense involved. For instance, aerobic tests typically require from five to twenty minutes of strenuous exercise. Further drawbacks include the use of sophisticated equipment. Additional drawbacks include, for instance, anaerobic fitness being limited to a power output of a stationary bicycle and not a power output of an individual being tested.

SUMMARY

The present invention provides methods and apparatus, including computer program products, for fitness assessment.

In general, in one aspect, a computer-implemented method includes, in a server system, receiving base data from a user, generating a fitness score in each of a plurality of categories from the base data, the categories selected from the group consisting of flexibility, cardiovascular endurance, muscle strength, body composition, and overall fitness, and generating a report in conjunction with the fitness score in each of the plurality of categories.

The invention can be implemented to realize one or more of the following advantages.

A fitness assessment method gives users an approach to assessing their fitness, becoming educated on components of fitness and their relevance, and improving fitness over time.

The method generates a specific numeric score representing a user's fitness.

The method can be targeted to specific users, such as adults, children, senior citizens, and so forth.

The method generates a numeric score that can be used as a comparison of other users' scores with the population at large, within a specific age bracket, within a specific locale, and so forth.

One implementation of the invention provides all of the above advantages.

Other features and advantages of the invention are apparent from the following description, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary fitness assessment system.

FIG. 2 is a flow diagram of a fitness assessment process.

FIGS. 3-12 are graphs.

Like reference numbers and designations in the various drawings indicate like elements.

DETAILED DESCRIPTION

As shown in FIG. 1, a fitness assessment system 8 includes an input/output (I/O) device 10 linked by a network 12 to a web server 14. The I/O device 10 can include a graphical user interface (GUI) 16 for display to a user 18. Example I/O devices 10 include a personal computer (PC), personal data assistant (PDA) with network access, pocket PC with network access, wireless telephone with network access, and so forth.

In general, a web server is a computer that is responsible for accepting Hypertext Transfer Protocol (HTTP) requests from clients and serving the clients Web pages, which are usually HTML documents. A web server is typically part of a client-server network.

The web server 14 includes a processor 20 and memory 22. Memory 22 includes an operating system 24, such as Linux or Windows®, and a fitness assessment process 100, described below.

The web server 14 can include a storage device 26. The storage device 26 can include data stored, for example, in a database 28.

As shown in FIG. 2, the fitness assessment process 100 includes receiving (102) base data from a user. A user can be an adult, a child or a senior citizen, for example. Base data can include, for example, one or more of the following: behavioral information such as gender, age, height, weight, servings of fruit per day, activity minutes per day, and so forth, and collected activity information such as waist circumference, number of pushups in a specified time period, ability to touch toes, and so forth.

From the inputted base data, process 100 generates (104) a fitness score from the base data in each of a number of categories, such as, for example, flexibility, cardiovascular endurance, muscle strength, body composition, overall fitness, and so forth.

Process 100 generates (106) a customized report. The customized report can indicate why each area of fitness, represented by the fitness category scores, is important, how the user scored and what the fitness scores convey.

Optionally, process 100 generates (108) suggestions on how to improve each of the fitness scores and/or links to fitness resources to help the user move toward improved fitness and fitness education. For example, during a user's initial visit to calculate their fitness scores and generate their fitness reports, the user can be introduced to a variety of additional content, products and services oriented to improve the user's fitness knowledge. For example, content can include links to web sites, suggestions for fitness activities, and the ability to track fitness improvement and comparisons to celebrity fitness scores.

Content can include tips around psychological issues pertaining to fitness, small fitness gear, books, videos, CDs and DVDs.

Content can include email, chat and/or phone access to experts, monthly emails to receive suggestions, and so forth.

As described in detail herein, metric scoring takes a person's age, gender, and score on a particular metric (e.g. resting heart rate) and provides that person with a score between 0 (poor) and 100 (excellent) for that metric. Component-of-fitness and composite-fitness scoring provides overall scores for each component of fitness (e.g. aerobic fitness) and an overall composite fitness score.

Metric scoring uses normative data to determine the scores. The normative data provide objective ratings of performance on the metrics and adjust those ratings for age and gender. The data illustrates three aspects. In a first aspect, the ratings are qualitative, e.g., “excellent,” “good,” “above average,” “average,” “below average,” “poor,” and “very poor.”In aspect, ages are provided as ranges. In a third aspect, metrics appear in a table as ranges.

As a result, the normative data do not directly generate a score between 0 and 100 for a specific age and metric score. Instead, process 100 uses the normative data to fit statistical models that provide those scores. The statistical models are mathematical equations that generate scores (between 0 and 100) from inputs of age, gender, and the metric scores, as fully described below. The component scores are averages of the metric scores, and the composite score is a weighted average of the component scores. The composite score uses weights that take into account the different fitness needs of different age groups, as fully described below.

Process 100 uses multiple linear regression that develops equations which translate the metrics for which there is normative data into scores. The metrics to which this methodology applies are resting heart rate, recovery heart rate, VO2 max (i.e., the maximum capacity to transport and utilize oxygen during incremental exercise), partial curl ups, pushups, and sit and reach. A body-mass index and waist measurement metrics use a different methodology, fully described below. In the following, we describe how models for each of the metrics were fit and the assumptions made.

All the multiple linear regression models process 100 have a similar structure. The models have two components, i.e., a set of predictor variables and a response.

The predictor variables are age, gender (except for balance metrics), and the metric score. Process 100 can include up to two-way interactions of those predictors. Those interactions allow the effect of a ten unit decrease in the metric to have different impacts on males and females, for example.

Age is categorized into ranges (e.g., often decades). When process fits the models, the average range from the interval as the predictor is used. When the category is a one sided interval (e,g., >65), process 100 considers that interval to be the same length as the other intervals in order to get a single age for that category. For instance, if other intervals were ten year intervals, >65 becomes 66-75 for an average age of 70.5. The metrics for resting heart rate and recovery heart rate are given as intervals and the average of the interval is used as the predictor in those cases.

When process 100 is applied to someone whose age or metric is not exactly at one of those average values, then process 100 interpolates what the effects of that person's particular age or metric should be. If someone's age or metric is outside the range of the normative data, then process 100 extrapolates what the effects should be.

The responses in process 100 are derived from the ratings given in the normative datasets. Since multiple linear regression dictates a response that does not have an absolute maximum or minimum, process 100 translates the ratings into “z-scores.” In general, in statistics, a standard score (also called z-score or normal score) is a dimensionless quantity derived by subtracting the population mean from an individual (raw) score and then dividing the difference by the population standard deviation.

Herein, the units on the z-score are a number of standard deviations above (or below) a mean in a normal distribution. In cases when the ratings in the normative datasets were subjective categories like “excellent,” “good,” “above average,” “below average,”

“poor,” “very poor,” process 100 assumes the top category is two standard deviations above the mean (a z-score of 2), the lowest category is two standard deviations below the mean (z-score=−2), and scores for the other categories are interpolated. When the ratings are given as percentages, process 100 uses the z-scores that correspond to those percentages in a normal distribution.

After the regression models are fit, the result is an equation that predicts a z-score for a person's particular age, gender, and achievement on a metric. That prediction can be translated to a number between 0 and 100 using a normal distribution function. For instance, when the predicted z-score is 1, the score is 84 since there is an 84% chance that a normally distributed random number is less than 1 standard deviation above the mean.

The regression models fit well. The lowest R-squared is 0.64 (for sharpened Romberg), and the rest are above 0.87. All of the regression F-statistics (and most of the coefficient t-statistics) have p-values that are very near zero. For each metric, process 100 reports the R-squared for the model fit.

For a resting heart rate the fitted equation is: z-score=6.7212+resting HR(−0.0972)+age(0.0123)+(if gender=M)(−0.7620)+resting HR*age(−0.0001)+resting HR*(if gender=M)(0.0074)+age*(if gender=M)(0.0002). The R-squared for this model fit is 0.95.

For a 25 year old male, for example, the equation is: z-score=6.7212+resting HR(−0.0972)+(25)(0.0123)+(−0.7620)+resting HR*(25)(−0.0001)+resting HR (0.0074)+(25)(0.0002).

For a 35 year old female the equation is: z-score=6.7212+resting HR(−0.0972)+(35)(0.0123)+resting HR*(35)(−0.0001).

As shown in FIG. 3, a graph 200 illustrate a relationship between the metric and the score for males and females at two ages. In this case, the ratings in the normative data are qualitative.

The following table shows the values assumed for each rating on the z-score and 0-100 score scale. The first column contains the ratings obtained from the normative data; the last column contains assumed scores for those ratings; and the middle column contains the z-scores we used in the regressions.

Ratingz-scorescore (0-100)
Excellent298
Good1.3391
Above average0.6775
Average050
Below average−0.6725
Poor−1.339
Very poor−22

For a recovery heart rate, the fitted equation is: z-score=4.7772+recovery HR(−0.0469)+age(0.0310)+(if gender=M)(−0.1757)+recovery HR*age(−0.0002)+recovery HR*(if gender=M)(−0.0016)+age*(if gender=M)(−0.0025). The R-squared for this model fit is 0.96.

As shown in FIG. 4, a graph 250 illustrates a relationship between the metric and the score for males and females at two ages. In this case, the ratings in the normative data are qualitative.

The following table shows the values assumed for each rating on the z-score and 0-100 score scale. The first column contains the ratings obtained from the normative data; the last column contains assumed scores for those ratings; and the middle column contains the z-scores used in the regressions.

Ratingz-scorescore (0-100)
Excellent298
Good1.3391
Above average0.6775
Average050
Below average−0.6725
Poor−1.339
Very poor−22

A VO2 max fitted equation is: z-score=−5.8306+VO2 max(0.1275)+age(0.0340)+(if gender=M)(−0.5171)+VO2 max*age(0.0001)+VO2 max*(if gender=M)(−0.0056)+age*(if gender=M)(−0.0079). The R-squared for this model fit is 0.89.

For example, for a 25 year old male the equation is: z-score=−5.8306+VO2 max(0.1275)+(25)(0.0340)+(−0.5171)+VO2 max*(25)(0.0001)+VO2 max (−0.0056)+(25)(−0.0079). For a 35 year old female it is: z-score=−5.8306+VO2 max(0.1275)+(35)(0.0340)+VO2 max*(35)(0.0001).

As shown in FIG. 5, a graph 300 illustrates a relationship between the metric and the score for males and females at two ages.

In this case, the ratings in the normative data are quantitative.

The following table shows the values assumed for each rating on the z-score and 0-100 score scale. The first column contains the ratings obtained from the normative data; the last column contains assumed scores for those ratings; and the middle column contains the z-scores used in the regressions.

Ratingz-scorescore (0-100)
0.91.2890
0.80.8480
0.70.5270
0.60.2560
0.50.0050
0.4−0.2540
0.3−0.5230
0.2−0.8420
0.1−1.2810

The VO2 max is calculated from the equation: These equations require height in meters and weight in kilograms. The equation is: VO2 max=34.142+[0.133*age]−[0.005 age2]+[11.403*gender]+[1.463*PAS]+[9.170*height]−[0.254*body weight]. This equation uses height in meters and weight in kilograms.

The partial curl ups fitted equation is: z-score=−1.5999+partial curl ups(0.04267)+age(0.01480)+(if gender=M)0.1702+partial curl ups*age(0.00002769)+partial curl ups*(if gender=M)(−0.01219)+age*(if gender=M)(−0.005785). Zero partial curl ups always gets a zero. The R-squared for this model fit is 0.87.

We have modified the equation so that a metric of 75 always gets a score of 100 since that is the maximum that is possible on this test. For example, for a 25 year old male the equation is: z-score=−1.5999+partial curl ups(0.04267)+25(0.01480)+0.1702+partial curl ups*25(0.00002769)+partial curl ups(−0.01219)+25(−0.005785). For a 35 year old female the equation is: z-score=−1.5999+partial curl ups(0.04267)+35(0.01480)+partial curl ups*35(0.00002769).

As shown in FIG. 6, a graph 350 illustrates a relationship between the metric and the score for males and females at two ages. In this case, the ratings in the normative data are quantitative.

The following table shows the values assumed for each rating on the z-score and 0-100 score scale. The first column contains the ratings obtained from the normative data; the last column contains assumed scores for those ratings; and the middle column contains the z-scores used in the regressions.

Ratingz-scorescore (0-100)
0.91.2890
0.80.8480
0.70.5270
0.60.2560
0.50.0050
0.4−0.2540
0.3−0.5230
0.2−0.8420
0.1−1.2810

For pushups the fitted equation is: z-score=−0.6574+pushups(0.1150)+age(0.0283)+(if gender=M)(−2.2338)+pushups*age(0.0016)+pushups*(if gender=M)(0.0283)+age*(if gender=M)(0.0264). Zero pushups gets a score of 0. The R-squared for this model fit is 0.94. For example, for a 25 year old male the equation is: z-score=−3.6574+pushups(0.1150)+(25)(0.0283)+(−2.2338)+pushups*(25) (0.0016)+pushups(0.0283)+(25)(0.0264). For a 35 year old female the equation is: z-score=−3.6574+pushups(0.1150)+(35)(0.0283)+pushups*(35) (0.0016).

As shown in FIG. 7, a graph 400 illustrates a relationship between the metric and the score for males and females at two ages. In this case, the ratings in the normative data are qualitative.

The following table shows the values assumed for each rating on the z-score and 0-100 score scale. The first column contains the ratings obtained from the normative data; the last column contains assumed scores for those ratings; and the middle column contains the z-scores used in the regressions.

Ratingz-scorescore (0-100)
Excellent298
Very good 11.4392
Very good 20.8680
Good 10.2961
Good 2−0.2939
Fair 1−0.8620
Fair 2−1.438
Needs−22
improvement

A sit and reach fitted equation is: z-score=−5.2189+sit and reach(0.2526)+age(0.0274)+(if gender=M)(1.0717)+sit and reach*age(−0.0003)+sit and reach*(if gender=M)(−0.0337)+age*(if gender=M)(0.0032). A reach of 0 gets a 0. For example, for a 25 year old male the equation is: z-score=−5.2189+sit and reach(0.2526)+(25)(0.0274)+(1.0717)+sit and reach*(25) (−0.0003)+sit and reach(−0.0337)+(25) (0.0032). The R-squared for this model fit is 0.98. Here, the sit and reach score is in inches. The data that are collected from the customers is in centimeters. To account for the “short” tape measure, 1.9 cm is subtracted from the number that the customers enter. The resulting number is divided by 2.54 to be converted to inches. The resulting number can be plugged into the equation.

For a 35 year old female the equation is: z-score=−5.2189+sit and reach(0.2526)+(35)(0.0274)+sit and reach*(35)(−0.0003).

As shown in FIG. 8, a graph 450 illustrates a relationship between the metric and the score for males and females at two ages. The ratings in the normative data are quantitative.

The following table shows the values assumed for each rating on the z-score and 0-100 score scale. The first column contains the ratings obtained from the normative data; the last column contains assumed scores for those ratings; and the middle column contains the z-scores used in the regressions.

Ratingz-scorescore (0-100)
0.91.2890
0.80.8480
0.70.5270
0.60.2560
0.50.0050
0.4−0.2540
0.3−0.5230
0.2−0.8420
0.1−1.2810

A one legged stance fitted equation is: z-score=−16.801891+seconds on one leg (0.595959)+age(0.225822)+seconds on one leg*age(−0.006762). 30 seconds gets a score of 100 and 0 seconds gets a score of 0. Gender is not used for this score.

For example, for a 25 year old the equation is: z-score=−16.801891+seconds on one leg (0.595959)+(25)(0.225822)+seconds on one leg*(25)(−0.006762). The R-squared for this model fit is 0.92.

As shown in FIG. 9, a graph 500 illustrates a relationship between the metric and the score for two ages. In this case, the ratings in the normative data are quantitative.

The following table shows the values assumed for each rating on the z-score and 0-100 score scale. The first column contains the ratings obtained from the normative data; the last column contains assumed scores for those ratings; and the middle column contains the z-scores used in the regressions.

Ratingz-scorescore (0-100)
0.91.2890
0.80.8480
0.70.5270
0.60.2560
0.50.0050
0.4−0.2540
0.3−0.5230
0.2−0.8420
0.1−1.2810

A sharpened Romberg fitted equation is: z-score=−10.14237149+seconds on test (0.16540932)+age(0.03630858). 60 seconds gets a score of 100 and 0 seconds gets a score of 0. Gender is not used for this score. Age and seconds on test interaction is dropped to improve the fidelity between the model and the data. For example, for a 25 year old the equation is: z-score=−10.14237149+seconds on test (0.16540932)+(25)(0.03630858). The R-squared for this model fit is 0.64.

As shown in FIG. 10, a graph 550 illustrates a relationship between the metric and the score for two ages. The ratings in the normative data are quantitative.

The following table shows the values assumed for each rating on the z-score and 0-100 score scale. The first column contains the ratings obtained from the normative data; the last column contains assumed scores for those ratings; and the middle column contains the z-scores used in the regressions.

Ratingz-scorescore (0-100)
0.91.2890
0.80.8480
0.70.5270
0.60.2560
0.50.0050
0.4−0.2540
0.3−0.5230
0.2−0.8420
0.1−1.2810

As shown FIG. 11, a graph 600 maps body mass index (BMI) to a score. BMI is a numerical computation regarding height and weight. BMI in graph 600 is calculated from weight in kilograms (kg) and height is meters (m). A score is zero for BMIs less than 15 or greater than 40.

Waist measurement scores are based on the following table. We assumed that very low risk gets a score of 100, low risk gets a score of 100 to 75, high risk gets a score of 75 to 25, and very high gets a score less than 25.

Riskfemalesmales
very low<70cm<80cm
Low70-89cm80-99cm
High89.1-109cm99.1-120cm
very high>/=110cm>/=121

As shown in FIG. 12, a graph 650 illustrates a relationship between the metric and the score for males and females.

If a metric entered is outside of the ranges shown below, then client receives message that the metric is outside of the allowable range and should be reentered.

age: 15-75

ht: 40-96

wt: 40-400

resting HR: 20-120

recovery HR: 40-220

vo2 max: 10-90

partial curl up: 0-150

pushup: 0-150

one leg stance 0-30

sharpened Romberg 0-60

sit and reach 0-77

waist 45-178

PAS 0-7

The four fitness scores are computed as follows:


Cardiovascular=(rest HR score+recovery HR score+VO2 max score)/3


Strength and endurance=(partial curl up score+pushup score)/2


(if BMI>18.45): Body Composition=(BMI score+waist score)/2


(if BMI<18.45): Body Composition=BMI score


Balance=(one legged score+sharpened Romberg score)/2

If there are missing data, then the following procedure should be followed:

If there is at least one metric in the component of fitness, then the average score should be computed using the metrics for which there are measurements. For instance, if the resting heart rate metric is missing, then:


Cardiovascular=(recovery HR score+VO2 max score)/2

Note that 2 is used in the denominator instead of 3.

If all metrics in a component of fitness are missing, then scores should not be computed, and the user should be told why.

The table below shows how the composite score is computed. Below is how test components should contribute to composite fitness test score. The norms for balance (titled balance test norms) are included so that each age group will receive a score for balance test along with all other fitness components.

aerobicbodystrength &
age rangefitnesscompositionenduranceflexibilitybalance
20-29303020200
30-39303020200
40-49232623235
50-592025252010
60-692020222315

There is one modification to the weights above. For the 20-39 yr old age group, if one legged stance (OLS) balance test and sharpened Romberg balance test (SRT) are 30 and 60 sec respectively, balance measure should not be incorporated into composite fitness score. If OLS test is below 30 sec or SRT is less than 60 sec, then reduce the composite fitness test score by 3 percent. If scores on both balance tests are less than 30 (OLS) and 60 sec (SRT), then reduce the composite fitness score by 6 percent.

The following are sample lifestyle questions:

In a typical week, how many days do you participate in at least 30 minutes of moderately intense physical activity (e.g., brisk walking)?

A 0-1

B 1-2

C 2-3

D 3-4

E 5 or more days

How many hours of television do you watch per day?

A 0, I do not watch television.

B 2 or less

C 2-3

D 3-4

E 5 or more

On average, how many hours of sleep do you get per night?

A Less than 4

B 4-5

C 5-6

D 6-7

E More than 7

How many servings of fruits and vegetables do you eat on a typical day?

A 0

B 1-2

C 2-3

D 4

E 5 or more

How many servings of food that are high in fiber do you eat in a typical day? (1 serving=1 slice of whole grain bread, ½ cup to vegetables, 1 medium-sized piece of fruit, or ¾ cup of high-fiber cereal)

A 0

B 1

C 2-3

D 4

E 5 or more

How many servings of foods that are high in saturated fat do you eat in a typical day? (1 serving=3.5 ounces of fatty meat or fried foods, or 1 ounce/slice of cheese)

A 0

B 1-2

C 3-4

D 5 or more

How many alcoholic beverages do you consume in an average day?(1 beverage=one 12 oz. beer, one 4 oz. glass of wine, or 1.5 oz. of an 80-proof distilled beverage)

A 0-2

B 3-4

C 5 or more

During an average day, how many cups of caffeinated drinks (e.g., tea, coffee, cola) do you consume?

A 0

B 1-2

C 3-4

D More than 5

On an average day, how many packs of cigarettes do you smoke?

A 0

B Less than 1

C 1

D 2 or more

In the past 12 months, how many days have your normal activities been difficult and/or interrupted because you were ill?

A 0

B 1-3

C 4-7

D 8-12

E 13 or more

How often do you experience the following?

RARELY SOMETIMES OFTEN

Difficulty concentrating

Irritability

Tense muscles

Disturbed sleeping

Fatigue with minimal exertion

You have suffered personal loss or hardship in the past 12 months? (For example, you have lost a job, become disabled, got divorced or separated, lost a loved one?)

A No

B Yes, 1 time

C Yes, 2 or more times

Do you make time to do things that are fun? (e.g., hobbies, vacation, entertainment, etc.)

A No

B Sometimes

C Yes

How do you rate your overall energy level on a typical day?

A High level of energy throughout the day

B High level of energy for only part of the day

C Tired throughout the day

Relative to others of the same age and gender, how would you rate

Yourself?

A Much less physically active

B Less physically active

C Equally physically active

D More physically active

E Much more physically active

Final lifestyle questions can include scoring instructions.

We are unable to score the lifestyle questions using data-driven regression modeling since there are no normative data on these questions. The approach we will use is as follows:

Answers to questions will be assigned a score from between 0 (worst)−and 100 (best), with 100 as the best score. For questions with 3 possible answers, worst=25, middle 63, best=100. For questions with 4 possible answers, worst=25, next=50, next=75, best=100. For 4 possible answers the lowest score is d=25 for Q6 and Q8 but d=10 for Q9

For questions with 5 possible answers, worst=24, next=43, next=62, next=81, best=100. All questions are coded so that answer a gets the worst score and the last answer gets the best score.

The total lifestyle score is a sample average of the scores from 1-6 above each questions):


Lifestyle score=[(Average of Qs1,2,15)+(Q3)+(Average of Qs4,5,6,7,8)+(Q9)+(Average of 10,11,12)+(Average of Qs 13, 14)]/6

The following table illustrates how data that are collected (including “trend” data and not including the lifestyle questions):

InformationHow used?
Age (years)Scoring
Gender (M or F)Scoring
Weight (lbs)Scoring
Height (inches)Scoring
Resting HRScoring
One legged stanceScoring
Heel to ToeScoring
StepScoring
Physical Activity LevelScoring
Push UpsScoring
Partial Curl UpsScoring
Sit & ReachScoring
Waist (inches)Scoring
Hip (inches)Trend
Upper arm (inches)Trend
Thigh (inches)Trend
Calf (inches)Trend
Wall sit (seconds)Trend
Plank (seconds)Trend
Calf stretch (inches)Trend
Shoulder reach (inches)Trend
Hamstring hurdle (inches)Trend

The invention can be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. The invention can be implemented as a computer program product, i.e., a computer program tangibly embodied in an information carrier, e.g., in a machine readable storage device or in a propagated signal, for execution by, or to control the operation of, data processing apparatus, e.g., a programmable processor, a computer, or multiple computers. A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network.

Method steps of the invention can be performed by one or more programmable processors executing a computer program to perform functions of the invention by operating on input data and generating output. Method steps can also be performed by, and apparatus of the invention can be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. Information carriers suitable for embodying computer program instructions and data include all forms of non volatile memory, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in special purpose logic circuitry.

It is to be understood that the foregoing description is intended to illustrate and not to limit the scope of the invention, which is defined by the scope of the appended claims. Other embodiments are within the scope of the following claims.