Patent Publication Number: US-11642044-B2

Title: Systems, methods, and apparatuses for peripheral arterial disease detection and mitigation thereof

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is a divisional application of U.S. application Ser. No. 15/443,944, entitled “SYSTEMS, METHODS, AND APPARATUSES FOR PERIPHERAL ARTERIAL DISEASE DETECTION AND MITIGATION THEREOF,” filed on Feb. 27, 2017, which is incorporated by reference herein. 
    
    
     BACKGROUND 
     Technical Field 
     The present application relates to methods, systems, and apparatuses for non-invasive procedures for detecting and mitigating peripheral arterial disease. 
     Background Art 
     Peripheral arterial disease is a common circulatory problem in which narrowed arteries reduce blood flow to a person&#39;s limbs. When peripheral arterial disease is developed, the extremities (e.g., a person&#39;s legs) do not receive enough blood flow to keep up with the demand. This causes various symptoms, most notably leg pain when walking. Severe peripheral arterial disease can lead to even more extreme issues, such as kidney failure, foot or leg amputation, a heart attack, or a stroke. Certain procedures, such as an angiogram or a blood test, can be used to detect the presence of peripheral arterial disease. However, such procedures are invasive and generally disliked by patients. 
     BRIEF SUMMARY 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. 
     Methods, systems, and apparatuses are described for non-invasive procedures for detecting and mitigating peripheral arterial disease, substantially as shown in and/or described herein in connection with at least one of the figures, as set forth more completely in the claims. 
     Further features and advantages of the invention, as well as the structure and operation of various embodiments, are described in detail below with reference to the accompanying drawings. It is noted that the invention is not limited to the specific embodiments described herein. Such embodiments are presented herein for illustrative purposes only. Additional embodiments will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES 
       The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate embodiments and, together with the description, further serve to explain the principles of the embodiments and to enable a person skilled in the pertinent art to make and use the embodiments. 
         FIG.  1 A  shows a block diagram of a first surface of a measuring apparatus for determining a calf circumference of a patient for diagnosing peripheral arterial disease in accordance with an embodiment. 
         FIG.  1 B  shows a block diagram of a second surface that opposes the first surface of the measuring apparatus depicted in  FIG.  1 A  in accordance with an embodiment. 
         FIG.  2    shows a diagram illustrating the measuring of the circumference of a patient&#39;s calf using a measuring apparatus in accordance with an embodiment. 
         FIG.  3    shows a block diagram of a measuring apparatus having a first surface that is color-coded in accordance with an embodiment. 
         FIG.  4    shows a block diagram of a measuring apparatus having a first surface that is color-coded using gradients in accordance with an embodiment. 
         FIG.  5    shows a block diagram of a computing device configured to assess whether a patient has peripheral arterial disease based on various types of data in accordance with an embodiment. 
         FIG.  6    shows a block diagram of a system for tracking compliance of a prescribed walking program in accordance with an embodiment. 
         FIG.  7    depicts a flowchart of a method for tracking compliance of a prescribed walking program in accordance with an embodiment. 
         FIG.  8    shows a block diagram of a computing device that includes a compliance application in accordance with an embodiment. 
         FIG.  9    depicts a flowchart of a method for transmitting messages of increasing severity as a patient continues to not comply with a prescribed walking program in accordance with an embodiment. 
         FIG.  10    is a block diagram of a computer system in accordance with an embodiment. 
     
    
    
     The features and advantages of the embodiments described herein will become more apparent from the detailed description set forth below when taken in conjunction with the drawings, in which like reference characters identify corresponding elements throughout. In the drawings, like reference numbers generally indicate identical, functionally similar, and/or structurally similar elements. The drawing in which an element first appears is indicated by the leftmost digit(s) in the corresponding reference number. 
     DETAILED DESCRIPTION 
     I. Introduction 
     The present specification discloses numerous example embodiments. The scope of the present patent application is not limited to the disclosed embodiments, but also encompasses combinations of the disclosed embodiments, as well as modifications to the disclosed embodiments. 
     References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. 
     Furthermore, it should be understood that spatial descriptions (e.g., “above,” “below,” “up,” “left,” “right,” “down,” “top,” “bottom,” “vertical,” “horizontal,” “front,” “rear,” etc.) used herein are for purposes of illustration only, and that practical implementations of the structures described herein can be spatially arranged in any orientation or manner. 
     Numerous exemplary embodiments are described as follows. It is noted that the section/subsection headings used herein are not intended to be limiting. Embodiments described in this document may be eligible for inclusion within multiple different sections or subsections. Furthermore, disclosed embodiments may be combined with each other in any manner. 
     Embodiments described herein are directed to non-invasive detection of peripheral arterial disease. For example, a measuring apparatus may be used to measure the calf circumference a patient. The measuring apparatus may have one or more text features or indicators printed thereupon that indicate the likelihood that the patient has peripheral arterial disease based on the measured calf circumference. The assessment may be further refined by using an application that assesses the likelihood of the patient having peripheral arterial disease using at least the calf circumference measurement, along with other information/data, such as a toe brachial index measurement associated with the patient, results of a walking test performed by the patient, and/or symptom-related information obtained from a questionnaire filled out by the patient. Based on the assessment, a healthcare practitioner (e.g., a doctor, a nurse, a physician&#39;s assistant, etc.) may prescribe a walking program for the patient to follow. An application may track compliance of the walking program and provide escalating reminders to the patient if the patient continues to fail to comply with the prescribed walking program. 
     In particular, a measuring apparatus for determining a calf circumference of a patient for diagnosing peripheral arterial disease is described herein. The measuring apparatus comprises a strip of material. The strip of material comprises a first surface and a second surface that opposes the first surface. At least one of the first surface or the second surface comprises a plurality of color-coded segments arranged in series. A first segment of the plurality of color-coded segments has a first color and corresponds to a first range of calf circumference that represents a high likelihood that the patient has peripheral arterial disease. A second segment of the plurality of color-coded segments has a second color that is different than the first color and corresponds to a second range of calf circumference that represents a low likelihood that the patient has peripheral arterial disease. 
     A method performed by a computer associated with a healthcare practitioner to determine compliance of a walking program prescribed by the healthcare practitioner is also described herein. In accordance with the method, first sensor data is received from a mobile device via a network that indicates a first number of steps a patient has taken within a predetermined time period. A determination is made that the first number of steps the patient has taken within the predetermined time period is not in compliance with the walking program prescribed by the healthcare practitioner. A first message having a first severity is transmitted via the network to a device associated with the patient indicating that the patient did not comply with the prescribed walking program. 
     A system is also described herein. The system includes at least one processor circuit and at least one memory that stores program code configured to be executed by the at least one process circuit. The program code includes a receiver configured to receive, via a network, first sensor data from a mobile device that indicates a first number of steps a patient has taken within a predetermined time period. The program code also includes a compliance determiner configured to determine that the first number of steps the patient has taken within the predetermined time period is not in compliance with a walking program prescribed by a healthcare practitioner. The program code further includes a transmitter configured to transmit, via the network, a first message having a first severity to a device associated with the patient indicating that the patient did not comply with the prescribed walking program. 
     These and further embodiments and variations are described in the next section. 
     II. Example Embodiments 
     A. Measuring Apparatus Evaluation Assessment 
       FIGS.  1 A and  1 B  are block diagrams of a measuring apparatus  100  for determining a calf circumference of a patient for diagnosing peripheral arterial disease in accordance with an embodiment. As shown in  FIGS.  1 A and  1 B , measuring apparatus  100  comprises a first surface  102  and a second surface  104  that opposes first surface  102 . First surface  102  may also be referred to as a front or outer surface of measuring apparatus  100 , and second surface  104  may also be referred to as a back or inner surface of measuring apparatus  100 . Measuring apparatus  100  may be a strip of material having a starting point  106  (a first edge) and an ending point  108  (a second edge opposed to the first edge). As shown in  FIGS.  1 A and  1 B , the strip may be a substantially rectangular shape, although embodiments described herein are not so limited. The material of which measuring apparatus  100  is made may comprise paper, plastic, cloth, fiber glass, metal, metal alloy, and/or any combination thereof. 
     First surface  102  may comprise a plurality of segments  110 ,  112 ,  114 , and  116  that are arranged in series between starting point  106  and ending point  108 . Each of segments  110 ,  112 ,  114 , and  116  may be defined via starting point  106 , ending point  108  and one or more segment markers  118 ,  120 , and  122  printed thereupon between them. For example, first segment  110  may be defined by the portion of first surface  102  between ending point  108  and segment marker  118 . Second segment  112  may be defined by the portion of first surface  102  between segment marker  118  and segment marker  120 . Third segment  114  may be defined by the portion of first surface  102  between segment marker  120  and segment marker  122 . Fourth segment  116  may be defined by the portion of first surface  102  between segment marker  122  and starting point  106 . 
     Each segment of measuring apparatus  100  may correspond to a calf thickness and corresponding likelihood that a patient has peripheral arterial disease. For instance, in an embodiment, first segment  110  corresponds to a first range of calf thickness that represents a low likelihood that the patient has peripheral arterial disease. Second segment  112  corresponds to a second range of calf thickness that represents a medium likelihood that the patient has peripheral arterial disease. Third segment  114  corresponds to a third range of calf thickness that represents a high likelihood that the patient has peripheral arterial disease. Fourth segment  116  may be a portion of measuring apparatus  110  that a user holds while wrapping measuring apparatus  100  around the calf of the patient and may not be used as an indicator of the likelihood that the patient has peripheral arterial disease. It is further noted that while measuring apparatus  100  is depicted as having four segments (i.e., segments  110 ,  112 ,  114 , and  116 ), measuring apparatus  100 , in other embodiments, other numbers of segments may be present that correspond to calf thicknesses and disease likelihoods. 
     As shown in  FIG.  1 A , one or more segments may include markings printed thereupon that are indicative of the likelihood that the patient has peripheral arterial disease. For example, first segment  110  may include a text feature  124  (“Low Likelihood”), second segment  112  may include a text feature  126  (“Medium Likelihood”), and third segment  114  may include a text feature  128  (“High Likelihood”). It is noted that text features  124 ,  126 , and  128  are merely exemplary and that any text and/or graphical feature may be used to represent various likelihoods of a patient having peripheral arterial disease. Fourth segment  116  may not include any markings printed thereupon (i.e., segment  116  may be blank) or may indicate fourth segment  116  as intended for a user&#39;s hand, etc. 
     Measuring apparatus  100  and each segment thereof may have a corresponding length. For instance, in accordance with an embodiment, the length of measuring apparatus is approximately 60 cm (e.g., measuring apparatus  100  may have a length between 59 and 61 cm). In accordance with such an embodiment, first segment  110  is approximately 17 centimeters in length (e.g., first segment  110  may have a length between 16-18 centimeters), second segment  112  is approximately 9 centimeters in length (e.g., second segment  112  may have a length between 8-10 centimeters), third segment  114  is 20 centimeters in length (e.g., third segment  110  may have a length between 19-21 centimeters), and fourth segment  116  is approximately 14 centimeters in length (e.g. fourth segment  116  may have a length between 13-15 centimeters). 
     In an embodiment, the length of each of segments  110 ,  112 , and  114  may be based on statistical analysis of data associated with a plurality of patients (e.g., thousands of patients). The data may be maintained in a public database (e.g., a National Health and Nutrition Examination Survey (NHANES) database) that includes information regarding patients with and without peripheral arterial disease. After applying a generalized linear statistical model and/or a random forest machine learning model to the data, and after controlling for traditional risk factors (e.g., age, ethnicity, smoking, hypertension, body mass index (BMI)), it has been observed that calf circumference is a strong independent predictor of the present of peripheral arterial disease. In particular, it has been observed that a calf circumference less than 34 cm is an indicator that a patient has a high likelihood of having peripheral arterial disease, a calf circumference greater than 43 cm is an indicator that a patient has a low likelihood of having peripheral arterial disease, and a calf circumference having a range therebetween (e.g., between 34 cm and 43 cm) is an indicator that a patient has a medium likelihood of having peripheral arterial disease. 
     As also shown in  FIG.  1 B , second surface  104  may be blank (i.e., second surface  104  may have not markings printed thereupon). Alternatively, second surface  104  may have the same markings as first surface  102 . That is, second surface  104  may be identical to first surface  102 . 
     The likelihood that the patient has peripheral arterial disease may be determined by measuring the circumference of the calf of the patient using measuring apparatus  100 . For example,  FIG.  2    shows a diagram  200  illustrating the measuring the circumference of a patient&#39;s calf using measuring apparatus  100  in accordance with an embodiment. As shown in  FIG.  2   , measuring apparatus  100  is wrapped around calf  202  such that first surface  102  faces away from calf  202  and a loop is formed around calf  202 . Measuring apparatus  100  may be moved up and down calf  202  in order to find the portion of calf  202  having the largest circumference. The likelihood that the patient has peripheral arterial disease can be obtained by reading the markings on first surface  202  at the point where starting point  106  of measuring apparatus  100  intersects second surface  104 . For example, if the point where starting point  106  intersects second surface  104  is within first segment  110 , it may be determined that the patient has a low likelihood of peripheral arterial disease. If the point where starting point  106  intersects second surface  104  is within second segment  112 , it may be determined that the patient has a medium likelihood of peripheral arterial disease. If the point where starting point  106  intersects second surface  104  is within third segment  114 , it may be determined that the patient has a high likelihood of peripheral arterial disease. In the example shown in  FIG.  2   , measuring apparatus  100  indicates that the patient has medium likelihood of having peripheral arterial disease. 
     In accordance with an embodiment, segments  110 ,  112 ,  114 , and  116  may be defined by different colors (in addition to or in lieu of using segment markers  118 ,  120 , and  122 ), thereby forming a color-coded measuring apparatus. For example,  FIG.  3    is a block diagram of measuring apparatus  100  in which first surface  102  is color-coded in accordance with an embodiment. As shown in  FIG.  3   , first segment  110  is defined by the portion of first surface  102  that has a first color (e.g., green), second segment  112  is defined by the portion of first surface  102  that has a second color (e.g., yellow or orange), third segment  114  is defined by the portion of first surface  102  that has a third color (e.g., red), and fourth segment  116  is defined by the portion of first surface  102  that has a fourth color (e.g., white). It is noted that the colors described above are merely exemplary and that each of segments  110 ,  112 ,  114 , and  116  may be any color, including a range of gray shades (i.e., grayscale) or other shades of a single or multiple colors. 
     In accordance with an embodiment, the color of each segments  110 ,  112 ,  114 , and  116  may be represented as a gradient. For example,  FIG.  4    is a block diagram of measuring apparatus  100  in which first surface  102  is color-coded using gradients in accordance with an embodiment. For instance, first segment  110  may be defined by the portion of first surface  102  having a first gradient being based on first color (e.g., green), second segment  112  may be defined by the portion of first surface  102  having a second gradient being based on second color (e.g., yellow or orange), and third segment  114  may be defined by the portion of first surface  102  having a third gradient being based on a third color (e.g., red). Fourth segment  116  may be defined by the portion of first surface  102  having no gradient and may simply be a solid color (e.g., white). It is noted that the gradients described above are merely exemplary and that each of segments  110 ,  112 ,  114 , and  116  may have gradients based on any color, including a range of gray shades (i.e., grayscale). 
     Measuring apparatus  100  may further include one or more additional features printed thereupon. For example, as further shown in  FIG.  4   , first surface  102  may include a set of length markings  402  that divide first surface  102  by standard units of length (e.g., centimeters). It is noted that while  FIG.  4    depicts the standard unit of length to be centimeters, any standard of unit of length may be used (e.g., inches, millimeters, etc.). As also shown in  FIG.  4   , first surface  102  may also include a first indicator  404 , a second indicator  406 , a third indicator  408 , a fourth indicator  410 , and/or a fifth indicator  412  printed thereupon. First indicator  404  may represent the mean of patients that do not have peripheral arterial disease. Second indicator  406  may represent the standard error of mean of patients without peripheral arterial disease. Third indicator  408  may represent the mean of patients that have peripheral arterial disease. Fourth indicator  410  may represent the mean plus the standard deviation of the patients that have peripheral arterial disease. Fifth indicator  412  may represent the mean minus the standard deviation of the patients that have peripheral arterial disease. The above-described means, standard error of means and standard deviations may be based on the statistical analysis performed on data associated with a plurality of patients as described above. 
     In an embodiment, second surface  104  (as shown in  FIG.  1 B ) may include the same color scheme as shown in  FIGS.  3  and  4    and/or each of markings  402 ,  404 ,  406 ,  408 ,  410  and/or  412  shown in  FIG.  4   , may include instructions for using measuring apparatus  100 , and/or may be marked and/or colored/shaded in other ways. 
     B. Screening Application 
     As described above in Subsection II.A, a measuring apparatus may be used to assess the likelihood as to whether a patient has peripheral arterial disease. The assessment may be further refined using additional data regarding the patient. For example, the data may be analyzed by an application to make a refined assessment as to whether the patient has peripheral arterial disease. 
       FIG.  5    is a block diagram of a computing device  500  configured to assess whether a patient has peripheral arterial disease based on various types of data in accordance with an example embodiment. Computing device  500  may be any type of stationary or mobile computing device, including a desktop computer (e.g., a personal computer, etc.), a mobile computer or computing device (e.g., a Palm® device, a RIM Blackberry® device, a personal digital assistant (PDA), a laptop computer, a notebook computer, a tablet computer (e.g., an Apple iPad™), a smart phone (e.g., an Apple iPhone, a Google Android™ phone, a Microsoft Windows® phone, etc.), or other type of computing device. As shown in  FIG.  5   , computing device  500  includes a screener application  502 . 
     Screener application  502  may be a software application that executes in hardware, and is configured to receive data associated with a patient and make an assessment as to whether the patient has peripheral arterial disease based on the data. The data includes one or more of measuring apparatus data  506 , exercise data  508 , diagnostic test data  510  and/or questionnaire data  512 . Screener application  502  may include a user interface  504  that enables a user to enter in measuring apparatus data  506 , exercise data  508 , diagnostic test data  510  and/or questionnaire data  512 . 
     Measurement apparatus data  506  may comprise one or more measurements taken using the measuring apparatus described above in Subsection A. For example, the measurement(s) may include the determined likelihood that the patient has peripheral arterial disease (e.g., a low likelihood, a medium likelihood, or a high likelihood) and/or the calf circumference (e.g., in centimeters). The determined likelihood and/or calf circumference is provided to screener application  502  via user interface  504 . 
     Exercise data  508  may comprise data associated with one or more exercises that the patient has performed. For example, it has been observed that peripheral arterial disease limits the walking ability of patients. A six-minute walk test has been shown to be a reliable and a reproducible method of assessing this limitation. The six-minute walk test assesses the distance that a patient walks at a normal pace during six minutes. Exercise data  508  may include results of a patient&#39;s six-minute walk test (i.e., the distance traveled (e.g., the number of feet, meters, etc.) in six minutes). The distance traveled may be indicative of whether the patient has peripheral arterial disease. For example, if the patient is able to walk less than a predetermined threshold (e.g., 300 meters) within six minutes, this may be an indication, or a determination, that the patient may have peripheral arterial disease. It is noted that the predetermined threshold is exemplary, and other thresholds may be used. It is further noted that the threshold may vary depending on certain characteristics of the patient (e.g., age, weight, etc.). The distance traveled is provided to screener application  502  via user interface  504 . 
     Diagnostic test data  510  may comprise data associated with one or more diagnostic tests performed on the patient. One such exam is a toe brachial index exam. It has been observed that patients with peripheral arterial disease have blockages in the arteries to the extremities. Thus, pressure in the involved extremity would be lower than that in the uninvolved extremity. Calcification of the patient&#39;s medium-sized arteries makes pressure measurement unreliable in the involved blood vessels. Toe pressure measurement avoids this error. Thus, a ratio of the toe pressure to the arm pressure (also referred to as the toe brachial index (TBI)) provides a measure of the presence (or absence) of peripheral arterial disease. The toe and/or arm pressure may be determined using known techniques, such, but not limited to, a continuous wave Doppler, a sphygmomanometer, and/or pressure cuffs. The TBI may be determine before and/or after the six-minute walk test. A TBI that is lower than 0.7 or higher than 1.3 may indicate the presence of peripheral arterial disease. Additionally, an absolute toe pressure less than 50 millimeters of mercury (mmHg) may indicate critical limb ischemia (if a leg wound forms, it may not heal). The determined TBI(s) are provided to screener application  502  via user interface  504 . 
     Questionnaire data  512  may comprise data collected via one or more questions provided to the patient that are directed to determining the severity of various patient symptoms. Such questions may inquire about the severity of pain and/or the numbness in the patient&#39;s leg and/or foot while walking, the severity of weakness or tiredness in the patient&#39;s leg and/or foot, the severity of pain in the patient&#39;s leg and/or foot while resting, whether the patient has had any pain in the leg and/or foot while resting, etc. The level of severity for each of the symptoms indicated by the patient may be indicative of peripheral arterial disease (where the more severe the symptoms, the more likely the patient has peripheral arterial disease). The answers to the questions are provided to screener application  502  via user interface  504 . 
     Screener application  502  may be configured to use the above-described measuring apparatus data  506 , exercise data  508 , diagnostic test data  510  and/or questionnaire data  512  to provide a comprehensive report of the patient and make an assessment as to whether the patient has peripheral arterial disease and the severity thereof. The severity of peripheral arterial disease may be based on a combination of the data that indicates whether the patient likely has peripheral arterial disease. For example, if each of measuring apparatus data  506 , exercise data  508 , diagnostic test data  510  and/or questionnaire data  512  are indicative of the patient having peripheral arterial disease, then screener application  502  may determine that patient has the most severe case of peripheral arterial disease. Conversely, if none of measuring apparatus data  506 , exercise data  508 , diagnostic test data  510  and/or questionnaire data  512  are indicative of the patient having peripheral arterial disease, screener application  502  may determine that the patient does not have peripheral arterial disease. A weighting of the various data may be combined to generate an overall assessment, which may be compared to one or more threshold levels to indicate the patient does have or does not have peripheral arterial disease, or provide some likelihood in between (e.g., medium likelihood). Severity of peripheral arterial disease may be adjudicated using non-invasive physiological measurements, which are important predictor of outcome than anatomical angiographic measurements. Machine learning techniques may be used and refined to further categorize likelihood and severity of peripheral arterial disease. 
     C. Prescribed Walking Program Compliance 
     After a determination is made that a patient likely has peripheral arterial disease, a healthcare practitioner may prescribe a walking program for the patient to follow. The goal of the walking program is to mitigate the effects of peripheral arterial disease. The patient may carry a computing device that executes a software application that tracks the distance the patient has travelled. The distance travelled may be transmitted to a computing device associated with the healthcare practitioner. The healthcare practitioner&#39;s computing device may assess whether the patient has complied with the prescribed walking program based on the received distance travelled. If it is determined that the patient has not complied with the prescribed walking program, the computing device may transmit escalating reminders to the patient that increase in severity. For example, the first time the patient does not comply with the prescribed walking program, the patient may receive a message via the application that tracks the distance travelled by the user reminding the patient to comply with the prescribed walking program (e.g., “Don&#39;t forget to walk 1000 steps today”). The second time the patient does not comply with the prescribed walking program, the patient may receive a more urgent message (e.g., text message) reminding the patient to comply with the prescribed walking program (“e.g., “URGENT: REFUSAL TO COMPLY WITH YOUR PRESCRIBED WALKING PROGRAM MAY WORSEN YOUR SYMPTOMS!!!”). Thereafter, the patient may receive a phone call from the healthcare practitioner to remind the patient to comply with the prescribed walking program, and ultimately, would receive a phone call from the healthcare practitioner to schedule an appointment therewith. 
       FIG.  6    is a block diagram of a system for tracking compliance of a prescribed walking program in accordance with an embodiment. As shown in  FIG.  6   , system  600  includes a mobile device  602  and a computing device  604 . Mobile device  602  and computing device  604  may each may comprise any of a wide variety of portable electronic devices mentioned herein or otherwise known, including but not limited to a smart phone, a tablet computer, a laptop computer, a wearable computing device, a wearable fitness device, a pedometer, a personal media player, or the like. Mobile device  602  and computing device  604  are each presented herein by way of example only. 
     As shown in  FIG.  6   , mobile device  602  includes a processing unit  606 . Processing unit  604  comprises a central processing unit (CPU), a microprocessor, a multi-core processor, or other integrated circuit that is configured to execute computer program instructions that are retrieved from memory (e.g., memory  614 ), thereby causing certain operations to be performed. As further shown in  FIG.  6   , processing unit  606  is connected to one or more user input components  608 , one or more user output component  610 , one or more sensors  612 , and a memory  614 . 
     User input component(s)  608  may comprise one or more of a touch screen, keypad, button, microphone, camera, or other component suitable for enabling a user to provide input to mobile device  602 . User output component(s)  610  may comprise one or more of a display, audio speaker, haptic feedback element, or other component suitable for providing output to a user of mobile device  602 . 
     Sensor(s)  612  may comprise an accelerometer and/or a gyroscope. The accelerometer may be configured to measure acceleration forces. In an embodiment, the accelerometer comprises a 3-axis accelerometer that is configured to measure acceleration along each of three orthogonal axes of a right-handed mobile device reference frame. The three axes may be denoted the x-axis, the y-axis, and the z-axis. In an embodiment in which mobile device  602  comprises a mobile phone having a generally rectangular display on one side thereof, the x-axis may run along the short side of the display, the y-axis may run along the long side of the display, and the z-axis may run perpendicular to and out of the front of the display. However, other mobile device reference frames may be used. Each acceleration measurement may be represented in meters per second squared (m/s 2 ) or other suitable unit of measurement. The gyroscope may be configured to measure orientation of mobile device  602 . In an embodiment, the gyroscope comprises a 3-axis MEMS gyroscope that is configured to measure a rate of rotation around each of the axes of the aforementioned mobile device reference frame. Each gyroscope measurement may be represented in radians per second (rad/s) or other suitable unit of measurement. 
     Memory  614  comprises one or more volatile or non-volatile memory devices that are operable to store computer program instructions (also referred to herein as computer program logic). These computer program instructions may be retrieved from memory  614  and executed by processing unit  606  in a well-known manner to cause processing unit  606  to perform certain operations. 
     As further shown in  FIG.  6   , memory  614  stores tracker application  616 . Tracker application  616  comprises computer program instructions that, when executed by processing unit  606 , causes processing unit  606  to perform an algorithm for determining a number of steps taken or a distance travelled by a patient within a predetermined time period using data collected from sensor(s)  612 . Based on the teachings provided herein, persons skilled in the relevant art(s) will appreciate that the method for determining a distance travelled by a patient predetermined time period can be implemented by other devices and systems as well. 
     Sensor data indicative of the number of steps and/or distance travelled may be provided to computing device  604  via network  618 . Network  618  may be a LAN (local area network), a WAN (wide area network), or any combination of networks, such as the Internet. Computing device  604  is coupled to network  618  through a communication link  620 , and mobile device  602  is coupled with network  618  through a communication link  622 . Communication links  620  and  622  may each include wired and/or wireless links. Examples of communication links  620  and  622  include IEEE 802.11 wireless LAN (WLAN) wireless links, Worldwide Interoperability for Microwave Access (Wi-MAX) links, cellular network links, wireless personal area network (PAN) links (e.g., Bluetooth™ links), Ethernet links, USB (universal serial bus) links, etc. 
     Computing device  604  may be a device associated with a healthcare practitioner. For example, computing device  604  may be located at the office, hospital, etc. of the healthcare practitioner. As shown in  FIG.  6   , computing device  604  may include a processing unit  624 , input component(s)  626 , user output component(s)  628 , and a memory  630 . Processing unit  624 , user input component(s)  626 , user output component(s)  628 , and memory  630  are examples of processing unit  606 , user input component(s)  608 , user output component(s)  610 , and memory  614 , and therefore, may operate in a similar manner as described above. As shown in  FIG.  6   , memory  630  stores a compliance application  632 . Compliance application  632  comprises computer program instructions that, when executed by processing unit  624 , causes processing unit  624  to perform an algorithm for determining whether a patient has complied with a walking program prescribed by the healthcare practitioner based on the sensor data provided by tracker application  616 . 
     For example, compliance application  632  may provide a user interface that enables a healthcare practitioner to enter in a walking program (e.g., using user input component(s)  626 ) that a patient is to comply with. Examples of a walking program may include, but are not limited to, walking 1000 meters in a day, 5000 meters in a week, etc. Compliance program  632  may compare the number of steps and/or distance travelled within the predetermined time period indicated by the sensor data received from mobile device  602  to the walking program and determine whether the patient has complied with the walking program. If compliance program  632  determines that the patient has not complied with the prescribed walking program, compliance program may transmit escalating reminders to the patient that increase in severity if the patient continues to not comply with the prescribed walking program. For example, the first time the patient does not comply with the prescribed walking program, compliance program  632  may transmit a command to tracker application  616  (e.g., via network  618 ) that causes tracker application  616  to display a message reminding the patient to comply with the walking program. The second time the patient does not comply with the prescribed walking program, compliance program  632  may cause a text message (e.g., a short messaging system (SMS) message) to be transmitted to mobile device  602  and/or other another computing device associated with the patient (e.g., the patient&#39;s smart phone, tablet, etc.). Alternatively, compliance program  632  may display a notification via computing device  604  (e.g., using user output component(s)  628 ) that indicates that the healthcare practitioner should transmit a text message (e.g., by using the healthcare practitioner&#39;s phone) to mobile device  602  and/or another computing device associated with the patient. The third time the patient does not comply with the prescribed walking program, compliance program  632  may display a notification via computing device  604  that indicates that the healthcare practitioner should call the patient to remind the patient to comply with the prescribed walking program. The fourth time the patient does not comply with the prescribed walking program, compliance program  632  may display a notification via computing device  604  that indicates that the healthcare practitioner should call the patient to schedule an appointment for the patient to visit the healthcare practitioner. It is noted that the reminders described do not necessarily have to be transmitted to the patient each time the patient fails to comply with the prescribed walking program. Instead, a reminder may be transmitted every Nth time a patient fails to comply with the prescribed walking program, where N is any integer greater than one. Furthermore, different numbers and types of reminders may be used in other embodiments. 
     Accordingly, in embodiments, compliance of a prescribed walking program may be tracked in many ways. For instance,  FIG.  7    shows a flowchart  700  of a method for tracking compliance of a prescribed walking program in accordance with an embodiment. The method of flowchart  700  may be implemented by a computing device  804  shown in  FIG.  8   .  FIG.  8    depicts a block diagram  800  of computing device  804  in accordance with an embodiment. Computing device  804  is an example of computing device  604  described above with reference to  FIG.  6   . As shown in  FIG.  8   , computing device  804  includes a compliance application  832 . Compliance application  832  is an example of compliance application  632  described above with reference to  FIG.  6   . Compliance application  832  includes compliance determiner  802 , a receiver  806 , and a transmitter  808 . Other structural and operational embodiments will be apparent to persons skilled in the relevant art(s) based on the following discussion regarding flowchart  700  and computing device  804 . 
     Flowchart  700  begins with step  702 . At step  702 , first sensor data is received via a network from a mobile device that indicates a first number of steps a patient has taken within a predetermined time period. For example, with reference to  FIG.  8   , receiver  806  may receive first sensor data  801  via a network (e.g., network  618 , as shown in  FIG.  6   ) from a mobile device (e.g., mobile device  602 , as shown in  FIG.  6   ). First sensor data  801  may indicate a first number of steps a patient has taken within a predetermined time period. First sensor data  801  is provided to compliance determiner  802 . 
     At step  704 , a determination is made that the first number of steps the patient has taken within the predetermined time period is not in compliance with the walking program prescribed by the healthcare practitioner. For example, with reference to  FIG.  8   , compliance determiner  802  determines that the first number of steps the patient has taken within the predetermined time period is not in compliance with the walking program prescribed by the healthcare practitioner. 
     At step  706 , a first message having a first severity is transmitted via the network to a device associated with the patient indicating that the patient did not comply with the prescribed walking program. For example, with reference to  FIG.  8   , compliance determiner  802  generates a first message  803  having a first severity that indicates that the patient did not comply with the prescribed walking program. First message  803  is provided to transmitter  808 . Transmitter  808  transmits first message  803  to a device associated with the patient via the network (e.g., network  618 ). 
     In accordance with one or more embodiments, the device associated with the patient is the mobile device (e.g., mobile device  602 , as shown in  FIG.  6   ). 
     In accordance with one or more embodiments, the device associated with the patient is a computing device associated with the user other than the mobile device (e.g., the patient&#39;s smart phone, tablet, etc.). 
     In accordance with one or more embodiments, the first message is configured to be displayed via an application executing on the device associated with the patient. For example, with reference to  FIG.  6   , the first message is configured to be displayed via tracker application  616  executing on mobile device  602 . For instance, tracker application  616  may cause user output component(s)  610  to display the first message. 
     In accordance with one or more embodiments, the prescribed walking program is based on a distance that the patient is able to walk within the predetermined time period. 
     In accordance with an embodiment, computing device  804  uses a single transceiver rather than a separate transmitter and receiver (i.e., receiver  806  and transmitter  808 ) for performing communication via the network. 
     In some example embodiments, one or more of steps  702 ,  704 , and/or  706  of flowchart  700  may not be performed. Moreover, operations in addition to or in lieu of steps  702 ,  704 , and/or  706  may be performed. Further, in some example embodiments, one or more of steps  702 ,  704 , and/or  706  may be performed out of order, in an alternate sequence, or partially (or completely) concurrently with each other or with other operations. 
     In accordance with one or more embodiments, compliance application  832  is configured to transmit messages of increasing severity as the patient continues to not comply with the prescribed walking program. For instance,  FIG.  9    shows a flowchart  900  of a method for transmitting messages of increasing severity as the patient continues to not comply with a prescribed walking program in accordance with an embodiment. The method of flowchart  900  may be implemented by a computing device  804  shown in  FIG.  8   . Accordingly,  FIG.  9    will be described with continued reference to  FIG.  8   . Other structural and operational embodiments will be apparent to persons skilled in the relevant art(s) based on the following discussion regarding flowchart  900  and computing device  804 . 
     Flowchart  900  begins with step  902 . At step  902 , second sensor data is received via the network from the mobile device that indicates a second number of steps a patient has taken within the predetermined time period. For example, with reference to  FIG.  8   , receiver  806  may receive second sensor data  805  via a network (e.g., network  618 , as shown in  FIG.  6   ) from a mobile device (e.g., mobile device  602 , as shown in  FIG.  6   ). Second sensor data  805  may indicate a second number of steps a patient has taken within the predetermined time period. First sensor data  805  is provided to compliance determiner  802 . 
     At step  804 , a determination is made that the second number of steps the patient has taken within the predetermined time period is not in compliance with the walking program prescribed by the healthcare practitioner. For example, with reference to  FIG.  8   , compliance determiner  802  determines that the second number of steps the patient has taken within the predetermined time period is not in compliance with the walking program prescribed by the healthcare practitioner. 
     At step  806 , a second message having a second severity that is greater than the first severity is transmitted via the network to the device associated with the patient indicating that the patient did not comply with the prescribed walking program. For example, with reference to  FIG.  8   , compliance determiner  802  generates a second message  807  having a second severity that is greater than the first severity indicates that the patient did not comply with the prescribed walking program. Second message  807  is provided to transmitter  808 . Transmitter  808  transmits second message  807  to a device associated with the patient via the network (e.g., network  618 ). 
     In accordance with one or more embodiments, the second message is a text message. 
     In some example embodiments, one or more of steps  902 ,  904 , and/or  906  of flowchart  900  may not be performed. Moreover, operations in addition to or in lieu of steps  902 ,  904 , and/or  906  may be performed. Further, in some example embodiments, one or more of steps  902 ,  904 , and/or  906  may be performed out of order, in an alternate sequence, or partially (or completely) concurrently with each other or with other operations. 
     D. Additional Embodiments 
     1. Tissue Loss Determination 
     Peripheral arterial disease is known to cause tissue loss. It has been observed that such tissue loss can result in the loss of calf circumference. The measuring apparatus described above in Subsection A may be also be used to quantify tissue loss. For example, the measuring apparatus may be used to periodically measure the calf circumference. If the measurements indicate that the measured calf circumference decreases over time, then it may be determined that the patient has suffered from tissue loss. 
     2. Fall Risk Assessment 
     A leading cause of death in the United States among the elderly is falling. One contributed factor may be a smaller calf circumference, along with other factors, such as visual acuity, age of shoes and certain environment issues (e.g., loose rugs, a lack of handicap handles at the home, etc.). In accordance with an embodiment, screener application  502 , as described above with reference to  FIG.  5   , may be configured to determine the risk of the patient falling based on the measured calf circumference and the additional factors described above. For example, the patient may be required to fill out a questionnaire asking the patient to identify the above-described factors. The patient&#39;s answers may be provided to screener application  502  as questionnaire data  512 , along with measuring apparatus data  506  (i.e., measured calf circumference). Screener application  502  may be configured to assess the risk of the patient falling based on questionnaire data  512  and/or measuring apparatus data  506  and provide a score of the risk of the patient falling accordingly. 
     III. Further Example Embodiments 
     A device, as defined herein, is a machine or manufacture as defined by 35 U.S.C. § 101. That is, as used herein, the term “device” refers to a machine or other tangible, manufactured object and excludes software and signals. Devices may include digital circuits, analog circuits, or a combination thereof. Devices may include integrated circuits (ICs), one or more processors (e.g., central processing units (CPUs), microprocessors, digital signal processors (DSPs), etc.) and/or may be implemented with any semiconductor technology, including one or more of a Bipolar Junction Transistor (BJT), a heterojunction bipolar transistor (HBT), a metal oxide field effect transistor (MOSFET) device, a metal semiconductor field effect transistor (MESFET) or other transconductor or transistor technology device. Such devices may use the same or alternative configurations other than the configuration illustrated in embodiments presented herein. 
     Techniques and embodiments, including methods, described herein may be implemented in hardware (digital and/or analog) or a combination of hardware and software and/or firmware. Techniques described herein may be implemented in one or more components. Embodiments may comprise computer program products comprising logic (e.g., in the form of program code or instructions as well as firmware) stored on any computer useable storage medium, which may be integrated in or separate from other components. Such program code, when executed in one or more processors, causes a device to operate as described herein. Devices in which embodiments may be implemented may include storage, such as storage drives, memory devices, and further types of computer-readable media. Examples of such computer-readable storage media include, but are not limited to, a hard disk, a removable magnetic disk, a removable optical disk, flash memory cards, digital video disks, random access memories (RAMs), read only memories (ROM), and the like. In greater detail, examples of such computer-readable storage media include, but are not limited to, a hard disk associated with a hard disk drive, a removable magnetic disk, a removable optical disk (e.g., CDROMs, DVDs, etc.), zip disks, tapes, magnetic storage devices, MEMS (micro-electromechanical systems) storage, nanotechnology-based storage devices, as well as other media such as flash memory cards, digital video discs, RAM devices, ROM devices, and the like. Such computer-readable storage media may, for example, store computer program logic, e.g., program modules, comprising computer executable instructions that, when executed, provide and/or maintain one or more aspects of functionality described herein with reference to the figures, as well as any and all components, steps and functions therein and/or further embodiments described herein. 
     Computer readable storage media are distinguished from and non-overlapping with communication media. Communication media embodies computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media as well as wireless media such as acoustic, RF, infrared and other wireless media. Example embodiments are also directed to such communication media. 
     The peripheral arterial disease assessment, fall risk assessment and/or the tracking of a prescribed walking program compliance embodiments and/or any further systems, sub-systems, and/or components disclosed herein may be implemented in hardware (e.g., hardware logic/electrical circuitry), or any combination of hardware with software (computer program code configured to be executed in one or more processors or processing devices) and/or firmware. 
     The embodiments described herein, including systems, methods/processes, and/or apparatuses, may be implemented using well known processing devices, telephones (smart phones and/or mobile phones), servers, electronic devices (e.g., consumer electronic devices) and/or, computers, such as a computer  1000  shown in  FIG.  10   . It should be noted that computer  1000  may represent communication devices, processing devices, servers, and/or traditional computers in one or more embodiments. For example, computing device  500 , screener application  502 , tracker application  616 , compliance application  632 , mobile device  602 , computing device  604 , computing device  804 , and/or compliance application  832  (as described above with reference to  FIGS.  5 ,  6 , and  8   , respectively), and/or any of the sub-systems, components or sub-components respectively contained therein, may be implemented using one or more computers  1000 . 
     Computer  1000  can be any commercially available and well known communication device, processing device, and/or computer capable of performing the functions described herein, such as devices/computers available from International Business Machines®, Apple®, Sun®, HP®, Dell®, Cray®, Samsung®, Nokia®, etc. Computer  1000  may be any type of computer, including a desktop computer, a server, etc. 
     Computer  1000  includes one or more processors (also called central processing units, or CPUs), such as a processor  1006 . Processor  1006  is connected to a communication infrastructure  1002 , such as a communication bus. In some embodiments, processor  1006  can simultaneously operate multiple computing threads. 
     Computer  1000  also includes a primary or main memory  1008 , such as random access memory (RAM). Main memory  1008  has stored therein control logic  1024  (computer software), and data. 
     Computer  1000  also includes one or more secondary storage devices  1010 . Secondary storage devices  1010  include, for example, a hard disk drive  1012  and/or a removable storage device or drive  1014 , as well as other types of storage devices, such as memory cards and memory sticks. For instance, computer  1000  may include an industry standard interface, such a universal serial bus (USB) interface for interfacing with devices such as a memory stick. Removable storage drive  1014  represents a floppy disk drive, a magnetic tape drive, a compact disk drive, an optical storage device, tape backup, etc. 
     Removable storage drive  1014  interacts with a removable storage unit  1016 . Removable storage unit  1016  includes a computer useable or readable storage medium  1018  having stored therein computer software  1026  (control logic) and/or data. Removable storage unit  1016  represents a floppy disk, magnetic tape, compact disk, DVD, optical storage disk, or any other computer data storage device. Removable storage drive  1014  reads from and/or writes to removable storage unit  1016  in a well-known manner. 
     Computer  1000  also includes input/output/display devices  1004 , such as touchscreens, LED and LCD displays, monitors, keyboards, pointing devices, etc. 
     Computer  1000  further includes a communication or network interface  1018 . Communication interface  1020  enables computer  1000  to communicate with remote devices. For example, communication interface  1020  allows computer  1000  to communicate over communication networks or mediums  1022  (representing a form of a computer useable or readable medium), such as LANs, WANs, the Internet, etc. Network interface  1020  may interface with remote sites or networks via wired or wireless connections. 
     Control logic  1028  may be transmitted to and from computer  1000  via the communication medium  1022 . 
     Any apparatus or manufacture comprising a computer useable or readable medium having control logic (software) stored therein is referred to herein as a computer program product or program storage device. This includes, but is not limited to, computer  1000 , main memory  1008 , secondary storage devices  1010 , and removable storage unit  1016 . Such computer program products, having control logic stored therein that, when executed by one or more data processing devices, cause such data processing devices to operate as described herein, represent embodiments of the invention. 
     Any apparatus or manufacture comprising a computer useable or readable medium having control logic (software) stored therein is referred to herein as a computer program product or program storage device. This includes, but is not limited to, a computer, computer main memory, secondary storage devices, and removable storage units. Such computer program products, having control logic stored therein that, when executed by one or more data processing devices, cause such data processing devices to operate as described herein, represent embodiments of the inventive techniques described herein. 
     IV. CONCLUSION 
     While various embodiments have been described above, it should be understood that they have been presented by way of example only, and not limitation. It will be apparent to persons skilled in the relevant art(s) that various changes in form and detail can be made therein without departing from the spirit and scope of the embodiments. Thus, the breadth and scope of the embodiments should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.