Abstract:
A cognitive behavioral therapy (CBT) method, system and application for treating disorders/conditions such as e.g., insomnia, smoking cessation, alcohol addiction, depression, and nightmares, among others.

Description:
CROSS REFERENCE TO RELATED APPLICATION 
       [0001]    This application claims priority to U.S. Provisional Application Ser. No. 62/193,419, filed Jul. 16, 2015, the entirety of which is incorporated herein by reference. 
     
    
     FIELD OF THE INVENTION 
       [0002]    Embodiments disclosed herein provide a cognitive behavioral therapy (CBT) method, system and application for treating disorders/conditions such as e.g., insomnia, smoking cessation, alcohol addiction, depression, and nightmares, among others. 
       BACKGROUND 
       [0003]    As used herein, “traditional therapy” is defined by the following case study steps for a patient dealing with a disorder:
       In episodic care, a patient has not seen a doctor for treatment for many months, yet the patient has many thoughts and symptoms that are not captured or the doctor must ask about and record using the rudimentary pen and paper technique.   The patient has to call the doctor&#39;s office, wait on the phone, and get an appointment typically weeks after the call.   The patient has to fill out many forms because of HIPAA laws; sometimes the patient fills out surveys/questionnaires, which are evidence-based screening tools, to rule out other disorders (e.g. restless leg syndrome, sleep apnea) in order to diagnose/treat the reason for visit (e.g., insomnia).   The patient has to wait long time in waiting room.   The patient is seen by a nurse or doctor to get his/her vital signs.   The doctor finally meets the patient who starts by asking many questions to get qualitative measures and objective measures.   The doctor asks the patient to review surveys/questionnaires and then needs time to score the answers.   The doctor decides to treat the patient with a CBT method, which is module-based—meaning usually in weekly or 2-week intervals; CBT can be face-to-face, telephone, or via a web portal. This, however, places a heavy burden on the patient to come to the office weekly for “face to face therapy.” Places a burden on patient to call-in as part of “phone CBT therapy” or places a burden on patient to use a web/Internet based CBT; assuming that the patient has broadband access.   The patient wishes to accelerate the program, rather than have it drag on for 6-8 weeks.   The patient is left “lost” needing a guide or a Sherpa on days when patient cannot see his/her physician/nurse; patient cannot share information with the care coordination team (doctor, nurse, psychologist, physical therapist).       
 
         [0014]    As can be seen, the traditional therapy methodology is slow, inefficient and does not provide the patient with the ability to accelerate its CBT program. Accordingly, there is a need and desire to provide the patient with a better CBT program, from initial diagnosis through the treatment plan, which is flexible to meet the patient&#39;s needs. Moreover, with the existence of wearable and other portable patient monitoring devices, there is a need and desire to automatically input data from these devices to better determine the health of the patient and status of the patient&#39;s treatment. 
       SUMMARY 
       [0015]    Embodiments disclosed herein combine therapy and/or treatment with the full, dynamic features of a mobile device (e.g., smartphone or tablet). 
         [0016]    The principles disclosed herein provide an alternative and much better CBT method than the traditional therapy experience discussed above. The principles disclosed herein are designed to allow a potential patient the ability to obtain CBT for a disorder/condition using a mobile device (i.e., his/her smartphone or tablet) running an application comprising the disclosed principles. For example, appointments can be scheduled using the mobile device via the disclosed application. The application will allow the patient to fill-out online surveys and other forms to provide an online pre-screening of the patient&#39;s reason for a visit or CBT (thus, using more of the face time with a doctor for treatment rather than paperwork). Wearable devices implementing health monitoring sensors could be used to feed the patient&#39;s vital signs to the application and thus wirelessly to the doctor or nurse (saving time). Once a CBT program is initiated, the application can guide the patient through the CBT and record the progress, to name a few features of the disclosed principles. 
         [0017]    In one embodiment, a computer implemented method for implementing cognitive behavioral therapy (CBT) for a patient is provided. The method comprises inputting a patient profile via an application executing on a processor of a patient device, said application being adapted to input data directly from the patient and from one or more wearable electronic devices associated with the patient; analyzing the profile to generate a CBT plan for the patient; monitoring patient data and other input data obtained as the patient implements the CBT plan to determine a status of the CBT plan; and determining if the CBT requires adjustment based on the monitored patient data and other input data. 
         [0018]    In another embodiment, a system for implementing cognitive behavioral therapy (CBT) for treating a patient is provided. The system comprises an application program to be executed by a processor of a patient device, said application being adapted to: input a patient profile by inputting data directly from the patient and from one or more wearable electronic devices associated with the patient; analyze the profile to generate a CBT plan for the patient; monitor patient data and other input data obtained as the patient implements the CBT plan to determine a status of the CBT plan; and determine if the CBT requires adjustment based on the monitored patient data and other input data. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0019]      FIG. 1  illustrates an example processing flow performed by an embodiment disclosed herein. 
           [0020]      FIG. 2  illustrates an example processing flow performed by an embodiment used for CBT for insomnia. 
           [0021]      FIG. 3  illustrates an example processing flow performed by an embodiment used for CBT for smoking. 
           [0022]      FIG. 4  illustrates an example system for implementing the disclosed application and method in accordance with the disclosed principles. 
           [0023]      FIGS. 5-9  illustrate example screenshots and functionality provided by the disclosed CBT application when used to treat insomnia in accordance with the disclosed principles. 
           [0024]      FIGS. 10-14  illustrate example screenshots and functionality provided by the disclosed CBT application when used for smoking cessation in accordance with the disclosed principles. 
       
    
    
     DETAILED DESCRIPTION 
       [0025]    In this disclosure the term patient, user or client maybe used interchangeably as a person or persons seeking treatment for a disorder/condition or disease that can be treated by a CBT program. 
         [0026]    Accordingly, in one embodiment, the principles disclosed herein can provide a flexible therapy/treatment for a patient&#39;s disorder/condition via an application running on the patient&#39;s mobile device. The application is evidence-based (i.e., proven in the clinical world with efficacy and/or effectiveness data). To build such an application, in one embodiment, booklets/programs that are used in traditional face-to-face sessions/hospitals/clinics are converted and stored in one or more databases accessible by or stored on the mobile device. In one embodiment, an information architect is used to interview the clinician and maintain the integrity of the treatment program while also keeping the special healthcare “tone and voice.” It should be appreciated that the format of a graphical user interface operated by the application is a complicated issue because of the device&#39;s small screen size. Moreover, the application will be able to communicate with a mobile health cloud system so a care coordination team can monitor and track the patient&#39;s treatment, and be a risk-detection platform. 
         [0027]    The disclosed principles will convert the traditional therapy into a simple and more enjoyable experience that will allow patients a better opportunity to obtain and follow-through on a prescribed therapy: 
         [0000]    
       
         
               
             
               
               
               
             
           
               
                 TABLE 1 
               
             
             
               
                   
               
               
                 Improvements over traditional therapy 
               
             
          
           
               
                   
                 From this 
                 To This 
               
               
                   
                   
               
               
                   
                 Linear, paper based 
                 Random access 
               
               
                   
                 Desk experience, face to face 
                 Anywhere experience 
               
               
                   
                 Rely on recall 
                 Triggered by life action 
               
               
                   
                 Start with lessons 
                 Start with an activity 
               
               
                   
                 Rely on user motivation 
                 Supplements motivation with 
               
               
                   
                   
                 device capabilities (reminders, 
               
               
                   
                   
                 gamifications, visualizations) 
               
               
                   
                   
               
             
          
         
       
     
         [0028]    In one example embodiment, and in reference to  FIG. 1 , a patient uses the disclosed application or activates the application by going through its different sections to perform CBT processing  10  as follows. 
         [0029]    The patient fills out a profile section on the application so that that application captures a baseline and rules out other diseases/disorders/conditions not related to the patient&#39;s current disease/disorder/condition (step  12 ). This can be a questionnaire provided by the application that is already established and used by the clinic (digitized and available to the mobile device), which can be scored based on the patient&#39;s answers. Alternatively, or in addition to, the application can co-create with the patient a plan on how to treat the patient&#39;s disorder, etc. (e.g., stop smoking). For a smoker wishing to stop smoking, a profile can be used to determine the type of smoker the patient is and to set up a preliminary goal/plan to cease smoking. Accordingly, based on the patient&#39;s input information and/or information input by one or more devices connected to or worn by the patient, the process  10  analyzes the patient&#39;s condition (step  14 ). In addition, the disclosed principles can create a “My Plan” which gathers results of activities and questionnaires into a personalized plan. At this point, the patient&#39;s CBT is initiated (step  16 ). 
         [0030]    At this point, the disclosed application can monitor inputs from the patient and/or one or more devices connected to or worn by the patient (step  18 ). The patient can track its habit (e.g., sleep, smoking, etc.) by using a calendar function on the mobile device, which can be input into the disclosed CBT application. For example, for a sleeping disorder, the times in which the patient slept and woke up can be entered and the application can do calculations to obtain metrics such as e.g., sleep efficiency or time in bed (that can be charted) 
         [0031]    The disclosed CBT application can allow the patient to journal and write narrative comments (e.g., the patient can use its phone to capture behaviors and events that may influence the therapy/disease (e.g., insomnia, depression, smoking cessation)). The logs are rich in data so that the application can calculate key metrics and create visualizations from them. The logs can track the patient&#39;s CBT progress. The patient can use the mobile device&#39;s camera to take pictures of his/her health condition. The logs can capture when a patient “slips” or has a “trigger” of its bad behavior—the disclosed CBT application provides easy access to the patient so that he/she can note triggers, cravings, etc. so the application can tailor a personalized plan (e.g., an individualized quitting plan for a smoker). With this information, the process can provide the patient with status and progress of the CBT (step  22 ). Changes to the CBT plan can be made by the patient and/or application if needed (step  22 ). The process continues at step  18  until the CBT has been completed. 
         [0032]    In one embodiment, the disclosed CBT application will have a “behavior” section that educates the patient about enhancing behaviors. The application can set goals and/or rewards for behavior changes. The application can tell what medications may help with stopping bad behavior. 
         [0033]    In one embodiment, the disclosed CBT application will have a “cognitive” section that addresses unhelpful thinking. For example, the CBT application advises the patient of smoking dangers and how to deal with common side-effects of smoking cessation; patients can create coping responses for challenging situations and get reminders when a tough event appears. 
         [0034]    In one embodiment, the disclosed CBT application will have a “maintenance” section that creates a personalized reference and provides tips for handling lapses. For example, the disclosed CBT application can monitor the patient and keep him/her on track; it can deal with withdrawal symptoms and side effects; and can offer tips and techniques for maintaining the therapy. 
         [0035]    In one embodiment, the disclosed CBT application will have a “bookshelf” section that provides a central location for reference information provided by the application. The disclosed CBT application leverages the interactivity of e-health solutions, including questionnaires, checklists, as well as easy access to key content and tools, and a bookshelf for reference material. It can incorporate reminders and encouraging messaging throughout the CBT. The disclosed CBT application can integrate with Zansors sensors (e.g., sleep, breathing, EKG, etc.) for automatic loading of objective data from the sensors. 
         [0036]    As shown in  FIG. 2 , the disclosed CBT application can implement CBT for insomnia. Based on data gathered by one or more processors through user and/or sensor input (e.g., see  FIG. 4 ), the CBT application can automatically determine appropriate CBT to address the user&#39;s specific insomnia issues and modify CBT based on feedback obtained while therapy is ongoing (e.g., steps  14 - 22  of  FIG. 1 ). For example, the CBT application may help users sleep better by monitoring their sleep behaviors and logs and, using an artificial intelligence process, providing recommendations for when the user should go to sleep and start their pre-bedtime time routine. The CBT application may analyze the patient&#39;s sleep logs, calculate sleep metrics, and, based on these metrics, recommend a time to go to sleep that will result in maximum sleep efficiency and, over time, longer total sleep time. 
         [0037]    The following metrics apply to the example process of  FIG. 2 . 
         [0000]    
       
         
               
             
               
               
               
             
           
               
                 TABLE 2 
               
             
             
               
                   
               
               
                 Insomnia CBT Metrics 
               
             
          
           
               
                 Metric 
                 Variable 
                 Unit 
               
               
                   
               
               
                 Time (user) got into bed 
                 a 
                 Clock time 
               
               
                 Time it took to fall asleep 
                 b 
                 Measured time 
               
               
                 Number of times (user) woke up 
                 c 
                 Dimensionless 
               
               
                 Time spent awake during the night (net/total) 
                 d 
                 Measured time 
               
               
                 Final time (user) woke up 
                 f 
                 Clock time 
               
               
                 Time (user) left bed 
                 g 
                 Clock time 
               
               
                 Specified number of days (user) has logged 
                 h 
                 Days 
               
               
                 successfully 
               
               
                   
               
             
          
         
       
     
         [0038]    Functions: 
         [0000]      Δ t  
 
         [0039]    Meaning: 
         [0040]    How long between two times 
         [0041]    Definition: 
         [0042]    Δt (t 1 , t 2 )=The time between clock times t 1  and t 2    
         [0043]    e.g. Δt(18:00, 19:15)=1 hour, 15 minutes=75 minutes 
         [0044]    Time in Bed—B
       Meaning:
           How long (user) was in bed (in hours and minutes or minutes)   
           Definition:       
 
         [0000]        B=Δt ( a,g ) 
         [0048]    Total Sleep Time—T
       Meaning:       
 
         [0050]    How long (user) was sleeping in a given night.
       Definition:       
 
         [0000]    
       
         
           
             
               
                 
                   T 
                   = 
                   
                     B 
                     - 
                     
                       [ 
                       
                         b 
                         + 
                         d 
                         + 
                         
                           Δ 
                            
                           
                               
                           
                            
                           
                             t 
                              
                             
                               ( 
                               
                                 f 
                                 , 
                                 g 
                               
                               ) 
                             
                           
                         
                       
                       ] 
                     
                   
                 
               
             
             
               
                 
                   = 
                   
                     
                       Δ 
                        
                       
                           
                       
                        
                       
                         t 
                          
                         
                           ( 
                           
                             a 
                             , 
                             g 
                           
                           ) 
                         
                       
                     
                     - 
                     
                       [ 
                       
                         b 
                         + 
                         d 
                         + 
                         
                           Δ 
                            
                           
                               
                           
                            
                           
                             t 
                              
                             
                               ( 
                               
                                 f 
                                 , 
                                 g 
                               
                               ) 
                             
                           
                         
                       
                       ] 
                     
                   
                 
               
             
           
         
       
       
         
           
             Mean Total Sleep Time— T 
           Meaning:
               The mean Total Sleep Time (T) for (user) over days logged (h)   
               Definition:   
         
           
         
       
     
         [0000]    
       
         
           
             
               T 
               _ 
             
             = 
             
               
                 1 
                 h 
               
                
               
                 ( 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     h 
                   
                    
                   
                       
                   
                    
                   
                     T 
                     i 
                   
                 
                 ) 
               
             
           
         
       
       
         
           
             Sleep Efficiency—F
           Meaning:   
         
           
         
       
     
         [0058]    How efficiently is (user) spending their time in bed? i.e. ratio of Time Asleep (T) to Time in Bed (B)
       Definition:       
 
         [0000]    
       
         
           
             
               
                 
                   F 
                   = 
                   
                     T 
                     B 
                   
                 
               
             
             
               
                 
                   = 
                   
                     
                       
                         Δ 
                          
                         
                             
                         
                          
                         
                           t 
                            
                           
                             ( 
                             
                               a 
                               , 
                               g 
                             
                             ) 
                           
                         
                       
                       - 
                       
                         [ 
                         
                           b 
                           + 
                           d 
                           + 
                           
                             Δ 
                              
                             
                                 
                             
                              
                             
                               t 
                                
                               
                                 ( 
                                 
                                   f 
                                   , 
                                   g 
                                 
                                 ) 
                               
                             
                           
                         
                         ] 
                       
                     
                     
                       Δ 
                        
                       
                           
                       
                        
                       
                         t 
                          
                         
                           ( 
                           
                             f 
                             , 
                             g 
                           
                           ) 
                         
                       
                     
                   
                 
               
             
           
         
       
       
         
           
             Mean Sleep Efficiency— F 
           Meaning:
               Mean Sleep Efficiency ( F ) for (user) over days logged (h)   
               Definition:   
         
           
         
       
     
         [0000]    
       
         
           
             
               F 
               _ 
             
             = 
             
               
                 1 
                 h 
               
                
               
                 ( 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     h 
                   
                    
                   
                       
                   
                    
                   
                     F 
                     i 
                   
                 
                 ) 
               
             
           
         
       
     
         [0064]    The following inputs and outputs apply to the example process of  FIG. 2 . 
         [0065]    Inputs: 
         [0000]    
       
         
               
             
               
               
               
               
             
           
               
                 TABLE 3 
               
             
             
               
                   
               
               
                 Sleep Restriction Therapy (SRT) Inputs 
               
             
          
           
               
                 Metric 
                 Variable 
                 Unit 
                 (Source) 
               
               
                   
               
               
                 Sleep Efficiency 
                 F 
                 Percent 
                 (Calculated) 
               
               
                 Total Sleep Time 
                 T 
                 Measured time 
                 (Calculated) 
               
               
                 Target Wakeup Time 
                 j 
                 Clock time 
                 (User) 
               
               
                 Sleep Time Goal 
                 k 
                 Measured time 
                 (User) 
               
               
                 Pre-Bedtime Routine Length 
                 L 
                 Measured time 
                 (Calculated) 
               
               
                 Required number of days at 
                 h 
                 Days 
                 (User) 
               
               
                 sustained good sleep (logging 
               
               
                 interval) 
               
               
                 Sleep Extension Time Interval 
                 t SRT   
                 Measured time 
                 (User) 
               
               
                   
               
             
          
         
       
     
         [0066]    Outputs: 
         [0000]    
       
         
               
             
               
               
               
               
             
           
               
                 TABLE 4 
               
             
             
               
                   
               
               
                 SRT Outputs 
               
             
          
           
               
                   
                 Metric 
                 Variable 
                 (Source) 
               
               
                   
                   
               
               
                   
                 Restricted Bedtime 
                 m 
                 (Calculated) 
               
               
                   
                 Pre-Bedtime Routine Start Time 
                 n 
                 (Calculated) 
               
               
                   
                 Restriction/Extension Chart 
                 (n/a) 
                 (Calculated) 
               
               
                   
                   
               
             
          
         
       
     
         [0067]    Functions: 
         [0068]    Trimmed Mean SRT Total Sleep Time— T   SRT , 
         [0069]    Meaning: 
         [0070]    The average sleep time in the previous h days. The day with the lowest sleep time is omitted. We compute the mean of the remaining (h−1) days as defined below, where the T 1 &#39;s are the sleep (minutes) for the (h−1) days being counted 
         [0071]    Note: this is used once, for the calculation of the initial restricted Sleep Time (T SRT(1) . 
         [0072]    Definition: 
         [0000]    
       
         
           
             
               
                 T 
                 _ 
               
               SRT 
             
             = 
             
               
                 1 
                 
                   ( 
                   
                     
                       h 
                       SRT 
                     
                     - 
                     1 
                   
                   ) 
                 
               
                
               
                 ( 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     
                       ( 
                       
                         
                           h 
                           SRT 
                         
                         - 
                         1 
                       
                       ) 
                     
                   
                    
                   
                       
                   
                    
                   
                     T 
                     i 
                   
                 
                 ) 
               
             
           
         
       
       
         
           
             Trimmed Mean SRT Sleep Efficiency— F   SRT    
           
         
       
     
         [0074]    Meaning: 
         [0075]    Over D days, the lowest value of F is thrown out, and  F   SRT  is calculated from remaining D−1 days. 
         [0076]    Note: this is a moving average, and is continually updated each day to include only the past D calendar days. 
         [0077]    Definition: 
         [0000]    
       
         
           
             
               
                 F 
                 _ 
               
               SRT 
             
             = 
             
               
                 1 
                 
                   ( 
                   
                     
                       h 
                       SRT 
                     
                     - 
                     1 
                   
                   ) 
                 
               
                
               
                 ( 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     
                       ( 
                       
                         
                           h 
                           SRT 
                         
                         - 
                         1 
                       
                       ) 
                     
                   
                    
                   
                       
                   
                    
                   
                     F 
                     i 
                   
                 
                 ) 
               
             
           
         
       
     
         [0078]    Pre-Bedtime Routine Length—L
       Meaning:       
 
         [0080]    The pre-bedtime routine is a series of activities the user does before getting in bed. Its length is simply how long it will take the user to complete all activities.
       Definition:       
 
         [0082]    Given (x) pre-bedtime activities—each with chronological length (l i ), 
         [0000]    
       
         
           
             L 
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 x 
               
                
               
                   
               
                
               
                 1 
                 i 
               
             
           
         
       
     
         [0083]    Restricted Bedtime—m
       Meaning:       
 
         [0085]    What time to get in bed on each day of the Sleep Restriction Therapy Schedule. 
         [0086]    Restricted bedtime starts one SRT Total Sleep Time increment (T SRT(i) ) before Target Wakeup Time (j). 
         [0087]    Note: details on T SRT(i)  are on Calculating SRT Total Sleep Time
       Definition:
           For the i th  day of SRT,   
               
 
         [0000]    
       
      
       m=j−T 
       SRT(i)  
      
       
         
           
             Pre-Bedtime Routine Start—n 
             Meaning: 
           
         
       
     
         [0092]    What clock-time each day of SRT that (user) should begin their Pre-Bedtime Routine in order to finish Pre-Bedtime Routine and be in bed by the Restricted Bedtime (m) of that day of SRT.
       Definition:
           For the i th  day of SRT,   
               
 
         [0000]    
       
      
       n=m−L  
      
       
         
           
             Restriction/Extension Chart 
             Definition: 
           
         
       
     
         [0097]    The Restriction/Extension Chart is a chart that displays information and provides feedback about (users′) ongoing SRT course. 
         [0098]    The following SRT total sleep time increment calculations apply to the example process of  FIG. 2 . 
         [0000]    
       
         
               
             
               
               
               
               
             
           
               
                 TABLE 5 
               
             
             
               
                   
               
               
                 SRT Calculation Metrics 
               
             
          
           
               
                 Metric 
                 Variable 
                 Unit 
                 (Source) 
               
               
                   
               
               
                 Trimmed Mean SRT Total 
                 
                   T 
                   SRT 
                 
                 Measured time 
                 (Calculated) 
               
               
                 Sleep Time 
               
               
                 Sleep Extension Time Interval 
                 t SRT   
                 Measured time 
                 (User) 
               
               
                 Sleep Time Goal 
                 k 
                 Measured time 
                 (User) 
               
               
                 Number of Sleep Extension 
                 Z 
                 Dimensionless 
                 (Calculated) 
               
               
                 Intervals 
               
               
                 Final Sleep Extension Interval 
                 t SRT(Z−1)   
                 Measured time 
                 (Calculated) 
               
               
                   
               
             
          
         
       
     
         [0099]    Initial Sleep Time Increment: 
         [0000]    
       
      
       T 
       SRT(1) 
       = T   
       SRT  
      
     
         [0100]    Final Sleep Time: 
         [0000]    
       
      
       T 
       SRT(Z) 
       =k  
      
     
         [0101]    Final Sleep Extension Interval: 
         [0000]    
       
         
           
             
               
                 
                   
                     t 
                     
                       SRT 
                        
                       
                         ( 
                         
                           Z 
                           - 
                           1 
                         
                         ) 
                       
                     
                   
                   = 
                   
                     
                       ( 
                       
                         
                           T 
                           
                             SRT 
                              
                             
                               ( 
                               Z 
                               ) 
                             
                           
                         
                         - 
                         
                           T 
                           
                             SRT 
                              
                             
                               ( 
                               1 
                               ) 
                             
                           
                         
                       
                       ) 
                     
                      
                     
                       mod 
                        
                       
                         ( 
                         
                           t 
                           SRT 
                         
                         ) 
                       
                     
                   
                 
               
             
             
               
                 
                   = 
                   
                     
                       ( 
                       
                         k 
                         - 
                         
                           
                             T 
                             _ 
                           
                           SRT 
                         
                       
                       ) 
                     
                      
                     
                       mod 
                        
                       
                         ( 
                         
                           t 
                           SRT 
                         
                         ) 
                       
                     
                   
                 
               
             
           
         
       
     
         [0102]    Number of Sleep Extension Intervals: 
         [0000]    
       
         
           
             Z 
             = 
             
               
                 ( 
                 
                   k 
                   - 
                   
                     ( 
                     
                       
                         
                           T 
                           _ 
                         
                         SRT 
                       
                       + 
                       
                         t 
                         
                           SRT 
                            
                           
                             ( 
                             
                               Z 
                               - 
                               1 
                             
                             ) 
                           
                         
                       
                     
                     ) 
                   
                 
                 ) 
               
               
                 t 
                 SRT 
               
             
           
         
       
     
         [0103]    SRT Total Sleep Time increment for i={1,2, . . . , Z−1, Z}: 
         [0000]    
       
      
       T 
       SRT(1) 
       = T   
       SRT  
      
     
         [0000]    
       
      
       T 
       SRT(2) 
       =T 
       SRT(1) 
       +t 
       SRT  
      
     
         [0000]        T   SRT(i)   =T   SRT(1) +( i− 1) t   SRT    
         [0000]        T   SRT(Z-1)   =T   SRT(1) +( Z− 2) t   SRT    
         [0000]        T   SRT(Z)   =T   SRT(1) +( Z− 2) t   SRT   +t   SRT(Z-1)    
         [0104]    CBT application may receive user-entered baseline sleep log data (step  102 ), and CBT application may use the baseline data to calculate T(bar) SRT , F(bar) SRT , and L (step  104 ). User may enter j, k, h SRT , and t SRT  (step  106 ), and T SRT(1)  (step  108 ). CBT application may give the user upcoming times (m), (n) automatically based on these calculations (step  110 ). At (n), CBT application may guide the user through a pre-bedtime routine (step  112 ). At (m), CBT application may remind the user to get into bed (step  114 ). 
         [0105]    CBT application may update the CBT based on feedback. For example, when the user awakens, CBT application may receive user-entered sleep log data (step  116 ) and determine whether the user allocated enough time to the pre-bedtime routine tasks (step  118 ). If not, CBT application may determine updated pre-bedtime routine task timing based on how long user took previously and advise user of the updated times (step  120 ). Thereafter, or if user allocated enough time, CBT application may determine whether F SRT  is greater than or equal to 85% (step  122 ). If so, CBT application may display a congratulatory message (step  124 ). CBT application may determine whether F(bar) SRT  is greater than or equal to 85% (step  126 ). If not, CBT application may give user times m and n (step  110 ), and if so, CBT application may display a congratulatory message (step  128 ) and update T SRT(i)  to T SRT(i+1)  (step  130 ). CBT application may determine whether i+1=Z (step  132 ). If not, CBT application may update m and n (step  134 ) and give m and n to user (step  110 ). If so, CBT application may display a congratulatory message (step  136 ) and indicate that user is finished with the course of SRT (step  138 ). 
         [0106]    As shown in  FIG. 3 , the disclosed CBT application can implement CBT for smoking cessation. Based on data gathered by one or more processors through user and/or sensor input (e.g., see  FIG. 4 ), the CBT application can automatically determine appropriate CBT to address the user&#39;s specific smoking cessation issues and modify CBT based on feedback obtained while therapy is ongoing (e.g., steps  14 - 22  of  FIG. 1 ). For example, the CBT application may help users determine appropriate smoking cessation therapies based on user reporting on whether previously tried therapies and/or other behaviors improve or worsen their smoking habits. 
         [0107]    CBT application may present a survey to the user (e.g., see  FIG. 14 ) and receive a user answer to a question, such as whether the user has tried nicotine replacement therapy (step  202 ). If the user has not tried the therapy, CBT application may recommend nicotine replacement therapy (step  204 ). If the user has tried the therapy, CBT application may determine an appropriate follow-up question, such as whether the therapy worked for three months (step  206 ). If it worked, CBT application may recommend nicotine replacement therapy (step  208 ). If it did not work, CBT application may determine another therapy to suggest, such as bupropion, and ask the user whether he or she has tried it (step  210 ). If not, CBT application may suggest trying bupropion (step  212 ). If so, CBT application may determine an appropriate follow-up question, such as whether it worked for three months (step  214 ). If it worked, CBT application may suggest trying bupropion (step  216 ). If not, CBT application may determine another therapy to suggest, such as a combination of nicotine replacement and bupropion, and ask the user whether he or she has tried it (step  218 ). If the user has not tried it, CBT application may recommend it (step  220 ). If the user has tried it, CBT application may determine an appropriate follow-up question, such as whether it worked for three months (step  222 ). If nicotine replacement and bupropion did not work, CBT application may recommend a different treatment, such as bupropion (step  224 ). If nicotine replacement and bupropion worked, CBT application may recommend it (step  226 ). After any recommendation, CBT application may provide a side effects link to give the user more information about the recommended therapy  228 . 
         [0108]    The example processing  100 / 200  of  FIGS. 2 and 3  illustrate two possible use cases and therapy paths for the CBT application. However, the CBT application is flexible enough to select, monitor, and modify any type of CBT. In some cases, a single user may use the CBT application to receive CBT for multiple maladies at the same time, for example. The CBT application provides a platform that can integrate any number of CBT courses into a single interface for a user. 
         [0109]    In one embodiment, the above described method, system and application are implemented in software (i.e., computer instructions) that are stored in a computer readable memory and executed by a processor on both a patient device and a system server.  FIG. 4  illustrates an example system  300  comprising a CBT server  304  for communicating with a patient&#39;s mobile device  302  to implement the principles disclosed herein. The server  304  includes or is connected to a memory  306  for storing computer instructions required to implement portions of the methods described herein and to store the various databases, user information and login/account data used during the above-described processes. The system  300  includes a database, which may also be stored in memory  306 , for user accounts, CBT pamphlet and clinical information, among other information required by the methods or applications disclosed herein. The server  304  can be accessed over a wired or wireless network  310  (shown as the Internet in this example) or via a cellular network  312 . 
         [0110]    Patient devices  302  include a mobile device (e.g., smartphone, tablet) that connects to the server  304  via the Internet/network  310  and/or a cellular network  312 . The device  302  will also include a processor, memory, input/output components and other devices (e.g., camera, GPS, accelerometer, etc.) that are useful for inputting and transmitting data disclosed herein. Although not shown, the system  300  can also receive inputs from one or more devices connected to or worn by the patient (e.g., health monitors, exercise monitors, sleep monitors, sleep apnea sensors/monitors, breathing sensors, heart rate monitors). 
         [0111]      FIGS. 5-9  illustrate example screenshots and functionality of pages provided on the patient&#39;s mobile device by the disclosed CBT application when used to treat insomnia. Insomnia maybe treated by: sleep hygiene and stimulus control; optimizing the sleep environment; Adopting sleep-enhancing behaviors and avoiding sleep-disturbing behaviors; uncovering and addressing unhelpful thinking that perpetuates poor sleep; and sleep restriction and extension, leveraging the body&#39;s natural fatigue response to slowly consolidate sleep over time. 
         [0112]      FIG. 5  illustrates two example pages  350 ,  400  associated with a sleep restriction functionality provided by the disclosed application (when executed on the patient&#39;s mobile device). In the first example page  350 , a sleep restriction worksheet is displayed on the device. The worksheet includes a field  352  for the patient&#39;s average sleep duration. This field  352  could be populated by an input from the patient, but in one embodiment, it is automatically populated based on the patient&#39;s sleep logs entered into the application via another page of the application. For example, the application determines the average sleep duration using the formula: average time in bed for all nights logged minus average time awake. The worksheet further includes fields  354  and  356  for respectively inputting the patient&#39;s target wake time and sleep time goal. These fields  354 ,  356  may be populated by inputs from the patient using time picker increments (of e.g., 5, 10, 15 minutes or more) or automatically populated based on the patient&#39;s sleep logs. 
         [0113]    Field  358  illustrates the patient&#39;s initial restricted bedtime as determined by the disclosed application based on the previously input information while field  360  provides a recommendation for when the patient should begin her/his pre-bedtime ritual (e.g., restricted bedtime minus 40 minutes). The worksheet also include fields  362 ,  364  allowing the patient to respectively input/edit sleep extension time intervals and the number of days the patient is at 85% efficiency before the sleep extension. Field  362  can have a default value (e.g., 20 minutes) and be changed by the patient. Field  364  can also have a default value (e.g., 4 days) and can be changed by the patient. Example page  400  illustrates a “how it works” write-up to help guide the patient through the CBT process (e.g., provides guidance on what some of the terms used in the profile and other sections mean). 
         [0114]      FIGS. 6-8  illustrate example pages  450 ,  500 ,  550   a ,  550   b ,  600   a  and  600   b  associated with a cognitive therapy functionality provided by the disclosed application (when executed on the patient&#39;s mobile device). Page  450 , for example, provides a description entitled “Taming Your Active Mind” that encourages the patient to take an “Active Mind Quiz” to determine the best strategy for the patient to tame his/her mind before going to bed. Page  500  illustrates an example of such a quiz  502 . The quiz  502  includes a question (e.g., “what do you think about when you&#39;re trying to sleep?”). Below the quiz are fields  504 ,  506 ,  508 ,  510 ,  512  containing typical responses. Field  512  (“A little bit of all of the above”) is shown as being selected by the patient. 
         [0115]    Pages  550   a ,  550   b  provide descriptions  552 ,  554  entitled “Try these exercises to help reduce bedtime worry.” These descriptions  552 ,  554  are part of the patient&#39;s CBT. Field  556  allows the patient to set “worry time” reminders (i.e., native reminders). Pages  600   a ,  600   b  provide the patient with the opportunity to provide “My Thoughts About Insomnia”  602 . These pages  600   a ,  600   b  include fields  602 ,  604 ,  606 ,  608 ,  610  for respectively inputting the patient&#39;s thoughts on: what is the cause of the insomnia; how the patient feels (emotionally) about his/her insomnia; what the patient believes is inhibiting sleep; what that consequences of the patient&#39;s insomnia are; and how the patient&#39;s thoughts regarding insomnia have evolved. It should be appreciated that the pages  450 ,  500 ,  550   a ,  550   b ,  600   a  and  600   b  associated with the cognitive therapy could include other messages and quizzes deemed appropriate for the patient&#39;s illness (insomnia in this example). 
         [0116]      FIG. 9  illustrates pages  650   a ,  650   b  associated with the “My plan” functionality provided by the disclosed application (when executed on the patient&#39;s mobile device). In the illustrated example, the patient is capable of obtaining quick access to his/her personalized plan and tools for better sleep in accordance with the disclosed principles. In the illustrated example, the patient may select pull down menus/plans for: a pre-bedtime routine  652 ; sleep schedule  654 ; a worry log  656 ; thoughts and attitudes  658 ; behavior recommendations  660 ; taming your active mind  662 ; and an insomnia profile  664 . It should be appreciated that the pages  650   a ,  650   b  associated with the patient&#39;s “My plan” could include other menus, tools and messages deemed appropriate for the patient&#39;s illness (insomnia in this example). 
         [0117]    Accordingly, the principles disclosed herein translate an evidence-based CBT for Insomnia program into the disclosed CBT application. CBT is medically proven to be superior to drugs in treating insomnia. The disclosed CBT application can be used in conjunction with a healthcare provider or alone. Features of the disclosed CBT application for treating insomnia include: (1) a Profile section that establishes a baseline and rules out serious diseases like Apnea and Restless Leg Syndrome; (2) an ongoing sleep log that tracks sleep, along with behaviors and events that influence sleep quality and quantity, allows for narrative comments. (Studies show that journaling is beneficial for improving health outcomes.) The Application calculates key metrics and creates visualizations based on the logs; (3) a behavior section educates the patient about sleep enhancing behaviors; (4) a cognitive section addresses unhelpful thinking; and (5) a maintenance section that creates a personalized reference and provides tips for handling lapses. The CBT application leverages the interactivity of e-health solutions, including questionnaires, checklists, as well as easy access to key content and tools, and a bookshelf for reference material; incorporates reminders and encouraging messaging; and can integrate with sleep monitors for automatic loading of sleep metrics. 
         [0118]      FIGS. 10-13  illustrate sample screenshots and functionality of pages provided on the patient&#39;s mobile device by the disclosed CBT application when used for smoking cessation. Smoking/Tobacco cessation can be treated by: Profiling the user&#39;s unique smoking behaviors; co-creating, with the user&#39;s participation, a plan to stop smoking; educating the patient about smoking dangers, medications to help with quitting, dealing with common side-effects of smoking cessation; setting goals and rewards for behavior change; and keeping the patient on track during the maintenance phase. 
         [0119]      FIG. 10  illustrates example pages  700 ,  750 ,  800 ,  850  associated with the smoking cessation CBT functionality provided by the disclosed application (when executed on the patient&#39;s mobile device). Page  700  illustrates an example menu  702  providing the user with many of the options to set up, perform and monitor the smoking cessation CBT that the disclosed application prescribes for the patient. Page  750  illustrates an example menu  752  for the “Profile Menu” illustrated on page  700 . The illustrated menu  752  includes pull down menus/descriptions for determining the patient&#39;s profile to be used with the application disclosed herein. For example, menu  752  includes an option  754  to help the patient determine “How Addicted Are You?”; an option  756  to select a “Medications Quiz”; an option  758  to help the patient determine his/her “Smoker Type Profile”; an option  760  for the patient to perform a “Self Assessment”; an option  762  to allow the user to “Change Plan”; and option  764  to allow the user to access a “Cost Calculator”; and option  766  to allow the user to “Reward Yourself” and an option  768  to allow the user to “Track Your Habit.” 
         [0120]    Page  800  illustrates the “How Addicted?” questionnaire referred to in page  750 . In the example, the questionnaire includes a question e.g., “How soon after you wake up do you smoke your first cigarette?” The page  800  includes selectors  802 ,  804 ,  806 ,  808  for selecting a typical patient response. Similarly, page  850  illustrates a medical survey. In this example, the page  850  includes the question “Have you tried Nicotine Replacement Therapy in conjunction with Bupropion?” A selector  852  is provided for a “yes” response and a selector  854  is provided for a “no”.  FIG. 10  illustrates a “no” response being selected. It should be appreciated that the pages  700 ,  750 ,  800 ,  850  associated with smoking cessation could include other messages and quizzes deemed appropriate for the patient&#39;s CBT. 
         [0121]      FIG. 11  illustrates additional example pages  900 ,  950 ,  1000   a ,  1000   b  associated with the smoking cessation CBT functionality provided by the disclosed application (when executed on the patient&#39;s mobile device). Page  900  illustrates fields  902 ,  904 ,  906  allowing the patient to input his/her opinion of the type of smoker he/she is (i.e., stimulator, handler, relaxer). As shown, each field  902 ,  904 ,  906  includes a selection box (with the box for field  902  being shown as selected) and a smoker type hyperlink in which the patient can click on to obtain a description of the type. 
         [0122]    Page  950  illustrates an example cost calculator page allowing the patient to calculate his/her spending costs for the purchase of cigarettes. For example, one field  952  allows the patient to enter a numerical value that can represent the number of cigarettes (via selector  954 ) or packs (via selector  956 ) the patient smokes per day (via selector  958 ) or week (via selector  960 ). In the illustrated example, the patient has entered  5  packs per week using field  952  and selectors  956 ,  960 . Field  962  allows the patient to enter a price for a pack of cigarettes, which allows the patient to click calculate to determine his/her costs spent on cigarette purchases for the day (via selector  958 ) or week (via selector  960 ). 
         [0123]    Pages  1000   a ,  1000   b ,  1000   c  ( FIG. 12 ),  1000   d  ( FIG. 12 ) are pages illustrating an example tobacco log. The log can be categorized as cigarette or craving via field  1002 . Example page  1000   a  has one field  1004  allowing the user to rate his/her craving using a sliding scale and another field  1006  allowing the user to rate his/her mood using a similar sliding scale. Page  1000   b  includes a fields  1008 ,  1010  allowing the user to select or input triggers, which could be a situation, person, location, activity, etc. that sets off the desire to have a cigarette. Example page  1000   c  illustrates graphs  1012 ,  1014 ,  1016  respectively showing the patient&#39;s cigarette usage, cravings, and craving severity based upon user and other information input into the application. Page  1000   d  shows a calendar  1018  and times  1020  per a selected day in which the patient experienced a craving or smoked a cigarette. It should be appreciated that the pages  900 ,  950 ,  1000   a ,  1000   b ,  1000   c ,  1000   d  associated with smoking cessation could include other messages, menus and illustrations deemed appropriate for the patient&#39;s CBT. 
         [0124]      FIG. 12  also illustrates an example page  1050  denoted as “anticipate”, which posts a scenario that the patient may experience and has a field  1052  for the patient to input an expected response to the scenario.  FIG. 13  illustrates two example “my plan” pages  1100   a ,  1100   b  and two example “bookshelf” pages  1150   a ,  1150   b  in accordance with the principles disclosed herein. Example page  1100   a  includes pull down menus/descriptions associated with the CBT plan developed by the application. For example, there is a menu option  1102  for the patient&#39;s quit date; option  1104  for the patient&#39;s tobacco usage; option  1106  for the patient&#39;s cost of smoking; option  1108  for the reason why the patient started smoking; option  1110  for the reason why the patient is still smoking; option  1112  for information on the patient&#39;s nicotine dependence; and option  1114  for the patient&#39;s smoker type. Page  1100   b  illustrates information associated with the patient&#39;s quit date. For example, there is a field  1116  to edit/modify the quit date and a chart  1118  showing the patient&#39;s recent tobacco usage. 
         [0125]    Page  1150   a  illustrates a menu for the bookshelf page. In the illustrated example, there is an option  1152  to obtain general information related to smoking cessation; option  1154  for obtaining information about medications; option  1156  for obtaining information regarding relaxation techniques; and an option  1158  for obtaining access to other resources. Page  1150   b  illustrates features when “general information” is selected from page  1150   a . The illustrated page  1150   b  includes an option  1160  related to patterns of smoking in the United States; option  1162  related to health consequences of smoking; option  1164  related to immediate physical effects; and option  1166  related to second hand smoke. It should be appreciated that the pages  1100   a ,  1100   b ,  1150   a ,  1150   b  associated with smoking cessation could include other messages, menus, illustrations and links to resources deemed appropriate for the patient&#39;s CBT. 
         [0126]      FIG. 14  illustrates a medications quiz  1200   a ,  1200   b  that may be used to gather data used by CBT application in  FIG. 3 , for example. CBT application may present a survey  1202 , receive user survey answers, and receive indication that the survey is complete 1204. CBT application may perform above-described analysis and provide functionality to allow the user to retake the quiz  1206  along with a recommended therapy  1208 . User may be able to provide feedback about the therapy through a self-assessment  1210 , for example providing feedback about whether the therapy is working and/or whether it is having other positive or negative effects on the user. 
         [0127]    Some examples of the CBT application described above may incorporate therapy directly into the application in addition to and/or alternatively to providing recommendations for external therapies. For example, CBT application may perform processing as described above and determine that self-determination theory (SDT) and/or guided meditation are appropriate treatments to recommend. 
         [0128]    SDT complements CBT by using framing and self-determinative messaging to make it easier for the user to develop the intrinsic motivation needed to both take on behavior change and stick with it after the program is over. 
         [0129]    Guided meditation may include recordings of guided meditation sessions by a licensed practitioner designed to complement, extend, and further personalize the cognitive and self-determinative aspects of the CBT application. This may include a set of recorded components that together can be used to create a targeted guided meditation experience for each health condition. The components may include, for example: 
         [0130]    1. Induction: using guided imagery to focus attention and block out thoughts. 
         [0131]    2. Deepening: continued use of guided imagery, breathing techniques, etc. to further quiet the conscious mind and take the subject deeper. 
         [0132]    3. Therapy: Connect directly with the subconscious mind with therapeutic suggestions that lead the subject to the desired outcome. 
         [0133]    4. Wakening: Making the transition back to the conscious mind. 
         [0134]    The disclosed principles translate an evidence-based tobacco cessation program, and Tobacco Tactics, into a mobile application. Mobile applications incorporate convenience and privacy and make it easier for people to track their habits and cravings. The disclosed CBT application can be used in conjunction with a healthcare provider or alone. Features of the disclosed CBT application for tobacco/smoking cessation include: (1) a profile section that uses several evidence-based clinical instruments to determine smoker type and set up a stop smoking plan; (2) smoking logs that establish baseline smoking habit and that allow a user to track progress towards becoming smoke-free. Logs track smoking incidents and triggers and allow for narrative comments. (Studies show that journaling is beneficial for improving health outcomes.); (3) a quitting section that helps the user create coping responses for challenging situations, including cognitive techniques for handling thoughts; (4) a maintenance section that covers withdrawal symptoms and side effects, offers tips and techniques for maintaining a tobacco-free lifestyle; (5) a “My Plan” section that gathers the results of all activities and questionnaires into a personalized plan for quitting; (6) a logs section that provides easy access to the important daily activity of noting triggers, cravings and smoking. These are then used to help tailor a personalized quitting program for the user; and (7) a bookshelf section that is a central location for reference information provided in the application. The CBT application may incorporate smokeless tobacco information and activities. 
         [0135]    The disclosed CBT method, system and application will work as a substitute for CBT-Insomnia and Tobacco Cessation therapy with a human therapist. The disclosed principles present the therapy as a stepped process, in much the same way a therapist would. There is a beginning, middle, and an end phase to the therapy. This means the patient experiences the therapy in stages. At the same time that it is presenting a linear process of the progressive therapy for insomnia, the disclosed embodiments also leverage the native capabilities of a digital content to provide random access to the key tools of the therapy: a sleep log, including metrics and visualizations based on that information; a “bookshelf” for ready access to reference materials, a repository for quizzes and interactive features that can be taken multiple times, and My Plan, a place where all the key information about the user&#39;s program is quickly available. The disclosed application also uses native capabilities of the mobile device for reminders and encouragers. 
         [0136]    The disclosed embodiments create a better experience that leads to better outcomes for the patient. People will not comply with any therapy (e.g., taking a pill, wearing a sleep apnea mask, etc.) if the experience is not beneficial. Medical applications create their own problems of getting too big and too wordy. The disclosed embodiments, however, reduce the use of multiple log menus and instead create one environment that uses features like “collapsible” screens and pull-out or swipe functions. The disclosed embodiments use less Yes-No questions, and more use tag features. The disclosed embodiments use the right amount of personalization and super-customization on one screen. 
         [0137]    Typical CBT systems and methods prompt the user with simplistic “reward yourself with each urge to overcome” that is believed to be too general and vague. The disclosed embodiments give a list of prompts that a patient rewards itself and looks back at their personalization. In sum, the disclosed embodiments combine both linear and non-linear presentations for maximum usefulness of a therapeutic application, which allows each user to move through the therapy at its own pace. The user can always access any section of the therapy to preview or review its contents. The process is presented in a flexible fashion, giving the user a sense of control over the experience. 
         [0138]    The disclosed embodiments link with the native capabilities of the mobile device, including access to media players, calendar and reminders, email and texting, posting to social networks, and pulling images from the camera for motivation. The disclosed embodiments have the capability to connect with a tracking device via Bluetooth to download sleep metrics. The devices can be a sleep quality tracker and potentially other activity trackers on the market such as e.g., the Fitbit, Fuel Band or Jawbone Up. While the disclosed embodiments can be used in a standalone fashion, they can also be used as part of a platform that links individual patients and their applications with a healthcare system via a HIPPA compliant, cloud based telemedicine system. The disclosed embodiments can be integrated with a mobile health cloud system/telemedicine system such as e.g., the one developed by Zansors. Key features include: 1) self-reporting data from the patient using the application; 2) a cloud analytics database that can handle unstructured data; 3) a health dashboard to share with a care coordination team (physician, nurse, sociologist, physical therapist, etc.) and 4) security and HIPAA/privacy compliance. Medical providers will have access to patient metrics, will be able to set thresholds for each patient, and will be able to reach out directly to the patient if need be. 
         [0139]    What is special/different/novel/non-obvious about the disclosed embodiments? The disclosed embodiments present CBT in a progression of sessions, much like in-person CBT therapy does. They are flexible, and allow the user to be in control and take sessions whenever they want to (e.g., jump around, see what&#39;s ahead, etc.). The disclosed embodiments present narrative information in interactive format, with instructional quizzes rather than articles. The disclosed embodiments provide greater clarity around the sleep restriction part of the program. The disclosed embodiments employ significantly better, more understandable visualizations of the data, including tagging which is integrated with visualizations so user has a better sense of the factors that may be influencing sleep amount and quality, smoking triggers, etc. The disclosed embodiments offer journaling, which has therapeutic as well as interpretive value; incorporates screening for 2 top sleep pathologies at the start, Apnea and RLS, using standard instruments. Moreover, CBT is not appropriate for people with these sleep diseases. Accordingly, the disclosed embodiments will identify people at risk for these diseases so they can get the care they need right away. 
         [0140]    It should be appreciated that the examples set forth herein are provided merely for the purpose of explanation and are in no way to be construed as limiting. While reference to various embodiments is made, the words used herein are words of description and illustration, rather than words of limitation. Further, although reference to particular means, materials, and embodiments are shown, there is no limitation to the particulars disclosed herein. Rather, the embodiments extend to all functionally equivalent structures, methods, and uses, such as are within the scope of the appended claims. 
         [0141]    Additionally, the purpose of the Abstract is to enable the patent office and the public generally, and especially the scientists, engineers and practitioners in the art who are not familiar with patent or legal terms or phraseology, to determine quickly from a cursory inspection the nature of the technical disclosure of the application. The Abstract is not intended to be limiting as to the scope of the present inventions in any way.