Concept Model of Weight Loss App

            The feasibility trial of a problem-solving weight loss application created a fundamental basis for exploring a new method of treating obesity. Similar to working with depression, the process of incorporating a problem-solving solution into a mobile app focused on changing actions of obese patients was the basis for this study. The application called The Habit app, along with a closed Facebook network for participants to collaborate resulted in a significant benefit in weight loss for the participants involved. The inclusion criteria were significantly broad, but the exclusion criteria helped to narrow down the size of the study to 43 participants. In this case the exclusion criteria had a few qualifiers that may be searched via LOINC, ICD-10, but other criteria that would have been identified via a survey such as asking about patient Facebook access, and smart phone usage. The app itself only collected general intake information (e.g. weight, date of birth, and height), but included mapping capabilities to connect to a wireless wearable device and created habit related criteria and notifications based on a series of habit related questions (Pagoto et al., 2018).

            The specific data that would be gathered for the study I reviewed that would be conducive to completing the study would be patient demographics, general intake information (e.g. height and weight), patient diagnosis and current medications, a family history, patient dietary and physical activity habits, if the patient smokes, and alcohol consumption information, ethnic background, gender, and spoken language. The general intake information would be taken during an in-person interview to include all information, and then loaded into the patient’s chart within the electronic health record. The information would then be accessible via a patient health record that the patient can access and review. The application itself would be interconnected to the electronic health record via an application processing interface function (API) to only extract and import specific data in real time. These data markers will include weight, physical activity, and a patient message board to enable communication between the physician and patient.

            Regarding data collection, I would find it more suitable to pursue a streaming process to replace the tradition batch processing technology. Since my plan would be to integrate the app with the EHR to receive real time events, incorporating a streaming process would reduce the latency of batch processing (i.e. any downtime) which may affect performance of the EHR. A streaming process such as Apache Kafka is an extremely scalable processing platform that can be used for disparate data of all sorts to keep up with today’s powerful streaming environment (Johansson, 2016).

            The specific medical data our device would collect is patients weight entered weekly, total daily calories with subgroups that include the three macros fats, proteins, and carbohydrates, hours of sleep calculated daily, physical activity in hours and minutes calculated daily to include steps monitored on a wearable device (e.g. Fitbit or Apple watch). We would also need a free text section to allow communication between the physician and patient. For the medical criteria not including free text section, I would recommend using the SNOMED CT as the preferred medical data vocabulary as it has a multitude of beneficial criteria that would create a semantic proof connection. SNOMED offers a plethora of benefits that include a broad scope of concepts, coding benefits, and can be optimized to meet requirements (The International Health Terminology Standards Development Organization, 2014). The idea is to create an application that sends real time events such as server sent events through a streaming API that can be processed in real time stored into a data base and shared with the consumer or the provider that will utilize the data. The data can then be retrieved and used to review patients weight loss over time in a controlled environment. The data could be used to help manage patients at high risk for diabetes and other medical conditions that can be a direct result of obesity.

Purpose	Vocabulary	Code Examples	Justification
Used to select patients for the study	SNOMED CT	414916001	Obesity (disorder)
Used to select patients for the study	SNOMED CT	238131007	Overweight (finding)
Used to select patients for the study	SNOMED CT	8943002	Weight gain (finding)
Used to monitor and observe weight loss	SNOMED CT	27113001	Body weight (observable entity)
Used as the baseline weight for the beginning of the study	SNOMED CT	162763007	On examination - weight (finding)

            The diagram represents an application that allows you to monitor your patient in real time. The solutions uses an API streaming processing interface to send events in real time. The data comes across using SNOMED CT semantic codes to provide specific data on patient weight, sleep, physical activity and diet. This information is then processed and stored into the consumers databases and sent to the consumer who can access the data via the EHR. The EHR will require single sign on authentication to access the API over the EHR. This would allow for seamless access to the data for the physician. The app also will have a messaging system that can transmit sms messages directly to the EHR and allow for the physician and patient to send and receive messages to improve patient and physician relationship. The application can also connect to a wireless device such as a Fitbit or Apple watch to track the patient’s movement. This is an extra layer and is not required but can be monitored to track the patients physical activity.

References:

Johansson, L. (2016). Part 1: Apache Kafka for beginners - What is Apache Kafka. Retrieved from: https://www.cloudkarafka.com/blog/2016-11-30-part1-kafka-for-beginners-what-is-apache-kafka.html

Pagoto, S., Tulu, B., Agu, E., Waring, M. E., Oleski, J. L., & Jake-Schoffman, D. E. (2018). Using the Habit App for Weight Loss Problem Solving: Development and Feasibility Study. JMIR mHealth and uHealth, 6(6), e145. doi:10.2196/mhealth.9801

The International Health Terminology Standards Development Organization. (2014). SNOMED CT – Adding Value to Electronic Health Records. Retrieved from: http://www.snomed.org/SNOMED/media/SNOMED/documents/SNOMED-CT_Adding-Value-to-EHR_20140219-(2).pdf