App Supports Self-management of Prosthetic Socket Fit Issues

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Clinicians know that for new prosthesis users the task of donning a prosthetic limb and achieving a comfortable fit can become a frustrating stumbling block. Due to the variable shape and volume fluctuations of the residual limb, along with the potential to misunderstand the donning sequence, an individual can experience difficulty obtaining a proper prosthetic socket fit without the help of a clinician. While many self-management educational materials exist to help clients manage the fit of their devices, sometimes those aren’t enough—especially for people with complex suspension solutions or mild cognitive impairments.

The result of this issue is twofold. First, clients become frustrated trying to achieve a comfortable fit and may attempt to figure out their own solutions, which can lead to preventable secondary complications associated with fit mismanagement. Second, clinicians are called upon to correct issues that could have been solved through patient self-management, which results in decreased clinic productivity and efficiency. Ultimately, the solution lies in empowering clients to self-manage their prosthetic socket fit, where they can solve socket comfort issues independently or be directed to contact their prosthetists if issues are beyond self-management. That is where limbWISE, the mobile app I developed with supporting assistants, comes into play.

I conceived limbWISE after spending a disproportionate amount of time trying to aid clients in the critical task of problem solving prosthetic socket fit issues, leaving me less time to spend on their functional mobility. I looked for a systematic and comprehensive solution, but one was not available. We went on to create decision trees that serve as a quick user guide to obtaining a proper fit, boiling down the most common fit issues of frequently prescribed prosthetic suspension types (Figure 1).

figure 1

To create the trees, an in-depth literature review was performed, looking at how other disciplines structure and develop their decision trees. I searched for “decision trees” in several scientific databases and then sorted through different types, choosing elements from each one. One common theme was that all of the flowcharts were made for clinicians, and not the clients themselves. Then we reviewed suspension types and common issues, examining several textbooks used in the Master of Science in Prosthetics and Orthotics (MSPO) program at the University of Hartford. The results of this work became the content for the decision trees, which were created using a free flowcharting software called LucidChart ( Each decision tree was printed out on 8.5 in. x 11 in. paper. Then the decision trees underwent a multiphase, expert panel review. Acceptability testing followed, which involved providing a current prosthesis user with the decision tree that matched his or her suspension type and soliciting evaluation and feedback on the ease of use, accuracy of the solutions, and potential benefits for a new user. Results of the study are in press at the time of this article, and can be seen here: Lee, D. J., and D. A. Veneri. “Development and Acceptability Testing of Decision Trees for Self-management of Prosthetic Socket Fit In Adults With Lower Limb Amputation.” Disability and Rehabilitation. In Press. DOI: 10.1080/09638288.2017.1286694.

A limitation of the decision trees became evident after the tests, specifically that they could appear too complicated because so much information was presented at once. To minimize the amount of information presented to the user at one time but still provide all the necessary information, we designed an interactive mobile app that allows simplified navigation of the decision trees.

The app distills the branching choices that were on the paper-based decision trees. It features a tracking system that guides the user through the process of solving his or her fit issue. A text prompt is provided, and the user presses a yes or no response button. After each response, a new solution or question is presented based on the previous answer. This continues until a solution is achieved. Ideally, the user will reach a solution and obtain a comfortable fit, such as adding a sock or tightening a strap. However, if the user is unable to obtain a comfortable fit due to issues that are beyond self-management, the app prompts the user to contact his or her prosthetist for further assistance. The experience with the app takes only a few minutes, and there are approximately 15-20 button pushes between starting and finishing the process, more or less depending on the complexity. In this way, the app empowers the client to address factors that can be self-managed but not forgo a clinician’s assistance when appropriate. My hope is that clients will experience less frustration, improved quality of life, and greater functional independence. From a clinical standpoint, my goal is to improve productivity by decreasing unscheduled visits for reasons that clients can manage independently.

The app currently addresses transtibial and transfemoral prostheses that fit too loosely on the residual limb. It covers the most common suspension types prescribed: suction variants, locking liners, and vacuum. A video of how the app functions can be viewed at The app has already undergone phase 1 pilot testing and is currently in phase 2 efficacy testing. Once testing is complete, a version will be available at no cost through the Android and Apple app stores. Expansion of the decision trees into other issues related to prosthetic fit and comfort are under way, and should be integrated into the app by the end of the year. Ultimately, I would like to see the app in the hands of every prosthesis user to help mitigate the arduous process they are undertaking as part of standard care.

The app was developed in partnership with the Connecticut Center for Advanced Technology (CCAT), East Hartford, and was funded by a seed grant for the University of Hartford College of Education, Nursing and Health Professions. Development would not have been possible without support from the university’s Department of Rehabilitation Sciences, students in the MSPO program, and prosthetists from Hanger Clinic and New England Orthotic and Prosthetic Systems. Each year, the MSPO students helped to further develop the decision trees as part of a faculty-led research project. I am extremely fortunate to have been able to work with such talented students who worked so diligently during their studies. I am proud to know that these students will be the future of the profession.

Daniel J. Lee, PT, DPT, GCS, is an assistant professor at the University of Hartford Department of Rehabilitation Sciences. He can be contacted at for more information about the limbWISE app or to participate in the research and development.

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