Innovation in the treatment of persistent pain in adults with Neurofibromatosis Type 1 (NF1): Implementation of the iCanCope mobile application

Perspectives of adults with neurofibromatosis regarding the design of psychosocial trials: Results from an anonymous online survey

BACKGROUND/AIMS

Individuals with neurofibromatosis, including neurofibromatosis 1 (NF1), neurofibromatosis 2 (NF2)-related schwannomatosis (SWN), and other forms of SWN, often experience disease manifestations and mental health difficulties for which psychosocial interventions may help. An anonymous online survey of adults with neurofibromatosis assessed their physical, social, and emotional well-being and preferences about psychosocial interventions to inform clinical trial design.

METHODS

Neurofibromatosis clinical researchers and patient representatives from the Response Evaluation in Neurofibromatosis and Schwannomatosis International Collaboration developed the survey. Eligibility criteria included age ≥ 18 years, self-reported diagnosis of NF1, NF2, or SWN, and ability to read and understand English. The online survey was distributed internationally by the Neurofibromatosis Registry and other neurofibromatosis foundations from June to August 2020.

RESULTS

Surveys were completed by 630 adults (18-81 years of age; M = 45.5) with NF1 (78%), NF2 (14%), and SWN (8%) who were mostly White, not Hispanic/Latino, female, and from the United States. The majority (91%) reported that their neurofibromatosis symptoms had at least some impact on daily life. In the total sample, 51% endorsed a mental health diagnosis, and 27% without a diagnosis believed they had an undiagnosed mental health condition. Participants indicated that neurofibromatosis affected their emotional (44%), physical (38%), and social (35%) functioning to a high degree. Few reported ever having participated in a drug (6%) or psychosocial (7%) clinical trial, yet 68% reported they "probably" or "definitely" would want to participate in a psychosocial trial if it targeted a relevant concern. Top treatment targets were anxiety, healthier lifestyle, and daily stress. Top barriers to participating in psychosocial trials were distance to clinic, costs, and time commitment. Respondents preferred interventions delivered by clinicians via individual sessions or a combination of group and individual sessions, with limited in-person and mostly remote participation. There were no significant group differences by neurofibromatosis type in willingness to participate in psychosocial trials (p = 0.27). Regarding interest in intervention targets, adults with SWN were more likely to prefer psychosocial trials for pain support compared to those with NF1 (p < 0.001) and NF2 (p < 0.001).

CONCLUSION

This study conducted the largest survey assessing physical symptoms, mental health needs, and preferences for psychosocial trials in adults with neurofibromatosis. Results indicate a high prevalence of disease manifestations, psychosocial difficulties, and untreated mental health problems in adults with neurofibromatosis and a high degree of willingness to participate in psychosocial clinical trials. Patient preferences should be considered when designing and implementing psychosocial interventions to develop the most feasible and meaningful studies.

Maintaining Engagement in Adults with Neurofibromatosis Type 1 to Use the iCanCope Mobile Application (iCanCope-NF)

INTRODUCTION

Neurofibromatosis Type 1 (NF1) is an autosomal dominant genetic condition in which chronic pain is a predominant issue. Given the rarity of the disease, there are limited psychosocial treatments for individuals with NF1 suffering with chronic pain. Using mobile applications can facilitate psychosocial treatments; however, there are consistent issues with engagement. Utilizing a mixed methodology, the current study evaluated the customized iCanCope mobile application for NF1 on increasing engagement through the usage of contingency management.

METHODS

A mixed methods study from a subset of data coming from a randomized clinical trial that occurred from January 2021 to August 2022 was undertaken. Two groups (iCC and iCC + CM) were exposed to the customized iCanCope mobile application in which engagement data were captured in real-time with daily check-ins for interference, sleep, mood, physical activity, energy levels, goal setting, and accessing article content (coping strategies). Additionally, semi-structured interviews were conducted to gain insight into the participants' experience at the end of the trial.

RESULTS

Adults (N = 72) were recruited via NF patient advocacy groups. Significant differences were noted between the groups in total articles read (p = 0.002), goals achieved (p = 0.017), and goals created (p = 008). Additionally, there were significant differences observed between user-generated goals and those that were app recommended (p < 0.001). Both groups qualitatively reported positive feedback on the customized mobile application, indicating that continued usage and engagement of the mobile application were acceptable.

CONCLUSIONS

Employing customized mobile applications for adults with NF1 along with contingency management can leverage self-managed pain treatments while providing auxiliary resources to this population.

Human Monkeypox Classification from Skin Lesion Images with Deep Pre-trained Network using Mobile Application

Recently, human monkeypox outbreaks have been reported in many countries. According to the reports and studies, quick determination and isolation of infected people are essential to reduce the spread rate. This study presents an Android mobile application that uses deep learning to assist this situation. The application has been developed with Android Studio using Java programming language and Android SDK 12. Video images gathered through the mobile device’s camera are dispatched to a deep convolutional neural network that runs on the same device. Camera2 API of the Android platform has been used for camera access and operations. The network then classifies images as positive or negative for monkeypox detection. The network’s training has been carried out using skin lesion images of monkeypox-infected people and other skin lesion images. For this purpose, a publicly available dataset and a deep transfer learning approach have been used. All training and testing steps have been applied on Matlab using different pre-trained networks. Then, the network that has the best accuracy has been recreated and trained using TensorFlow. The TensorFlow model has been adapted to mobile devices by converting to the TensorFlow Lite model. The TensorFlow Lite model has been then embedded into the mobile application together with the TensorFlow Lite library for monkeypox detection. The application has been run on three devices successfully. During the run-time, the inference times have been gathered. 197 ms, 91 ms, and 138 ms average inference times have been observed. The presented system allows people with body lesions to quickly make a preliminary diagnosis. Thus, monkeypox-infected people can be encouraged to act rapidly to see an expert for a definitive diagnosis. According to the test results, the system can classify the images with 91.11% accuracy. In addition, the proposed mobile application can be trained for the preliminary diagnosis of other skin diseases.