Baek S, et al. Korean J Radiol. 2015 Mar-Apr;16(2):286-296. http://dx.doi.org/10.3348/kjr.2015.16.2.286

Despite the growing adoption of the mobile health (mHealth) applications (apps), few studies address concerns with low retention rates. This study aimed to investigate how the usage patterns of mHealth app functions affect user retention. We collected individual usage logs for 1,439 users of single tethered personal health record app, which spanned an 18-months period from August 2011 to January 2013. The user logs contained timestamps whenever an individual uses each function, which enables us to identify the usage patterns based on the intensity of using a particular function in the app. We then estimated how these patterns were related to 1) the app usage over time (using the random effect model) and 2) the probability of stopping the use of the application (using the Cox proportional hazard model). The analyses suggested that the users utilize the app most at the time of the adoption and gradually reduce their usage over time. The average duration of use after starting the app was 25.62 weeks (SD: 18.41). The degree of the usage reduction, however, decreases as the self-monitoring function is more frequently used (coefficient = 0.002, P = 0.013); none of the other functions has this effect. Moreover, engaging with the self-monitoring function frequently (coefficient = −0.18, P = 0.003) and regularly (coefficient = 0.10, P = 0.001) significantly also reduces the probability of abandoning the application. Specifically, the estimated survival rate indicates that, after 40 weeks since the adoption, the probability of the regular users of self-monitoring to stay in use was about 80% while that of non-user was about 60%. This study provides the empirical evidence that sustained use of mHealth app is closely linked to the regular usage on self-monitoring function. The implications can be extended to the education of users and physicians to produce better outcomes as well as application development for effective user interfaces.

Background: Autism spectrum disorder (ASD) is characterized by abnormalities in social communication and limited and repetitive behavioral patterns. Children with ASD who lack social communication skills will eventually not interact with others and will lack peer relationships when compared to ordinary people. Thus, it is necessary to develop a program to improve social communication abilities using digital technology in people with ASD. Objective: We intend to develop and apply a metaverse-based child social skills training program aimed at improving the social interaction abilities of children with ASD aged 7-12 years. We plan to compare and analyze the biometric information collected through wearable devices when applying the metaverse-based social skills training program to evaluate emotional changes in children with ASD in stressful situations. Methods: This parallel randomized controlled study will be conducted on children aged 7-12 years diagnosed with ASD. A metaverse-based social skills training program using digital technology will be administered to children who voluntarily wish to participate in the research with consent from their legal guardians. The treatment group will participate in the metaverse-based social skills training program developed by this research team once a week for 60 minutes per session for 4 weeks. The control group will not intervene during the experiment. The treatment group will use wearable devices during the experiment to collect real-time biometric information. Results: The study is expected to recruit and enroll participants in March 2022. After registering the participants, the study will be conducted from March 2022 to May 2022. This research will be jointly conducted by Yonsei University and Dobrain Co Ltd. Children participating in the program will use the internet-based platform. Conclusions: The metaverse-based Program for the Education and Enrichment of Relational Skills (PEERS) will be effective in improving the social skills of children with ASD, similar to the offline PEERS program. The metaverse-based PEERS program offers excellent accessibility and is inexpensive because it can be administered at home; thus, it is expected to be effective in many children with ASD. If a method can be applied to detect children's emotional changes early using biometric information collected through wearable devices, then emotional changes such as anxiety and anger can be alleviated in advance, thus reducing issues in children with ASD. Trial Registration: Clinical Research Information Service KCT0006859; https://tinyurl.com/4r3k7cmj International Registered Report Identifier (IRRID): PRR1-10.2196/35960

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: There are many perspectives on the advantages of introducing blockchain in the medical field, but there are no published feasibility studies to regarding the storage, propagation, and management of personal health records (PHRs) using blockchain technology. Objective: The purpose of this study was to investigate the usefulness of blockchain in the medical field in relation to transactions with and propagation of PHRs in a private blockchain. Methods: We constructed a private blockchain network using Ethereum version 1.8.4 and conducted verification though de-identified PHRs of 300 patients. The private blockchain network consisted of one hospital node and 300 patient nodes. In order to verify the effectiveness of blockchain-based PHR management, one PHR was loaded in a transaction between the hospital and patient nodes and propagated to the whole network. We obtained and analyzed the time and gas required for data transaction and propagation on the blockchain network. For reproducibility, these processes were repeated 100 times. Results: Of 300 patient records, 74 (24.7%) were not loaded in the private blockchain due to the data block size of the transaction block. The remaining 226 individual health records were classified into groups A (80 patients with outpatient visit data less than 1-year-old), B (84 patients with outpatient data from between 1 and 3 years before data collection), and C (62 patients with outpatient data 3 to 5 years old) according to their outpatient record period. According to mean transaction time in the blockchain, C (128.7 seconds) had the shortest time, followed by A (132.2 seconds) and C (159.0 seconds). Mean propagation times for groups A, B, and C was, 1494.2 seconds, 2138.9 seconds, and 4111.4 seconds, respectively; mean file sizes were 5.6 KB, 18.6 KB, and 45.38 KB, respectively. The mean gas consumption values were 1,900,767; 4,224,341; and 4,112,784 for groups A, B, and C, respectively. Conclusions: This study confirms that it is possible to exchange PHR data in a private blockchain network. However, to develop a blockchain-based PHR platform that can be used in real practice, many improvements are required, including in terms of data size, personal information protection, and reduced operating costs.

Shin SY, et al. J Korean Med Sci. 2015 Jan;30(1):7-15. https://doi.org/10.3346/jkms.2015.30.1.7

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Lung cancer patients experience various symptoms during treatment. Although pulmonary rehabilitation is an effective way to improve these symptoms, limited medical environment makes it difficult to provide seamless adequate rehabilitation for lung cancer patients. Objective: This study aims to investigate the effects of pulmonary rehabilitation for patients with non-small cell lung cancer by means of a personalized pulmonary rehabilitation program that uses real-time mobile patient health data. Methods: A prospective clinical trial was conducted with 64 non-small cell lung cancer patients aged between 20 and 80 years, diagnosed with lung cancer at Asan Medical Center. A 12-week personalized pulmonary rehabilitation program was administered to investigate the efficacy of the rehabilitation app developed. The participants were randomized into the fixed exercise or fixed-interactive exercise group. At 6 weeks, changes in walking distances during 6 minutes (6MWD) and grade of dyspnea (mMRC), and at 12weeks those in QoL and service satisfaction, were observed. Paired T-test were used for analysis of the variables. Results: An mHealth pulmonary rehabilitation app and real-time patient monitoring website were developed. Significant changes were observed in the intervention group at 12 weeks for 6MWT from 433.43±65.60m to 471.25±75.69m (p=0.001), and 0.94±0.66 to 0.61±0.82 (p=0.017) for mMRC. The intervention showed a significant improvement in the quality of life (EQ-VAS) in compared to the baseline (p=0.002) Conclusions: A personalized mHealth-based pulmonary rehabilitation app for recording and monitoring real-time health data of patients with non-small cell lung cancer can plays a supplementary role in traditional center-based rehabilitation programs. Our clinical trial results support the use of this technology to encourage changes in 6MWT and to improve mMRC.