An mHealth Management Platform for Patients with Chronic Obstructive Pulmonary Disease (efil breath): Randomized Controlled Trial

Kwon, Hee Kyu; Lee, Sungin; Jung, Eun Ji; Kim, Sanghee; Lee, Jung Kyu; Kim, Deog Kyeom; Kim, Tae Hyung; Lee, Seung Hyeun; Lee, Myoung Kyu; Song, Seungjae; Shin, Kichul

doi:10.2196/10502

Cited by 50 publications

(115 citation statements)

References 30 publications

Supporting

Mentioning

112

Contrasting

Unclassified

Order By: Relevance

“…Use of the apps and patient monitoring website within the mHealth rehabilitation platform are described elsewhere [28]. The platform was developed as a comprehensive rehabilitation-focused platform for patients with chronic obstructive pulmonary disease [28], lung cancer, breast cancer, and others. Figures 2 and 3 show screenshots of the app for the fixed exercise program.…”

Section: Methodsmentioning

confidence: 99%

Mobile Health Management Platform–Based Pulmonary Rehabilitation for Patients With Non–Small Cell Lung Cancer: Prospective Clinical Trial

Ji¹,

Kwon²,

Lee³

et al. 2019

JMIR Mhealth Uhealth

Self Cite

View full text Add to dashboard Cite

Background Lung cancer patients experience various symptoms during treatment. Although pulmonary rehabilitation is an effective way to improve these symptoms, a medical environment of limited availability makes it difficult to provide seamless and adequate rehabilitation for lung cancer patients. Objective This study aimed to investigate the effects of a personalized pulmonary rehabilitation program using real-time mobile patient health data for patients with non–small cell lung cancer. Methods We conducted a prospective clinical trial in 64 patients with non–small cell lung cancer aged between 20 and 80 years at a large tertiary hospital in Seoul, South Korea. A 12-week personalized pulmonary rehabilitation program, called efil breath, was administered to determine the effectiveness of the newly developed rehabilitation app. Participants were randomly allocated to the fixed exercise or fixed-interactive exercise group (which received the personalized program). We measured changes in 6-minute walk distance (6MWD) and dyspnea (modified Medical Research Council [mMRC] score) at 6 weeks; and quality of life and service satisfaction at 12 weeks. We used the paired t test to analyze the variables. Results Patients used the newly developed mobile health pulmonary rehabilitation app and a real-time patient monitoring website. In all participants, significant changes were observed in 6MWD at 12 weeks from a mean of 433.43m (SD 65.60) to 471.25m (SD 75.69; P =.001), and mMRC from a mean score of 0.94 (0.66) to 0.61 (SD 0.82; P =.02). The intervention significantly improved their quality of life (EuroQol-visual analog scale [EQ-VAS]) compared with baseline (mean score 76.05, SD 12.37 vs 82.09, SD 13.67, respectively; P =.002). Conclusions A personalized mobile health–based pulmonary rehabilitation app for recording and monitoring real-time health data of patients with non–small cell lung cancer can supplement traditional health care center–based rehabilitation programs. This technology can encourage improvement of physical activity, dyspnea, and quality of life.

show abstract

Section: Methodsmentioning

confidence: 99%

Mobile Health Management Platform–Based Pulmonary Rehabilitation for Patients With Non–Small Cell Lung Cancer: Prospective Clinical Trial

Ji¹,

Kwon²,

Lee³

et al. 2019

JMIR Mhealth Uhealth

Self Cite

View full text Add to dashboard Cite

show abstract

“…According to published literature, during the development of maintenance programs, clinical improvements in quality of life last about 6 to 9 months [ 9 , 49 ]. Thus, our study conducted a follow-up of greater duration than the usual ones in the literature, where the usual follow-up period ranges from 3 to 6 months [ 15 , 50 - 53 ], and was able to verify that at 1-year follow-up the HappyAir group studied showed significant differences in health-related quality of life over time from the values presented prior to pulmonary rehabilitation until the evaluations carried out at 6 and 12 months, with a gradual improvement in their values, as shown objectively with the SGRQ and CAT questionnaires.…”

Section: Discussionmentioning

confidence: 99%

Development and Preliminary Evaluation of the Effects of an mHealth Web-Based Platform (HappyAir) on Adherence to a Maintenance Program After Pulmonary Rehabilitation in Patients With Chronic Obstructive Pulmonary Disease: Randomized Controlled Trial

Jiménez-Reguera¹,

López²,

Fitch³

et al. 2020

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

Background Pulmonary rehabilitation is one of the main interventions to reduce the use of health resources, and it promotes a reduction in chronic obstructive pulmonary disease (COPD) costs. mHealth systems in COPD aim to improve adherence to maintenance programs after pulmonary rehabilitation by promoting the change in attitude and behavior necessary for patient involvement in the management of the disease. Objective This study aimed to assess the effects of an integrated care plan based on an mHealth web-based platform (HappyAir) on adherence to a 1-year maintenance program applied after pulmonary rehabilitation in COPD patients. Methods COPD patients from three hospitals were randomized to a control group or an intervention group (HappyAir group). Patients from both groups received an 8-week program of pulmonary rehabilitation and educational sessions about their illness. After completion of the process, only the HappyAir group completed an integrated care plan for 10 months, supervised by an mHealth system and therapeutic educator. The control group only underwent the scheduled check-ups. Adherence to the program was rated using a respiratory physiotherapy adherence self-report (CAP FISIO) questionnaire. Other variables analyzed were adherence to physical activity (Morisky-Green Test), quality of life (Chronic Obstructive Pulmonary Disease Assessment Test, St. George’s Respiratory Questionnaire, and EuroQOL-5D), exercise capacity (6-Minute Walk Test), and lung function. Results In total, 44 patients were recruited and randomized in the control group (n=24) and HappyAir group (n=20). Eight patients dropped out for various reasons. The CAP FISIO questionnaire results showed an improvement in adherence during follow-up period for the HappyAir group, which was statistically different compared with the control group at 12 months (56.1 [SD 4.0] vs 44.0 [SD 13.6]; P=.004) after pulmonary rehabilitation. Conclusions mHealth systems designed for COPD patients improve adherence to maintenance programs as long as they are accompanied by disease awareness and patient involvement in management. Trial Registration ClinicalTrials.gov NCT04479930; https://clinicaltrials.gov/ct2/show/NCT04479930

show abstract

“…The proposed SW is targeted towards patients diagnosed with PCa, who have not undergone radical prostatectomy, as well as physicians. LifeSemantics Corp., which has an experience of developing a mHealth management platform for patients [ 22 – 24 ], has contributed to the development of the Dr. Answer AI SW for PCa.…”

Section: Discussionmentioning

confidence: 99%

“…developing a mHealth management platform for patients [22][23][24], has contributed to the development of the Dr. Answer AI SW for PCa.…”

Section: Plos Onementioning

confidence: 99%

Dr. Answer AI for prostate cancer: Clinical outcome prediction model and service

et al. 2020

View full text Add to dashboard Cite

The importance of clinical outcome prediction models using artificial intelligence (AI) is being emphasized owing to the increasing necessity of developing a clinical decision support system (CDSS) employing AI. Therefore, in this study, we proposed a "Dr. Answer" AI software based on the clinical outcome prediction model for prostate cancer treated with radical prostatectomy. Methods The Dr. Answer AI was developed based on a clinical outcome prediction model, with a user-friendly interface. We used 7,128 clinical data of prostate cancer treated with radical prostatectomy from three hospitals. An outcome prediction model was developed to calculate the probability of occurrence of 1) tumor, node, and metastasis (TNM) staging, 2) extracapsular extension, 3) seminal vesicle invasion, and 4) lymph node metastasis. Random forest and k-nearest neighbors algorithms were used, and the proposed system was compared with previous algorithms. Results Random forest exhibited good performance for TNM staging (recall value: 76.98%), while knearest neighbors exhibited good performance for extracapsular extension, seminal vesicle invasion, and lymph node metastasis (80.24%, 98.67%, and 95.45%, respectively). The Dr. Answer AI software consisted of three primary service structures: 1) patient information, 2) clinical outcome prediction, and outcomes according to the National Comprehensive Cancer Network guideline.

show abstract

An mHealth Management Platform for Patients with Chronic Obstructive Pulmonary Disease (efil breath): Randomized Controlled Trial

Cited by 50 publications

References 30 publications

Mobile Health Management Platform–Based Pulmonary Rehabilitation for Patients With Non–Small Cell Lung Cancer: Prospective Clinical Trial

Mobile Health Management Platform–Based Pulmonary Rehabilitation for Patients With Non–Small Cell Lung Cancer: Prospective Clinical Trial

Development and Preliminary Evaluation of the Effects of an mHealth Web-Based Platform (HappyAir) on Adherence to a Maintenance Program After Pulmonary Rehabilitation in Patients With Chronic Obstructive Pulmonary Disease: Randomized Controlled Trial

Dr. Answer AI for prostate cancer: Clinical outcome prediction model and service

Contact Info

Product

Resources

About