Factors Associated with Dietary Control and Physical Activity in the Management of Metabolic Syndrome in Korean Menopausal Women.

The increased prevalence of metabolic syndrome (MetS) among menopausal women necessitates successful management strategies such as applying dietary restrictions and engaging in physical activity to improve their health and quality of life. We investigate factors associated with dietary control and physical activity in 564 menopausal Korean women classified as having MetS who partook in the 2016 and 2017 Korean National Health and Nutrition Examination Survey (KNHANES). To determine socio-demographic characteristics, lifestyle features, and MetS-related characteristics associated with dietary control and physical activity, multiple logistic regression analysis was performed. Of the women with MetS 36.1% applied diet control and 39.5% engaged in physical activity. Women who applied dietary control strategies to improve their health were more likely to be in the 40-49 age group (odds ratio (OR): 3.38; 95% confidence interval (CI): 1.25-9.18), to engage in physical activity (OR: 2.24; CI: 1.43-3.52), and to take hypertension medication (OR: 1.66; CI: 1.04-2.67) or diabetes mellitus medication (OR: 2.99; CI: 1.80-4.97). Physically active menopausal women with MetS were more likely to also engage in dieting (OR: 2.32; CI: 1.42-3.51). Accordingly, suggestions can be provided to healthcare workers in designing, not only individual approaches to lifestyle modification but also comprehensive interventions including dietary control and physical activity for menopausal MetS women. Health-care interventions like dietary control, which provide additional support to vulnerable MetS women, should target women aged 60 or above or those who do not take medicines for hypertension and diabetes mellitus.

Effect of self-monitoring on long-term patient engagement with mobile health applications.

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.

Blood Culture Testing via a Mobile App That Uses a Mobile Phone Camera: A Feasibility Study.

BACKGROUND: To evaluate patients with fever of unknown origin or those with suspected bacteremia, the precision of blood culture tests is critical. An inappropriate step in the test process or error in a parameter could lead to a false-positive result, which could then affect the direction of treatment in critical conditions. Mobile health apps can be used to resolve problems with blood culture tests, and such apps can hence ensure that point-of-care guidelines are followed and processes are monitored for blood culture tests. OBJECTIVE: In this pilot project, we aimed to investigate the feasibility of using a mobile blood culture app to manage blood culture test quality. We implemented the app at a university hospital in South Korea to assess the potential for its utilization in a clinical environment by reviewing the usage data among a small group of users and by assessing their feedback and the data related to blood culture sampling. METHODS: We used an iOS-based blood culture app that uses an embedded camera to scan the patient identification and sample number bar codes. A total of 4 medical interns working at 2 medical intensive care units (MICUs) participated in this project, which spanned 3 weeks. App usage and blood culture sampling parameters (including sampler, sampling site, sampling time, and sample volume) were analyzed. The compliance of sampling parameter entry was also measured. In addition, the participants' opinions regarding patient safety, timeliness, efficiency, and usability were recorded. RESULTS: In total, 356/644 (55.3%) of all blood culture samples obtained at the MICUs were examined using the app, including 254/356 (71.3%) with blood collection volumes of 5-7 mL and 256/356 (71.9%) with blood collection from the peripheral veins. The sampling volume differed among the participants. Sampling parameters were completely entered in 354/356 cases (99.4%). All the participants agreed that the app ensured good patient safety, disagreed on its timeliness, and did not believe that it was efficient. Although the bar code scanning speed was acceptable, the Wi-Fi environment required improvement. Moreover, the participants requested feedback regarding their sampling quality. CONCLUSIONS: Although this app could be used in the clinical setting, improvements in the app functions, environment network, and internal policy of blood culture testing are needed to ensure hospital-wide use.

Assessment of healthy behaviors for metabolic syndrome among Korean adults: a modified information-motivation-behavioral skills with psychological distress.

BACKGROUND: Since the worldwide incidence of metabolic syndrome (Mets) has rapidly increased, healthy behaviors such as weight control, engaging in physical activity, and healthy diet have been crucial in the management of Mets. The purpose of this study was to examine healthy behaviors practice and factors that affect the practice in relation to Mets on the basis of a modified Information-Motivation-Behavioral skills model (IMB) with psychological distress, which is a well-known factor affecting healthy behaviors among individuals with Mets. METHODS: Study participants were 267 community dwelling adults (M age: 54.0 ± 8.1 years) with Mets who were attending public health centers located in Seoul, South Korea. A structured questionnaire was administered in the areas of information, motivation, behavioral skills, and practice of Mets healthy behaviors and levels of psychological distress from May 2014 to September 2014. Structural equation modeling was used to test the modified IMB model. RESULTS: The modified IMB model had a good fit with the data, indicating that motivation and behavioral skills directly influenced the practice of Mets healthy behaviors, whereas information and psychological distress directly influenced motivation and influenced the practice of healthy behaviors through behavioral skills. These components of the modified IMB model explained 29.8 % of the variance in healthy behaviors for Mets. CONCLUSION: Findings suggested that strengthening motivation and behavioral skills for healthy behaviors can directly enhance healthy behavior practice. Providing information about Mets related healthy behaviors and strategies for psychological distress management can be used as the first line evidence based intervention to systemically enhance motivation and behavioral skills among individuals with Mets.

Which Users Should Be the Focus of Mobile Personal Health Records? Analysis of User Characteristics Influencing Usage of a Tethered Mobile Personal Health Record.

BACKGROUND: This study was conducted to analyze the usage pattern of a hospital-tethered mobile personal health records (m-PHRs) application named My Chart in My Hand (MCMH) and to identify user characteristics that influence m-PHR usage. MATERIALS AND METHODS: Access logs to MCMH and its menus were collected for a total of 18 months, from August 2011 to January 2013. Usage patterns between users without a patient identification number (ID) and users with a patient ID were compared. Users with a patient ID were divided into light and heavy user groups by the median number of monthly access. Multiple linear regression models were used to assess MCMH usage pattern by characteristics of MCMH user with a patient ID. RESULTS: The total number of MCMH logins was 105,603, and the median number of accesses was 15 times. Users (n = 7,096) mostly accessed the "My Chart" menu, but "OPD [outpatient department] Service Support" and "Health Management" menus were also frequently used. Patients with chronic diseases, experience of hospital visits including emergency room and OPD, and age group of 0-19 years were more frequently found among users with a patient ID (n = 2,186) (p < 0.001). A similar trend was found in the heavy user group (n = 1,123). Submenus of laboratory result, online appointment, and medication lists that were accessed mostly by users with a patient ID were associated with OPD visit and chronic diseases. CONCLUSIONS: This study showed that focuses on patients with chronic disease and more hospital visits and empowerment functions in a tethered m-PHR would be helpful to pursue the extensive use.

[Effects of Dietary and Physical Activity Interventions on Metabolic Syndrome: A Meta-analysis].

PURPOSE: This study identified effects of dietary and physical activity interventions including dietary interventions or physical activity interventions alone or combined dietary-physical activity interventions to improve symptoms in metabolic syndrome including abdominal obesity, high triglycerides, low high density lipoprotein cholesterol, elevated blood pressure, and elevated fasting glucose through meta-analysis. METHODS: Articles on metabolic syndrome X published from 1988 to 2013 were searched through electronic databases, Google Scholar, and reference reviews. Methodological quality was assessed by the checklist, SIGN (Scottish Intercollegiate Guidelines Network). RESULTS: In the meta-analysis, there were 9 articles reporting 13 interventions with 736 participants. Using random effect models, the dietary and/or physical activity interventions showed a lower mean difference in waist circumference (-1.30 cm, 95% CI:-2.44~-0.15, p=.027). The combined dietary-physical activity interventions showed a lower mean difference in waist circumference (-2.77 cm, 95% CI:-4.77~-0.76, p=.007) and systolic blood pressure (-5.44 mmHg, 95% CI:-10.76~-0.12, p=.044). Additionally, interventions of over 24 weeks yielded a lower mean difference in waist circumference (-2.78 cm, 95% CI:-4.69~-0.87, p=.004) and diastolic blood pressure (-1.93 mmHg, 95% CI:-3.63~-0.22, p=.026). CONCLUSION: The findings indicate that dietary and/or physical activity interventions for metabolic syndrome reduce central obesity with no adverse effects. This finding provides objective evidences for dietary and physical activity management on metabolic syndrome as an efficient intervention.

Smartphone applications with sensors used in a tertiary hospital-current status and future challenges.

Smartphones have been widely used recently to monitor heart rate and activity, since they have the necessary processing power, non-invasive and cost-effective sensors, and wireless communication capabilities. Consequently, healthcare applications (apps) using smartphone-based sensors have been highlighted for non-invasive physiological monitoring. In addition, several healthcare apps have received FDA clearance. However, in spite of their potential, healthcare apps with smartphone-based sensors are mostly used outside of hospitals and have not been widely adopted for patient care in hospitals until recently. In this paper, we describe the experience of using smartphone apps with sensors in a large medical center in Korea. Among >20 apps developed in our medical center, four were extensively analyzed ("My Cancer Diary", "Point-of-Care HIV Check", "Blood Culture" and "mAMIS"), since they use smartphone-based sensors such as the camera and barcode reader to enter data into the electronic health record system. By analyzing the usage patterns of these apps for data entry with sensors, the current limitations of smartphone-based sensors in a clinical setting, hurdles against adoption in the medical center, benefits of smartphone-based sensors and potential future research directions could be evaluated.

Daily collection of self-reporting sleep disturbance data via a smartphone app in breast cancer patients receiving chemotherapy: a feasibility study.

BACKGROUND: Improvements in mobile telecommunication technologies have enabled clinicians to collect patient-reported outcome (PRO) data more frequently, but there is as yet limited evidence regarding the frequency with which PRO data can be collected via smartphone applications (apps) in breast cancer patients receiving chemotherapy. OBJECTIVE: The primary objective of this study was to determine the feasibility of an app for sleep disturbance-related data collection from breast cancer patients receiving chemotherapy. A secondary objective was to identify the variables associated with better compliance in order to identify the optimal subgroups to include in future studies of smartphone-based interventions. METHODS: Between March 2013 and July 2013, patients who planned to receive neoadjuvant chemotherapy for breast cancer at Asan Medical Center who had access to a smartphone app were enrolled just before the start of their chemotherapy and asked to self-report their sleep patterns, anxiety severity, and mood status via a smartphone app on a daily basis during the 90-day study period. Push notifications were sent to participants daily at 9 am and 7 pm. Data regarding the patients' demographics, interval from enrollment to first self-report, baseline Beck's Depression Inventory (BDI) score, and health-related quality of life score (as assessed using the EuroQol Five Dimensional [EQ5D-3L] questionnaire) were collected to ascertain the factors associated with compliance with the self-reporting process. RESULTS: A total of 30 participants (mean age 45 years, SD 6; range 35-65 years) were analyzed in this study. In total, 2700 daily push notifications were sent to these 30 participants over the 90-day study period via their smartphones, resulting in the collection of 1215 self-reporting sleep-disturbance data items (overall compliance rate=45.0%, 1215/2700). The median value of individual patient-level reporting rates was 41.1% (range 6.7-95.6%). The longitudinal day-level compliance curve fell to 50.0% at day 34 and reached a nadir of 13.3% at day 90. The cumulative longitudinal compliance curve exhibited a steady decrease by about 50% at day 70 and continued to fall to 45% on day 90. Women without any form of employment exhibited the higher compliance rate. There was no association between any of the other patient characteristics (ie, demographics, and BDI and EQ5D-3L scores) and compliance. The mean individual patient-level reporting rate was higher for the subgroup with a 1-day lag time, defined as starting to self-report on the day immediately after enrollment, than for those with a lag of 2 or more days (51.6%, SD 24.0 and 29.6%, SD 25.3, respectively; P=.03). CONCLUSIONS: The 90-day longitudinal collection of daily self-reporting sleep-disturbance data via a smartphone app was found to be feasible. Further research should focus on how to sustain compliance with this self-reporting for a longer time and select subpopulations with higher rates of compliance for mobile health care.

Lessons learned from the development of health applications in a tertiary hospital.

BACKGROUND: Adoption of smart devices for hospital use has been increasing with the development of health applications (apps) for patient point-of-care and hospital management. To promote the use of health apps, we describe the lessons learned from developing 12 health apps in the largest tertiary hospital in Korea. MATERIALS AND METHODS: We reviewed and analyzed 12 routinely used apps in three categories-Smart Clinic, Smart Patient, and Smart Hospital-based on target users and functions. The log data for each app were collected from the date of release up until December 2012. RESULTS: Medical personnel accessed a mobile electronic medical record app classified as Smart Clinic an average of 452 times per day. Smart Hospital apps are actively used to communicate with each other. Patients logged on to a mobile personal health record app categorized as Smart Patient an average of 222 times per day. As the mobile trend, the choice of supporting operating system (OS) is more difficult. By developing these apps, a monitoring system is needed for evaluation. CONCLUSIONS: We described the lessons learned regarding OS support, device choice, and developmental strategy. The OS can be chosen according to market share or hospital strategic plan. Smartphones were favored compared with tablets. Alliance with an information technology company can be the best way to develop apps. Health apps designed for smart devices can be used to improve healthcare. However, to develop health apps, hospitals must define their future goals and carefully consider all the aspects.