The Impact of Using Mobile-Enabled Devices on Patient Engagement in Remote Monitoring Programs
Agboola S, Havasy R, Myint-U K, Kvedar J, Jethwani K. The impact of using mobile-enabled devices on patient engagement in remote monitoring programs. J Diabetes Sci Technol. 2013 May 1;7(3):623-9. PubMed PMID: 23759394.Read More...
Different types of data transmission technologies are used in remote monitoring (RM) programs. This study reports on a retrospective analysis of how participants engage, based on the type of data transfer technology used in a blood pressure (BP) RM program, and its potential impact on RM program design and outcomes.
Thirty patients, aged 23-84 years (62 ± 14 years), who had completed at least 2 months in the program and were not participating in any other clinical trial were identified from the Remote Monitoring Data Repository. Half of these patients used wireless-based data transfer devices [wireless-based device (WBD)] while the other half used telephone modem-based data transfer devices [modem-based device (MBD)]. Participants were matched by practice and age. Engagement indices, which include frequency of BP measurements, frequency of data uploads, time to first BP measurement, and time to first data upload, were compared in both groups using the Wilcoxon-Mann-Whitney two-sample rank-sum test. Help desk call data were analyzed by Chi square test.
The frequency of BP measurements and data uploads was significantly higher in the WBD group versus the MBD group [median = 0.66 versus 0.2 measurements/day (p = .01) and 0.46 versus 0.01 uploads/day (p < .001), respectively]. Time to first upload was significantly lower in the WBD group (median = 4 versus 7 days; p = .02), but time to first BP measurement did not differ between the two groups (median = 2 versus 1 day; p = .98).
Wireless transmission ensures instantaneous transmission of readings, providing clinicians timely data to intervene on. Our findings suggest that mobile-enabled wireless technologies can positively impact patient engagement, outcomes, and operational workflow in RM programs.
Perspectives on Acne: What Twitter Can Teach Health Care Providers
Shive M, Bhatt M, Cantino A, Kvedar J, Jethwani K. Perspectives on acne: what Twitter can teach health care providers. JAMA Dermatol. 2013 May;149(5):621-2. doi: 10.1001/jamadermatol.2013.248. PubMed PMID: 23677100.Read More...
Acne is one of the most common skin diseases, with an estimated prevalence of 50 million people in the United States alone, and has a significant impact on quality of life. The high prevalence and seriousness of acne makes crafting innovative avenues for patient education about this disease very important. Twitter has become a popular social networking phenomenon with a user base of over 140 million active users and 340 million tweets per day. Its popularity makes it a potentially powerful source of information and route of communication for acne, especially since the Internet can be an adolescent's primary sources of health information.
Assessing Hospital Readmission Risk Factors in Heart Failure Patients Enrolled in a Telemonitoring Program
Zai AH, Ronquillo JG, Nieves R, Chueh HC, Kvedar JC, Jethwani K. Assessing hospital readmission risk factors in heart failure patients enrolled in a telemonitoring program. Int J Telemed Appl. 2013;2013:305819. doi: 10.1155/2013/305819. Epub 2013 Apr 27. PubMed PMID: 23710170.Read More...
The purpose of this study was to validate a previously developed heart failure readmission predictive algorithm based on psychosocial factors, develop a new model based on patient-reported symptoms from a telemonitoring program, and assess the impact of weight fluctuations and other factors on hospital readmission. Clinical, demographic, and telemonitoring data was collected from 100 patients enrolled in the Partners Connected Cardiac Care Program between July 2008 and November 2011. 38% of study participants were readmitted to the hospital within 30 days. Ten different heart-failure-related symptoms were reported 17,389 times, with the top three contributing approximately 50% of the volume. The psychosocial readmission model yielded an AUC of 0.67, along with sensitivity 0.87, specificity 0.32, positive predictive value 0.44, and negative predictive value 0.8 at a cutoff value of 0.30. In summary, hospital readmission models based on psychosocial characteristics, standardized changes in weight, or patient-reported symptoms can be developed and validated in heart failure patients participating in an institutional telemonitoring program. However, more robust models will need to be developed that use a comprehensive set of factors in order to have a significant impact on population health.
The Future of Connected Health in Preventive Medicine
Agboola SO, Ball M, Kvedar JC, Jethwani K. The future of Connected Health in preventive medicine. QJM. 2013 Sep;106(9):791-4. doi: 10.1093/qjmed/hct088. Epub 2013 Apr 18. PubMed PMID: 23598385.Read More...
Over the last decade, Connected Health (CH) has shown great value in the management of chronic disease (CD), but has limited application in preventing these diseases that remain a huge burden to the society. Technological advances have made determination of genetic predisposition to disease possible and have gained wide use in oncology to develop more effective and individualized treatment strategies-Personalized Medicine. There is growing interest in the application of these genetic tests in predicting risk for complex genetic diseases; even, direct-to-consumer tests are increasingly becoming available and affordable. CH has shown great potential in collecting phenotypic data, which can be overlaid on genomic data to deliver a more precise and personalized preventive care that better engages patients. The goal of a CH program that uses genetic data would be to monitor individuals' risk factors and predict the onset of CD. This prediction would be coupled with coaching to delay or prevent the onset of disease. However, the challenge remains that many CDs are due to complex interaction between genes and modifiable environmental risk factors that are still under-studied.
Is There Such a Thing as an Online Health Lifestyle?
Hale, Timothy M. 2013. “Is There Such a Thing as an Online Health Lifestyle? Examining the Relationship Between Social Status, Internet Access, and Health Behaviors.” Information, Communication & Society, iFirst:1-18. DOI:10.1080/1369118X.2013.777759.Read More...
The purpose of this paper is to examine the use of the Internet for health-related purposes and whether this usage is part of larger pattern of health-promoting behaviors, or health lifestyle. Pierre Bourdieu's concept of habitus provides the key theoretical concept that links health lifestyle and the digital inequality framework to explain how social conditions (i.e. social status and quality of Internet access) influence attitudes and behaviors. Path analysis is used to examine the relationship between key endogenous variables on attitudes, health behavior, health status, and online health-related activities, while controlling for demographics and other factors. Data comes from the National Cancer Institute's 2007 Health Information National Trends Survey. The results demonstrate that online health behaviors can be usefully conceptualized as elements of health lifestyle. The combination of health lifestyle and digital inequality provides a broader theoretical framework that highlights the importance of social conditions to influence people's Internet habitus and routine health-promoting behaviors. The combination of health lifestyle and digital inequality provides a useful theoretical framework for future research investigating persistent social disparities in health and the potential for the growing reliance on information and communication technologies to contribute to socially patterned health outcomes.
Partners HealthCare Center for Connected Health
Ternullo J, Jethwani K, Lane S, Myint-U K, Havasy R, Carter M, Kvedar J. Partners HealthCare Center for Connected Health. Telemed J E Health. 2013 May;19(5):363-7. doi: 10.1089/tmj.2012.0294. Epub 2013 Jan 18. PubMed PMID: 23330595.Read More...
This article reviews the history, current status, and future plans of the Partners HealthCare Center for Connected Health (the Center). Established in 1995 by Harvard Medical School teaching hospitals, the Center develops strategies to move healthcare from the hospital and doctor's office into the day-to-day lives of patients. It leverages information technology to help manage chronic conditions, maintain health and wellness, and improve adherence to prescribed regimen, patient engagement, and clinical outcomes. Since inception, it has served over 30,000 patients. The Center's core functions include videoconference-based real-time virtual visits, home vital sign monitoring, store-and-forward online consultations, social media, mobile technology, and other novel methods of providing care and enabling health and wellness remotely and independently of traditional time and geographic constraints. It offers a wide range of services, programs, and research activities. The Center comprises over 40 professionals with various technical and professional skills. Internally within Partners HealthCare, the role of the Center is to collaborate, guide, advise, and support the experimentation with and the deployment and growth of connected health technologies, programs, and services. Annually, the Center engages in a deliberative planning process to guide its annual research and operational agenda. The Center enjoys a diversified revenue stream. Funding sources include institutional operating budget/research funds from Partners HealthCare, public and private competitive grants and contracts, philanthropic contributions, ad hoc funding arrangements, and longer-term contractual arrangements with third parties.