The eHealth/mHealth Research Interest Group (EMRIG) was formed in the spring of 2013 and is currently headed by Associate Director Deborah Cornman (PhD, InCHIP) and Assistant Professor Debarchana Ghosh (PhD, Geography). This group is comprised of researchers at UConn and other institutions who are interested in electronic health/mobile health research and the use of mobile technologies, social media, web-based interventions, sensors, and other new technologies to assess and modify health behavior. Members are informed through the group’s listserv and website about relevant funding opportunities, presentations, conferences, webinars, trainings, publications, and new developments in eHealth/mHealth. The group sponsors events and activities in eHealth/mHealth, assists members in identifying potential research collaborators and technology experts, holds periodic meetings, and keeps members abreast of what is happening in the field. The primary goal of these various activities is to foster new multidisciplinary research collaborations that address important individual and public health issues in new and innovative ways. In August 2014, the EMRIG expanded to also become a CICATS Core Interest Group (CIG), which means that the eHealth/mHealth research interest group is co-sponsored by both InCHIP on the Storrs campus and the Connecticut Institute for Clinical and Translational Science (CICATS) at UConn Health.
To join the eHealth/mHealth Research Interest Group, contact Deborah Cornman, InCHIP Associate Director and EMRIG co-chair, at Deborah.Cornman@uconn.edu.
Conferences and Colloquia
American Telemedicine Association
April 23-25, 2017, Orlando, FL
Past InCHIP Events/Activities
- March 24, 2016, 12-1pm – EMRIG Workshop Matt Larson, Janel Wright, and Eli Strassfeld, UConn Procurement Services
- March 2, 2017, 12:30-1:30pm – InCHIP Lecture Chandra Osborn, PhD, MPH, One Drop, Informed Data Systems Inc.
- February 23, 2017, 12:30-1:30pm – InCHIP Lecture J. Graham Thomas, PhD, Alpert Medical School of Brown University
- January 26, 2017, 12:30-1:30pm – InCHIP Lecture Sheana Bull, PhD, MPH, University of Colorado, Aurora
- October 15, 2015, 12:30-1:30pm – InCHIP Lecture Jennifer Stinson, PhD, CPNP; University of Toronto
- May 7, 2015, 12:30-1:30pm – InCHIP Lecture Gary Bennett, PhD – Duke University
- March 31, 2015: Digital Health Science Café at Costa del Sol: This networking event co-hosted by CICATS and InCHIP was a huge success! Researchers from UConn, UConn Health Center, and community agencies convened to present their digital health research interests and potential areas where they’re looking for collaborators. We are looking forward to seeing new research collaborations in the field of digital health in the near future.
- Thursday, October 16, 2014, 12:30-1:30pm – InCHIP Lecture Sherry Pagoto, PhD – Associate Professor at the University of Massachusetts Medical School and co-founder of the UMass Center for mHealth and Social Media
- May 6, 2014 – Presentation on UConn’s Digital Media Center Tim Hunter – Professor and Head of the Digital Media & Design Department, and Director of the Digital Media Center, University of Connecticut
- April 24, 2014 – InCHIP Lecture John Mangano, MBA – Vice President, comScore
- April 17, 2014 – InCHIP Lecture Suzanne Mitchell, MD, MS – Assistant Professor of Family Medicine at Boston University School of Medicine
- April 10, 2014 – InCHIP Lecture Tim Bickmore, PhD – Associate Professor in the College of Computer and Information Science at Northeastern University
- “Automated Health Counselors for Underserved Populations”
- View streaming video
See below for eHealth/mHealth-related funding opportunities in the InCHIP External Funding Opportunities database. To filter the database by funding opportunities in other health domains, please visit the External Funding Opportunities main page.
eHealth/mHealth Research Resources
Interesting and Noteworthy Publications
Linked Patient-Reported Outcomes Data From Patients With Multiple Sclerosis Recruited on an Open Internet Platform to Health Care Claims Databases Identifies a Representative Population for Real-Life Data Analysis in Multiple Sclerosis
Authors: Risson V., Ghodge B., Bonzani I.C., Korn J.R., Medin J., Saraykar T., Sengupta S., Saini D., and Olson M.
Journal: Journal of Medical Internet Research
Date Published: September 22, 2016
Background: An enormous amount of information relevant to public health is being generated directly by online communities.
Objective: To explore the feasibility of creating a dataset that links patient-reported outcomes data, from a Web-based survey of US patients with multiple sclerosis (MS) recruited on open Internet platforms, to health care utilization information from health care claims databases. The dataset was generated by linkage analysis to a broader MS population in the United States using both pharmacy and medical claims data sources.
Methods: US Facebook users with an interest in MS were alerted to a patient-reported survey by targeted advertisements. Eligibility criteria were diagnosis of MS by a specialist (primary progressive, relapsing-remitting, or secondary progressive), â¥12-month history of disease, age 18-65 years, and commercial health insurance. Participants completed a questionnaire including data on demographic and disease characteristics, current and earlier therapies, relapses, disability, health-related quality of life, and employment status and productivity. A unique anonymous profile was generated for each survey respondent. Each anonymous profile was linked to a number of medical and pharmacy claims datasets in the United States. Linkage rates were assessed and survey respondentsâ representativeness was evaluated based on differences in the distribution of characteristics between the linked survey population and the general MS population in the claims databases.
Results: The advertisement was placed on 1,063,973 Facebook usersâ pages generating 68,674 clicks, 3719 survey attempts, and 651 successfully completed surveys, of which 440 could be linked to any of the claims databases for 2014 or 2015 (67.6% linkage rate). Overall, no significant differences were found between patients who were linked and not linked for educational status, ethnicity, current or prior disease-modifying therapy (DMT) treatment, or presence of a relapse in the last 12 months. The frequencies of the most common MS symptoms did not differ significantly between linked patients and the general MS population in the databases. Linked patients were slightly younger and less likely to be men than those who were not linkable.
Conclusions: Linking patient-reported outcomes data, from a Web-based survey of US patients with MS recruited on open Internet platforms, to health care utilization information from claims databases may enable rapid generation of a large population of representative patients with MS suitable for outcomes analysis.
Can Facebook Be Used for Research? Experiences Using Facebook to Recruit Pregnant Women for a Randomized Controlled Trial
Authors: Adam L.M., Manca D.P., and Bell R.C.
Journal: Journal of Medical Internet Research
Date Published: September 21, 2016
Background: Recruitment is often a difficult and costly part of any human research study. Social media and other emerging means of mass communication hold promise as means to complement traditional strategies used for recruiting participants because they can reach a large number of people in a short amount of time. With the ability to target a specified audience, paid Facebook advertisements have potential to reach future research participants of a specific demographic. This paper describes the experiences of a randomized controlled trial in Edmonton, Alberta, attempting to recruit healthy pregnant women between 8 and 20 weeksâ gestation for participation in a prenatal study. Various traditional recruitment approaches, in addition to paid Facebook advertisements were trialed.
Objective: To evaluate the effectiveness of paid advertisements on Facebook as a platform for recruiting pregnant women to a randomized controlled trial in comparison with traditional recruitment approaches.
Methods: Recruitment using traditional approaches occurred for 7 months, whereas Facebook advertisements ran for a total of 26 days. Interested women were prompted to contact the study staff for a screening call to determine study eligibility. Costs associated with each recruitment approach were recorded and used to calculate the cost to recruit eligible participants. Performance of Facebook advertisements was monitored using Facebook Ads Manager.
Results: Of the 115 women included, 39.1% (n=45) of the women who contacted study staff heard about the study through Facebook, whereas 60.9% (n=70) of them heard about it through traditional recruitment approaches. During the 215 days (~7 months) that the traditional approaches were used, the average rate of interest was 0.3 (0.2) women/day, whereas the 26 days of Facebook advertisements resulted in an average rate of interest of 2.8 (1.7) women/day. Facebook advertisements cost Can $506.91 with a cost per eligible participant of Cad $20.28. In comparison, the traditional approaches cost Cad $1087, with approximately Cad $24.15 per eligible participant. Demographic characteristics of women were similar between the 2 recruitment methods except that women recruited using Facebook were significantly earlier in their pregnancy than those recruited using traditional approaches (P<.03).
Conclusions: Paid Facebook advertisements hold promise as a platform for reaching pregnant women. The relative ease of placing an advertisement, the comparable cost per participant recruited, and the dramatically improved recruitment rates in comparison with traditional approaches highlight the importance of combining novel and traditional recruitment approaches to recruit women for pregnancy-related studies.
Trial Registration: ClinicalTrials.gov NCT02711644; https://clinicaltrials.gov/ct2/show/NCT02711644 (Archived by WebCite at http://www.webcitation.org/6kKpagpMk)
Authors: Agarwal V., Zhang L., Zhu J., Fang S., Cheng T., Hong C., and Shah N.H.
Journal: Journal of Medical Internet Research
Date Published: September 21, 2016
Background: By recent estimates, the steady rise in health care costs has deprived more than 45 million Americans of health care services and has encouraged health care providers to better understand the key drivers of health care utilization from a population health management perspective. Prior studies suggest the feasibility of mining population-level patterns of health care resource utilization from observational analysis of Internet search logs; however, the utility of the endeavor to the various stakeholders in a health ecosystem remains unclear.
Objective: The aim was to carry out a closed-loop evaluation of the utility of health care use predictions using the conversion rates of advertisements that were displayed to the predicted future utilizers as a surrogate. The statistical models to predict the probability of userâs future visit to a medical facility were built using effective predictors of health care resource utilization, extracted from a deidentified dataset of geotagged mobile Internet search logs representing searches made by users of the Baidu search engine between March 2015 and May 2015.
Methods: We inferred presence within the geofence of a medical facility from location and duration information from usersâ search logs and putatively assigned medical facility visit labels to qualifying search logs. We constructed a matrix of general, semantic, and location-based features from search logs of users that had 42 or more search days preceding a medical facility visit as well as from search logs of users that had no medical visits and trained statistical learners for predicting future medical visits. We then carried out a closed-loop evaluation of the utility of health care use predictions using the show conversion rates of advertisements displayed to the predicted future utilizers. In the context of behaviorally targeted advertising, wherein health care providers are interested in minimizing their cost per conversion, the association between show conversion rate and predicted utilization score, served as a surrogate measure of the modelâs utility.
Results: We obtained the highest area under the curve (0.796) in medical visit prediction with our random forests model and daywise features. Ablating feature categories one at a time showed that the model performance worsened the most when location features were dropped. An online evaluation in which advertisements were served to users who had a high predicted probability of a future medical visit showed a 3.96% increase in the show conversion rate.
Conclusions: Results from our experiments done in a research setting suggest that it is possible to accurately predict future patient visits from geotagged mobile search logs. Results from the offline and online experiments on the utility of health utilization predictions suggest that such prediction can have utility for health care providers.
A Comparison of Recruitment Methods for an mHealth Intervention Targeting Mothers: Lessons from the Growing Healthy Program
Authors: Laws R.A., Litterbach E.K.V., Denney-Wilson E.A., Russell C.G., Taki S., Ong K.L., Elliott R.M., Lymer S.J., and Campbell K.J.
Journal: Journal of Medical Internet Research
Date Published: September 15, 2016
Background: Mobile health (mHealth) programs hold great promise for increasing the reach of public health interventions. However, mHealth is a relatively new field of research, presenting unique challenges for researchers. A key challenge is understanding the relative effectiveness and cost of various methods of recruitment to mHealth programs.
Objective: The objectives of this study were to (1) compare the effectiveness of various methods of recruitment to an mHealth intervention targeting healthy infant feeding practices, and (2) explore factors influencing practitioner referral to the intervention.
Methods: The Growing healthy study used a quasi-experimental design with an mHealth intervention group and a concurrent nonrandomized comparison group. Eligibility criteria included: expectant parents (>30 weeks of gestation) or parents with an infant <3 months old, ability to read and understand English, own a mobile phone, â¥18 years old, and living in Australia. Recruitment to the mHealth program consisted of: (1) practitioner-led recruitment through Maternal and Child Health nurses, midwives, and nurses in general practice; (2) face-to-face recruitment by researchers; and (3) online recruitment. Participantsâ baseline surveys provided information regarding how participants heard about the study, and their sociodemographic details. Costs per participant recruited were calculated by taking into account direct advertising costs and researcher time/travel costs. Practitioner feedback relating to the recruitment process was obtained through a follow-up survey and qualitative interviews.
Results: A total of 300 participants were recruited to the mHealth intervention. The cost per participant recruited was lowest for online recruitment (AUD $14) and highest for practice nurse recruitment (AUD $586). Just over half of the intervention group (50.3%, 151/300) were recruited online over a 22-week period compared to practitioner recruitment (29.3%, 88/300 over 46 weeks) and face-to-face recruitment by researchers (7.3%, 22/300 over 18 weeks). No significant differences were observed in participant sociodemographic characteristics between recruitment methods, with the exception that practitioner/face-to-face recruitment resulted in a higher proportion of first-time parents (68% versus 48%, P=.002). Less than half of the practitioners surveyed reported referring to the program often or most of the time. Key barriers to practitioner referral included lack of time, difficulty remembering to refer, staff changes, lack of parental engagement, and practitioner difficulty in accessing the app.
Conclusions: Online recruitment using parenting-related Facebook pages was the most cost effective and timely method of recruitment to an mHealth intervention targeting parents of young infants. Consideration needs to be given to addressing practitioner barriers to referral, to further explore if this can be a viable method of recruitment.
The Impact of Automated Brief Messages Promoting Lifestyle Changes Delivered Via Mobile Devices to People with Type 2 Diabetes: A Systematic Literature Review and Meta-Analysis of Controlled Trials
Authors: Arambepola C., Ricci-Cabello I., Manikavasagam P., Roberts N., French D.P., and Farmer A.
Journal: Journal of Medical Internet Research
Date Published: April 19, 2016
Background: Brief automated messages have the potential to support self-management in people with type 2 diabetes, but their effect compared with usual care is unclear.
Objective: To examine the effectiveness of interventions to change lifestyle behavior delivered via automated brief messaging in patients with type 2 diabetes.
Methods: A systematic literature review of controlled trials examined the impact of interventions, delivered by brief messaging, and intended to promote lifestyle change in people with type 2 diabetes, on behavioral and clinical outcomes. Bibliographic databases searched included Medline, Embase, CINAHL, PsycINFO, and ISI WoK. Two reviewers independently screened citations. We extracted information on study risk of bias, setting (high versus low- and middle-income countries) and intervention characteristics (including use of theory and behavior-change techniques). Outcome measures included acceptability of the interventions and their impact on 1) determinants of lifestyle behavior (knowledge about diabetes, self-efficacy, attitudes towards self-management), 2) lifestyle behavior (diet, physical activity), and 3) clinical and patient-reported outcomes. Where possible, we pooled data using random-effects meta-analyses to obtain estimates of effect size of intervention compared to usual care.
Results: We identified 15 trials (15 interventions) meeting our inclusion criteria. Most interventions were delivered via short message service text messaging (n=12) and simultaneously targeted diet and physical activity (n=11). Nine interventions consisted of unidirectional messages, whereas six consisted of bidirectional messages, with patients receiving automated tailored feedback based on self-reported data. The acceptability of the interventions, and their impact on lifestyle behavior and its determinants, were examined in a low proportion of trials, with heterogeneous results being observed. In 13 trials (1155 patients) where data were available, there was a difference in glycated hemoglobin of -0.53% (95% CI -0.59% to -0.47%) between intervention groups compared to usual care. In five trials (406 patients) there was a non-significant difference in body mass index of -0.25 kg/m2 (95% CI -1.02 to 0.52). Interventions based on unidirectional messages produced similar effects in the outcomes examined, compared to those based on bidirectional messages. Interventions conducted in low- and middle-income countries showed a greater impact than those conducted in high-income countries. In general, trials were not free of bias and did not use explicit theory.
Conclusions: Automated brief messages strategies can improve health outcomes in people with type 2 diabetes. Larger, methodologically robust trials are needed to confirm these positive results.
Guidelines for Reporting of Health Interventions Using Mobile Phones: Mobile Health (mHealth) Evidence Reporting and Assessment (mERA) Checklist
Authors: Agarwal S., LeFevre A.E., Lee J., L’Engle K., Mehl G., Sinha C., and Labrique A. for the WHO mHealth Technical Evidence Review Group
Date Published: March 17, 2016
To improve the completeness of reporting of mobile health (mHealth) interventions, the World Health Organization (WHO) mHealth Technical Evidence Review Group developed the mHealth evidence reporting and assessment (mERA) checklist. The development process for mERA consisted of convening an expert group to recommend an appropriate approach, convening a global expert review panel for checklist development, and pilot testing the checklist. The guiding principle for the development of these criteria was to identify a minimum set of information needed to define what the mHealth intervention is (content), where it is being implemented (context), and how it was implemented (technical features), to support replication of the intervention. This paper presents the resulting 16 item checklist and a detailed explanation and elaboration for each item, with illustrative reporting examples. Through widespread adoption, we expect that the use of these guidelines will standardize the quality of mHealth evidence reporting, and indirectly improve the quality of mHealth evidence.
Authors: Muntaner A., Vidal-Conti J., and Palou P.
Journal: Health Informatics Journal
Date Published: February 3, 2015
Physical inactivity is a health problem that affects people worldwide and has been identified as the fourth largest risk factor for overall mortality (contributing to 6% of deaths globally). Many researchers have tried to increase physical activity levels through traditional methods without much success. Thus, many researchers are turning to mobile technology as an emerging method for changing health behaviours. This systematic review sought to summarise and update the existing scientific literature on increasing physical activity through mobile device interventions, taking into account the methodological quality of the studies. The articles were identified by searching the PubMed, SCOPUS and SPORTDiscus databases for studies published between January 2003 and December 2013. Studies investigating efforts to increase physical activity through mobile phone or even personal digital assistant interventions were included. The search results allowed the inclusion of 11 studies that gave rise to 12 publications. Six of the articles included in this review reported significant increases in physical activity levels. The number of studies using mobile devices for interventions has increased exponentially in the last few years, but future investigations with better methodological quality are needed to draw stronger conclusions regarding how to increase physical activity through mobile device interventions.