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Electronic Proceedings, Presentations and Posters of Leading Conferences
BOSTON, MA - OCTOBER 25-27, 2017
Call for Abstracts
The 2017 Connected Health Conference will be holding a poster session in collaboration with the Journal of Medical Internet Research (JMIR). Poster presentations offer an opportunity to share your work with an international audience interested in Connected Health. Posters will be on display in the exhibit hall with special designated times for presentations. Topics presented showcase work relevant to the Conference theme, including:
Review Call for Abstracts
Review Poster Session FAQs
iproc (iproceedings) is a peer-review and publishing platform for conference papers, abstracts, posters, and presentations. JMIR Publications partners with leading conferences such as Medicine 2.0 or the Connected Health Symposium to provide peer-review and editing services, and/or to publish proceedings, posters, or abstracts. If you are a conference organizer or conference chair running a leading medical or technology conference, and wish to outsource the submission and peer-reviewing process, or are interested in hosting a virtual poster show or wish to publish electronic proceedings, or if you are looking for a permanent and open dissemination venue for presentations at your conference, please contact us to discuss partnership options. Starting in 2017, we will also accept individual submissions from researchers who wish to disseminate their poster presented at a major peer-reviewed conference.
The Connected Health Symposium is a change-agent conference that promotes innovative thinking and the application of personal consumer health technologies to support new models of care delivery. This year's theme, “The Internet of Healthy Things: Integrating Connected Health into Real World Care Delivery," focuses on trends at the intersection of technology and new models of health care delivery. The Symposium, presented by Partners HealthCare, convenes thought leaders in an effort to grow the rapidly-expanding connected health marketplace. The audience is a high-profile gathering of innovators, researchers, industry representatives, and policymakers who gather for knowledge-sharing across the connected health landscape. Over 100 speakers and more than 1,000 attendees come together to define the future of care delivery and impact the day-to-day lives of patients.
As in the last year, there are no submission/publication fees (unless presenters want to turn their submissions into full papers published in a JMIR journal) and poster presenters will enjoy free registration (no registration fees) at the symposium. And if that's not enough, presenters get 20% discount on APFs if they publish their full work in JMIR.
What is different in 2016 from previous years is that we no longer require an expanded abstract (5 pages) - a 500 word abstract is enough!
Background: Patient-centeredness is an important element of high-quality care. Patient portals can contribute to the patient-centered care and are defined as “an online gateway for patients to gather and share information mostly provided by one health institution.” While portals can have positive effects, its implementation has a major impact on the healthcare institutions providing those. Little is known about the organizational factors that facilitate or hinder successful implementation. Knowledge of the specific barriers and facilitators of different stakeholders may be useful for future implementations. Objective: The objective of this study is to identify the barriers and facilitators of patient portal implementation among different stakeholders within the hospital organization. Methods: Purposive sampling was used to select hospitals of different classes. Two university medical centers (UMCs), 3 mid-size hospitals and 2 general hospitals were included. Per hospital three stakeholders were interviewed including: 1) medical professionals, 2) managers, and 3) IT employees. Semi-structured interviews were conducted using the comprehensive model of Grol and Wensing, which describes barriers and facilitators of change in healthcare practice. Barriers and facilitators can occur on six levels: 1) Innovation, 2) Individual professional, 3) Patient, 4) Social Context, 5) Organizational Context, 6) Economic Context. Two researchers independently selected and coded quotes by using this model. Additional factors related to technical and portal characteristics were added by using the model of McGinn et al developed for implementation of electronic medical records Results: In total, we identified 382 quotes in 34 categories. Twenty-five categories were common for all stakeholders groups, including 16 barriers and 13 facilitators. Positive aspects related to ‘advantage in practice’ were mentioned most frequently, followed by positive ‘attitude’ and ‘motivation to change’. The main barriers were ‘resources’ (eg lack of staff), ‘opinion of colleagues’ (eg, negative beliefs) and ‘privacy and security’ (eg, strict regulations). Similarities and differences were found between stakeholder groups and hospital classes. For example, medical professionals and IT employees considered 'resources' as an essential barrier. However, their perspectives differed regarding 'opinion of colleagues' as this was a major barrier for medical professionals (eg doctors with negative attitudes), but a facilitator for IT employees (eg, portal implementation can drive a positive change). Results of mid-size and general hospitals were largely comparable, whereas differences were identified for the UMCs. Conclusions: The model of Grol and Wensing proved to be useful in elicitation and classification of barriers and facilitators to portal implementation. However, technical and aspects related to portal characteristics (such as 'privacy and security' and 'perceived ease of use') were missing, and were added from the McGinn model. Barriers and facilitators occurred at various levels and differed between hospital classes and stakeholder groups on several aspects (eg 'opinion of colleagues' and 'cost issues'). This underscores the added value of involving multiple stakeholders in future portal implementations. The identified set of barriers and facilitators may be useful to make strategic and efficient implementation plans.
Background: Difficulty falling asleep and staying asleep are common problems that affect over 30 million Americans. Additionally, we know that military personnel and Veterans often have insomnia problems post deployment. Home sleep monitors can be used to diagnose sleep disorders and determine if the sleep issue cause is a physical issue, such as obstructive sleep apnea. Some non-physiological causes may be improved by focusing on behavioral change, which can be assisted by mobile health (mHealth) technologies. In addition, mHealth apps are an increasingly popular method to deliver behavioral change interventions for a variety of conditions, with the cognitive behavioral therapy for Insomnia Coach app (CBT-i Coach) being particularly popular (it has been downloaded over 80,000 times in 86 countries). Objective: In this pilot trial we assessed the usability and feasibility of mobile health information technologies (HITs) designed to reduce sleep problems in post-9/11 Veterans with chronic insomnia. We used the CBT-i Coach mobile app (based on cognitive behavioral therapy for insomnia) and supplemented it with usage instructions to enhance self-management. Participants also used a home-based sleep monitor (WatchPAT) to obtain objective sleep data to assess possible sleep apnea and to provide subjects with objective data to motivate behavioral change. Methods: Thirty-eight post-9/11 Veterans met criteria for insomnia on the Insomnia Severity Index (ISI). We assessed feasibility and usability of the HITs over a 6-week intervention with a pre-post design. The WatchPAT was used to screen for sleep apnea, and those with moderate to severe apnea were withdrawn from the trial and referred for further assessment. Participants were given a self-management guide which detailed when to use different elements of the CBT-i Coach app, including guidance to complete a sleep diary each morning. Assessments were completed at the beginning, middle, and end of the 6-week intervention. Results: Of the 38 enrolled, 18 participants were withdrawn for moderate or severe sleep apnea as measured by the WatchPAT, and 9 withdrew for personal reasons. Post-intervention qualitative interviews revealed that many participants found both the CBT-i Coach app and WatchPAT easy to use. Participants also liked tracking their daily sleep and seeing graphical results of their sleep changes over time, with only 2 of the final 11 participants completing CBT-I Coach sleep diaries less than 85% of the time. Exploratory analyses on the 11 completers also revealed significant but modest differences between baseline ISI scores (M=16.63, SD = 5.55) and post-intervention follow-up (M=12.82, SD = 3.74; t (10) = 4.14, P<.01). Conclusions: We found good usability of the combined CBT-i Coach app and WatchPAT sleep intervention and determined that feasibility was reasonable, with more than half of those not excluded due to apnea completing all assessments. The pilot demonstrated reasonable feasibility and usability of the mobile HIT tools which could provide an accessible adjunct or alternative to in-person cognitive behavioral therapy for insomnia to improve the health and wellbeing of busy individuals.
Background: In 2011, the Department of Orthopedics and Rehabilitation at UMass Medical School was awarded an AHRQ grant to establish a national registry of comprehensive total joint replacement (TJR) outcomes registry, FORCE-TJR. This lead to the development of an infrastructure for successful longitudinal direct-to-patient data capture, which has now been translated into a product to support orthopedic outcomes measurement in hospitals and surgeon practices around the country. Objective: To use data from the FORCE-TJR registry to demonstrate the benefits of direct-to-patient data capture for data consistency and completeness. Methods: To be a comprehensive TJR outcomes registry, FORCE-TJR required the development of a data capture system that supported complete and consistent research quality data. For this work, we first explored how our data capture system differs from other commercial and research based outcomes measurement systems. We then queried our integrated database of patient-reported outcomes, risk factors, adverse events, and claims to demonstrate how these differences affect data consistency and completeness. Results: We found that direct-to-patient data capture led to more complete measurement of adverse events as 25% of post-TJR ER visits, hospital readmission, and early revisions occurred outside of the surgical hospital. Direct-to-patient data capture increased the capture of key risk factors, such as morbid obesity, eight-fold as compared to claims data. Finally, when compared to “in-office” outcomes measurement, web-based, direct-to-patient methods increased data completion rates from 53% to 86%. Conclusions: Web-based, direct-to-patient outcomes data collection improves data consistency and completeness as compared to other data capture methods supporting the collection of research quality data.
Background: The Veterans Health Administration (VA) strives to increase access and patient-centered care. “Annie” is VA’s first national automated text messaging system aimed to give patients a self-management tool to take charge of their health and become more engaged in their own care. We examined early patient/provider experiences with Annie as part of a limited field test and subsequently examined if the texting system would improve outcomes. Objective: To understand early experiences using Annie and subsequently pilot and evaluate automated text messages to improve Veteran self-management and adherence to medications and appointments. Methods: We conducted a national two-phase study in VA. In Phase 1, five sites conducted limited field testing and engaged 43 respondents (23 patients, 20 providers). Respondents completed surveys and qualitative interviews focused on provider adoption, integration into clinic workflow, patient ease of use, perceptions of program effectiveness, and barriers to use. In Phase 2, seven sites implemented Annie. Four intervention sites received a toolkit of materials and facilitation calls intended to enhance Annie implementation. An additional three sites received Annie without implementation support and two matched comparison sites did not receive Annie. A mixed-methods evaluation is underway, and includes pre and post patient and provider surveys and interviews, medical chart abstraction, and process measure analysis. Results: Analysis of Phase 1 data revealed that all participating Veterans felt positive about their ability to receive a text message. All but one Veteran used a smart phone and had been using a cell phone for four years or more, with 83% sending and receiving text messages several times a day. Sixty-seven percent of Veterans agreed that Annie was easy to use, 11% felt they needed to learn a lot to use Annie, 46% felt Annie helped them take better care of their health and become more connected with their clinical team, and 91% would recommend Annie to another Veteran. Among providers, 67% agreed leadership and management would support Annie implementation and 60% would recommend Annie to another provider. Analysis of Phase 1 interviews revealed five lessons to support implementation: 1) the importance of identifying a resource person who is able to bridge technology and clinical issues; 2) promoting the evidence, innovation, and patient empowerment associated with Annie to providers; 3) focusing early Annie enrollment efforts on patients comfortable with technology and who do not need intensive follow up; 4) advertising the adaptability of Annie texting protocols; and 5) the value of Annie as a health coaching tool. In Phase 2 of Annie implementation we are collecting data on patient and provider experiences with Annie, including usability, clinical workflow fit, and clinical benefits such as improved medication adherence and fewer missed labs and visits. Conclusions: Technologies develop rapidly and hold the allure of efficiently doing things that were once cumbersome or not possible to do in a busy clinical setting. The Annie automated text-messaging system may be able to help in this regard by offering support through self-management, coaching, and education outside of clinical encounters. Findings will inform iterative development of Annie and its national rollout.
Background: Timely forecast of influenza activity is critical for a public health system to prepare for an influenza epidemic and mitigate its burden. Currently, influenza surveillance relies on traditional data sources such as reports from health care providers, which lag behind real-time by several days to weeks. In an effort to reduce the time lag, internet search information, voluntary web-based records, and electronic health records have been suggested as the alternative data sources for influenza surveillance. However, low specificity, low rate of report, or privacy concerns limits the use of such data. Objective: FeverCoach mobile application provides tailored information to help caregivers manage a febrile child. Using the self-reported diagnosis data submitted to the app, we developed a new algorithm that accurately predicted the influenza trend in South Korea. Methods: Users of FeverCoach agreed to the use of de-identified data for research purposes. The app shows information about use of antipyretics and adjuvant way to relieve fever when users enter the child’s age, sex, body temperature, and the duration of fever. Users can choose from the list of 21 candidate diseases including Influenza after a physician office visit. Additional information about the disease was provided following submission of the diagnosis. Public influenza-like illness (ILI) data was obtained from the Korea Centers for Disease Control and Prevention (KCDC) website. The data was collected from September 2016 to March 2017. Ordinary least squares linear regression was used to build a model using the data from the app to predict the influenza trend. To perform linear regression, we calculate logit(Pcdc) and logit(Papp) where logit(p) is natural log of p/(1-p), Pcdc is (ILI visit counts)/(total patient visit counts) and Papp is (Influenza report on FeverCoach)/(total diagnosis report on FeverCoach). Results: We collected 13,014 self-reported diagnoses. Of all users, 81% of the children were under 5 years of age. The animated visualization of spatiotemporal diagnosis report is available online at https://www.youtube.com/watch?v=-8kDXz43gO8. Ordinary least square regression showed significant association between logit(Pcdc) and logit(Papp) (R2=0.860, P<.001). Using this regression model, we could detect an influenza epidemic 5 days before the 2016-2017 season’s influenza epidemic alert by KCDC. Conclusions: We found that it is possible to predict influenza epidemics earlier than KCDC with a relatively small amoount data. Collection of specific and accurate data was made possible by targeting a well-defined population.
Background: HIV-infected individuals with comorbidities like drug addiction have difficulty staying in medical care and adhering to antiretroviral medication which, if taken regularly, results in viral suppression and greatly reduces risk of transmission to others. Since cocaine is not amenable to pharmacological interventions, there is an urgent need for behavioral interventions. Innovative and cost-effective strategies to improve medication adherence and optimize HIV treatment outcomes may be provided by mHealth. Very little, however, is known about the acceptability and feasibility of mHealth among HIV-infected drug users. Objective: To assess feasibility, acceptability, and barriers and facilitators of implementing an mHealth intervention among HIV-infected cocaine users. Methods: Focus groups were conducted with 5 groups (N=20) of HIV-infected individuals who self-reported cocaine use in the past 30 days, and 3 groups (N=8) of HIV and substance use providers. Participants were recruited from clinical and community venues including HIV and drug treatment clinics, a mobile medical unit, and support groups. We asked about: previous experience with smartphones and computers; barriers and facilitators of mobile technology for health purposes; attitudes toward receiving different types of feedback about adherence behaviors. Data was analyzed using content analysis to identify salient themes from responses, facilitated by the qualitative data analysis software NVivo. Results: Results highlighted the pattern and preference of cell phone/mobile device usage among HIV-infected cocaine users. Usage reasons included staying connected with their social network, receiving text reminders for appointments, information seeking, scheduling and recreational use. Text reminders were preferred over phone calls due to reasons of privacy, accessibility and economizing “minutes”. Patient privacy and confidence in the electronic medical system, however, were important themes among patients. Although cell phones were considered useful, some patients reported very limited computer use because of distrusting technology and worries that their medical information, particularly their HIV status, could be hacked through the phone. From providers’ perspectives, the personal interaction with patients, including via cell phones, was important to them and was often hampered by interruptions in patients’ phone use (number changes, disconnections, running out of pre-paid minutes). Communication via text message or phone calls would be most appropriate for professionals directly in charge and in continuous contact with patients, such as social workers and case managers, as opposed to physicians. Conclusions: Participants’ beliefs and suggestions were helpful in informing the design of a subsequent mHealth pilot randomized control trial. We incorporated findings in the following ways: (1) personalized feedback from a clinician along with automated reminders and feedback; (2) an extra layer of security was added on the smartphone app; and (3) facilitated mobility and convenience by providing backpacks for all devices, considering HIV-infected participants’ concerns about their transient and unstable living conditions. Understanding potential users’ and stakeholders’ perspectives is an important step in developing effective mHealth strategies to help people manage their health behaviors.
Background: Depression is the leading cause of disability worldwide. Heavy drinking often co-occurs with Major Depressive Disorder (MDD), preventing the amelioration of symptoms and increasing disability. New technology-based assessment tools such as ecological momentary assessment (EMA) and wearable sensors provide the opportunity for a more detailed examination of the interplay between these two conditions. While the association between low mood and heavy drinking has been extensively examined, multi-method assessments including EMA, sensors and clinician-rated measures have not been utilized to study the association between depression and heavy alcohol use. Objective: To examine the association between depressive symptoms and alcohol consumption by integrating multiple sources of data including EMA, clinical assessment, and wearable sensors. Methods: Individuals with MDD complete an 8-week protocol that involves tracking depressive symptoms and alcohol consumption daily through an EMA. Mood is captured via surveys delivered twice daily that include 10 items of the Positive and Negative Affect Scale (PANAS) assessing negative affect (NA) and positive affect (PA). Participants wear Empatica E4 wristband sensors that track electrodermal activity (EDA) and accelerometer data 23 hours/day. The clinician-rated Hamilton Depression Rating Scale (HDRS) is administered biweekly to assess depressive symptoms. MovisensXS, the app delivering the EMA, tracks text messages, phone calls, location, app usage, and screen on/off behavior. Results: To date, 16 of 30 projected participants have completed the study. All participants are expected to complete the study by 10/2017. Preliminary analyses confirmed the accuracy of the daily mood ratings. There was a significant linear relationship between NA/PA ratio from EMA ratings and the clinician-based ratings (P=1.3e-6). To focus on the association of low mood and drinking behavior, we solely included instances where NA>=PA and observed a significant association (P=0.001) between low mood (as a ratio of total NA divided by PA) and higher alcohol use. Analyses will be repeated for all 30 participants. E4 accelerometer data and location data will help elucidate whether mobility moderates the association between mood and depression, such that individuals who drink at home may exhibit greater depressive symptomatology. Finally, the association between EDA and alcohol use will be examined. Final results will be presented at the Connected Health Conference. Conclusions: To date, results show a significant association between low mood and alcohol consumption. Results of planned analyses will further clarify the temporal association between mood and alcohol use among depressed patients, and possible moderators and mediators of this relationship. A precise understanding of the association between low mood, physiological states and heavy drinking will facilitate the development of “just-in-time” ecological momentary interventions for both reduction of depressed mood and heavy drinking.
Background: Many older adults develop chronic diseases, such as heart disease and diabetes, which are correlated with low levels of physical activity. Chronic diseases can result in a decreased quality of life, increased health care costs, and premature mortality. Adults, specifically older adults, who started using wearable activity trackers (WATs) have exhibited an increase in daily activity levels. Although WAT use has increased, only 7% of older adults use a WAT. The use of WATs has the potential to facilitate chronic condition self-management, with patients engaging in personalized care and health care providers receiving accurate data about patient physical activity. One benefit of WATs is the opportunity to develop social relationships. Social relationships have as much impact on physical health as physical activity. Older adults with larger networks show higher levels of health. Objective: The purpose of this study is to explore how WATs connect older adults to those around them and to determine the benefits of sharing WAT data with healthcare providers. Methods: Ten focus groups and 20 interviews were conducted with older adults who had varying levels of WAT use. Each participant was categorized as one of the following; long-term user (used WATs for six months or more); short-term user (used WATs for less than six months); former user; never user. Discussion topics included WAT social aspects, the frequency and benefits of sharing individual WAT data with healthcare providers, and strategies to increase the number of long-term WAT users among older adults. Results: Preliminary data suggests that WATs have the potential to better connect people socially through their competition and gamification aspects. Trackers are able to connect numerous people together and turn an individual's health journey into an engaging and communal game. Some older adults also reported taking their WAT data to their healthcare provider. Sharing the WAT data made many of them feel like they were taking charge of their own health. The features that were reported as most commonly talked about with providers were sleep patterns, steps taken, and heart rate. A difference between the long-term and former users studied was their level in social interaction. Preliminary data suggests that more long-term users reported sharing their data with others than former users. Conclusions: Initial analysis suggests that WAT users can benefit more from social interactions with their WATs. Tracking activity with others holds a person accountable and can make it more enjoyable. Sharing WAT data with healthcare providers has been suggested to comfort older adults by making them feel more in control of their life. Older adults can potentially talk with their doctors more intelligently about their activity levels through their WAT.
Background: There is increasing interest in incorporating voice-activated technology (VAT), such as Amazon Alexa, Google Home, and Microsoft Cortana, into the existing connected health, mHealth, and mobile medical app ecosystems. VATs allow for natural-language interactions and offer patients the promise of increased usability, greater engagement, and improved adherence to treatments and/or medications. Despite this interest, there is little ethnographic data on patients’ use of VAT or unmet needs. This data is critical to developing VAT applications that interact with medical devices, where regulatory or design control considerations require a higher level of rigor compared to unregulated consumer applications. As first-mover, Amazon Alexa technology has dominated the VAT market; customer reviews of Alexa-enabled devices outnumber the next closest technology 19 to 1. We hypothesized that Amazon Alexa was a good proxy for VAT users at large, and that systematic coding and analysis of 95,000 reviews for Amazon Alexa devices could provide insights that would accelerate follow-on research efforts to support development of user-centered VAT applications for connected health. Objective: Primarily, we sought to explore whether Amazon reviews could be used to develop initial research hypotheses, pain points, and user insights, in much the same way complaint reviews inform early development of medical devices and interventions. Secondarily, we explored whether VAT reviews could be used to identify unmet needs around VAT-for-healthcare applications. Methods: We conducted an exploratory, manual retrospective analysis of 28,271 full-text user reviews for Amazon’s Echo and Dot devices, including all reviews from February to July 2017. This represented approximately 31% of all available Amazon Alexa review data. Two authors (CT/AC) screened each review for relevance, defined as any mention of an issue related to use, misuse, unintended/unexpected event, or novel application of technology. Relevant reviews were manually coded by the authors into one or more of nine categories. Results: There were 284/28,271 user reviews (~1%) that were relevant, yielding valuable user-related insights in our areas of interest. Most relevant reviews focused on Healthcare-Related Workarounds (141), Quality of Life Improvement (159), and Physical Disability (93). We also found relevant, useful information related to Neurological Disorder/Disability (39), Unauthorized Interactions (23), Unexpected Use Settings (33), Natural Language Barriers/Advantages (50), Companionship (50), and Noteworthy Benefits to Healthcare (16). We found the reviews to contain significant detail, allowing us to generate initial insights without the expenditure and complexity of traditional user research. Conclusions: The results of our manual review and coding provided unexpectedly rich information regarding unique device uses, curious workarounds, and unexpected complications. This analysis offers an early effort to improve understanding of how this type of technology may be used in the medical field. Given the currently sparse literature in this space, our study provides a roadmap for future studies centered around VATs in digital health. All remaining reviews should be similarly analyzed and catalogued for future use. Such investigations could involve more detailed exploration of patient practices using other user research methods in order to inform future development in this area.
Background: Colorectal cancer (CRC) is the third leading cause of death due to cancer in the United States. Compared to other racial/ethnic groups, African Americans have the highest CRC morbidity and mortality rates. Despite the proven efficacy of CRC screening, more than one-third of African Americans have not received a colonoscopy screening within the recommended time frame (one colonoscopy per ten years). It is critical to improve this group’s colonoscopy screening uptake to reduce the burden of CRC among African Americans. Objective: The primary goal was to develop a tablet app, e-motivate, which incorporates motivational interviewing principles to increase African Americans’ colonoscopy screening uptake. Two-step field testing was conducted to examine the app’s efficacy. Methods: Participants (N=40) were African American primary care patients over the age of 50 (recommended age to begin screening for CRC). Immediately after receiving a colonoscopy screening referral, patients field-tested e-motivate in the primary care office, which took approximately 20 minutes. For Field Test 1, 20 participants used the app and engaged in a think-aloud exercise to assess the intervention’s feasibility. The feedback from Field Test 1 was used to modify the app. Field test procedures were repeated on an additional 20 participants to confirm feasibility. The feedback from the Field Test 2 was used to further modify the app. Results: In Field Test 1, descriptive statistics were run to determine the usability and acceptability of the app. The mean overall score on the System Usability Scale of 86.62 (possible range from 0 to 100) indicates high usability. The mean score on the Acceptability E-Scale of 4.8 (possible range from 1 to 5) indicates high acceptability of the app. Qualitative thematic analysis revealed that participants found the e-motivate 1.0 app to be user-friendly and helpful. Some participants reported difficulty with certain app functions (e.g., using a slider icon). The participants’ suggestions were used to guide the development of the e-motivate app 2.0. Field Test 2 is ongoing and results will be reported in the final poster presentation. Iterations to follow will be based on patient feedback. Conclusions: The two-step field test approach focused on user-centered design and directly informed the development of a user-friendly, patient-driven app with optimal user satisfaction and engagement to help improve screening colonoscopy uptake in African Americans. The next critical step in the app’s development is to test the efficacy of e-motivate in a randomized clinical trial. If the app is successful in the RCT there is a strong case for integrating e-motivate into standard clinical practices with the ultimate goal of reducing the preventable and unequal burden of CRC among African Americans. In the future, the digital prescribing platform RxUniverse, an efficient program which enables physicians to “prescribe” evidence-based mobile health applications to a large population of patients, can be used to bulk prescribe e-motivate. Trial Registration: Improving Colonoscopy Screening Uptake to Reduce the Burden of CRC Among African Americans
Background: As of 2017, an estimated 5.5 million Americans are living with Alzheimer’s disease and related dementias (ADRD). Information and support for individuals with ADRD and their caregivers are critically needed. Technological advancements have provided patients and caregivers with tools that can provide information and education in areas such as improving awareness about the disease, disease management, and caregiving skills training. Mobile applications (apps) are an example of these tools. Studies have been conducted to assess the content of mobile apps focused on other health issues such as diabetes, weight management, and cancer; however, little is known about ADRD-related mobile apps. To our knowledge, this is the first comprehensive review of apps focused on ADRD. Objective: The objective of this study was to review the content of ADRD-related mobile apps. Methods: ADRD-related mobile apps were searched using keywords such as “Alzheimer”, “Alzheimer’s Disease” and “Dementia” in the App store for iOS-supported apps and Google Play Store for Android-supported apps. Apps were included for final review based on description, and inclusion and exclusion criteria. Three reviewers coded characteristics of the app (e.g. developer, version, number of installations, user ratings), target users, purpose, content of information provided, and technical aspects. Descriptive statistics, including frequencies and percentages, were used to analyze the data. Results: A total of 38 apps were included in the review (16 were only available in iOS; 9 were only available in Android; 13 apps were available in both operating systems). IT companies developed 36.8% of the apps reviewed, followed by non-profit organizations (18.4%), and health-consulting organizations (10.5%). Very few apps were developed by government agencies (5.3%) or pharmaceutical companies (5.3%). Most apps were intended for caregivers of individuals with ADRD (63.2%), followed by the general population (44.7%). The main purpose of the apps was for disease management (55.3%), skills training (42.1%), disease and treatment information (34.2%), and to improve disease awareness (29.0%). Very few apps had a goal of providing peer support (2.6%). Most of the content was focused on caregiving (63.2%) and disease management (50.0%). Other information frequently presented included signs and symptoms of ADRD (34.2%), types of ADRD (31.6%), financial and legal issues (29.0%), resources for supporting patients (29.0%), and healthy lifestyle for ADRD prevention (29.0%). Few apps contained information about differences between typical aging and ADRD symptoms (13.2%), and health insurance option for ADRD patients (10.5%). Few apps had video (23.7%) or audio (2.6%) lectures or tutorials. Interactive features were limited; few apps had a function of sharing (18.4%), an app community (10.5%), or sending reminders (7.9%). Conclusions: ADRD mobile apps that provide caregiving information can potentially benefit individuals who are supporting ADRD patients. Most ADRD mobile apps reviewed did not cover certain aspects related to ADRD, such as how to differentiate ADRD symptoms from typical aging. In addition, information provided by the apps was mainly presented in the form of text with limited audio/video options. There are opportunities for further development of ADRD apps with respect to content and format.
Background: Urothelial bladder cancer kills over 16,000 people annually. Approximately 30% of affected patients have cancer cells invading the muscularis propria at the time of diagnosis. Standard management for muscle-invasive bladder cancer (MIBC) patients involves radical cystectomy and pelvic lymph node dissection. Approximately 50% of these patients will develop fatal metastatic recurrence. In an attempt to eradicate micrometastatic disease, neoadjuvant chemotherapy (NAC) was integrated into treatment. Even though studies show that this improves these patients’ prognosis, population-based studies have demonstrated that NAC is still underutilized. The difficulty of predicting an individual patient’s outcome with cystectomy alone and the potential added benefit with NAC was cited as a common reason for this. Objective: The aim of this study is to develop a web-based app for MIBC patients treated with cystectomy, with or without NAC, designed to improve prediction and enhance communication of these patients’ prognosis. Methods: This study included patients from the National Cancer Database (2003 through 2011) who were diagnosed with MIBC and were subsequently treated with cystectomy. Patient, tumor, and facility-level predictors were incorporated in the outcome prediction model and a state-transition model was synthesized to calculate the 5-year death risk with and without NAC. Internal and external cross-validations were performed to validate the predictions. Using U.S. Life Tables, bladder cancer-specific and other cause mortality were distinguished from all cause mortality rates. The effect of NAC was integrated using a literature-derived hazard ratio (HR). Finally, a web-based tool was developed using the state transition model and usability testing was performed. Results: A total of 9,824 patients who had MIBC and underwent cystectomy met the eligibility criteria and were included in the prediction model (Figure 1). Factors such as race, advanced age, higher clinical T stage, and higher comorbidity index were associated with shorter survival. On the other hand, factors like private insurance, higher income, and undergoing cystectomy at a higher volume facility were associated with longer survival. The prediction model was well-calibrated across geographical regions. Individualized survival estimates of each patient can be generated using the web-based app (BladderCancerRisk.org) by feeding in the predictor variables and a user-defined HR associated with the effect of NAC. The output of the tool is displayed using infographics (Figures 2 and 3). A cohort consisting of 13 clinicians field-tested the usability of the tool. Conclusions: A web-based user-friendly app was developed for patients with MIBC treated with cystectomy, with or without NAC, which individualizes outcome prediction and communication in these patients, and may also facilitate physician-patient shared decision-making. This app can be easily accessed or prescribed by the physicians using the Rx Universe platform (a digital platform that enables physicians to directly “prescribe” evidence-based mobile health applications to patients).
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