Browsing by Subject "Mobile Applications"

Now showing 1 - 9 of 9
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    ItemOpen Access
    An Initial Evaluation of the Impact of Pokémon GO on Physical Activity.
    (Journal of the American Heart Association, 2017-05-16) Xian, Ying; Xu, Hanzhang; Xu, Haolin; Liang, Li; Hernandez, Adrian F; Wang, Tracy Y; Peterson, Eric D
    Pokémon GO is a location-based augmented reality game. Using GPS and the camera on a smartphone, the game requires players to travel in real world to capture animated creatures, called Pokémon. We examined the impact of Pokémon GO on physical activity (PA).A pre-post observational study of 167 Pokémon GO players who were self-enrolled through recruitment flyers or online social media was performed. Participants were instructed to provide screenshots of their step counts recorded by the iPhone Health app between June 15 and July 31, 2016, which was 3 weeks before and 3 weeks after the Pokémon GO release date. Of 167 participants, the median age was 25 years (interquartile range, 21-29 years). The daily average steps of participants at baseline was 5678 (SD, 2833; median, 5718 [interquartile range, 3675-7279]). After initiation of Pokémon GO, daily activity rose to 7654 steps (SD, 3616; median, 7232 [interquartile range, 5041-9744], pre-post change: 1976; 95% CI, 1494-2458, or a 34.8% relative increase [P<0.001]). On average, 10 000 "XP" points (a measure of game progression) was associated with 2134 additional steps per day (95% CI, 1673-2595), suggesting a potential dose-response relationship. The number of participants achieving a goal of 10 000+ steps per day increased from 15.3% before to 27.5% after (odds ratio, 2.06; 95% CI, 1.70-2.50). Increased PA was also observed in subgroups, with the largest increases seen in participants who spent more time playing Pokémon GO, those who were overweight/obese, or those with a lower baseline PA level.Pokémon GO participation was associated with a significant increase in PA among young adults. Incorporating PA into gameplay may provide an alternative way to promote PA in persons who are attracted to the game.URL: http://www.clinicaltrials.gov. Unique identifier: NCT02888314.
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    ItemOpen Access
    Cell phone intervention for you (CITY): A randomized, controlled trial of behavioral weight loss intervention for young adults using mobile technology.
    (Obesity (Silver Spring, Md.), 2015-11) Svetkey, Laura P; Batch, Bryan C; Lin, Pao-Hwa; Intille, Stephen S; Corsino, Leonor; Tyson, Crystal C; Bosworth, Hayden B; Grambow, Steven C; Voils, Corrine; Loria, Catherine; Gallis, John A; Schwager, Jenifer; Bennett, Gary G

    Objective

    To determine the effect on weight of two mobile technology-based (mHealth) behavioral weight loss interventions in young adults.

    Methods

    Randomized, controlled comparative effectiveness trial in 18- to 35-year-olds with BMI ≥ 25 kg/m(2) (overweight/obese), with participants randomized to 24 months of mHealth intervention delivered by interactive smartphone application on a cell phone (CP); personal coaching enhanced by smartphone self-monitoring (PC); or Control.

    Results

    The 365 randomized participants had mean baseline BMI of 35 kg/m(2) . Final weight was measured in 86% of participants. CP was not superior to Control at any measurement point. PC participants lost significantly more weight than Controls at 6 months (net effect -1.92 kg [CI -3.17, -0.67], P = 0.003), but not at 12 and 24 months.

    Conclusions

    Despite high intervention engagement and study retention, the inclusion of behavioral principles and tools in both interventions, and weight loss in all treatment groups, CP did not lead to weight loss, and PC did not lead to sustained weight loss relative to Control. Although mHealth solutions offer broad dissemination and scalability, the CITY results sound a cautionary note concerning intervention delivery by mobile applications. Effective intervention may require the efficiency of mobile technology, the social support and human interaction of personal coaching, and an adaptive approach to intervention design.
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    Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation.
    (The New England journal of medicine, 2019-11) Perez, Marco V; Mahaffey, Kenneth W; Hedlin, Haley; Rumsfeld, John S; Garcia, Ariadna; Ferris, Todd; Balasubramanian, Vidhya; Russo, Andrea M; Rajmane, Amol; Cheung, Lauren; Hung, Grace; Lee, Justin; Kowey, Peter; Talati, Nisha; Nag, Divya; Gummidipundi, Santosh E; Beatty, Alexis; Hills, Mellanie True; Desai, Sumbul; Granger, Christopher B; Desai, Manisha; Turakhia, Mintu P; Apple Heart Study Investigators
    BACKGROUND:Optical sensors on wearable devices can detect irregular pulses. The ability of a smartwatch application (app) to identify atrial fibrillation during typical use is unknown. METHODS:Participants without atrial fibrillation (as reported by the participants themselves) used a smartphone (Apple iPhone) app to consent to monitoring. If a smartwatch-based irregular pulse notification algorithm identified possible atrial fibrillation, a telemedicine visit was initiated and an electrocardiography (ECG) patch was mailed to the participant, to be worn for up to 7 days. Surveys were administered 90 days after notification of the irregular pulse and at the end of the study. The main objectives were to estimate the proportion of notified participants with atrial fibrillation shown on an ECG patch and the positive predictive value of irregular pulse intervals with a targeted confidence interval width of 0.10. RESULTS:We recruited 419,297 participants over 8 months. Over a median of 117 days of monitoring, 2161 participants (0.52%) received notifications of irregular pulse. Among the 450 participants who returned ECG patches containing data that could be analyzed - which had been applied, on average, 13 days after notification - atrial fibrillation was present in 34% (97.5% confidence interval [CI], 29 to 39) overall and in 35% (97.5% CI, 27 to 43) of participants 65 years of age or older. Among participants who were notified of an irregular pulse, the positive predictive value was 0.84 (95% CI, 0.76 to 0.92) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular pulse notification and 0.71 (97.5% CI, 0.69 to 0.74) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular tachogram. Of 1376 notified participants who returned a 90-day survey, 57% contacted health care providers outside the study. There were no reports of serious app-related adverse events. CONCLUSIONS:The probability of receiving an irregular pulse notification was low. Among participants who received notification of an irregular pulse, 34% had atrial fibrillation on subsequent ECG patch readings and 84% of notifications were concordant with atrial fibrillation. This siteless (no on-site visits were required for the participants), pragmatic study design provides a foundation for large-scale pragmatic studies in which outcomes or adherence can be reliably assessed with user-owned devices. (Funded by Apple; Apple Heart Study ClinicalTrials.gov number, NCT03335800.).
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    ItemOpen Access
    Learnings From the Pilot Implementation of Mobile Medical Milestones Application.
    (Journal of graduate medical education, 2016-10) Page, Cristen P; Reid, Alfred; Coe, Catherine L; Carlough, Martha; Rosenbaum, Daryl; Beste, Janalynn; Fagan, Blake; Steinbacher, Erika; Jones, Geoffrey; Newton, Warren P

    Background

    Implementation of the educational milestones benefits from mobile technology that facilitates ready assessments in the clinical environment. We developed a point-of-care resident evaluation tool, the Mobile Medical Milestones Application (M3App), and piloted it in 8 North Carolina family medicine residency programs.

    Objective

    We sought to examine variations we found in the use of the tool across programs and explored the experiences of program directors, faculty, and residents to better understand the perceived benefits and challenges of implementing the new tool.

    Methods

    Residents and faculty completed presurveys and postsurveys about the tool and the evaluation process in their program. Program directors were interviewed individually. Interviews and open-ended survey responses were analyzed and coded using the constant comparative method, and responses were tabulated under themes.

    Results

    Common perceptions included increased data collection, enhanced efficiency, and increased perceived quality of the information gathered with the M3App. Residents appreciated the timely, high-quality feedback they received. Faculty reported becoming more comfortable with the tool over time, and a more favorable evaluation of the tool was associated with higher utilization. Program directors reported improvements in faculty knowledge of the milestones and resident satisfaction with feedback.

    Conclusions

    Faculty and residents credited the M3App with improving the quality and efficiency of resident feedback. Residents appreciated the frequency, proximity, and specificity of feedback, and faculty reported the app improved their familiarity with the milestones. Implementation challenges included lack of a physician champion and competing demands on faculty time.
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    ItemOpen Access
    Mobile health devices: will patients actually use them?
    (Journal of the American Medical Informatics Association : JAMIA, 2016-05) Shaw, Ryan J; Steinberg, Dori M; Bonnet, Jonathan; Modarai, Farhad; George, Aaron; Cunningham, Traven; Mason, Markedia; Shahsahebi, Mohammad; Grambow, Steven C; Bennett, Gary G; Bosworth, Hayden B
    Although mobile health (mHealth) devices offer a unique opportunity to capture patient health data remotely, it is unclear whether patients will consistently use multiple devices simultaneously and/or if chronic disease affects adherence. Three healthy and three chronically ill participants were recruited to provide data on 11 health indicators via four devices and a diet app. The healthy participants averaged overall weekly use of 76%, compared to 16% for those with chronic illnesses. Device adherence declined across all participants during the study. Patients with chronic illnesses, with arguably the most to benefit from advanced (or increased) monitoring, may be less likely to adopt and use these devices compared to healthy individuals. Results suggest device fatigue may be a significant problem. Use of mobile technologies may have the potential to transform care delivery across populations and within individuals over time. However, devices may need to be tailored to meet the specific patient needs.
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    Opening the Duke electronic health record to apps: Implementing SMART on FHIR.
    (International journal of medical informatics, 2017-03) Bloomfield, Richard A; Polo-Wood, Felipe; Mandel, Joshua C; Mandl, Kenneth D
    Recognizing a need for our EHR to be highly interoperable, our team at Duke Health enabled our Epic-based electronic health record to be compatible with the Boston Children's project called Substitutable Medical Apps and Reusable Technologies (SMART), which employed Health Level Seven International's (HL7) Fast Healthcare Interoperability Resources (FHIR), commonly known as SMART on FHIR.We created a custom SMART on FHIR-compatible server infrastructure written in Node.js that served two primary functions. First, it handled API management activities such rate-limiting, authorization, auditing, logging, and analytics. Second, it retrieved the EHR data and made it available in a FHIR-compatible format. Finally, we made required changes to the EHR user interface to allow us to integrate several compatible apps into the provider- and patient-facing EHR workflows.After integrating SMART on FHIR into our Epic-based EHR, we demonstrated several types of apps running on the infrastructure. This included both provider- and patient-facing apps as well as apps that are closed source, open source and internally-developed. We integrated the apps into the testing environment of our desktop EHR as well as our patient portal. We also demonstrated the integration of a native iOS app.In this paper, we demonstrate the successful implementation of the SMART and FHIR technologies on our Epic-based EHR and subsequent integration of several compatible provider- and patient-facing apps.
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    ItemOpen Access
    The Evolution of U.S. Spectrum Values Over Time
    (Economic Research Initiatives at Duke (ERID) Working Paper, 2018-02-12) Connolly, Michelle P; Sá, Nelson; Zaman, Azeem; Roark, Christopher; Trivedi, Akshaya
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    Theoretically Guided Iterative Design of the Sense2Quit App for Tobacco Cessation in Persons Living with HIV.
    (International journal of environmental research and public health, 2023-02) Schnall, Rebecca; Trujillo, Paul; Alvarez, Gabriella; Michaels, Claudia L; Brin, Maeve; Huang, Ming-Chun; Chen, Huan; Xu, Wenyao; Cioe, Patricia A
    The use of mobile health (mHealth technology) can be an effective intervention when considering chronic illnesses. Qualitative research methods were used to identify specific content and features for a mobile app for smoking cessation amongst people living with HIV (PWH). We conducted five focus group sessions followed by two Design Sessions with PWH who were or are currently chronic cigarette smokers. The first five groups focused on the perceived barriers and facilitators to smoking cessation amongst PWH. The two Design Sessions leveraged the findings from the focus group sessions and were used to determine the optimal features and user interface of a mobile app to support smoking cessation amongst PWH. Thematic analysis was conducted using the Health Belief Model and Fogg's Functional Triad. Seven themes emerged from our focus group sessions: history of smoking, triggers, consequences of quitting smoking, motivation to quit, messages to help quit, quitting strategies, and mental health-related challenges. Functional details of the app were identified during the Design Sessions and used to build a functional prototype.
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    Track: A randomized controlled trial of a digital health obesity treatment intervention for medically vulnerable primary care patients.
    (Contemporary clinical trials, 2016-05) Foley, Perry; Steinberg, Dori; Levine, Erica; Askew, Sandy; Batch, Bryan C; Puleo, Elaine M; Svetkey, Laura P; Bosworth, Hayden B; DeVries, Abigail; Miranda, Heather; Bennett, Gary G

    Introduction

    Obesity continues to disproportionately affect medically vulnerable populations. Digital health interventions may be effective for delivering obesity treatment in low-resource primary care settings.

    Methods

    Track is a 12-month randomized controlled trial of a digital health weight loss intervention in a community health center system. Participants are 351 obese men and women aged 21 to 65years with an obesity-related comorbidity. Track participants are randomized to usual primary care or to a 12-month intervention consisting of algorithm-generated tailored behavior change goals, self-monitoring via mobile technologies, daily self-weighing using a network-connected scale, skills training materials, 18 counseling phone calls with a Track coach, and primary care provider counseling. Participants are followed over 12months, with study visits at baseline, 6, and 12months. Anthropometric data, blood pressure, fasting lipids, glucose and HbA1C and self-administered surveys are collected. Follow-up data will be collected from the medical record at 24months.

    Results

    Participants are 68% female and on average 50.7years old with a mean BMI of 35.9kg/m(2). Participants are mainly black (54%) or white (33%); 12.5% are Hispanic. Participants are mostly employed and low-income. Over 20% of the sample has hypertension, diabetes and hyperlipidemia. Almost 27% of participants currently smoke and almost 20% score above the clinical threshold for depression.

    Conclusions

    Track utilizes an innovative, digital health approach to reduce obesity and chronic disease risk among medically vulnerable adults in the primary care setting. Baseline characteristics reflect a socioeconomically disadvantaged, high-risk patient population in need of evidence-based obesity treatment.