Assessing the Use of Mobile Health Technology by Patients: An Observational Study in Primary Care Clinics

Ramirez, Veronica; Johnson, Emily; Gonzalez, Cesar; Ramirez, Vanessa; Rubino, Barbara; Rossetti, Gina

doi:10.2196/mhealth.4928

Cited by 105 publications

(79 citation statements)

References 14 publications

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“…This acceptability of mHealth is consistent with a prior study of a culturally diverse low-income population at two primary care clinics in California [29]. In this study, 86% of respondents said they were interested in using mHealth to improve their health.…”

Section: Discussionsupporting

confidence: 89%

Engaging Gatekeeper-Stakeholders in Development of a Mobile Health Intervention to Improve Medication Adherence Among African American and Pacific Islander Elderly Patients With Hypertension

Yazdanshenas¹,

Bazargan²,

Jones³

et al. 2016

JMIR Mhealth Uhealth

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BackgroundApproximately 70 million people in the United States have hypertension. Although antihypertensive therapy can reduce the morbidity and mortality associated with hypertension, often patients do not take their medication as prescribed.ObjectiveThe goal of this study was to better understand issues affecting the acceptability and usability of mobile health technology (mHealth) to improve medication adherence for elderly African American and Native Hawaiian and Pacific Islander patients with hypertension.MethodsIn-depth interviews were conducted with 20 gatekeeper-stakeholders using targeted open-ended questions. Interviews were deidentified, transcribed, organized, and coded manually by two independent coders. Analysis of patient interviews used largely a deductive approach because the targeted open-ended interview questions were designed to explore issues specific to the design and acceptability of a mHealth intervention for seniors.ResultsA number of similar themes regarding elements of a successful intervention emerged from our two groups of African American and Native Hawaiian and Pacific Islander gatekeeper-stakeholders. First was the need to teach participants both about the importance of adherence to antihypertensive medications. Second, was the use of mobile phones for messaging and patients need to be able to access ongoing technical support. Third, messaging needs to be short and simple, but personalized, and to come from someone the participant trusts and with whom they have a connection. There were some differences between groups. For instance, there was a strong sentiment among the African American group that the church be involved and that the intervention begin with group workshops, whereas the Native Hawaiian and Pacific Islander group seemed to believe that the teaching could occur on a one-to-one basis with the health care provider.ConclusionsInformation from our gatekeeper-stakeholder (key informant) interviews suggests that the design of a mHealth intervention to improve adherence to antihypertensives among the elderly could be very similar for African Americans and Native Hawaiian and Pacific Islanders. The main difference might be in the way in which the program is initiated (possibly through church-based workshops for African Americans and by individual providers for Native Hawaiian and Pacific Islanders). Another difference might be who sends the messages with African Americans wanting someone outside the health care system, but Native Hawaiian and Pacific Islanders preferring a provider.

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Section: Discussionsupporting

confidence: 89%

Engaging Gatekeeper-Stakeholders in Development of a Mobile Health Intervention to Improve Medication Adherence Among African American and Pacific Islander Elderly Patients With Hypertension

Yazdanshenas¹,

Bazargan²,

Jones³

et al. 2016

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

“…12, 13 The majority of Americans now use smartphones and patients are relying more on mobile app to regularly monitor their health. 14, 15 Smartphone devices already have the capability to nearly continuously identify a user's location in real-time, and publicly available datasets enable pre-specification of a myriad of location types. Push notifications via mobile apps (as was utilized in the current study) or automated calls or texts triggered by some activity on an app allow for customized messaging or questions in response to an app-detected condition.…”

Section: Discussionmentioning

confidence: 99%

Smartphone-Based Geofencing to Ascertain Hospitalizations

Nguyen

Olgin

Pletcher

et al. 2017

Circ: Cardiovascular Quality and Outcomes

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Background Ascertainment of hospitalizations is critical to assess quality of care and the effectiveness and adverse effects of various therapies. Smartphones, mobile geo-locators that are ubiquitous, have not been leveraged to ascertain hospitalizations. Therefore, we evaluated the use of smartphone-based “geofencing” to track hospitalizations. Methods and Results Participants ages ≥ 18 years installed a mobile application programmed to “geofence” all hospitals using global positioning systems and cell phone tower triangulation and to trigger a smartphone-based questionnaire when located in a hospital for ≥ 4 hours. An “in-person” study included consecutive consenting patients scheduled for electrophysiology and cardiac catheterization procedures. A “remote” arm invited Health eHeart Study participants that consented and engaged with the study via the internet only. The accuracy of application-detected hospitalizations was confirmed by medical record review as the reference standard. Of 22 eligible “in-person” patients, 17 hospitalizations were detected (sensitivity 77%; 95% confidence interval [CI] 55-92%). The length of stay according to the application was positively correlated with the length of stay ascertained via the electronic medical record (r=0.53, p=0.03). In the remote arm, the application was downloaded by 3,443 participants residing in all 50 U.S. states; 243 hospital visits at 119 different hospitals were detected through the application. The positive predictive value for an application-reported hospitalization was 65% (95% CI 57-72%). Conclusions Mobile application-based ascertainment of hospitalizations can be achieved with modest accuracy. This first proof of concept may ultimately be applicable to “geofencing” other types of prespecified locations to facilitate healthcare research and patient care.

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“…These decisions can be made individually or in collaboration with health care professionals, and ODLs such as PGHD can be used for personal tracking and improvement as well as to inform clinical care [13,17,18]. PGHD collected through a range of different types of smart devices and other new technologies can provide patients with innovative ways to actively manage their health [11,19-21], improving patient self-knowledge [12], and management of health concerns, including diabetes [4-6,14,22], physical activity [8,10], and behavioral health triggers such as anxiety [7,9,23]. …”

Section: Introductionmentioning

confidence: 99%

Integrating Patient-Generated Health Data Into Clinical Care Settings or Clinical Decision-Making: Lessons Learned From Project HealthDesign

et al. 2016

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BackgroundPatient-generated health data (PGHD) are health-related data created or recorded by patients to inform their self-care and understanding about their own health. PGHD is different from other patient-reported outcome data because the collection of data is patient-driven, not practice- or research-driven. Technical applications for assisting patients to collect PGHD supports self-management activities such as healthy eating and exercise and can be important for preventing and managing disease. Technological innovations (eg, activity trackers) are making it more common for people to collect PGHD, but little is known about how PGHD might be used in outpatient clinics.ObjectiveThe objective of our study was to examine the experiences of health care professionals who use PGHD in outpatient clinics.MethodsWe conducted an evaluation of Project HealthDesign Round 2 to synthesize findings from 5 studies funded to test tools designed to help patients collect PGHD and share these data with members of their health care team. We conducted semistructured interviews with 13 Project HealthDesign study team members and 12 health care professionals that participated in these studies. We used an immersion-crystallization approach to analyze data. Our findings provide important information related to health care professionals’ attitudes toward and experiences with using PGHD in a clinical setting.ResultsHealth care professionals identified 3 main benefits of PGHD accessibility in clinical settings: (1) deeper insight into a patient’s condition; (2) more accurate patient information, particularly when of clinical relevance; and (3) insight into a patient’s health between clinic visits, enabling revision of care plans for improved health goal achievement, while avoiding unnecessary clinic visits. Study participants also identified 3 areas of consideration when implementing collection and use of PGHD data in clinics: (1) developing practice workflows and protocols related to PGHD collection and use; (2) data storage, accessibility at the point of care, and privacy concerns; and (3) ease of using PGHD data.ConclusionsPGHD provides value to both patients and health care professionals. However, more research is needed to understand the benefit of using PGHD in clinical care and to identify the strategies and clinic workflow needs for optimizing these tools.

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Assessing the Use of Mobile Health Technology by Patients: An Observational Study in Primary Care Clinics

Cited by 105 publications

References 14 publications

Engaging Gatekeeper-Stakeholders in Development of a Mobile Health Intervention to Improve Medication Adherence Among African American and Pacific Islander Elderly Patients With Hypertension

Engaging Gatekeeper-Stakeholders in Development of a Mobile Health Intervention to Improve Medication Adherence Among African American and Pacific Islander Elderly Patients With Hypertension

Smartphone-Based Geofencing to Ascertain Hospitalizations

Integrating Patient-Generated Health Data Into Clinical Care Settings or Clinical Decision-Making: Lessons Learned From Project HealthDesign

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