Mobile Health App for Japanese Adult Patients With Asthma: Clinical Observational Study.

BACKGROUND: Inappropriate asthma control reduces quality of life and causes increased exacerbations. Mobile health (mHealth) employs information and communication technology for surveying health-related issues. OBJECTIVE: This noninterventional, observational study assessed current real-world asthma control levels among Japanese patients with asthma and cough variant asthma (CVA) using the Zensoku-Log app. METHODS: We developed the app using the ResearchKit platform and conducted a mobile-based, self-reporting, observational survey among patients with asthma and CVA. The app was downloaded 7855 times between February 2016 and February 2018, and enabled collection of data on symptoms, comorbidities, quality of life, medications, asthma control, and adherence. RESULTS: Of the 1744 eligible participants (median age 33 years; range 20-74 years; male-to-female ratio 38.7:61.3), 50.97% (889/1744) reported unscheduled visits, 62.84% (1096/1744) reported regularly scheduled visits, 23.14% (402/1737) smoked, and 40.75% (705/1730) had pets. In addition, 91.89% (1598/1739) of participants had atopic predisposition, including allergic rhinitis and atopic dermatitis. Daily inhaled corticosteroid and oral corticosteroid treatment had been prescribed for 89.45% (1552/1735) and 22.07% (383/1735) of participants, respectively. Although an asthma control questionnaire demonstrated poor asthma control in 58.48% (1010/1727), a leukotriene receptor antagonist, theophylline, and a long-acting muscarinic antagonist had been prescribed for only 30.66% (532/1735), 15.91% (276/1735), and 4.38% (76/1735), respectively. The Adherence Starts with Knowledge 12 total score was 29. In the 421 participants who repeated the questionnaire, asthma control increased significantly between the initial and last rounds (P=.002). CONCLUSIONS: Users of this mHealth app in Japan had poorly controlled asthma and may need more treatment for asthma and their comorbidities. Repeated app users demonstrated improved asthma control. TRIAL REGISTRATION: UMIN Clinical Trial Registry UMIN000021043; https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000023913.

Stratification of Individual Symptoms of Contact Lens-Associated Dry Eye Using the iPhone App DryEyeRhythm: Crowdsourced Cross-Sectional Study.

BACKGROUND: Discontinuation of contact lens use is mainly caused by contact lens-associated dry eye. It is crucial to delineate contact lens-associated dry eye's multifaceted nature to tailor treatment to each patient's individual needs for future personalized medicine. OBJECTIVE: This paper aims to quantify and stratify individual subjective symptoms of contact lens-associated dry eye and clarify its risk factors for future personalized medicine using the smartphone app DryEyeRhythm (Juntendo University). METHODS: This cross-sectional study included iPhone (Apple Inc) users in Japan who downloaded DryEyeRhythm. DryEyeRhythm was used to collect medical big data related to contact lens-associated dry eye between November 2016 and January 2018. The main outcome measure was the incidence of contact lens-associated dry eye. Univariate and multivariate adjusted odds ratios of risk factors for contact lens-associated dry eye were determined by logistic regression analyses. The t-distributed Stochastic Neighbor Embedding algorithm was used to depict the stratification of subjective symptoms of contact lens-associated dry eye. RESULTS: The records of 4454 individuals (median age 27.9 years, SD 12.6), including 2972 female participants (66.73%), who completed all surveys were included in this study. Among the included participants, 1844 (41.40%) were using contact lenses, and among those who used contact lenses, 1447 (78.47%) had contact lens-associated dry eye. Multivariate adjusted odds ratios of risk factors for contact lens-associated dry eye were as follows: younger age, 0.98 (95% CI 0.96-0.99); female sex, 1.53 (95% CI 1.05-2.24); hay fever, 1.38 (95% CI 1.10-1.74); mental illness other than depression or schizophrenia, 2.51 (95% CI 1.13-5.57); past diagnosis of dry eye, 2.21 (95% CI 1.63-2.99); extended screen exposure time >8 hours, 1.61 (95% CI 1.13-2.28); and smoking, 2.07 (95% CI 1.49-2.88). The t-distributed Stochastic Neighbor Embedding analysis visualized and stratified 14 groups based on the subjective symptoms of contact lens-associated dry eye. CONCLUSIONS: This study identified and stratified individuals with contact lens-associated dry eye and its risk factors. Data on subjective symptoms of contact lens-associated dry eye could be used for prospective prevention of contact lens-associated dry eye progression.

Exploring the weight bias of professionals working in the field of obesity with a mobile IAT: a pilot study.

Background: Obesity is common in many industrialized nations and often accompanied by related health issues. Furthermore, individuals living with overweight or obesity are often confronted with stigmatization in their daily lives. These problems may be aggravated if the objectivity of health care professionals is compromised due to (unconscious) prejudices. If pharmaceutical companies, regulatory agencies, and health insurers are also susceptible to these biases, decisions related to the development, approval, and reimbursement of obesity-related therapies may be negatively impacted. Materials and Methods: The 'Implicit Association Test' (IAT) is a psychometric test allowing to measure these attitudes and could therefore assist to reveal unconscious preferences. A self-developed mobile version, in the form of a ResearchKit-based IAT app was employed in the presented study. The objective was to determine (potential) weight bias and its characteristics for professionals attending a national obesity-related conference in Germany (G1), compared to a control group (without stated interest in the topic, G2) - both using the mobile app - and a historical control (G3) based on data provided by Project Implicit acquired by a web app. Results: Explicit evaluations of G1 were neutral at a higher percentage compared with G2 and G3, while implicit preference toward lean individuals did not differ significantly between G2 and G3, and G1. Conclusion: The greater discrepancy between the (more neutral) explicit attitude and the unconscious preference pointing in the anti-obesity direction could indicate an underestimated bias for the professional participants in G1. Implicit preference is often ingrained from childhood on, and difficult to overcome. Thus, even for professionals, it may unconsciously influence decisions made in the care they provide. Professionals in any given health care sector directed at obesity care should thus be made aware of this inconsistency to enable them to consciously counteract this potential effect.

DryEyeRhythm: A reliable and valid smartphone application for the diagnosis assistance of dry eye.

PURPOSE: Undiagnosed or inadequately treated dry eye disease (DED) decreases the quality of life. We aimed to investigate the reliability, validity, and feasibility of the DryEyeRhythm smartphone application (app) for the diagnosis assistance of DED. METHODS: This prospective, cross-sectional, observational, single-center study recruited 82 participants (42 with DED) aged ≥20 years (July 2020-May 2021). Patients with a history of eyelid disorder, ptosis, mental disease, Parkinson's disease, or any other disease affecting blinking were excluded. Participants underwent DED examinations, including the Japanese version of the Ocular Surface Disease Index (J-OSDI) and maximum blink interval (MBI). We analyzed their app-based J-OSDI and MBI results. Internal consistency reliability and concurrent validity were evaluated using Cronbach's alpha coefficients and Pearson's test, respectively. The discriminant validity of the app-based DED diagnosis was assessed by comparing the results of the clinical-based J-OSDI and MBI. The app feasibility and screening performance were evaluated using the precision rate and receiver operating characteristic curve analysis. RESULTS: The app-based J-OSDI showed good internal consistency (Cronbach's α = 0.874). The app-based J-OSDI and MBI were positively correlated with their clinical-based counterparts (r = 0.891 and r = 0.329, respectively). Discriminant validity of the app-based J-OSDI and MBI yielded significantly higher total scores for the DED cohort (8.6 ± 9.3 vs. 28.4 ± 14.9, P < 0.001; 19.0 ± 11.1 vs. 13.2 ± 9.3, P < 0.001). The app's positive and negative predictive values were 91.3% and 69.1%, respectively. The area under the curve (95% confidence interval) was 0.910 (0.846-0.973) with concurrent use of the app-based J-OSDI and MBI. CONCLUSIONS: DryEyeRhythm app is a novel, non-invasive, reliable, and valid instrument for assessing DED.

Leveraging Mobile-Based Sensors for Clinical Research to Obtain Activity and Health Measures for Disease Monitoring, Prevention, and Treatment.

Clinical researchers are using mobile-based sensors to obtain detailed and objective measures of the activity and health of research participants, but many investigators lack expertise in integrating wearables and sensor technologies effectively into their studies. Here, we describe the steps taken to design a study using sensors for disease monitoring in older adults and explore the benefits and drawbacks of our approach. In this study, the Geriatric Acute and Post-acute Fall Prevention Intervention (GAPcare), we created an iOS app to collect data from the Apple Watch's gyroscope, accelerometer, and other sensors; results of cognitive and fitness tests; and participant-entered survey data. We created the study app using ResearchKit, an open-source framework developed by Apple for medical research that includes neuropsychological tests (e.g., of executive function and memory), gait speed, balance, and other health assessments. Data is transmitted via an Application Programming Interface (API) from the app to REDCap for researchers to monitor and analyze in real-time. Employing the lessons learned from GAPcare could help researchers create study-tailored research apps and access timely information about their research participants from wearables and smartphone devices for disease prevention, monitoring, and treatment.

Implementation of Mobile Psychological Testing on Smart Devices: Evaluation of a ResearchKit-Based Design Approach for the Implicit Association Test.

Objective: To determine whether a framework-based approach for mobile apps is appropriate for the implementation of psychological testing, and equivalent to established methods. Methods: Apple's ResearchKit was used for implementing native implicit association test methods (IAT), and an exemplary app was developed to examine users' implicit attitudes toward overweight or thin individuals. For comparison, a web-based IAT app, based on code provided by Project Implicit, was used. Adult volunteers were asked to test both versions on an iPad with touch as well as keyboard input (altogether four tests per participant, random order). Latency values were recorded and used to calculate parameters relevant to the implicit setting. Measurements were analyzed with respect to app type and input method, as well as test order (ANOVA and χ2 tests). Results: Fifty-one datasets were acquired (female, n = 21; male, n = 30, average age 35 ± 4.66 years). Test order and combination of app type and input method influenced the latency values significantly (both P<0.001). This was not mirrored for the D scores or average number of errors vs. app type combined with input method (D scores: P = 0.66; number of errors: P = 0.733) or test order (D scores: P = 0.096; number of errors: P = 0.85). Post-hoc power analysis of the linear ANOVA showed 0.8 by f 2=0.25, with α = 0.05 and 4 predictors. Conclusions: The results suggest that a native mobile implementation of the IAT may be comparable to established implementations. The validity of the acquired measurements seems to depend on the properties of the chosen test rather than the specifics of the chosen platform or input method.

The feasibility of using Apple's ResearchKit for recruitment and data collection: Considerations for mental health research.

In 2015, Apple launched an open-source software framework called ResearchKit. ResearchKit provides an infrastructure for conducting remote, smartphone-based research trials through the means of Apple's App Store. Such trials may have several advantages over conventional trial methods including the removal of geographic barriers, frequent assessments of participants in real-life settings, and increased inclusion of seldom-heard communities. The aim of the current study was to explore the feasibility of participant recruitment and the potential for data collection in the non-clinical population in a smartphone-based trial using ResearchKit. As a case example, an app called eMovit, a behavioural activation (BA) app with the aim of helping users to build healthy habits was used. The study was conducted over a 9-month period. Any iPhone user with access to the App Stores of The Netherlands, Belgium, and Germany could download the app and participate in the study. During the study period, the eMovit app was disseminated amongst potential users via social media posts (Twitter, Facebook, LinkedIn), paid social media advertisements (Facebook), digital newsletters and newspaper articles, blogposts and other websites. In total, 1,788 individuals visited the eMovit landing page. A total of 144 visitors subsequently entered Apple's App Store through that landing page. The eMovit product page was viewed 10,327 times on the App Store. With 79 installs, eMovit showed a conversion rate of 0.76% from product view to install of the app. Of those 79 installs, 53 users indicated that they were interested to participate in the research study and 36 subsequently consented and completed the demographics and the participants quiz. Fifteen participants completed the first PHQ-8 assessment and one participant completed the second PHQ-8 assessment. We conclude that from a technological point of view, the means provided by ResearchKit are well suited to be integrated into the app process and thus facilitate conducting smartphone-based studies. However, this study shows that although participant recruitment is technically straightforward, only low recruitment rates were achieved with the dissemination strategies applied. We argue that smartphone-based trials (using ResearchKit) require a well-designed app dissemination process to attain a sufficient sample size. Guidelines for smartphone-based trial designs and recommendations on how to work with challenges of mHealth research will ensure the quality of these trials, facilitate researchers to do more testing of mental health apps and with that enlarge the evidence-base for mHealth.

Patient and public involvement in mobile health-based research for hay fever: a qualitative study of patient and public involvement implementation process.

BACKGROUND: Smartphones are being increasingly used for research owing to their multifunctionality and flexibility, and crowdsourced research using smartphone applications (apps) is effective in the early detection and management of chronic diseases. We developed the AllerSearch app to gather real-world data on individual subjective symptoms and lifestyle factors related to hay fever. This study established a foundation for interactive research by adopting novel, diverse perspectives accrued through implementing the principles of patient and public involvement (PPI) in the development of our app. METHODS: Patients and members of the public with a history or family history of hay fever were recruited from November 2019 to December 2021 through a dedicated website, social networking services, and web briefing according to the PPI Guidebook 2019 by the Japan Agency for Medical Research and Development. Nine opinion exchange meetings were held from February 2020 to December 2021 to collect opinions and suggestions for updating the app. After each meeting, interactive evaluations from PPI contributors and researchers were collected. The compiled suggestions were then incorporated into the app, establishing an active feedback loop fed by the consistently interactive infrastructure. RESULTS: Four PPI contributors (one man and three women) were recruited, and 93 items were added/changed in the in-app survey questionnaire in accordance with discussions from the exchange meetings. The exchange meetings emphasized an atmosphere and opportunity for participants to speak up, ensuring frequent opportunities for them to contribute to the research. In March 2020, a public website was created to display real-time outcomes of the number of participants and users' hay-fever-preventative behaviors. In August 2020, a new PPI-implemented AllerSearch app was released. CONCLUSIONS: This study marks the first research on clinical smartphone apps for hay fever in Japan that implements PPI throughout its timeline from research and development to the publication of research results. Taking advantage of the distinct perspectives offered by PPI contributors, a step was taken toward actualizing a foundation for an interactive research environment. These results should promote future PPI research and foster the establishment of a social construct that enables PPI efforts in various fields.

Patient and public involvement (PPI) plays an important role in promoting effective execution of health science research, as well as in the establishment of a social agreement and infrastructure for the care of various diseases, including cancer, chronic diseases, and allergic illnesses. Hay fever is one of the most common allergic diseases, affecting more than 30 million people in Japan. It is known for its myriad factors and diverse presentations. Previously, we developed a mobile health (mHealth) smartphone application (app) for hay fever­AllerSearch­released in February 2018. This app is capable of collecting relevant digital phenotypes and user-provided information, which are used in providing tailored, evidence-based suggestions. To our knowledge, no other studies have been conducted on the implementation of PPI in mHealth. Since hay fever presents with a wide variety of symptoms and risk factors, PPI principles appear well-suited for eliciting insights from the patient/public population and for incorporating new, expert perspectives into the research process. In this study, we included PPI contributors in the research plan, app development, and evaluation. Most notably, the survey questionnaire and user interface of the app was tailored based on PPI feedback. The updated AllerSearch app was released during this study period. Since hay fever is a widespread and variable illness, the multifaceted input from patients and public experts enabled by PPI implementation holds promise for improving society-wide healthcare and in empowering a culture toward medical involvement.

Heterogeneity of eye drop use among symptomatic dry eye individuals in Japan: large-scale crowdsourced research using DryEyeRhythm application.

PURPOSE: To determine eye drop type and usage frequency and investigate risk factors for no eye drop use in individuals with symptomatic dry eye (DE) in Japan. STUDY DESIGN: Crowdsourced observational study. METHODS: This study was conducted using the DryEyeRhythm smartphone application between November 2016 and September 2019. Data collected included the type and frequency of eye drop use, demographics, medical history, lifestyle, and self-reported symptoms. Symptomatic DE was defined as an Ocular Surface Disease Index total score of ≥ 13. Risk factors for no eye drop use were identified using multivariate logistic regression analyses. RESULTS: Among 2619 individuals with symptomatic DE, 1876 did not use eye drops. The most common eye drop type was artificial tears (53.4%), followed by hyaluronic acid 0.1% (33.1%) and diquafosol sodium 3% (18.7%). Risk factors (odds ratio [95% confidence interval]) for no eye drop use were age (0.97 [0.97-0.98]), body mass index (1.04 [1.01-1.07]), brain disease (0.38 [0.15-0.98]), collagen disease (0.30 [0.13-0.68]), mental illness other than depression and schizophrenia (0.65 [0.45-0.93]), cataract surgery (0.12 [0.02-0.59]), ophthalmic surgery other than cataract and laser-assisted in situ keratomileusis (0.55 [0.34-0.88]), current (0.47 [0.38-0.57]) or past (0.58 [0.43-0.77]) contact lens use, >8 h screen exposure time (1.38 [1.05-1.81]), <6 h (1.24 [1.01-1.52]) and >9 h (1.34 [1.04-1.72]) sleep time, and water intake (0.97 [0.94-0.98]). CONCLUSION: Many participants with symptomatic DE did not use optimized eye drop treatment and identified risk factors for no eye drop use. The DryEyeRhythm application may help improve DE treatment.

The Digital Marshmallow Test (DMT) Diagnostic and Monitoring Mobile Health App for Impulsive Behavior: Development and Validation Study.

BACKGROUND: The classic Marshmallow Test, where children were offered a choice between one small but immediate reward (eg, one marshmallow) or a larger reward (eg, two marshmallows) if they waited for a period of time, instigated a wealth of research on the relationships among impulsive responding, self-regulation, and clinical and life outcomes. Impulsivity is a hallmark feature of self-regulation failures that lead to poor health decisions and outcomes, making understanding and treating impulsivity one of the most important constructs to tackle in building a culture of health. Despite a large literature base, impulsivity measurement remains difficult due to the multidimensional nature of the construct and limited methods of assessment in daily life. Mobile devices and the rise of mobile health (mHealth) have changed our ability to assess and intervene with individuals remotely, providing an avenue for ambulatory diagnostic testing and interventions. Longitudinal studies with mobile devices can further help to understand impulsive behaviors and variation in state impulsivity in daily life. OBJECTIVE: The aim of this study was to develop and validate an impulsivity mHealth diagnostics and monitoring app called Digital Marshmallow Test (DMT) using both the Apple and Android platforms for widespread dissemination to researchers, clinicians, and the general public. METHODS: The DMT app was developed using Apple's ResearchKit (iOS) and Android's ResearchStack open source frameworks for developing health research study apps. The DMT app consists of three main modules: self-report, ecological momentary assessment, and active behavioral and cognitive tasks. We conducted a study with a 21-day assessment period (N=116 participants) to validate the novel measures of the DMT app. RESULTS: We used a semantic differential scale to develop self-report trait and momentary state measures of impulsivity as part of the DMT app. We identified three state factors (inefficient, thrill seeking, and intentional) that correlated highly with established measures of impulsivity. We further leveraged momentary semantic differential questions to examine intraindividual variability, the effect of daily life, and the contextual effect of mood on state impulsivity and daily impulsive behaviors. Our results indicated validation of the self-report sematic differential and related results, and of the mobile behavioral tasks, including the Balloon Analogue Risk Task and Go-No-Go task, with relatively low validity of the mobile Delay Discounting task. We discuss the design implications of these results to mHealth research. CONCLUSIONS: This study demonstrates the potential for assessing different facets of trait and state impulsivity during everyday life and in clinical settings using the DMT mobile app. The DMT app can be further used to enhance our understanding of the individual facets that underlie impulsive behaviors, as well as providing a promising avenue for digital interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT03006653; https://www.clinicaltrials.gov/ct2/show/NCT03006653.

A ResearchKit app to deliver paediatric electronic consent: Protocol of an observational study in adolescents with arthritis.

ResearchKit is an open-source software framework designed to streamline the process of screening and consenting participants into research studies. By digitizing traditionally analog processes, ResearchKit has potential to increase the reach, efficiency, and scalability of mobile health (mHealth) research. The model has been successfully applied in adult settings. However, to our knowledge, no group has sought to adapt ResearchKit for a paediatric research environment in Canada. The potential benefits for building paediatric mHealth apps compatible with remote eConsent are numerous: (1) access to studies can be broadened from small groups of children and families who live in close proximity to research sites to whole populations across geographical boundaries, (2) increased convenience for study participants because they can complete consent on their smartphone from their home, rather than in person or on paper, and (3) large-scale study enrollment can be conducted with fewer resources than traditional face-to-face methods. We describe the rationale and design of a proof-of-concept observational study focused on implementing remote eConsent in a Canadian paediatric population. A community-based sample of adolescents with arthritis will be remotely onboarded to use the iCanCope pain self-management app for 8-weeks. Outcomes will focus on: (1) fidelity and acceptability of the eConsent process, (2) fidelity of the iCanCope app in terms of engagement and acceptability, (3) participant study experience including level of perceived support and acceptability of study tasks, and (4) clinical outcomes related to use of the iCanCope app over an 8-week period.

Association between dry eye and depressive symptoms: Large-scale crowdsourced research using the DryEyeRhythm iPhone application.

PURPOSE: Dry eye (DE) disease and depression are increasing in modern times. We investigated the association between DE and depressive symptoms using the iPhone application, DryEyeRhythm. METHODS: This large-scale crowdsourced observational study was conducted within iPhone users in Japan who downloaded DryEyeRhythm. Participants with a Zung Self-rating Depression Scale (SDS) score ≥ 40 were defined as having depressive symptoms, and those with an Ocular Surface Disease Index (OSDI) score ≥ 13 were defined as having DE symptoms (mild, 13-22; moderate, 23-32; and severe, 33-100). We compared SDS scores between participants with normal eye and mild, moderate, and severe OSDI-based DE symptoms. Logistic regression analyses were used to determine the association between DE severity and depressive symptoms after adjustment for demographic characteristics, medical history, and lifestyle habits. RESULTS: This study included 4454 participants (mean age, 27.9 ± 12.6 years; female, 66.7%). Participants with SDS scores ≥40 accounted for 58.2%, 70.9%, 79.4%, and 85.0% of normal controls and participants with mild, moderate, and severe DE symptoms, respectively (P trend < 0.001). The adjusted odds ratios (95% confidence interval) for depressive symptoms (SDS score of ≥40) were 1.62 (1.35-1.95) for mild, 2.39 (1.92-2.97) for moderate, and 3.29 (2.70-4.00) for severe DE symptoms. CONCLUSION: This large-scale crowdsourced clinical study using DryEyeRhythm suggests that depressive symptoms are more common in individuals with more severe DE symptoms. DryEyeRhythm could play a role in earlier prevention or future prospective interventions for depressive symptoms in individuals with DE symptoms.

International ResearchKit App for Women with Menstrual Pain: Development, Access, and Engagement.

BACKGROUND: Primary dysmenorrhea is a common condition in women of reproductive age. A previous app-based study undertaken by our group demonstrated that a smartphone app supporting self-acupressure introduced by a health care professional can reduce menstrual pain. OBJECTIVE: This study aims to evaluate whether a specific smartphone app is effective in reducing menstrual pain in 18- to 34-year-old women with primary dysmenorrhea in a self-care setting. One group of women has access to the full-featured study app and will be compared with 2 control groups who have access to fewer app features. Here, we report the trial design, app development, user access, and engagement. METHODS: On the basis of the practical implications of the previous app-based study, we revised and reengineered the study app and included the ResearchKit (Apple Inc) framework. Behavior change techniques (BCTs) were implemented in the app and validated by expert ratings. User access was estimated by assessing recruitment progress over time. User evolution and baseline survey respondent rate were assessed to evaluate user engagement. RESULTS: The development of the study app for a 3-armed randomized controlled trial required a multidisciplinary team. The app is accessible for the target population free of charge via the Apple App Store. In Germany, within 9 months, the app was downloaded 1458 times and 328 study participants were recruited using it without external advertising. A total of 98.27% (5157/5248) of the app-based baseline questions were answered. The correct classification of BCTs used in the app required psychological expertise. CONCLUSIONS: Conducting an innovative app study requires multidisciplinary effort. Easy access and engagement with such an app can be achieved by recruitment via the App Store. Future research is needed to investigate the determinants of user engagement, optimal BCT application, and potential clinical and self-care scenarios for app use. TRIAL REGISTRATION: ClinicalTrials.gov NCT03432611; https://clinicaltrials.gov/ct2/show/NCT03432611 (Archived by WebCite at http://www.webcitation.org/75LLAcnCQ).

The FDA MyStudies app: a reusable platform for distributed clinical trials and real-world evidence studies.

We developed a mobile application and secure patient data storage platform, FDA MyStudies, to address privacy, engagement, and extensibility challenges in mobile clinical research. The system extends the capabilities of the mobile frameworks Apple ResearchKit and ResearchStack through an intuitive front-end application and secure storage environment that can support health research studies. The platform supports single or multisite studies via role-based access and can be implemented within highly secure data environments. As a proof-of-concept, pregnant women participated in a descriptive study via the app in which data not routinely captured in electronic health records (EHR) were collected and linked with existing patient data to provide a more wholistic view of the patient and illustrate how patient data combined with EHR data could be used to support public health research.

The Googlization of health research: from disruptive innovation to disruptive ethics.

Consumer-oriented mobile technologies offer new ways of capturing multidimensional health data, and are increasingly seen as facilitators of medical research. This has opened the way for large consumer tech companies, like Apple, Google, Amazon and Facebook, to enter the space of health research, offering new methods for collecting, storing and analyzing health data. While these developments are often portrayed as 'disrupting' research in beneficial ways, they also raise many ethical issues. These can be organized into three clusters: questions concerning the quality of research; privacy/informed consent; and new power asymmetries based on access to data and control over technological infrastructures. I argue that this last cluster, insofar as it may affect future research agendas, deserves more critical attention.