The landscape of research on smartphone medical apps: Coherent taxonomy, motivations, open challenges and recommendations

Real-Time Fault-Tolerant mHealth System: Comprehensive Review of Healthcare Services, Opens Issues, Challenges and Methodological Aspects

The burden on healthcare services in the world has increased substantially in the past decades. The quality and quantity of care have to increase to meet surging demands, especially among patients with chronic heart diseases. The expansion of information and communication technologies has led to new models for the delivery healthcare services in telemedicine. Therefore, mHealth plays an imperative role in the sustainable delivery of healthcare services in telemedicine. This paper presents a comprehensive review of healthcare service provision. It highlights the open issues and challenges related to the use of the real-time fault-tolerant mHealth system in telemedicine. The methodological aspects of mHealth are examined, and three distinct and successive phases are presented. The first discusses the identification process for establishing a decision matrix based on a crossover of 'time of arrival of patient at the hospital/multi-services' and 'hospitals' within mHealth. The second phase discusses the development of a decision matrix for hospital selection based on the MAHP method. The third phase discusses the validation of the proposed system.

An overview on the emerging area of identification, characterization, and assessment of health apps

The need to characterize and assess health apps has inspired a significant amount of research in the past years, in search for methods able to provide potential app users with relevant, meaningful knowledge. This article presents an overview of the recent literature in this field and categorizes - by discussing some specific examples - the various methodologies introduced so far for the identification, characterization, and assessment of health apps. Specifically, this article outlines the most significant web-based resources for app identification, relevant frameworks for descriptive characterization of apps' features, and a number of methods for the assessment of quality along its various components (e.g., evidence base, trustworthiness, privacy, or user engagement). The development of methods to characterize the apps' features and to assess their quality is important to define benchmarks and minimum requirements. Similarly, such methods are important to categorize potential risks and challenges in the field so that risks can be minimized, whenever possible, by design. Understanding methods to assess apps is key to raise the standards of quality of health apps on the market, towards the final goal of delivering apps that are built on the pillars of evidence-base, reliability, long-term effectiveness, and user-oriented quality.

Balancing Between Privacy and Patient Needs for Health Information in the Age of Participatory Health and Social Media: A Scoping Review

OBJECTIVES

 With the increased use of participatory health enabling technologies, such as social media, balancing the need for health information with patient privacy and confidentiality has become a more complex and immediate concern. The purpose of this paper produced by the members of the IMIA Participatory Health and Social Media (PHSM) working group is to investigate patient needs for health information using participatory health enabling technologies, while balancing their needs for privacy and confidentiality.

METHODS

 Six domain areas including media sharing platforms, patient portals, web-based platforms, crowdsourcing websites, medical avatars, and other mobile health technologies were identified by five members of the IMIA PHSM working group as relevant to participatory health and the balance between data sharing and patient needs for privacy and confidentiality. After identifying the relevant domain areas, our scoping review began by searching several databases such as PubMed, MEDLINE, Scopus, and Google Scholar using a variety of key search terms.

RESULTS

 A total of 1,973 studies were identified, of which 68 studies met our inclusion criteria and were included in the analysis. Results showed that challenges for balancing patient needs for information and privacy and confidentiality concerns included: cross-cultural understanding, clinician and patient awareness, de-identification of data, and commercialization of patient data. Some opportunities identified were patient empowerment, connecting participatory health enabling technologies with clinical records, open data sharing agreement, and e-consent.

CONCLUSION

 Balancing between privacy and patient needs for health information in the age of participatory health and social media offers several opportunities and challenges. More people are engaging in actively managing health through participatory health enabling technologies. Such activity often includes sharing health information and with this comes a perennial tension between balancing individual needs and the desire to uphold privacy and confidentiality. We recommend that guidelines for both patients and clinicians, in terms of their use of participatory health-enabling technologies, are developed to ensure that patient privacy and confidentiality are protected, and a maximum benefit can be realized.

The Feasibility and Usability of RunningCoach: A Remote Coaching System for Long-Distance Runners

Studies have shown that about half of the injuries sustained during long-distance running involve the knee. Cadence (steps per minute) has been identified as a factor that is strongly associated with these running-related injuries, making it a worthwhile candidate for further study. As such, it is critical for long-distance runners to minimize their risk of injury by running at an appropriate running cadence. In this paper, we present the results of a study on the feasibility and usability of RunningCoach, a mobile health (mHealth) system that remotely monitors running cadence levels of runners in a continuous fashion, among other variables, and provides immediate feedback to runners in an effort to help them optimize their running cadence.

Detecting Acute Otitis Media Symptom Episodes Using a Mobile App: Cohort Study

BACKGROUND

Population cohort studies are useful to study infectious diseases episodes not attended by health care services, but conventional paper diaries and questionnaires to capture cases are prone to noncompliance and recall bias. Use of smart technology in this setting may improve case finding.

OBJECTIVE

The objective of our study was to validate an interactive mobile app for monitoring occurrence of acute infectious diseases episodes in individuals, independent of health care seeking, using acute otitis media (AOM) symptom episodes in infants as a case study. We were interested in determining participant compliance and app performance in detecting and ascertaining (parent-reported) AOM symptom episodes with this novel tool compared with traditional methods used for monitoring study participants.

METHODS

We tested the InfectieApp research app to detect AOM symptom episodes. In 2013, we followed 155 children aged 0 to 3 years for 4 months. Parents recorded the presence of AOM symptoms in a paper diary for 4 consecutive months and completed additional disease questionnaires when AOM symptoms were present. In 2015 in a similar cohort of 69 children, parents used an AOM diary and questionnaire app instead.

RESULTS

During conventional and app-based recording, 93.13% (17,244/18,516) and 94.56% (7438/7866) of symptom diaries were returned, respectively, and at least one symptom was recorded for 32.50% (n=5606) and 43.99% (n=3272) of diary days (P<.01). The incidence of AOM symptom episodes was 605 and 835 per 1000 child-years, respectively. Disease questionnaires were completed for 59% (17/29) of episodes when participants were using conventional recording, compared with 100% (18/18) for app-based recording.

CONCLUSIONS

The use of the study's smart diary app improved AOM case finding and disease questionnaire completeness. For common infectious diseases that often remain undetected by health care services, use of this technology can substantially improve the accurateness of disease burden estimates.

The ALFA4Hearing Model (At-a-Glance Labeling for Features of Apps for Hearing Health Care) to Characterize Mobile Apps for Hearing Health Care

PURPOSE

The aim of the study was to analyze, by using the ALFA4Hearing model (At-a-Glance Labeling for Features of Apps for Hearing Health Care), a sample of apps over a wide range of services in the hearing health care (HHC) domain in order to take a first picture of the current scenario of apps for HHC.

METHOD

We tested 120 apps, and we characterized them by using the ALFA4Hearing model, which includes 29 features in 5 components (Promoters, Services, Implementation, Users, and Descriptive Information). We analyzed (a) the distribution of the 29 features in the sample, (b) the relationship between the Implementation features and the Services provided by the apps, and (c) the distribution of the 29 features in apps for professional use.

RESULTS

The analysis of our sample of apps by means of the ALFA4Hearing model highlighted interesting trends and emerging challenges. Also, results suggested many potential opportunities for research and clinical practice, such as greater involvement of stakeholders, improved evidence base, higher technical quality, and usability.

CONCLUSIONS

The ALFA4Hearing model is able to represent, at a glance, a large amount of information about apps for HHC, highlighting trends and challenges. It might be useful to HHC professionals as a basis for app characterization and informed decision making.

Proof of concept of a 45-second cardiorespiratory fitness self-test for coronary artery disease patients based on accelerometry

Cardiorespiratory fitness (CRF) provides important diagnostic and prognostic information. It is measured directly via laboratory maximal testing or indirectly via submaximal protocols making use of predictor parameters such as submaximal V˙O2, heart rate, workload, and perceived exertion. We have established an innovative methodology, which can provide CRF prediction based only on body motion during a periodic movement. Thirty healthy subjects (40% females, 31.3 ± 7.8 yrs, 25.1 ± 3.2 BMI) and eighteen male coronary artery disease (CAD) (56.6 ± 7.4 yrs, 28.7 ± 4.0 BMI) patients performed a V˙O2peak test on a cycle ergometer as well as a 45 second squatting protocol at a fixed tempo (80 bpm). A tri-axial accelerometer was used to monitor movements during the squat exercise test. Three regression models were developed to predict CRF based on subject characteristics and a new accelerometer-derived feature describing motion decay. For each model, the Pearson correlation coefficient and the root mean squared error percentage were calculated using the leave-one-subject-out cross-validation method (r_cv, RMSE_cv). The model built with all healthy individuals’ data showed an r_cv = 0.68 and an RMSE_cv = 16.7%. The CRF prediction improved when only healthy individuals with normal to lower fitness (CRF<40 ml/min/kg) were included, showing an r_cv = 0.91 and RMSE_cv = 8.7%. Finally, our accelerometry-based CRF prediction CAD patients, the majority of whom taking β-blockers, still showed high accuracy (r_cv = 0.91; RMSE_cv = 9.6%). In conclusion, motion decay and subject characteristics could be used to predict CRF in healthy people as well as in CAD patients taking β-blockers, accurately. This method could represent a valid alternative for patients taking β-blockers, but needs to be further validated in a larger population.

Nursing students' perceptions of using smartphones in the community practicum: A qualitative study

BACKGROUND

Smartphones have the potential to revolutionize the way in which nurses practice by facilitating access to evidence-based resources, however their integration in nursing practice remain variable. Millennials tend to be more comfortable with technology, yet find themselves limited in their ability to use smartphones within the context of clinical practice.

METHODS/DATA SOURCES

Using a qualitative descriptive design, we explored nursing students' perceptions of using smartphones in the community practicum. Individual semi-structured interviews were conducted and verbatim transcripts were subjected to data analysis.

PARTICIPANTS

The sample consisted of 8 undergraduate and graduate nursing students. Participants were recruited using a purposive sampling strategy.

RESULTS

Students' narratives describe unclear expectations regarding the use of smartphones that force them to adopt individualized strategies to maintain their professional image and avoid negative consequences.

CONCLUSION

A cultural shift will be required at the academic and organizational levels if we are to foster acceptance of smartphones in community practice going forward.

Users of the main smartphone operating systems (iOS, Android) differ only little in personality

The increasingly widespread use of mobile phone applications (apps) as research tools and cost-effective means of vast data collection raises new methodological challenges. In recent years, it has become a common practice for scientists to design apps that run only on a single operating system, thereby excluding large numbers of users who use a different operating system. However, empirical evidence investigating any selection biases that might result thereof is scarce. Henceforth, we conducted two studies drawing from a large multi-national (Study 1; N = 1,081) and a German-speaking sample (Study 2; N = 2,438). As such Study 1 compared iOS and Android users across an array of key personality traits (i.e., well-being, self-esteem, willingness to take risks, optimism, pessimism, Dark Triad, and the Big Five). Focusing on Big Five personality traits in a broader scope, in addition to smartphone users, Study 2 also examined users of the main computer operating systems (i.e., Mac OS, Windows). In both studies, very few significant differences were found, all of which were of small or even tiny effect size mostly disappearing after sociodemographics had been controlled for. Taken together, minor differences in personality seem to exist, but they are of small to negligible effect size (ranging from OR = 0.919 to 1.344 (Study 1), η_p² = .005 to .036 (Study 2), respectively) and may reflect differences in sociodemographic composition, rather than operating system of smartphone users.

Progress on Broadband Access to the Internet and Use of Mobile Devices in the United States

Healthy People 2020 (HP2020) aims to improve population health outcomes through several objectives, including health communication and health information technology. We used 7 administrations of the Health Information National Trends Survey to examine HP2020 goals toward access to the Internet through broadband and mobile devices (N = 34 080). We conducted descriptive analyses and obtained predicted marginals, also known as model-adjusted risks, to estimate the association between demographic characteristics and use of mobile devices. The HP2020 target (7.7% of the US population) for accessing the Internet through a cellular network was surpassed in 2014 (59.7%), but the HP2020 target (83.2%) for broadband access fell short (63.8%). Sex and age were associated with accessing the Internet through a cellular network throughout the years (Wald F test, P <.05). The increase in the percentage of people accessing the Internet through mobile devices presents an opportunity for technology-based health interventions that should be explored.