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Journal Description

JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a sister journal of JMIR, the leading eHealth journal. JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, Scopus, and Science Citation Index Expanded (SCIE), and in June 2018 received an Impact Factor of 4.541, which ranks the journal #2 (behind JMIR) out of 25 journals in the medical informatics category indexed by the Science Citation Index Expanded (SCIE) by Thomson Reuters/Clarivate

The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics.

JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research

JMIR mHealth and uHealth features a rapid and thorough peer-review process, professional copyediting, professional production of PDF, XHTML, and XML proofs.

JMIR mHealth and uHealth adheres to the same quality standards as JMIR and all articles published here are also cross-listed in the Table of Contents of JMIR, the worlds' leading medical journal in health sciences / health services research and health informatics.

 

Recent Articles:

  • Entering drinks on the Drink Less app. Source: Image created by the Authors; Copyright: The Authors; URL: http://mhealth.jmir.org/2018/12/e11175/; License: Creative Commons Attribution (CC-BY).

    Predictors of Engagement, Response to Follow Up, and Extent of Alcohol Reduction in Users of a Smartphone App (Drink Less): Secondary Analysis of a Factorial...

    Abstract:

    Background: Digital interventions for alcohol can help achieve reductions in hazardous and harmful alcohol consumption. The Drink Less app was developed using evidence and theory, and a factorial randomized controlled trial (RCT) suggested that 4 of its intervention modules may assist with drinking reduction. However, low engagement is an important barrier to effectiveness, and low response to follow up is a challenge for intervention evaluation. Research is needed to understand what factors influence users’ level of engagement, response to follow up, and extent of alcohol reduction. Objective: This study aimed to investigate associations between user characteristics, engagement, response to follow up, and extent of alcohol reduction in an app-based intervention, Drink Less. Methods: This study involved a secondary data analysis of a factorial RCT of the Drink Less app. Participants (N=672) were aged 18 years or older, lived in the United Kingdom, and had an Alcohol Use Disorders Identification Test score >7 (indicative of excessive drinking). Sociodemographic and drinking characteristics were assessed at baseline. Engagement was assessed in the first month of use (number of sessions, time on app, number of days used, and percentage of available screens viewed). Response to follow up and extent of alcohol reduction (change in past week consumption) were measured after 1 month. Associations were assessed using unadjusted and adjusted linear or logistic regression models. Results: Age (all unstandardized regression coefficients [B] >.02, all P<.001) and post-16 educational qualifications (all B>.18, all P<.03) were positively associated with all engagement outcomes. Age (odds ratio [OR] 1.04, P<.001), educational qualifications (OR 2.11, P<.001), and female gender (OR 1.58, P=.02) were positively associated with response to follow up. Engagement outcomes predicted response to follow up (all OR>1.02, all P<.001) but not the extent of alcohol reduction (all −.14.07). Baseline drinking characteristics were the only variables associated with the extent of alcohol reduction among those followed up (all B>.49, all P<.001). Conclusions: Users of the alcohol reduction app, Drink Less, who were older and had post-16 educational qualifications engaged more and were more likely to respond at 1-month follow up. Higher baseline alcohol consumption predicted a greater extent of alcohol reduction among those followed up but did not predict engagement or response to follow up. Engagement was associated with response to follow up but was not associated with the extent of alcohol reduction, which suggests that the Drink Less app does not have a dose-response effect. Trial Registration: International Standard Randomised Controlled Trial Number ISRCTN40104069; http://www.isrctn.com/ISRCTN40104069 (Archived by WebCite at http://www.webcitation.org/746HqygIV)

  • Source: Pexels; Copyright: Tookapic; URL: https://www.pexels.com/photo/bed-bedroom-boredom-girl-73290/; License: Public Domain (CC0).

    Use of Mobile Devices to Help Cancer Patients Meet Their Information Needs in Non-Inpatient Settings: Systematic Review

    Abstract:

    Background: The shift from inpatient to outpatient cancer care means that patients are now required to manage their condition at home, away from regular supervision by clinicians. Subsequently, research has consistently reported that many patients with cancer have unmet information needs during their illness. Mobile devices, such as mobile phones and tablet computers, provide an opportunity to deliver information to patients remotely. To date, no systematic reviews have evaluated how mobile devices have been used specifically to help patients meet to their information needs. Objective: A systematic review was conducted to identify studies that describe the use of mobile interventions to enable patients with cancer meet their cancer-related information needs in non-inpatient settings, and to describe the effects and feasibility of these interventions. Methods: MEDLINE, Embase, and PsycINFO databases were searched up until January 2017. Search terms related to “mobile devices,” “information needs,” and “cancer” were used. There were no restrictions on study type in order to be as inclusive as possible. Study participants were patients with cancer undergoing treatment. Interventions had to be delivered by a mobile or handheld device, attempt to meet patients’ cancer-related information needs, and be for use in non-inpatient settings. Critical Appraisal Skills Programme checklists were used to assess the methodological quality of included studies. A narrative synthesis was performed and findings were organized by common themes found across studies. Results: The initial search yielded 1020 results. We included 23 articles describing 20 studies. Interventions aimed to improve the monitoring and management of treatment-related symptoms (17/20, 85%), directly increase patients’ knowledge related to their condition (2/20, 10%), and improve communication of symptoms to clinicians in consultations (1/20, 5%). Studies focused on adult (17/20; age range 24-87 years) and adolescent (3/20; age range 8-18 years) patients. Sample sizes ranged from 4-125, with 13 studies having 25 participants or fewer. Most studies were conducted in the United Kingdom (12/20, 52%) or United States (7/20, 30%). Of the 23 articles included, 12 were of medium quality, 9 of poor quality, and 2 of good quality. Overall, interventions were reported to be acceptable and perceived as useful and easy to use. Few technical problems were encountered. Adherence was generally consistent and high (periods ranged from 5 days to 6 months). However, there was considerable variation in use of intervention components within and between studies. Reported benefits of the interventions included improved symptom management, patient empowerment, and improved clinician-patient communication, although mixed findings were reported for patients’ health-related quality of life and anxiety. Conclusions: The current review highlighted that mobile interventions for patients with cancer are only meeting treatment or symptom-related information needs. There were no interventions designed to meet patients’ full range of cancer-related information needs, from information on psychological support to how to manage finances during cancer, and the long-term effects of treatment. More comprehensive interventions are required for patients to meet their information needs when managing their condition in non-inpatient settings. Controlled evaluations are needed to further determine the effectiveness of these types of intervention.

  • Runners at the Trollinger Marathon event 2017. Source: Nasse-Design KG, Daniel Nasse; Copyright: Nasse-Design KG, Daniel Nasse; URL: http://www.nasse-design.de; License: Licensed by the authors.

    Technology Adoption, Motivational Aspects, and Privacy Concerns of Wearables in the German Running Community: Field Study

    Abstract:

    Background: Despite the availability of a great variety of consumer-oriented wearable devices, perceived usefulness, user satisfaction, and privacy concerns have not been fully investigated in the field of wearable applications. It is not clear why healthy, active citizens equip themselves with wearable technology for running activities, and what privacy and data sharing features might influence their individual decisions. Objective: The primary aim of the study was to shed light on motivational and privacy aspects of wearable technology used by healthy, active citizens. A secondary aim was to reevaluate smart technology adoption within the running community in Germany in 2017 and to compare it with the results of other studies and our own study from 2016. Methods: A questionnaire was designed to assess what wearable technology is used by runners of different ages and sex. Data on motivational factors were also collected. The survey was conducted at a regional road race event in May 2017, paperless via a self-implemented app. The demographic parameters of the sample cohort were compared with the event’s official starter list. In addition, the validation included comparison with demographic parameters of the largest German running events in Berlin, Hamburg, and Frankfurt/Main. Binary logistic regression analysis was used to investigate whether age, sex, or course distance were associated with device use. The same method was applied to analyze whether a runner’s age was predictive of privacy concerns, openness to voluntary data sharing, and level of trust in one’s own body for runners not using wearables (ie, technological assistance considered unnecessary in this group). Results: A total of 845 questionnaires were collected. Use of technology for activity monitoring during events or training was prevalent (73.0%, 617/845) in this group. Male long-distance runners and runners in younger age groups (30-39 years: odds ratio [OR] 2.357, 95% CI 1.378-4.115; 40-49 years: OR 1.485, 95% CI 0.920-2.403) were more likely to use tracking devices, with ages 16 to 29 years as the reference group (OR 1). Where wearable technology was used, 42.0% (259/617) stated that they were not concerned if data might be shared by a device vendor without their consent. By contrast, 35.0% (216/617) of the participants would not accept this. In the case of voluntary sharing, runners preferred to exchange tracked data with friends (51.7%, 319/617), family members (43.4%, 268/617), or a physician (32.3%, 199/617). A large proportion (68.0%, 155/228) of runners not using technology stated that they preferred to trust what their own body was telling them rather than trust a device or an app (50-59 years: P<.001; 60-69 years: P=.008). Conclusions: A total of 136 distinct devices by 23 vendors or manufacturers and 17 running apps were identified. Out of 4, 3 runners (76.8%, 474/617) always trusted in the data tracked by their personal device. Data privacy concerns do, however, exist in the German running community, especially for older age groups (30-39 years: OR 1.041, 95% CI 0.371-0.905; 40-49 years: OR 1.421, 95% CI 0.813-2.506; 50-59 years: OR 2.076, 95% CI 1.813-3.686; 60-69 years: OR 2.394, 95% CI 0.957-6.183).

  • Identified challenge categories of physiological sensing mHealth project in remote or low-resource settings. A fieldworker in the trial was conducting a measurement for a child. Source: The Authors; Copyright: Walter Karlen; URL: http://mhealth.jmir.org/2018/12/e11896/; License: Licensed by JMIR.

    Data Integrity–Based Methodology and Checklist for Identifying Implementation Risks of Physiological Sensing in Mobile Health Projects: Quantitative and...

    Abstract:

    Background: Mobile health (mHealth) technologies have the potential to bring health care closer to people with otherwise limited access to adequate health care. However, physiological monitoring using mobile medical sensors is not yet widely used as adding biomedical sensors to mHealth projects inherently introduces new challenges. Thus far, no methodology exists to systematically evaluate these implementation challenges and identify the related risks. Objective: This study aimed to facilitate the implementation of mHealth initiatives with mobile physiological sensing in constrained health systems by developing a methodology to systematically evaluate potential challenges and implementation risks. Methods: We performed a quantitative analysis of physiological data obtained from a randomized household intervention trial that implemented sensor-based mHealth tools (pulse oximetry combined with a respiratory rate assessment app) to monitor health outcomes of 317 children (aged 6-36 months) that were visited weekly by 1 of 9 field workers in a rural Peruvian setting. The analysis focused on data integrity such as data completeness and signal quality. In addition, we performed a qualitative analysis of pretrial usability and semistructured posttrial interviews with a subset of app users (7 field workers and 7 health care center staff members) focusing on data integrity and reasons for loss thereof. Common themes were identified using a content analysis approach. Risk factors of each theme were detailed and then generalized and expanded into a checklist by reviewing 8 mHealth projects from the literature. An expert panel evaluated the checklist during 2 iterations until agreement between the 5 experts was achieved. Results: Pulse oximetry signals were recorded in 78.36% (12,098/15,439) of subject visits where tablets were used. Signal quality decreased for 1 and increased for 7 field workers over time (1 excluded). Usability issues were addressed and the workflow was improved. Users considered the app easy and logical to use. In the qualitative analysis, we constructed a thematic map with the causes of low data integrity. We sorted them into 5 main challenge categories: environment, technology, user skills, user motivation, and subject engagement. The obtained categories were translated into detailed risk factors and presented in the form of an actionable checklist to evaluate possible implementation risks. By visually inspecting the checklist, open issues and sources for potential risks can be easily identified. Conclusions: We developed a data integrity–based methodology to assess the potential challenges and risks of sensor-based mHealth projects. Aiming at improving data integrity, implementers can focus on the evaluation of environment, technology, user skills, user motivation, and subject engagement challenges. We provide a checklist to assist mHealth implementers with a structured evaluation protocol when planning and preparing projects.

  • Anonymized picture of the participants working on design tasks during the workshop. Source: Image created by the Authors; Copyright: The Authors; URL: http://mhealth.jmir.org/2018/12/e11579/; License: Creative Commons Attribution + NoDerivatives (CC-BY-ND).

    Creating Gameful Design in mHealth: A Participatory Co-Design Approach

    Abstract:

    Background: Gameful designs (gamification), using design pieces and concepts typically found in the world of games, is a promising approach to increase users’ engagement with, and adherence to, electronic health and mobile health (mHealth) tools. Even though both identifying and addressing users’ requirements and needs are important steps of designing information technology tools, little is known about the users’ requirements and preferences for gameful designs in the context of self-management of chronic conditions. Objective: This study aimed to present findings as well as the applied methods and design activities from a series of participatory design workshops with patients with chronic conditions, organized to generate and explore user needs, preferences, and ideas to the implementation of gameful designs in an mHealth self-management app. Methods: We conducted three sets of two consecutive co-design workshops with a total of 22 participants with chronic conditions. In the workshops, we applied participatory design methods to engage users in different activities such as design games, scenario making, prototyping, and sticky notes exercises. The workshops were filmed, and the participants’ interactions, written products, ideas, and suggestions were analyzed thematically. Results: During the workshops, the participants identified a wide range of requirements, concerns, and ideas for using the gameful elements in the design of an mHealth self-management app. Overall inputs on the design of the app concerned aspects such as providing a positive user experience by promoting collaboration and not visibly losing to someone or by designing all feedback in the app to be uplifting and positive. The participants provided both general inputs (regarding the degree of competitiveness, use of rewards, or possibilities for customization) and specific inputs (such as being able to customize the look of their avatars or by having rewards that can be exchanged for real-world goods in a gift shop). However, inputs also highlighted the importance of making tools that provide features that are meaningful and motivating on their own and do not only have to rely on gameful design features to make people use them. Conclusions: The main contribution in this study was users’ contextualized and richly described needs and requirements for gamefully designed mHealth tools for supporting chronic patients in self-management as well as the methods and techniques used to facilitate and support both the participant’s creativity and communication of ideas and inputs. The range, variety, and depth of the inputs from our participants also showed the appropriateness of our design approach and activities. These findings may be combined with literature and relevant theories to further inform in the selection and application of gameful designs in mHealth apps, or they can be used as a starting point for conducting more participatory workshops focused on co-designing gameful health apps.

  • Source: Bigstock; Copyright: Wavebreak Media Ltd; URL: https://www.bigstockphoto.com/image-198974353/stock-photo-hand-of-woman-using-mobile-phone-while-having-a-glass-of-wine-in-restaurant; License: Licensed by the authors.

    Developing Typologies of User Engagement With the BRANCH Alcohol-Harm Reduction Smartphone App: Qualitative Study

    Abstract:

    Background: Understanding how users engage with electronic screening and brief intervention (eSBI) is a critical research objective to improve effectiveness of app-based interventions to reduce harmful alcohol consumption. Although quantitative measures of engagement provide a strong indicator of how the user engages with an app at the group level, they do not elucidate finer-grained details of how apps function from an individual, experiential perspective and why, or how, users engage with an intervention in a particular manner. Objective: The aim of this study was to (1) understand why and how participants engaged with the BRANCH app, (2) explore facilitators and barriers to engagement with app features, (3) explore how the BRANCH app impacted drinking behavior, (4) use these data to identify typologies of users of the BRANCH app in terms of engagement behaviors, and (5) identify future eSBI app design implications. Methods: In total, 20 one-to-one semistructured telephone interviews were conducted with participants recruited from a randomized controlled trial, which evaluated the effectiveness of engagement-promoting strategies in the BRANCH app targeting harmful drinking in young adults (aged 18-30 years). The topic guide explored users’ current engagement levels with existing health promotion apps, their views toward the effectiveness of such apps, and what they liked and disliked about BRANCH, specifically focusing on how they engaged with the app. Framework analysis was used to develop typologies of user engagement. Results: The study identified 3 typologies of engagers. Trackers were defined by their motivations to use health-tracking apps to monitor and understand quantified self-data. They did not have intentions necessarily to cut down and predominantly used only the drinking diary. Cut-downers were motivated to use the app because they wanted to reduce their alcohol consumption Unlike Trackers, they did not use a range of different health apps daily, but saw the BRANCH app as an opportunity to test out a different method of trying to cut down their alcohol use. This typology used more features than Trackers, such as the goal setting function. Noncommitters were characterized as a group of users who were initially enthusiastic about using the app; however, this enthusiasm quickly waned and they gained no benefit from it. Conclusions: This was the first study to identify typologies of user engagement with eSBI apps. Although in need of replication, it provides a first step in understanding independent categories of eSBI users, who may benefit from apps tailored to a user’s typology or motivation. It also provides new evidence to suggest that apps may be used more effectively as a tool to raise awareness of drinking, instead of reducing alcohol use, and be a step in the care pathway, identifying at-risk individuals and signposting them to more intensive treatment. Trial Registration: International Standard Randomised Controlled Trial Number ISRCTN70980706; http://www.isrctn.com /ISRCTN70980706 (Archived by WebCite at http://www.webcitation.org/73vfDXYEZ)

  • Source: Pexels; Copyright: Oliur Rahman; URL: https://www.pexels.com/photo/smart-watch-smartwatch-futuristic-technology-9051/; License: Public Domain (CC0).

    The Accuracy of Smart Devices for Measuring Physical Activity in Daily Life: Validation Study

    Abstract:

    Background: Wearables for monitoring physical activity (PA) are increasingly popular. These devices are not only used by consumers to monitor their own levels of PA but also by researchers to track the behavior of large samples. Consequently, it is important to explore how accurately PA can be tracked via these devices. Objectives: The aim of this study was, therefore, to investigate convergent validity of 3 Android Wear smartwatches—Polar M600 (Polar Electro Oy, Kempele, Finland), Huawei Watch (Huawei Technologies Co, Ltd, Shenzhen, Guangdong, China), Asus Zenwatch3 (AsusTek Computer Inc, Taipei, Taiwan)—and Fitbit Charge with an ActiGraph accelerometer for measuring steps and moderate to vigorous physical activity (MVPA) on both a day level and 15-min level. Methods: A free-living protocol was used in which 36 adults engaged in usual daily activities over 2 days while wearing 2 different wearables on the nondominant wrist and an ActiGraph GT3X+ accelerometer on the hip. Validity was evaluated on both levels by comparing each wearable with the ActiGraph GT3X+ accelerometer using correlations and Bland-Altman plots in IBM SPSS 24.0. Results: On a day level, all devices showed strong correlations (Spearman r=.757-.892) and good agreement (interclass correlation coefficient, ICC=.695-.885) for measuring steps, whereas moderate correlations (Spearman r=.557-.577) and low agreement (ICC=.377-.660) for measuring MVPA. Bland-Altman revealed a systematic overestimation of the wearables for measuring steps but a variation between over- and undercounting of MVPA. On a 15-min level, all devices showed strong correlations (Spearman r=.752-.917) and good agreement (ICC=.792-.887) for measuring steps, whereas weak correlations (Spearman r=.116-.208) and low agreement (ICC=.461-.577) for measuring MVPA. Bland-Altman revealed a systematic overestimation of the wearables for steps but under- or overestimation for MVPA depending on the device. Conclusions: In sum, all 4 consumer-level devices can be considered accurate step counters in free-living conditions. This study, however, provides evidence of systematic bias for all devices in measurement of MVPA. The results on a 15-min level also indicate that these devices are not sufficiently accurate to provide correct real-time feedback.

  • Source: Pxhere; Copyright: Mohamed Hassan; URL: https://pxhere.com/en/photo/1440395; License: Public Domain (CC0).

    A Mobile App for Assisting Users to Make Informed Selections in Security Settings for Protecting Personal Health Data: Development and Feasibility Study

    Abstract:

    Background: On many websites and mobile apps for personal health data collection and management, there are security features and privacy policies available for users. Users sometimes are given an opportunity to make selections in a security setting page; however, it is challenging to make informed selections in these settings for users who do not have much education in information security as they may not precisely know the meaning of certain terms mentioned in the privacy policy or understand the consequences of their selections in the security and privacy settings. Objective: The aim of this study was to demonstrate several commonly used security features such as encryption, user authentication, and access control in a mobile app and to determine whether this brief security education is effective in encouraging users to choose stronger security measures to protect their personal health data. Methods: A mobile app named SecSim (Security Simulator) was created to demonstrate the consequences of choosing different options in security settings. A group of study participants was recruited to conduct the study. These participants were asked to make selections in the security settings before and after they viewed the consequences of security features. At the end of the study, a brief interview was conducted to determine the reason for their selections in the security settings. Their selections before and after the security education were compared in order to determine the effectiveness of the security education. The usability of the app was also evaluated. Results: In total, 66 participants finished the study and provided their answers in the app and during a brief interview. The comparison between the pre- and postsecurity education selection in security settings indicated that 21% (14/66) to 32% (21/66) participants chose a stronger security measure in text encryption, access control, and image encryption; 0% (0/66) to 2% (1/66) participants chose a weaker measure in these 3 security features; and the remainder kept their original selections. Several demographic characteristics such as marital status, years of experience using mobile devices, income, employment, and health status showed an impact on the setting changes. The usability of the app was good. Conclusions: The study results indicate that a significant percentage of users (21%-32%) need guidance to make informed selection in security settings. If websites and mobile apps can provide embedded security education for users to understand the consequences of their security feature selection and the meaning of commonly used security features, it may help users to make the best choices in terms of security settings. Our mobile app, SecSim, offers a unique approach for mobile app users to understand commonly used security features. This app may be incorporated into other apps or be used before users make selections in their security settings.

  • Source: Adobe Stock Photos; Copyright: Дмитрий Днепровский; URL: https://stock.adobe.com/ca/images/a-woman-s-hand-with-a-smart-watch-bottle-with-water-in-hand-close-up-sitting-on-the-floor-black-sportswear-looks-time-black-sneakers-with-white-soles-wooden-floor/197461632?prev_url=detail; License: Licensed by the authors.

    Accuracy of Wrist-Worn Activity Monitors During Common Daily Physical Activities and Types of Structured Exercise: Evaluation Study

    Abstract:

    Background: Wrist-worn activity monitors are often used to monitor heart rate (HR) and energy expenditure (EE) in a variety of settings including more recently in medical applications. The use of real-time physiological signals to inform medical systems including drug delivery systems and decision support systems will depend on the accuracy of the signals being measured, including accuracy of HR and EE. Prior studies assessed accuracy of wearables only during steady-state aerobic exercise. Objective: The objective of this study was to validate the accuracy of both HR and EE for 2 common wrist-worn devices during a variety of dynamic activities that represent various physical activities associated with daily living including structured exercise. Methods: We assessed the accuracy of both HR and EE for two common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) during dynamic activities. Over a 2-day period, 20 healthy adults (age: mean 27.5 [SD 6.0] years; body mass index: mean 22.5 [SD 2.3] kg/m2; 11 females) performed a maximal oxygen uptake test, free-weight resistance circuit, interval training session, and activities of daily living. Validity was assessed using an HR chest strap (Polar) and portable indirect calorimetry (Cosmed). Accuracy of the commercial wearables versus research-grade standards was determined using Bland-Altman analysis, correlational analysis, and error bias. Results: Fitbit and Garmin were reasonably accurate at measuring HR but with an overall negative bias. There was more error observed during high-intensity activities when there was a lack of repetitive wrist motion and when the exercise mode indicator was not used. The Garmin estimated HR with a mean relative error (RE, %) of −3.3% (SD 16.7), whereas Fitbit estimated HR with an RE of −4.7% (SD 19.6) across all activities. The highest error was observed during high-intensity intervals on bike (Fitbit: −11.4% [SD 35.7]; Garmin: −14.3% [SD 20.5]) and lowest error during high-intensity intervals on treadmill (Fitbit: −1.7% [SD 11.5]; Garmin: −0.5% [SD 9.4]). Fitbit and Garmin EE estimates differed significantly, with Garmin having less negative bias (Fitbit: −19.3% [SD 28.9], Garmin: −1.6% [SD 30.6], P<.001) across all activities, and with both correlating poorly with indirect calorimetry measures. Conclusions: Two common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) show good HR accuracy, with a small negative bias, and reasonable EE estimates during low to moderate-intensity exercise and during a variety of common daily activities and exercise. Accuracy was compromised markedly when the activity indicator was not used on the watch or when activities involving less wrist motion such as cycle ergometry were done.

  • The eB2 app. Source: The Authors; Copyright: The Authors; URL: http://mhealth.jmir.org/2018/11/e197/; License: Licensed by JMIR.

    Combining Continuous Smartphone Native Sensors Data Capture and Unsupervised Data Mining Techniques for Behavioral Changes Detection: A Case Series of the...

    Abstract:

    Background: The emergence of smartphones, wearable sensor technologies, and smart homes allows the nonintrusive collection of activity data. Thus, health-related events, such as activities of daily living (ADLs; eg, mobility patterns, feeding, sleeping, ...) can be captured without patients’ active participation. We designed a system to detect changes in the mobility patterns based on the smartphone’s native sensors and advanced machine learning and signal processing techniques. Objective: The principal objective of this work is to assess the feasibility of detecting mobility pattern changes in a sample of outpatients with depression using the smartphone’s sensors. The proposed method processed the data acquired by the smartphone using an unsupervised detection technique. Methods: In this study, 38 outpatients from the Hospital Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) participated. The Evidence-Based Behavior (eB2) app was downloaded by patients on the day of recruitment and configured with the assistance of a physician. The app captured the following data: inertial sensors, physical activity, phone calls and message logs, app usage, nearby Bluetooth and Wi-Fi connections, and location. We applied a change-point detection technique to location data on a sample of 9 outpatients recruited between April 6, 2017 and December 14, 2017. The change-point detection was based only on location information, but the eB2 platform allowed for an easy integration of additional data. The app remained running in the background on patients’ smartphone during the study participation. Results: The principal outcome measure was the identification of mobility pattern changes based on an unsupervised detection technique applied to the smartphone’s native sensors data. Here, results from 5 patients’ records are presented as a case series. The eB2 system detected specific mobility pattern changes according to the patients’ activity, which may be used as indicators of behavioral and clinical state changes. Conclusions: The proposed technique could automatically detect changes in the mobility patterns of outpatients who took part in this study. Assuming these mobility pattern changes correlated with behavioral changes, we have developed a technique that may identify possible relapses or clinical changes. Nevertheless, it is important to point out that the detected changes are not always related to relapses and that some clinical changes cannot be detected by the proposed method.

  • Source: Flickr; Copyright: flightlog; URL: https://www.flickr.com/photos/flightlog/5952781835/in/photolist-a52yDz-4Q7eY-jSQCWa-ArXkUV-jSSjd3-jSSnWA-jSRm8r-SvJ3kB-FzZ6fa-jSQ1yR-jSQSvn-jSTKfL-4yfMB-jSRyCD-fuKWK-jSRjGF-5ofCsN-DGpq3m-Pkhof-itY6m6-8AxAi1-YuYfkM-5EWs2-68C2m2-9FbNqD-Pii5H-ggzb6F-gajGzk-A; License: Creative Commons Attribution (CC-BY).

    Mobile Phone, Computer, and Internet Use Among Older Homeless Adults: Results from the HOPE HOME Cohort Study

    Abstract:

    Background: The median age of single homeless adults is approximately 50 years. Older homeless adults have poor social support and experience a high prevalence of chronic disease, depression, and substance use disorders. Access to mobile phones and the internet could help lower the barriers to social support, social services, and medical care; however, little is known about access to and use of these by older homeless adults. Objective: This study aimed to describe the access to and use of mobile phones, computers, and internet among a cohort of 350 homeless adults over the age of 50 years. Methods: We recruited 350 participants who were homeless and older than 50 years in Oakland, California. We interviewed participants at 6-month intervals about their health status, residential history, social support, substance use, depressive symptomology, and activities of daily living (ADLs) using validated tools. We performed clinical assessments of cognitive function. During the 6-month follow-up interview, study staff administered questions about internet and mobile technology use. We assessed participants’ comfort with and use of multiple functions associated with these technologies. Results: Of the 343 participants alive at the 6-month follow-up, 87.5% (300/343) completed the mobile phone and internet questionnaire. The median age of participants was 57.5 years (interquartile range 54-61). Of these, 74.7% (224/300) were male, and 81.0% (243/300) were black. Approximately one-fourth (24.3%, 73/300) of the participants had cognitive impairment and slightly over one-third (33.6%, 100/300) had impairments in executive function. Most (72.3%, 217/300) participants currently owned or had access to a mobile phone. Of those, most had feature phones, rather than smartphones (89, 32.1%), and did not hold annual contracts (261, 94.2%). Just over half (164, 55%) had ever accessed the internet. Participants used phones and internet to communicate with medical personnel (179, 64.6%), search for housing and employment (85, 30.7%), and to contact their families (228, 82.3%). Those who regained housing were significantly more likely to have mobile phone access (adjusted odds ratio [AOR] 3.81, 95% CI 1.77-8.21). Those with ADL (AOR 0.53, 95% CI 0.31-0.92) and executive function impairment (AOR 0.49; 95% CI 0.28-0.86) were significantly less likely to have mobile phones. Moderate to high risk amphetamine use was associated with reduced access to mobile phones (AOR 0.27, 95% CI 0.10-0.72). Conclusions: Older homeless adults could benefit from portable internet and phone access. However, participants had a lower prevalence of smartphone and internet access than adults aged over 65 years in the general public or low-income adults. Participants faced barriers to mobile phone and internet use, including financial barriers and functional and cognitive impairments. Expanding access to these basic technologies could result in improved outcomes.

  • A self-care app for cancer patients. Source: Image created by the Authors; Copyright: Michael Mikolasek; URL: http://mhealth.jmir.org/2018/11/e11271/; License: Licensed by JMIR.

    Adherence to a Mindfulness and Relaxation Self-Care App for Cancer Patients: Mixed-Methods Feasibility Study

    Abstract:

    Background: Cancer is highly prevalent worldwide and can cause high levels of distress in patients, which is often neglected in medical care. Smartphone apps are readily available and therefore seem promising to deliver distress-reducing interventions such as mindfulness and relaxation programs. Objective: This study aimed to evaluate the feasibility of a mindfulness and relaxation app for cancer patients. We looked at characteristics of participating patients in a mobile health (mHealth) study, including adherence to the app intervention, predictors for adherence, and patients’ feedback regarding the app. Methods: In this prospective observational study with a mixed-methods approach, cancer patients received a mindfulness and relaxation self-care app. Cancer patients were recruited online and through hospitals in Switzerland. We assessed self-reported measures (eg, quality of life, anxiety, depressive symptoms, openness to experience, resistance to change) at baseline, and the app gathered data on patients’ practicing time. With 8 semistructured interviews, we obtained patients’ feedback about the app and recommendations for improvements. We looked at 3 dimensions of the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework (reach, adoption, and maintenance) and analyzed data for adherence for the first 10 weeks of the app intervention. We report descriptive statistics for patient characteristics and app use. For the prediction of adherence, we used Kaplan-Meier analyses with log-rank tests and a Cox proportional hazards regression. Results: Data from 100 cancer patients (74 female) showed that 54 patients were using the app exercises continuously until week 10. In continuous app users, the median number of exercises per week dropped from 4 (interquartile range, IQR 1-7) at week 1 to a median of 2 (IQR 1-4) at week 10. Our analyses revealed 4 significant predictors for better adherence: female gender, higher openness to experience, higher resistance to change, and more depressive symptoms. Interviews revealed that the patients generally were satisfied with the app but also made suggestions on how to improve it. Conclusions: Our study indicates that a mindfulness and relaxation mHealth intervention for cancer patients is feasible with acceptable adherence and largely positive feedback from patients. Trial Registration: German Clinical Trials Register DRKS00010481; https://www.drks.de/drks_web/navigate.do?navigation Id=trial.HTML&TRIAL_ID=DRKS00010481 (Archived by WebCite at http://www.webcitation.org/73xGE1B0P)

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  • Validity Evaluation of the Fitbit Charge2 and the Garmin VivoSmart HR+ in Free-Living Environments in an Old Adult Cohort

    Date Submitted: Dec 11, 2018

    Open Peer Review Period: Dec 14, 2018 - Feb 8, 2019

    Background: Few studies have investigated the validity of mainstream wrist-based activity trackers in healthy older adults in real-life, as opposed to laboratory settings. Objective: This study explor...

    Background: Few studies have investigated the validity of mainstream wrist-based activity trackers in healthy older adults in real-life, as opposed to laboratory settings. Objective: This study explored the performance of two wrist-worn trackers (Fitbit Charge2 and Garmin Vivo Smart HR+) estimating steps, energy expenditure, moderate-to-vigorous physical activity (MVPA) levels, and sleep parameters (TST and WASO) against gold-standard technologies in a cohort of healthy older adults in free-living environment. Methods: Overall, 20 subjects (> 65 years) took part in the study. The devices were worn by the subjects for 24 hours, and the results were compared against validated technology (ActiGraph and New Lifestyles NL-2000i). Mean error, MAPE, ICC and Bland-Altman plots were computed for all the parameters considered. Results: Regarding step-counting, all the trackers are highly correlated with each other (ICCs > 0.89). While Fitbit tends to over-count steps (MPE: 12.36%), Garmin and ActiGraph under-count with a MPE of 9.36% and 11.53%. Garmin had poor ICC values when considering energy expenditure compared against the criterion. Fitbit has moderate-to-good ICCs when compared against the other activity trackers, and showed the best results (MAPE: 12.25%) even if underestimated the amount of calories burnt. For MVPA levels estimation, the wristband trackers were highly correlated (ICC = 0.96), however, were moderately correlated against the criterion and over-estimated the minutes of MVPA activity. When analyzing the sleep parameters, the ICCs were poor for all cases, except when comparing Fitbit with the criterion, which showed a moderate agreement. TST is slightly over-estimated with the Fitbit, although providing good results with an average MAPE equal to 10.13%. On the other hand, WASO estimation was poorer and was over-estimated by Fitbit but under-estimated by Garmin. Again, Fitbit was the most accurate, with an average MAPE of 49.7%. Conclusions: Results suggest that the tested well-known devices could be adopted to estimate steps, energy expenditure, and sleep duration with an acceptable level of accuracy in the population of interest, while clinicians should be cautious in considering other parameters for clinical and research purposes.

  • Mindfulness-based Smoking Cessation Enhanced with Mobile Technology (iQuit Mindfully): Pilot Randomized Controlled Trial

    Date Submitted: Dec 11, 2018

    Open Peer Review Period: Dec 14, 2018 - Feb 8, 2019

    Background: Mindfulness training increases rates of smoking cessation and lapse recovery, and between-session mHealth messages could enhance treatment engagement and effectiveness. Personalized, in-th...

    Background: Mindfulness training increases rates of smoking cessation and lapse recovery, and between-session mHealth messages could enhance treatment engagement and effectiveness. Personalized, in-the-moment text messaging support could be particularly useful for low-income smokers with fewer smoking cessation resources. Objective: This pilot study examined feasibility of a text messaging program (“iQuit Mindfully”) as an adjunct to in-person Mindfulness-Based Addiction Treatment (MBAT) for smoking cessation. Methods: Participants (N=71; 70% African American, 61% annual household income <$30K; 41%

  • Standards for mobile health-related apps

    Date Submitted: Dec 7, 2018

    Open Peer Review Period: Dec 11, 2018 - Feb 5, 2019

    Background: In recent years, the considerable increase in the number of mobile health apps has made healthcare more accessible and affordable for all. However, the exponential growth in mHealth soluti...

    Background: In recent years, the considerable increase in the number of mobile health apps has made healthcare more accessible and affordable for all. However, the exponential growth in mHealth solutions has occurred with almost no control or regulation of any kind. Despite some recent initiatives, there is still no specific regulation procedure, accreditation system or standards to help the development of the apps, mitigate risks or guarantee quality. Objective: The main aim of this study is to provide a set of standards for mobile health-related apps on the basis of what is available from guidelines, frameworks, and standards in the field of health app development. Methods: To identify the most important criteria, we used three strategies. First, we conducted a systematic review of all the studies published on health-related apps. Second, we searched for health-app recommendations on the websites of professional organizations. Finally, we looked for standards governing the development of software for medical devices on the specialized webs of regulatory organizations. Then, we compiled the criteria we had identified and determined which of them could be regarded as essential, recommendable or desirable. Results: We identified a total of 168 criteria from the systematic review, 282 criteria from published guidelines, and 53 criteria from the standards of medical devices. These criteria were then grouped and subsumed under 8 categories, which included 36 important criteria for health apps. Of these 7 were considered to be essential, 18 recommendable, and 11 desirable. The more essential criteria an mHealth application has, the greater its quality. Conclusions: This set of standards can be easily used by health care providers, developers, patients and other stakeholders, both to guide the development of mHealth related apps and to measure the quality of an mHealth app.

  • Evaluating effects of a mobile health application in reducing patients' care needs and improving quality of life after oral cancer surgery.

    Date Submitted: Dec 7, 2018

    Open Peer Review Period: Dec 11, 2018 - Feb 5, 2019

    Background: Oral cancer patients experience different degrees of comorbidity after medical and surgical treatment and also must face psychological distress and uncertainty during the disease course....

    Background: Oral cancer patients experience different degrees of comorbidity after medical and surgical treatment and also must face psychological distress and uncertainty during the disease course. Patients may encounter physical function obstacles, psychosocial issues and diminishing quality of life. Poorer quality of life creates a higher demand for care, greater demand for health information, and increased psychological needs, care support, medical communication needs, and assistance with physical functioning and activities of daily life. Objective: This study aimed to investigate the use of a mobile health application to provide information and education for patients after undergoing oral cancer surgery, and to evaluate changes in care needs and quality of life after the intervention. Methods: A convenience sample of 100 patients with postoperative oral cancer was recruited from Far Eastern Memorial Hospital (New Taipei City, Taiwan) between March 2017 and December 2017. Patients were divided into two groups: an experimental group (n=50) receiving an education and information intervention from a mobile health application and a control group (n=50) receiving standard care and nursing guidance. The mobile health application was used from discharge to three months after oral cancer surgery. Patients’ data were collected using a self-administered structured questionnaire. Data were analyzed using logistic regression analysis. Results: At baseline, overall scores for quality of life in the experimental and control groups were 32.15 and 28.99, respectively; after 3 months of education/information intervention via mobile health application, the overall scores for quality of life were 24.91 and 24.63, respectively, but without statistical significance. Among patients’ care needs, the physiological needs decreased significantly in the experimental group after the intervention compared with those of the control group (p=0.015). Multivariate linear regression indicated that physiological needs also decreased significantly in the experimental group compared with the control group after adjusting for age and sex (p=0.022). Acceptability of the APP by patients was measured by use intentions, perceived usefulness and perceived ease of use. At baseline, mean experimental group scores were 2.54 for use intentions, 2.52 for perceptual usefulness, and 2.32 for perceived ease of use, with no significant differences with control group scores. After 3 months intervention, experimental group scores for use intentions, perceived usefulness and perceived ease of use were 3.02, 2.95, and 3.01, respectively, representing significant increases in each after APP intervention (p<0.01). Conclusions: The mobile health application intervention improves quality of life and reduces physiological needs significantly in patients with postoperative oral cancer, and use of the device was readily accepted. These findings may constitute an empirical basis for postoperative care delivered by healthcare practitioners. Results of this study suggest that mobile health applications can be incorporated easily into routine care of oral cancer patients to provide medical information conveniently and improve patients’ self-management and quality of life.

  • Evaluation of Self-Management Support Functions in Apps for People with Persistent Pain: A Systematic Review

    Date Submitted: Dec 10, 2018

    Open Peer Review Period: Dec 11, 2018 - Dec 20, 2018

    Background: Smartphone applications (apps) are a potential mechanism for development of self-management skills in people with persistent pain. However, the inclusion of best practice content items in...

    Background: Smartphone applications (apps) are a potential mechanism for development of self-management skills in people with persistent pain. However, the inclusion of best practice content items in available pain management apps fostering core self-management skills for self-management support is not known. Objective: To evaluate the contents of smartphone apps for people with persistent pain facilitating self-management support and appraise the app quality. Methods: A systematic search was performed in the New Zealand App Store and Google Play Store. Apps were included if they were designed for people with persistent pain, provided information on pain self-management strategies and were available in English. App contents were evaluated using an a priori 14-item self-management support (SMS-14) checklist. App quality was assessed using the 23-item Mobile Apps Rating Scale. Results: Of the 939 apps screened, 19 apps met inclusion criteria. Meditation and guided relaxation were the most frequently included self-management strategies. Overall, the included apps met a median of 4 (range 1-8) of the SMS-14 checklist. Three apps (Curable, PainScale-Pain Diary and Coach and SuperBetter) met the largest number of items (8 out of 14) to foster self-management of pain. Self-monitoring of symptoms (n=11) and self-tailoring of strategies (n=9) were frequently featured functions, while few apps had features facilitating social support and communicating with clinicians. No apps provided information tailored to cultural needs of the user. The app quality mean scores using Mobile Apps Rating Scale ranged from 2.7 to 4.5 (out of 5.0). While use of two apps (Headspace and SuperBetter) have been shown to improve health outcomes, none of the included apps have been evaluated in people with persistent pain. Conclusions: Of the three apps (Curable, PainScale-Pain Diary and Coach and SuperBetter) that met the largest number of items to support skills in self-management of pain, two apps (PainScale-Pain Diary and Coach and SuperBetter) were free, suggesting the potential for using apps as a scalable, wide-reaching intervention to complement face-to-face care. However, none provided culturally-tailored information. While two apps (Headspace and SuperBetter) were validated to show improved health outcomes, none were tested in people with persistent pain. Both users and clinicians should be aware of such limitations and make informed choices in using or recommending apps as a self-management tool. For better integration of apps in clinical practice, concerted efforts are required among app developers, clinicians and people with persistent pain in developing apps, and evaluating for clinical efficacy. Clinical Trial: Not applicable

  • A smartphone app to assist smoking cessation amongst Aboriginal Australians: findings from a pilot randomised controlled trial

    Date Submitted: Dec 6, 2018

    Open Peer Review Period: Dec 10, 2018 - Feb 4, 2019

    Background: Mobile health (mHealth) apps have potential to increase smoking cessation but little research has been conducted with Aboriginal communities in Australia. Objective: We conducted a pilot s...

    Background: Mobile health (mHealth) apps have potential to increase smoking cessation but little research has been conducted with Aboriginal communities in Australia. Objective: We conducted a pilot study to assess the feasibility, acceptability and explore the effectiveness of a novel mHealth application to assist Aboriginal people to quit smoking. Methods: Design: A single-blinded, pilot, randomised, controlled trial (RCT) and a process evaluation comprising usage analytics data and in-depth interviews Setting and participants: Aboriginal current smokers (aged >16 years willing to make a quit attempt in the next month) recruited from Aboriginal Community Controlled Health Services and a government telephone coaching service in New South Wales Intervention: A multifaceted Android or iOS app comprising a personalised profile and quit plan, text and in app motivational messages, and a challenge feature allowing users to ‘compete’ with others vs usual cessation support services Outcomes: Self-reported continuous smoking abstinence, verified by carbon monoxide breath testing at 6 months. Secondary outcomes included point prevalence of abstinence and use of smoking cessation therapies and services. Results: A total of 49 participants were recruited for the pilot RCT. Competing service delivery priorities, the lack of resources for research and lack of support for randomisation to a control group were the major recruitment barriers. At baseline, 47% of participants had tried to quit in recent weeks with very few having accessed support services, medication or nicotine replacement therapy. At 6 months follow-up only one participant (intervention arm) was abstinent. There were however, non-significant increases in smoking cessation medication/ nicotine replacement use in the intervention arm (18% intervention vs 9% control) and use of cessation support services (41% intervention vs 28% control). The process evaluation highlighted low to moderate app usage (3-10 new users/ month and 4–8 returning users/month), an average of 2.9 sessions/user/month and 6.3 minutes per session. Key themes from interviews with intervention participants (n=15) included: (1) the powerful influence of prevailing social norms around acceptability of smoking; (2) high usage of mobile devices for phone, text and social media but very low use of other smartphone apps; (3) the role of family and social group support in supporting quit attempts; and (4) low awareness and utilisation of smoking cessation support services. Despite broad acceptability of the app, participants also recommended technical improvements to improve functionality, greater customisation of text messages, integration with existing social media platforms and gamification features. Conclusions: Smoking cessation apps need to be integrated with commonly used functions of mobile phones and draw on social networks to support their use. Although they have potential to increase utilisation of cessation support services and treatments, more research is needed to identify optimal implementation models. Robust evaluation is critical to determine their impact, however, an RCT design is unlikely to be feasible in this setting and alternative approaches are recommended. Clinical Trial: ACTRN12616001550493

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