JMIR Publications

Recent Articles:

• 

  mHealth for Wellness, Behavior Change...

  Dec 14, 2018

  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...

  View Authors View Abstract

  Authors List:

  • Claire Garnett  
  • Olga Perski  
  • Ildiko Tombor  
  • Robert West  
  • Susan Michie  
  • Jamie Brown  

  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)

  • JMIR Mhealth Uhealth 2018;6(12):e11175

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Reviews

  Dec 14, 2018

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

  View Authors View Abstract

  Authors List:

  • Rebecca Richards  
  • Paul Kinnersley  
  • Kate Brain  
  • Grace McCutchan  
  • John Staffurth  
  • Fiona Wood  

  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.

  • JMIR Mhealth Uhealth 2018;6(12):e10026

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Wearable Devices and Sensors

  Dec 14, 2018

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

  View Authors View Abstract

  Authors List:

  • Martin Wiesner  
  • Richard Zowalla  
  • Julian Suleder  
  • Maximilian Westers  
  • Monika Pobiruchin  

  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).

  • JMIR Mhealth Uhealth 2018;6(12):e201

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• Wearable Devices and Sensors

  Dec 14, 2018

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

  View Authors View Abstract

  Authors List:

  • Jia Zhang  
  • Laura Tüshaus  
  • Néstor Nuño Martínez  
  • Monica Moreo  
  • Hector Verastegui  
  • Stella M Hartinger  
  • Daniel Mäusezahl  
  • Walter Karlen  

  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.

  • JMIR Mhealth Uhealth 2018;6(12):e11896

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Design and Formative Evaluation of...

  Dec 14, 2018

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

  View Authors View Abstract

  Authors List:

  • Stian Jessen  
  • Jelena Mirkovic  
  • Cornelia M Ruland  

  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.

  • JMIR Mhealth Uhealth 2018;6(12):e11579

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Usability and user perceptions of...

  Dec 13, 2018

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

  View Authors View Abstract

  Authors List:

  • Joanna Milward  
  • Paolo Deluca  
  • Colin Drummond  
  • Andreas Kimergård  

  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)

  • JMIR Mhealth Uhealth 2018;6(12):e11692

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Fitness Trackers and Smart...

  Dec 13, 2018

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

  View Authors View Abstract

  Authors List:

  • Laurent Degroote  
  • Ilse De Bourdeaudhuij  
  • Maïté Verloigne  
  • Louise Poppe  
  • Geert Crombez  

  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.

  • JMIR Mhealth Uhealth 2018;6(12):e10972

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Security and Privacy of mHealth and...

  Dec 11, 2018

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

  View Authors View Abstract

  Authors List:

  • Leming Zhou  
  • Bambang Parmanto  
  • Zakiy Alfikri  
  • Jie Bao  

  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.

  • JMIR Mhealth Uhealth 2018;6(12):e11210

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Fitness Trackers and Smart...

  Dec 10, 2018

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

  View Authors View Abstract

  Authors List:

  • Ravi Kondama Reddy  
  • Rubin Pooni  
  • Dessi P Zaharieva  
  • Brian Senf  
  • Joseph El Youssef  
  • Eyal Dassau  
  • Francis J Doyle III  
  • Mark A Clements  
  • Michael R Rickels  
  • Susana R Patton  
  • Jessica R Castle  
  • Michael C Riddell  
  • Peter G Jacobs  

  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.

  • JMIR Mhealth Uhealth 2018;6(12):e10338

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Wearable Devices and Sensors

  Dec 10, 2018

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

  View Authors View Abstract

  Authors List:

  • Sofian Berrouiguet  
  • David Ramírez  
  • María Luisa Barrigón  
  • Pablo Moreno-Muñoz  
  • Rodrigo Carmona Camacho  
  • Enrique Baca-García  
  • Antonio Artés-Rodríguez  

  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.

  • JMIR Mhealth Uhealth 2018;6(12):e197

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  Use and User Demographics of mHealth

  Dec 10, 2018

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

  View Authors View Abstract

  Authors List:

  • Maria C Raven  
  • Lauren M Kaplan  
  • Marina Rosenberg  
  • Lina Tieu  
  • David Guzman  
  • Margot Kushel  

  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.

  • JMIR Mhealth Uhealth 2018;6(12):e10049

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.

• 

  mHealth for Wellness, Behavior Change...

  Dec 6, 2018

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

  View Authors View Abstract

  Authors List:

  • Michael Mikolasek  
  • Claudia M Witt  
  • Jürgen Barth  

  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)

  • JMIR Mhealth Uhealth 2018;6(12):e11271

  • Choose one of the following formats:

  • Share this article:

    Or, copy the article link to your clipboard:

    Article URL

  • Citation

    • END

      for: Endnote

    • BibTeX

      for: BibDesk and LaTeX

    • RIS

      for: RefMan, Procite, Endnote, RefWorks

    Add this article to your Mendeley library Add this article to your CiteULike library Copy Citation to Clipboard

  • Metrics

    Discover Social Media mentions by hovering over the donut. Click the 'details' link for a full report.