JMIR Publications

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  mHealth for Wellness, Behavior Change...

  Dec 11, 2019

  Improving Pacific Adolescents’ Physical Activity Toward International Recommendations: Exploratory Study of a Digital Education App Coupled With Activity...

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  Authors List:

  • Olivier Galy  
  • Kalina Yacef  
  • Corinne Caillaud  

  Abstract:

  Background: The prevalence of overweight and obesity in children and adolescents has dramatically increased in the Pacific Island countries and territories over the last decade. Childhood overweight and obesity not only have short-term consequences but are also likely to lead to noncommunicable diseases in adulthood. A major factor contributing to the rising prevalence is an insufficient amount of daily moderate-to-vigorous physical activity (MVPA). In the Pacific region, less than 50% of children and adolescents meet the international recommendations of 11,000 steps and 60 min of MVPA per day. Although studies have shown the potential of digital technologies to change behaviors, none has been proposed to guide adolescents toward achieving these recommendations. Objective: The aims of this study were (1) to investigate whether a technology-based educational program that combines education, objective measures of physical activity (PA), and self-assessment of goal achievement would be well received by Pacific adolescents and help change their PA behaviors toward the international PA recommendations and (2) to create more insightful data analysis methods to better understand PA behavior change. Methods: A total of 24 adolescents, aged 12 to 14 years, participated in a 4-week program comprising 8 1-hour modules designed to develop health literacy and physical skills. This self-paced user-centered program was delivered via an app and provided health-related learning content as well as goal setting and self-assessment tasks. PA performed during the 4-week program was captured by an activity tracker to support learning and help the adolescents self-assess their achievements against personal goals. The data were analyzed using a consistency rate and daily behavior clustering to reveal any PA changes, particularly regarding adherence to international recommendations. Results: The consistency rate of daily steps revealed that the adolescents reached 11,000 steps per day 48% (approximately 3.4 days per week) of the time in the first week of the program, and this peaked at 59% (approximately 4.1 days per week) toward the end of the program. PA data showed an overall increase during the program, particularly in the less active adolescents, who increased their daily steps by 15% and ultimately reached 11,000 steps more frequently. The consistency of daily behavior clustering showed a 27% increase in adherence to international recommendations in the least active adolescents. Conclusions: Technology-supported educational programs that include self-monitored PA via activity trackers can be successfully delivered to adolescents in schools in remote Pacific areas. New data mining techniques enable innovative analyses of PA engagement based on the international recommendations.

  • JMIR Mhealth Uhealth 2019;7(12):e14854

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  Viewpoint

  Dec 10, 2019

  Lessons Learned: Recommendations For Implementing a Longitudinal Study Using Wearable and Environmental Sensors in a Health Care Organization

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  Authors List:

  • Michelle L'Hommedieu  
  • Justin L'Hommedieu  
  • Cynthia Begay  
  • Alison Schenone  
  • Lida Dimitropoulou  
  • Gayla Margolin  
  • Tiago Falk  
  • Emilio Ferrara  
  • Kristina Lerman  
  • Shrikanth Narayanan  

  Abstract:

  Although traditional methods of data collection in naturalistic settings can shed light on constructs of interest to researchers, advances in sensor-based technology allow researchers to capture continuous physiological and behavioral data to provide a more comprehensive understanding of the constructs that are examined in a dynamic health care setting. This study gives examples for implementing technology-facilitated approaches and provides the following recommendations for conducting such longitudinal, sensor-based research, with both environmental and wearable sensors in a health care setting: pilot test sensors and software early and often; build trust with key stakeholders and with potential participants who may be wary of sensor-based data collection and concerned about privacy; generate excitement for novel, new technology during recruitment; monitor incoming sensor data to troubleshoot sensor issues; and consider the logistical constraints of sensor-based research. The study describes how these recommendations were successfully implemented by providing examples from a large-scale, longitudinal, sensor-based study of hospital employees at a large hospital in California. The knowledge gained from this study may be helpful to researchers interested in obtaining dynamic, longitudinal sensor data from both wearable and environmental sensors in a health care setting (eg, a hospital) to obtain a more comprehensive understanding of constructs of interest in an ecologically valid, secure, and efficient way.

  • JMIR Mhealth Uhealth 2019;7(12):e13305

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  Text-messaging (SMS, WeChat...

  Dec 9, 2019

  Analysis of the Implementation, User Perspectives, and Feedback From a Mobile Health Intervention for Individuals Living With Hypertension (DREAM-GLOBAL):...

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  Authors List:

  • Jordan Barsky  
  • Rebekah Hunter  
  • Colin McAllister  
  • Karen Yeates  
  • Norm Campbell  
  • Peter Liu  
  • Nancy Perkins  
  • Diane Hua-Stewart  
  • Marion A Maar  
  • Sheldon W Tobe  

  Abstract:

  Background: DREAM-GLOBAL (Diagnosing hypertension—Engaging Action and Management in Getting Lower Blood Pressure in Indigenous and low- and middle-income countries) studied a SMS text messaging–based system for blood pressure measurement and hypertension management in Canadian Aboriginal and Tanzanian communities. The use of SMS text messages is an emerging point of interest in global health care initiatives because of their scalability, customizability, transferability, and cost-effectiveness. Objective: The study aim was to assess the effect on the difference in blood pressure reduction of active hypertension management messages or passive health behavior messages. The system was designed to be implemented in remote areas with wireless availability. This study described the implementation and evaluation of technical components, including quantitative data from the transmission of blood pressure measurements and qualitative data collected on the operational aspects of the system from participants, health care providers, and community leadership. Methods: The study was implemented in six remote Indigenous Canadian and two rural Tanzanian communities. Blood pressure readings were taken by a community health worker and transmitted to a mobile phone via Bluetooth, then by wireless to a programmed central server. From the server, the readings were sent to the participant’s own phone as well. Participants also received biweekly tailored SMS text messages on their phones. Quantitative data on blood pressure reading transmissions were collected from the study central server. Qualitative data were collected by surveys, focus groups, and key informant interviews of participants, health care providers, and health leadership. Results: In Canada, between February 2014 and February 2017, 2818 blood pressure readings from 243 patients were transmitted to the central server. In Tanzania, between October 2014 and August 2015, 1165 readings from 130 patients were transmitted to the central server. The use of Bluetooth technology enabled the secure, reliable transmission of information from participants to their health care provider. The timing and frequency were satisfactory to 137 of 187 (73.2%) of participants, supporting the process of sending weekly messages twice on Mondays and Thursdays at 11 am. A total of 97.0% (164/169) of the participants surveyed said they would recommend participation in the DREAM-GLOBAL program to a friend or relative with hypertension. Conclusions: In remote communities, the DREAM-GLOBAL study helped local health care providers deliver a blood pressure management program that enabled patients and community workers to feel connected. The technical components of the study were implemented as planned, and patients felt supported in their management through the SMS text messaging and mobile health program. Technological issues were solved with troubleshooting. Overall, the technical aspects of this research program enhanced clinical care and study evaluation and were well received by participants, health care workers, and community leadership. Clinical Trial: Clinicaltrials.gov NCT02111226; https://clinicaltrials.gov/ct2/show/NCT02111226.

  • JMIR Mhealth Uhealth 2019;7(12):e12639

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  mHealth for Data Collection and Research

  Dec 5, 2019

  Clustering Insomnia Patterns by Data From Wearable Devices: Algorithm Development and Validation Study

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  Authors List:

  • Sungkyu Park  
  • Sang Won Lee  
  • Sungwon Han  
  • Meeyoung Cha  

  Abstract:

  Background: As societies become more complex, larger populations suffer from insomnia. In 2014, the US Centers for Disease Control and Prevention declared that sleep disorders should be dealt with as a public health epidemic. However, it is hard to provide adequate treatment for each insomnia sufferer, since various behavioral characteristics influence symptoms of insomnia collectively. Objective: We aim to develop a neural-net based unsupervised user clustering method towards insomnia sufferers in order to clarify the unique traits for each derived groups. Unlike the current diagnosis of insomnia that requires qualitative analysis from interview results, the classification of individuals with insomnia by using various information modalities from smart bands and neural-nets can provide better insight into insomnia treatments. Methods: This study, as part of the precision psychiatry initiative, is based on a smart band experiment conducted over 6 weeks on individuals with insomnia. During the experiment period, a total of 42 participants (19 male; average age 22.00 [SD 2.79]) from a large university wore smart bands 24/7, and 3 modalities were collected and examined: sleep patterns, daily activities, and personal demographics. We considered the consecutive daily information as a form of images, learned the latent variables of the images via a convolutional autoencoder (CAE), and clustered and labeled the input images based on the derived features. We then converted consecutive daily information into a sequence of the labels for each subject and finally clustered the people with insomnia based on their predominant labels. Results: Our method identified 5 new insomnia-activity clusters of participants that conventional methods have not recognized, and significant differences in sleep and behavioral characteristics were shown among groups (analysis of variance on rank: F_4,37=2.36, P=.07 for the sleep_min feature; F_4,37=9.05, P<.001 for sleep_efficiency; F_4,37=8.16, P<.001 for active_calorie; F_4,37=6.53, P<.001 for walks; and F_4,37=3.51, P=.02 for stairs). Analyzing the consecutive data through a CAE and clustering could reveal intricate connections between insomnia and various everyday activity markers. Conclusions: Our research suggests that unsupervised learning allows health practitioners to devise precise and tailored interventions at the level of data-guided user clusters (ie, precision psychiatry), which could be a novel solution to treating insomnia and other mental disorders.

  • JMIR Mhealth Uhealth 2019;7(12):e14473

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  mHealth in a Clinical Setting

  Dec 3, 2019

  Medical Videography Using a Mobile App: Retrospective Analysis

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  Authors List:

  • Julia C Cambron  
  • Kirk D Wyatt  
  • Christine M Lohse  
  • Page Y Underwood  
  • Thomas R Hellmich  

  Abstract:

  Background: As mobile devices and apps grow in popularity, they are increasingly being used by health care providers to aid clinical care. At our institution, we developed and implemented a point-of-care clinical photography app that also permitted the capture of video recordings; however, the clinical findings it was used to capture and the outcomes that resulted following video recording were unclear. Objective: The study aimed to assess the use of a mobile clinical video recording app at our institution and its impact on clinical care. Methods: A single reviewer retrospectively reviewed video recordings captured between April 2016 and July 2017, associated metadata, and patient records. Results: We identified 362 video recordings that were eligible for inclusion. Most video recordings (54.1%; 190/351) were captured by attending physicians. Specialties recording a high number of video recordings included orthopedic surgery (33.7%; 122/362), neurology (21.3%; 77/362), and ophthalmology (15.2%; 55/362). Consent was clearly documented in the medical record in less than one-third (31.8%; 115/362) of the records. People other than the patient were incidentally captured in 29.6% (107/362) of video recordings. Although video recordings were infrequently referenced in notes corresponding to the clinical encounter (12.2%; 44/362), 7.7% (22/286) of patients were video recorded in subsequent clinical encounters, with 82% (18/22) of these corresponding to the same finding seen in the index video. Store-and-forward telemedicine was documented in clinical notes in only 2 cases (0.5%; 2/362). Videos appeared to be of acceptable quality for clinical purposes. Conclusions: Video recordings were captured in a variety of clinical settings. Documentation of consent was inconsistent, and other individuals were incidentally included in videos. Although clinical impact was not always clearly evident through retrospective review because of limited documentation, potential uses include documentation for future reference and store-and-forward telemedicine. Repeat video recordings of the same finding provide evidence of use to track the findings over time. Clinical video recordings have the potential to support clinical care; however, documentation of consent requires standardization.

  • JMIR Mhealth Uhealth 2019;7(12):e14919

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  Wearable Devices and Sensors

  Dec 2, 2019

  Use of Mobile Health Apps and Wearable Technology to Assess Changes and Predict Pain During Treatment of Acute Pain in Sickle Cell Disease: Feasibility Study

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  Authors List:

  • Amanda Johnson  
  • Fan Yang  
  • Siddharth Gollarahalli  
  • Tanvi Banerjee  
  • Daniel Abrams  
  • Jude Jonassaint  
  • Charles Jonassaint  
  • Nirmish Shah  

  Abstract:

  Background: Sickle cell disease (SCD) is an inherited red blood cell disorder affecting millions worldwide, and it results in many potential medical complications throughout the life course. The hallmark of SCD is pain. Many patients experience daily chronic pain as well as intermittent, unpredictable acute vaso-occlusive painful episodes called pain crises. These pain crises often require acute medical care through the day hospital or emergency department. Following presentation, a number of these patients are subsequently admitted with continued efforts of treatment focused on palliative pain control and hydration for management. Mitigating pain crises is challenging for both the patients and their providers, given the perceived unpredictability and subjective nature of pain. Objective: The objective of this study was to show the feasibility of using objective, physiologic measurements obtained from a wearable device during an acute pain crisis to predict patient-reported pain scores (in an app and to nursing staff) using machine learning techniques. Methods: For this feasibility study, we enrolled 27 adult patients presenting to the day hospital with acute pain. At the beginning of pain treatment, each participant was given a wearable device (Microsoft Band 2) that collected physiologic measurements. Pain scores from our mobile app, Technology Resources to Understand Pain Assessment in Patients with Pain, and those obtained by nursing staff were both used with wearable signals to complete time stamp matching and feature extraction and selection. Following this, we constructed regression and classification machine learning algorithms to build between-subject pain prediction models. Results: Patients were monitored for an average of 3.79 (SD 2.23) hours, with an average of 5826 (SD 2667) objective data values per patient. As expected, we found that pain scores and heart rate decreased for most patients during the course of their stay. Using the wearable sensor data and pain scores, we were able to create a regression model to predict subjective pain scores with a root mean square error of 1.430 and correlation between observations and predictions of 0.706. Furthermore, we verified the hypothesis that the regression model outperformed the classification model by comparing the performances of the support vector machines (SVM) and the SVM for regression. Conclusions: The Microsoft Band 2 allowed easy collection of objective, physiologic markers during an acute pain crisis in adults with SCD. Features can be extracted from these data signals and matched with pain scores. Machine learning models can then use these features to feasibly predict patient pain scores.

  • JMIR Mhealth Uhealth 2019;7(12):e13671

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  mHealth for Wellness, Behavior Change...

  Nov 29, 2019

  A Baby Formula Designed for Chinese Babies: Content Analysis of Milk Formula Advertisements on Chinese Parenting Apps

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  Authors List:

  • Jing Zhao  
  • Mu Li  
  • Becky Freeman  

  Abstract:

  Background: China is the largest market for infant formula. With the increasing use of smartphones, apps have become the latest tool used to promote milk formula. Formula manufacturers and distributors both have seized the popularity of apps as an avenue for marketing. Objective: This study aimed to identify and analyze milk formula ads featured on Chinese pregnancy and parenting apps, to build the first complete picture of app-based milk formula marketing techniques being used by milk formula brand variants on these apps, and to more fully understand the ad content that potentially undermines public health messaging about infant and young child feeding. Methods: We searched for free-to-download Chinese parenting apps in the 360 App Store, the biggest Android app store in China. The final sample consisted of 353 unique formula ads from the 79 apps that met the inclusion criteria. We developed a content analysis coding tool for categorizing the marketing techniques used in ads, which included a total of 22 coding options developed across 4 categories: emotional imagery, marketing elements, claims, and advertising disclosure. Results: The 353 milk formula ads were distributed across 31 companies, 44 brands, and 79 brand variants. Overall, 15 of 31 corporations were international with the remaining 16 being Chinese owned. An image of a natural pasture was the most commonly used emotional image among the brand variants (16/79). All variants included branding elements, and 75 variants linked directly to e-shops. Special price promotions were promoted by nearly half (n=39) of all variants. A total of 5 variants included a celebrity endorsement in their advertising. A total of 25 of the 79 variants made a product quality claim. Only 14 variants made a direct advertisement disclosure. Conclusions: The purpose of marketing messages is to widen the use of formula and normalize formula as an appropriate food for all infants and young children, rather than as a specialized food for those unable to breastfeed. Policy makers should take steps to establish an appropriate regulatory framework and provide detailed monitoring and enforcement to ensure that milk formula marketing practices do not undermine breastfeeding norms and behaviors.

  • JMIR Mhealth Uhealth 2019;7(11):e14219

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  Wearable Devices and Sensors

  Nov 27, 2019

  Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial

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  Authors List:

  • Arne Mueller  
  • Holger Alfons Hoefling  
  • Amir Muaremi  
  • Jens Praestgaard  
  • Lorcan C. Walsh  
  • Ola Bunte  
  • Roland Martin Huber  
  • Julian  Fürmetz  
  • Alexander Martin Keppler  
  • Matthias Schieker  
  • Wolfgang Böcker  
  • Ronenn Roubenoff  
  • Sophie Brachat  
  • Daniel S. Rooks  
  • Ieuan Clay  

  Abstract:

  Background: Digital technologies and advanced analytics have drastically improved our ability to capture and interpret health-relevant data from patients. However, only limited data and results have been published that demonstrate accuracy in target indications, real-world feasibility, or the validity and value of these novel approaches. Objective: This study aimed to establish accuracy, feasibility, and validity of continuous digital monitoring of walking speed in frail, elderly patients with sarcopenia and to create an open source repository of raw, derived, and reference data as a resource for the community. Methods: Data described here were collected as a part of 2 clinical studies: an independent, noninterventional validation study and a phase 2b interventional clinical trial in older adults with sarcopenia. In both studies, participants were monitored by using a waist-worn inertial sensor. The cross-sectional, independent validation study collected data at a single site from 26 naturally slow-walking elderly subjects during a parcours course through the clinic, designed to simulate a real-world environment. In the phase 2b interventional clinical trial, 217 patients with sarcopenia were recruited across 32 sites globally, where patients were monitored over 25 weeks, both during and between visits. Results: We have demonstrated that our approach can capture in-clinic gait speed in frail slow-walking adults with a residual standard error of 0.08 m per second in the independent validation study and 0.08, 0.09, and 0.07 m per second for the 4 m walk test (4mWT), 6-min walk test (6MWT), and 400 m walk test (400mWT) standard gait speed assessments, respectively, in the interventional clinical trial. We demonstrated the feasibility of our approach by capturing 9668 patient-days of real-world data from 192 patients and 32 sites, as part of the interventional clinical trial. We derived inferred contextual information describing the length of a given walking bout and uncovered positive associations between the short 4mWT gait speed assessment and gait speed in bouts between 5 and 20 steps (correlation of 0.23) and longer 6MWT and 400mWT assessments with bouts of 80 to 640 steps (correlations of 0.48 and 0.59, respectively). Conclusions: This study showed, for the first time, accurate capture of real-world gait speed in slow-walking older adults with sarcopenia. We demonstrated the feasibility of long-term digital monitoring of mobility in geriatric populations, establishing that sufficient data can be collected to allow robust monitoring of gait behaviors outside the clinic, even in the absence of feedback or incentives. Using inferred context, we demonstrated the ecological validity of in-clinic gait assessments, describing positive associations between in-clinic performance and real-world walking behavior. We make all data available as an open source resource for the community, providing a basis for further study of the relationship between standardized physical performance assessment and real-world behavior and independence.

  • JMIR Mhealth Uhealth 2019;7(11):e15191

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  mHealth for Symptom and Disease...

  Nov 26, 2019

  Mobile Health Apps for Self-Management of Rheumatic and Musculoskeletal Diseases: Systematic Literature Review

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  Authors List:

  • Aurélie Najm  
  • Laure Gossec  
  • Catherine Weill  
  • David Benoist  
  • Francis Berenbaum  
  • Elena Nikiphorou  

  Abstract:

  Background: Although the increasing availability of mobile health (mHealth) apps may enable people with rheumatic and musculoskeletal diseases (RMDs) to better self-manage their health, there is a general lack of evidence on ways to ensure appropriate development and evaluation of apps. Objective: This study aimed to obtain an overview on existing mHealth apps for self-management in patients with RMDs, focusing on content and development methods. Methods: A search was performed up to December 2017 across 5 databases. For each publication relevant to an app for RMDs, information on the disease, purpose, content, and development strategies was extracted and qualitatively assessed. Results: Of 562 abstracts, 32 were included in the analysis. Of these 32 abstracts, 11 (34%) referred to an app linked to a connected device. Most of the apps targeted rheumatoid arthritis (11/32, 34%). The top three aspects addressed by the apps were pain (23/32, 71%), fatigue (15/32, 47%), and physical activity (15/32, 47%). The development process of the apps was described in 84% (27/32) of the articles and was of low to moderate quality in most of the cases. Despite most of the articles having been published within the past two years, only 5 apps were still commercially available at the time of our search. Moreover, only very few studies showed improvement of RMD outcome measures. Conclusions: The development process of most apps was of low or moderate quality in many studies. Owing to the increasing RMD patients’ willingness to use mHealth apps for self-management, optimal standards and quality assurance of new apps are mandatory.

  • JMIR Mhealth Uhealth 2019;7(11):e14730

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  mHealth for Patient Education

  Nov 25, 2019

  Improving Medication Information Presentation Through Interactive Visualization in Mobile Apps: Human Factors Design

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  Authors List:

  • Don Roosan  
  • Yan Li  
  • Anandi Law  
  • Huy Truong  
  • Mazharul Karim  
  • Jay Chok  
  • Moom Roosan  

  Abstract:

  Background: Despite the detailed patient package inserts (PPIs) with prescription drugs that communicate crucial information about safety, there is a critical gap between patient understanding and the knowledge presented. As a result, patients may suffer from adverse events. We propose using human factors design methodologies such as hierarchical task analysis (HTA) and interactive visualization to bridge this gap. We hypothesize that an innovative mobile app employing human factors design with an interactive visualization can deliver PPI information aligned with patients’ information processing heuristics. Such an app may help patients gain an improved overall knowledge of medications. Objective: The objective of this study was to explore the feasibility of designing an interactive visualization-based mobile app using an HTA approach through a mobile prototype. Methods: Two pharmacists constructed the HTA for the drug risperidone. Later, the specific requirements of the design were translated using infographics. We transferred the wireframes of the prototype into an interactive user interface. Finally, a usability evaluation of the mobile health app was conducted. Results: A mobile app prototype using HTA and infographics was successfully created. We reiterated the design based on the specific recommendations from the usability evaluations. Conclusions: Using HTA methodology, we successfully created a mobile prototype for delivering PPI on the drug risperidone to patients. The hierarchical goals and subgoals were translated into a mobile prototype.

  • JMIR Mhealth Uhealth 2019;7(11):e15940

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  mHealth for Symptom and Disease...

  Nov 22, 2019

  Mood Monitoring Over One Year for People With Chronic Obstructive Pulmonary Disease Using a Mobile Health System: Retrospective Analysis of a Randomized...

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  Authors List:

  • Maxine E Whelan  
  • Carmelo Velardo  
  • Heather Rutter  
  • Lionel Tarassenko  
  • Andrew J Farmer  

  Abstract:

  Background: Comorbid anxiety and depression can add to the complexity of managing treatment for people living with chronic obstructive pulmonary disease (COPD). Monitoring mood has the potential to identify individuals who might benefit from additional support and treatment. Objective: We used data from the sElf-management anD support proGrammE (EDGE) trial to examine: (1) the extent to which the mood-monitoring components of a mobile health system for patients with COPD were used by participants; (2) the levels of anxiety and depression symptoms among study participants; (3) the extent to which videos providing advice about coping with low mood were viewed; and (4) the characteristics of participants with differing levels of mood and utilization of mood monitoring. Methods: A total of 107 men and women with a clinical diagnosis of COPD, aged ≥40 years old, were recruited to the intervention arm of the EDGE trial. Participants were invited to complete the Patient Health Questionnaire-8 and the Generalized Anxiety Disorder-7 test every four weeks using a tablet computer. Mood disturbance based on these measures was defined as a score ≥5 on either scale. Participants reporting a mood disturbance were automatically directed (signposted) to a stress or mood management video. Study outcomes included measures of health status, respiratory quality of life, and symptoms of anxiety and depression. Results: Overall, 94 (87.9%) participants completed the 12-month study. A total of 80 participants entered at least one response each month for at least ten months. On average, 16 participants (range 8-38 participants) entered ≥2 responses each month. Of all the participants, 47 (50%) gave responses indicating a mood disturbance. Participants with a mood disturbance score for both scales (n=47) compared with those without (n=20) had lower health status (P=.008), lower quality of life (P=.009), and greater anxiety (P<.001) and increased depression symptoms (P<.001). Videos were viewed by 64 (68%) people over 12 months. Of the 220 viewing visualizations, 70 (34.7%) began after being signposted. Participants signposted to the stress management video (100%; IQR 23.3-100%) watched a greater proportion of it compared to those not signposted (38.4%; IQR 16.0-68.1%; P=.03), whereas duration of viewing was not significantly different for the mood management video. Conclusions: Monitoring of anxiety and depression symptoms for people with COPD is feasible. More than half of trial participants reported scores indicating a mood disturbance during the study. Signposting participants to an advisory video when reporting increased symptoms of a mood disturbance resulted in a longer view-time for the stress management video. The opportunity to elicit measures of mood regularly as part of a health monitoring system could contribute to better care for people with COPD.

  • JMIR Mhealth Uhealth 2019;7(11):e14946

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  Augmented Reality (incl. Google...

  Nov 21, 2019

  Opportunities and Pitfalls in Applying Emotion Recognition Software for Persons With a Visual Impairment: Simulated Real Life Conversations

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  Authors List:

  • Hendrik Buimer  
  • Renske Schellens  
  • Tjerk Kostelijk  
  • Abdellatif Nemri  
  • Yan Zhao  
  • Thea Van der Geest  
  • Richard Van Wezel  

  Abstract:

  Background: A large part of the communication cues exchanged between persons is nonverbal. Persons with a visual impairment are often unable to perceive these cues, such as gestures or facial expression of emotions. In a previous study, we have determined that visually impaired persons can increase their ability to recognize facial expressions of emotions from validated pictures and videos by using an emotion recognition system that signals vibrotactile cues associated with one of the six basic emotions. Objective: The aim of this study was to determine whether the previously tested emotion recognition system worked equally well in realistic situations and under controlled laboratory conditions. Methods: The emotion recognition system consists of a camera mounted on spectacles, a tablet running facial emotion recognition software, and a waist belt with vibrotactile stimulators to provide haptic feedback representing Ekman’s six universal emotions. A total of 8 visually impaired persons (4 females and 4 males; mean age 46.75 years, age range 28-66 years) participated in two training sessions followed by one experimental session. During the experiment, participants engaged in two 15 minute conversations, in one of which they wore the emotion recognition system. To conclude the study, exit interviews were conducted to assess the experiences of the participants. Due to technical issues with the registration of the emotion recognition software, only 6 participants were included in the video analysis. Results: We found that participants were quickly able to learn, distinguish, and remember vibrotactile signals associated with the six emotions. A total of 4 participants felt that they were able to use the vibrotactile signals in the conversation. Moreover, 5 out of the 6 participants had no difficulties in keeping the camera focused on the conversation partner. The emotion recognition was very accurate in detecting happiness but performed unsatisfactorily in recognizing the other five universal emotions. Conclusions: The system requires some essential improvements in performance and wearability before it is ready to support visually impaired persons in their daily life interactions. Nevertheless, the participants saw potential in the system as an assistive technology, assuming their user requirements can be met.

  • JMIR Mhealth Uhealth 2019;7(11):e13722

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