JMIR Publications

JMIR mHealth and uHealth

Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

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Journal Description

JMIR mhealth and uhealth (mobile and ubiquitous health) (JMU, ISSN 2291-5222) is a new spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2014: 3.4). JMIR mHealth and uHealth has a projected impact factor (2015) of about 2.03. 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 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.
In addition to peer-reviewing paper submissions by researchers, JMIR mHealth and uHealth offers peer-review of medical apps itself (developers can submit an app for peer-review here).

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 is indexed in PubMed Central/PubMed and also the Thomson Reuters Emerging Sources Citation IndexESCI

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:

  • Source: https://pixabay.com/en/tablet-computer-notepad-technology-602968, CC0 Licensed, modified using image from author.

    Using a Mobile App to Promote Smoking Cessation in Hospitalized Patients

    Abstract:

    Background: The potential of interactive health education for preventive health applications has been widely demonstrated. However, use of mobile apps to promote smoking cessation in hospitalized patients has not been systematically assessed. Objective: This study was conducted to assess the feasibility of using a mobile app for the hazards of smoking education delivered via touch screen tablets to hospitalized smokers. Methods: Fifty-five consecutive hospitalized smokers were recruited. Patient sociodemographics and smoking history was collected at baseline. The impact of the mobile app was assessed by measuring cognitive and behavioral factors shown to promote smoking cessation before and after the mobile app use including hazards of smoking knowledge score (KS), smoking attitudes, and stages of change. Results: After the mobile app use, mean KS increased from 27(3) to 31(3) (P<0.0001). Proportion of patients who felt they “cannot quit smoking” reduced from 36% (20/55) to 18% (10/55) (P<0.03). Overall, 13% (7/55) of patients moved toward a more advanced stage of change with the proportion of patients in the preparation stage increased from 40% (22/55) to 51% (28/55). Multivariate regression analysis demonstrated that knowledge gains and mobile app acceptance did not depend on age, gender, race, computer skills, income, or education level. The main factors affecting knowledge gain were initial knowledge level (P<0.02), employment status (P<0.05), and high app acceptance (P<0.01). Knowledge gain was the main predictor of more favorable attitudes toward the mobile app (odds ratio (OR)=4.8; 95% confidence interval (CI) (1.1, 20.0)). Attitudinal surveys and qualitative interviews identified high acceptance of the mobile app by hospitalized smokers. Over 92% (51/55) of the study participants recommended the app for use by other hospitalized smokers and 98% (54/55) of the patients were willing to use such an app in the future. Conclusions: Our results suggest that a mobile app promoting smoking cessation is well accepted by hospitalized smokers. The app can be used for interactive patient education and counseling during hospital stays. Development and evaluation of mobile apps engaging patients in their care during hospital stays is warranted.

  • Source: https://www.flickr.com/photos/56155476@N08/6660032213, CC0 2.0 Attribution: Brad Flickinger.

    Using Mobile Apps to Promote a Healthy Lifestyle Among Adolescents and Students: A Review of the Theoretical Basis and Lessons Learned

    Abstract:

    Background: European adolescents and students tend to have low levels of physical activity and eat unhealthy foods, and the prevalence of overweight and obesity has increased, which poses a public health challenge. Mobile apps play an important role in their daily lives, suggesting their potential to be used in health-promoting strategies. Objective: This review aimed to explore how mobile apps can contribute to the promotion of healthy nutrition, physical activity, and prevention of overweight in adolescents and students. For the apps identified, the review describes the content, the theoretical mechanisms applied, and lessons learned. Methods: The databases Scopus, MEDLINE, Embase, and PsycINFO were searched for English-language publications from January 2009 to November 2013. Studies were included if (1) the primary component of the intervention involves an app; (2) the intervention targets healthy nutrition, or physical activity, or overweight prevention; and (3) the target group included adolescents or students (aged 12-25 years). Results: A total of 15 studies were included, which describe 12 unique apps. Ten of these apps functioned as monitoring tools for assessing dietary intake or physical activity levels. The other apps used a Web-based platform to challenge users to exercise and to allow users to list and photograph their problem foods. For 5 apps, the behavioral theory underpinning their development was clearly specified. Frequently applied behavior change techniques are prompting self-monitoring of behavior and providing feedback on performance. Apps can function self-contained, but most of them are used as part of therapy or to strengthen school programs. From the age of 10 years users may be capable of using apps. Only 4 apps were developed specifically for adolescents. All apps were tested on a small scale and for a short period. Conclusions: Despite large potential and abundant usage by young people, limited research is available on apps and health promotion for adolescents. Apps seem to be a promising health promotion strategy as a monitoring tool. Apps can enable users to set targets, enhance self‐monitoring, and increase awareness. Three apps incorporated social features, making them “social media,” but hardly any evidence appeared available about their potential.

  • Wearable fitness trackers.  Top L-R: Jawbone UP24, Nike Fuelband, Polar Loop, Misfit Shine with sport band . Centre: Misfit Shine with action Clip. Bottom L - R: Withings Pulse, Fitbit Zip, Spark Activity Tracker.

    Behavior Change Techniques Present in Wearable Activity Trackers: A Critical Analysis

    Abstract:

    Background: Wearable activity trackers are promising as interventions that offer guidance and support for increasing physical activity and health-focused tracking. Most adults do not meet their recommended daily activity guidelines, and wearable fitness trackers are increasingly cited as having great potential to improve the physical activity levels of adults. Objective: The objective of this study was to use the Coventry, Aberdeen, and London-Refined (CALO-RE) taxonomy to examine if the design of wearable activity trackers incorporates behavior change techniques (BCTs). A secondary objective was to critically analyze whether the BCTs present relate to known drivers of behavior change, such as self-efficacy, with the intention of extending applicability to older adults in addition to the overall population. Methods: Wearing each device for a period of 1 week, two independent raters used CALO-RE taxonomy to code the BCTs of the seven wearable activity trackers available in Canada as of March 2014. These included Fitbit Flex, Misfit Shine, Withings Pulse, Jawbone UP24, Spark Activity Tracker by SparkPeople, Nike+ FuelBand SE, and Polar Loop. We calculated interrater reliability using Cohen's kappa. Results: The average number of BCTs identified was 16.3/40. Withings Pulse had the highest number of BCTs and Misfit Shine had the lowest. Most techniques centered around self-monitoring and self-regulation, all of which have been associated with improved physical activity in older adults. Techniques related to planning and providing instructions were scarce. Conclusions: Overall, wearable activity trackers contain several BCTs that have been shown to increase physical activity in older adults. Although more research and development must be done to fully understand the potential of wearables as health interventions, the current wearable trackers offer significant potential with regard to BCTs relevant to uptake by all populations, including older adults.

  • Source: http://www.canstockphoto.com/woman-using-activity-tracker-26816164.html. Can Stock Photo, Inc. Standard License.

    Community Engagement to Optimize the Use of Web-Based and Wearable Technology in a Cardiovascular Health and Needs Assessment Study: A Mixed Methods Approach

    Abstract:

    Background: Resource-limited communities in Washington, D.C. have high rates of obesity-related cardiovascular disease in addition to inadequate physical activity (PA) facilities and limited Internet access. Engaging community members in the design and implementation of studies to address these health disparities is essential to the success of community-based PA interventions. Objective: The objective of the study was to use qualitative and quantitative methods to evaluate the feasibility and acceptability of PA-monitoring wristbands and Web-based technology by predominantly African American, church-based populations in resource-limited Washington, D.C. neighborhoods. Methods: To address cardiovascular health in at-risk populations in Washington, D.C., we joined community leaders to establish a community advisory board, the D.C. Cardiovascular Health and Obesity Collaborative (D.C. CHOC). As their first initiative, the Washington, D.C. Cardiovascular Health and Needs Assessment intends to evaluate cardiovascular health, social determinants of health, and PA-monitoring technologies. At the recommendation of D.C. CHOC members, we conducted a focus group and piloted the proposed PA-monitoring system with community members representing churches that would be targeted by the Cardiovascular Health and Needs Assessment. Participants (n=8) agreed to wear a PA-monitoring wristband for two weeks and to log cardiovascular health factors on a secure Internet account. Wristbands collected accelerometer-based data that participants uploaded to a wireless hub at their church. Participants agreed to return after two weeks to participate in a moderated focus group to share experiences using this technology. Feasibility was measured by Internet account usage, wristband utilization, and objective PA data. Acceptability was evaluated through thematic analysis of verbatim focus group transcripts. Results: Study participants (5 males, 3 females) were African American and age 28-70 years. Participant wristbands recorded data on 10.1±1.6 days. Two participants logged cardiovascular health factors on the website. Focus group transcripts revealed that participants felt positively about incorporating the device into their church-based populations, given improvements were made to device training, hub accessibility, and device feedback. Conclusions: PA-monitoring wristbands for objectively measuring PA appear to be a feasible and acceptable technology in Washington, D.C., resource-limited communities. User preferences include immediate device feedback, hands-on device training, explicit instructions, improved central hub accessibility, and designation of a church member as a trained point-of-contact. When implementing technology-based interventions in resource-limited communities, engaging the targeted community may aid in early identification of issues, suggestions, and preferences. ClinicalTrial: Trial Registration: ClinicalTrials.gov NCT01927783; https://clinicaltrials.gov/ct2/show/NCT01927783 (Archived by WebCite at http://www.webcitation.org/6f8wL117u)

  • Image Source: Beat Wearable Sensor System, copyright Centre for Global eHealth Innovation,
Licensed under Creative Commons Attribution cc-by 2.0 https://creativecommons.org/licenses/by/2.0/.

    Development of a Wearable Cardiac Monitoring System for Behavioral Neurocardiac Training: A Usability Study

    Abstract:

    Background: Elevated blood pressure is one of the main risk factors for death globally. Behavioral neurocardiac training (BNT) is a complementary approach to blood pressure and stress management that is intended to exercise the autonomic reflexes, improve stress recovery, and lower blood pressure. BNT involves cognitive-behavioral therapy with a paced breathing technique and heart rate variability biofeedback. BNT is limited to in-clinic delivery and faces an accessibility barrier because of the need for clinical oversight and the use of complex monitoring tools. Objective: The objective of this project was to design, develop, and evaluate a wearable electrocardiographic (ECG) sensor system for the delivery of BNT in a home setting. Methods: The wearable sensor system, Beat, consists of an ECG sensor and a mobile app. It was developed iteratively using the principles of test-driven Agile development and user-centered design. A usability study was conducted at Toronto General Hospital to evaluate feasibility and user experience and identify areas of improvement. Results: The Beat sensor was designed as a modular patch to be worn on the user’s chest and uses standard ECG electrodes. It streams a single-lead ECG wirelessly to a mobile phone using Bluetooth Low Energy. The use of small, low-power electronics, a low device profile, and a tapered enclosure allowed for a device that can be unobtrusively worn under clothing. The sensor was designed to operate with a mobile app that guides users through the BNT exercises to train them to a slow-paced breathing technique for stress recovery. The BNT app uses the ECG captured by the sensor to provide heart rate variability biofeedback in the form of a real-time heart rate waveform to complement and reinforce the impact of the training. Usability testing (n=6) indicated that the overall response to the design and user experience of the system was perceived positively. All participants indicated that the system had a positive effect on stress management and that they would use it at home. Areas of improvement were identified, which focused primarily on the delivery of training and education on BNT through the app. Conclusions: The outcome of this project was a wearable sensor system to deliver BNT at home. The system has the potential to offer a complementary approach to blood pressure and stress management at home and reduce current accessibility barriers.

  • Parent using ezPARENT. Copyright: the authors.

    Parent Use and Efficacy of a Self-Administered, Tablet-Based Parent Training Intervention: A Randomized Controlled Trial

    Abstract:

    Background: Parent training programs are traditionally delivered in face-to-face formats and require trained facilitators and weekly parent attendance. Implementing face-to-face sessions is challenging in busy primary care settings and many barriers exist for parents to attend these sessions. Tablet-based delivery of parent training offers an alternative to face-to-face delivery to make parent training programs easier to deliver in primary care settings and more convenient and accessible to parents. We adapted the group-based Chicago Parent Program (CPP) to be delivered as a self-administered, tablet-based program called the ezParent program. Objective: The purpose of this study was to (1) assess the feasibility of the ezParent program by examining parent satisfaction with the program and the percent of modules completed, (2) test the efficacy of the ezParent program by examining the effects compared with a control condition for improving parenting and child behavior in a sample of low-income ethnic minority parents of young children recruited from a primary care setting, and (3) compare program completion and efficacy with prior studies of the group-based CPP. Methods: The study used a two-group randomized controlled trial (RCT) design with repeated measures follow up. Subjects (n=79) were randomly assigned to an intervention or attention control condition. Data collection was at baseline and 12 and 24 weeks post baseline. Parents were recruited from a large, urban, primary care pediatric clinic. ezParent module completion was calculated as the percentage of the six modules completed by the intervention group parents. Attendance in the group-based CPP was calculated as the percentage of attendance at sessions 1 through 10. Satisfaction data were summarized using item frequencies. Parent and child data were analyzed using a repeated measures analysis of variance (RM-ANOVA) with simple contrasts to determine if there were significant intervention effects on the outcome measures. Effect sizes for between group comparisons were calculated for all outcome variables and compared with CPP group based archival data. Results: italic>ezParent module completion rate was 85.4% (34.2/40; 95% confidence interval [CI] = 78.4%-93.7%) and was significantly greater (P<.05) than face-to-face CPP group attendance (135.2/267, 50.6%) attendance of sessions; 95% CI = 46.8%-55.6%). ezParent participants reported the program as very helpful (35/40, 88.0%) and they would highly recommend the program (33/40, 82.1%) to another parent. ezParent participants showed greater improvements in parenting warmth (F1,77 = 4.82, P<.05) from time 1 to 3. No other significant differences were found. Cohen’s d effect sizes for intervention group improvements in parenting warmth, use of corporal punishment, follow through, parenting stress, and intensity of child behavior problems were comparable or greater than those of the group-based CPP. Conclusions: Data from this study indicate the feasibility and acceptability of the ezParent program in a low-income, ethnic minority population of parents and comparable effect sizes with face-to-face delivery for parents.

  • Image Credit: (c) andri333, from (https://pixabay.com/en/iphone-apple-inc-iphone-6s-phone-1125136/), licensed under cc-by-nc 3.0.

    Assessing the Use of Mobile Health Technology by Patients: An Observational Study in Primary Care Clinics

    Abstract:

    Background: There is significant potential for mobile health technology to improve health outcomes for patients with chronic diseases. However, there is a need for further development of mobile health technology that would help to improve the health of lower-income communities. Objective: The study objective was to assess mobile phone and app usage among a culturally diverse patient population, and to determine whether patients would be interested in using mobile health technology to help manage their chronic diseases. Methods: An observational study was conducted with patients of the Internal Medicine resident primary care clinics of Los Angeles County and University of Southern California (LAC+USC) Medical Center. Self-reported information regarding demographics, current mobile phone usage, current mobile health app and social media usage, barriers to using mobile phones or mobile health apps, and interest in using a mobile health app was collected. Results: Ninety-one percent of patients owned a mobile phone, with 76% (169/223) of these reporting having a mobile phone with Internet capability. Fifty-seven percent of subjects used mobile apps on their mobile phones, and 32% (41/130) of these used mobile apps related to their health. Eighty-six percent (207/241) of respondents voiced interest in using a mobile app to improve their health, and 40% (88/221) stated they would use such an app daily. Patients stated they would find the mobile health app most useful for nutrition, exercise, and obtaining general information on medical conditions. Conclusions: Despite the fact that the majority of our primary care patients were of lower socioeconomic status, they utilized mobile phones with Internet and mobile app capabilities to a great extent. There was substantial interest among our patients in using mobile health technology to both manage chronic disease and improve overall health. Given that cultural, educational, and socioeconomic disparities strongly correlate with higher rates of chronic diseases such as obesity, diabetes and hypertension, access to culturally relevant mobile health tools may empower patients in these populations to improve health outcomes.

  • Source: https://www.flickr.com/photos/comedynose/4739614082/in/photolist-8dPL9b-hUiqWc-CH46i6-hqQpiQ-A7tmRn-auKw44-of1MYm-pfSLdS-czSEx5-8f4wQf-DM2Sgo-Ca4Rcv-sbyf7J-7rr9Cw, Image Public Domain.

    Young People’s Views and Experiences of a Mobile Phone Texting Intervention to Promote Safer Sex Behavior

    Abstract:

    Background: The risk of poor sexual health, including unplanned pregnancy and sexually transmitted infections (STIs), is greatest amongst young people. Innovative and acceptable interventions to improve sexual health are required. Mobile phone text messaging (short message service, SMS) interventions have the potential to reach large numbers of people at relatively low cost, but greater understanding is needed on how these interventions should be developed and how they work. Objectives: The aim of this paper is to explore young people’s views of and experiences with a mobile phone text messaging intervention to promote safer sex behavior. Methods: We undertook qualitative interviews with young people aged 16 to 24 years as part of a pilot trial of a sexual health intervention delivered by text message in the United Kingdom. Study participants received sexual health promotion text messages based on behavior-change techniques. The message content, tailored by gender and STI status, included support for correct STI treatment and promotion of safer sex behaviors. Young people were eligible if they had received a positive chlamydia test or had more than one partner and at least one episode of unprotected sex in the last year. Telephone interviews were conducted 2 to 3 weeks after initiation of the intervention. A semi-structured topic guide was followed to explore participant experiences and a thematic analysis was conducted. Results: We conducted 16 telephone interviews with participants who had received the text intervention and an additional four interviews with those in the control group (13 women and 7 men). Intervention participants found text messages easy to understand and appearing to come from a friendly and trustworthy source. They considered the frequency and timing of messages to be appropriate, and delivery via mobile phones convenient. Receipt of support by text message allowed recipients to assimilate information at their own pace, and prompted reflection on and sharing of messages with friends, family members, and partners, thus providing opportunities for education and discussion. For some recipients, the messages had increased their knowledge of how to correctly use condoms. Some described how the messages had increased their confidence and reduced stigma, enabling them to disclose infection to a partner and/or to do so sooner and more calmly. Discussing the messages with a partner reportedly enabled some women to negotiate condom use. Conclusion: From the perspective of the recipients, the tone, frequency, and content of the text messaging-based sexual health intervention was acceptable and appropriate. Their accounts indicated that the intervention increased knowledge, confidence, and safer sex behaviors. A large-scale randomized controlled trial (RCT) is needed to assess effectiveness.

  • Creating Effective Mobile Phone Apps to Optimize Antiretroviral Therapy Adherence: Perspectives From Stimulant-Using HIV-Positive Men Who Have Sex With Men

    Abstract:

    Background: The use of stimulant drugs among men who have sex with men (MSM) with human immunodeficiency virus (HIV) is associated with decreased odds of antiretroviral therapy (ART) adherence and elevated risk of forward HIV transmission. Advancing tailored and innovative mobile phone–based ART adherence app interventions for stimulant-using HIV-positive MSM requires greater understanding of their needs and preferences in this emerging area. Objective: The purpose of this study is to (1) assess reasons that stimulant-using HIV-positive MSM download and sustain their use of mobile phone apps in general, and (2) obtain feedback on features and functions that these men prefer in a mobile phone app to optimize their ART adherence. Methods: Focus groups were conducted with stimulant-using HIV-positive MSM (24-57 years of age; mostly non-Hispanic white; 42% once a week or more frequent stimulant drug use) in San Francisco and Minneapolis. Our aim was to explore the mobile phone app features and functions that they considered when deciding to download and sustain their use of general apps over time, as well as specific features and functions that they would like to see incorporated into an ART adherence mobile app. Focus groups were audiorecorded and transcribed verbatim. Thematic analysis was applied to transcripts using line-by-line open coding and organizing codes into meaningful themes. Results: Men reported that they currently had a variety of health and wellness, social media and networking, gaming and entertainment, and utility apps on their mobile phones. Downloading apps to their mobile phones was influenced by the cost of the app, recommendations by a trusted source, and the time it takes to download. In addition, downloading and sustained use of apps was more likely to occur when men had control over most features of the app and apps were perceived to be useful, engaging, secure, and credible. Participants suggested that ART adherence mobile phone apps include social networking features, connections to local resources and their medical chart, and breaking HIV news and updates. Although some men expressed concerns about daily self-monitoring of HIV medication doses, many appreciated receiving a summary of their medication adherence over time and suggested that feedback about missed doses be delivered in an encouraging and humorous manner. Conclusions: In this study, we were able to recruit a relatively high proportion (42%) of HIV-positive MSM reporting weekly or more stimulant use. These results suggest critical design elements that may need to be considered during development of ART adherence-related mobile phone apps for this, and possibly other, high-risk groups. In particular, finding the optimal balance of security, engagement, usefulness, control capabilities, and credibility will be critical to sustained used of HIV treatment apps.

  • Sathani Senior Center, Minneapolis, MN. Copyright: the authors.

    Older Adults’ Experiences Using a Commercially Available Monitor to Self-Track Their Physical Activity

    Abstract:

    Background: Physical activity contributes to older adults’ autonomy, mobility, and quality of life as they age, yet fewer than 1 in 5 engage in activities as recommended. Many older adults track their exercise using pencil and paper, or their memory. Commercially available physical activity monitors (PAM) have the potential to facilitate these tracking practices and, in turn, physical activity. An assessment of older adults’ long-term experiences with PAM is needed to understand this potential. Objective: To assess short and long-term experiences of adults >70 years old using a PAM (Fitbit One) in terms of acceptance, ease-of-use, and usefulness: domains in the technology acceptance model. Methods: This prospective study included 95 community-dwelling older adults, all of whom received a PAM as part of randomized controlled trial piloting a fall-reducing physical activity promotion intervention. Ten-item surveys were administered 10 weeks and 8 months after the study started. Survey ratings are described and analyzed over time, and compared by sex, education, and age. Results: Participants were mostly women (71/95, 75%), 70 to 96 years old, and had some college education (68/95, 72%). Most participants (86/95, 91%) agreed or strongly agreed that the PAM was easy to use, useful, and acceptable both 10 weeks and 8 months after enrolling in the study. Ratings dropped between these time points in all survey domains: ease-of-use (median difference 0.66 points, P=.001); usefulness (median difference 0.16 points, P=.193); and acceptance (median difference 0.17 points, P=.032). Differences in ratings by sex or educational attainment were not statistically significant at either time point. Most participants 80+ years of age (28/37, 76%) agreed or strongly agreed with survey items at long-term follow-up, however their ratings were significantly lower than participants in younger age groups at both time points. Conclusions: Study results indicate it is feasible for older adults (70-90+ years of age) to use PAMs when self-tracking their physical activity, and provide a basis for developing recommendations to integrate PAMs into promotional efforts. Trial Registration: Clinicaltrials.gov NCT02433249; https://clinicaltrials.gov/ct2/show/NCT02433249 (Archived by WebCite at http://www.webcitation.org/6gED6eh0I)

  • Image Source: STIHL, http://www.stihlusa.com/products/protective-and-work-wear/ (fair use).

    User Perceptions of ¡Protéjase!: An Intervention Designed to Increase Protective Equipment Use Among Mexican Immigrant and Mexican American Farmworkers

    Abstract:

    Background: Farmworkers’ exposures to pesticides are reduced when they wear personal protective equipment (PPE), and mobile health (mHealth) platforms can potentially deliver information to farmworkers to help promote PPE use. However, little is known about the feasibility of using mHealth platforms to promote farmworkers’ use of PPE. Objective: The objective of the study was to describe the development and feasibility-testing of Protect Yourself! (¡Protéjase!), an intervention designed to increase PPE use. As the vast majority of farmworkers in the United States are from Mexico, we examined the intervention in a primarily Mexican-origin farmworker population. Methods: ¡Protéjase was developed in several steps. First, we performed ethnographic observations to understand what prevents PPE use. Next, we developed program components that met the challenges uncovered in the ethnographic observations, seeking direct feedback from farmworkers on each component. Feasibility was assessed using surveys and focus groups. Material was provided in Spanish or English at the preference of the participant. Finally, we pilot tested each component of the intervention, including: (1) PPE that was provided to each worker for their personal use during the intervention trial, and (2) delivery of an application-based tool that promoted the use of PPE through daily individualized messaging. Results: 55 farmworkers enrolled in the study, but only 41 of 55 (75%) completed the entire pilot intervention trial. Results focus on the evaluation of the intervention, and include only those who completed the entire trial. Among farmworkers who completed the entire intervention trial, all but two farmworkers were born in Mexico and were Spanish speaking. Still, all study participants self-identified as Mexican or Mexican-American. When asked what changes were needed in the intervention’s messaging or delivery to increase user satisfaction, 22 out of 41 participants (54%) felt that no changes were needed. However, 16 of 41 participants (39%) suggested small changes to messaging (eg, refer to long pants as pants only) to improve their understanding of the messages. Finally, a small number (3 of 41 participants, 7%) felt that messages were difficult to read, primarily due to low literacy. Conclusions: The ¡Protéjase! mHealth program demonstrated very good feasibility, satisfaction, and acceptance; potential improvements (eg, small modifications in messaging to increase farmworkers’ use) were noted. Overall, the PPE provided to workers as well as the mHealth platform were both perceived as useful for promoting PPE use.

  • Image Credit: (c) Wang et al, licensed under cc-by-nc 3.0. This study focused on two kinds of apps: diet apps (left) and physical activity apps (right). It identified how the apps affected their users (blue arrows). In addition, this study also identified whether using apps affected the user's diet and physical activity (white arrows).

    Diet and Physical Activity Apps: Perceived Effectiveness by App Users

    Abstract:

    Background: Diet and physical activity apps are two types of health apps that aim to promote healthy eating and energy expenditure through monitoring of dietary intake and physical activity. No clear evidence showing the effectiveness of using these apps to promote healthy eating and physical activity has been previously reported. Objective: This study aimed to identify how diet and physical activity (PA) apps affected their users. It also investigated if using apps was associated with changes in diet and PA. Methods: First, 3 semi-structured focus group discussions concerning app usability were conducted (15 app users and 8 nonusers; mean age 24.2 years, SD 6.4), including outcome measures such as motivations, experiences, opinions, and adherence. Results from the discussions were used to develop a questionnaire. The questionnaire, which contained questions about behavior changes, app usage, perceived effectiveness, and opinions of app usability, was answered by 500 Norwegians, with a mean age of 25.8 years (SD 5.1). Results: App users found diet and PA apps effective in promoting healthy eating and exercising. These apps affected their actions, health consciousness, and self-education about nutrition and PA; and were also a part of their social lives. Over half of the users perceived that apps were effective in assisting them to eat healthily and to exercise more. Diet apps were more effective when they were frequently used and over a long period of time, compared to infrequent or short-term use (P=.01 and P=.02, respectively). Users who used diet and PA apps, perceived apps as more effective than users who only used one type of app (all P<.05). App users were better at maintaining diet and PA behaviors than nonusers (all P<.05). Young adults found apps fun to use, but sometimes time consuming. They wanted apps to be designed to meet their personal expectations. Conclusions: App usage influenced action, consciousness, self-education about nutrition and PA, and social life. It facilitated maintaining a healthy diet and exercising more. Diet and PA apps of the future can be further strengthened by being tailored to meet personal needs.

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  • Engaging Gatekeeper-stakeholders in Development of a Mobile Health Intervention to Improve Medication Adherence among African American and Pacific Islander Elderly Patients with Hypertension

    Date Submitted: Apr 25, 2016

    Open Peer Review Period: Apr 29, 2016 - Jun 24, 2016

    Background: Approximately 70 million people in the US have hypertension. While antihypertensive therapy can reduce the morbidity and mortality associated with hypertension, often patients do not take...

    Background: Approximately 70 million people in the US have hypertension. While antihypertensive therapy can reduce the morbidity and mortality associated with hypertension, often patients do not take their medication as prescribed. Objective: The goal of this study was to better understand issues affecting the acceptability and usability of mobile health technology (mHealth) to improve medication adherence for elderly African American (AA) and Native Hawaiian and Pacific Islander (NHPI) patients with hypertension. Methods: In-depth interviews were conducted with 20 Gatekeeper-stakeholders using targeted open-ended questions. Interviews were de-identified, transcribed, organized and coded manually by two independent coders. Analysis of patient interviews used largely a deductive approach because the targeted open-ended interview questions were designed to explore issues specific to the design and acceptability of a mHealth intervention for seniors. Results: A number of similar themes regarding elements of a successful intervention emerged from our two groups of AA and NHPI Gatekeeper-stakeholders. First was the need to teach participants both about the importance of adherence to antihypertensive medications; second, the use of smart/cell phones for messaging and patients need to be able to access ongoing technical support; third, messaging needs to be short and simple, but personalized, and to come from someone the participant trusts and with whom they have a connection. There were some differences between groups. For instance, there was a strong sentiment among AA that the church be involved and that the intervention begin with group workshops, while NHPI seemed to believe that the teaching could occur on a one-to-one basis with the health care provider. Conclusions: Information from our Gatekeeper-stakeholder (key informant) interviews suggests that the design of the mHealth intervention to improve adherence to antihypertensives among the elderly could be very similar between AAs and NHPIs. The main difference might be in the way in which the program is initiated (possibly through church-based workshops for AA and by individual providers for NHPIs). Another difference might be who sends the messages with AA wanting someone outside the health care system, but NHPI preferring a provider.

  • Investigating the Perceptions of Care Coordinators on Using Behavior Theory-Based Mobile Health Technology with Medicaid Populations: A Grounded Theory Study

    Date Submitted: Apr 20, 2016

    Open Peer Review Period: Apr 22, 2016 - Jun 17, 2016

    Background: Medicaid populations are less engaged in their healthcare than the rest of the population, translating to worse health outcomes and increased healthcare costs. Since theory-based mobile he...

    Background: Medicaid populations are less engaged in their healthcare than the rest of the population, translating to worse health outcomes and increased healthcare costs. Since theory-based mobile health (mHealth) interventions have been shown to increase patient engagement, mobile phones may be an optimal strategy to reach this population. There is a deep disconnect between developers of mHealth technology and health behavior researchers, so there is a lack of data on what components of theory-based mHealth increase patient engagement. Objective: This study aims to address this gap between academia and practice by conducting research using the health behavior-theory based patient-provider text-messaging platform, Sense Health, which integrates Transtheoretical Model and Stages of Change (TTM), Social Cognitive Theory (SCT), Supportive Accountability, and Motivational Interviewing. Methods: Interviews based in grounded-theory methodology were conducted with 10 care managers to triangulate the findings of internal user activity data and to further understand perceptions of the relationship between mHealth and patient engagement. Results: The interviews with care managers yield a grounded theory model including four intertwined relationships revolving around patient engagement: between Sense Health, client-care manager relationships, and communication; Sense Health, literacy, and access to care; support, Sense Health, and communication; and Sense Health, patient accountability, and patient motivation. Conclusions: Sense Health features tied to health behavior theory appear to be effective in improving patient engagement. Two-way communication (Supportive Accountability), trusted relationships (Supportive Accountability, SCT), personalized messages (TTM), and patient input (TTM, SCT, Motivational Interviewing) appeared as the most relevant components in achieving desired outcomes. Additionally, reminder messages were noted as especially useful in making Medicaid patients accountable, and in turn engaging them in their health and healthcare. These findings expose how this theory-centered platform drives engagement, allowing Sense Health, and future mHealth interventions that aim to improvement patient engagement in Medicaid populations, to improve their technology. Clinical Trial: Columbia University Medical Center Institutional Review Board (IRB-AAAQ5254)

  • SMS-Based Intervention Targeting Alcohol Consumption Among University Students: Findings from a Formative Development Study

    Date Submitted: Apr 12, 2016

    Open Peer Review Period: Apr 12, 2016 - Jun 7, 2016

    Background: Drinking of alcohol among university students is a global phenomenon with heavy episodic drinking being accepted despite several potential negative consequences. Half of all young adults i...

    Background: Drinking of alcohol among university students is a global phenomenon with heavy episodic drinking being accepted despite several potential negative consequences. Half of all young adults in Sweden attend university making the health and well-being of this group a public health concern. There is emerging evidence that text messaging (SMS) interventions are effective to promote behaviour change among students. However, it is still unclear how effectiveness can be optimized through intervention design or how user interest and adherence can be maximised. Objective: To develop an SMS-based intervention targeting alcohol drinking among university students using formative research. Methods: A formative research design was used including an iterative revision process based on input from end users and experts. Data were collected via focus groups (n=7) with students and a panel evaluation involving students (n=15) and experts (n=5). Student participants were recruited from five universities in Sweden. A semi-structured interview guide was used in the focus groups and included questions on alcohol culture, message content and intervention format. The panel evaluation asked participants to rate to what degree preliminary messages were understandable, usable and had a good tone on a scale from 1 to 4 (1 = very low degree; 4 = very high degree). Participants could also write their own comments for each message. Qualitative data were analysed using qualitative descriptive analysis. Quantitative data were analysed using descriptive statistics. The SMS messages and the intervention format were revised continuously, in parallel with data collection. A behaviour change technique analysis was conducted on the final version of the program. Results: The focus group data showed that, overall, students were positive towards the SMS intervention. Messages that were neutral, motivated, clear and tangible engaged students. Students expressed that they preferred short, concise messages and confirmed that a 6-week intervention was an appropriate duration. However, there was limited consensus regarding SMS frequency, personalization of messages and timing. Overall, messages scored high on understanding (3.86, SD 0.43), usability (3.70, SD 0.61) and tone (3.78, SD 0.53). Participants added comments to 67 of 70 messages, including suggestions for change in wording, order of messages, and feedback on why a message was unclear or needed major revision. Comments also included positive feedback that confirmed the value of the messages. Twenty-three behaviour change techniques, aimed at, for example, addressing self-regulatory skills, were identified in the final program. Conclusions: The formative research design was valuable and resulted in significant changes to the intervention. All the original SMS messages were changed and new messages were added. The findings showed that, overall, students were positive towards receiving support through SMS and that neutral, motivated, clear and tangible messages promoted engagement. However, limited consensus was found on the timing, frequency and tailoring of messages.

  • A mobile phone app for dietary intake assessment (e-EPIDEMIOLOGY): comparison with a food frequency questionnaire

    Date Submitted: Mar 22, 2016

    Open Peer Review Period: Mar 22, 2016 - May 17, 2016

    Background: Background: There is a great necessity for new methods of evaluation of dietary intake that overcome the limitations of traditional methods such as food frequency questionnaires (FFQ). O...

    Background: Background: There is a great necessity for new methods of evaluation of dietary intake that overcome the limitations of traditional methods such as food frequency questionnaires (FFQ). Objective: The objective of this study was to validate e-EPIDEMIOLOGY as a tool for the determination of habitual intake of certain foods/drinks, using a traditional FFQ as a reference method. Methods: University students between the ages of 18 and 24 years of age recorded the consumption of certain foods/drinks using an application for mobile phones (e-EPIDEMIOLOGY) during 28 consecutive days and then filled out a conventional FFQ on paper at the end of the study period. The agreement between the category of habitual consumption for each of the foods/drinks included in the study was evaluated using cross-classification analysis and average weighted kappa statistic. Results: 75 participants completed the study (23% male and 77% female). Cross-classification analysis showed that more than 69% of the participants were correctly classified into the same category and less than 3% were misclassified into an opposite category. The average weighted kappa statistic was moderate (k = 0.43). Conclusions: e-EPIDEMIOLOGY could be considered a reasonably and moderately valid tool in epidemiological studies to measure single foods/drinks habitual intake in young adults, as a valuable alternative to conventional paper FFQ. Objective: Introduction Characterization of the intake of foods/drinks has been used in the last few decades in numerous epidemiological studies, though with different objectives: 1) In descriptive studies, as a basis for the planification of certain Public Health policies related to diet; 2) In analytical observational studies, in order to define relationships between the consumption of certain foods/drinks and disease; and 3) In analytical experimental studies that evaluate Public Health policies whose intent is to modify certain concrete dietary habits [1-4]. Traditional methods that evaluate dietary intake, such as dietary registries and 24 hour recall questionnaires (short-term methods) and FFQ (long-term instruments) present important limitations. The short-term tools allow collection of data that includes quantities of all of the foods/drinks consumed by a person during a certain number of days. Dietary registries that require weighing of foods are time-consuming and suppose a great deal of work for study participants, which can lead to deviations from normal food intake (especially underestimation of quantities), as well as low rates of participation and compliance. Dietary registries and 24 hour recall questionnaires also require trained personnel and are short-term memory dependent [5-8]. It is also important to remember that in order to determine habitual dietetic intake (the long-term mean consumption of foods) using these short-term tools, it would be necessary to repeat multiple times, which would only worsen the problem. Long-term recall methods allow information to be collected about the consumption of a series of foods/drinks during prolonged periods of time (weeks or months), classifying a person according to the consumption category applied to each of the foods/drinks considered. FFQ depend mostly on the memory of the subject interviewed, do not allow for intrapersonal variation in the recording of daily food consumption during the time period of the study and do not allow precise estimation of food portion size. Both long-term and short-term tools if use conventional techniques (paper and pen) to collect information, with posterior manual introduction for statistical analysis, which increments research costs and time consumption considerably [3,7]. For these reasons, improvement upon traditional methods for the determination of dietary intake still remains one of the most important challenges in nutritional epidemiology [2-4,9]. Improvement of self-reporting that contributes to greater precision of habitual dietary intake would represent a considerable boon for researchers, as well as for society as a whole, keeping in mind the important repercussions that the results and conclusions of these studies can have on the general population. Traditional techniques that evaluate dietary intake should be substituted by new solutions, or nutritional research and treatments for nutritional problems will remain restricted and deficient [10]. Recently certain dietary registries and 24-hour recall mobile phone applications have been developed that could reduce the limitations of these methods, with promising results [1-4,11]. However, and though FFQ are the most practical, accessible and commonly utilized tools in research that determines habitual dietary intake [6,9,12], up until now, no long-term instrument has been developed that takes advantage of mobile technology and serves as an alternative to traditional FFQ. Use of mobile phones is extense in Spain, with 95.0 % of Spaniards having used a mobile phone in the last 3 months [13]. This facilitates the introduction of new methods of evaluation of dietary intake that include mobile technology. In any case, these new technologies need to be developed according to different local conditions and evaluated with objective measures [4]. Our research team has developed a new application for mobile phones called e-EPIDEMIOLOGY, designed to collect individual consumption data of a series of foods/drinks. The objective of this study was to compare data recorded with e-EPIDEMIOLOGY with that registered through a conventional paper FFQ for the same foods/drinks, in order to evaluate its potential as research tool for the determination of habitual dietary intake. Methods: Methods Study Sample This study was performed among medical and pharmaceutical students of the University of Seville (Andalusia, Spain, Southern Europe). Different events were programmed at both faculties in which the research team personally presented the project to the students. At the end of each presentation, interested students and those that fit inclusion criteria signed up for a personal interview. Of the 127 students that were interested, 88 were eligible and were signed up for the interview, in which the study protocol was explained. Finally 76 students decided to participate in the validation study. Of these, 75 completed both the application e-EPIDEMIOLOGY and the conventional paper FFQ. The period of participant recruitment spanned from October 2014 to January 2016. The inclusion criteria were: University of Seville student from the medical or pharmaceutical schools, between the ages of 18-24 years old and the owner of a mobile phone with access to the Internet and an Android operating system. As an incentive, all participants were entered into a raffle of a tablet at the completion of the study. The study was performed according to directives established in the Helsinki Declaration and the Biomedical Research Law [14], and all procedures on human beings were approved by the Ethical Committee for Experimentation of the University of Seville. Written informed consent was obtained from all participants. e-EPIDEMIOLOGY, a mobile phone application Participants downloaded the application e-EPIDEMIOLOGY to their personal mobile phones. This application was developed for mobile phones with an Android operating system and permitted the recording of daily consumption of the foods/drinks selected for the study. At the end of each day a notice would appear on the participants mobile phone, informing them that it was time to use the application. At this moment, the participant could access the application and register the number of standard portions that had been consumed during that day of each of the foods/drinks included in the study. The list of foods appeared every day in the same order to facilitate completion of the application. This list consisted of 12 items which referred to 10 different foods/drinks: fruit, vegetables, red meat (lamb, beef, and pork), chicken/turkey, fish, legumes, sweets, prepared foods, soft drinks and alcoholic beverages. These were selected for the study because they provide consumption patterns that range from daily to sporadic for the population [9]. These were also considered to be markers for healthy (fruits, vegetables, fish and legumes) and unhealthy (sweets, prepared foods, and soft drinks) dietary habits [15]. When accessing the first food/drink on the list, the number of standard portions of this food/drink consumed throughout the day were introduced. The button 'Next' was then pressed to go on to the next item in order to record all foods/drinks consumed that day (Figure 1). One could correct errors by pressing 'Go Back' and changing the number introduced. After filling out e-EPIDEMIOLOGY, the data was automatically saved and sent to the research administrator's website via Wi-Fi or 3G/4G, after which time the user could not access or change answers to the questionnaire. This process was repeated every day for each user during 28 days. The time necessary to complete e-EPIDEMIOLOGY was about 1 minute per day. Each participant selected the time of day at which the reminder would be set, during the interval between 8 pm and midnight (after having consumed all of the foods and beverages for that day). The application was blocked at midnight until the next day at 8 pm. Saved data could be consulted pressing the 'Historial' button, though this information could not be changed or eliminated. Figure 1. Screen captures of the application e-EPIDEMIOLOGY. The application used to register daily consumption of selected foods/drinks was based on a questionnaire elaborated using the FFQ from the European Health Survey [16] (Appendix 1). Standardized portions were added after testing a previous prototype of e-EPIDEMIOLOGY (results not published) and were obtained from a FFQ validated for the Spanish population [17]. The application also allows for registry of other lifestyle habits (hours of sleep, oral hygiene, physical activity and tobacco consumption). The application recorded this information using a different questionnaire with 11 items, also based on validated instruments from the European Health Survey [16]. Appendix 1. Questionnaire used in e-EPIDEMIOLOGY, with weights / measurements of standardized rations of selected foods/drinks. 1. How many pieces of fruit have you eaten today? (1 piece = aprox. 100 g) (Include fresh-squeezed juice (1 ration = aprox. 200 ml)) 2. How many portions of vegetables have you eaten today? (1 portion = aprox. 150 g) 3. How many portions of legumes (lentils, garbanzos, beans, etc.) have you eaten today? (1 portion = aprox. 60 g) 4. How many portions of chicken/turkey have you eaten today? (1 portion = aprox. 150 g) 5. How many portions of fish have you eaten today? (1 portion = aprox. 150 g) 6. How many portions of red meat (beef, pork, lamb) have you eaten today? (1 portion = aprox. 150 g) 7. How many servings of soft drinks have you had today? (1 serving = aprox. 250 ml) 8. How many portions of comercially produced sweets (not home-made) (cookies/pastries) have you eaten today? (1 piece = aprox. 100 g) 9. How many portions of prepared/frozen foods have you eaten today (croquettes, pizza, etc.)? (1 portion = aprox. 80 g) 10. Have you consumed alcoholic beverages today? 11. What kind of alcoholic beverage have you consumed? 12. How many servings of beer/wine/spirits or mixed drinks have you consumed today? (1 serving of beer = aprox. 200 ml / 1 glass of wine = aprox. 100 ml / 1 serving of spirits or mixed drinks = aprox. 50 ml (of alcohol)) Anthropomorphic measurements Researchers used the personal interview to both explain the study protocol and collect anthropomorphic data using standard procedure. Height was measured in centimeters, with a precision of 0.5 cm, and weight in kilograms, with a precision of 0.1 kg (wearing lightweight clothing, with shoes off and pockets empty). With this data, BMI (Body Mass Index) (Kg/m2), was calculated using categories defined by the WHO [18]. Procedure All participants completed a questionnaire during the personal interview in which demographic data was collected, such as date of birth, gender, birthplace, and current place of residence. Participants were instructed in the use of e-EPIDEMIOLOGY with a personal demonstration of use of the application, as well as estimation of standardized portion sizes, and were reminded to maintain their habitual diet. The recording of foods/drinks intake was to be completed during 28 consecutive days using the application. Participants were recruited to the study during the entire period of research, so that all seasons, days of the month and week were included in the sample. As a reference, a conventional paper FFQ was filled out at the end of each period of the study, through personal interviews and at the convenience of the participants. The FFQ utilized was based on a validated questionnaire used in the European Health Survey (Appendix 2) [16]. Standardized portion sizes were obtained from an FFQ validated for the Spanish population [17]. Both the questionnaires used in the application and the paper FFQ had the same items (Appendixes 1 y 2), the only difference being that in e-EPIDEMIOLOGY the questionnaire refers to daily consumption while the FFQ refers to consumption during the previous 28 days. Appendix 2. Questionnaire utilized for conventional paper FFQ, with weights / measurements of standardized portions of selected foods/drinks. 1. How many pieces of fruit did you habitually consume in the last 28 days? (1 piece = aprox. 100 g) (Include fresh-squeezed juice (1 portion = aprox. 200 ml)) Categories a 2. How many portions of vegetables did you habitually consume in the last 28 days? (1 portion = aprox. 150 g) Categories a 3. How many portions of legumes (lentils, garbanzos, beans, etc.) did you habitually consume in the last 28 days? (1 portion = aprox. 60 g) Categories a 4. How many portions of chicken/turkey did you habitually consume in the last 28 days? (1 portion = aprox. 150 g) Categories a 5. How many portions of fish did you habitually consume in the last 28 days? (1 portion = aprox. 150 g) Categories a 6. How many portions of red meat (beef, pork, lamb) did you habitually consume in the last 28 days? (1 portion = aprox. 150 g) Categories a 7. How many servings of soft drinks did you habitually consume in the last 28 days? (1 serving = aprox. 250 ml) Categories a 8. How many portions of comercially produced sweets (not home-made) (cookies/pastries) did you habitually consume in the last 28 days? (1 piece = aprox. 100 g) Categories a 9. How many portions of prepared/frozen foods have you habitually eaten (croquettes, pizza, etc.) in the last 28 days? (1 portion = aprox. 80 g) Categories a 10. Have you consumed alcoholic beverages in the last 28 days? Yes 􀂉No 11. What kind of alcoholic beverages have you consumed in the last 28 days? 􀂉 Beer Wine 􀂉Spirits/mixed drinks Others 12. How many servings of beer/wine/spirits or mixed drinks did you consume in the last 28 days? (1 serving of beer = aprox. 200 ml / 1 glass of wine = aprox. 100 ml/ 1 serving of spirits or mixed drinks = aprox. 50 ml (of alcohol)) Categories a a The different categories were: : Less than once a week / Once or twice a week / 3-4 times a week / 5-6 times a week / Once or twice a day / 3 times or more a day All of the personal data collected in this study remained anonymous and confidential and were treated according to the current Spanish legislation [19]. To that end, each participant was assigned a personal alphanumeric code, so that no-one, including the researchers, could link personal information to the results obtained. The code was introduced the first time the participant accessed the application, and when completing the demographic questionnaire and paper FFQ, for organizational purposes. Codification y revision of data For each participant, the data collected from the FFQ for each of the 10 foods/drinks mentioned previously were categorized. The frequency of consumption of foods/drinks ítems was categorized into six subgroups, ranging from “Less than once a week” to “3 times or more a day” (Appendix 2). For the same foods/drinks, the data from the 28 days using e-EPIDEMIOLOGY were recorded as daily consumption. This data was transformed in order to include it in one of the same categories of habitual consumption included in the FFQ. This was made possible because both the FFQ and e-EPIDEMIOLOGY used the same standardized portion sizes. For example, suppose that a participant consumes an average of 0.25 standard rations of fish daily during 28 days using e-EPIDEMIOLOGY. This average consumption represents 1.75 standard portions per week (0.25 x 7 = 1.75), which would be classified in the category “Once or twice a week.” The data collected from the conventional paper FFQ were manually introduced in the data base by the research team. These were reviewed in order to avoid data entry errors. Data collected from e-EPIDEMIOLOGY were saved without modifications in a separate data base. Posteriorly, one set of data was removed due to obvious inconsistency: one participant registered the consumption of 200 standardized portions of legumes in one day. Statistical analysis Due to the lack of agreement on the best way of presenting results from validation studies, it is necessary to use more than one statistical method in order to give credence to the results. In this study cross-classification analysis and the weighted kappa statistic were used. e-EPIDEMIOLOGY and conventional FFQ are designed to rank individuals rather than to assess their absolute level of intake, thus a correlation coefficient would not apply. To assess agreement, subjects were classified into categories of intake by e-EPIDEMIOLOGY and the reference method, and the percentage of subjects correctly classified into the same category and grossly misclassified into the opposite category were calculated. With cross-classification, the percentages misclassified clearly illustrate the likely impact of measurement error; however, the percentage of agreement will include agreement that can be accounted for by chance. Weighted kappa statistic is a summary measure of cross-classification that allows for the agreement expected by chance and has the added advantage over the kappa statistic in that it allows for the degree of misclassification. However, both the cross-classification analysis and the weighted kappa statistic are still dependent on the number of categories used. In order to limit this dependence, the six original categories were reorganized into three (Category 1: “Less than once a week” and “Once or twice a week”; Category 2: “3-4 times a week” and “5-6 times a week”; Category: “Once or twice a day” and “3 times or more a day”), in order to apply Masson and colleagues criteria [20] to evaluate agreement and misclassification. The inter-rater agreement of two assessment methods was analysed by weighted kappa statistic [21], assigning partial credit to scores using the Stata prerecorded weights. If there was complete agreement, a weight of 1.00 was assigned. Slight disagreements (off by one) were given a weight of 0.50 and 0.00 if there was a complete disagreement. Values of kappa over 0.80 indicate very good agreement, between 0.61 and 0.80 good agreement, 0.41-060 moderate agreement, 0.21-0.40 fair agreement and < 0.20 poor agreement [20]. All statistical analysis was performed using STATA version MP 13.1 (Stata Corp LP, Texas, USA) and a P value <.05 was considered statistically significant [22]. Results: Results 76 individuals participated in the study, but one participant did not finish neither the application nor the FFQ. This individual’s data was not used for posterior analysis. The study potentially could have generated 2128 separate data entries using e-EPIDEMIOLOGY (76 participants x 28 días = 2128). 2054 separate data entries were obtained (96.5%), meaning the application was completed on 2054 days and not completed on 74 days (28 of which are due to the participant mentioned previously that did not complete any days of the application). Of the rest of the participants 56 individuals (74.7%) completed the application every day and the remaining 19 filled out the application at least 24 of the 28 days. Among the participants, the mean age was 21.0 years. 22.7% were males and 77.3% were females. 14.7% were smokers. About categories of BMI (Kg/m2): 4.0%, underweight; 77.3%, normal range; 14.7%, overweight; 4.0%, obesity (Table 1). The percentage of individuals correctly classified into the same category ranged from 53% (vegetables) to 80% (legumes), while the percentage of individuals misclassified into an opposite category ranged from 0% (red meat, soft drinks and prepared foods) to 7% (sweets) (Table 2). Weighted kappa statistic values showed good agreement for fruit (k = 0.61), moderate agreement for chicken/turkey, soft drinks and alcoholic beverages (k = 0.53-0.57) and fair agreement for vegetables, legumes, fish, red meat, sweets and prepared foods (k = 0.21-0.40) (Table 3). Table 1. Characteristics of participants in the study. Age, years, mean (SD) 21.0 (1.7) Gender, N (%) Male 17 (22.7) Female 58 (77.3) Smoking status, N (%) No 64 (85.3) Yes 11 (14.7) BMIa, kg/m2, N (%) Underweight 3 (4.0) Normal range 58 (77.3) Overweight 11 (14.7) Obesity 3 (4.0) a BMI: body mass index Table 2. Cross-classification analysis derived from e-EPIDEMIOLOGY and conventional paper FFQ. Comparison Agreement (%) Same category Adjacent category Extreme category Fruit 72.0 22.7 5.3 Vegetables 53.3 42.7 4.0 Legumes 80.0 18.7 1.3 Chicken/turkey 72.0 22.7 5.3 Fish 72.0 26.7 1.3 Red meat 70.7 29.3 0.0 Soft drinks 70.7 29.3 0.0 Sweets 54.7 38.7 6.7 Prepared foods 74.7 25.3 0.0 Alcoholic beverages 77.3 21.3 1.3 Average 69.7 27.7 2.5 Table 3. Percentage agreement, percentage expected agreement and weighted kappa statistic derived from e-EPIDEMIOLOGY and conventional paper FFQ. Comparison Agreement (%) Expected agreement (%) Weighted kappa P Fruit 83.3 56.9 0.61 < 0.01 Vegetables 74.7 57.5 0.40 < 0.01 Legumes 89.3 83.5 0.34 0.01 Chicken/turkey 83.3 64.6 0.53 < 0.01 Fish 85.3 76.8 0.37 < 0.01 Red meat 85.3 75.8 0.39 < 0.01 Soft drinks 85.3 66.1 0.57 < 0.01 Sweets 74.0 59.4 0.36 < 0.01 Prepared foods 87.3 83.8 0.21 0.02 Alcoholic beverages 88.0 73.8 0.54 < 0.01 Average - - 0.43 - Conclusions: Discussion Principal Findings This is the first study that evaluates an alternative to traditional FFQ using mobile technologies. Recently, certain dietary registries and recall questionnaires that use mobile technologies have been developed, with promising results [1-4,11]. However, until now, none have been developed that evaluate long term intake, as well as benefit from mobile technologies and serve as alternatives to the conventional paper FFQ. An accurate method is one that measures what the method intends to measure, i.e. the “truth”. In the context of dietary studies “the truth” represents actual intake over the period of the study [6]. However, there is not, and probably never will be, a method that can estimate dietary intake without error [23]. The semi-quantitative FFQ is the primary dietary assessment method used in epidemiological studies [20]. Results from such studies can be interpreted with greater confidence if the questionnaire has a quantified validity. To assess the true validity of a FFQ would require measuring with high accuracy the usual self-selected diet of free-living individual over several months, which is not feasible. Therefore, research assesses relative validity by comparing the FFQ with an alternative dietary assessment method with its own limitations [6,20]. In epidemiological studies, the odds ratio or relative risk of disease in relation to nutrient intake is the most common measure association presented. Consequently, FFQ must be able to rank individuals along the distribution of intake, so that individuals with low intakes can be separated from those with high intakes. Therefore, obtaining absolute nutrient intakes is not necessary. As long as FFQ can rank individuals, relative risk estimates will be accurate [20]. It is also unnecessary to record exact consumption of nutrients in those descriptive studies which aim to help plan concrete Public Health measures related to diet, or in analytical experimental studies which aim to help create policies that will modify certain dietary habits. In the first case, one would only need to record the amounts consumed of certain kinds of foods, while in the second, it would be necessary to measure the change in consumption over time. The fact that there is error in dietary measurements does not mean that dietary data should not be collected but simply that it is important to determine the nature of the errors associated with dietary data so that these can be taken into account in evaluating the data [6]. In this study the relative validity of e-EPIDEMIOLOGY was determined using a previously validated conventional paper FFQ [16]. Often in previous studies, long-term methods such as an FFQ were compared to short-term methods, such as 24 hour recalls, each of these presenting their own limitations. It would seem more interesting to compare the results of two long-term instruments that allow categorization of individuals based on their consumption of certain foods/drinks, without recording total consumption. This would allow analysis of the capability of e-EPIDEMIOLOGY to minimize some of the limitations that, in theory, are shared with conventional paper FFQ. The objective of this study was to compare data collected with e-EPIDEMIOLOGY with registries obtained with conventional paper FFQ and evaluate its potential as a tool for the determination of habitual dietary intake in research. Cross-classification analysis showed that more than 69% of the participants were correctly classified into the same category and less than 3% were misclassified into an opposite category, which depicted good agreement and lower misclassification between two methods, thus demonstrating that e-EPIDEMIOLOGY is able to ranking subjects on a range of nutrient intakes. Presenting data categorized in categories provides compact information concerning the capacity of both methods to allocate individuals according to dietary intake distribution [24]. The average weighted kappa statistic was moderate (k = 0.43), with values over 0.35 in 8 of the 10 foods/drinks selected for the study. Thus, due to the moderate agreement with conventional paper FFQ estimated by the mean weighted kappa statistics, as well as its good ability to classify individuals into categories, e-EPIDEMIOLOGY could be considered a reasonable and moderately valid instrument for correctly ranking subjects into classes of food groups, according to Masson’s criteria [20]. FFQ have been demonstrated to be valid for ranking subjects on a range of food intakes even though there is an on-going need for the refinement of these tools [25]. Though e-EPIDEMIOLOGY can be considered a valid tool for the classification of individuals into categories of habitual consumption of foods/drinks, the results of this study show that there is obvious disagreement between both instruments (cross-classification analysis showed that 28% of the participants were incorrectly classified into an adjacent category and 2.5% were misclassified into an opposite category). Misclassification of participants could have important negative on the results and conclusions of studies that use such tools. The analysis of the characteristics of each of these methods shows that e-EPIDEMIOLOGY is a more precise and more refined method than the traditional paper FFQ for the correct classification of individuals into categories of habitual consumption. This held true when there was not agreement between both methods. In terms of precision, both methods have in common that, for each of the foods/drinks considered, both use the same question to measure the frequency of consumption. For example, both ask: 'How many portions of fish have you eaten? (1 portion = aprox. 150 g)' The difference between both methods lies in that e-EPIDEMIOLOGY this question is answered at the end of each day during the study period, while the FFQ is completed at the end of 28 days. Consequently, both methods present the same difficulties in the precise estimation of portion size, given that standardized ration size are used in both. However, e-EPIDEMIOLOGY permits daily collection of information, FFQ only allows collection of information at the end of the study period. This minimizes the dependence on the memory of the participant in e-EPIDEMIOLOGY in comparison to the FFQ, keeping in mind that the recollection of past consumption of foods can be influenced by more recent food consumption [7]. e-EPIDEMIOLOGY allows for daily intrapersonal variability in the collection of consumption of foods/drinks. Among university students, who made up our study sample, dietary intake is variable from day to day, with sporadic changes in food intake (skipping meals, snacking, school events that interfere with meal time), as well as frequent dining out. These aspects interfere with the precise determination of habitual dietary intake [1], especially in the case of FFQ, where data is collected only once at the end of an extended time period. Repeated applications of traditional short term instruments, such as dietary registries and 24 hour recalls, can modify habitual intake due to the excessive work load for participants. Any tool that aports a simple method that facilitates data collection about dietary intake without changing behavior is an important advance [1]. Despite repeated use, the modification of habitual intake seems unlikely through the use of e-EPIDEMIOLOGY, due to the reduced workload that using this application presents (1 minute/day). In relation to refinement, one must consider other aspects related to its use, such as research costs and the ease of digitalization of data. If information is recorded in a traditional manner (pen and paper), such as with the FFQ used in this study, the costs increase due to need for interviewers and for the digitalization of data for posterior statistical analysis, which also increases time consumption [3,7]. The use of mobile technologies as tools for self-reporting (for example, e-EPIDEMIOLOGY) can eliminate the need for interviewers, and permits the instant digitalization of data [2,15,26], minimizing costs and facilitating research. If this study had compared the application e-EPIDEMIOLOGY with a FFQ applied through a mobile phone application, those aspects related to cost and digitalization of data would have been equalized though this would not have affected the precision of the FFQ, due to the fact that this does not depend on the format used, but on other considerations aforementioned. In this study a new instrument, using mobile technology (e-EPIDEMIOLOGY), was compared with the more frequently used paper FFQ, for this reason an electronic FFQ (website or mobile phone application) was not used. FFQ are tools that allow collection of data related to consumption of foods/drinks, including those consumed sporadically, in one use. This characteristic would seem to make it advantageous compared to e-EPIDEMIOLOGY in terms of the time necessary to obtain information from all of the participants in the study. The FFQ only requires a few minutes to complete, while e-EPIDEMIOLOGY requires completion every day for 28 days. From a practical point of view, this aspect does not suppose a great difference for the researcher. For example, in the current study the participants were recruited between October of 2014 and January of 2016. If an FFQ were used, the information from the 76 participants would be available in January 2016, when the last participant had completed the questionnaire. If only e-EPIDEMIOLOGY had been applied, the data from the 76 participants would be available in February 2016, the last day of completion of the application. In a study were recruitment occurred during 16 months, this difference of one month seems irrelevant. However, the application also has the many advantages mentioned previously and even allows analysis of the participant's behavior during different days of the week, during vacations, etc. Another advantage is that the investigators receive the data directly to their computer. Limitations One of the possible limitations of this study is the possible rate of non-response. On the one hand, those who did not fill out any days of e-EPIDEMIOLOGY and, on the other, those who did not fill out the application during one or a few days of the 28 days duration of the study. Of the 76 participants in the study, 56 completed the application every day (74.7%), 19 completed the application at least 24 of the 28 days of the study and just one individual did not complete any of the 28 days. Some of the characteristics of these types of mobile technologies, such as asinchrony [27-30], the ease with which privacy can be maintained [31], as well as the light workload for the participants (1 minute per day), helped to increase participation and could have contributed to minimize the rate of non-response. Young people have expressed their preference for those methods of evaluation of dietary intake that utilize new technologies, as they can easily be incorporated into their lifestyles and are more amenable than traditional pen and paper methods [1,2]. In any case, the possible limitation presented by the rate of non-response was minimized, as no statistically significant differences were found in any of the variables studied (age, gender, tobacco consumption, BMI), after analysing the basic characteristics of responders and non-responders. Another possible limitation lies in the fact that access to these technologies is not universal, excluding especially vulnerable groups, such as students from poorer social strata. In the environment in which this study was performed, the percentage of students with mobile phones with internet access is very high, which minimizes this possible limitation. Future Studies To further study the potential of e-EPIDEMIOOGY as a tool for the evaluation of habitual dietary intake, the research team intends to perform future investigations in different socio-demographic groups in order to increase the representativity of the results and conclusions obtained, of the general population. As no similar studies were found in the literature, we could not compare with previous experiences in order to help select a follow-up time, so the duration of 28 days was chosen because it fit the characteristics and objectives of this study. We would like to evaluate e-EPIDEMIOLOGY, modifying the follow-up time, reducing from daily data input to input 2-3 times a week, as well as varying the foods/drinks selected. It is worth noting that another advantage of these technologies is that the applications are easily modifiable (varying the questions, standardized portions, time of completion of the application, reminder time, etc.) permitting adaptation to different sociocultural idiosyncrasies present worldwide. Another line of study would be to analyse the impact of factors that can affect the validity of data collected with e-EPIDEMIOLOGY, such as age, gender, IMC and health related behavior (level of physical activity, tobacco consumption, etc.). Due to the small sample size, separate statistical analysis was not performed for males and females, nor for distinct categories of BMI, physical activity, etc. In all of these future projects, the development of the process will include estimation of portion size using digital images in order to decrease error. Conclusions e-EPIDEMIOLOGY could be considered a reasonably and moderately valid tool in epidemiological studies to measure habitual intake of certain food/drinks in young adults, as a valuable alternative to conventional paper FFQ. Along with the growing popularity of mobile phones among young adults, this instrument is likely to be accepted in this population and could reduce some of the inherent limitations present in paper FFQ, such as dependence on the memory of participants and the impossibility to reflect intrapersonal variability in daily consumption of foods/drinks. Future studies should aim to explore the validity of e-EPIDEMIOLOGY in different samples, modifying foods and drinks analysed, duration of the study, etc. This could confirm its value as a tool to determine habitual dietary intake in both descriptive and analytical epidemiological studies (both observational and experimental). Acknowledgements We would like to thank the participants in the validation study. This research was partly supported by funding from the Research Plan of the University of Seville. Authors' Contributions LMB performed the conception and design of the study, developed the application, analysed and interpreted data, and wrote the article; BS and MDG were involved in data collection and interpretation of the data and contributed in drafting the article; and all authors were involved in editing the final draft of the article and revising it critically and approving the manuscript. Conflicts of Interest None declared.

  • A Review of Safe Sex Messages within Smartphone Applications

    Date Submitted: Mar 13, 2016

    Open Peer Review Period: Mar 14, 2016 - May 10, 2016

    Background: Smartphone applications provide a new platform for entertainment, information distribution and health promotion activities, as well as for dating and casual sexual encounters. Previous res...

    Background: Smartphone applications provide a new platform for entertainment, information distribution and health promotion activities, as well as for dating and casual sexual encounters. Previous research has shown high acceptability of sexual health interventions via smartphone apps, however, sexual health promotion apps were infrequently downloaded and underutilized. Therefore, using established apps to integrate sexual health promotion might be a more effective method. Objective: To critically review popular sex-related apps and dating apps, in order to ascertain if there are currently any sexual health messages present in the apps. Methods: Part 1: We used the search term “sexual” to search for free apps in Apple iTunes store and Android Google Play store, and analyzed sexual health content on the 137 apps included. Part 2: The search term “dating” was used to search for free geosocial networking apps in the Apple iTunes and Android Google Play stores. The apps were downloaded to test out functionality, and to search for sexual health messages therein. Results: Part 1: Of the included 137 apps, 15 (11%) contained sexual health messages, and 14 (10%) contained messages for sexual assault/violence. The majority of the apps did not contain any sexual health messages. Part 2: Sixty dating apps were reviewed: 44 (73.3%) apps targeting heterosexual users, 9 (15%) apps targeting men who have sex with men (MSM), 3 (5%) apps targeting lesbian women, and 4 (6.7%) apps for group dating. Only 9 dating apps contained sexual health messages, of which 7 targeted MSM. Conclusions: The majority of sex-related apps and dating apps contained no sexual health messages that could educate and remind users of their sexual risks. Sexual health practitioners and public health departments will need to work with app developers to promote sexual health within existing popular apps. For those apps that already contain sexual health messages, further study to investigate the effectiveness of the messages is needed.

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