JMIR Publications

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  Engagement with and Adherence to...

  Apr 19, 2019

  Predicting Outcomes from Engagement With Specific Components of an Internet-Based Physical Activity Intervention With Financial Incentives: Process Analysis...

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  Authors List:

  • Jennifer M Murray  
  • David P French  
  • Christopher C Patterson  
  • Frank Kee  
  • Aisling Gough  
  • Jianjun Tang  
  • Ruth F Hunter  

  Abstract:

  Background: Investigating participant engagement and nonusage attrition can help identify the likely active ingredients of electronic health interventions. Research on engagement can identify which intervention components predict health outcomes. Research on nonusage attrition is important to make recommendations for retaining participants in future studies. Objective: This study aimed to investigate engagement and nonusage attrition in the Physical Activity Loyalty (PAL) scheme, a 6-month complex physical activity intervention in workplaces in Northern Ireland. The intervention included financial incentives with reward redemption and self-regulation techniques. Specific objectives were (1) to determine whether engagement in specific intervention components predicted physical activity at 6 months, (2) to determine whether engagement in specific intervention components predicted targeted mediators at 6 months, and (3) to investigate predictors of nonusage attrition for participants recording daily activity via the PAL scheme physical activity monitoring system and logging onto the website. Methods: Physical activity was assessed at baseline and 6 months using pedometers (Yamax Digiwalker CW-701, Japan). Markers of engagement and website use, monitoring system use, and reward redemption were collected throughout the scheme. Random-effects generalized least-squares regressions determined whether engagement with specific intervention components predicted 6-month physical activity and mediators. Cox proportional hazards regressions were used to investigate predictors of nonusage attrition (days until first 2-week lapse). Results: A multivariable generalized least-squares regression model (n=230) showed that the frequency of hits on the website’s monitoring and feedback component (regression coefficient [b]=50.2; SE=24.5; P=.04) and the percentage of earned points redeemed for financial incentives (b=9.1; SE=3.3; P=.005) were positively related to 6-month pedometer steps per day. The frequency of hits on the discussion forum (b=−69.3; SE=26.6; P=.009) was negatively related to 6-month pedometer steps per day. Reward redemption was not related to levels of more internal forms of motivation. Multivariable Cox proportional hazards regression models identified several baseline predictors associated with nonusage attrition. These included identified regulation (hazard ratio [HR] 0.88, 95% CI 0.81-0.97), recovery self-efficacy (HR 0.88, 95% CI 0.80-0.98), and perceived workplace environment safety (HR 1.07, 95% CI 1.02-1.11) for using the physical activity monitoring system. The EuroQoL health index (HR 0.33, 95% CI 0.12-0.91), financial motivation (HR 0.93, 95% CI 0.87-0.99), and perceived availability of physical activity opportunities in the workplace environment (HR 0.96, 95% CI 0.93-0.99) were associated with website nonusage attrition. Conclusions: Our results provide evidence opposing one of the main hypotheses of self-determination theory by showing that financial rewards are not necessarily associated with decreases in more internal forms of motivation when offered as part of a complex multicomponent intervention. Identifying baseline predictors of nonusage attrition can help researchers to develop strategies to ensure maximum intervention adherence. Trial Registration: ISRCTN Registry ISRCTN17975376; http://www.isrctn.com/ISRCTN17975376 (Archived by WebCite at http://www.webcitation.org/76VGZsZug)

  • J Med Internet Res 2019;21(4):e11394

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  Quality/Credibility of eHealth...

  Apr 18, 2019

  Effects of Mock Facebook Workday Comments on Public Perception of Professional Credibility: A Field Study in Canada

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  Authors List:

  • Cynthia Weijs  
  • Jason Coe  
  • Serge Desmarais  
  • Shannon Majowicz  
  • Andria Jones-Bitton  

  Abstract:

  Background: There is considerable discussion of risks to health professionals’ reputations and employment from personal social media use, though its impacts on professional credibility and the health professional-client relationship are unknown. Objective: The aim of this study was to test the extent to which workday comments posted to health professionals’ personal Facebook profiles influence their credibility and affect the professional-client relationship. Methods: In a controlled field study, participants (members of the public) reviewed randomly assigned mock Facebook profiles of health professionals. The 2×2×2 factorial design of mock profiles included gender (female/male), health profession (physician/veterinarian), and workday comment type (evident frustration/ambiguous). Participants then rated the profile owner’s credibility on a visual analog scale. An analysis of variance test compared ratings. Mediation analyses tested the importance of credibility ratings on participants’ willingness to become a client of the mock health professional. Results: Participants (N=357) rated health professionals whose personal Facebook profile showed a comment with evident frustration rather than an ambiguous workday comment as less credible (P<.001; mean difference 11.18 [SE 1.28]; 95% CI 8.66 to 13.70). Furthermore, participants indicated they were less likely to become clients of the former when they considered credibility (standardized beta=.69; P<.001). Credibility explained 86% of the variation in the relationship between the type of workday comment and the participant’s willingness to become a client of the health professional. Conclusions: This study provides the first evidence of the impact of health professionals’ personal online disclosures on credibility and the health relationship. Public perceptions about professionalism and credibility are integral to developing the evidence base for e-professionalism guidelines and encouraging best practices in social media use.

  • J Med Internet Res 2019;21(4):e12024

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  New Methods

  Apr 18, 2019

  Wearable Sensors Reveal Menses-Driven Changes in Physiology and Enable Prediction of the Fertile Window: Observational Study

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  Authors List:

  • Brianna Mae Goodale  
  • Mohaned Shilaih  
  • Lisa Falco  
  • Franziska Dammeier  
  • Györgyi Hamvas  
  • Brigitte Leeners  

  Abstract:

  Background: Previous research examining physiological changes across the menstrual cycle has considered biological responses to shifting hormones in isolation. Clinical studies, for example, have shown that women’s nightly basal body temperature increases from 0.28 to 0.56 ˚C following postovulation progesterone production. Women’s resting pulse rate, respiratory rate, and heart rate variability (HRV) are similarly elevated in the luteal phase, whereas skin perfusion decreases significantly following the fertile window’s closing. Past research probed only 1 or 2 of these physiological features in a given study, requiring participants to come to a laboratory or hospital clinic multiple times throughout their cycle. Although initially designed for recreational purposes, wearable technology could enable more ambulatory studies of physiological changes across the menstrual cycle. Early research suggests that wearables can detect phase-based shifts in pulse rate and wrist skin temperature (WST). To date, previous work has studied these features separately, with the ability of wearables to accurately pinpoint the fertile window using multiple physiological parameters simultaneously yet unknown. Objective: In this study, we probed what phase-based differences a wearable bracelet could detect in users’ WST, heart rate, HRV, respiratory rate, and skin perfusion. Drawing on insight from artificial intelligence and machine learning, we then sought to develop an algorithm that could identify the fertile window in real time. Methods: We conducted a prospective longitudinal study, recruiting 237 conception-seeking Swiss women. Participants wore the Ava bracelet (Ava AG) nightly while sleeping for up to a year or until they became pregnant. In addition to syncing the device to the corresponding smartphone app daily, women also completed an electronic diary about their activities in the past 24 hours. Finally, women took a urinary luteinizing hormone test at several points in a given cycle to determine the close of the fertile window. We assessed phase-based changes in physiological parameters using cross-classified mixed-effects models with random intercepts and random slopes. We then trained a machine learning algorithm to recognize the fertile window. Results: We have demonstrated that wearable technology can detect significant, concurrent phase-based shifts in WST, heart rate, and respiratory rate (all P<.001). HRV and skin perfusion similarly varied across the menstrual cycle (all P<.05), although these effects only trended toward significance following a Bonferroni correction to maintain a family-wise alpha level. Our findings were robust to daily, individual, and cycle-level covariates. Furthermore, we developed a machine learning algorithm that can detect the fertile window with 90% accuracy (95% CI 0.89 to 0.92). Conclusions: Our contributions highlight the impact of artificial intelligence and machine learning’s integration into health care. By monitoring numerous physiological parameters simultaneously, wearable technology uniquely improves upon retrospective methods for fertility awareness and enables the first real-time predictive model of ovulation.

  • J Med Internet Res 2019;21(4):e13404

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  Review

  Apr 17, 2019

  Augmented Reality in Emergency Medicine: A Scoping Review

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  Authors List:

  • Brendan William Munzer  
  • Mohammad Mairaj Khan  
  • Barbara Shipman  
  • Prashant Mahajan  

  Abstract:

  Background: Augmented reality is increasingly being investigated for its applications to medical specialties as well as in medical training. Currently, there is little information about its applicability to training and care delivery in the context of emergency medicine. Objective: The objective of this article is to review current literature related to augmented reality applicable to emergency medicine and its training. Methods: Through a scoping review utilizing Scopus, MEDLINE, and Embase databases for article searches, we identified articles involving augmented reality that directly involved emergency medicine or was in an area of education or clinical care that could be potentially applied to emergency medicine. Results: A total of 24 articles were reviewed in detail and were categorized into three groups: user-environment interface, telemedicine and prehospital care, and education and training. Conclusions: Through analysis of the current literature across fields, we were able to demonstrate that augmented reality has utility and feasibility in clinical care delivery in patient care settings, in operating rooms and inpatient settings, and in education and training of emergency care providers. Additionally, we found that the use of augmented reality for care delivery over distances is feasible, suggesting a role in telehealth. Our results from the review of the literature in emergency medicine and other specialties reveal that further research into the uses of augmented reality will have a substantial role in changing how emergency medicine as a specialty will deliver care and provide education and training.

  • J Med Internet Res 2019;21(4):e12368

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  Mobile Health (mhealth)

  Apr 17, 2019

  Validation of Two Automatic Blood Pressure Monitors With the Ability to Transfer Data via Bluetooth

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  Authors List:

  • Madeleine Wetterholm  
  • Stephanie Erika Bonn  
  • Christina Alexandrou  
  • Marie Löf  
  • Ylva Trolle Lagerros  

  Abstract:

  Background: Patients with chronic diseases are in need of regular health controls. Diabetes mellitus type 2 is currently the most prevalent chronic metabolic disease. A majority of diabetic patients have at least one comorbid chronic disease, where hypertension is the most common. The standard for blood pressure (BP) measurement is manual BP monitoring at health care clinics. Nevertheless, several advantages of self-measured BP have been documented. With BP data transfer from an automatic BP monitor via Bluetooth to software, for example, a smartphone app, home measurement could effectively be integrated into regular care. Objective: The aim of this study was to validate two commercially available automatic BP monitors with the ability to transfer BP data via Bluetooth (Beurer BM 85 and Andersson Lifesense BDR 2.0), against manual BP monitoring in patients with type 2 diabetes. Methods: A total of 181 participants with type 2 diabetes were recruited from 6 primary care centers in Stockholm, Sweden. BP was first measured using a manual BP monitor and then measured using the two automatic BP monitors. The mean differences between the automatic and manual measurements were calculated by subtracting the manual BP monitor measurement from the automatic monitor measurement. Validity of the two automatic BP monitors was further assessed using Spearman rank correlation coefficients and the Bland-Altman method. Results: In total, 180 participants, 119 men and 61 women, were included. The mean age was 60.1 (SD 11.4) years and the mean body mass index was 30.4 (SD 5.4) kg/m2. The mean difference between the Beurer BM 85 and the manual BP monitor was 11.1 (SD 11.2) mmHg for systolic blood pressure (SBP) and 8.0 (SD 8.1) mmHg for diastolic blood pressure (DBP). The mean difference between the Andersson Lifesense BDR 2.0 and the manual BP monitor was 3.2 (SD 10.8) mmHg for SBP and 4.2 (SD 7.2) mmHg for DBP. The automatic BP measurements were significantly correlated (P<.001) with the manual BP measurement values (Andersson Lifesense BDR 2.0: r=0.78 for SBP and r=0.71 for DBP; Beurer BM 85: r=0.78 for SBP and r=0.69 for DBP). Conclusions: The two automatic BP monitors validated measure sufficiently accurate on a group level, with the Andersson Lifesense BDR 2.0 more often falling within the ranges for what is acceptable in clinical practice compared with the Beurer BM 85.

  • J Med Internet Res 2019;21(4):e12772

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  Demographics of Users, Social &...

  Apr 17, 2019

  Comparison of Smartphone Ownership, Social Media Use, and Willingness to Use Digital Interventions Between Generation Z and Millennials in the Treatment of...

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  Authors List:

  • Brenda L Curtis  
  • Robert D Ashford  
  • Katherine I Magnuson  
  • Stacy R Ryan-Pettes  

  Abstract:

  Background: Problematic substance use in adolescence and emerging adulthood is a significant public health concern in the United States due to high recurrence of use rates and unmet treatment needs coupled with increased use. Consequently, there is a need for both improved service utilization and availability of recovery supports. Given the ubiquitous use of the internet and social media via smartphones, a viable option is to design digital treatments and recovery support services to include internet and social media platforms. Objective: Although digital treatments delivered through social media and the internet are a possibility, it is unclear how interventions using these tools should be tailored for groups with problematic substance use. There is limited research comparing consumer trends of use of social media platforms, use of platform features, and vulnerability of exposure to drug cues online. The goal of this study was to compare digital platforms used among adolescents (Generation Zs, age 13-17) and emerging adults (Millennials, age 18-35) attending outpatient substance use treatment and to examine receptiveness toward these platforms in order to support substance use treatment and recovery. Methods: Generation Zs and Millennials enrolled in outpatient substance use treatment (n=164) completed a survey examining social media use, digital intervention acceptability, frequency of substance exposure, and substance use experiences. Generation Zs (n=53) completed the survey in July 2018. Millennials (n=111) completed the survey in May 2016. Results: Generation Zs had an average age of 15.66 (SD 1.18) years and primarily identified as male (50.9%). Millennials had an average age of 27.66 (SD 5.12) years and also primarily identified as male (75.7%). Most participants owned a social media account (Millennials: 82.0%, Generation Zs: 94.3%) and used it daily (Millennials: 67.6%, Generation Zs: 79.2%); however, Generation Zs were more likely to use Instagram and Snapchat, whereas Millennials were more likely to use Facebook. Further, Generation Zs were more likely to use the features within social media platforms (eg, instant messaging: Millennials: 55.0%, Generation Zs: 79.2%; watching videos: Millennials: 56.8%, Generation Zs: 81.1%). Many participants observed drug cues on social media (Millennials: 67.5%, Generation Zs: 71.7%). However, fewer observed recovery information on social media (Millennials: 30.6%, Generation Zs: 34.0%). Participants felt that social media (Millennials: 55.0%, Generation Zs: 49.1%), a mobile phone app (Millennials: 36.9%, Generation Zs: 45.3%), texting (Millennials: 28.8%, Generation Zs: 45.3%), or a website (Millennials: 39.6%, Generation Zs: 32.1%) would be useful in delivering recovery support. Conclusions: Given the high rates of exposure to drug cues on social media, disseminating recovery support within a social media platform may be the ideal just-in-time intervention needed to decrease the rates of recurrent drug use. However, our results suggest that cross-platform solutions capable of transcending generational preferences are necessary and one-size-fits-all digital interventions should be avoided.

  • J Med Internet Res 2019;21(4):e13050

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  e-Mental Health and Cyberpsychology

  Apr 17, 2019

  Internet-Delivered Cognitive Behavioral Therapy for Anxiety Disorders in Open Community Versus Clinical Service Recruitment: Meta-Analysis

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  Authors List:

  • Geke Romijn  
  • Neeltje Batelaan  
  • Robin Kok  
  • Jeroen Koning  
  • Anton van Balkom  
  • Nickolai Titov  
  • Heleen Riper  

  Abstract:

  Background: Ample studies have shown the effectiveness of internet-delivered cognitive behavioral therapy (iCBT) for anxiety disorders. These studies recruited their participants mainly from the community and, to a lesser extent, from within routine care services. Little is known about whether different recruitment strategies lead to different treatment effects. Objective: This meta-analysis compared clinical results obtained in trials with recruitment from the community versus results obtained in trials with clinical service recruitment and explored factors that may mediate differences in treatment outcome. Methods: We included randomized controlled trials in which the clinical effects of iCBT for anxiety disorders were compared with a control condition (waitlist controls or face-to-face cognitive behavioral therapy). We classified trials as open recruitment trials (recruitment from the community) or clinical service recruitment trials (recruitment through outpatient clinics). Pooled effect sizes based on measures examining anxiety symptoms, depressive symptoms, and quality of life were computed for each type of trial. Subgroup analyses examined whether clinical results from open recruitment trials differed from those obtained in clinical service recruitment trials. Additional analyses explored which demographic, clinical, and treatment-related factors contributed to differences in effect sizes of open recruitment versus clinical service recruitment trials. Results: We included 42 studies with 53 comparisons (43 open recruitment comparisons and 10 clinical recruitment comparisons). Analyses of anxiety measures revealed, first, that iCBT open recruitment studies with waitlist control comparators showed a significantly higher effect size for decrease in anxiety symptoms than did those with clinical recruitment (Q=10.09; P=.001). This association between recruitment method and effect size was no longer significant in a multivariate metaregression with treatment adherence and exclusion of patients with depressive symptoms entered as additional predictors of effect size. Second, effect size for decrease in anxiety symptoms did not differ significantly between clinical recruitment and open recruitment studies with face-to-face cognitive behavioral therapy comparators. The effects of open recruitment trials and clinical recruitment trials did not differ significantly for the secondary outcomes, compared with face-to-face cognitive behavioral therapy and waitlist controls. Conclusions: iCBT was effective in samples recruited in clinical practice, but effect sizes were smaller than those found in trials with an open recruitment method for studies with waitlist control comparators. Hence, for patients with anxiety disorders in routine care, the impact of iCBT may not be as positive as for study participants recruited from the community. The difference between open recruitment trials and clinical service recruitment trials might be partly explained by patients’ greater therapy adherence in open recruitment trials and the stricter exclusion of patients with severe depressive symptoms in these studies. Since most trials in this meta-analysis applied an open recruitment method, more studies with routine care populations are needed to further validate these findings.

  • J Med Internet Res 2019;21(4):e11706

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  Mobile Health (mhealth)

  Apr 17, 2019

  Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational...

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  Authors List:

  • Chul-Hyun Cho  
  • Taek Lee  
  • Min-Gwan Kim  
  • Hoh Peter In  
  • Leen Kim  
  • Heon-Jeong Lee  

  Abstract:

  Background: Virtually, all organisms on Earth have their own circadian rhythm, and humans are no exception. Circadian rhythms are associated with various human states, especially mood disorders, and disturbance of the circadian rhythm is known to be very closely related. Attempts have also been made to derive clinical implications associated with mood disorders using the vast amounts of digital log that is acquired by digital technologies develop and using computational analysis techniques. Objective: This study was conducted to evaluate the mood state or episode, activity, sleep, light exposure, and heart rate during a period of about 2 years by acquiring various digital log data through wearable devices and smartphone apps as well as conventional clinical assessments. We investigated a mood prediction algorithm developed with machine learning using passive data phenotypes based on circadian rhythms. Methods: We performed a prospective observational cohort study on 55 patients with mood disorders (major depressive disorder [MDD] and bipolar disorder type 1 [BD I] and 2 [BD II]) for 2 years. A smartphone app for self-recording daily mood scores and detecting light exposure (using the installed sensor) were provided. From daily worn activity trackers, digital log data of activity, sleep, and heart rate were collected. Passive digital phenotypes were processed into 130 features based on circadian rhythms, and a mood prediction algorithm was developed by random forest. Results: The mood state prediction accuracies for the next 3 days in all patients, MDD patients, BD I patients, and BD II patients were 65%, 65%, 64%, and 65% with 0.7, 0.69, 0.67, and 0.67 area under the curve (AUC) values, respectively. The accuracies of all patients for no episode (NE), depressive episode (DE), manic episode (ME), and hypomanic episode (HME) were 85.3%, 87%, 94%, and 91.2% with 0.87, 0.87, 0.958, and 0.912 AUC values, respectively. The prediction accuracy in BD II patients was distinctively balanced as high showing 82.6%, 74.4%, and 87.5% of accuracy (with generally good sensitivity and specificity) with 0.919, 0.868, and 0.949 AUC values for NE, DE, and HME, respectively. Conclusions: On the basis of the theoretical basis of chronobiology, this study proposed a good model for future research by developing a mood prediction algorithm using machine learning by processing and reclassifying digital log data. In addition to academic value, it is expected that this study will be of practical help to improve the prognosis of patients with mood disorders by making it possible to apply actual clinical application owing to the rapid expansion of digital technology.

  • J Med Internet Res 2019;21(4):e11029

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  Chatbots and Conversational Agents

  Apr 16, 2019

  Designing a Chatbot for a Brief Motivational Interview on Stress Management: Qualitative Case Study

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  Authors List:

  • SoHyun Park  
  • Jeewon Choi  
  • Sungwoo Lee  
  • Changhoon Oh  
  • Changdai Kim  
  • Soohyun La  
  • Joonhwan Lee  
  • Bongwon Suh  

  Abstract:

  Background: In addition to addiction and substance abuse, motivational interviewing (MI) is increasingly being integrated in treating other clinical issues such as mental health problems. Most of the many technological adaptations of MI, however, have focused on delivering the action-oriented treatment, leaving its relational component unexplored or vaguely described. This study intended to design a conversational sequence that considers both technical and relational components of MI for a mental health concern. Objective: This case study aimed to design a conversational sequence for a brief motivational interview to be delivered by a Web-based text messaging application (chatbot) and to investigate its conversational experience with graduate students in their coping with stress. Methods: A brief conversational sequence was designed with varied combinations of MI skills to follow the 4 processes of MI. A Web-based text messaging application, Bonobot, was built as a research prototype to deliver the sequence in a conversation. A total of 30 full-time graduate students who self-reported stress with regard to their school life were recruited for a survey of demographic information and perceived stress and a semistructured interview. Interviews were transcribed verbatim and analyzed by Braun and Clarke’s thematic method. The themes that reflect the process of, impact of, and needs for the conversational experience are reported. Results: Participants had a high level of perceived stress (mean 22.5 [SD 5.0]). Our findings included the following themes: Evocative Questions and Clichéd Feedback; Self-Reflection and Potential Consolation; and Need for Information and Contextualized Feedback. Participants particularly favored the relay of evocative questions but were less satisfied with the agent-generated reflective and affirming feedback that filled in-between. Discussing the idea of change was a good means of reflecting on themselves, and some of Bonobot’s encouragements related to graduate school life were appreciated. Participants suggested the conversation provide informational support, as well as more contextualized feedback. Conclusions: A conversational sequence for a brief motivational interview was presented in this case study. Participant feedback suggests sequencing questions and MI-adherent statements can facilitate a conversation for stress management, which may encourage a chance of self-reflection. More diversified sequences, along with more contextualized feedback, should follow to offer a better conversational experience and to confirm any empirical effect.

  • J Med Internet Res 2019;21(4):e12231

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  Infodemiology and Infoveillance

  Apr 16, 2019

  Using a Facebook Forum to Cope With Narcolepsy After Pandemrix Vaccination: Infodemiology Study

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  Authors List:

  • Karin Blomberg  
  • Mats Eriksson  
  • Rickard Böö  
  • Åke Grönlund  

  Abstract:

  Background: In 2010, newly diagnosed narcolepsy cases among children and adolescents were seen in several European countries as a consequence of comprehensive national vaccination campaigns with Pandemrix against H1N1 influenza. Since then, a large number of people have had to live with narcolepsy and its consequences in daily life, such as effects on school life, social relationships, and activities. Initially, the adverse effects were not well understood and there was uncertainty about whether there would be any financial compensation. The situation remained unresolved until 2016, and during these years affected people sought various ways to join forces to handle the many issues involved, including setting up a social media forum. Objective: Our aim was to examine how information was shared, and how opinions and beliefs about narcolepsy as a consequence of Pandemrix vaccination were formed through discussions on social media. Methods: We used quantitative and qualitative methods to investigate a series of messages posted in a social media forum for people affected by narcolepsy after vaccination. Results: Group activity was high throughout the years 2010 to 2016, with peaks corresponding to major narcolepsy-related events, such as the appearance of the first cases in 2010, the first payment of compensation in 2011, and passage of a law on compensation in July 2016. Unusually, most (462/774, 59.7%) of the group took part in discussions and only 312 of 774 (40.3%) were lurkers (compared with the usual 90% rule of thumb for participation in an online community). The conversation in the group was largely factual and had a civil tone, even though there was a long struggle for the link between the vaccine and narcolepsy to be acknowledged and regarding the compensation issue. Radical, nonscientific views, such as those expounded by the antivaccination movement, did not shape the discussions in the group but were being actively expressed elsewhere on the internet. At the outset of the pandemic, there were 18 active Swedish discussion groups on the topic, but most dissolved quickly and only one Facebook group remained active throughout the period. Conclusions: The group studied is a good example of social media use for self-help through a difficult situation among people affected by illness and disease. This shows that social media do not by themselves induce trench warfare but, given a good group composition, can provide a necessary forum for managing an emergency situation where health care and government have failed or are mistrusted, and patients have to organize themselves so as to cope.

  • J Med Internet Res 2019;21(4):e11419

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  Demographics of Users, Social &...

  Apr 16, 2019

  Web-Based Health Information Technology: Access Among Latinos Varies by Subgroup Affiliation

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  Authors List:

  • Mariaelena Gonzalez  
  • Ashley Sanders-Jackson  
  • Tashelle Wright  

  Abstract:

  Background: There are significant health technology gaps between Latinos and non-Hispanic whites and between first- and second-generation Latinos. Objective: This study aimed to examine disparities in Web-based health information–seeking behavior (HISB) and patient portal use among Latinos, taking into account nativity and subethnic affiliation. Methods: We analyzed US-born, non-Hispanic whites and Latinos adults (N=49,259) and adult internet users (N=36,214) in the 2015 to 2016 National Health Interview Survey using a binary logistic regression controlling for individual difference level variables. Outcomes were internet use, HISB (health information-seeking online and using a chat group for health information), and patient portal use (using a computer to schedule an appointment, filling a prescription, and communicating with a provider). Results: We found that US-born Mexicans (odds ratio [OR] 0.81, 95% CI 0.66-0.99), foreign-born Mexicans (OR 0.35, 95% CI 0.29-0.42), foreign-born Puerto Ricans (OR 0.62, 95% CI 0.44-0.87), foreign-born Central and South Americans (OR 0.42, 95% CI 0.33-0.53), and foreign-born other Latinos (OR 0.34, 95% CI 0.24-0.49) had lower odds of using the internet than US-born non-Hispanic whites. The relationship between subgroup affiliation and Web-based HISB varied by type of technology. US-born Mexicans (OR 0.77, 95% CI 0.66-0.9), foreign-born Mexicans (OR 0.51, 95% CI 0.43-0.61), foreign-born Central and South Americans (OR 0.53, 95% CI 0.43-0.64), and foreign-born other Latinos (OR 0.56, 95% CI 0.4-0.79) had lower odds of looking up health information online than US-born non-Hispanic whites. Controlling for age, sex, education, income to federal poverty level, and region, foreign-born Central and South Americans (OR 0.61, 95% CI 0.41-0.92) and foreign-born other Latinos (OR 0.26, 95% CI 0.1-0.68) had lower odds of filling a prescription using a computer than US-born non-Hispanic whites. Foreign-born Mexicans (OR 0.51, 95% CI 0.36-0.72) and foreign-born Central and South Americans (OR 0.7, 95% CI 0.5-0.99) have lower odds of emailing a health care provider than US-born non-Hispanic whites. Posthoc analyses were conducted among Mexican-Americans to see if age was significant in predicting Web-based HISB or other patient portal use. We found individuals aged 18 to 30 years had higher odds of using the internet (OR 3.46, 95% CI 2.61-4.59) and lower odds of looking up health information online (OR 0.75, 95% CI 0.58-0.96). A posthoc analysis was conducted among Mexican-Americans to see if nativity predicted Web-based HISB and patient portal use. We found that US-born individuals had higher odds (OR 52.9, 95% CI 1.2-1.93) of looking up health information online compared with foreign-born individuals. Conclusions: We found Latino subgroups do not use health information channels equally, and attempts to target Latinos should take ethnicity and nativity into account.

  • J Med Internet Res 2019;21(4):e10389

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  New Methods

  Apr 15, 2019

  Understanding User Experience: Exploring Participants’ Messages With a Web-Based Behavioral Health Intervention for Adolescents With Chronic Pain

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  Authors List:

  • Annie T Chen  
  • Aarti Swaminathan  
  • William R Kearns  
  • Nicole M Alberts  
  • Emily F Law  
  • Tonya M Palermo  

  Abstract:

  Background: Delivery of behavioral health interventions on the internet offers many benefits, including accessibility, cost-effectiveness, convenience, and anonymity. In recent years, an increased number of internet interventions have been developed, targeting a range of conditions and behaviors, including depression, pain, anxiety, sleep disturbance, and eating disorders. Human support (coaching) is a common component of internet interventions that is intended to boost engagement; however, little is known about how participants interact with coaches and how this may relate to their experience with the intervention. By examining the data that participants produce during an intervention, we can characterize their interaction patterns and refine treatments to address different needs. Objective: In this study, we employed text mining and visual analytics techniques to analyze messages exchanged between coaches and participants in an internet-delivered pain management intervention for adolescents with chronic pain and their parents. Methods: We explored the main themes in coaches’ and participants’ messages using an automated textual analysis method, topic modeling. We then clustered participants’ messages to identify subgroups of participants with similar engagement patterns. Results: First, we performed topic modeling on coaches’ messages. The themes in coaches’ messages fell into 3 categories: Treatment Content, Administrative and Technical, and Rapport Building. Next, we employed topic modeling to identify topics from participants’ message histories. Similar to the coaches’ topics, these were subsumed under 3 high-level categories: Health Management and Treatment Content, Questions and Concerns, and Activities and Interests. Finally, the cluster analysis identified 4 clusters, each with a distinguishing characteristic: Assignment-Focused, Short Message Histories, Pain-Focused, and Activity-Focused. The name of each cluster exemplifies the main engagement patterns of that cluster. Conclusions: In this secondary data analysis, we demonstrated how automated text analysis techniques could be used to identify messages of interest, such as questions and concerns from users. In addition, we demonstrated how cluster analysis could be used to identify subgroups of individuals who share communication and engagement patterns, and in turn facilitate personalization of interventions for different subgroups of patients. This work makes 2 key methodological contributions. First, this study is innovative in its use of topic modeling to provide a rich characterization of the textual content produced by coaches and participants in an internet-delivered behavioral health intervention. Second, to our knowledge, this is the first example of the use of a visual analysis method to cluster participants and identify similar patterns of behavior based on intervention message content.

  • J Med Internet Res 2019;21(4):e11756

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