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Web-Based Cognitive Behavioral Therapy Blended With Face-to-Face Sessions for Major Depression: Randomized Controlled Trial
Posted:2018-09-21 10:00:02 UTC-04:00
Background: Meta-analyses of several randomized controlled trials have shown that cognitive behavioral therapy (CBT) has comparable efficacy to antidepressant medication, but therapist availability and cost-effectiveness is a problem. Objective: This study aimed to evaluate the effectiveness of Web-based CBT blended with face-to-face sessions that reduce therapist time in patients with major depression who were unresponsive to antidepressant medications. Methods: A 12-week, assessor-masked, parallel-group, waiting- list controlled, randomized trial was conducted at 3 medical institutions in Tokyo. Outpatients aged 20-65 years with a primary diagnosis of major depression who were taking ≥1 antidepressant medications at an adequate dose for ≥6 weeks and had a 17-item GRID-Hamilton Depression Rating Scale (HAMD) score of ≥14 were randomly assigned (1:1) to blended CBT or waiting-list groups using a computer allocation system, stratified by the study site with the minimization method, to balance age and baseline GRID-HAMD score. The CBT intervention was given in a combined format, comprising a Web-based program and 12 45-minute face-to-face sessions. Thus, across 12 weeks, a participant could receive up to 540 minutes of contact with a therapist, which is approximately two-thirds of the therapist contact time provided in the conventional CBT protocol, which typically provides 16 50-minute sessions. The primary outcome was the alleviation of depressive symptoms, as measured by a change in the total GRID-HAMD score from baseline (at randomization) to posttreatment (at 12 weeks). Moreover, in an exploratory analysis, we investigated whether the expected positive effects of the intervention were sustained during follow-up, 3 months after the posttreatment assessment. Analyses were performed on an intention-to-treat basis, and the primary outcome was analyzed using a mixed-effects model for repeated measures. Results: We randomized 40 participants to either blended CBT (n=20) or waiting-list (n=20) groups. All patients completed the 12-week treatment protocol and were included in the intention-to-treat analyses. Participants in the blended CBT group had significantly alleviated depressive symptoms at week 12, as shown by greater least squares mean changes in the GRID-HAMD score, than those in the waiting list group (−8.9 points vs −3.0 points; mean between-group difference=−5.95; 95% CI −9.53 to −2.37; P<.001). The follow-up effects within the blended CBT group, as measured by the GRID-HAMD score, were sustained at the 3-month follow-up (week 24) and posttreatment (week 12): posttreatment, 9.4 (SD 5.2), versus follow-up, 7.2 (SD 5.7); P=.009. Conclusions: Although our findings warrant confirmation in larger and longer term studies with active controls, these suggest that a combined form of CBT is effective in reducing depressive symptoms in patients with major depression who are unresponsive to antidepressant medications. Trial Registration: University Hospital Medical Information Network Clinical Trials Registry: UMIN000009242; https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000010852 (Archived by WebCite at http://www.webcitation. org/729VkpyYL)
This is the abstract only. Read the full article on the JMIR site. JMIR is the leading open access journal for eHealth and healthcare in the Internet age.
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Robotic Services Acceptance in Smart Environments With Older Adults: User Satisfaction and Acceptability Study
Posted:2018-09-21 09:30:02 UTC-04:00
Background: In Europe, the population of older people is increasing rapidly. Many older people prefer to remain in their homes but living alone could be a risk for their safety. In this context, robotics and other emerging technologies are increasingly proposed as potential solutions to this societal concern. However, one-third of all assistive technologies are abandoned within one year of use because the end users do not accept them. Objective: The aim of this study is to investigate the acceptance of the Robot-Era system, which provides robotic services to permit older people to remain in their homes. Methods: Six robotic services were tested by 35 older users. The experiments were conducted in three different environments: private home, condominium, and outdoor sites. The appearance questionnaire was developed to collect the users’ first impressions about the Robot-Era system, whereas the acceptance was evaluated through a questionnaire developed ad hoc for Robot-Era. Results: A total of 45 older users were recruited. The people were grouped in two samples of 35 participants, according to their availability. Participants had a positive impression of Robot-Era robots, as reflected by the mean score of 73.04 (SD 11.80) for DORO’s (domestic robot) appearance, 76.85 (SD 12.01) for CORO (condominium robot), and 75.93 (SD 11.67) for ORO (outdoor robot). Men gave ORO’s appearance an overall score higher than women (P=.02). Moreover, participants younger than 75 years understood more readily the functionalities of Robot-Era robots compared to older people (P=.007 for DORO, P=.001 for CORO, and P=.046 for ORO). For the ad hoc questionnaire, the mean overall score was higher than 80 out of 100 points for all Robot-Era services. Older persons with a high educational level gave Robot-Era services a higher score than those with a low level of education (shopping: P=.04; garbage: P=.047; reminding: P=.04; indoor walking support: P=.006; outdoor walking support: P=.03). A higher score was given by male older adults for shopping (P=.02), indoor walking support (P=.02), and outdoor walking support (P=.03). Conclusions: Based on the feedback given by the end users, the Robot-Era system has the potential to be developed as a socially acceptable and believable provider of robotic services to facilitate older people to live independently in their homes.
This is the abstract only. Read the full article on the JMIR site. JMIR is the leading open access journal for eHealth and healthcare in the Internet age.
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Improving Prediction of Risk of Hospital Admission in Chronic Obstructive Pulmonary Disease: Application of Machine Learning to Telemonitoring Data
Posted:2018-09-21 09:00:02 UTC-04:00
Background: Telemonitoring of symptoms and physiological signs has been suggested as a means of early detection of chronic obstructive pulmonary disease (COPD) exacerbations, with a view to instituting timely treatment. However, algorithms to identify exacerbations result in frequent false-positive results and increased workload. Machine learning, when applied to predictive modelling, can determine patterns of risk factors useful for improving prediction quality. Objective: Our objectives were to (1) establish whether machine learning techniques applied to telemonitoring datasets improve prediction of hospital admissions and decisions to start corticosteroids, and (2) determine whether the addition of weather data further improves such predictions. Methods: We used daily symptoms, physiological measures, and medication data, with baseline demography, COPD severity, quality of life, and hospital admissions from a pilot and large randomized controlled trial of telemonitoring in COPD. We linked weather data from the United Kingdom meteorological service. We used feature selection and extraction techniques for time series to construct up to 153 predictive patterns (features) from symptom, medication, and physiological measurements. We used the resulting variables to construct predictive models fitted to training sets of patients and compared them with common symptom-counting algorithms. Results: We had a mean 363 days of telemonitoring data from 135 patients. The two most practical traditional score-counting algorithms, restricted to cases with complete data, resulted in area under the receiver operating characteristic curve (AUC) estimates of 0.60 (95% CI 0.51-0.69) and 0.58 (95% CI 0.50-0.67) for predicting admissions based on a single day’s readings. However, in a real-world scenario allowing for missing data, with greater numbers of patient daily data and hospitalizations (N=57,150, N+=55, respectively), the performance of all the traditional algorithms fell, including those based on 2 days’ data. One of the most frequently used algorithms performed no better than chance. All considered machine learning models demonstrated significant improvements; the best machine learning algorithm based on 57,150 episodes resulted in an aggregated AUC of 0.74 (95% CI 0.67-0.80). Adding weather data measurements did not improve the predictive performance of the best model (AUC 0.74, 95% CI 0.69-0.79). To achieve an 80% true-positive rate (sensitivity), the traditional algorithms were associated with an 80% false-positive rate: our algorithm halved this rate to approximately 40% (specificity approximately 60%). The machine learning algorithm was moderately superior to the best symptom-counting algorithm (AUC 0.77, 95% CI 0.74-0.79 vs AUC 0.66, 95% CI 0.63-0.68) at predicting the need for corticosteroids. Conclusions: Early detection and management of COPD remains an important goal given its huge personal and economic costs. Machine learning approaches, which can be tailored to an individual’s baseline profile and can learn from experience of the individual patient, are superior to existing predictive algorithms and show promise in achieving this goal. Trial Registration: International Standard Randomized Controlled Trial Number ISRCTN96634935; http://www.isrctn.com/ISRCTN96634935 (Archived by WebCite at http://www.webcitation.org/722YkuhAz)
This is the abstract only. Read the full article on the JMIR site. JMIR is the leading open access journal for eHealth and healthcare in the Internet age.
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Use and the Users of a Patient Portal: Cross-Sectional Study
Posted:2018-09-17 09:30:02 UTC-04:00
Background: Patient portals offer patients access to their medical information and tools to communicate with health care providers. It has been shown that patient portals have the potential to positively impact health outcomes and efficiency of health care. It is therefore important that health care organizations identify the patients who use or do not use the patient portal and explore the reasons in either case. The Unified Theory of Acceptance and Use of Technology (UTAUT) is a frequently used theory for explaining the use of information technology. It consists of the following constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention to use. Objective: This study aimed to explore the prevalence of patient portal use and the characteristics of patients who use or do not use a patient portal. The main constructs of UTAUT, together with demographics and disease- and care-related characteristics, have been measured to explore the predictive factors of portal use. Methods: A cross-sectional study was conducted in the outpatient departments for adult patients of a university hospital in the Netherlands. Following outcomes were included: self-reported portal use, characteristics of users such as demographics, disease- and care-related data, eHealth literacy (modified score), and scores of UTAUT constructs. Descriptive analyses and univariate and multivariate logistic regression were also conducted. Results: In the analysis, 439 adult patients were included. Furthermore, 32.1% (141/439) identified as being a user of the patient portal; 31.2% (137/439) indicated as nonusers, but being aware of the existence of the portal; and 36.6% (161/439) as being nonusers not aware of the existence of the portal. In the entire study population, the factors of being chronically ill (odds ratio, OR 1.62, 95% CI 1.04-2.52) and eHealth literacy (modified score; OR 1.12, 95% CI 1.07-1.18) best predicted portal use. In users and nonusers who were aware of the portal, UTAUT constructs were added to the multivariate logistic regression, with chronically ill and modified eHealth literacy sum score. Effort expectancy (OR 13.02, 95% CI 5.68-29.87) and performance expectancy (OR 2.84, 95% CI 1.65-4.90) are shown to significantly influence portal use in this group. Conclusions: Approximately one-third of the patients of a university hospital self-reported using the patient portal; most expressed satisfaction. At first sight, being chronically ill and higher scores on the modified eHealth literacy scale explained portal use. Adding UTAUT constructs to the model revealed that effort expectancy (ease of use and knowledge and skills related to portal use) and performance expectancy (perceived usefulness) influenced portal use. Interventions to improve awareness of the portal and eHealth literacy skills of patients and further integration of the patient portal in usual face-to-face care are needed to increase use and potential subsequent patient benefits.
This is the abstract only. Read the full article on the JMIR site. JMIR is the leading open access journal for eHealth and healthcare in the Internet age.
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Discordance Between Human Papillomavirus Twitter Images and Disparities in Human Papillomavirus Risk and Disease in the United States: Mixed-Methods Analysis
Posted:2018-09-14 10:15:02 UTC-04:00
Background: Racial and ethnic minorities are disproportionately affected by human papillomavirus (HPV)-related cancer, many of which could have been prevented with vaccination. Yet, the initiation and completion rates of HPV vaccination remain low among these populations. Given the importance of social media platforms for health communication, we examined US-based HPV images on Twitter. We explored inconsistencies between the demographics represented in HPV images and the populations that experience the greatest burden of HPV-related disease. Objective: The objective of our study was to observe whether HPV images on Twitter reflect the actual burden of disease by select demographics and determine to what extent Twitter accounts utilized images that reflect the burden of disease in their health communication messages. Methods: We identified 456 image tweets about HPV that contained faces posted by US users between November 11, 2014 and August 8, 2016. We identified images containing at least one human face and utilized Face++ software to automatically extract the gender, age, and race of each face. We manually annotated the source accounts of these tweets into 3 types as follows: government (38/298, 12.8%), organizations (161/298, 54.0%), and individual (99/298, 33.2%) and topics (news, health, and other) to examine how images varied by message source. Results: Findings reflected the racial demographics of the US population but not the disease burden (795/1219, 65.22% white faces; 140/1219, 11.48% black faces; 71/1219, 5.82% Asian faces; and 213/1219, 17.47% racially ambiguous faces). Gender disparities were evident in the image faces; 71.70% (874/1219) represented female faces, whereas only 27.89% (340/1219) represented male faces. Among the 11-26 years age group recommended to receive HPV vaccine, HPV images contained more female-only faces (214/616, 34.3%) than males (37/616, 6.0%); the remainder of images included both male and female faces (365/616, 59.3%). Gender and racial disparities were present across different image sources. Faces from government sources were more likely to depict females (n=44) compared with males (n=16). Of male faces, 80% (12/15) of youth and 100% (1/1) of adults were white. News organization sources depicted high proportions of white faces (28/38, 97% of female youth and 12/12, 100% of adult males). Face++ identified fewer faces compared with manual annotation because of limitations with detecting multiple, small, or blurry faces. Nonetheless, Face++ achieved a high degree of accuracy with respect to gender, race, and age compared with manual annotation. Conclusions: This study reveals critical differences between the demographics reflected in HPV images and the actual burden of disease. Racial minorities are less likely to appear in HPV images despite higher rates of HPV incidence. Health communication efforts need to represent populations at risk better if we seek to reduce disparities in HPV infection.
This is the abstract only. Read the full article on the JMIR site. JMIR is the leading open access journal for eHealth and healthcare in the Internet age.
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Using Partially-Observed Facebook Networks to Develop a Peer-Based HIV Prevention Intervention: Case Study
Posted:2018-09-14 09:30:02 UTC-04:00
Background: This is a case study from an HIV prevention project among young black men who have sex with men. Individual-level prevention interventions have had limited success among young black men who have sex with men, a population that is disproportionately affected by HIV; peer network–based interventions are a promising alternative. Facebook is an attractive digital platform because it enables broad characterization of social networks. There are, however, several challenges in using Facebook data for peer interventions, including the large size of Facebook networks, difficulty in assessing appropriate methods to identify candidate peer change agents, boundary specification issues, and partial observation of social network data. Objective: This study aimed to explore methodological challenges in using social Facebook networks to design peer network–based interventions for HIV prevention and present techniques to overcome these challenges. Methods: Our sample included 298 uConnect study respondents who answered a bio-behavioral survey in person and whose Facebook friend lists were downloaded (2013-2014). The study participants had over 180,000 total Facebook friends who were not involved in the study (nonrespondents). We did not observe friendships between these nonrespondents. Given the large number of nonrespondents whose networks were partially observed, a relational boundary was specified to select nonrespondents who were well connected to the study respondents and who may be more likely to influence the health behaviors of young black men who have sex with men. A stochastic model-based imputation technique, derived from the exponential random graph models, was applied to simulate 100 networks where unobserved friendships between nonrespondents were imputed. To identify peer change agents, the eigenvector centrality and keyplayer positive algorithms were used; both algorithms are suitable for identifying individuals in key network positions for information diffusion. For both algorithms, we assessed the sensitivity of identified peer change agents to the imputation model, the stability of identified peer change agents across the imputed networks, and the effect of the boundary specification on the identification of peer change agents. Results: All respondents and 78.9% (183/232) of nonrespondents selected as peer change agents by eigenvector on the imputed networks were also selected as peer change agents on the observed networks. For keyplayer, the agreement was much lower; 42.7% (47/110) and 35.3% (110/312) of respondent and nonrespondent peer change agents, respectively, selected on the imputed networks were also selected on the observed network. Eigenvector also produced a stable set of peer change agents across the 100 imputed networks and was much less sensitive to the specified relational boundary. Conclusions: Although we do not have a gold standard indicating which algorithm produces the most optimal set of peer change agents, the lower sensitivity of eigenvector centrality to key assumptions leads us to conclude that it may be preferable. The methods we employed to address the challenges in using Facebook networks may prove timely, given the rapidly increasing interest in using online social networks to improve population health.
This is the abstract only. Read the full article on the JMIR site. JMIR is the leading open access journal for eHealth and healthcare in the Internet age.
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Automatic Extraction of Mental Health Disorders From Domestic Violence Police Narratives: Text Mining Study
Posted:2018-09-13 10:45:02 UTC-04:00
Background: Vast numbers of domestic violence (DV) incidents are attended by the New South Wales Police Force each year in New South Wales and recorded as both structured quantitative data and unstructured free text in the WebCOPS (Web-based interface for the Computerised Operational Policing System) database regarding the details of the incident, the victim, and person of interest (POI). Although the structured data are used for reporting purposes, the free text remains untapped for DV reporting and surveillance purposes. Objective: In this paper, we explore whether text mining can automatically identify mental health disorders from this unstructured text. Methods: We used a training set of 200 DV recorded events to design a knowledge-driven approach based on lexical patterns in text suggesting mental health disorders for POIs and victims. Results: The precision returned from an evaluation set of 100 DV events was 97.5% and 87.1% for mental health disorders related to POIs and victims, respectively. After applying our approach to a large-scale corpus of almost a half million DV events, we identified 77,995 events (15.83%) that mentioned mental health disorders, with 76.96% (60,032/77,995) of those linked to POIs versus 16.47% (12,852/77,995) for the victims and 6.55% (5111/77,995) for both. Depression was the most common mental health disorder mentioned in both victims (22.30%, 3258) and POIs (18.73%, 8918), followed by alcohol abuse for POIs (12.24%, 5829) and various anxiety disorders (eg, panic disorder, generalized anxiety disorder) for victims (11.43%, 1671). Conclusions: The results suggest that text mining can automatically extract targeted information from police-recorded DV events to support further public health research into the nexus between mental health disorders and DV.
This is the abstract only. Read the full article on the JMIR site. JMIR is the leading open access journal for eHealth and healthcare in the Internet age.
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Reach of Messages in a Dental Twitter Network: Cohort Study Examining User Popularity, Communication Pattern, and Network Structure
Posted:2018-09-13 09:45:09 UTC-04:00
Background: Increasing the reach of messages disseminated through Twitter promotes the success of Twitter-based health education campaigns. Objective: This study aimed to identify factors associated with reach in a dental Twitter network (1) initially and (2) sustainably at individual and network levels. Methods: We used instructors’ and students’ Twitter usernames from a Saudi dental school in 2016-2017 and applied Gephi (a social network analysis tool) and social media analytics to calculate user and network metrics. Content analysis was performed to identify users disseminating oral health information. The study outcomes were reach at baseline and sustainably over 1.5 years. The explanatory variables were indicators of popularity (number of followers, likes, tweets retweeted by others), communication pattern (number of tweets, retweets, replies, tweeting/ retweeting oral health information or not). Multiple logistic regression models were used to investigate associations. Results: Among dental users, 31.8% had reach at baseline and 62.9% at the end of the study, reaching a total of 749,923 and dropping to 37,169 users at the end. At an individual level, reach was associated with the number of followers (baseline: odds ratio, OR=1.003, 95% CI=1.001-1.005 and sustainability: OR=1.002, 95% CI=1.0001-1.003), likes (baseline: OR=1.001, 95% CI=1.0001-1.002 and sustainability: OR=1.0031, 95% CI=1.0003-1.002), and replies (baseline: OR=1.02, 95% CI=1.005-1.04 and sustainability: OR=1.02, 95% CI=1.004-1.03). At the network level, users with the least followers, tweets, retweets, and replies had the greatest reach. Conclusions: Reach was reduced by time. Factors increasing reach at the user level had different impact at the network level. More than one strategy is needed to maximize reach.
This is the abstract only. Read the full article on the JMIR site. JMIR is the leading open access journal for eHealth and healthcare in the Internet age.
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Audio-/Videorecording Clinic Visits for Patient’s Personal Use in the United States: Cross-Sectional Survey
Posted:2018-09-12 10:00:02 UTC-04:00
Background: Few clinics in the United States routinely offer patients audio or video recordings of their clinic visits. While interest in this practice has increased, to date, there are no data on the prevalence of recording clinic visits in the United States. Objective: Our objectives were to (1) determine the prevalence of audiorecording clinic visits for patients’ personal use in the United States, (2) assess the attitudes of clinicians and public toward recording, and (3) identify whether policies exist to guide recording practices in 49 of the largest health systems in the United States. Methods: We administered 2 parallel cross-sectional surveys in July 2017 to the internet panels of US-based clinicians (SERMO Panel) and the US public (Qualtrics Panel). To ensure a diverse range of perspectives, we set quotas to capture clinicians from 8 specialties. Quotas were also applied to the public survey based on US census data (gender, race, ethnicity, and language other than English spoken at home) to approximate the US adult population. We contacted 49 of the largest health systems (by clinician number) in the United States by email and telephone to determine the existence, or absence, of policies to guide audiorecordings of clinic visits for patients’ personal use. Multiple logistic regression models were used to determine factors associated with recording. Results: In total, 456 clinicians and 524 public respondents completed the surveys. More than one-quarter of clinicians (129/456, 28.3%) reported that they had recorded a clinic visit for patients’ personal use, while 18.7% (98/524) of the public reported doing so, including 2.7% (14/524) who recorded visits without the clinician’s permission. Amongst clinicians who had not recorded a clinic visit, 49.5% (162/327) would be willing to do so in the future, while 66.0% (346/524) of the public would be willing to record in the future. Clinician specialty was associated with prior recording: specifically oncology (odds ratio [OR] 5.1, 95% CI 1.9-14.9; P=.002) and physical rehabilitation (OR 3.9, 95% CI 1.4-11.6; P=.01). Public respondents who were male (OR 2.11, 95% CI 1.26-3.61; P=.005), younger (OR 0.73 for a 10-year increase in age, 95% CI 0.60-0.89; P=.002), or spoke a language other than English at home (OR 1.99; 95% CI 1.09-3.59; P=.02) were more likely to have recorded a clinic visit. None of the large health systems we contacted reported a dedicated policy; however, 2 of the 49 health systems did report an existing policy that would cover the recording of clinic visits for patient use. The perceived benefits of recording included improved patient understanding and recall. Privacy and medicolegal concerns were raised. Conclusions: Policy guidance from health systems and further examination of the impact of recordings—positive or negative—on care delivery, clinician-related outcomes, and patients’ behavioral and health-related outcomes is urgently required.
This is the abstract only. Read the full article on the JMIR site. JMIR is the leading open access journal for eHealth and healthcare in the Internet age.
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Mindfulness-Based Resilience Training in the Workplace: Pilot Study of the Internet-Based Resilience@Work (RAW) Mindfulness Program
Posted:2018-09-11 09:45:03 UTC-04:00
Background: The impact of mental illness on society is far reaching and has been identified as the leading cause of sickness absence and work disability in most developed countries. By developing evidence-based solutions that are practical, affordable, and accessible, there is potential to deliver substantial economic benefits while improving the lives of individual workers. Academic and industry groups are now responding to this public health issue. A key focus is on developing practical solutions that enhance the mental health and psychological resilience of workers. A growing body of research suggests resilience training may play a pivotal role in the realm of public health and prevention, particularly with regards to protecting the long-term well-being of workers. Objective: Our aim is to examine whether a mindfulness-based resilience-training program delivered via the internet is feasible and engaging to a group of high-risk workers. Additionally, we aim to measure the effect of the Resilience@Work Resilience@Work Mindfulness program on measures of resilience and related skills. Methods: The current pilot study recruited 29 full-time firefighters. Participants were enrolled in the 6-session internet-based resilience-training program and were administered questionnaires prior to training and directly after the program ended. Measurements examined program feasibility, psychological resilience, experiential avoidance, and thought entanglement. Results: Participants reported greater levels of resilience after Resilience@Work training compared to baseline, with a mean increase in their overall resilience score of 1.5 (95% CI -0.25 to 3.18, t14=1.84, P=.09). Compared to baseline, participants also reported lower levels of psychological inflexibility and experiential avoidance following training, with a mean decrease of -1.8 (95% CI -3.78 to 0.20, t13=-1.94, P=.07). With regards to cognitive fusion (thought entanglement), paired-samples t tests revealed a trend towards reduction in mean scores post training (P=.12). Conclusions: This pilot study of the Resilience@Work program suggests that a mindfulness-based resilience program delivered via the Internet is feasible in a high-risk workplace setting. In addition, the firefighters using the program showed a trend toward increased resilience and psychological flexibility. Despite a number of limitations, the results of this pilot study provide some valuable insights into what form of resilience training may be viable in occupational settings particularly among those considered high risk, such as emergency workers. To the best of our knowledge, this is the first time a mindfulness-based resilience-training program delivered wholly via the internet has been tested in the workplace.
This is the abstract only. Read the full article on the JMIR site. JMIR is the leading open access journal for eHealth and healthcare in the Internet age.
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