Journal of Medical Internet Research

Web-Based Counseling for Problem Gambling: Exploring Motivations and Recommendations

2013-05-24T10:36:01-04:00

Background: For highly stigmatized disorders, such as problem gambling, Web-based counseling has the potential to address common barriers to treatment, including issues of shame and stigma. Despite the exponential growth in the uptake of immediate synchronous Web-based counseling (ie, provided without appointment), little is known about why people choose this service over other modes of treatment. Objective: The aim of the current study was to determine motivations for choosing and recommending Web-based counseling over telephone or face-to-face services. Methods: The study involved 233 Australian participants who had completed an online counseling session for problem gambling on the Gambling Help Online website between November 2010 and February 2012. Participants were all classified as problem gamblers, with a greater proportion of males (57.4%) and 60.4% younger than 40 years of age. Participants completed open-ended questions about their reasons for choosing online counseling over other modes (ie, face-to-face and telephone), as well as reasons for recommending the service to others. Results: A content analysis revealed 4 themes related to confidentiality/anonymity (reported by 27.0%), convenience/accessibility (50.9%), service system access (34.2%), and a preference for the therapeutic medium (26.6%). Few participants reported helpful professional support as a reason for accessing counseling online, but 43.2% of participants stated that this was a reason for recommending the service. Those older than 40 years were more likely than younger people in the sample to use Web-based counseling as an entry point into the service system (P=.045), whereas those engaged in nonstrategic gambling (eg, machine gambling) were more likely to access online counseling as an entry into the service system than those engaged in strategic gambling (ie, cards, sports; P=.01). Participants older than 40 years were more likely to recommend the service because of its potential for confidentiality and anonymity (P=.04), whereas those younger than 40 years were more likely to recommend the service due to it being helpful (P=.02). Conclusions: This study provides important information about why online counseling for gambling is attractive to people with problem gambling, thereby informing the development of targeted online programs, campaigns, and promotional material.

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Web-Based Newborn Screening System for Metabolic Diseases: Machine Learning Versus Clinicians

2013-05-23T10:19:41-04:00

Background: A hospital information system (HIS) that integrates screening data and interpretation of the data is routinely requested by hospitals and parents. However, the accuracy of disease classification may be low because of the disease characteristics and the analytes used for classification. Objective: The objective of this study is to describe a system that enhanced the neonatal screening system of the Newborn Screening Center at the National Taiwan University Hospital. The system was designed and deployed according to a service-oriented architecture (SOA) framework under the Web services .NET environment. The system consists of sample collection, testing, diagnosis, evaluation, treatment, and follow-up services among collaborating hospitals. To improve the accuracy of newborn screening, machine learning and optimal feature selection mechanisms were investigated for screening newborns for inborn errors of metabolism. Methods: The framework of the Newborn Screening Hospital Information System (NSHIS) used the embedded Health Level Seven (HL7) standards for data exchanges among heterogeneous platforms integrated by Web services in the C# language. In this study, machine learning classification was used to predict phenylketonuria (PKU), hypermethioninemia, and 3-methylcrotonyl-CoA-carboxylase (3-MCC) deficiency. The classification methods used 347,312 newborn dried blood samples collected at the Center between 2006 and 2011. Of these, 220 newborns had values over the diagnostic cutoffs (positive cases) and 1557 had values that were over the screening cutoffs but did not meet the diagnostic cutoffs (suspected cases). The original 35 analytes and the manifested features were ranked based on F score, then combinations of the top 20 ranked features were selected as input features to support vector machine (SVM) classifiers to obtain optimal feature sets. These feature sets were tested using 5-fold cross-validation and optimal models were generated. The datasets collected in year 2011 were used as predicting cases. Results: The feature selection strategies were implemented and the optimal markers for PKU, hypermethioninemia, and 3-MCC deficiency were obtained. The results of the machine learning approach were compared with the cutoff scheme. The number of the false positive cases were reduced from 21 to 2 for PKU, from 30 to 10 for hypermethioninemia, and 209 to 46 for 3-MCC deficiency. Conclusions: This SOA Web service–based newborn screening system can accelerate screening procedures effectively and efficiently. An SVM learning methodology for PKU, hypermethioninemia, and 3-MCC deficiency metabolic diseases classification, including optimal feature selection strategies, is presented. By adopting the results of this study, the number of suspected cases could be reduced dramatically.

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Predictors of Participant Retention in a Guided Online Self-Help Program for University Students: Prospective Cohort Study

2013-05-22T10:13:36-04:00

Background: Attrition is a persistent issue in online self-help programs, but limited research is available on reasons for attrition or successful methods for improving participant retention. One potential approach to understanding attrition and retention in such programs is to examine person-related variables (eg, beliefs and attitudes) that influence behavior. Theoretical models, such as the Theory of Planned Behavior, that describe conditions influencing human behavior may provide a useful framework for predicting participant retention in online-based program. Objective: We examined predictors of participant retention in a guided online anxiety, depression, and stress self-help program for university students using the theory of planned behavior. We also explored whether age, symptom severity, and type of coaching (ie, email vs phone) affected participant retention. Methods: 65 university students with mild to moderate depression, anxiety, and stress were enrolled in this prospective cohort study. Participants completed a questionnaire based on the theory of planned behavior prior to commencing the online-based program and the Depression Anxiety and Stress Scale (DASS) during the assessment module of the program. Participant retention was operationalized as the number of program modules completed. Results: Perceived control over completing the online program significantly predicted intention to complete the program (F3,62=6.7; P=.001; adjusted R2=.2; standardized beta=.436, P=.001). Age (standardized beta=.319, P=.03) and perceived behavioral control (standardized beta=.295, P=.05) predicted the number of program modules completed (F3,61=3.20, P=.03, adjusted R2 =.11). Initial level of distress (ie, symptom severity) did not predict participant retention (P=.55). Participants who chose phone-based coaching completed more program modules than participants who chose email-based coaching (Mann-Whitney’s U=137; P=.004). Conclusions: Participants’ age, level of perceived behavioral control, and choice of interaction (ie, phone-based or email-based coaching) were found to influence retention in this online-based program.

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Mapping mHealth Research: A Decade of Evolution

2013-05-21T10:59:47-04:00

Background: For the last decade, mHealth has constantly expanded as a part of eHealth. Mobile applications for health have the potential to target heterogeneous audiences and address specific needs in different situations, with diverse outcomes, and to complement highly developed health care technologies. The market is rapidly evolving, making countless new mobile technologies potentially available to the health care system; however, systematic research on the impact of these technologies on health outcomes remains scarce. Objective: To provide a comprehensive view of the field of mHealth research to date and to understand whether and how the new generation of smartphones has triggered research, since their introduction 5 years ago. Specifically, we focused on studies aiming to evaluate the impact of mobile phones on health, and we sought to identify the main areas of health care delivery where mobile technologies can have an impact. Methods: A systematic literature review was conducted on the impact of mobile phones and smartphones in health care. Abstracts and articles were categorized using typologies that were partly adapted from existing literature and partly created inductively from publications included in the review. Results: The final sample consisted of 117 articles published between 2002 and 2012. The majority of them were published in the second half of our observation period, with a clear upsurge between 2007 and 2008, when the number of articles almost doubled. The articles were published in 77 different journals, mostly from the field of medicine or technology and medicine. Although the range of health conditions addressed was very wide, a clear focus on chronic conditions was noted. The research methodology of these studies was mostly clinical trials and pilot studies, but new designs were introduced in the second half of our observation period. The size of the samples drawn to test mobile health applications also increased over time. The majority of the studies tested basic mobile phone features (eg, text messaging), while only a few assessed the impact of smartphone apps. Regarding the investigated outcomes, we observed a shift from assessment of the technology itself to assessment of its impact. The outcome measures used in the studies were mostly clinical, including both self-reported and objective measures. Conclusions: Research interest in mHealth is growing, together with an increasing complexity in research designs and aim specifications, as well as a diversification of the impact areas. However, new opportunities offered by new mobile technologies do not seem to have been explored thus far. Mapping the evolution of the field allows a better understanding of its strengths and weaknesses and can inform future developments.

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.

Crowdsourcing a Normative Natural Language Dataset: A Comparison of Amazon Mechanical Turk and In-Lab Data Collection

2013-05-20T09:03:54-04:00

Background: Crowdsourcing has become a valuable method for collecting medical research data. This approach, recruiting through open calls on the Web, is particularly useful for assembling large normative datasets. However, it is not known how natural language datasets collected over the Web differ from those collected under controlled laboratory conditions. Objective: To compare the natural language responses obtained from a crowdsourced sample of participants with responses collected in a conventional laboratory setting from participants recruited according to specific age and gender criteria. Methods: We collected natural language descriptions of 200 half-minute movie clips, from Amazon Mechanical Turk workers (crowdsourced) and 60 participants recruited from the community (lab-sourced). Crowdsourced participants responded to as many clips as they wanted and typed their responses, whereas lab-sourced participants gave spoken responses to 40 clips, and their responses were transcribed. The content of the responses was evaluated using a take-one-out procedure, which compared responses to other responses to the same clip and to other clips, with a comparison of the average number of shared words. Results: In contrast to the 13 months of recruiting that was required to collect normative data from 60 lab-sourced participants (with specific demographic characteristics), only 34 days were needed to collect normative data from 99 crowdsourced participants (contributing a median of 22 responses). The majority of crowdsourced workers were female, and the median age was 35 years, lower than the lab-sourced median of 62 years but similar to the median age of the US population. The responses contributed by the crowdsourced participants were longer on average, that is, 33 words compared to 28 words (P<.001), and they used a less varied vocabulary. However, there was strong similarity in the words used to describe a particular clip between the two datasets, as a cross-dataset count of shared words showed (P<.001). Within both datasets, responses contained substantial relevant content, with more words in common with responses to the same clip than to other clips (P<.001). There was evidence that responses from female and older crowdsourced participants had more shared words (P=.004 and .01 respectively), whereas younger participants had higher numbers of shared words in the lab-sourced population (P=.01). Conclusions: Crowdsourcing is an effective approach to quickly and economically collect a large reliable dataset of normative natural language responses.

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.

Online Social Network Use by Health Care Providers in a High Traffic Patient Care Environment

2013-05-17T10:37:18-04:00

Background: The majority of workers, regardless of age or occupational status, report engaging in personal Internet use in the workplace. There is little understanding of the impact that personal Internet use may have on patient care in acute clinical settings. Objective: The objective of this study was to investigate the volume of one form of personal Internet use—online social networking (Facebook)—generated by workstations in the emergency department (ED) in contrast to measures of clinical volume and severity. Methods: The research team analyzed anonymous network utilization records for 68 workstations located in the emergency medicine department within one academic medical center for 15 consecutive days (12/29/2009 to 1/12/2010). This data was compared to ED work index (EDWIN) data derived by the hospital information systems. Results: Health care workers spent an accumulated 4349 minutes (72.5 hours) browsing Facebook, staff cumulatively visited Facebook 9369 times and spent, on average, 12.0 minutes per hour browsing Facebook. There was a statistically significant difference in the time spent on Facebook according to time of day (19.8 minutes per hour versus 4.3 minutes per hour, P<.001). There was a significant, positive correlation between EDWIN scores and time spent on Facebook (r=.266, P<.001). Conclusions: Facebook use constituted a substantive percentage of staff time during the 15-day observation period. Facebook use increased with increased patient volume and severity within the ED.

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Internet Use Among Older Adults: Association With Health Needs, Psychological Capital, and Social Capital

2013-05-16T10:24:44-04:00

Background: Previous studies have identified socioeconomic status and health status as predictors of older adults’ computer and Internet use, but researchers have not examined the relationships between older adults’ health needs and psychological capital (emotional well-being and self-efficacy) and social capital (social integration/ties and support networks) to different types of Internet use. Objective: This study examined (1) whether older adults’ health conditions and psychological and social capital differentiate Internet users from nonusers, and (2) whether the Internet users differed in their types of Internet use on the basis of their health conditions and psychological and social capital. Methods: Data for this study came from the National Health and Aging Trends Study, which is based on a nationally representative sample of US Medicare beneficiaries aged 65 years and older. The sample for this study were those who resided in the community in their own or others’ homes (N=6680). Binary logistic regression analysis was used to compare health needs, psychological capital, and social capital among (1) any type of Internet users and nonusers, (2) Internet users who engaged in health-related tasks and Internet users who did not, (3) Internet users who engaged in shopping/banking tasks and Internet users who did not, and (4) Internet users only used the Internet for email/texting and all other Internet users. Results: Depressive and anxiety symptoms, measures of psychological capital, were negatively associated with Internet use among older adults (odds ratio [OR] 0.83, 95% CI 0.70-0.98, P=.03 and OR 0.79, 95% CI 0.65-0.97, P=.03, respectively), whereas most measures of social capital were positively associated with Internet use. Having more chronic medical conditions and engaging in formal volunteering increased the odds of Internet use for health-related tasks by 1.15 (95% CI 1.08-1.23, P<.001) and 1.28 (95% CI 1.05-1.57, P=.02), respectively, but anxiety symptoms decreased the odds (OR 0.74, 95% CI 0.55-0.99, P=.05). Religious service attendance was negatively associated with Internet use for shopping/banking activities (OR 0.75, 95% CI 0.62-0.91, P=.01). Anxiety symptoms increased the odds of using the Internet only for emails/texting (OR 1.75, 95% CI 1.12-2.75, P=.02), but formal volunteering decreased the odds (OR 0.63, 95% CI 0.43-0.92, P=.02). Other correlates of Internet use solely for emails/texting were older age (80-84 years and ≥85 years), a black or “other” racial/ethnic background, a high school education or less than high school, and lower income. Conclusions: The findings point to the importance of social capital in facilitating older adults’ learning and adoption of Internet technology. Older adults who used the Internet for email/texting purposes only were the most socially and economically disadvantaged group of Internet users. Computer/Internet training for older adults and computer/Internet use for various purposes need to consider the significant role their social capital can play.

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.

Evaluation of a Web-Based Social Network Electronic Game in Enhancing Mental Health Literacy for Young People

2013-05-15T11:01:04-04:00

Background: Internet-based learning programs provide people with massive health care information and self-help guidelines on improving their health. The advent of Web 2.0 and social networks renders significant flexibility to embedding highly interactive components, such as games, to foster learning processes. The effectiveness of game-based learning on social networks has not yet been fully evaluated. Objectives: The aim of this study was to assess the effectiveness of a fully automated, Web-based, social network electronic game on enhancing mental health knowledge and problem-solving skills of young people. We investigated potential motivational constructs directly affecting the learning outcome. Gender differences in learning outcome and motivation were also examined. Methods: A pre/posttest design was used to evaluate the fully automated Web-based intervention. Participants, recruited from a closed online user group, self-assessed their mental health literacy and motivational constructs before and after completing the game within a 3-week period. The electronic game was designed according to cognitive-behavioral approaches. Completers and intent-to-treat analyses, using multiple imputation for missing data, were performed. Regression analysis with backward selection was employed when examining the relationship between knowledge enhancement and motivational constructs. Results: The sample included 73 undergraduates (42 females) for completers analysis. The gaming approach was effective in enhancing young people’s mental health literacy (d=0.65). The finding was also consistent with the intent-to-treat analysis, which included 127 undergraduates (75 females). No gender differences were found in learning outcome (P=.97). Intrinsic goal orientation was the primary factor in learning motivation, whereas test anxiety was successfully alleviated in the game setting. No gender differences were found on any learning motivation subscales (P>.10). We also found that participants’ self-efficacy for learning and performance, as well as test anxiety, significantly affected their learning outcomes, whereas other motivational subscales were statistically nonsignificant. Conclusions: Electronic games implemented through social networking sites appear to effectively enhance users’ mental health literacy.

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.

Making Sense of Mobile- and Web-Based Wellness Information Technology: Cross-Generational Study

2013-05-14T10:32:44-04:00

Background: A recent trend in personal health and wellness management is the development of computerized applications or information and communication technologies (ICTs) that support behavioral change, aid the management of chronic conditions, or help an individual manage their wellness and engage in a healthier lifestyle. Objective: To understand how individuals across 3 generations (young, middle-aged, and older) think about the design and use of collaborative health and wellness management technologies and what roles these could take in their lives. Methods: Face-to-face semistructured interviews, paper prototype systems, and video skits were used to assess how individuals from 3 age cohorts (young: 18-25 years; middle-aged: 35-50 years; and older: ≥65 years) conceptualize the role that health and wellness computing could take in their lives. Results: A total of 21 participants in the 3 age cohorts took part (young: n=7; middle-aged: n=7; and older: n=7). Young adults expected to be able to actively manage the presentation of their health-related information. Middle-aged adults had more nuanced expectations that reflect their engagement with work and other life activities. Older adults questioned the sharing of health information with a larger audience, although they saw the value in 1-way sharing between family members or providing aggregated information. Conclusions: Our findings inform our suggestions for improving the design of future collaborative health and wellness applications that target specific age groups. We recommend that collaborative ICT health applications targeting young adults should integrate with existing social networking sites, whereas those targeting middle-aged and older adults should support small social networks that rely on intimate personal relationships. Systems that target middle-aged adults should support episodic needs, such as time-sensitive, perhaps intermittent, goal setting. They should also have a low barrier to entry, allowing individuals who do not normally engage with the Internet to participate with the application for the specific purposes of health engagement. Collaborative ICT health applications targeting older adults should allow discreet 1-way sharing, and also support sharing of information in aggregate with others’ data. These systems should also provide mechanisms to preselect recipients of different kinds of data, or to easily direct specific information to individuals in real time.

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Internet-Delivered Interpersonal Psychotherapy Versus Internet-Delivered Cognitive Behavioral Therapy for Adults With Depressive Symptoms: Randomized Controlled Noninferiority Trial

2013-05-13T10:26:11-04:00

Background: Face-to-face cognitive behavioral therapy (CBT) and interpersonal psychotherapy (IPT) are both effective treatments for depressive disorders, but access is limited. Online CBT interventions have demonstrated efficacy in decreasing depressive symptoms and can facilitate the dissemination of therapies among the public. However, the efficacy of Internet-delivered IPT is as yet unknown. Objective: This study examines whether IPT is effective, noninferior to, and as feasible as CBT when delivered online to spontaneous visitors of an online therapy website. Methods: An automated, 3-arm, fully self-guided, online noninferiority trial compared 2 new treatments (IPT: n=620; CBT: n=610) to an active control treatment (MoodGYM: n=613) over a 4-week period in the general population. Outcomes were assessed using online self-report questionnaires, the Center for Epidemiological Studies Depression scale (CES-D) and the Client Satisfaction Questionnaire (CSQ-8) completed immediately following treatment (posttest) and at 6-month follow-up. Results: Completers analyses showed a significant reduction in depressive symptoms at posttest and follow-up for both CBT and IPT, and were noninferior to MoodGYM. Within-group effect sizes were medium to large for all groups. There were no differences in clinical significant change between the programs. Reliable change was shown at posttest and follow-up for all programs, with consistently higher rates for CBT. Participants allocated to IPT showed significantly lower treatment satisfaction compared to CBT and MoodGYM. There was a dropout rate of 1294/1843 (70%) at posttest, highest for MoodGYM. Intention-to-treat analyses confirmed these findings. Conclusions: Despite a high dropout rate and lower satisfaction scores, this study suggests that Internet-delivered self-guided IPT is effective in reducing depressive symptoms, and may be noninferior to MoodGYM. The completion rates of IPT and CBT were higher than MoodGYM, indicating some progress in refining Internet-based self-help. Internet-delivered treatment options available for people suffering from depression now include IPT. Trial Registration: International Standard Randomized Controlled Trial Number (ISRCTN): 69603913; http://www.controlled-trials.com/ISRCTN69603913 (Archived by WebCite at http://www.webcitation.org/6FjMhmE1o)

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.