Related Articles

Background

The field of eHealth holds promise for supporting and enabling health behavior change and the prevention and management of chronic disease.

Objective

In order to establish areas of congruence and controversy among contributors to the early development, evaluation, and dissemination of eHealth applications, as well as the desire to inform an evaluation research funding agenda, 38 semistructured, qualitative interviews were conducted among stakeholders in eHealth between May 2002 and September 2003.

Methods

Participants were asked about their perspectives on the credibility, value, and future potential of information technology for health behavior change and chronic disease management. Interviews were coded and analyzed for emergent themes using qualitative methods.

Results

Consistent themes were identified across stakeholder groups, with slight differences in emphasis. These topics included the following: (1) consensus and standardization—most stakeholders expressed a strong desire for a more coordinated, rigorous effort to define and integrate the field; (2) evaluation methods and challenges—demonstrating outcomes is required to establish eHealth quality and efficacy, but stakeholders were not satisfied with the sensitivity, validity, and reliability of existing outcome measures; (3) quality, value, and future potential—the intersection between eHealth’s potential cost-effectiveness, efficiency, and improved clinical status among users generated a high degree of interest; and (4) health disparities—many stakeholders contended that traditionally underserved populations will particularly benefit from eHealth applications, although others argued that the underserved are also disadvantaged in terms of access to technology.

Conclusions

Recommendations included the need for improvement and formalization of development and evaluation standards across private and public sectors, additional research on the technology needs and preferences of traditionally underserved populations, and long-term epidemiologic studies of the impact of eHealth on outcomes and cost-effectiveness.

Background

The prevention of obesity and health concerns related to obesity are major challenges worldwide. The use of eHealth communication and the tailoring of information delivered via the Internet at the individual level may increase the effectiveness of interventions. Mastering behaviors related to nutrition, physical activity, and weight management are the main issues in preventing obesity, and the need for interdisciplinary knowledge within this area is obvious.

Objective

The objectives were to review the literature on tailored health communication and to present an interdisciplinary analysis of studies on “second” generation tailored interventions aimed at behavior change in nutrition, physical activity, or weight management.

Methods

A literature search was conducted of the main electronic information sources on health communication. Selection criteria were defined, and 23 intervention studies were selected. The content analysis focused on the following: study designs, objectives of behavior change, target groups, sample sizes, study lengths, attrition rates, theories applied, intervention designs, computer-based channels used, statistically significant outcomes from the perspective of tailoring, and possible biases of the studies. However, this was not a structured meta-analysis and cannot be replicated as such.

Results

Of the 23 studies, 21 were randomized controlled trials, and all focused on behavior change: 10 studies focused on behavior change in nutrition, 7 on physical activity, 2 on nutrition and physical activity, and 4 on weight management. The target groups and the number of participants varied: 8 studies included more than 500 participants, and 6 studies included less than 100. Most studies were short; the duration of 20 studies was 6 months or less. The Transtheoretical Model was applied in 14 of the 23 studies, and feedback as a tailoring mechanism was used in addition to an Internet site (or program) in 15 studies and in addition to email in 11 studies. Self-reporting was used in 15 studies, and 14 studies did not have a no-information control group. Tailoring was more effective in nutrition interventions than in physical activity and weight management interventions. The outcomes were mixed or negative in 4 studies of physical activity interventions and in 3 studies of weight management. The use of a no-information control group seemed to have been linked to statistically significant between-group effects in measuring physical activity. This bias effect related to intervention design may explain the differences in the outcomes of the physical activity studies.

Conclusions

Tailoring was shown to have been an effective method in nutrition interventions, but the results for physical activity were mixed, which is in line with previous studies. Nevertheless, the effect of possible biases, such as relying solely on self-reports and on intervention design without a no-information control group, should not be underestimated. Thus, the issue of bias merits more attention in planning interventions and in future meta-analyses.

Obesity is often cited as the most prevalent chronic health condition and highest priority public health problem in the United States. There is a limited but growing body of evidence suggesting that mobile eHealth behavioral interventions, if properly designed, may be effective in promoting and sustaining successful weight loss and weight maintenance behavior changes.

This paper reviews the current literature on the successes and failures of public health, provider-administered, and self-managed behavioral health interventions for weight loss. The prevailing theories of health behavior change are discussed from the perspective of how this knowledge can serve as an evidence base to inform the design of mobile eHealth weight loss interventions. Tailored informational interventions, which, in recent years, have proven to be the most effective form of conventional health behavior intervention for weight loss, are discussed. Lessons learned from the success of conventional tailored informational interventions and the early successes of desktop computer–assisted self-help weight management interventions are presented, as are design principles suggested by Social Cognitive Theory and the Social Marketing Model.

Relevant computing and communications technology convergence trends are also discussed. The recent trends in rapid advancement, convergence, and public adoption of Web-enabled cellular telephone and wireless personal digital assistant (PDA) devices provide timely opportunities to deliver the mass customization capabilities, reach, and interactivity required for the development, administration, and adoption of effective population-level eHealth tailored informational interventions for obesity.

Background

Consumer eHealth interventions are of a growing importance in the individual management of health and health behaviors. However, a range of access, resources, and skills barriers prevent health care consumers from fully engaging in and benefiting from the spectrum of eHealth interventions. Consumers may engage in a range of eHealth tasks, such as participating in health discussion forums and entering information into a personal health record. eHealth literacy names a set of skills and knowledge that are essential for productive interactions with technology-based health tools, such as proficiency in information retrieval strategies, and communicating health concepts effectively.

Objective

We propose a theoretical and methodological framework for characterizing complexity of eHealth tasks, which can be used to diagnose and describe literacy barriers and inform the development of solution strategies.

Methods

We adapted and integrated two existing theoretical models relevant to the analysis of eHealth literacy into a single framework to systematically categorize and describe task demands and user performance on tasks needed by health care consumers in the information age. The method derived from the framework is applied to (1) code task demands using a cognitive task analysis, and (2) code user performance on tasks. The framework and method are applied to the analysis of a Web-based consumer eHealth task with information-seeking and decision-making demands. We present the results from the in-depth analysis of the task performance of a single user as well as of 20 users on the same task to illustrate both the detailed analysis and the aggregate measures obtained and potential analyses that can be performed using this method.

Results

The analysis shows that the framework can be used to classify task demands as well as the barriers encountered in user performance of the tasks. Our approach can be used to (1) characterize the challenges confronted by participants in performing the tasks, (2) determine the extent to which application of the framework to the cognitive task analysis can predict and explain the problems encountered by participants, and (3) inform revisions to the framework to increase accuracy of predictions.

Conclusions

The results of this illustrative application suggest that the framework is useful for characterizing task complexity and for diagnosing and explaining barriers encountered in task completion. The framework and analytic approach can be a potentially powerful generative research platform to inform development of rigorous eHealth examination and design instruments, such as to assess eHealth competence, to design and evaluate consumer eHealth tools, and to develop an eHealth curriculum.

Background

Web-based and mobile health interventions (also called “Internet interventions” or "eHealth/mHealth interventions") are tools or treatments, typically behaviorally based, that are operationalized and transformed for delivery via the Internet or mobile platforms. These include electronic tools for patients, informal caregivers, healthy consumers, and health care providers. The Consolidated Standards of Reporting Trials (CONSORT) statement was developed to improve the suboptimal reporting of randomized controlled trials (RCTs). While the CONSORT statement can be applied to provide broad guidance on how eHealth and mHealth trials should be reported, RCTs of web-based interventions pose very specific issues and challenges, in particular related to reporting sufficient details of the intervention to allow replication and theory-building.

Objective

To develop a checklist, dubbed CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile HEalth Applications and onLine TeleHealth), as an extension of the CONSORT statement that provides guidance for authors of eHealth and mHealth interventions.

Methods

A literature review was conducted, followed by a survey among eHealth experts and a workshop.

Results

A checklist instrument was constructed as an extension of the CONSORT statement. The instrument has been adopted by the Journal of Medical Internet Research (JMIR) and authors of eHealth RCTs are required to submit an electronic checklist explaining how they addressed each subitem.

Conclusions

CONSORT-EHEALTH has the potential to improve reporting and provides a basis for evaluating the validity and applicability of eHealth trials. Subitems describing how the intervention should be reported can also be used for non-RCT evaluation reports. As part of the development process, an evaluation component is essential; therefore, feedback from authors will be solicited, and a before-after study will evaluate whether reporting has been improved.

Background

Co-occurrence of different behaviors was investigated using the theoretical underpinnings of the Transtheoretical Model, the Theory of Triadic Influence and the concept of Transfer.

Purpose

To investigate relationships between different health behaviors' stages of change, how behaviors group, and whether study participants cluster in terms of their behaviors.

Method

Relationships across stages for different behaviors were assessed in three studies with N = 3,519, 965, and 310 individuals from the USA and Germany by telephone and internet surveys using correlational analyses, factor analyses, and cluster analyses.

Results

Consistently stronger correlations were found between nutrition and physical activity (r = 0.16–0.26, p < 0.01) than between non-smoking and nutrition (r = 0.08–0.16, p < 0.03), or non-smoking and physical activity (r = 0.01–0.21). Principal component analyses of investigated behaviors indicated two factors: a “health-promoting” factor and a “health-risk” factor. Three distinct behavioral patterns were found in the cluster analyses.

Conclusion

Our results support the assumption that individuals who are in a higher stage for one behavior are more likely to be in a higher stage for another behavior as well. If the aim is to improve a healthy lifestyle, success in one behavior can be used to facilitate changes in other behaviors—especially if the two behaviors are both health-promoting or health-risky. Moreover, interventions should be targeted towards the different behavioral patterns rather than to single behaviors. This might be achieved by addressing transfer between behaviors.

Background

eHealth tools on the Internet have the potential to help people manage their health and health care. However, little is known about the distribution and use of different kinds of eHealth tools across the population or within population subgroups.

Objective

The purpose of this study was to examine the prevalence and predictors of participation in specific online health-related activities.

Methods

A secondary data analysis of the National Cancer Institute’s Health Information National Trends Survey (HINTS) 2005 was conducted to study three online behaviors among Internet users (n = 3244): searching for health information for oneself, participating in a support group for those with similar health or medical conditions, and purchasing medicine or vitamins.

Results

A total of 58% of Internet users reported searching for health information for themselves, 3.8% used online support groups, and 12.8% bought medicine or vitamins online in the past year. Multivariate analysis found that those seeking health information were more likely to be women (OR = 2.23, 95% CI = 1.60, 3.09), have cable or satellite Internet connections (OR = 1.73, 95% CI = 1.22, 2.45) or DSL connections (OR = 1.94, 95% CI = 1.36, 2.76), have Internet access from work (OR = 2.43, 95% CI = 1.27, 4.67) or from home and work (OR = 1.73, 95% CI = 1.31, 2.30), and report more hours of weekday Internet use (OR = 4.12, 95% CI = 2.41, 7.07). Those with a high school education or less (OR = 0.44, 95% CI = 0.31, 0.63) and those with some college (OR = 0.66, 95% CI = 0.49, 0.89) were less likely to search for health information. Online support groups were more likely to be used by those with “fair” health (OR = 3.28, 95% CI = 1.21, 8.92) and “poor” health (OR = 5.98, 95% CI = 1.49, 24.07) and those with lower incomes (OR = 2.64, 95% CI = 1.09, 6.41) and less likely to be used by those with Internet access both at home and work (OR = 0.56, 95% CI = 0.35, 0.90). Those who were age 35-49 (OR = 2.16, 95% CI = 1.43, 3.26), age 50-64 (OR = 2.44, 95% CI = 1.53, 3.89), and age 65-74 (OR = 2.18, 95% CI = 1.30, 3.67) and those who were married (OR = 1.93, 95% CI = 1.13, 3.30) were more likely to purchase medicine or vitamins online.

Conclusions

The Internet was most widely used as a health information resource, with less participation in the purchase of medicine and vitamins and in online support groups. Results suggest that modifying survey questions to better capture forms of online support and medications purchased could provide greater understanding of the nature of participation in these activities.

Background

Treatments and organizational changes supported by eHealth are beginning to play an important role in improving disease treatment outcome and providing cost-efficient care management. “Improvehealth.eu” is a novel eHealth service to support the treatment of patients with depressive disorder. It offers active patient engagement and collaborative care management by combining Web- and mobile-based information and communication technology systems and access to care managers.

Objectives

Our objective was to assess the feasibility of a novel eHealth service.

Methods

The intervention—the “Improvehealth.eu” service—was explored in the course of a pilot study comparing two groups of patients receiving treatment as usual and treatment as usual with eHealth intervention. We compared patients’ medication adherence and outcome measures between both groups and additionally explored usage and overall perceptions of the intervention in intervention group.

Results

The intervention was successfully implemented in a pilot with 46 patients, of whom 40 were female. Of the 46 patients, 25 received treatment as usual, and 21 received the intervention in addition to treatment as usual. A total of 55% (12/25) of patients in the former group and 45% (10/21) in the latter group finished the 6-month pilot. Available case analysis indicated an improvement of adherence in the intervention group (odds ratio [OR] = 10.0, P = .03). Intention-to-treat analysis indicated an improvement of outcome in the intervention group (ORs ranging from 0.35 to 18; P values ranging from .003 to .20), but confidence intervals were large due to small sample sizes. Average duration of use of the intervention was 107 days. The intervention was well received by 81% (17/21) of patients who reported feeling actively engaged, in control of their disease, and that they had access to a high level of information. In all, 33% (7/21) of the patients also described drawbacks of the intervention, mostly related to usability issues.

Conclusions

The results of this pilot study indicate that the intervention was well accepted and helped the patients in the course of treatment. The results also suggest the potential of the intervention to improve both medication adherence and outcome measures of treatment, including reduction of depression severity and patients becoming “healthy.”

Background

Internet-delivered interventions can effectively change health risk behaviors, but the actual use of these interventions by the target group once they access the website is often very low (high attrition, low adherence). Therefore, it is relevant and necessary to focus on factors related to use of an intervention once people arrive at the intervention website. We focused on user perceptions resulting in e-loyalty (ie, intention to visit an intervention again and to recommend it to others). A background theory for e-loyalty, however, is still lacking for Internet-delivered interventions.

Objective

The objective of our study was to propose and validate a conceptual model regarding user perceptions and e-loyalty within the field of eHealth.

Methods

We presented at random 3 primary prevention interventions aimed at the general public and, subsequently, participants completed validated measures regarding user perceptions and e-loyalty. Time on each intervention website was assessed by means of server registrations.

Results

Of the 592 people who were invited to participate, 397 initiated the study (response rate: 67%) and 351 (48% female, mean age 43 years, varying in educational level) finished the study (retention rate: 88%). Internal consistency of all measures was high (Cronbach alpha > .87). The findings demonstrate that the user perceptions regarding effectiveness (betarange .21–.41) and enjoyment (betarange .14–.24) both had a positive effect on e-loyalty, which was mediated by active trust (betarange .27–.60). User perceptions and e-loyalty had low correlations with time on the website (r range .04–.18).

Conclusions

The consistent pattern of findings speaks in favor of their robustness and contributes to theory validation regarding e-loyalty. The importance of a theory-driven solution to a practice-based problem (ie, low actual use) needs to be stressed in view of the importance of the Internet in terms of intervention development. Longitudinal studies are needed to investigate whether people will actually revisit intervention websites and whether this leads to changes in health risk behaviors.

Summary

Objective

To investigate current use of the internet and eHealth amongst adults.

Design

Focus groups were conducted to explore participants' attitudes to and reasons for health internet use.

Main outcome measures

The focus group data were analysed and interpreted using thematic analysis.

Results

Three superordinate themes exploring eHealth behaviours were identified: decline in expert authority, pervasiveness of health information on the internet and empowerment. Results showed participants enjoyed the immediate benefits of eHealth information and felt empowered by increased knowledge, but they would be reluctant to lose face-to-face consultations with their GP.

Conclusions

Our findings illustrate changes in patient identity and a decline in expert authority with ramifications for the practitioner–patient relationship and subsequent implications for health management more generally.

Background

Many people in Western countries do not follow public health physical activity (PA) recommendations. Web-based interventions provide cost- and time-efficient means of delivering individually targeted lifestyle modification at a population level.

Objective

To examine whether access to a website with individually tailored feedback and suggestions on how to increase PA led to improved PA, anthropometrics, and health measurements.

Methods

Physically inactive adults (n = 12,287) participating in a nationwide eHealth survey and health examination in Denmark were randomly assigned to either an intervention (website) (n = 6055) or a no-intervention control group (n = 6232) in 2008. The intervention website was founded on the theories of stages of change and of planned behavior and, apart from a forum page where a physiotherapist answered questions about PA and training, was fully automated. After 3 and again after 6 months we emailed participants invitations to answer a Web-based follow-up questionnaire, which included the long version of the International Physical Activity Questionnaire. A subgroup of participants (n = 1190) were invited to a follow-up health examination at 3 months.

Results

Less than 22.0% (694/3156) of the participants logged on to the website once and only 7.0% (222/3159) logged on frequently. We found no difference in PA level between the website and control groups at 3- and 6-month follow-ups. By dividing participants into three groups according to use of the intervention website, we found a significant difference in total and leisure-time PA in the website group. The follow-up health examination showed no significant reductions in body mass index, waist circumference, body fat percentage, and blood pressure, or improvements in arm strength and aerobic fitness in the website group.

Conclusions

Based on our findings, we suggest that active users of a Web-based PA intervention can improve their level of PA. However, for unmotivated users, single-tailored feedback may be too brief. Future research should focus on developing more sophisticated interventions with the potential to reach both motivated and unmotivated sedentary individuals.

Trial Registration

Clinicaltrials.gov NCT01295203; http://clinicaltrials.gov/ct2/show/NCT01295203 (Archived by WebCite at http://www.webcitation.org/6B7HDMqiQ)

Objectives

Factors such as an ageing and rapidly growing population, an increase in chronic disease rates and a global shortage of health professionals place increased pressure on Australian health departments to deliver more with less. To address the challenge faced by clinicians and support staff, the Queensland Department of Health established an eHealth strategy in 2006 with a vision to deliver a patient centric, networked model of care.

Methods

Queensland Health's eHealth program is a complex program which brings together the outputs and products of numerous projects to provide new clinical capabilities across the state. To ensure the potential benefits of the Queensland Government investment are realised, the eHealth program is implementing comprehensive benefits management to plan for key outcomes and benefits, support projects to deliver those benefits and ensure that they are delivered through ongoing measurement.

Results

The first stage of the eHealth program is already delivering benefits across the health department with a number of projects currently live in numerous sites across Queensland.

Conclusions

By adopting an evidence based benefits management approach, Queensland Health's eHealth program is able to demonstrate the achievement of these benefits with tangible evidence that will create momentum for change in the short term, provide the evidence for future funding applications in the medium term, and build an understanding of the economic impacts of eHealth in the long term.

As the use of eHealth grows and diversifies globally, the concept of eHealth literacy – a foundational skill set that underpins the use of information and communication technologies (ICT) for health – becomes more important than ever to understand and advance. EHealth literacy draws our collective attention to the knowledge and complex skill set that is often taken for granted when people interact with technology to address information, focusing our attention on learning and usability issues from the clinical through to population health level. Just as the field of eHealth is dynamic and evolving, so too is the context where eHealth literacy is applied and understood. The original Lily Model of eHealth literacy and scale used to assess it were developed at a time when the first generation of web tools gained prominence before the rise of social media. The rapid shifts in the informational landscape created by Web 2.0 tools and environments suggests it might be time to revisit the concept of eHealth Literacy and consider what a second release might look like.

Objective This study examines adolescents’ willingness to use the internet and other forms of technology for health promotion purposes (i.e., “eHealth promotion” willingness) and determines if a relationship exists between adolescents’ behavioral risks and their eHealth promotion willingness. Methods A total of 332 adolescents provided data at a routine medical check-up, including assessments of technology access, eHealth promotion willingness, and multiple behavioral risk factors for child- and adult-onset disease (body mass index, physical activity, smoking, sun protection, depression). Results The level of access to technology among the sample was high, with moderate willingness to engage in eHealth promotion. After adjusting for adolescents’ access to technology, the presence of multiple behavioral risk factors was positively associated with willingness to use technology for health promotion purposes (β =.12, p =.03). Conclusions Adolescents with both single and multiple behavioral risk factors are in need of health promotion to prevent the onset of disease later in life. eHealth appears to be an acceptable and promising intervention approach with this population.

Background

Researchers and practitioners have developed numerous online interventions that encourage people to reduce their drinking, increase their exercise, and better manage their weight. Motivations to develop eHealth interventions may be driven by the Internet’s reach, interactivity, cost-effectiveness, and studies that show online interventions work. However, when designing online interventions suitable for public campaigns, there are few evidence-based guidelines, taxonomies are difficult to apply, many studies lack impact data, and prior meta-analyses are not applicable to large-scale public campaigns targeting voluntary behavioral change.

Objectives

This meta-analysis assessed online intervention design features in order to inform the development of online campaigns, such as those employed by social marketers, that seek to encourage voluntary health behavior change. A further objective was to increase understanding of the relationships between intervention adherence, study adherence, and behavioral outcomes.

Methods

Drawing on systematic review methods, a combination of 84 query terms were used in 5 bibliographic databases with additional gray literature searches. This resulted in 1271 abstracts and papers; 31 met the inclusion criteria. In total, 29 papers describing 30 interventions were included in the primary meta-analysis, with the 2 additional studies qualifying for the adherence analysis. Using a random effects model, the first analysis estimated the overall effect size, including groupings by control conditions and time factors. The second analysis assessed the impacts of psychological design features that were coded with taxonomies from evidence-based behavioral medicine, persuasive technology, and other behavioral influence fields. These separate systems were integrated into a coding framework model called the communication-based influence components model. Finally, the third analysis assessed the relationships between intervention adherence and behavioral outcomes.

Results

The overall impact of online interventions across all studies was small but statistically significant (standardized mean difference effect size d = 0.19, 95% confidence interval [CI] = 0.11 - 0.28, P < .001, number of interventions k = 30). The largest impact with a moderate level of efficacy was exerted from online interventions when compared with waitlists and placebos (d = 0.28, 95% CI = 0.17 - 0.39, P < .001, k = 18), followed by comparison with lower-tech online interventions (d = 0.16, 95% CI = 0.00 - 0.32, P = .04, k = 8); no significant difference was found when compared with sophisticated print interventions (d = –0.11, 95% CI = –0.34 to 0.12, P = .35, k = 4), though online interventions offer a small effect with the advantage of lower costs and larger reach. Time proved to be a critical factor, with shorter interventions generally achieving larger impacts and greater adherence. For psychological design, most interventions drew from the transtheoretical approach and were goal orientated, deploying numerous influence components aimed at showing users the consequences of their behavior, assisting them in reaching goals, and providing normative pressure. Inconclusive results suggest a relationship between the number of influence components and intervention efficacy. Despite one contradictory correlation, the evidence suggests that study adherence, intervention adherence, and behavioral outcomes are correlated.

Conclusions

These findings demonstrate that online interventions have the capacity to influence voluntary behaviors, such as those routinely targeted by social marketing campaigns. Given the high reach and low cost of online technologies, the stage may be set for increased public health campaigns that blend interpersonal online systems with mass-media outreach. Such a combination of approaches could help individuals achieve personal goals that, at an individual level, help citizens improve the quality of their lives and at a state level, contribute to healthier societies.

Context

The term eHealth is widely used by many individuals, academic institutions, professional bodies, and funding organizations. It has become an accepted neologism despite the lack of an agreed-upon clear or precise definition. We believe that communication among the many individuals and organizations that use the term could be improved by comprehensive data about the range of meanings encompassed by the term.

Objective

To report the results of a systematic review of published, suggested, or proposed definitions of eHealth.

Data Sources

Using the search query string “eHealth” OR “e-Health” OR “electronic health”, we searched the following databases: Medline and Premedline (1966-June 2004), EMBASE (1980-May 2004), International Pharmaceutical Abstracts (1970-May 2004), Web of Science (all years), Information Sciences Abstracts (1966-May 2004), Library Information Sciences Abstracts (1969-May 2004), and Wilson Business Abstracts (1982-March 2004). In addition, we searched dictionaries and an Internet search engine.

Study Selection

We included any source published in either print format or on the Internet, available in English, and containing text that defines or attempts to define eHealth in explicit terms. Two of us independently reviewed titles and abstracts of citations identified in the bibliographic databases and Internet search, reaching consensus on relevance by discussion.

Data Extraction

We retrieved relevant reports, articles, references, letters, and websites containing definitions of eHealth. Two of us qualitatively analyzed the definitions and coded them for content, emerging themes, patterns, and novel ideas.

Data Synthesis

The 51 unique definitions that we retrieved showed a wide range of themes, but no clear consensus about the meaning of the term eHealth. We identified 2 universal themes (health and technology) and 6 less general (commerce, activities, stakeholders, outcomes, place, and perspectives).

Conclusions

The widespread use of the term eHealth suggests that it is an important concept, and that there is a tacit understanding of its meaning. This compendium of proposed definitions may improve communication among the many individuals and organizations that use the term.

This paper reviews trends and issues in health and in the information and communication technologies (ICT) market as they relate to the deployment of eHealth solutions in Latin America and the Caribbean. Heretofore designed for industrialized countries and large organizations, eHealth solutions are being proposed as an answer to a variety of health-system management problems and health care demands faced by all health organizations including those in developing societies. Particularly, eHealth is seen as especially useful in the operational support of the new health care models being implemented in many countries. The authors examine those developments vis-à-vis the characteristics of the Latin American and the Caribbean health-sector organizational preparedness and technological infrastructure, and propose policy and organizational actions to foster the development of eHealth solutions in the region.

Background

The Internet is a trusted source of health information for growing majorities of Web users. The promise of online health interventions will be realized with the development of purely online theory-based programs for Web users that are evaluated for program effectiveness and the application of behavior change theory within the online environment. Little is known, however, about the demographic, behavioral, or psychosocial characteristics of Web-health users who represent potential participants in online health promotion research. Nor do we understand how Web users’ psychosocial characteristics relate to their health behavior—information essential to the development of effective, theory-based online behavior change interventions.

Objective

This study examines the demographic, behavioral, and psychosocial characteristics of Web-health users recruited for an online social cognitive theory (SCT)-based nutrition, physical activity, and weight gain prevention intervention, the Web-based Guide to Health (WB-GTH).

Methods

Directed to the WB-GTH site by advertisements through online social and professional networks and through print and online media, participants were screened, consented, and assessed with demographic, physical activity, psychosocial, and food frequency questionnaires online (taking a total of about 1.25 hours); they also kept a 7-day log of daily steps and minutes walked.

Results

From 4700 visits to the site, 963 Web users consented to enroll in the study: 83% (803) were female, participants’ mean age was 44.4 years (SD 11.03 years), 91% (873) were white, and 61% (589) were college graduates; participants’ median annual household income was approximately US $85,000. Participants’ daily step counts were in the low-active range (mean 6485.78, SD 2352.54) and overall dietary levels were poor (total fat g/day, mean 77.79, SD 41.96; percent kcal from fat, mean 36.51, SD 5.92; fiber g/day, mean 17.74, SD 7.35; and fruit and vegetable servings/day, mean 4.03, SD 2.33). The Web-health users had good self-efficacy and outcome expectations for health behavior change; however, they perceived little social support for making these changes and engaged in few self-regulatory behaviors. Consistent with SCT, theoretical models provided good fit to Web-users’ data (root mean square error of the approximation [RMSEA] < .05). Perceived social support and use of self-regulatory behaviors were strong predictors of physical activity and nutrition behavior. Web users’ self-efficacy was also a good predictor of healthier levels of physical activity and dietary fat but not of fiber, fruits, and vegetables. Social support and self-efficacy indirectly predicted behavior through self-regulation, and social support had indirect effects through self-efficacy.

Conclusions

Results suggest Web-health users visiting and ultimately participating in online health interventions may likely be middle-aged, well-educated, upper middle class women whose detrimental health behaviors put them at risk of obesity, heart disease, some cancers, and diabetes. The success of Internet physical activity and nutrition interventions may depend on the extent to which they lead users to develop self-efficacy for behavior change, but perhaps as important, the extent to which these interventions help them garner social-support for making changes. Success of these interventions may also depend on the extent to which they provide a platform for setting goals, planning, tracking, and providing feedback on targeted behaviors.

Diabetes self-management (DSM) training helps prevent diabetic complications. eHealth approaches may improve its optimal use. The aims were to determine a) acceptability of e-HealthyStrides© (an interactive, Internet-based, patient-driven, diabetes self-management support and social networking program) among Morehouse Community Physicians’ Network diabetics; b) efficacy for DSM behavior change c) success factors for use of e-HealthyStrides©. Baseline characteristics of pilot study participants are reported. Of those approached, 13.8% agreed to participate. Among participants, 96% were Black, 77% female; age 56±9.2 years; education: 44% college or higher and 15% less than 12th grade; 92.5% with home computers. Over half (51%) failed the Diabetes Knowledge Test. Nearly half (47%) were at goal A1C; 24% at goal blood pressure; 3% at goal LDL cholesterol level. Median (SD) Diabetes Empowerment Scale score = 3.93 (0.72) but managing psychosocial aspects = 3.89 (0.89) scored lower than other domains. There was low overall confidence for DSM behaviors. Assistance with healthy eating was the most frequently requested service. Requestors were more obese with worse A1C than others. Chronic care delivery scored average with high scores for counseling and problem solving but low scores for care coordination and follow up.

Background

eHealth applications for out-of-hospital monitoring and treatment follow-up have been advocated for many years as a promising tool to improve treatment compliance, promote individualized care and obtain a person-centred care. Despite these benefits and a large number of promising projects, a major breakthrough in everyday care is generally still lacking. Inappropriate organization for eHealth technology, reluctance from users in the introduction of new working methods, and resistance to information and communication technology (ICT) in general could be reasons for this. Another reason may be attitudes towards the potential in out-of-hospital eHealth applications. It is therefore of interest to study the general opinions among healthcare professionals to ICT in healthcare, as well as the attitudes towards using ICT as a tool for patient monitoring and follow-up at home. One specific area of interest is in-home follow-up of elderly patients with chronic heart failure (CHF). The aim of this paper is to investigate the attitudes towards ICT, as well as distance monitoring and follow-up, among healthcare professionals working with this patient group.

Method

This paper covers an attitude survey study based on responses from 139 healthcare professionals working with CHF care in Swedish hospital departments, i.e. cardiology and medicine departments. Comparisons between physicians and nurses, and in some cases between genders, on attitudes towards ICT tools and follow-up at home were performed.

Results

Out of the 425 forms sent out, 139 were collected, and 17 out of 21 counties and regions were covered in the replies. Among the respondents, 66% were nurses, 30% physicians and 4% others. As for gender, 90% of nurses were female and 60% of physicians were male. Internet was used daily by 67% of the respondents. Attitudes towards healthcare ICT were found positive as 74% were positive concerning healthcare ICT today, 96% were positive regarding the future of healthcare ICT, and 54% had high confidence in healthcare ICT. Possibilities for distance monitoring/follow-up are good according to 63% of the respondents, 78% thought that this leads to increased patient involvement, and 80% thought it would improve possibilities to deliver better care. Finally, 72% of the respondents said CHF patients would benefit from home monitoring/follow-up to some extent, and 19% to a large extent. However, the best method of follow-up was considered to be home visits by nurse, or phone contact.

Conclusion

The results indicate that a majority of the healthcare professionals in this study are positive to both current and future use of ICT tools in healthcare and home follow-up. Consequently other factors have to play an important role in the slow penetration of out-of-hospital eHealth applications in daily healthcare practice.

A health behavioral counseling curriculum grounded in Motivational Interviewing and the Transtheoretical Model of behavior change was developed to enhance knowledge and clinical skill among physician assistant (PA) students in managing cancer risk behaviors. A literature and curriculum review informed course content, teaching strategies, and learning activities. The course was evaluated over two pilot years. Students demonstrated increased knowledge and skills regarding the basic principles of the intervention models. The course was integrated into the pre-clinical year of PA training and will be disseminated, beginning with a faculty development workshop for all PA training programs in Texas, USA.

Compliance with colorectal cancer screening recommendations requires considerable conscious effort on the part of the individual patient, making an individual's decisions about engagement in screening an important contributor to compliance or noncompliance. The objective of this paper was to examine the effectiveness of individual-level behavior theories and their associated constructs in accounting for engagement in colorectal cancer screening behavior. We reviewed the literature examining constructs from formal models of individual-level health behavior as factors associated with compliance with screening for colorectal cancer. All published studies examining one or more constructs from the health belief model, theory of planned behavior, transtheoretical model, or social cognitive theory and their relation to screening behavior or behavioral intentions were included in the analysis. By and large, results of studies supported the theory-based predictions for the influence of constructs on cancer screening behavior. However, the evidence base for many of these relations, especially for models other than the health belief model, is quite limited. Suggestions are made for future research on individual-level determinants of colorectal cancer screening.

Background

This study was conceived to analyze how exercise and weight management psychosocial variables, derived from several health behavior change theories, predict weight change in a short-term intervention. The theories under analysis were the Social Cognitive Theory, the Transtheoretical Model, the Theory of Planned Behavior, and Self-Determination Theory.

Methods

Subjects were 142 overweight and obese women (BMI = 30.2 ± 3.7 kg/m2; age = 38.3 ± 5.8y), participating in a 16-week University-based weight control program. Body weight and a comprehensive psychometric battery were assessed at baseline and at program's end.

Results

Weight decreased significantly (-3.6 ± 3.4%, p < .001) but with great individual variability. Both exercise and weight management psychosocial variables improved during the intervention, with exercise-related variables showing the greatest effect sizes. Weight change was significantly predicted by each of the models under analysis, particularly those including self-efficacy. Bivariate and multivariate analyses results showed that change in variables related to weight management had a stronger predictive power than exercise-specific predictors and that change in weight management self-efficacy was the strongest individual correlate (p < .05). Among exercise predictors, with the exception of self-efficacy, importance/effort and intrinsic motivation towards exercise were the stronger predictors of weight reduction (p < .05).

Conclusion

The present models were able to predict 20–30% of variance in short-term weight loss and changes in weight management self-efficacy accounted for a large share of the predictive power. As expected from previous studies, exercise variables were only moderately associated with short-term outcomes; they are expected to play a larger explanatory role in longer-term results.

Background

Increased access to the Internet and the availability of efficacious eHealth interventions offer great promise for assisting adults with diabetes to change and maintain health behaviors. A key concern is whether levels of engagement in Internet programs are sufficient to promote and sustain behavior change.

Objective

This paper used automated data from an ongoing Internet-based diabetes self-management intervention study to calculate various indices of website engagement. The multimedia website involved goal setting, action planning, and self-monitoring as well as offering features such as “Ask an Expert” to enhance healthy eating, physical activity, and medication adherence. We also investigated participant characteristics associated with website engagement and the relationship between website use and 4-month behavioral and health outcomes.

Methods

We report on participants in a randomized controlled trial (RCT) who were randomized to receive (1) the website alone (n = 137) or (2) the website plus human support (n = 133) that included additional phone calls and group meetings. The website was available in English and Spanish and included features to enhance engagement and user experience. A number of engagement variables were calculated for each participant including number of log-ins, number of website components visited at least twice, number of days entering self-monitoring data, number of visits to the “Action Plan” section, and time on the website. Key outcomes included exercise, healthy eating, and medication adherence as well as body mass index (BMI) and biological variables related to cardiovascular disease risk.

Results

Of the 270 intervention participants, the average age was 60, the average BMI was 34.9 kg/m2, 130 (48%) were female, and 62 (23%) self-reported Latino ethnicity. The number of participant visits to the website over 4 months ranged from 1 to 119 (mean 28 visits, median 18). Usage decreased from 70% of participants visiting at least weekly during the first 6 weeks to 47% during weeks 7 to 16. There were no significant differences between website only and website plus support conditions on most of the engagement variables. In total, 75% of participants entered self-monitoring data at least once per week. Exercise action plan pages were visited more often than medication taking and healthy eating pages (mean of 4.3 visits vs 2.8 and 2.0 respectively, P < .001). Spearman nonparametric correlations indicated few significant associations between patient characteristics and summary website engagement variables, and key factors such as ethnicity, baseline computer use, age, health literacy, and education were not related to use. Partial correlations indicated that engagement, especially in self-monitoring, was most consistently related to improvement in healthy eating (r = .20, P = .04) and reduction of dietary fat (r = -.31, P = .001). There was also a significant correlation between self-monitoring and improvement in exercise (r = .20, P = .033) but not with medication taking.

Conclusions

Participants visited the website fairly often and used all of the theoretically important sections, but engagement decreased over 4 months. Usage rates and patterns were similar for a wide range of participants, which has encouraging implications for the potential reach of online interventions.

Trial Registration

NCT00987285; http://clinicaltrials.gov/show/NCT00987285 (Archived by WebCite at http://www.webcitation.org/5vpe4RHTV)

Background

For effective health promotion using health information technology (HIT), it is mandatory that health consumers have the behavioral intention to measure, store, and manage their own health data. Understanding health consumers’ intention and behavior is needed to develop and implement effective and efficient strategies.

Objective

To develop and verify the extended Technology Acceptance Model (TAM) in health care by describing health consumers’ behavioral intention of using HIT.

Methods

This study used a cross-sectional descriptive correlational design. We extended TAM by adding more antecedents and mediating variables to enhance the model’s explanatory power and to make it more applicable to health consumers’ behavioral intention. Additional antecedents and mediating variables were added to the hypothetical model, based on their theoretical relevance, from the Health Belief Model and theory of planned behavior, along with the TAM. We undertook structural equation analysis to examine the specific nature of the relationship involved in understanding consumers’ use of HIT. Study participants were 728 members recruited from three Internet health portals in Korea. Data were collected by a Web-based survey using a structured self-administered questionnaire.

Results

The overall fitness indices for the model developed in this study indicated an acceptable fit of the model. All path coefficients were statistically significant. This study showed that perceived threat, perceived usefulness, and perceived ease of use significantly affected health consumers’ attitude and behavioral intention. Health consumers’ health status, health belief and concerns, subjective norm, HIT characteristics, and HIT self-efficacy had a strong indirect impact on attitude and behavioral intention through the mediators of perceived threat, perceived usefulness, and perceived ease of use.

Conclusions

An extended TAM in the HIT arena was found to be valid to describe health consumers’ behavioral intention. We categorized the concepts in the extended TAM into 3 domains: health zone, information zone, and technology zone.