Improvement of Physical Activity by a Kiosk-based Electronic Screening and Brief Intervention in Routine Primary Health Care: Patient-Initiated Versus Staff-Referred

Background

Interactive behavior change technology (eg, computer programs, Internet websites, and mobile phones) may facilitate the implementation of lifestyle behavior interventions in routine primary health care. Effective, fully automated solutions not involving primary health care staff may offer low-cost support for behavior change.

Objectives

We explored the effectiveness of an electronic screening and brief intervention (e-SBI) deployed through a stand-alone information kiosk for promoting physical activity among sedentary patients in routine primary health care. We further tested whether its effectiveness differed between patients performing the e-SBI on their own initiative and those referred to it by primary health care staff.

Methods

The e-SBI screens for the physical activity level, motivation to change, attitudes toward performing the test, and physical characteristics and provides tailored feedback supporting behavior change. A total of 7863 patients performed the e-SBI from 2007 through 2009 in routine primary health care in Östergötland County, Sweden. Of these, 2509 were considered not sufficiently physically active, and 311 of these 2509 patients agreed to participate in an optional 3-month follow-up. These 311 patients were included in the analysis and were further divided into two groups based on whether the e-SBI was performed on the patient´s own initiative (informed by posters in the waiting room) or if the patient was referred to it by staff. A physical activity score representing the number of days being physically active was compared between baseline e-SBI and the 3-month follow-up. Based on physical activity recommendations, a score of 5 was considered the cutoff for being sufficiently physically active.

Results

In all, 137 of 311 patients (44%) were sufficiently physically active at the 3-month follow-up. The proportion becoming sufficiently physically active was 16/55 (29%), 40/101 (40%), and 81/155 (52%) for patients with a physical activity score at baseline of 0, 1 to 2, and 3 to 4, respectively. The patient-initiated group and staff-referred group had similar mean physical activity scores at baseline (2.1, 95% confidence interval [CI] 1.8-2.3, versus 2.3, 95% CI 2.1-2.5) and at follow-up, (4.1, 95% CI 3.4-4.7, vs 4.2, 95% CI 3.7-4.8).

Conclusions

Among the sedentary patients in primary health care who participated in the follow-up, the e-SBI appeared effective at promoting short-term improvement of physical activity for about half of them. The results were similar when the e-SBI was patient-initiated or staff-referred. The e-SBI may be a low-cost complement to lifestyle behavior interventions in routine primary health care and could work as a stand-alone technique not requiring the involvment of primary health care staff.     

A framework for characterizing eHealth literacy demands and barriers

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.     

Why business modeling is crucial in the development of eHealth technologies

The impact and uptake of information and communication technologies that support health care are rather low. Current frameworks for eHealth development suffer from a lack of fitting infrastructures, inability to find funding, complications with scalability, and uncertainties regarding effectiveness and sustainability. These issues can be addressed by defining a better implementation strategy early in the development of eHealth technologies. A business model, and thus business modeling, help to determine such an implementation strategy by involving all important stakeholders in a value-driven dialogue on what the technology should accomplish. This idea also seems promising to eHealth, as it can contribute to the whole development of eHealth technology. We therefore suggest that business modeling can be used as an effective approach to supporting holistic development of eHealth technologies. The contribution of business modeling is elaborated in this paper through a literature review that covers the latest business model research, concepts from the latest eHealth and persuasive technology research, evaluation and insights from our prior eHealth research, as well as the review conducted in the first paper of this series. Business modeling focuses on generating a collaborative effort of value cocreation in which all stakeholders reflect on the value needs of the others. The resulting business model acts as the basis for implementation. The development of eHealth technology should focus more on the context by emphasizing what this technology should contribute in practice to the needs of all involved stakeholders. Incorporating the idea of business modeling helps to cocreate and formulate a set of critical success factors that will influence the sustainability and effectiveness of eHealth technology.