[Conference paper] ePHoRt Project: A Web-Based Platform for Home Motor Rehabilitation – Abstract

Abstract

ePHoRt is a project that aims to develop a web-based system for the remote monitoring of rehabilitation exercises in patients after hip replacement surgery. The tool intends to facilitate and enhance the motor recovery, due to the fact that the patients will be able to perform the therapeutic movements at home and at any time. As in any case of rehabilitation program, the time required to recover is significantly diminished when the individual has the opportunity to practice the exercises regularly and frequently. However, the condition of such patients prohibits transportations to and from medical centers and many of them cannot afford a private physiotherapist. Thus, low-cost technologies will be used to develop the platform, with the aim to democratize its access. By taking into account such a limitation, a relevant option to record the patient’s movements is the Kinect motion capture device. The paper describes an experiment that evaluates the validity and accuracy of this visual capture by a comparison to an accelerometer sensor. The results show a significant correlation between both systems and demonstrate that the Kinect is an appropriate tool for the therapeutic purpose of the project.

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via ePHoRt Project: A Web-Based Platform for Home Motor Rehabilitation | SpringerLink

[Abstract+References] XOOM: An End-User Development Tool for Web-Based Wearable Immersive Virtual Tours

Abstract

XOOM is a novel interactive tool that allows non ICT-specialists to create web-based applications of Wearable Immersive Virtual Reality (WIVR) technology that use 360° realistic videos as interactive virtual tours. These applications are interesting for various domains that range from gaming, entertainment, cultural heritage, and tourism to education, professional training, therapy and rehabilitation. 360° interactive videos are displayed on smart-phones placed on head-mounted VR viewers. Users explore the virtual environment and interact with active elements through head direction and movements. The virtual scenarios can be seen also on external displays (e.g., TV monitors or projections) to enable other users to participate in the experience, and to control the VR space if needed, e.g., for education, training or therapy purposes. XOOM provides the functionality to create applications of this kind, import 360° videos, concatenate them, and superimpose active elements on the virtual scenes, so that the resulting environment is more interactive and is customized to the requirement of a specific domain and user target. XOOM also supports automatic data gathering and visualizations (e.g., through heat-maps) of the users’ experience, which can be inspected for analytics purposes, as well as for user evaluation (e.g., in education, training, or therapy contexts). The paper describes the design and implementation of XOOM, and reports a case study in the therapeutic context.

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Source: XOOM: An End-User Development Tool for Web-Based Wearable Immersive Virtual Tours | SpringerLink

[ARTICLE] mHealth or eHealth? Efficacy, Use, and Appreciation of a Web-Based Computer-Tailored Physical Activity Intervention for Dutch Adults: A Randomized Controlled Trial – Full Text

ABSTRACT

Background: Until a few years ago, Web-based computer-tailored interventions were almost exclusively delivered via computer (eHealth). However, nowadays, interventions delivered via mobile phones (mHealth) are an interesting alternative for health promotion, as they may more easily reach people 24/7.

Objective: The first aim of this study was to compare the efficacy of an mHealth and an eHealth version of a Web-based computer-tailored physical activity intervention with a control group. The second aim was to assess potential differences in use and appreciation between the 2 versions.

Methods: We collected data among 373 Dutch adults at 5 points in time (baseline, after 1 week, after 2 weeks, after 3 weeks, and after 6 months). We recruited participants from a Dutch online research panel and randomly assigned them to 1 of 3 conditions: eHealth (n=138), mHealth (n=108), or control condition (n=127). All participants were asked to complete questionnaires at the 5 points in time. Participants in the eHealth and mHealth group received fully automated tailored feedback messages about their current level of physical activity. Furthermore, they received personal feedback aimed at increasing their amount of physical activity when needed. We used analysis of variance and linear regression analyses to examine differences between the 2 study groups and the control group with regard to efficacy, use, and appreciation.

Results: Participants receiving feedback messages (eHealth and mHealth together) were significantly more physically active after 6 months than participants in the control group (B=8.48, df=2, P=.03, Cohen d=0.27). We found a small effect size favoring the eHealth condition over the control group (B=6.13, df=2, P=.09, Cohen d=0.21). The eHealth condition had lower dropout rates (117/138, 84.8%) than the mHealth condition (81/108, 75.0%) and the control group (91/127, 71.7%). Furthermore, in terms of usability and appreciation, the eHealth condition outperformed the mHealth condition with regard to participants receiving (t₁₈₂=3.07, P=.002) and reading the feedback messages (t₁₈₁=2.34, P=.02), as well as the clarity of the messages (t₁₈₁=1.99, P=.049).

Conclusions: We tested 2 Web-based computer-tailored physical activity intervention versions (mHealth and eHealth) against a control condition with regard to efficacy, use, usability, and appreciation. The overall effect was mainly caused by the more effective eHealth intervention. The mHealth app was rated inferior to the eHealth version with regard to usability and appreciation. More research is needed to assess how both methods can complement each other.

Trial Registration: Netherlands Trial Register: NTR4503; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4503 (Archived by WebCite at http://www.webcitation.org/6lEi1x40s)

Introduction

Insufficient physical activity is considered to be a major public health issue worldwide [1,2]. The Dutch public health guidelines recommend adults to engage in moderate- to vigorous-intensity physical activity for at least 30 minutes on at least 5 days per week [3,4]. Studies suggest that sufficient physical activity can effectively prevent numerous chronic diseases and mental health issues [2,4–6]. Lee et al [7] argued that 6% to 10% of worldwide deaths caused by noncommunicable diseases, such as cancer, cardiovascular diseases, and diabetes, can be attributed to physical inactivity. Therefore, there is a need for interventions that increase the level of physical activity and can reach a broad population cost effectively [1].

Empirical research suggests that Web-based computer-tailored interventions are a promising solution [8]. These interventions provide tailored information and feedback via the Internet and therefore have some important advantages. First, Web-based computer-tailored interventions can adapt intervention materials according to the specific situation, characteristics, and needs of an individual and accordingly make information more personally relevant for the individual [9–11]. Second, research has shown that tailored messages are more likely to be read, understood, discussed with others, and remembered by the receiver [12–14]. Third, due to the fact that more and more people are using the Internet to search for health-related information and health advice [15–17], Web-based computer-tailored health interventions offer an effective method to reach a broad population cost effectively [18–22]. Fourth, even though a broad population is targeted simultaneously, each individual can make use of the intervention privately at any given point in time or place [18,23].

Until a few years ago, Web-based computer-tailored interventions were almost exclusively delivered via computer. This medium of delivery has formed the term eHealth (electronic Health). The concept of eHealth has been described as the use of the Internet and related technologies to deliver health-related information and interventions [23]. Even though eHealth has been shown to be an efficient strategy to lower costs and deliver health messages more interactively, it also has several disadvantages. One of the major problems with eHealth interventions is the high percentage of dropout [24,25].

To make interventions even more accessible, and thereby decrease chances of dropout, health promotion professionals are increasingly interested in the use of mHealth (mobile Health). mHealth refers to the delivery of health messages and interventions via mobile phones or tablets by making use of telecommunication and multimedia technologies [26–31]. In the Netherlands, almost 70% of Dutch households use the Internet via mobile phones and approximately 45% use tablets [32]. Based on the increasing usage of mobile phones as a lifestyle device, it has been argued that mHealth might increase the use of interventions and thereby also their efficacy [28,29]. Whereas computers and laptops are relatively stationary, mobile phones and tablets can be carried and used everywhere [33]. People are able to use mHealth independent of time or space, which could improve the usage and evaluation of interventions compared with eHealth [28,31,33].

Most people already use their phones for a variety of personal and work-related matters, such as social networking, calendaring, financial tracking, or emailing [33]. This leads to the assumption that the inclusion of health-related information would be advisable. However, previous research shows some pitfalls of mHealth. First, mobile phone technology is a rapidly changing field that introduces new apps, communication possibilities, and additional gadgets nearly by the day. This makes it difficult for intervention developers to keep up with the newest technologies and interests of their users [34,35]. Second, although using text messaging can be a very effective way of communicating, some intervention messages might be too long or difficult to be presented in such a short manner. This restricted communication can lead to more misunderstandings between the participant and health professional, which in turn can influence the effectiveness of the intervention [36]. And third, both participants and health professionals claim to feel unsure about the safety of private and sensitive information. Although this concern can also arise in the eHealth sector, the inferior but rapidly growing mHealth sector evokes skepticism on both sides [37].

To examine whether mHealth can improve the use and efficacy and reduce dropout rates of Web-based computer-tailored interventions, this study examined the effects of an mHealth and eHealth intervention on physical activity compared with a control group. Both interventions were identical with regard to content but differed in the medium of delivery. The main aim of the study was to examine the efficacy of the 2 versions on physical activity and to compare them with a control group. A secondary aim was to study potential differences in dropout and appreciation of the mHealth and eHealth intervention.

Figure 1. Flowchart of the participation of respondents.

Continue —> JMIR-mHealth or eHealth? Efficacy, Use, and Appreciation of a Web-Based Computer-Tailored Physical Activity Intervention for Dutch Adults: A Randomized Controlled Trial | Gomez Quiñonez | Journal of Medical Internet Research

[ARTICLE] Increasing Patient Engagement in Rehabilitation Exercises Using Computer-Based Citizen Science – Full Text HTML

Abstract

Patient motivation is an important factor to consider when developing rehabilitation programs. Here, we explore the effectiveness of active participation in web-based citizen science activities as a means of increasing participant engagement in rehabilitation exercises, through the use of a low-cost haptic joystick interfaced with a laptop computer. Using the joystick, patients navigate a virtual environment representing the site of a citizen science project situated in a polluted canal. Participants are tasked with following a path on a laptop screen representing the canal. The experiment consists of two conditions: in one condition, a citizen science component where participants classify images from the canal is included; and in the other, the citizen science component is absent. Both conditions are tested on a group of young patients undergoing rehabilitation treatments and a group of healthy subjects. A survey administered at the end of both tasks reveals that participants prefer performing the scientific task, and are more likely to choose to repeat it, even at the cost of increasing the time of their rehabilitation exercise. Furthermore, performance indices based on data collected from the joystick indicate significant differences in the trajectories created by patients and healthy subjects, suggesting that the low-cost device can be used in a rehabilitation setting for gauging patient recovery.

Continue–>  PLOS ONE: Increasing Patient Engagement in Rehabilitation Exercises Using Computer-Based Citizen Science.

Participate: Transition Planning for Youth with TBI

The NIDRR-funded project on Defining Success: Web-Based Transition Training for Students with Traumatic Brain Injury (TBI) is seeking parents of children with TBI to participate in a study assessing the effectiveness of a web-based intervention for youth with TBI transitioning from school. Qualifying participants will take a short online survey before and after visiting the Transition Web website over two months, after which they will receive $75 in compensation. For more information, email transitionweb@cbirt.org.