Hermie Hermens

A randomized controlled trial on providing ankle-foot orthoses in patients with (sub-)acute stroke: Short-term kinematic and spatiotemporal effects and effects of timing

Initial walking function is often limited after stroke, and regaining walking ability is an important goal in rehabilitation. Various compensatory movement strategies to ensure sufficient foot-clearance are reported. Ankle-foot orthoses (AFOs) are often prescribed to improve foot-clearance and may influence these strategies. However, research studying effects of actual AFO-provision early after stroke is limited. We conducted an explorative randomized controlled trial and aimed to study the short-term effects of AFO-provision on kinematic and spatiotemporal parameters in patients early after stroke. In addition, we studied whether timing of AFO-provision influenced these effects. Unilateral hemiparetic patients maximal six weeks post-stroke were randomly assigned to AFO-provision: early (at inclusion) or delayed (eight weeks later). Three-dimensional gait-analysis with and without AFO in randomized order was performed within two weeks after AFO-provision. Twenty subjects (8 early, 12 delayed) were analyzed. We found significant positive effects of AFO-provision for ankle dorsiflexion at initial contact, foot-off and during swing (-3.6° (7.3) vs 3.0° (3.9); 0.0° (7.4) vs 5.2° (3.7); and -6.1° (7.8) vs 2.6° (3.5), respectively), all p<0.001. No changes in knee, hip and pelvis angles were found after AFO-provision, except for knee (+2.3°) and hip flexion (+1.6°) at initial contact, p≤0.001. Significant effects of AFO-provision were found for cadence (+2.1 steps/min, p=0.026), stride duration (-0.08s, p=0.015) and single support duration (+1.0%, p=0.002). Early or delayed AFO-provision after stroke did not affect results. In conclusion, positive short-term effects of AFO-provision were found on ankle kinematics early after stroke. Timing of AFO-provision did not influence the results.nnnTRIAL REGISTRATION NUMBERnNTR1930.

Six-month effects of early or delayed provision of an ankle-foot orthosis in patients with (sub)acute stroke: a randomized controlled trial:

Objective: To study the six-month clinical effects of providing ankle-foot orthoses at different moments (early or delayed) in (sub)acute stroke; this is a follow-up to a published trial. Design: Randomized controlled trial. Setting: Rehabilitation centre. Subjects: Unilateral hemiparetic stroke subjects maximal six weeks post-stroke with indication for ankle-foot orthosis use. Interventions: Subjects were randomly assigned to early (at inclusion; week 1) or delayed provision (eight weeks later; week 9). Outcome measures: Functional tests assessing balance and mobility were performed bi-weekly for 17u2009weeks and at week 26. Results: In all, 33 subjects were randomized. No differences at week 26 were found between both groups for any of the outcome measures. However, results suggest that early provision leads to better outcomes in the first 11–13u2009weeks. Berg Balance Scale (Pu2009=u20090.006), Functional Ambulation Categories (Pu2009=u20090.033) and 6-minute walk test (Pu2009<u20090.001) showed significantly different patterns over time. Clinically relevant but statistically non-significant differences of 4–10u2009weeks in reaching independent walking with higher balance levels were found, favouring early provision. Conclusion: No six-month differences in functional outcomes of providing ankle-foot orthoses at different moments in the early rehabilitation after stroke were found. Results suggest that there is a period of 11–13u2009weeks in which early provision may be beneficial, possibly resulting in early independent and safe walking. However, our study was underpowered. Further research including larger numbers of subjects is warranted.

Exploring Determinants of Patient Adherence to a Portal-Supported Oncology Rehabilitation Program: Interview and Data Log Analyses

Background Telemedicine applications often do not live up to their expectations and often fail once they have reached the operational phase. Objective The objective of this study was to explore the determinants of patient adherence to a blended care rehabilitation program, which includes a Web portal, from a patient’s perspective. Methods Patients were enrolled in a 12-week oncology rehabilitation treatment supported by a Web portal that was developed in cooperation with patients and care professionals. Semistructured interviews were used to analyze thought processes and behavior concerning patient adherence and portal use. Interviews were conducted with patients close to the start and the end of the treatment. Besides, usage data from the portal were analyzed to gain insights into actual usage of the portal. Results A total of 12 patients participated in the first interview, whereas 10 participated in the second round of interviews. Furthermore, portal usage of 31 patients was monitored. On average, 11 persons used the portal each week, with a maximum of 20 in the seventh week and a drop toward just one person in the weeks in the follow-up period of the treatment. From the interviews, it was derived that patients’ behavior in the treatment and use of the portal was primarily determined by extrinsic motivation cues (eg, stimulation by care professionals and patient group), perceived severity of the disease (eg, physical and mental condition), perceived ease of use (eg, accessibility of the portal and the ease with which information is found), and perceived usefulness (eg, fit with the treatment). Conclusions The results emphasized the impact that care professionals and fellow patients have on patient adherence and portal usage. For this reason, the success of blended care telemedicine interventions seems highly dependent on the willingness of care professionals to include the technology in their treatment and stimulate usage among patients.

A maturity model for interoperability in eHealth

Interoperability, the ability of different technological applications to exchange data, is viewed by many as an important goal for eHealth, as it can save money and improve the quality of care and patient safety. However, creating an interoperable infrastructure for eHealth is a difficult task. In this paper, we present a maturity model that aids eHealth developers to determine what level of interoperability they should strive for, and that allows researchers to benchmark interoperable eHealth infrastructures in terms of maturity. For each level in the model, we illustrate what the interoperable infrastructure looks like from the technical point of view, we list implications for working procedures and we discuss the role of standardization. The maturity model has five levels. At level 0, there is no interoperability: The eHealth application is a silo. At level 1, Peer-to-peer systems, single applications are linked for simple data exchange. At level 2, Distributed systems, multiple applications are linked to achieve a common objective. At level 3, Integrated systems, applications from different suppliers are linked in a closed infrastructure. And at level 4, Universal interoperability, finally, applications are linked in an open infrastructure from which everybody is free to (dis)connect. We demonstrate the application of the maturity model via the case of an interoperable eHealth infrastructure for primary care. Reaching the most technically advanced form of interoperability (level 4) is not a goal eHealth developers should always strive for. They should set their goal with regard to the desired interoperability level for their situation and should then determine what they should do in terms of technique, working procedures, and standardization.

Should I see a Healthcare Professional or can I perform Self-care: Self-Referral Decision Support for Patients with Low Back Pain

When people get low back pain (LBP), it is not always evident when to see a general practitioner (GP) or physiotherapist, or to perform self-care. A direct correct referral is essential for effective treatment to prevent the development of chronic LBP the utmost. In the context of designing a tool that is able to provide a referral advice to a patient, 63 healthcare professionals (GPs and physiotherapists) participated in a vignette study. They had to judge 32 LBP cases on 1. see a general practitioner, 2. see a physiotherapist, and 3. perform self-care. In total, 1288 vignettes were judged. Multinomial regression analysis showed that Weight Loss, Trauma, and Nocturnal Pain are the three most significant predictive variables. A decision tree was generated that showed the same conclusion. This decision tree is the basis to build a tool that provides personalized referral advice to patients with LBP from the very beginning.

Understanding the Acceptance of an eHealth Technology in the Early Stages of Development: An End-User Walkthrough Approach and Two Case Studies

Background Studies that focus on the acceptance of an electronic health (eHealth) technology generally make use of surveys. However, results of such studies hold little value for a redesign, as they focus only on quantifying end-user appreciation of general factors (eg, perceived usefulness). Objective We present a method for understanding end-user acceptance of an eHealth technology, early in the development process: The eHealth End-User Walkthrough. Methods During a walkthrough, a participant is guided by using the technology via a scenario, a persona, and a low-fidelity protoype. A participant is questioned about factors that may affect acceptance during and after the demonstration. We show the value of the method via two case studies. Results During the case studies, participants commented on whether they intend to use a technology and why they would (not) use its main features. They also provided redesign advice or input for additional functions. Finally, the sessions provide guidance for the generation of business models and implementation plans. Conclusions The eHealth End-User Walkthrough can aid design teams in understanding the acceptance of their eHealth application in a very early stage of the design process. Consequently, it can prevent a mismatch between technology and end-users’ needs, wishes and context.

Evaluation of three machine learning models for self-referral decision support on low back pain in primary care

BACKGROUNDnMost people experience low back pain (LBP) at least once in their life and for some patients this evolves into a chronic condition. One way to prevent acute LBP from transiting into chronic LBP, is to ensure that patients receive the right interventions at the right moment. We started research in the design of a clinical decision support system (CDSS) to support patients with LBP in their self-referral to primary care. For this, we explored the possibilities of using supervised machine learning. We compared the performances of the three classification models - i.e. 1. decision tree, 2. random forest, and 3. boosted tree - to get insight in which model performs best and whether it is already acceptable to use this model in real practice.nnnMETHODSnThe three models were generated by means of supervised machine learning with 70% of a training dataset (1288 cases with 65% GP, 33% physio, 2% self-care cases). The cases in the training dataset were fictive cases on low back pain collected during a vignette study with primary healthcare professionals. We also wanted to know the performance of the models on real-life low back pain cases that were not used to train the models. Therefore we also collected real-life cases on low back pain as test dataset. These cases were collected with the help of patients and healthcare professionals in primary care. For each model, the performance was measured during model validation - with 30% of the training dataset -as well as during model testing - with the test dataset containing real-life cases. The total observed accuracy as well as the kappa, and the sensitivity, specificity, and precision were used as performance measures to compare the models.nnnRESULTSnFor the training dataset, the total observed accuracies of the decision tree, the random forest and boosted tree model were 70%, 69%, and 72% respectively. For the test dataset, the total observed accuracies were 71%, 53%, and 71% respectively. The boosted tree appeared to be the best for predicting a referral advice with a fair accuracy (Kappa between 0.2 and 0.4). Next to this, the measured evaluation measures show that all models provided a referral advice better than just a random guess. This means that all models learned some implicit knowledge of the provided referral advices in the training dataset.nnnCONCLUSIONSnThe study showed promising results on the possibility of using machine learning in the design of our CDSS. The boosted tree model performed best on the classification of low back pain cases, but still has to be improved. Therefore, new cases have to be collected, especially cases that are classified as self-care cases. This to be sure that also the self-care advice can be predicted well by the model.

The influence of early or delayed provision of ankle-foot orthoses on pelvis, hip and knee kinematics in patients with sub-acute stroke : A randomized controlled trial

BACKGROUNDnCompensatory pelvis, hip- and knee movements are reported after stroke to overcome insufficient foot-clearance. Ankle-foot orthoses (AFOs) are often used to improve foot-clearance, but the optimal timing of AFO-provision post-stroke is unknown. Early AFO-provision to prevent foot-drop might decrease the development of compensatory movements, but it is unknown whether timing of AFO-provision affects post-stroke kinematics.nnnRESEARCH QUESTIONSn1) To compare the effect of AFO-provision at two different points in time (early versus delayed) on frontal pelvis and hip, and sagittal hip and knee kinematics in patients with sub-acute stroke. Effects were assessed after 26 weeks; 2) To study whether possible changes in kinematics or walking speed during the 26-weeks follow-up period differed between both groups.nnnMETHODnAn explorative randomized controlled trial was performed, including unilateral hemiparetic patients maximal six weeks post-stroke with indication for AFO-use. Subjects were randomly assigned to AFO-provision early (at inclusion) or delayed (eight weeks later). 3D gait-analysis with and without AFO was performed in randomized order. Measurements were performed in study-week 1, 9, 17 and 26.nnnRESULTSnTwenty-six subjects (15 early, 11 delayed) were analyzed. After 26 weeks, no differences in kinematics were found between both groups for any of the joint angles, both for the without and with AFO-condition. Changes in kinematics during the 26-weeks follow-up period did not differ between both groups for any of the joint angles during walking without AFO. Significant differences in changes in walking speed during the 26-weeks follow-up were found (pu202f=u202f0.034), corresponding to the first eight weeks after AFO-provision.nnnSIGNIFICANCEnResults indicate that early or delayed AFO-use post-stroke does not influence pelvis, hip and knee movements after 26 weeks, despite that AFO-use properly corrected drop-foot. AFOs should be provided to improve drop-foot post-stroke, but not with the intention to influence development of compensatory patterns around pelvis and hip.

Barriers and requirements for achieving interoperable eHealth technology in primary care

Despite its great promises, eHealth is not yet structurally embedded within the IT infrastructure of primary care. This is mainly due to the fact that healthcare technologies have been developed without coordination and a centralized approach [1], which in turn has led to a lack of shared standards among the different systems for exchanging data (interoperability). The benefits and barriers for interoperability in healthcare have thoroughly been described in literature. Benefits include the availability of up to date information, improved quality of care and cost savings, while barriers include information overload, costs, security & privacy, and liability issues [2,3]. None of these overviews, however, have listed the barriers and prerequisites towards interoperability within primary care on the level of single healthcare professionals in relation to their daily work practice.