Introduction: Patient information holds an important role in knee arthroplasty surgery regarding patients’ expectations and outcomes after surgery. The purpose of the present study was to explore the experiences and opinions of patients undergoing knee arthroplasty (KA) surgery on an information brochure provided preoperatively. Methods: A qualitative case study of 8 patients using individual semi-structured interviews was conducted to explore patients’ opinions on an information brochure in KA surgery. Results: Patients rated the brochure as good and recommended its use. Unsatisfactory information regarding wound healing, pain expectations, postoperative exercises and use of walking aids was reported. Patients stated that the table of contents was insufficient and the size of the brochure (A4-format) too large. Patients reported to have no need for additional digital sources (e.g. applications, websites). Conclusion: These opinions support the use of an information brochure. The reported opinions were used to improve the brochure. Future research should focus on the improvement of information sources by involving patients (and other users) in the development process in which the information is tailored towards patient needs.
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Background: Limited information is available on the experiences of patients during rehabilitation after anterior cruciate ligament reconstruction (ACLR). Aim: The current study aimed to identify factors that differentiated positive and negative patient experiences during rehabilitation after ACLR. Method and Design: A survey-based study with an online platform was used to identify factors that differentiated positive and negative patient experiences during rehabilitation after ACLR. Seventy-two patients (age 27.8 [8.8] y) after ACLR participated. Data were analyzed and themes were identified by comparing categories and subcategories on similarity. Main Findings: Positive patient experiences were room for own input, supervision, attention, knowledge, honesty, and professionalism of the physiotherapist. Additionally, a varied and structured rehabilitation program, adequate facilities, and contact with other patients were identified as positive patient experiences. Negative experiences were a lack of attention, lack of professionalism of the physiotherapists, a lack of sport-specific field training, a lack of goal setting, a lack of adequate facilities, and health insurance costs. Conclusions: The current study identified factors that differentiated positive and negative patient experiences during rehabilitation after ACLR. These findings can help physiotherapists in understanding the patient experiences during rehabilitation after ACLR.
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Introduction: Success of e-health relies on the extent to which the related technology, such as the electronic device, is accepted by its users. However, there has been limited research on the patients’ perspective on use of e-health-related technology in rehabilitation care. Objective: To explore the usage of common electronic devices among rehabilitation patients with access to email and investigate their preferences regarding their usage in rehabilitation. Methods: Adult patients who were admitted for inpatient and/or outpatient rehabilitation and were registered with an email address were invited to complete an electronic questionnaire regarding current and preferred use of information and communication technologies in rehabilitation care. Results: 190 out of 714 invited patients completed the questionnaire, 94 (49%) female, mean age 49 years (SD 16). 149 patients (78%) used one or more devices every day, with the most frequently used devices were: PC/laptop (93%), smartphone (57%) and tablet (47%). Patients mostly preferred to use technology for contact with health professionals (mean 3.15, SD 0.79), followed by access to their personal record (mean 3.09, SD 0.78) and scheduling appointments with health professionals (mean 3.07, SD 0.85). Conclusion: Most patients in rehabilitation used one or more devices almost every day and wish to use these devices in rehabilitation. https://doi.org/10.1080/17483107.2017.1358302
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Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
Low back pain is the leading cause of disability worldwide and a significant contributor to work incapacity. Although effective therapeutic options are scarce, exercises supervised by a physiotherapist have shown to be effective. However, the effects found in research studies tend to be small, likely due to the heterogeneous nature of patients' complaints and movement limitations. Personalized treatment is necessary as a 'one-size-fits-all' approach is not sufficient. High-tech solutions consisting of motions sensors supported by artificial intelligence will facilitate physiotherapists to achieve this goal. To date, physiotherapists use questionnaires and physical examinations, which provide subjective results and therefore limited support for treatment decisions. Objective measurement data obtained by motion sensors can help to determine abnormal movement patterns. This information may be crucial in evaluating the prognosis and designing the physiotherapy treatment plan. The proposed study is a small cohort study (n=30) that involves low back pain patients visiting a physiotherapist and performing simple movement tasks such as walking and repeated forward bending. The movements will be recorded using sensors that estimate orientation from accelerations, angular velocities and magnetometer data. Participants complete questionnaires about their pain and functioning before and after treatment. Artificial analysis techniques will be used to link the sensor and questionnaire data to identify clinically relevant subgroups based on movement patterns, and to determine if there are differences in prognosis between these subgroups that serve as a starting point of personalized treatments. This pilot study aims to investigate the potential benefits of using motion sensors to personalize the treatment of low back pain. It serves as a foundation for future research into the use of motion sensors in the treatment of low back pain and other musculoskeletal or neurological movement disorders.
For English see below In dit project werkt het Lectoraat ICT-innovaties in de Zorg van hogeschool Windesheim samen met zorganisaties de ZorgZaak, De Stouwe, en IJsselheem en daarnaast Zorgcampus Noorderboog, Zorgtrainingscentrum Regio Zwolle, Patiëntenfederatie NPCF, VitaalThuis, ActiZ, Vilans, V&VN, Universiteit Twente en het Lectoraat Innoveren in de Ouderenzorg van Windesheim aan het in staat stellen van wijkverpleegkundigen om autonoom en doelmatig, op basis van klinisch redeneren, eHealth te indiceren en in te zetten bij cliënten. De aanleiding voor dit project wordt gevormd door de wijzigingen per 1 januari 2015 in de Zorgverzekeringswet. Wijkverpleegkundigen zijn sindsdien zelf verantwoordelijk voor de indicatiestelling en zorgtoewijzing voor verzorging en verpleging thuis: zij moeten bepalen welke zorg hun cliënten nodig hebben gezien hun individuele situaties, en hoe die zorg het best geleverd kan worden. Zorgverzekeraars leggen hierbij minimumeisen op, o.a. met betrekking tot de inzet van eHealth. Wijkverpleegkundigen hebben op dit moment echter niet of nauwelijks ervaring met het inzetten en toepassen van technologische toepassingen zoals eHealth. Vraagarticulatie leidde tot de volgende praktijkvraagstelling: 1. Hoe kunnen wijkverpleegkundigen worden voorzien in hun informatiebehoefte over eHealth? 2. Hoe kunnen wijkverpleegkundigen worden ondersteund in hun klinisch redeneren over het inzetten van eHealth bij hun cliënten? 3. Hoe kunnen wijkverpleegkundigen worden ondersteund bij het inzetten van eHealth in hun zorgproces? Het project levert hiertoe drie bijdragen: - De eerste bijdrage is een duurzaam geborgde keuzehulp (een app voor tablet of smartphone) waarmee wijkverpleegkundigen toegang hebben tot de benodigde informatie over eHealth-toepassingen en die aansluit bij de manier waarop wijkverpleegkundigen zorg indiceren (bijvoorbeeld door relaties te leggen tussen NIC-interventies en bijpassende eHealth-toepassingen). - Informatievoorziening is niet een afdoende antwoord op de handelingsverlegenheid van de wijkverpleegkundige omdat eHealth sterk in ontwikkeling is en blijft waardoor er altijd een discrepantie zal bestaan tussen de beschikbare en de benodigde informatie. . De tweede bijdrage van dit project is daarom kennis over (en inzicht in) het klinisch redeneren over de inzet van eHealth. Deze kennis wordt in het project doorvertaald naar een trainingsmodule die erop is gericht om het klinisch redeneren van wijkverpleegkundigen over het inzetten van eHealth en andere thuiszorgtechnologie bij hun cliënten te versterken. - De derde bijdrage van dit project omhelst inbedding van bovengenoemde resultaten in het verpleegkunde-onderwijs van onder meer Windesheim en in nascholingstrajecten voor wijkverpleegkundigen. Voor duurzame, bredere inbedding in het onderwijs wordt samengewerkt met regionale zorgonderwijsnetwerken. In this project the research group IT-innovations in Health Care of Windesheim University of Applied Sciences cooperates with care organisations de ZorgZaak, De Stouwe, and IJsselheem, and stakeholders Zorgcampus Noorderboog, Zorgtrainingscentrum Regio Zwolle, Patiëntenfederatie NPCF, VitaalThuis, ActiZ, Vilans, V&VN, University of Twente, and research group Innovation of Care of Older Adults of Windesheim to enable home care nurses to autonomously and adequately, based on clinical reasoning, allocate eHealth and implement it in patient care. The motivation behind this project lies in the alterations in the care insurance legislation per January 2015. Since then, home care nurses are responsible for the care allocation of all care at home: they determine which care their clients require, taking into account the individual situations, and how this care can best be delivered. Care insurance companies impose minimum requirements for this allocation of home care, among others concerning the implementation of eHealth. Home care nurses, however, have no or limited information about and experience with technical applications like eHealth. Articulation of the demands of home care nurses resulted in the following questions: 1. How can home care nurses be provided with information concerning eHealth? 2. How can home care nurses be supported in their clinical reasoning about the deployment of eHealth by their patients? 3. How can home care nurses be supported when deploying eHealth in their care process? This project contributes in three ways: " The first contribution is a sustainable selection tool (an app for tablet or smartphone) to be used by home care nurses to provide them with the required information about eHealth applications. This selection tool will work in accordance with how home care nurses allocate care, e.g. by relating NIC-interventions to matching eHealth applications. " Providing information is an insufficient, although necessary, answer to the demands of home care nurses because of continuously developing eHealth applications. Hence, the second contribution of this project is knowledge about (and insight in) the clinical reasoning about the deployment of eHealth. This knowledge will be converted into a training module aimed at strengthening the clinical reasoning about the deployment of eHealth by their patients. " The third contribution of this project concerns embedding the selection tool and the training module in regular education (among others at Windesheim) and in refresher courses for home care nurses. Cooperation with regional care education networks will ensure sustainable and broad embedding of both the selection tool and the training module.
