Which Comprehensive Geriatric Assessment (CGA) instruments are currently used in Germany: a survey.

BACKGROUND: The Comprehensive Geriatric Assessment (CGA) records geriatric syndromes in a standardized manner, allowing individualized treatment tailored to the patient's needs and resources. Its use has shown a beneficial effect on the functional outcome and survival of geriatric patients. A recently published German S1 guideline for level 2 CGA provides recommendations for the use of a broad variety of different assessment instruments for each geriatric syndrome. However, the actual use of assessment instruments in routine geriatric clinical practice and its consistency with the guideline and the current state of literature has not been investigated to date. METHODS: An online survey was developed by an expert group of geriatricians and sent to all licenced geriatricians (n = 569) within Germany. The survey included the following geriatric syndromes: motor function and self-help capability, cognition, depression, pain, dysphagia and nutrition, social status and comorbidity, pressure ulcers, language and speech, delirium, and frailty. Respondents were asked to report which geriatric assessment instruments are used to assess the respective syndromes. RESULTS: A total of 122 clinicians participated in the survey (response rate: 21%); after data cleaning, 76 data sets remained for analysis. All participants regularly used assessment instruments in the following categories: motor function, self-help capability, cognition, depression, and pain. The most frequently used instruments in these categories were the Timed Up and Go (TUG), the Barthel Index (BI), the Mini Mental State Examination (MMSE), the Geriatric Depression Scale (GDS), and the Visual Analogue Scale (VAS). Limited or heterogenous assessments are used in the following categories: delirium, frailty and social status. CONCLUSIONS: Our results show that the assessment of motor function, self-help capability, cognition, depression, pain, and dysphagia and nutrition is consistent with the recommendations of the S1 guideline for level 2 CGA. Instruments recommended for more frequent use include the Short Physical Performance Battery (SPPB), the Montreal Cognitive Assessment (MoCA), and the WHO-5 (depression). There is a particular need for standardized assessment of delirium, frailty and social status. The harmonization of assessment instruments throughout geriatric departments shall enable more effective treatment and prevention of age-related diseases and syndromes.

Connecting real-world digital mobility assessment to clinical outcomes for regulatory and clinical endorsement-the Mobilise-D study protocol.

BACKGROUND: The development of optimal strategies to treat impaired mobility related to ageing and chronic disease requires better ways to detect and measure it. Digital health technology, including body worn sensors, has the potential to directly and accurately capture real-world mobility. Mobilise-D consists of 34 partners from 13 countries who are working together to jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes have the potential to provide a better, safer, and quicker way to assess, monitor, and predict the efficacy of new interventions on impaired mobility. The overarching objective of the study is to establish the clinical validity of digital outcomes in patient populations impacted by mobility challenges, and to support engagement with regulatory and health technology agencies towards acceptance of digital mobility assessment in regulatory and health technology assessment decisions. METHODS/DESIGN: The Mobilise-D clinical validation study is a longitudinal observational cohort study that will recruit 2400 participants from four clinical cohorts. The populations of the Innovative Medicine Initiative-Joint Undertaking represent neurodegenerative conditions (Parkinson's Disease), respiratory disease (Chronic Obstructive Pulmonary Disease), neuro-inflammatory disorder (Multiple Sclerosis), fall-related injuries, osteoporosis, sarcopenia, and frailty (Proximal Femoral Fracture). In total, 17 clinical sites in ten countries will recruit participants who will be evaluated every six months over a period of two years. A wide range of core and cohort specific outcome measures will be collected, spanning patient-reported, observer-reported, and clinician-reported outcomes as well as performance-based outcomes (physical measures and cognitive/mental measures). Daily-living mobility and physical capacity will be assessed directly using a wearable device. These four clinical cohorts were chosen to obtain generalizable clinical findings, including diverse clinical, cultural, geographical, and age representation. The disease cohorts include a broad and heterogeneous range of subject characteristics with varying chronic care needs, and represent different trajectories of mobility disability. DISCUSSION: The results of Mobilise-D will provide longitudinal data on the use of digital mobility outcomes to identify, stratify, and monitor disability. This will support the development of widespread, cost-effective access to optimal clinical mobility management through personalised healthcare. Further, Mobilise-D will provide evidence-based, direct measures which can be endorsed by regulatory agencies and health technology assessment bodies to quantify the impact of disease-modifying interventions on mobility. TRIAL REGISTRATION: ISRCTN12051706.

Hidden Markov Model based stride segmentation on unsupervised free-living gait data in Parkinson's disease patients.

BACKGROUND: To objectively assess a patient's gait, a robust identification of stride borders is one of the first steps in inertial sensor-based mobile gait analysis pipelines. While many different methods for stride segmentation have been presented in the literature, an out-of-lab evaluation of respective algorithms on free-living gait is still missing. METHOD: To address this issue, we present a comprehensive free-living evaluation dataset, including 146.574 semi-automatic labeled strides of 28 Parkinson's Disease patients. This dataset was used to evaluate the segmentation performance of a new Hidden Markov Model (HMM) based stride segmentation approach compared to an available dynamic time warping (DTW) based method. RESULTS: The proposed HMM achieved a mean F1-score of 92.1% and outperformed the DTW approach significantly. Further analysis revealed a dependency of segmentation performance to the number of strides within respective walking bouts. Shorter bouts ([Formula: see text] strides) resulted in worse performance, which could be related to more heterogeneous gait and an increased diversity of different stride types in short free-living walking bouts. In contrast, the HMM reached F1-scores of more than 96.2% for longer bouts ([Formula: see text] strides). Furthermore, we showed that an HMM, which was trained on at-lab data only, could be transferred to a free-living context with a negligible decrease in performance. CONCLUSION: The generalizability of the proposed HMM is a promising feature, as fully labeled free-living training data might not be available for many applications. To the best of our knowledge, this is the first evaluation of stride segmentation performance on a large scale free-living dataset. Our proposed HMM-based approach was able to address the increased complexity of free-living gait data, and thus will help to enable a robust assessment of stride parameters in future free-living gait analysis applications.

Recommendations for the Organization of Multidisciplinary Clinical Care Teams in Parkinson's Disease.

BACKGROUND: Optimal management in expert centers for Parkinson's disease (PD) usually involves pharmacological and non-pharmacological interventions, delivered by a multidisciplinary approach. However, there is no guideline specifying how this model should be organized. Consequently, the nature of multidisciplinary care varies widely. OBJECTIVE: To optimize care delivery, we aimed to provide recommendations for the organization of multidisciplinary care in PD. METHODS: Twenty expert centers in the field of multidisciplinary PD care participated. Their leading neurologists completed a survey covering eight themes: elements for optimal multidisciplinary care; team members; role of patients and care partners; team coordination; team meetings; inpatient versus outpatient care; telehealth; and challenges towards multidisciplinary care. During a consensus meeting, outcomes were incorporated into concept recommendations that were reviewed by each center's multidisciplinary team. Three patient organizations rated the recommendations according to patient priorities. Based on this feedback, a final set of recommendations (essential elements for delivery of multidisciplinary care) and considerations (desirable elements) was developed. RESULTS: We developed 30 recommendations and 10 considerations. The patient organizations rated the following recommendations as most important: care is organized in a patient-centered way; every newly diagnosed patient has access to a core multidisciplinary team; and each team has a coordinator. A checklist was created to further facilitate its implementation. CONCLUSION: We provide a practical tool to improve multidisciplinary care for persons with PD at the organizational level. Future studies should focus on implementing these recommendations in clinical practice, evaluating their potential applicability and effectiveness, and comparing alternative models of PD care.

Assessment of gait parameters and physical function in patients with advanced cancer participating in a 12-week exercise and nutrition programme: A controlled clinical trial.

OBJECTIVE: Gait is a sensitive marker for functional declines commonly seen in patients treated for advanced cancer. We tested the effect of a combined exercise and nutrition programme on gait parameters of advanced-stage cancer patients using a novel wearable gait analysis system. METHODS: Eighty patients were allocated to a control group with nutritional support or to an intervention group additionally receiving whole-body electromyostimulation (WB-EMS) training (2×/week). At baseline and after 12 weeks, physical function was assessed by a biosensor-based gait analysis during a six-minute walk test, a 30-s sit-to-stand test, a hand grip strength test, the Karnofsky Index and EORTC QLQ-C30 questionnaire. Body composition was measured by bioelectrical impedance analysis and inflammation by blood analysis. RESULTS: Final analysis included 41 patients (56.1% male; 60.0 ± 13.0 years). After 12 weeks, the WB-EMS group showed higher stride length, gait velocity (p < .05), six-minute walking distance (p < .01), bodyweight and skeletal muscle mass, and emotional functioning (p < .05) compared with controls. Correlations between changes in gait and in body composition, physical function and inflammation were detected. CONCLUSION: Whole-body electromyostimulation combined with nutrition may help to improve gait and functional status of cancer patients. Sensor-based mobile gait analysis objectively reflects patients' physical status and could support treatment decisions.

Balance and mobility in geriatric patients : Assessment and treatment of neurological aspects.

BACKGROUND: Personal autonomy in advanced age critically depends on mobility in the environment. Geriatric patients are often not able to walk safely with sufficient velocity. In many cases, multiple factors contribute to the deficit. Diagnostic identification of single components enables a specific treatment. OBJECTIVE: This article describes the most common neurological causes of imbalance and impaired gait that are relevant for a pragmatic approach for the assessment of deficits in clinical and natural environments taking into account the physiology of balance and gait control, typical morbidities in older people and the potential of innovative assessment technologies. MATERIAL AND METHODS: Expert opinion based on a narrative review of the literature and with reference to selected research topics. RESULTS AND DISCUSSION: Common neurological causes of impaired balance and mobility are sensory deficits (reduced vision, peripheral neuropathy, vestibulopathy), neurodegeneration in disorders with an impact on movement control and motoric functions (Parkinsonian syndromes, cerebellar ataxia, vascular encephalopathy) and functional (psychogenic) disorders, particularly a fear of falling. Clinical tests and scores in laboratory environments are complemented by the assessment in the natural environment. Wearable sensors, mobile smartphone-based assessment of symptoms and functions and adopted strategies for analysis are currently emerging. Use of these data enables a personalized treatment. Furthermore, sensor-based assessment ensures that effects are measured objectively.

A roadmap for implementation of patient-centered digital outcome measures in Parkinson's disease obtained using mobile health technologies.

Obtaining reliable longitudinal information about everyday functioning from individuals with Parkinson's disease (PD) in natural environments is critical for clinical care and research. Despite advances in mobile health technologies, the implementation of digital outcome measures is hindered by a lack of consensus on the type and scope of measures, the most appropriate approach for data capture (eg, in clinic or at home), and the extraction of timely information that meets the needs of patients, clinicians, caregivers, and health care regulators. The Movement Disorder Society Task Force on Technology proposes the following objectives to facilitate the adoption of mobile health technologies: (1) identification of patient-centered and clinically relevant digital outcomes; (2) selection criteria for device combinations that offer an acceptable benefit-to-burden ratio to patients and that deliver reliable, clinically relevant insights; (3) development of an accessible, scalable, and secure platform for data integration and data analytics; and (4) agreement on a pathway for approval by regulators, adoption into e-health systems and implementation by health care organizations. We have developed a tentative roadmap that addresses these needs by providing the following deliverables: (1) results and interpretation of an online survey to define patient-relevant endpoints, (2) agreement on the selection criteria for use of device combinations, (3) an example of an open-source platform for integrating mobile health technology output, and (4) recommendations for assessing readiness for deployment of promising devices and algorithms suitable for regulatory approval. This concrete implementation guidance, harmonizing the collaborative endeavor among stakeholders, can improve assessments of individuals with PD, tailor symptomatic therapy, and enhance health care outcomes. © 2019 International Parkinson and Movement Disorder Society.

Multimodal Assessment of Parkinson's Disease: A Deep Learning Approach.

Parkinson's disease is a neurodegenerative disorder characterized by a variety of motor symptoms. Particularly, difficulties to start/stop movements have been observed in patients. From a technical/diagnostic point of view, these movement changes can be assessed by modeling the transitions between voiced and unvoiced segments in speech, the movement when the patient starts or stops a new stroke in handwriting, or the movement when the patient starts or stops the walking process. This study proposes a methodology to model such difficulties to start or to stop movements considering information from speech, handwriting, and gait. We used those transitions to train convolutional neural networks to classify patients and healthy subjects. The neurological state of the patients was also evaluated according to different stages of the disease (initial, intermediate, and advanced). In addition, we evaluated the robustness of the proposed approach when considering speech signals in three different languages: Spanish, German, and Czech. According to the results, the fusion of information from the three modalities is highly accurate to classify patients and healthy subjects, and it shows to be suitable to assess the neurological state of the patients in several stages of the disease. We also aimed to interpret the feature maps obtained from the deep learning architectures with respect to the presence or absence of the disease and the neurological state of the patients. As far as we know, this is one of the first works that considers multimodal information to assess Parkinson's disease following a deep learning approach.

Dynamic footprint based locomotion sway assessment in α-synucleinopathic mice using Fast Fourier Transform and Low Pass Filter.

BACKGROUND: Sway is a crucial gait characteristic tightly correlated with the risk of falling in patients with Parkinsons disease (PD). So far, the swaying pattern during locomotion has not been investigated in rodent models using the analysis of dynamic footprint recording obtained from the CatWalk gait recording and analysis system. NEW METHODS: We present three methods for describing locomotion sway and apply them to footprint recordings taken from C57BL6/N wild-type mice and two different α-synuclein transgenic PD-relevant mouse models (α-synm-ko, α-synm-koxα-synh-tg). Individual locomotion data were subjected to three different signal processing analytical approaches: the first two methods are based on Fast Fourier Transform (FFT), while the third method uses Low Pass Filters (LPF). These methods use the information associated with the locomotion sway and generate sway-related parameters. RESULTS: The three proposed methods were successfully applied to the footprint recordings taken from all paws as well as from front/hind-paws separately. Nine resulting sway-related parameters were generated and successfully applied to differentiate between the mouse models under study. Namely, α-synucleinopathic mice revealed higher sway and sway itself was significantly higher in the α-synm-koxα-synh-tg mice compared to their wild-type littermates in eight of the nine sway-related parameters. COMPARISON WITH EXISTING METHOD: Previous locomotion sway index computation is based on the estimated center of mass position of mice. CONCLUSIONS: The methods presented in this study provide a sway-related gait characterization. Their application is straightforward and may lead to the identification of gait pattern derived biomarkers in rodent models of PD.

Towards Mobile Gait Analysis: Concurrent Validity and Test-Retest Reliability of an Inertial Measurement System for the Assessment of Spatio-Temporal Gait Parameters.

The purpose of this study was to assess the concurrent validity and test-retest reliability of a sensor-based gait analysis system. Eleven healthy subjects and four Parkinson's disease (PD) patients were asked to complete gait tasks whilst wearing two inertial measurement units at their feet. The extracted spatio-temporal parameters of 1166 strides were compared to those extracted from a reference camera-based motion capture system concerning concurrent validity. Test-retest reliability was assessed for five healthy subjects at three different days in a two week period. The two systems were highly correlated for all gait parameters ( r > 0.93 ). The bias for stride time was 0 ± 16 ms and for stride length was 1.4 ± 6.7 cm. No systematic range dependent errors were observed and no significant changes existed between healthy subjects and PD patients. Test-retest reliability was excellent for all parameters (intraclass correlation (ICC) > 0.81) except for gait velocity (ICC > 0.55). The sensor-based system was able to accurately capture spatio-temporal gait parameters as compared to the reference camera-based system for normal and impaired gait. The system's high retest reliability renders the use in recurrent clinical measurements and in long-term applications feasible.

Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment.

The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring. Deep learning recently broke records in speech and image classification, but it has not been fully investigated as a potential approach to analyze wearable sensor data. We collected data from ten patients with idiopathic Parkinson's disease using inertial measurement units. Several motor tasks were expert-labeled and used for classification. We specifically focused on the detection of bradykinesia. For this, we compared standard machine learning pipelines with deep learning based on convolutional neural networks. Our results showed that deep learning outperformed other state-of-the-art machine learning algorithms by at least 4.6 % in terms of classification rate. We contribute a discussion of the advantages and disadvantages of deep learning for sensor-based movement assessment and conclude that deep learning is a promising method for this field.