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Background: Motor and cognitive deficits and consequently mobility problems are common in geriatric patients. The currently available methods for diagnosis and for the evaluation of treatment in this vulnerable cohort are limited. The aims of the ComOn (COgnitive and Motor interactions in the Older populatioN) study are (i) to define quantitative markers with clinical relevance for motor and cognitive deficits, (ii) to investigate the interaction between both motor and cognitive deficits and (iii) to assess health status as well as treatment outcome of 1000 geriatric inpatients in hospitals of Kiel (Germany), Brescia (Italy), Porto (Portugal), Curitiba (Brazil) and Bochum (Germany).

Background: Older adults have increased risks of balance issues and falls when walking and performing turns in daily situations. Changes of prioritization during different walking situations associated with dual tasking may contribute to these deficits. The objective of this study was therefore to investigate whether older adults demonstrate changes of prioritization during different walking paths.Methods: In total, 1,054 subjects with an age range from 50 to 83 years were selected from the first follow-up visit of the TREND (Tuebinger evaluation of Risk factors for Early detection of Neurodegenerative Disorders) study. They were classified according to their performance on the Trail Making Test (TMT) into good and poor TMT performers (based on recent results showing that cognitive flexibility affects prioritization strategies during straight walking). Absolute dual-task performance and relative dual-task costs (DTC, relative performance under dual-task conditions compared with single-task conditions) were assessed in two paradigms: walking while subtracting serial 7 s and walking while checking boxes on a clipboard. Both tasks were performed on straight and curved paths.Results: Overall, the poor TMT performers group performed worse in all single and dual tasks. Interestingly, the relative change in performance measured by dual-task costs differed in the groups between the two walking paths. On straight paths, poor TMT performers had a similar DTC of walking to that of good performers (p = 0.10) but had a significantly lower DTC of subtracting (p = 0.02). On curved paths, poor performers had a similar DTC of subtracting (p = 0.10), but their DTC of walking was significantly higher (p < 0.0001).Conclusion: Given that walking on curved paths is considered more difficult than that on straight paths and that the serial subtracting dual task is more difficult than the box checking dual task, this study in older adults provides evidence for the existence of a (walking) situation-dependent change of prioritization. If confirmed in other studies, situation-dependent change of prioritization should be included as a potential factor contributing to gait and balance impairments, and increased fall risk in older adults.

Static balance is a commonly used health measure in clinical practice. Usually, static balance parameters are assessed via force plates or, more recently, with inertial measurement units (IMUs). Multiple parameters have been developed over the years to compare patient groups and understand changes over time. However, the day-to-day variability of these parameters using IMUs has not yet been tested in a neurogeriatric cohort. The aim of the study was to examine day-to-day variability of static balance parameters of five experimental conditions in a cohort of neurogeriatric patients using data extracted from a lower back-worn IMU. A group of 41 neurogeriatric participants (age: 78 ± 5 years) underwent static balance assessment on two occasions 12–24 h apart. Participants performed a side-by-side stance, a semi-tandem stance, a tandem stance on hard ground with eyes open, and a semi-tandem assessment on a soft surface with eyes open and closed for 30 s each. The intra-class correlation coefficient (two-way random, average of the k raters’ measurements, ICC2, k) and minimal detectable change at a 95% confidence level (MDC95%) were calculated for the sway area, velocity, acceleration, jerk, and frequency. Velocity, acceleration, and jerk were calculated in both anterior-posterior (AP) and medio-lateral (ML) directions. Nine to 41 participants could successfully perform the respective balance tasks. Considering all conditions, acceleration-related parameters in the AP and ML directions gave the highest ICC results. The MDC95% values for all parameters ranged from 39% to 220%, with frequency being the most consistent with values of 39–57%, followed by acceleration in the ML (43–55%) and AP direction (54–77%). The present results show moderate to poor ICC and MDC values for IMU-based static balance assessment in neurogeriatric patients. This suggests a limited reliability of these tasks and parameters, which should induce a careful selection of potential clinically relevant parameters.

Healthy adults and neurological patients show unique mobility patterns over the course of their lifespan and disease. Quantifying these mobility patterns could support diagnosing, tracking disease progression and measuring response to treatment. This quantification can be done with wearable technology, such as inertial measurement units (IMUs). Before IMUs can be used to quantify mobility, algorithms need to be developed and validated with age and disease-specific datasets. This study proposes a protocol for a dataset that can be used to develop and validate IMU-based mobility algorithms for healthy adults (18–60 years), healthy older adults (>60 years), and patients with Parkinson’s disease, multiple sclerosis, a symptomatic stroke and chronic low back pain. All participants will be measured simultaneously with IMUs and a 3D optical motion capture system while performing standardized mobility tasks and non-standardized activities of daily living. Specific clinical scales and questionnaires will be collected. This study aims at building the largest dataset for the development and validation of IMU-based mobility algorithms for healthy adults and neurological patients. It is anticipated to provide this dataset for further research use and collaboration, with the ultimate goal to bring IMU-based mobility algorithms as quickly as possible into clinical trials and clinical routine.