Background: Gait impairments are common and highly disabling for Parkinson's disease (PD) patients. With the development of technology-based tools, it is now possible to measure the spatiotemporal parameters of gait with a reduced margin of error, thereby enabling a more accurate characterization of impairment. Objective: To summarize and critically appraise the characteristics of technology-based gait analysis in PD and to provide mean and standard deviation values for spatiotemporal gait parameters. Methods: A systematic review was conducted using the databases CENTRAL, MEDLINE, Embase, and PEDro from their inception to September 2019 to identify all observational and experimental studies conducted in PD or atypical parkinsonism that included a technology-based gait assessment. Two reviewers independently screened citations and extracted data. Results: We included 95 studies, 82.1% (n = 78) reporting a laboratory gait assessment and 61.1% (n = 58 studies) using a wearable sensor. The most frequently reported parameters were gait velocity, stride and step length, and cadence. A statistically significant difference was found when comparing the mean values of each of these parameters in PD patients versus healthy controls. No statistically significant differences were found in the mean value of the parameters when comparing wearable versus non-wearable sensors, different types of wearable sensors, and different sensor locations. Conclusion: Our results provide useful information for performing objective technology-based gait assessment in PD, as well as mean values to better interpret the results. Further studies should explore the clinical meaningfulness of each parameter and how they behave in a free-living context and throughout disease progression.

Problems with fatigue and sleep are highly prevalent in patients with chronic diseases and often rated among the most disabling symptoms, impairing their activities of daily living and the health-related quality of life (HRQoL). Currently, they are evaluated primarily via Patient Reported Outcomes (PROs), which can suffer from recall biases and have limited sensitivity to temporal variations. Objective measurements from wearable sensors allow to reliably quantify disease state, changes in the HRQoL, and evaluate therapeutic outcomes. This work investigates the feasibility of capturing continuous physiological signals from an electrocardiography-based wearable device for remote monitoring of fatigue and sleep and quantifies the relationship of objective digital measures to self-reported fatigue and sleep disturbances. 136 individuals were followed for a total of 1,297 recording days in a longitudinal multi-site study conducted in free-living settings and registered with the German Clinical Trial Registry (DRKS00021693). Participants comprised healthy individuals (N = 39) and patients with neurodegenerative disorders (NDD, N = 31) and immune mediated inflammatory diseases (IMID, N = 66). Objective physiological measures correlated with fatigue and sleep PROs, while demonstrating reasonable signal quality. Furthermore, analysis of heart rate recovery estimated during activities of daily living showed significant differences between healthy and patient groups. This work underscores the promise and sensitivity of novel digital measures from multimodal sensor time-series to differentiate chronic patients from healthy individuals and monitor their HRQoL. The presented work provides clinicians with realistic insights of continuous at home patient monitoring and its practical value in quantitative assessment of fatigue and sleep, an area of unmet need.

Introduction: Functional mobility (FM) is a concept that incorporates the capacity of a person to move independently and safely to accomplish tasks. It has been proposed as a Parkinson's disease (PD) functional and global health outcome. In this study, we aimed to identify which kinematic and clinical outcomes changes better predict FM changes when PD patients are submitted to a specialized multidisciplinary program.Methods: PD patients engaged in a pre-defined specialized multidisciplinary program were assessed at admission and discharge. Change from baseline was calculated for all kinematic and clinical outcomes, and Timed Up and Go (TUG) was defined as the primary outcome for FM. A stepwise multivariate linear regression was performed to identify which outcome measures better predict TUG changes.Results: Twenty-four patients were included in the study. The changes in TUG Cognitive test, supervised step length, and free-living (FL) step time asymmetry were identified as the best predictors of TUG changes. The supervised step length and FL step time asymmetry were able to detect a small to moderate effect of the intervention (d values ranging from −0.26 to 0.42).Conclusions: Our results support the use of kinematic outcome measures to evaluate the efficacy of multidisciplinary interventions on PD FM. The TUG Cognitive, step length, and FL step time asymmetry were identified as having the ability to predict TUG changes. More studies are needed to identify the minimal clinically important difference for step length and FL step time asymmetry in response to a multidisciplinary intervention for PD FM.

Background Caregivers’ influence on young children’s eating behaviors is widely recognized. Nutritional interventions that focus on the promotion of children’s healthy diet should actively involve parents, focusing on their feeding behaviors and practices. Methods This work aims to describe the development and study protocol of the SmartFeeding4Kids (SF4K) program, an online self-guided 7-session intervention for parents of young (2–6 years old) children. The program is informed by social cognitive, self-regulation, and habit formation theoretical models and uses self-regulatory techniques as self-monitoring, goal setting, and feedback to promote behavior change. We propose to examine the intervention efficacy on children’s intake of fruit, vegetables, and added sugars, and parental feeding practices with a two-arm randomized controlled with four times repeated measures design (baseline, immediately, 3 and 6 months after intervention). Parental perceived barriers about food and feeding, food parenting self-efficacy, and motivation to change will be analyzed as secondary outcomes. The study of the predictors of parents’ dropout rates and the trajectories of parents’ and children’s outcomes are also objectives of this work. Discussion The SmartFeeding4Kids program relies on technological resources to deliver parents’ self-regulation techniques that proved effective in promoting health behaviors. The study design can enhance the knowledge about the most effective methodologies to change parental feeding practices and children’s food intake. As a self-guided online program, SmartFeeding4Kids might overcome parents’ attrition more effectively, besides being easy to disseminate and cost-effective. Trial registration The study was registered in ClinicalTrials.gov (NCT04591496) on October 19, 2020.

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IntroductionThe SmartFeeding4Kids (SF4K) program is an online self-guided intervention for parents with the propose of changing parental feeding practices and children’s dietary intake, focusing on the intake of added sugars, fruit, vegetables, and legumes. This paper aims to describe children’s dietary pattern at baseline through a 24-h food recall, the SmartKidsDiet24.MethodsOverall, 89 participants recorded at least one meal of the 3-day food recall. Mean age was 36.22 ± 6.05 years and 53.09 ± 15.42 months old for parents and children, respectively. Of these, 22 participants were considered to have 2 days of near complete 24-h food recalls. Children’s dietary intake are reported for these 22 participants based on parents reports and, thus, represent estimations only, as it remains unknown whether children consumed other non-reported foods.ResultsFruit was the group with the highest daily intake among children (mean 1.77 ± 1.10 portions/day), followed by added sugar foods (mean 1.48 ± 0.89 portions/day), vegetables [median 1.27 (1.64) portions/day] and legumes [median 0.12 (0.39) portions/day]. Fruit intake was positively correlated with vegetable intake (p = 0.008). Regarding Dietary Reference Values accomplishment, 13.6% of children exceeded the daily safe and adequate intake of sodium, 77.3% did not meet potassium and fiber recommendations, and 31.8% did not meet vitamin C recommendations.DiscussionAll children did not meet calcium, vitamin B12 and vitamin D intake recommendations. Our findings further justify the need for dietary interventions in this field, to improve young children’s diets.Clinical trial registrationClinicalTrials.gov, identifier NCT04591496.

There is growing interest in monitoring gait patterns in people with neurological conditions. The democratisation of wearable inertial sensors has enabled the study of gait in free living environments. One pivotal aspect of gait assessment in uncontrolled environments is the ability to accurately recognise gait instances. Previous work has focused on wavelet transform methods or general machine learning models to detect gait; the former assume a comparable gait pattern between people and the latter assume training datasets that represent a diverse population. In this paper, we argue that these approaches are unsuitable for people with severe motor impairments and their distinct gait patterns, and make the case for a lightweight personalised alternative. We propose an approach that builds on top of a general model, fine-tuning it with personalised data. A comparative proof-of-concept evaluation with general machine learning (NN and CNN) approaches and personalised counterparts showed that the latter improved the overall accuracy in 3.5% for the NN and 5.3% for the CNN. More importantly, participants that were ill-represented by the general model (the most extreme cases) had the recognition of gait instances improved by up to 16.9% for NN and 20.5% for CNN with the personalised approaches. It is common to say that people with neurological conditions, such as Parkinson’s disease, present very individual motor patterns, and that in a sense they are all outliers; we expect that our results will motivate researchers to explore alternative approaches that value personalisation rather than harvesting datasets that are may be able to represent these differences.

Oral anticoagulation significantly reduces the incidence of dementia in atrial fibrillation patients. However, this protective effect has not been compared between Direct Oral Anticoagulants (DOAC) and Vitamin K antagonists’ anticoagulants (VKA). We conducted an electronic search for potentially eligible studies through the bibliographic databases MEDLINE, CENTRAL, ClinicalTrials.gov, EMBASE and Web of Science. The outcome of interest was dementia. Random-effects meta-analysis was performed. Nine observational studies were included and 1,175,609 atrial fibrillation patients were enrolled. DOAC therapy was associated with a significant reduction when compared with patients under VKA therapy (hazard ratio 0.89; 95% confidence interval 0.80–0.99). The grade of confidence of our results was very low due to the risk of bias. DOAC therapy is associated with a significant decrease in the risk of dementia when compared with VKA therapy. However, the low certainty of the evidence along with the paucityof clinical trials dedicated to answering this important question underscores a need for global clinical research initiatives.