Objective: Gait may be a useful biomarker that can be objectively measured with wearable technology to classify Parkinson's disease (PD). This study aims to: (i) comprehensively quantify a battery of commonly utilized gait digital characteristics (spatiotemporal and signal-based), and (ii) identify the best discriminative characteristics for the optimal classification of PD. Methods: Six partial least square discriminant analysis (PLS-DA) models were trained on subsets of 210 characteristics measured in 142 subjects (81 people with PD, 61 controls (CL)). Results: Models accuracy ranged between 70.42-88.73% (AUC: 78.4-94.5%) with a sensitivity of 72.84-90.12% and a specificity of 60.3-86.89%. Signal-based digital gait characteristics independently gave 87.32% accuracy. The most influential characteristics in the classification models were related to root mean square values, power spectral density, step velocity and length, gait regularity and age. Conclusions: This study highlights the importance of signal-based gait characteristics in the development of tools to help classify PD in the early stages of the disease.

Strong associations exist between quality of life and physical activity for those living in aged residential care (ARC). Suitable and reliable tools are required to quantify physical activity for descriptive and evaluative purposes. We calculated the number of days required for reliable walking outcomes indicative of physical activity in an ARC population using a trunk-worn device. ARC participants (n = 257) wore the device for up to 7 days. Reasons for data loss were also recorded. The volume, pattern, and variability of walking was calculated. For 197 participants who wore the device for at least 3 days, linear mixed models determined the impact of week structure and number of days required to achieve reliable outcomes, collectively and then stratified by care level. The average days recorded by the wearable device was 5.2 days. Day of the week did not impact walking activity. Depending on the outcome and level of care, 2–5 days was sufficient for reliable estimates. This study provides informative evidence for future studies aiming to use a wearable device located on the trunk to quantify physical activity walking out in the ARC population.

Rationale: The effect of pharmacological and non-pharmacological interventions on physical activity (PA) outcomes is not fully elucidated in patients with COPD. The objectives of the present study were to provide estimation of treatment effects of all available interventions on PA outcomes in patients with COPD and to provide recommendations regarding the future role of PA outcomes in pharmacological trials.Materials and methods: This review was conducted according to the Cochrane Handbook for Systematic Reviews of Interventions and reported in line with PRISMA. Records were identified through searches of 12 scientific databases; the most updated search was performed in January 2023.Results: 74 studies published from 2000 to 2021 were included, with a total of 8140 COPD patients. Forced expiratory volume in 1 s % predicted ranged between 31% and 74%, with a mean of 55%. Steps/day constituted the most frequently assessed PA outcome in interventional studies. Compared to usual care, PA behavioural modification interventions resulted in improvements in the mean (95% CI) steps/day when implemented alone (by 1035 (576–1493); p<0.00001) or alongside exercise training (by 679 (93–1266); p=0.02). Moreover, bronchodilator therapy yielded a favourable difference of 396 (125–668; p=0.004) steps/day, compared to placebo.Conclusions: PA behavioural modification and pharmacological interventions lead to significant improvements in steps/day, compared to control and placebo groups, respectively. Compared to bronchodilator therapy, PA behavioural modification interventions were associated with a 2-fold greater improvement in steps/day. Large-scale pharmacological studies are needed to establish an intervention-specific minimal clinically important difference for PA outcomes as well as their convergent validity to accelerate qualification as potential biomarkers and efficacy end-points for regulatory approval.

Hemiparesis is a disabling consequence of stroke, causing abnormal gait patterns with biomechanical asymmetries. Gait mechanics for stroke survivors appear resistant to conventional rehabilitation. Auditory rhythmical cueing (ARC) represents an emerging intervention option. To determine effective gait interventions, objective measures of gait collected from real-world environments may be required in addition to standard clinical outcomes to aid understanding of gait mechanics. This study reports on the ability of wearable activity monitors to quantify an ARC intervention for fifty-nine stroke survivors randomised into an ARC gait and balance training programme or an equivalent training programme without ARC. Gait assessments were undertaken at baseline and at 6 weeks for 4-metre walks and continuously for 7 days following each home assessment using a wearable activity monitor. The success rates of data collection using the wearable activity monitors ranged from 64 to 95%. Forty-eight Digital Mobility Outcomes representing a broad range of gait mechanics were calculated. Visualisation of all DMOs using radar plots indicated changes from baseline in both groups, with individual data indicating large variability in response to the intervention and control programme. Including wearable activity monitors to evaluate gait interventions for stroke survivors provides additional value to traditional methods and aids understanding of individual responses; as such, they should be used for future intervention-based research.

Background: The binding substrates of [¹⁸F]flutemetamol and [¹¹C]PiB PET consist of fibrillar amyloid‐β (Aβ) aggregates in morphologically and structurally distinct diffuse plaques (DP) and cored plaques (CP) which are present at different proportions across brain regions in Alzheimer’s disease (AD). To account for this heterogeneity, an unbiased quantitative measure integrating plaque size and density of amyloid‐β (Aβ) fibrils in plaques could help avoid false positive results, improve imaging‐to‐autopsy correlations, and further advance our understanding of pathological substrates for binding of amyloid PET tracers.Method: Sixteen subjects underwent [¹⁸F]flutemetamol PET imaging in the pivotal Phase III end‐of‐life study (Study GE067‐007 and GE067‐026) and later came to autopsy (mean imaging‐to‐autopsy interval was 111 ± 87.6 days). [¹⁸F]flutemetamol SUVRpons values in four neocortical regions were correlated with region‐matched postmortem measures of cyano‐flutemetamol labeling of all plaques, as well as CP and DP separately, using percent area coverage, mean grayscale intensity of fluorescence, or a combination of the two measures referred to as integrated density.Result: In the analysis of all cyano‐flutemetamol‐labeled plaques, [¹⁸F]flutemetamol SUVRpons values were more closely associated with integrated density measures than percent area coverage and mean grayscale values. Similarly, when CP and DP were analyzed separately, the integrated density values showed the closest association with neocortical region‐matched [¹⁸F]flutemetamol SUVRpons values. When area coverage of CP and DP were each expressed as a fraction of total plaque area coverage with or without integrating fluorescence intensity values, [¹⁸F]flutemetamol SUVRpons values within each region of interest were associated with the DP, but not the CP, area coverage measure.Conclusion: The association of antemortem [¹⁸F]flutemetamol PET and region‐matched postmortem integrated density measurements of cyano‐flutemetamol labeled plaques further supports the hypothesis that amyloid PET signal is influenced both by plaque size and Aβ fibril density within DP and CP. Further elucidating the relative contributions of each plaque type to the retention of amyloid PET radioligands could influence how a positive amyloid PET signal is interpreted in relation to postmortem pathology data.

Background: The prevention of symptomatic Alzheimer’s disease (AD) is a major endeavor currently in the field, with the prevalence of subjects on the AD continuum (i.e. presence of amyloid‐β [Aβ] pathology) being much higher than previously estimated. However, the extent to which Aβ pathology and its interaction with common risk factors in the aging population relates to disease progression is still limited. The AMYPAD Prognostic and Natural History Study (PNHS) aims to evaluate the value of amyloid‐PET for predicting AD‐related disease progression.Method: A total of 1423 non‐demented subjects were included from the AMYPAD‐PNHS pan‐European cohort (17 sites across 10 Parent Cohorts), for who a baseline [¹⁸F]flutemetamol or [¹⁸F]florbetaben amyloid‐PET scans that passed quality control was available (Table‐1). Amyloid burden was assessed using the Centiloid (CL) method (whole cerebellum reference region). Independent linear mixed‐effect models were used to assess the predictive value of continuous CL, and its interaction with time, across cognitive domains: mini‐mental state examination (MMSE), memory, attention, language fluency, visuospatial, and executive functions. Years from baseline, age, sex, years of education, cognitive dementia rating (CDR), and APOE‐ε4 were used as independent variables. Clinical follow‐up was 3.4 ± 1.8 years (up to ±10 years) on average.Result: At baseline, 90% of the subjects were classified as cognitively unimpaired (CDR = 0). Based on CL, most subjects were categorized as amyloid‐negative (59%, 0±9CL), followed by grey‐zone (24%, 25±11CL), and amyloid‐positive (17%, 82±21CL). Baseline CL and its interaction with time were predictive of MMSE scores, memory, attention, and language fluency. Baseline CL, but not its interaction with time, was predictive of executive function. Finally, the interaction of baseline CL with time, but not its main effect, was predictive of visuospatial functioning (Figure‐1). Age and sex were significant contributors to nearly all models.Conclusion: Higher baseline CL was associated with lower cognitive performance in all explored domains, suggesting that emerging cerebral amyloid accumulation in non‐demented individuals is predictive of overall cognitive function. Also, higher CL values were predictive of decline across cognitive domains, except for executive function. Further analysis is ongoing, including different confounding factors, longitudinal PET outcomes, and other biomarkers of the disease.