Sitting time and patterns of activity in post-stroke rehabilitation: week versus weekend activity

Background: High levels of active task practice are recommended after stroke. However, the in-patient rehabilitation day is largely spent sitting. Understanding patterns of sitting across the rehabilitation week may facilitate strategies to promote greater activity. We aimed to compare differences in weekday and weekend sitting time and 24-hour activity patterns during the last week of in patient rehabilitation. Methods: Participants with stroke (n=34) from two rehabilitation units wore an activity monitor continuously during the final 7-days of in-patient rehabilitation. Linear mixed models (adjusted for waking hours) were performed with activity time as the outcome and weekday and weekend as the exposure. Patterns of activity accumulation were determined by averaging patient activity in 60-minute epochs, and then generating a heat map of activity level as a function of time. Results: Participant mean age was 68 [SD 13] years (53% male) mean NIHSS score 7 [SD 5]. There was no significant difference in total sitting time between weekdays and weekends. On the weekend, mean walking time was 8.35 minutes less (95% CI -12.13, -4.56 p ≤0.001), and steps/day were 624 fewer (95% CI -951, -296 p ≤0.001) than during the week. Activity patterns were similar across weekdays and weekends, with more morning than afternoon activity observed. Conclusion: Sitting time did not change in relation to the 7-day rehabilitation week, while walking (time and steps) was less on weekends. Morning activity was observably greater than afternoon activity across the 7-days. Strategies targeting afternoon, evening and weekend activity may increase overall physical activity during rehabilitation

Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting.

OBJECTIVE: To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. METHODS: We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI. RESULTS: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10-8; and LINC00539/ZDHHC20, p = 5.82 × 10-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p [BI] = 9.38 × 10-25; p [SSBI] = 5.23 × 10-14 for hypertension), smoking (p [BI] = 4.4 × 10-10; p [SSBI] = 1.2 × 10-4), diabetes (p [BI] = 1.7 × 10-8; p [SSBI] = 2.8 × 10-3), previous cardiovascular disease (p [BI] = 1.0 × 10-18; p [SSBI] = 2.3 × 10-7), stroke (p [BI] = 3.9 × 10-69; p [SSBI] = 3.2 × 10-24), and MRI-defined white matter hyperintensity burden (p [BI] = 1.43 × 10-157; p [SSBI] = 3.16 × 10-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy. CONCLUSION: In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI

A single case study using Jintronix software for stroke rehabilitation and Kinect motion tracking for physical rehabilitation using a putt to stand aid and standby table

Background: Technology using rehabilitation software may provide an engaging way to increase the amount of exercises that a client performs to address low physical activity levels in rehabilitation and the need for high repetitions of movements or exercises for effective physical rehabilitation. The ability to use motion capture software with individuals who require assistive devices to stand is not known. Aims: to determine if motion capture software can be feasibly used for physical rehabilitation in stroke clients who required assistance to stand. Methods: A single case study using Jintronix software for stroke rehabili- tation and Kinect motion tracking system for upper and lower limb physi- cal rehabilitation using a pull to stand aid and standby table. The exercise program included 10 exercises–5forupperlimb and 5 for standing balance and leg strength. Results: One 88 yo stroke survivor (12/52) participated. Clients need to have more than 20 degrees range of motion into flexion or adduction at their shoulder to avoid interference with the frame. Active-assisted upper limb activities are possible in this case. One stepping game is not possible in the standing frame. Weight shift and balance activities can be per- formed and the tracking system is able to give feedback on performance. Conclusion/Discussion: Use of motion tracking software to increase total activity time in rehabilitation, increase repetition of movements of both the upper and lower limbs and give feedback on performance are all possible using the Kinect system in clients who are not able to stand independentl

Protocol of a 12-month multifactorial eHealth programme targeting balance, dual-tasking and mood to prevent falls in older people: the StandingTall+ randomised controlled trial

Introduction Falls have a multifactorial aetiology, which may limit the effectiveness of the common approach of exercise as the sole intervention strategy. Multifactorial interventions could be more effective in people at high risk of falling; however, the focus of such interventions has traditionally been quite narrow. This paper describes the design of a randomised controlled trial that will evaluate the effectiveness of an eHealth programme, which addresses cumulative effects of key fall-risk factors across the triad of physical, affective and cognitive functions on falls in older people.Methods and analysis 518 older people aged 65 years and over with high fall risk, defined as having a history of falls in the past 6 months, self-reported fear of falling or being aged 80 years or over, will be recruited via local advertisements, newsletters and presentations, and randomised to an intervention or health education control group. The intervention comprises balance exercise, cognitive-motor exercise and cognitive–behavioural therapy, with their dosage based on participant’s baseline balance, executive function and mood. The primary outcome is the rate of falls in the 12 months after randomisation. Secondary outcomes at 6 and 12 months comprise programme adherence, healthcare use, physical activity, balance and mobility, cognitive function, psychological well-being, quality of life, health literacy and user experience and attitudes towards the programme. Data will be analysed following intention to treat to gauge real-world effectiveness. We will further determine complier averaged causal effects to correct for varying adherence and conduct economic analyses to gain insight into cost-effectiveness and cost–utility.Ethics and dissemination Ethical approval was obtained from the University of New South Wales (UNSW) Human Research Ethics Committee in December 2017. Outcomes will be disseminated via peer-reviewed articles, conference presentations, community events and media releases.Trial registration number ACTRN12619000540112

Dementia prevention : the time to act is now

In 2012, the Australian Government declared dementia as the ninth National Health Priority Area. Eight years later, dementia is the greatest cause of disability in Australians aged over 65 years, the second leading cause of mortality, and the highest in women.1 Today, more than 459 000 Australians live with dementia, and this number is expected to exceed one million by 2056.2 The societal, economic and health care burden of dementia is unprecedented, with significant impacts on individuals, caregivers and families. In addition to therapeutic advances, improved and timely diagnosis and coordinated person-centred care, dementia prevention and risk-factor management are our best chance to make a difference.3 How do we tackle dementia prevention cost-effectively in the post-pandemic era

Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based study - Supplemental data

This file contains the supplemental data to the article entitled "Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based study". It contains additional methods paragraphs 1 to 4, supplemental tables 1 to 17, supplemental figures 1 to 7, and additional reference

Data from: Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting

Objective: We explored genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. Methods: We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely small sub-cortical BI (SSBI), in eighteen population-based cohorts (N=20,949) from five ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in seven population-based cohorts (N=6,862, 1,483 with BI, 630 with SBBI), and tested associations with related phenotypes including ischemic stroke and pathologically-defined BI. Results: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, P=1.77×10-8 and LINC00539/ZDHHC20, P=5.82×10-9. Both have been associated with blood pressure (BP) related phenotypes, but did not replicate in the smaller follow-up sample nor show associations with related phenotypes. Age and sex-adjusted associations with BI and SSBI were observed for BP traits (P-value for BI, P[BI]=9.38×10-25; P[SSBI]=5.23×10-14 for hypertension), smoking (P[BI]=4.4×10-10; P[SSBI]=1.2×10-4), diabetes (P[BI]=1.7×10-8; P[SSBI]=2.8×10-3), previous cardiovascular disease (P[BI]=1.0×10-18; P[SSBI]=2.3×10-7), stroke (P[BI]=3.9×10-69; P[SSBI]=3.2×10-24), and MRI-defined white matter hyperintensity burden (P[BI]=1.43×10-157; P[SSBI]=3.16×10-106), but not with body-mass-index or cholesterol. GRS of BP traits were associated with BI and SSBI (P≤0.0022), without indication of directional pleiotropy. Conclusions: In this multi-ethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI