Patient and Public Involvement (PPI) in outcome selection in breast cancer and nephrology trials

Acknowledgements We would like to acknowledge our PPI colleagues on Twitter who read and commented on our work, helping bring the importance of PPI in clinical trials to the fore. Funding There was no direct funding received for this research. The HRB CRF-C at UCC facilitated the placement of an undergraduate BSc Public Health Sciences student, CB, who led this study under supervision. The HRB Clinical Research Facility receives core funding from the Health Research Board, Ireland, and matched funding from University College Cork. The Health Services Research Unit, University of Aberdeen, receives core funding from the Chief Scientist Office of the Scottish Government Health Directorates.Peer reviewedPublisher PD

Including children's voices in a multiple stakeholder study on a community-wide approach to improving quality in early years settings

This article will explore the use of visual participatory research methods with young children. These methods have been utilized to add young children's voices to research on the impact of a quality improvement strategy in an early years’ settings involved in a community-based prevention and early intervention programme. The main objective of the intervention programme is to measurably improve the lives of children (pre-birth to six years) and families through universal and targeted services in an urban community which experiences high levels of socio-economic deprivation. Children were offered the opportunity to share their views with the researchers through a variety of participatory rights-based approaches including drawing, photo-elicitation, photography, and conversations. Adding children's voices to the programme evaluation can help us to understand children's experiences and produces better policy and better services and also to interrogate the adult-centric quantitative data and adult perspectives generated in the ongoing project evaluation

Gait Asymmetry Post-Stroke: Determining Valid and Reliable Methods Using a Single Accelerometer Located on the Trunk

Asymmetry is a cardinal symptom of gait post-stroke that is targeted during rehabilitation. Technological developments have allowed accelerometers to be a feasible tool to provide digital gait variables. Many acceleration-derived variables are proposed to measure gait asymmetry. Despite a need for accurate calculation, no consensus exists for what is the most valid and reliable variable. Using an instrumented walkway (GaitRite) as the reference standard, this study compared the validity and reliability of multiple acceleration-derived asymmetry variables. Twenty-five post-stroke participants performed repeated walks over GaitRite whilst wearing a tri-axial accelerometer (Axivity AX3) on their lower back, on two occasions, one week apart. Harmonic ratio, autocorrelation, gait symmetry index, phase plots, acceleration, and jerk root mean square were calculated from the acceleration signals. Test–retest reliability was calculated, and concurrent validity was estimated by comparison with GaitRite. The strongest concurrent validity was obtained from step regularity from the vertical signal, which also recorded excellent test–retest reliability (Spearman’s rank correlation coefficients (rho) = 0.87 and Intraclass correlation coefficient (ICC21) = 0.98, respectively). Future research should test the responsiveness of this and other step asymmetry variables to quantify change during recovery and the effect of rehabilitative interventions for consideration as digital biomarkers to quantify gait asymmetry

Empirical algebraic modelling of live weight of Irish dairy cows over lactation

The aim of this study is to derive an equation that has the ability to model live weight of Irish dairy cows over lactation. The data set consisted of 6899 cows from 63 herds, of which 428 were from experimental herds and 6471 cows were from commercial herds. An initial examination focussed on time series techniques, as the data are of a time series nature. Splines were also examined to determine the dimensions of a model required to represent the data. As an incomplete gamma function, which was previously used to model milk yield, has been used in other studies to model live weight, various milk yield models were investigated. Finally, live weight changes between two calvings were modelled as a function of age, days in milk and pregnancy. As multicollinearity was evident in this function, the variance inflation factor was examined and principal component analysis was carried out on the variables responsible for multicollinearity. The proposed live weight model has a better fit than previous models, weak multicollinearity and the residuals are homoskedastic, independent and normally distributed. This live weight model therefore provides an acceptable level of accuracy in representing the shape of the live weight curve for Irish dairy cows and can be easily modified for different environmental scenarios

Evaluation of daily walking activity and gait profiles: a novel application of a time series analysis framework

Wearable technology allows an in-depth analysis of gait behaviour in free-living environments. This investigation aimed to use Alzheimer's disease as an example to apply the time series analysis technique of Statistical Parametric Mapping (SPM) to create daily gait profiles and test if they differed from cognitively intact controls. A framework of macro (habitual walking behaviours) and micro characteristics (spatiotemporal gait variables) characteristics were calculated on an hourly basis. SPM showed that select micro gait characteristics differed from controls at specific hours of the day. Therefore, the application of SPM may provide a more in-depth reflection of activity and gait time-dependent fluctuations than commonly used whole day values. Considering macro and micro gait hour-by- hour may have applications towards disease management, personalized care, monitoring medication and targeted interventions for people with a range of neurodegenerative diseases

Empirical Algebraic Modelling of Lactation Curves Using Irish Data

The purpose of this study was to find a well-fitting,robust, single equation model to describe the shape of lactation curves for Irish dairy cows. The suitability of a number of algebraic models that depict lactation curves was examined, using Irish test day data. The analysis was carried out on a total of 14,956 lactation records from commercial and experimental herds and included both autumn and spring calving animals ‘Goodness of fit’ and adherence of the various models to the assumptions of regression analysis were examined. Multicollinearity posed a severe problem in the application of the best-fit model but omitting one of the variables from the estimation procedure reduced this effect. The modified model, referred to as the Ali-B model, is a single equation model that can be easily updated and incorporated into computer code. This is in contrast with the Standard Lactation Curve (SLAC) method, a method of interpolation, which is currently adopted by the Irish industry. The effects of seasonal factors on milk production were estimated and added to the Ali-B model to create a production profile for cows calving in specific months. The Ali-B model provides an acceptable level of accuracy in representing the shape of the lactation curve for Irish dairy cows, and can be easily modified for different environmental scenarios

Quantifying Reliable Walking Activity with a Wearable Device in Aged Residential Care: How Many Days Are Enough?

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

Variable neurologic phenotype in a GEFS+ family with a novel mutation in SCN1A

AbstractPurposeTo describe the spectrum of clinical disease in a mutliplex family with an autosomal dominant form of generalized epilepsy with febrile seizures plus (GEFS+) and determine its genetic etiology.MethodsMedical and family history was obtained on 11 clinically affected individuals and their relatives across three generations through medical chart review and home visits. A candidate gene approach including haplotype analysis and direct sequencing was used.ResultsAn epilepsy-associated haplotype was identified on 2q24. Direct sequencing of the entire SCN1A gene identified seven sequence variants. However, only one of these, c.1162 T>C, was not found in population controls. This transition in exon 8 of SCN1A predicts a substitution (Y388H) of a highly conserved tyrosine residue in the loop between transmembrane segments S5 and S6 of the sodium channel protein (Nav1.1). Clinical features in mutation carriers of this novel missense mutation were highly variable, ranging from febrile seizures to severe refractory epilepsy.ConclusionA novel missense mutation in the pore-forming region of the sodium channel gene SCN1A causes GEFS+ with a variable phenotype that includes mood and anxiety disorders, as well as ataxia, expanding the GEFS+ spectrum to include neuropsychiatric disease

Complications in the first 5 years following cataract surgery in infants with and without intraocular lens implantation in the Infant Aphakia Treatment Study

PURPOSE: To compare rates and severity of complications between infants undergoing cataract surgery with and without intraocular lens (IOL) implantation. DESIGN: Prospective randomized clinical trial. METHODS: A total of 114 infants were enrolled in the Infant Aphakia Treatment Study, a randomized, multi-center (12) clinical trial comparing the treatment of unilateral aphakia in patients under 7 months of age with a primary IOL implant or contact lens. The rate, character, and severity of intraoperative complications, adverse events, and additional intraocular surgeries during the first 5 postoperative years in the 2 groups were examined. RESULTS: There were more patients with intraoperative complications (28% vs 11%, P = .031), adverse events (81% vs 56%, P = .008), and more additional intraocular surgeries (72% vs 16%, P < .0001) in the IOL group than in the contact lens group. However, the number of patients with adverse events in the contact lens group increased (15 to 24) in postoperative years 2-5 compared to the first postoperative year, while it decreased (44 to 14) in years 2-5 compared to the first postoperative year in the IOL group. If only one half of the patients in the contact lens (aphakic) group eventually undergo secondary IOL implantation, the number of additional intraocular surgeries in the 2 groups will be approximately equal. CONCLUSION: The increased rate of complications, adverse events, and additional intraocular surgeries associated with IOL implantation in infants <7 months of age militates toward leaving babies aphakic if it is considered likely that the family will be successful with contact lens correction

The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control

Quantifying gait and postural control adds valuable information that aids in understanding neurological conditions where motor symptoms predominate and cause considerable functional impairment. Disease-specific clinical scales exist; however, they are often susceptible to subjectivity, and can lack sensitivity when identifying subtle gait and postural impairments in prodromal cohorts and longitudinally to document disease progression. Numerous devices are available to objectively quantify a range of measurement outcomes pertaining to gait and postural control; however, efforts are required to standardise and harmonise approaches that are specific to the neurological condition and clinical assessment. Tools are urgently needed that address a number of unmet needs in neurological practice. Namely, these include timely and accurate diagnosis; disease stratification; risk prediction; tracking disease progression; and decision making for intervention optimisation and maximising therapeutic response (such as medication selection, disease staging, and targeted support). Using some recent examples of research across a range of relevant neurological conditions—including Parkinson’s disease, ataxia, and dementia— we will illustrate evidence that supports progress against these unmet clinical needs. We summarise the novel ‘big data’ approaches that utilise data mining and machine learning techniques to improve disease classification and risk prediction, and conclude with recommendations for future direction