BackgroundCognitive symptoms are an underrecognized aspect of depression that are often untreated. High-frequency cognitive assessment holds promise for improving disease and treatment monitoring. Although we have previously found it feasible to remotely assess cognition and mood in this capacity, further work is needed to ascertain the optimal methodology to implement and synthesize these techniques. ObjectiveThe objective of this study was to examine (1) longitudinal changes in mood, cognition, activity levels, and heart rate over 6 weeks; (2) diurnal and weekday-related changes; and (3) co-occurrence of fluctuations between mood, cognitive function, and activity. MethodsA total of 30 adults with current mild-moderate depression stabilized on antidepressant monotherapy responded to testing delivered through an Apple Watch (Apple Inc) for 6 weeks. Outcome measures included cognitive function, assessed with 3 brief n-back tasks daily; self-reported depressed mood, assessed once daily; daily total step count; and average heart rate. Change over a 6-week duration, diurnal and day-of-week variations, and covariation between outcome measures were examined using nonlinear and multilevel models. ResultsParticipants showed initial improvement in the Cognition Kit N-Back performance, followed by a learning plateau. Performance reached 90% of individual learning levels on average 10 days after study onset. N-back performance was typically better earlier and later in the day, and step counts were lower at the beginning and end of each week. Higher step counts overall were associated with faster n-back learning, and an increased daily step count was associated with better mood on the same (P<.001) and following day (P=.02). Daily n-back performance covaried with self-reported mood after participants reached their learning plateau (P=.01). ConclusionsThe current results support the feasibility and sensitivity of high-frequency cognitive assessments for disease and treatment monitoring in patients with depression. Methods to model the individual plateau in task learning can be used as a sensitive approach to better characterize changes in behavior and improve the clinical relevance of cognitive data. Wearable technology allows assessment of activity levels, which may influence both cognition and mood.

0.00001; p = 0.05) for every week that they were enrolled in the study, which equated to a deterioration of 1.5% in FEV 1 annually (0.1 z-score, 95% CI, -0.26 to -0.001; p = 0.05), although there was a significant positive interaction between FEV 1 z-score and total number of weeks of exercise training completed (0.02, 95% CI, 0.01-0.04;p = 0.01).Children who exercised more regularly offset some of the deterioration in FEV 1 z-score that they might otherwise have experienced.Extrapolated data showed that children who attend at least 52 weeks of training over 24 months might expect an improvement of 1.0 in FEV 1 z-score (95% CI, 0.5-2.1;p = 0.01), which equated to an annual improvement of 7.5% in FEV 1 (Figure 1).This effect was not realized in children who did not attend regular exercise sessions.There was also a significant dose-related effect of exercise on FVC and FEF 25-75 .A dose-related effect was not demonstrated for LCI. Figure 1.(abstract 271): Moderate-to vigorous-intensity physical activity profiles of Project Fizzyo participants.

by enabling people of all ages to perform the activities that matter to them now and in the future.In the United Kingdom, PwCF are not routinely offered an occupational therapy review within specialist care, and there is limited knowledge of the impact of CF on their ADLs.Methods: In December 2021, a pilot occupational therapy role was introduced at a single adult center covering a mixed caseload including CF and non-CF respiratory conditions.Referrals were triaged through oral discussions before being accepted.Information was collected on age, sex, presence of CF complications, employment status, and reasons for referral and will continue to be collected for the duration of a 12-month pilot.Results: From December 2021 to March 2022, referrals to occupational therapy were accepted for 19 PwCF (6 male, 13 female; aged 18-69).CFrelated arthritis (CFRA) was a complication that 52% (n = 10) experienced, and 79% (n = 15) had CF-related diabetes (CFRD).None of the PwCF referred were in full-time employment.Reasons for referral were personal ADLs (n = 15), including transfers, mobility, bathing, grooming, toileting, dressing, and managing treatments; instrumental ADLs (n = 11), including meal preparation, laundry, shopping, and driving; leisure (n = 3) including forming new relationships and sports and rest (n = 1).Most referrals indicated difficulties in more than one category.Conclusions: Adults with CF-related complications such as CFRD and CFRA are overrepresented in referrals to occupational therapy within this small sample size.Personal ADLs appeared to be the most commonly reported area of difficulty.Preliminary results suggest that people with CF-related complications may benefit from occupational therapy assessment and intervention to improve ADL performance and overall quality of life.

Digital health is a burgeoning technology sector seeking to improve healthcare and clinical trial outcomes using software applications. However, there is little information on patients’ experiences of, and motivations for, using these systems.

Introduction Daily physiotherapy is believed to mitigate the progression of cystic fibrosis (CF) lung disease. However, physiotherapy airway clearance techniques (ACTs) are burdensome and the evidence guiding practice remains weak. This paper describes the protocol for Project Fizzyo, which uses innovative technology and analysis methods to remotely capture longitudinal daily data from physiotherapy treatments to measure adherence and prospectively evaluate associations with clinical outcomes. Methods and analysis A cohort of 145 children and young people with CF aged 6–16 years were recruited. Each participant will record their usual physiotherapy sessions daily for 16 months, using remote monitoring sensors: (1) a bespoke ACT sensor, inserted into their usual ACT device and (2) a Fitbit Alta HR activity tracker. Real-time breath pressure during ACTs, and heart rate and daily step counts (Fitbit) are synced using specific software applications. An interrupted time-series design will facilitate evaluation of ACT interventions (feedback and ACT-driven gaming). Baseline, mid and endpoint assessments of spirometry, exercise capacity and quality of life and longitudinal clinical record data will also be collected. This large dataset will be analysed in R using big data analytics approaches. Distinct ACT and physical activity adherence profiles will be identified, using cluster analysis to define groups of individuals based on measured characteristics and any relationships to clinical profiles assessed. Changes in adherence to physiotherapy over time or in relation to ACT interventions will be quantified and evaluated in relation to clinical outcomes. Ethics and dissemination Ethical approval for this study (IRAS: 228625) was granted by the London-Brighton and Sussex NREC (18/LO/1038). Findings will be disseminated via peer-reviewed publications, at conferences and via CF clinical networks. The statistical code will be published in the Fizzyo GitHub repository and the dataset stored in the Great Ormond Street Hospital Digital Research Environment. Trial registration number ISRCTN51624752; Pre-results.

Heart rate (HR) data from activity trackers can facilitate longitudinal measures of moderate to vigorous physical activity (MVPA), but identifying MVPA HR threshold is challenging due to the variability and age-related differences in resting and peak HR (RHR & PHR) in children and young people (CYP). We aimed to identify age-related MVPA HR thresholds in CYP with CF (CYPwCF). Continuous HR data were collected from 140 CYPwCF (6-16yrs) wearing a Fitbit Alta HR for >8 daytime hours, over >7 days. Daily RHR was calculated from the mean of the 5 lowest discrete HR minutes (Logan et al. 2000 Med Sci Sports Exc) and a RHR regression line was created from plotting median daily RHR against age. CYPwCF also completed a 10m modified shuttle walk test (10m-MSWT), wearing a single lead ECG (Polar H10 chest strap). PHR was the highest HR recorded during the 10m-MSWT. PHR values <180bpm were excluded (submaximal test) and a PHR regression line was created by plotting PHR against age. MVPA HR threshold was generated using the age-normalised regression lines and the American College of Sports Medicine (2014) threshold equation for MVPA: HR>(0.4x(PHR–RHR))+RHR. The MVPA HR threshold decreased by 1 bpm/year: 125bpm at 6yrs to 114bpm at 16yrs (fig 1). This study provides a useful method to identify normative age-related HR thresholds to ensure accurate estimation of MVPA in a wide paediatric age range.

Background Cognitive symptoms are common in major depressive disorder and may help to identify patients who need treatment or who are not experiencing adequate treatment response. Digital tools providing real-time data assessing cognitive function could help support patient treatment and remediation of cognitive and mood symptoms. Objective The aim of this study was to examine feasibility and validity of a wearable high-frequency cognitive and mood assessment app over 6 weeks, corresponding to when antidepressant pharmacotherapy begins to show efficacy. Methods A total of 30 patients (aged 19-63 years; 19 women) with mild-to-moderate depression participated in the study. The new Cognition Kit app was delivered via the Apple Watch, providing a high-resolution touch screen display for task presentation and logging responses. Cognition was assessed by the n-back task up to 3 times daily and depressed mood by 3 short questions once daily. Adherence was defined as participants completing at least 1 assessment daily. Selected tests sensitive to depression from the Cambridge Neuropsychological Test Automated Battery and validated questionnaires of depression symptom severity were administered on 3 occasions (weeks 1, 3, and 6). Exploratory analyses examined the relationship between mood and cognitive measures acquired in low- and high-frequency assessment. Results Adherence was excellent for mood and cognitive assessments (95% and 96%, respectively), did not deteriorate over time, and was not influenced by depression symptom severity or cognitive function at study onset. Analyses examining the relationship between high-frequency cognitive and mood assessment and validated measures showed good correspondence. Daily mood assessments correlated moderately with validated depression questionnaires (r=0.45-0.69 for total daily mood score), and daily cognitive assessments correlated moderately with validated cognitive tests sensitive to depression (r=0.37-0.50 for mean n-back). Conclusions This study supports the feasibility and validity of high-frequency assessment of cognition and mood using wearable devices over an extended period in patients with major depressive disorder.

The success of next generation sequencing technologies potentially opens up new horizons in clinical research and practice. But before one can really benefit from genomic clinics of the future, multiple issues must be addressed. Human genomics research relies on the availability of genomic datasets that are needed to test a hypothesis. Although a large amount of data is generated around the world, individual researchers still often lack access to it. Exemplary collaborative practices demonstrated during the realisation of the Human Genome Project do not reflect the state of data sharing in the community today: data sharing is not the default, but the exception. Data sharing has continually been recognised as important, not only for the advancement of scientific knowledge, but also for the preservation of information: verification of conclusions and safeguarding against misconduct. But data sharing in human genomics is a multifaceted challenge. Ethical considerations combined with the uniqueness of the genome of an individual require special precautions to enable sharing whilst protecting data privacy. Here, we investigate the current extent of human genomic data sharing by examining the data handling processes and needs of human genomics researchers in different settings. We explore how researchers are including data access and data sharing in their current workflows and whether any bottlenecks need to be addressed to enable more efficient data collaborations.

DNAdigest's mission is to investigate and address the issues hindering efficient and ethical genomic data sharing in the human genomics research community. We conducted contextual interviews with human genomics researchers in clinical, academic or industrial R&D settings about their experience with accessing and sharing human genomic data. The qualitative interviews were followed by an online survey which provided quantitative support for our findings. Here we present the generalised workflow for accessing human genomic data through both public and restricted-access repositories and discuss reported points of frustration and their possible improvements. We discuss how data discoverability and accessibility are lacking in current mechanisms and how these are the prerequisites for adoption of best practices in the research community. We summarise current initiatives related to genomic data discovery and present a new data discovery platform available at http://nucleobase.co.uk.