Interventions to improve or maintain lower-limb function among ambulatory adolescents with cerebral palsy: a cross-sectional survey of current practice in the UK.

2 Objectives: To describe physiotherapy management to improve or maintain lower-limb 1 function among adolescents with cerebral palsy, classified in Gross Motor Function 2 Classification System levels I-III, in the United Kingdom. 3 Methods: A list of interventions was identified using a nominal group technique and 4 developed into a survey, which was distributed to approximately 2,100 paediatric 5 physiotherapists in the UK through the Association of Paediatric Chartered Physiotherapists 6 and a private physiotherapy clinic in London between April and June 2015. One-hundred and 7 thirty-five physiotherapists completed the survey. Survey respondents indicated how 8 frequently they used each intervention (i.e. “frequently”, “sometimes”, “rarely”, “never”) in 9 the past year. 10 Results: Provision of explanations to the child, liaison with families, liaison with health 11 professionals, provision of advice to schools, and stretching were the most frequently used 12 interventions with 90%, 90%, 86%, 79%, and 76% of respondents, respectively, reporting 13 that they frequently used each. The interventions most commonly reported as “never” used 14 were conductive education (88%), MOVE programme (85%), functional electrical 15 stimulation (82%), body-weight supported treadmill training (80%), and rebound therapy 16 (71%). 17 Conclusions: This study suggests that a large number of interventions are used by 18 physiotherapists in the UK to improve or maintain lower-limb function among adolescents 19 with CP, not all of which are evidence-based

Multicomponent frailty assessment tools for older people with psychiatric disorders: a systematic review

Objective: To review evidence evaluating the use of multi-component frailty assessment tools in assessing frailty in older adults with psychiatric disorders. Methods: A systematic literature review was conducted to identify all multi-component frailty assessment tools (i.e. a tool that assesses ≥2 indicators of frailty). The items of each frailty assessment tool were compared to DSM-5 diagnostic criteria for psychiatric disorders to assess construct overlap. Studies conducted in community, inpatient and outpatient clinical settings were considered for inclusion. Participants: Adults aged ≥60 years old. Results: 5,639 studies in total were identified following the removal of duplicates; 97 of which were included for review. Of the 48 multi-component frailty assessment tools identified, no tool had been developed for, or validated in, older adult populations with psychiatric disorder. 24/48 frailty assessment tools contained a psychological assessment domain, with 18/48 tools using presence of depressed mood and/or anxiety as a frailty indicator. Common areas of construct overlap in frailty assessment tools and DSM-5 diagnostic criteria included weight loss (29/48) and fatigue (21/48). Conclusions: Significant construct overlap exists between the indicators of frailty as conceptualised in existing frailty assessment tools and DSM-5 diagnostic criteria for common psychiatric disorders, including Major Depressive Episode and Generalised Anxiety Disorder, which has the potential to confound frailty assessment results. Further research is necessary to establish a reliable and valid tool to assess frailty in this population

Associations between gait kinematics, gross motor function and physical activity among young people with cerebral palsy: A cross sectional study

INTRODUCTION: The aim of this study was to investigate the association between gait parameters, gross motor function and physical activity (PA) in young people with cerebral palsy (CP). METHODS: Thirty-eight adolescents aged between 10–19 years with spastic CP in GMFCS levels I-III (mean [standard deviation] age 13.7 [2.4] yr; 53%female) were included in this cross-sectional study. Hip, knee and ankle joint excursion and stance time was assessed using 3D gait analysis. Self-selected walking speed was assessed during a timed 10 m overground walk and treadmill walking. Gross motor function was assessed using dimensions D and E of the Gross Motor Function Measure (GMFM-66). Moderate-to-vigorous PA, light PA and step-count were assessed using an accelerometer. Linear regression was used to examine associations. RESULTS: After adjusting for age, sex and GMFCS level, percentage stance time was associated with dimension E of the GMFM-66 (β= –0.29, 95%CI –0.54 to –0.05). There was no evidence that any other gait parameters were associated with GMFM-66 dimensions D or E. There was also no evidence that gait parameters or GMFM-66 dimensions D or E were associated with step-count or time in PA after adjusting for age, sex and GMFCS level. DISCUSSION: The findings provide an insight into the complexity of the relationship between gait quality or ability at the impairment level, function as measured in a controlled environment, and the performance of habitual PA, which is essential for health among children with CP

Co-designing resources to support the transition from child to adult health services for young people with cerebral palsy: A design thinking approach

IntroductionDesign thinking is a human-centred process that aims to identify the needs of end-users and iteratively develop solutions. Involving end-users in the development and design of solutions may enhance effectiveness by increasing focus on the needs of the target population. This paper describes the process of co-designing resources to support the transition from child-centred to adult-orientated health services using a design thinking approach.MethodsFive co-design workshops were conducted remotely with a young person advisory group and parent advisory group. A design thinking process guided by the Stanford D.School approach was used to understand the transition needs of young people and their parents and iteratively develop solutions to improve end-user experience.ResultsEight resource prototypes were generated: (1) designated transition coordinator, (2) digital stories of transition experience (3) written informational support (4) transition website, (5) transition checklists and worksheets (6) transition app, (7) transition programme or course and (8) educational programme for health professionals.ConclusionDesign thinking is a feasible approach to identify, characterise and prioritise resources collaboratively with end-user partners

Digital health tools for the passive monitoring of depression: a systematic review of methods

The use of digital tools to measure physiological and behavioural variables of potential relevance to mental health is a growing field sitting at the intersection between computer science, engineering, and clinical science. We summarised the literature on remote measuring technologies, mapping methodological challenges and threats to reproducibility, and identified leading digital signals for depression. Medical and computer science databases were searched between January 2007 and November 2019. Published studies linking depression and objective behavioural data obtained from smartphone and wearable device sensors in adults with unipolar depression and healthy subjects were included. A descriptive approach was taken to synthesise study methodologies. We included 51 studies and found threats to reproducibility and transparency arising from failure to provide comprehensive descriptions of recruitment strategies, sample information, feature construction and the determination and handling of missing data. The literature is characterised by small sample sizes, short follow-up duration and great variability in the quality of reporting, limiting the interpretability of pooled results. Bivariate analyses show consistency in statistically significant associations between depression and digital features from sleep, physical activity, location, and phone use data. Machine learning models found the predictive value of aggregated features. Given the pitfalls in the combined literature, these results should be taken purely as a starting point for hypothesis generation. Since this research is ultimately aimed at informing clinical practice, we recommend improvements in reporting standards including consideration of generalisability and reproducibility, such as wider diversity of samples, thorough reporting methodology and the reporting of potential bias in studies with numerous features

The Association Between Home Stay and Symptom Severity in Major Depressive Disorder: Preliminary Findings From a Multicenter Observational Study Using Geolocation Data From Smartphones

BACKGROUND: Most smartphones and wearables are currently equipped with location sensing (using GPS and mobile network information), which enables continuous location tracking of their users. Several studies have reported that various mobility metrics, as well as home stay, that is, the amount of time an individual spends at home in a day, are associated with symptom severity in people with major depressive disorder (MDD). Owing to the use of small and homogeneous cohorts of participants, it is uncertain whether the findings reported in those studies generalize to a broader population of individuals with MDD symptoms. OBJECTIVE: The objective of this study is to examine the relationship between the overall severity of depressive symptoms, as assessed by the 8-item Patient Health Questionnaire, and median daily home stay over the 2 weeks preceding the completion of a questionnaire in individuals with MDD. METHODS: We used questionnaire and geolocation data of 164 participants with MDD collected in the observational Remote Assessment of Disease and Relapse-Major Depressive Disorder study. The participants were recruited from three study sites: King's College London in the United Kingdom (109/164, 66.5%); Vrije Universiteit Medisch Centrum in Amsterdam, the Netherlands (17/164, 10.4%); and Centro de Investigación Biomédica en Red in Barcelona, Spain (38/164, 23.2%). We used a linear regression model and a resampling technique (n=100 draws) to investigate the relationship between home stay and the overall severity of MDD symptoms. Participant age at enrollment, gender, occupational status, and geolocation data quality metrics were included in the model as additional explanatory variables. The 95% 2-sided CIs were used to evaluate the significance of model variables. RESULTS: Participant age and severity of MDD symptoms were found to be significantly related to home stay, with older (95% CI 0.161-0.325) and more severely affected individuals (95% CI 0.015-0.184) spending more time at home. The association between home stay and symptoms severity appeared to be stronger on weekdays (95% CI 0.023-0.178, median 0.098; home stay: 25th-75th percentiles 17.8-22.8, median 20.9 hours a day) than on weekends (95% CI -0.079 to 0.149, median 0.052; home stay: 25th-75th percentiles 19.7-23.5, median 22.3 hours a day). Furthermore, we found a significant modulation of home stay by occupational status, with employment reducing home stay (employed participants: 25th-75th percentiles 16.1-22.1, median 19.7 hours a day; unemployed participants: 25th-75th percentiles 20.4-23.5, median 22.6 hours a day). CONCLUSIONS: Our findings suggest that home stay is associated with symptom severity in MDD and demonstrate the importance of accounting for confounding factors in future studies. In addition, they illustrate that passive sensing of individuals with depression is feasible and could provide clinically relevant information to monitor the course of illness in patients with MDD

Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study

Background Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks. A key question for the field is the extent to which participants can adhere to research protocols and the completeness of data collected. We aimed to describe drop out and data completeness in a naturalistic multimodal longitudinal RMT study, in people with a history of recurrent MDD. We further aimed to determine whether those experiencing a depressive relapse at baseline contributed less complete data. Methods Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) is a multi-centre, prospective observational cohort study conducted as part of the Remote Assessment of Disease and Relapse – Central Nervous System (RADAR-CNS) program. People with a history of MDD were provided with a wrist-worn wearable device, and smartphone apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks, and cognitive assessments. Participants were followed-up for a minimum of 11 months and maximum of 24 months. Results Individuals with a history of MDD (n = 623) were enrolled in the study,. We report 80% completion rates for primary outcome assessments across all follow-up timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. In total, 110 participants had > 50% data available across all data types. Conclusions RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible. We found comparable levels of data availability in active and passive forms of data collection, demonstrating that both are feasible in this patient group

Multilingual markers of depression in remotely collected speech samples: A preliminary analysis

Background: Speech contains neuromuscular, physiological and cognitive components, and so is a potential biomarker of mental disorders. Previous studies indicate that speaking rate and pausing are associated with major depressive disorder (MDD). However, results are inconclusive as many studies are small and underpowered and do not include clinical samples. These studies have also been unilingual and use speech collected in controlled settings. If speech markers are to help understand the onset and progress of MDD, we need to uncover markers that are robust to language and establish the strength of associations in real-world data. // Methods: We collected speech data in 585 participants with a history of MDD in the United Kingdom, Spain, and Netherlands as part of the RADAR-MDD study. Participants recorded their speech via smartphones every two weeks for 18 months. Linear mixed models were used to estimate the strength of specific markers of depression from a set of 28 speech features. // Results: Increased depressive symptoms were associated with speech rate, articulation rate and intensity of speech elicited from a scripted task. These features had consistently stronger effect sizes than pauses. // Limitations: Our findings are derived at the cohort level so may have limited impact on identifying intra-individual speech changes associated with changes in symptom severity. The analysis of features averaged over the entire recording may have underestimated the importance of some features. // Conclusions: Participants with more severe depressive symptoms spoke more slowly and quietly. Our findings are from a real-world, multilingual, clinical dataset so represent a step-change in the usefulness of speech as a digital phenotype of MDD