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Algorithms are used to aid human decision makers by making predictions and recommending decisions. Currently, these algorithms are trained to optimize prediction accuracy. What if they were optimized to control final decisions? In this paper, we study a decision-aid algorithm that learns about the human decision maker and provides ''personalized recommendations'' to influence final decisions. We first consider fixed human decision functions which map observable features and the algorithm's recommendations to final decisions. We characterize the conditions under which perfect control over final decisions is attainable. Under fairly general assumptions, the parameters of the human decision function can be identified from past interactions between the algorithm and the human decision maker, even when the algorithm was constrained to make truthful recommendations. We then consider a decision maker who is aware of the algorithm's manipulation and responds strategically. By posing the setting as a variation of the cheap talk game [Crawford and Sobel, 1982], we show that all equilibria are partition equilibria where only coarse information is shared: the algorithm recommends an interval containing the ideal decision. We discuss the potential applications of such algorithms and their social implications.

The feedback that users provide through their choices (e.g., clicks, purchases) is one of the most common types of data readily available for training search and recommendation algorithms. However, myopically training systems based on choice data may only improve short-term engagement, but not the long-term sustainability of the platform and the long-term benefits to its users, content providers, and other stakeholders. In this paper, we thus develop a new framework in which decision makers (e.g., platform operators, regulators, users) can express long-term goals for the behavior of the platform (e.g., fairness, revenue distribution, legal requirements). These goals take the form of exposure or impact targets that go well beyond individual sessions, and we provide new control-based algorithms to achieve these goals. In particular, the controllers are designed to achieve the stated long-term goals with minimum impact on short-term engagement. Beyond the principled theoretical derivation of the controllers, we evaluate the algorithms on both synthetic and real-world data. While all controllers perform well, we find that they provide interesting trade-offs in efficiency, robustness, and the ability to plan ahead.

Datasets for training recommender systems are often subject to distribution shift induced by users' and recommenders' selection biases. In this paper, we study the impact of selection bias on datasets with different quantization. We then leverage two differently quantized datasets from different source distributions to mitigate distribution shift by applying the inverse probability scoring method from causal inference. Empirically, our approach gains significant performance improvement over single-dataset methods and alternative ways of combining two datasets., In Workshop on Distribution Shifts, 36th Conference on Neural Information Processing Systems (NeurIPS 2022)

Motivated by perception-based control problems in autonomous systems, this paper addresses the problem of developing feedback controllers to regulate the inputs and the states of a dynamical system to optimal solutions of an optimization problem when one has no access to exact measurements of the system states. In particular, we consider the case where the states need to be estimated from high-dimensional sensory data received only at discrete time intervals. We develop a sampled-data feedback controller that is based on adaptations of a projected gradient descent method, and that includes neural networks as integral components to estimate the state of the system from perceptual information. We derive sufficient conditions to guarantee (local) input-to-state stability of the control loop. Moreover, we show that the interconnected system tracks the solution trajectory of the underlying optimization problem up to an error that depends on the approximation errors of the neural network and on the time-variability of the optimization problem; the latter originates from time-varying safety and performance objectives, input constraints, and unknown disturbances. As a representative application, we illustrate our results with numerical simulations for vision-based autonomous driving., This is an extended version of the paper accepted to IFAC World Congress 2023 for publication, containing proofs

Online convex optimization (OCO) is a widely used framework in online learning. In each round, the learner chooses a decision in a convex set and an adversary chooses a convex loss function, and then the learner suffers the loss associated with their current decision. However, in many applications the learner's loss depends not only on the current decision but on the entire history of decisions until that point. The OCO framework and its existing generalizations do not capture this, and they can only be applied to many settings of interest after a long series of approximation arguments. They also leave open the question of whether the dependence on memory is tight because there are no non-trivial lower bounds. In this work we introduce a generalization of the OCO framework, ``Online Convex Optimization with Unbounded Memory'', that captures long-term dependence on past decisions. We introduce the notion of $p$-effective memory capacity, $H_p$, that quantifies the maximum influence of past decisions on present losses. We prove an $O(\sqrt{H_p T})$ upper bound on the policy regret and a matching (worst-case) lower bound. As a special case, we prove the first non-trivial lower bound for OCO with finite memory~\citep{anavaHM2015online}, which could be of independent interest, and also improve existing upper bounds. We demonstrate the broad applicability of our framework by using it to derive regret bounds, and to improve and simplify existing regret bound derivations, for a variety of online learning problems including online linear control and an online variant of performative prediction., Major revision with slight changes to the framework, improved upper bounds, new matching lower bounds, and improved regret bounds for the applications

Research Paper for the course PSYC 2017. This paper discusses the correlation between humor and personality traits.

Content creators compete for user attention. Their reach crucially depends on algorithmic choices made by developers on online platforms. To maximize exposure, many creators adapt strategically, as evidenced by examples like the sprawling search engine optimization industry. This begets competition for the finite user attention pool. We formalize these dynamics in what we call an exposure game, a model of incentives induced by algorithms, including modern factorization and (deep) two-tower architectures. We prove that seemingly innocuous algorithmic choices, e.g., non-negative vs. unconstrained factorization, significantly affect the existence and character of (Nash) equilibria in exposure games. We proffer use of creator behavior models, like exposure games, for an (ex-ante) pre-deployment audit. Such an audit can identify misalignment between desirable and incentivized content, and thus complement post-hoc measures like content filtering and moderation. To this end, we propose tools for numerically finding equilibria in exposure games, and illustrate results of an audit on the MovieLens and LastFM datasets. Among else, we find that the strategically produced content exhibits strong dependence between algorithmic exploration and content diversity, and between model expressivity and bias towards gender-based user and creator groups., presented at ICLR 2023 (top 5%)

Prediction systems face exogenous and endogenous distribution shift -- the world constantly changes, and the predictions the system makes change the environment in which it operates. For example, a music recommender observes exogeneous changes in the user distribution as different communities have increased access to high speed internet. If users under the age of 18 enjoy their recommendations, the proportion of the user base comprised of those under 18 may endogeneously increase. Most of the study of endogenous shifts has focused on the single decision-maker setting, where there is one learner that users either choose to use or not. This paper studies participation dynamics between sub-populations and possibly many learners. We study the behavior of systems with \emph{risk-reducing} learners and sub-populations. A risk-reducing learner updates their decision upon observing a mixture distribution of the sub-populations $\mathcal{D}$ in such a way that it decreases the risk of the learner on that mixture. A risk reducing sub-population updates its apportionment amongst learners in a way which reduces its overall loss. Previous work on the single learner case shows that myopic risk minimization can result in high overall loss~\citep{perdomo2020performative, miller2021outside} and representation disparity~\citep{hashimoto2018fairness, zhang2019group}. Our work analyzes the outcomes of multiple myopic learners and market forces, often leading to better global loss and less representation disparity.

In the long term, reinforcement learning (RL) is considered by many AI theorists to be the most promising path to artificial general intelligence. This places RL practitioners in a position to design systems that have never existed before and lack prior documentation in law and policy. Public agencies could intervene on complex dynamics that were previously too opaque to deliberate about, and long-held policy ambitions would finally be made tractable. In this whitepaper we illustrate this potential and how it might be technically enacted in the domains of energy infrastructure, social media recommender systems, and transportation. Alongside these unprecedented interventions come new forms of risk that exacerbate the harms already generated by standard machine learning tools. We correspondingly present a new typology of risks arising from RL design choices, falling under four categories: scoping the horizon, defining rewards, pruning information, and training multiple agents. Rather than allowing RL systems to unilaterally reshape human domains, policymakers need new mechanisms for the rule of reason, foreseeability, and interoperability that match the risks these systems pose. We argue that criteria for these choices may be drawn from emerging subfields within antitrust, tort, and administrative law. It will then be possible for courts, federal and state agencies, and non-governmental organizations to play more active roles in RL specification and evaluation. Building on the "model cards" and "datasheets" frameworks proposed by Mitchell et al. and Gebru et al., we argue the need for Reward Reports for AI systems. Reward Reports are living documents for proposed RL deployments that demarcate design choices., 60 pages

Additional file 2. SPIRIT 2013 checklist.

Additional file 1. Tabulated summary of feasibility outcomes.

Barn banter describes and analyzes the sardonic and whimsical verbal exchanges of humor as they function both to develop an elaborated sense of imaginative camaraderie, and to typify constructions of personhood, mutuality and community among horses and those who ride with them in three contemporary South Dakota boarding barns. Although a broad literature on the anthropomorphism of animals, including horses, exists, there is a lack of data on what we call the ‘equine-o-morphism’ of humans. Barn banter, as a kind of insider’s knowledge and shared argot, is not only a playful way of minding animals, but also, in practice, develops horses’ potentials as actors and partners with their humans to form an elaborated and highly intimate sense of co-being or shared identity for horse and rider that situates them as a pair within the wider barn community.

Building systems that are good for society in the face of complex societal effects requires a dynamic approach. Recent approaches to machine learning (ML) documentation have demonstrated the promise of discursive frameworks for deliberation about these complexities. However, these developments have been grounded in a static ML paradigm, leaving the role of feedback and post-deployment performance unexamined. Meanwhile, recent work in reinforcement learning has shown that the effects of feedback and optimization objectives on system behavior can be wide-ranging and unpredictable. In this paper we sketch a framework for documenting deployed and iteratively updated learning systems, which we call Reward Reports. Taking inspiration from various contributions to the technical literature on reinforcement learning, we outline Reward Reports as living documents that track updates to design choices and assumptions behind what a particular automated system is optimizing for. They are intended to track dynamic phenomena arising from system deployment, rather than merely static properties of models or data. After presenting the elements of a Reward Report, we discuss a concrete example: Meta's BlenderBot 3 chatbot. Several others for game-playing (DeepMind's MuZero), content recommendation (MovieLens), and traffic control (Project Flow) are included in the appendix.

Modern nonlinear control theory seeks to endow systems with properties such as stability and safety, and has been deployed successfully across various domains. Despite this success, model uncertainty remains a significant challenge in ensuring that model-based controllers transfer to real world systems. This paper develops a data-driven approach to robust control synthesis in the presence of model uncertainty using Control Certificate Functions (CCFs), resulting in a convex optimization based controller for achieving properties like stability and safety. An important benefit of our framework is nuanced data-dependent guarantees, which in principle can yield sample-efficient data collection approaches that need not fully determine the input-to-state relationship. This work serves as a starting point for addressing important questions at the intersection of nonlinear control theory and non-parametric learning, both theoretical and in application. We validate the proposed method in simulation with an inverted pendulum in multiple experimental configurations., Comment: 8 pages, 2 figures, submitted to Conference on Decision & Control (CDC) 2021

Trial registration number ISRCTN15644451. © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ. Objective: To determine whether adding web-based support (e-coachER) to an exercise referral scheme (ERS) increases objectively assessed physical activity (PA). Design: Multicentre trial with participants randomised to usual ERS alone (control) or usual ERS plus e-coachER (intervention). Setting: Primary care and ERS in three UK sites from 2015 to 2018. Participants: 450 inactive ERS referees with chronic health conditions. Interventions: Participants received a pedometer, PA recording sheets and a user guide for the web-based support. e-coachER interactively encouraged the use of the ERS and other PA options. Main outcome measures: Primary and key secondary outcomes were: objective moderate-to-vigorous PA (MVPA) minutes (in ≥10 min bouts and without bouts), respectively, after 12 months. Secondary outcomes were: other accelerometer-derived and self-reported PA measures, ERS attendance, EQ-5D-5L, Hospital Anxiety and Depression Scale and beliefs about PA. All outcomes were collected at baseline, 4 and 12 months. Primary analysis was an intention to treat comparison between intervention and control arms at 12-month follow-up. Results: There was no significant effect of the intervention on weekly MVPA at 12 months between the groups recorded in ≥10 min bouts (mean difference 11.8 min of MVPA, 95% CI: -2.1 to 26.0; p=0.10) or without bouts (mean difference 13.7 min of MVPA, 95% CI: -26.8 to 54.2; p=0.51) for 232 participants with usable data. There was no difference in the primary or secondary PA outcomes at 4 or 12 months. Conclusion: Augmenting ERS referrals with web-based behavioural support had only a weak, non-significant effect on MVPA. Trial registration number: ISRCTN15644451. National Institute for Health Research (NIHR), Health Technology Assessment Programme (grant reference: 13/25/20).

In this work, we consider how preference models in interactive recommendation systems determine the availability of content and users' opportunities for discovery. We propose an evaluation procedure based on stochastic reachability to quantify the maximum probability of recommending a target piece of content to an user for a set of allowable strategic modifications. This framework allows us to compute an upper bound on the likelihood of recommendation with minimal assumptions about user behavior. Stochastic reachability can be used to detect biases in the availability of content and diagnose limitations in the opportunities for discovery granted to users. We show that this metric can be computed efficiently as a convex program for a variety of practical settings, and further argue that reachability is not inherently at odds with accuracy. We demonstrate evaluations of recommendation algorithms trained on large datasets of explicit and implicit ratings. Our results illustrate how preference models, selection rules, and user interventions impact reachability and how these effects can be distributed unevenly., Comment: to appear ICML 2021

Recommender systems operate in an inherently dynamical setting. Past recommendations influence future behavior, including which data points are observed and how user preferences change. However, experimenting in production systems with real user dynamics is often infeasible, and existing simulation-based approaches have limited scale. As a result, many state-of-the-art algorithms are designed to solve supervised learning problems, and progress is judged only by offline metrics. In this work we investigate the extent to which offline metrics predict online performance by evaluating eleven recommenders across six controlled simulated environments. We observe that offline metrics are correlated with online performance over a range of environments. However, improvements in offline metrics lead to diminishing returns in online performance. Furthermore, we observe that the ranking of recommenders varies depending on the amount of initial offline data available. We study the impact of adding exploration strategies, and observe that their effectiveness, when compared to greedy recommendation, is highly dependent on the recommendation algorithm. We provide the environments and recommenders described in this paper as Reclab: an extensible ready-to-use simulation framework at https://github.com/berkeley-reclab/RecLab.

While real-world decisions involve many competing objectives, algorithmic decisions are often evaluated with a single objective function. In this paper, we study algorithmic policies which explicitly trade off between a private objective (such as profit) and a public objective (such as social welfare). We analyze a natural class of policies which trace an empirical Pareto frontier based on learned scores, and focus on how such decisions can be made in noisy or data-limited regimes. Our theoretical results characterize the optimal strategies in this class, bound the Pareto errors due to inaccuracies in the scores, and show an equivalence between optimal strategies and a rich class of fairness-constrained profit-maximizing policies. We then present empirical results in two different contexts -- online content recommendation and sustainable abalone fisheries -- to underscore the applicability of our approach to a wide range of practical decisions. Taken together, these results shed light on inherent trade-offs in using machine learning for decisions that impact social welfare.

Modern nonlinear control theory seeks to develop feedback controllers that endow systems with properties such as safety and stability. The guarantees ensured by these controllers often rely on accurate estimates of the system state for determining control actions. In practice, measurement model uncertainty can lead to error in state estimates that degrades these guarantees. In this paper, we seek to unify techniques from control theory and machine learning to synthesize controllers that achieve safety in the presence of measurement model uncertainty. We define the notion of a Measurement-Robust Control Barrier Function (MR-CBF) as a tool for determining safe control inputs when facing measurement model uncertainty. Furthermore, MR-CBFs are used to inform sampling methodologies for learning-based perception systems and quantify tolerable error in the resulting learned models. We demonstrate the efficacy of MR-CBFs in achieving safety with measurement model uncertainty on a simulated Segway system.

In order to certify performance and safety, feedback control requires precise characterization of sensor errors. In this paper, we provide guarantees on such feedback systems when sensors are characterized by solving a supervised learning problem. We show a uniform error bound on nonparametric kernel regression under a dynamically-achievable dense sampling scheme. This allows for a finite-time convergence rate on the sub-optimality of using the regressor in closed-loop for waypoint tracking. We demonstrate our results in simulation with simplified unmanned aerial vehicle and autonomous driving examples., Comment: to appear at L4DC 2021

BACKGROUND: There is modest evidence that exercise referral schemes increase physical activity in inactive individuals with chronic health conditions. There is a need to identify additional ways to improve the effects of exercise referral schemes on long-term physical activity. OBJECTIVES: To determine if adding the e-coachER intervention to exercise referral schemes is more clinically effective and cost-effective in increasing physical activity after 1 year than usual exercise referral schemes. DESIGN: A pragmatic, multicentre, two-arm randomised controlled trial, with a mixed-methods process evaluation and health economic analysis. Participants were allocated in a 1 : 1 ratio to either exercise referral schemes plus e-coachER (intervention) or exercise referral schemes alone (control). SETTING: Patients were referred to exercise referral schemes in Plymouth, Birmingham and Glasgow. PARTICIPANTS: There were 450 participants aged 16-74 years, with a body mass index of 30-40 kg/m2, with hypertension, prediabetes, type 2 diabetes, lower limb osteoarthritis or a current/recent history of treatment for depression, who were also inactive, contactable via e-mail and internet users. INTERVENTION: e-coachER was designed to augment exercise referral schemes. Participants received a pedometer and fridge magnet with physical activity recording sheets, and a user guide to access the web-based support in the form of seven 'steps to health'. e-coachER aimed to build the use of behavioural skills (e.g. self-monitoring) while strengthening favourable beliefs in the importance of physical activity, competence, autonomy in physical activity choices and relatedness. All participants were referred to a standard exercise referral scheme. PRIMARY OUTCOME MEASURE: Minutes of moderate and vigorous physical activity in ≥ 10-minute bouts measured by an accelerometer over 1 week at 12 months, worn ≥ 16 hours per day for ≥ 4 days including ≥ 1 weekend day. SECONDARY OUTCOMES: Other accelerometer-derived physical activity measures, self-reported physical activity, exercise referral scheme attendance and EuroQol-5 Dimensions, five-level version, and Hospital Anxiety and Depression Scale scores were collected at 4 and 12 months post randomisation. RESULTS: Participants had a mean body mass index of 32.6 (standard deviation) 4.4 kg/m2, were referred primarily for weight loss and were mostly confident self-rated information technology users. Primary outcome analysis involving those with usable data showed a weak indicative effect in favour of the intervention group (n = 108) compared with the control group (n = 124); 11.8 weekly minutes of moderate and vigorous physical activity (95% confidence interval -2.1 to 26.0 minutes; p = 0.10). Sixty-four per cent of intervention participants logged on at least once; they gave generally positive feedback on the web-based support. The intervention had no effect on other physical activity outcomes, exercise referral scheme attendance (78% in the control group vs. 75% in the intervention group) or EuroQol-5 Dimensions, five-level version, or Hospital Anxiety and Depression Scale scores, but did enhance a number of process outcomes (i.e. confidence, importance and competence) compared with the control group at 4 months, but not at 12 months. At 12 months, the intervention group incurred an additional mean cost of £439 (95% confidence interval -£182 to £1060) compared with the control group, but generated more quality-adjusted life-years (mean 0.026, 95% confidence interval 0.013 to 0.040), with an incremental cost-effectiveness ratio of an additional £16,885 per quality-adjusted life-year. LIMITATIONS: A significant proportion (46%) of participants were not included in the primary analysis because of study withdrawal and insufficient device wear-time, so the results must be interpreted with caution. The regression model fit for the primary outcome was poor because of the considerable proportion of participants [142/243 (58%)] who recorded no instances of ≥ 10-minute bouts of moderate and vigorous physical activity at 12 months post randomisation. FUTURE WORK: The design and rigorous evaluation of cost-effective and scalable ways to increase exercise referral scheme uptake and maintenance of moderate and vigorous physical activity are needed among patients with chronic conditions. CONCLUSIONS: Adding e-coachER to usual exercise referral schemes had only a weak indicative effect on long-term rigorously defined, objectively assessed moderate and vigorous physical activity. The provision of the e-coachER support package led to an additional cost and has a 63% probability of being cost-effective based on the UK threshold of £30,000 per quality-adjusted life-year. The intervention did improve some process outcomes as specified in our logic model. TRIAL REGISTRATION: Current Controlled Trials ISRCTN15644451. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 63. See the NIHR Journals Library website for further project information.

Introduction Accidental urine leakage is a distressing problem that affects around one in three women. The main types of urinary incontinence (UI) are stress, urgency and mixed, with stress being most common. Current UK guidelines recommend that women with UI are offered at least 3 months of pelvic floor muscle training (PFMT). There is evidence that PFMT is effective in treating UI, however it is not clear how intensively women have to exercise to give the maximum sustained improvement in symptoms, and how we enable women to achieve this. Biofeedback is an adjunct to PFMT that may help women exercise more intensively for longer, and thus may improve continence outcomes when compared with PFMT alone. A Cochrane review was inconclusive about the benefit of biofeedback, indicating the need for further evidence. Methods and analysis This multicentre randomised controlled trial will compare the effectiveness and cost-effectiveness of PFMT versus biofeedback-mediated PFMT for women with stress UI or mixed UI. The primary outcome is UI severity at 24 months after randomisation. The primary economic outcome measure is incremental cost per quality-adjusted life-year at 24 months. Six hundred women from UK community, outpatient and primary care settings will be randomised and followed up via questionnaires, diaries and pelvic floor assessment. All participants are offered six PFMT appointments over 16 weeks. The use of clinic and home biofeedback is added to PFMT for participants in the biofeedback group. Group allocation could not be masked from participants and healthcare staff. An intention-to-treat analysis of the primary outcome will estimate the mean difference between the trial groups at 24 months using a general linear mixed model adjusting for minimisation covariates and other important prognostic covariates, including the baseline score. Ethics and dissemination Approval granted by the West of Scotland Research Ethics Committee 4 (16/LO/0990). Written informed consent will be obtained from participants by the local research team. Serious adverse events will be reported to the data monitoring and ethics committee, the ethics committee and trial centres as required. A Standard Protocol Items: Recommendations for Interventional Trials checklist and figure are available for this protocol. The results will be published in international journals and included in the relevant Cochrane review. Trial registration number ISRCTN57746448; Pre-results.

RAM Evidence Briefing paper. (PDF 829 kb)

This document summarizes the medication inventory conducted during the Wave V survey. It also documents the protocol for assigning therapeutic classes to those medications. Whenever possible, the inventory and protocol in the Wave V survey and at the in-home biomarker exam mirrored those of Wave IV to ensure comparability of data within and between waves.

Motivated by vision-based control of autonomous vehicles, we consider the problem of controlling a known linear dynamical system for which partial state information, such as vehicle position, is extracted from complex and nonlinear data, such as a camera image. Our approach is to use a learned perception map that predicts some linear function of the state and to design a corresponding safe set and robust controller for the closed loop system with this sensing scheme. We show that under suitable smoothness assumptions on both the perception map and the generative model relating state to complex and nonlinear data, parameters of the safe set can be learned via appropriately dense sampling of the state space. We then prove that the resulting perception-control loop has favorable generalization properties. We illustrate the usefulness of our approach on a synthetic example and on the self-driving car simulation platform CARLA., Comment: This revision includes reframing the local generalization problem, with relaxed the assumptions so that the robust problem depends on a local slope bound rather than a Lipschitz constant, and provide a method for learning the slope bound from data. We also include additional experiments with a CNN perception module

Machine learning (ML) is increasingly deployed in real world contexts, supplying actionable insights and forming the basis of automated decision-making systems. While issues resulting from biases pre-existing in training data have been at the center of the fairness debate, these systems are also affected by technical and emergent biases, which often arise as context-specific artifacts of implementation. This position paper interprets technical bias as an epistemological problem and emergent bias as a dynamical feedback phenomenon. In order to stimulate debate on how to change machine learning practice to effectively address these issues, we explore this broader view on bias, stress the need to reflect on epistemology, and point to value-sensitive design methodologies to revisit the design and implementation process of automated decision-making systems., Presented at the 2018 Workshop on Fairness, Accountability and Transparency in Machine Learning during ICML 2018, Stockholm, Sweden

We consider adaptive control of the Linear Quadratic Regulator (LQR), where an unknown linear system is controlled subject to quadratic costs. Leveraging recent developments in the estimation of linear systems and in robust controller synthesis, we present the first provably polynomial time algorithm that provides high probability guarantees of sub-linear regret on this problem. We further study the interplay between regret minimization and parameter estimation by proving a lower bound on the expected regret in terms of the exploration schedule used by any algorithm. Finally, we conduct a numerical study comparing our robust adaptive algorithm to other methods from the adaptive LQR literature, and demonstrate the flexibility of our proposed method by extending it to a demand forecasting problem subject to state constraints.

Rehabilitation Training (ReTrain) is a group-based approach to functional training post stroke. ReTrain has recently been evaluated through a pilot randomised controlled trial.\ud \ud Objective: This article reports on the acceptability of the intervention as described by trial participants.\ud \ud Design: A qualitative approach was undertaken. Of the 45 participants recruited into the trial, 23 were randomised to receive ReTrain. Following a sampling strategy, 10 participants undertook 1:1 semistructured audio-recorded interviews. Transcripts were analysed following a modified Framework Approach.\ud \ud Results: Six themes were developed including exploration of: the physical and psychological impacts of training,the perceived mechanisms of change, the interaction of the group and approach of the trainer. A further theme considered the reported longer term impact of participation. Overall, the results indicated the acceptability of the intervention, but also key areas for potential modification in the definitive trial. These include a need to consider potential impact on both physical and psychological function, careful consideration of dosing and fatigue and the interpersonal factors that facilitate appropriate level of delivery, the trainer to participant ratio, and enhancing features that support continuation of activity postintervention.\ud \ud Conclusion: Overall, this study supports the acceptability of ReTrain and the development of a definitive trial evaluation of this intervention to full.

Patient-related measures. (PDF 446 kb)

Exemplar of GP participant information sheet and consent form. (PDF 127 kb)

Additional trial information (consistent with SPIRIT 2013 checklist) not included in the main protocol document. (PDF 344 kb)

Behaviour change techniques employed in FREE in relation to general practitioners. (PDF 413 kb)

Completed PRISMA-P+ checklist. (DOCX 29 kb)

Teacher interview schedule. (DOCX 16 kb)

Exemplar of patient participant information sheets and consent form. (PDF 173 kb)

General practitioner-related measures. (PDF 418 kb)

Criteria for quality appraisal. (DOCX 15 kb)

Behaviour change techniques employed in FREE in relation to patients. (PDF 409 kb)

Fidelity to delivery (quality) checklist. (DOCX 16 kb)

Child focus group schedule. (DOCX 16 kb)

SPIRIT checklist. (PDF 277 kb)

Search strategy. (DOCX 15 kb)

Fidelity to delivery (content) checklist. (DOCX 13 kb)

Purpose: Upper limb disability following stroke may have multiple effects on the individual. Existing assessment instruments tend to focus on impairment and function and may miss other changes that are personally important. This study aimed to identify personally significant impacts of upper limb disability following stroke. Methods: Accounts by stroke survivors, in the form of web-based diaries (blogs) and stories, were sought using a blog search engine and in stroke-related web-sites. Thematic analysis using the World Health Organisation’s International Classification of Functioning Disability and Health (ICF) was used to identify personal impacts of upper limb disability following stroke. Results: Ninety-nine sources from at least four countries were analysed. Many impacts were classifiable using the ICF, but a number of additional themes emerged, including emotional, cognitive and behavioural changes. Blogs and other web-based accounts were easily accessible and rich sources of data, although using them raised several methodological issues, including potential sample bias. Conclusions: A range of impacts was identified, some of which (such as use of information technology and alienation from the upper limb) are not addressed in current assessment instruments. They should be considered in post-stroke assessments. Blogs may help in the development of more comprehensive assessments.Implications for RehabilitationA comprehensive assessment of the upper limb following stroke should include the impact of upper limb problems on social participation, as well as associated emotional, cognitive and behavioural changes.Using personalised assessment instruments alongside standardised measures may help ensure that these broader domains are considered in discussions between clinicians and patients.Rehabilitation researchers should investigate whether and how these domains could be addressed and operationalised in standard upper limb assessment instruments. A comprehensive assessment of the upper limb following stroke should include the impact of upper limb problems on social participation, as well as associated emotional, cognitive and behavioural changes. Using personalised assessment instruments alongside standardised measures may help ensure that these broader domains are considered in discussions between clinicians and patients. Rehabilitation researchers should investigate whether and how these domains could be addressed and operationalised in standard upper limb assessment instruments.

Background: Promoting physical activity (PA) via primary care exercise referral schemes (ERS) is common but there is no rigorous evidence for long term changes in PA (Pavey et al, 2011) among those with chronic conditions. From July 2015, for 15 months, the e-coachER trial began to recruit 1400 patients (in SW England, Birmingham and Glasgow) with one or more chronic conditions including diabetes, obesity, hypertension, osteoarthritis, or depression, who are eligible and about to attend an ERS. The two-arm parallel RCT is powered to determine if the addition of a web-based, interactive, theory-driven and evidence-based support system called e-coachER (hosted on the ‘LifeGuide’ platform) will result in at least 10% more patients who do 150 mins or more per week of accelerometer assessed moderate or vigorous physical activity (MVPA) at 12 months. Recruitment into the trial is within primary care, using both mail-merged patient invitations and opportunistic GP invitations (and exercise referrals). Within the trial, after participants are screened, provide consent and complete baseline assessments, they are randomised to receive usual ERS at each site or usual ERS plus a mailed Welcome Pack with registration details to access e-coachER on-line. Inclusion criteria for entering the trial are: (1) Aged 16-74 years; (2) with one or more of the following: obesity (BMI 30-35), hypertension (SBP 140-179 or DBP 90-109, type 2 diabetes, lower limb osteoarthritis, recent history of treatment for depression; (3) Participants who are in the two lowest (of four) groups using the GP Physical Activity Questionnaire; (4) have an e-mail address and access to the internet; (5) Eligible for an ERS. The intervention rationale, design and content are reported in another presentation. Aims: This presentation will provide initial findings from a 3 month internal pilot phase with a focus on trial recruitment and initial intervention engagement. We will present data on the achievement of our targets for recruiting 180 participants across the three sites and proportion who initially go online to register and use e-coachER. We also aim, based on detailed process evaluation work, to present a summary of factors influencing recruitment and intervention engagement, and any adaptations needed for the recruitment process. Methods: Data is being captured to inform a CONSORT chart with details on number of patient invitations, number and reasons for trial exclusion, and numbers and characteristics of those recruited. The LifeGuide system will provide data on numbers/proportion assigned to the intervention arm who register and start to use e-coachER. Qualitative data on recruitment processes and intervention engagement, as part of the process evaluation, will be summarised. Results: Due to a number of factors recruitment began 2-4 months later than planned across the 3 sites. Data will be presented on achieved recruitment and e-coachER engagement relative to our targets in the internal pilot trial. Qualitative data will be presented on the acceptability and feasibility of the trial methods and intervention. Conclusion: A summary of what we have learned about recruitment and intervention engagement will be presented and ideas for improvement, as appropriate. Supported with funding from the NIHR (HTA) Project: 13/25/20

Young adulthood is associated with increased responsibilities, including financial independence, career development, and starting a family. Young Americans between the ages of 25-34 spend more time at work and caring for others, and have less free time than younger age groups.1 With more time dedicated to these responsibilities, young adults may not have as much time to maintain a healthy lifestyle. Over time, less healthy lifestyle behaviors set health trajectories into adulthood, increasing the risks of morbidities and chronic disease in the future. Lifestyle and health related behavioral choices are important factors for understanding health. Physical inactivity and poor diet are major risk factors for obesity and other chronic diseases such as hypertension, cardiovascular disease, diabetes, and some cancers.2-6 Inadequate sleep is also related to poor health status, including obesity, hypertension, and Type 2 diabetes.2 This brief provides a snapshot of the lifestyle habits of young adults in the U.S. in 2008. Using data from the National Longitudinal Study of Adolescent to Adult Health (Add Health), we examine the following lifestyle behaviors known to be related to health: sleep and sleep problems, physical activity, sedentary behaviors, fast food and sweet beverage intake, and smoking habits. We address the following questions: • How do young adults’ lifestyle behaviors compare to guidelines and recommendations for sleep, diet, physical activity and smoking? • How do young adults’ lifestyle behaviors compare to reported national averages? • How do patterns in these characteristics change by gender, age, race/ethnicity and obesity status?

status: published