UK Preschool-aged children’s physical activity levels in childcare and at home: a cross-sectional exploration

Background Young children are thought to be inactive in childcare, but little is known about location-specific activity levels. This observational study sought to describe the in-care and out-of-care activity patterns of preschool-aged children and explore differences in physical activity level by childcare attendance. Methods Three to four-year-old children were recruited from 30 preschool and nursery ‘settings’ in Cambridgeshire, UK. Average minutes per hour (min/h) spent sedentary (SED), in light physical activity (LPA) and in moderate-to-vigorous PA (MVPA) were measured by accelerometry for up to 7 days (mean: 6.7 ± 1.1). Weekly childcare attendance patterns were reported by parents. The within-child association between childcare attendance and outcomes was assessed using two- and three-level hierarchical regression; sex by care (in/out) interactions were considered. Results Two hundred and two children (51 % female) had valid activity data for ≥2 days. Children, and particularly boys, were less sedentary and more active when in care compared to at home (SED: Boys: β (SE): −6.4 (0.5) min/h, Girls: −4.8 (0.5); LPA: Boys: 0.6 (0.4), Girls: 1.8 (0.4); MVPA: Boys: 5.7 (0.5); Girls: 3.0 (0.4)). Differences between in-care and at-home activity were largest in the (early) mornings and early evenings for boys; no compensation in at-home activity occurred later in the day. On days when children were in care part-time (1–5 h) or full-time (>5 h), they were significantly less sedentary and more active compared with non-care days. Conclusions Young children, and particularly boys, accumulate more MVPA in care compared to at home. Future research should identify factors accounting for this difference and consider targeting non-care time in intervention efforts to increase higher-intensity activity and decrease sedentary time in preschoolers

Sensitivity to interaural timing differences within high-frequency sounds

Interaural Timing Differences (ITDs) are a cue for sound localisation. In response to low-frequency sounds, sensitivity to ITDs can be conveyed by the fine-structure of the sound waveform. In response to high-frequency sounds, sensitivity to ITDs can only be conveyed by the amplitude modulated envelope of the sound waveform. Sensitivity to ITDs within high-frequency sounds has classically been described as poorer than in response to low-frequency sounds. However, using a "transposed" sound stimulus, it has been shown that human sensitivity to ITDs in high-frequency sounds can be equivalent to sensitivity to ITDs in low-frequency sounds. In the present study, sensitivity to ITDs was investigated in the responses of neurons from the Inferior Colliculus of the guinea pig using transposed, and conventional, stimuli. A neural correlate of the improvement in sensitivity to ITDs provided by transposed tones was found. ITD-tuning functions had greater depths of modulation in response to transposed tones as compared to conventional stimuli, and neural discrimination thresholds for ITDs in transposed tones were similar to those obtained in response to low-frequency tones. Neural coding of ITDs at low frequencies has been shown to depend on a neuron's frequency tuning. Therefore, the responses of neurons were examined for evidence of frequency-dependent tuning to ITDs in the envelope of high-frequency stimuli. The frequency-dependent ITD-tuning that was found contradicts a model of ITD coding proposed in 1948 by Jeffress. ITD-coding at high-frequencies, similarly to at low- frequencies, may use a population of neurons which are broadly tuned to ITDs. It is suggested that sensitivity to ITDs in the envelope of high-frequency sounds is restricted both by peripheral processing and also by an upper fm above which sensitivity to ITDs does not occur. For these reasons, the physiological relevance of sensitivity to ITDs in the envelope of high-frequency may be limited

The effect of social preference on academic diligence in adolescence

In the current study, we were interested in whether adolescents show a preference for social stimuli compared with non-social stimuli in the context of academic diligence, that is, the ability to expend effort on tedious tasks that have long-term benefits. Forty-five female adolescents (aged 11–17) and 46 female adults (aged 23–33) carried out an adapted version of the Academic Diligence Task (ADT). We created two variations of the ADT: a social ADT and non-social ADT. Individuals were required to freely split their time between an easy, boring arithmetic task and looking at a show-reel of photographs of people (in the social ADT) or landscapes (in the non-social ADT). Individuals also provided enjoyment ratings for both the arithmetic task and the set of photographs they viewed. Adolescents reported enjoying the social photographs significantly more than the non-social photographs, with the converse being true for adults. There was no significant difference in the time spent looking at the social photographs between the adolescents and adults. However, adults spent significantly more time than adolescents looking at the non-social photographs, suggesting that adolescents were less motivated to look at the non-social stimuli. Further, the correlation between self-reported enjoyment of the pictures and choice behaviour in the ADT was stronger for adults than for adolescents in the non-social condition, revealing a greater discrepancy between self-reported enjoyment and ADT choice behaviour for adolescents. Our results are discussed within the context of the development of social cognition and introspective awareness between adolescence and adulthood

Lifestyle Advice Combined with Personalized Estimates of Genetic or Phenotypic Risk of Type 2 Diabetes, and Objectively Measured Physical Activity: A Randomized Controlled Trial

$\textbf{Background}$ Information about genetic and phenotypic risk of type 2 diabetes is now widely available and is being incorporated into disease prevention programs. Whether such information motivates behavior change or has adverse effects is uncertain. We examined the effect of communicating an estimate of genetic or phenotypic risk of type 2 diabetes in a parallel group, open, randomized controlled trial. $\textbf{Methods and Findings}$ We recruited 569 healthy middle-aged adults from the Fenland Study, an ongoing population-based, observational study in the east of England (Cambridgeshire, UK). We used a computer-generated random list to assign participants in blocks of six to receive either standard lifestyle advice alone (control group, $n$ = 190) or in combination with a genetic ($n$ = 189) or a phenotypic ($n$ = 190) risk estimate for type 2 diabetes (intervention groups). After 8 wk, we measured the primary outcome, objectively measured physical activity (kJ/kg/day), and also measured several secondary outcomes (including self-reported diet, self-reported weight, worry, anxiety, and perceived risk). The study was powered to detect a between-group difference of 4.1 kJ/kg/d at follow-up. 557 (98%) participants completed the trial. There were no significant intervention effects on physical activity (difference in adjusted mean change from baseline: genetic risk group versus control group 0.85 kJ/kg/d (95% CI −2.07 to 3.77, $p$ = 0.57); phenotypic risk group versus control group 1.32 (95% CI −1.61 to 4.25, $p$ = 0.38); and genetic risk group versus phenotypic risk group −0.47 (95% CI −3.40 to 2.46, $p$ = 0.75). No significant differences in self-reported diet, self-reported weight, worry, and anxiety were observed between trial groups. Estimates of perceived risk were significantly more accurate among those who received risk information than among those who did not. Key limitations include the recruitment of a sample that may not be representative of the UK population, use of self-reported secondary outcome measures, and a short follow-up period. $\textbf{Conclusions}$ In this study, we did not observe short-term changes in behavior associated with the communication of an estimate of genetic or phenotypic risk of type 2 diabetes. We also did not observe changes in worry or anxiety in the study population. Additional research is needed to investigate the conditions under which risk information might enhance preventive strategies. (Current Controlled Trials ISRCTN09650496; Date applied: April 4, 2011; Date assigned: June 10, 2011). $\textbf{Trial Registration}$ The trial is registered with Current Controlled Trials, ISRCTN09650496.This trial was conducted at the MRC Epidemiology Unit in Cambridge, UK. It was funded by the Medical Research Council (MC_U106179474), the Sixth Framework Programme (LSHM-CT-2006-037197), and the National Institute for Health Research (RP-PG- 0606-1259)

Effect on cardiovascular disease risk factors of interventions to alter consultations between practitioners and patients with type 2 diabetes: A systematic review and meta-analysis of trials in primary care

$\textbf{Objective:}$ To examine the effect on cardiovascular (CVD) risk factors of interventions to alter consultations between practitioners and patients with type 2 diabetes. $\textbf{Search Strategy:}$ Electronic and manual citation searching to identify relevant randomized controlled trials (RCTs). $\textbf{Inclusion Criteria:}$ RCTs that compared usual care to interventions to alter consultations between practitioners and patients. The population was adults aged over 18 years with type 2 diabetes. Trials were set in primary care. $\textbf{Data extraction and synthesis:}$ We recorded if explicit theory-based interventions were used, how consultations were measured to determine whether interventions had an effect on these and calculated weighted mean differences for CVD risk factors including glycated haemoglobin (HbA$_{1c}$), systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), LDL cholesterol (LDL-C) and HDL cholesterol (HDL-C). $\textbf{Results:}$ We included seven RCTs with a total of 2277 patients with type 2 diabetes. A range of measures of the consultation was reported, and underlying theory to explain intervention processes was generally undeveloped and poorly applied. There were no overall effects on CVD risk factors; however, trials were heterogeneous. Subgroup analysis suggested some benefit among studies in which interventions demonstrated impact on consultations; statistically significant reductions in HbA$_{1c}$ levels (weighted mean difference, −0.53%; 95% CI: [−0.77, −0.28]; $P$<.0001; $I^{2}$ =46%). $\textbf{Conclusions:}$ Evidence of effect on CVD risk factors from interventions to alter consultations between practitioners and patients with type 2 diabetes was heterogeneous and inconclusive. This could be explained by variable impact of interventions on consultations. More research is required that includes robust measures of the consultations and better development of theory to elucidate mechanisms.HDM was an Academic Clinical Fellow, Andrew Cooper is funded by the University of Cambridge MRC Epidemiology Unit (grant code: MC_UU_12015/4), SJG is an NIHR Senior Investigator. The primary care unit is a member of the National Institute for Health Research (NIHR) School for Primary Care Research and supported by NIHR Research funds. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health

Offering statins to a population attending health checks with a 10-year cardiovascular disease risk between 10% and 20.

BACKGROUND: In 2014 the UK National Institute for Health and Care Excellence recommended reducing the threshold for offering statin therapy to patients from a 10-year modelled risk of cardiovascular disease (CVD) of 20% to 10%. AIM: To describe the response of patients in UK primary care with a CVD risk between 10% and 20% to an invitation to attend a consultation to discuss statins. DESIGN AND SETTING: Review of electronic medical records at one GP practice in the East of England. METHOD: We invited all patients who had attended an NHS Health Check at the practice, had a QRisk(®) score between 10% and 20%, and were not prescribed statins to attend designated clinics in the practice to discuss starting statins. We reviewed the medical records to identify those who had attended the clinics and those who had chosen to start a statin. RESULTS: Of 410 patients invited, 100 (24.4%) patients attended the designated clinics and 45 (11%) chose to start a statin. Those who chose to start a statin were older and with a higher QRisk(®) than those who did not. Among those who attended, individuals who started a statin had a higher QRisk(®) than those who did not and were more likely to be current or ex-smokers. CONCLUSIONS: The proportion choosing to start a statin was substantially lower than previously estimated. Large population-based studies with long-term follow-up are needed to assess the impact on health and workload of this change in guidance.JU-S is funded by a National Institute of Health Research Clinical Lectureship. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, or the Department of Health.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1111/ijcp.1274

Change in cardio-protective medication and health-related quality of life after diagnosis of screen-detected diabetes: Results from the ADDITION-Cambridge cohort.

AIMS: Establishing a balance between the benefits and harms of treatment is important among individuals with screen-detected diabetes, for whom the burden of treatment might be higher than the burden of the disease. We described the association between cardio-protective medication and health-related quality of life (HRQoL) among individuals with screen-detected diabetes. METHODS: 867 participants with screen-detected diabetes underwent clinical measurements at diagnosis, one and five years. General HRQoL (EQ5D) was measured at baseline, one- and five-years, and diabetes-specific HRQoL (ADDQoL-AWI) and health status (SF-36) at one and five years. Multivariable linear regression was used to quantify the association between change in HRQoL and change in cardio-protective medication. RESULTS: The median (IQR) number of prescribed cardio-protective agents was 2 (1 to 3) at diagnosis, 3 (2 to 4) at one year and 4 (3 to 5) at five years. Change in cardio-protective medication was not associated with change in HRQoL from diagnosis to one year. From one year to five years, change in cardio-protective agents was not associated with change in the SF-36 mental health score. One additional agent was associated with an increase in the SF-36 physical health score (2.1; 95%CI 0.4, 3.8) and an increase in the EQ-5D (0.05; 95%CI 0.02, 0.08). Conversely, one additional agent was associated with a decrease in the ADDQoL-AWI (-0.32; 95%CI -0.51, -0.13), compared to no change. CONCLUSIONS: We found little evidence that increases in the number of cardio-protective medications impacted negatively on HRQoL among individuals with screen-detected diabetes over five years.ADDITION-Cambridge was supported by the Wellcome Trust (grant reference No G061895) the Medical Research Council (grant reference no: G0001164), National Health Service R&D support funding (including the Primary Care Research and Diabetes Research Networks), and the National Institute for Health Research. We received an unrestricted grant from University of Aarhus, Denmark, to support the ADDITION-Cambridge trial. Bio-Rad provided equipment to undertake capillary glucose screening by HbA1c in general practice. The Primary Care Research Unit is supported by NIHR Research funds. SJG receives support from the Department of Health NIHR Programme Grant funding scheme (RP-PG-0606-1259). This article presents independent research funded by the NIHR under the Programme Grants for Applied Research programme (RP-PG-0606-1259]. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.diabres.2015.04.01

Causal pathways linking environmental change with health behaviour change: Natural experimental study of new transport infrastructure and cycling to work.

BACKGROUND: Mechanisms linking changes to the environment with changes in physical activity are poorly understood. Insights into mechanisms of interventions can help strengthen causal attribution and improve understanding of divergent response patterns. We examined the causal pathways linking exposure to new transport infrastructure with changes in cycling to work. METHODS: We used baseline (2009) and follow-up (2012) data (N=469) from the Commuting and Health in Cambridge natural experimental study (Cambridge, UK). Exposure to new infrastructure in the form of the Cambridgeshire Guided Busway was defined using residential proximity. Mediators studied were changes in perceptions of the route to work, theory of planned behaviour constructs and self-reported use of the new infrastructure. Outcomes were modelled as an increase, decrease or no change in weekly cycle commuting time. We used regression analyses to identify combinations of mediators forming potential pathways between exposure and outcome. We then tested these pathways in a path model and stratified analyses by baseline level of active commuting. RESULTS: We identified changes in perceptions of the route to work, and use of the cycle path, as potential mediators. Of these potential mediators, only use of the path significantly explained (85%) the effect of the infrastructure in increasing cycling. Path use also explained a decrease in cycling among more active commuters. CONCLUSION: The findings strengthen the causal argument that changing the environment led to changes in health-related behaviour via use of the new infrastructure, but also show how some commuters may have spent less time cycling as a result.The Commuting and Health in Cambridge study was developed by David Ogilvie, Simon Griffin, Andy Jones and Roger Mackett and initially funded under the auspices of the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence. Funding from the British Heart Foundation, Economic and Social Research Council, Medical Research Council, National Institute for Health Research and the Wellcome Trust, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. The study was subsequently funded by the National Institute for Health Research Public Health Research programme (project number 09/3001/06). RP, SG and DO are supported by the Medical Research Council [Unit Programme number MC_UP_12015/6] and JP is supported by a National Institute for Health Research (NIHR) post-doctoral fellowship (PDF 2012-05-157). The views and opinions expressed herein are those of the authors and do not necessarily reflect those of the NIHR PHR programme or the Department of Health. The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of the manuscript. We thank all staff from the MRC Epidemiology Unit Functional Group Team, in particular for study coordination and data collection (led by Cheryl Chapman and Fiona Whittle) and data management. We also thank Alice Dalton for computing the proximity measures used in this analysis and Louise Foley for her contribution to preparing the questionnaire data for analysis.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.ypmed.2016.02.04

Risk prediction tools for cancer in primary care.

This is the final version of the article. Available from the publisher via the DOI in this record.Numerous risk tools are now available, which predict either current or future risk of a cancer diagnosis. In theory, these tools have the potential to improve patient outcomes through enhancing the consistency and quality of clinical decision-making, facilitating equitable and cost-effective distribution of finite resources such as screening tests or preventive interventions, and encouraging behaviour change. These potential uses have been recognised by the National Cancer Institute as an 'area of extraordinary opportunity' and an increasing number of risk prediction models continue to be developed. The data on predictive utility (discrimination and calibration) of these models suggest that some have potential for clinical application; however, the focus on implementation and impact is much more recent and there remains considerable uncertainty about their clinical utility and how to implement them in order to maximise benefits and minimise harms such as over-medicalisation, anxiety and false reassurance. If the potential benefits of risk prediction models are to be realised in clinical practice, further validation of the underlying risk models and research to assess the acceptability, clinical impact and economic implications of incorporating them in practice are needed