The spectrum effect in tests for risk prediction, screening, and diagnosis.

The spectrum effect describes the variation between settings in performance of tests used to predict, screen for, and diagnose disease. In particular, the predictive use of a test may be different when it is applied in a general population rather than in the study sample in which it was first developed. This article discusses the impact of the spectrum effect on measures of test performance, and its implications for the development, evaluation, application, and implementation of such tests.JUS is supported by a National Institute of Health Research (NIHR) Clinical Lectureship. 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. SJS is supported by the Medical Research Council www.mrc.ac.uk [Unit Programme number MC_UU_12015/1].This is the final version of the article. It first appeared from the BMJ Group via https://doi.org/10.1136/bmj.i313

Reliability and validity of a domain-specific last 7-d sedentary time questionnaire

Purpose: The objective of this study is to examine test-retest reliability, criterion validity, and absolute agreement of a self-report, last 7-d sedentary behavior questionnaire (SIT-Q-7d), which assesses total daily sedentary time as an aggregate of sitting/lying down in five domains (meals, transportation, occupation, nonoccupational screen time, and other sedentary time). Dutch (DQ) and English (EQ) versions of the questionnaire were examined. Methods: Fifty-one Flemish adults (ages 39.4 +/- 11.1 yr) wore a thigh accelerometer (activPAL3 (TM)) and simultaneously kept a domain log for 7 d. The DQ was subsequently completed twice (median test-retest interval: 3.3 wk). Thigh-acceleration sedentary time was log annotated to create comparable domain-specific and total sedentary time variables. Four hundred two English adults (ages 49.6 +/- 7.3 yr) wore a combined accelerometer and HR monitor (Actiheart (R)) for 6 d to objectively measure total sedentary time. The EQ was subsequently completed twice (median test-retest interval: 3.4 wk). In both samples, the questionnaire reference frame overlapped with the criterion measure administration period. All participants had five or more valid days of criterion data, including one or more weekend day. Results: Test-retest reliability (intraclass correlation coefficient (95% CI)) was fair to good for total sedentary time (DQ: 0.68 (0.50-0.81); EQ: 0.53 (0.44-0.62)) and poor to excellent for domain-specific sedentary time (DQ: from 0.36 (0.10-0.57) (meals) to 0.66 (0.46-0.79) (occupation); EQ: from 0.45 (0.35-0.54) (other sedentary time) to 0.76 (0.71-0.81) (meals)). For criterion validity (Spearman rho), significant correlations were found for total sedentary time (DQ: 0.52; EQ: 0.22; all P <0.001). Compared with domain-specific criterion variables (DQ), modest-to-strong correlations were found for domain-specific sedentary time (from 0.21 (meals) to 0.76 (P < 0.001) (screen time)). The questionnaire generally overestimated sedentary time compared with criterion measures. Conclusion: The SIT-Q-7d appears to be a useful tool for ranking individuals in large-scale observational studies examining total and domain-specific sitting

Lexical neutrality in environmental health research: Reflections on the term walkability.

Neighbourhood environments have important implications for human health. In this piece, we reflect on the environments and health literature and argue that precise use of language is critical for acknowledging the complex and multifaceted influence that neighbourhood environments may have on physical activity and physical activity-related outcomes. Specifically, we argue that the term "neighbourhood walkability", commonly used in the neighbourhoods and health literature, constrains recognition of the breadth of influence that neighbourhood environments might have on a variety of physical activity behaviours. The term draws attention to a single type of physical activity and implies that a universal association exists when in fact the literature is quite mixed. To maintain neutrality in this area of research, we suggest that researchers adopt the term "neighbourhood physical activity environments" for collective measures of neighbourhood attributes that they wish to study in relation to physical activity behaviours or physical activity-related health outcomes

Accelerometery as a measure of modifiable physical activity in high-risk elderly preoperative patients: a prospective observational pilot study.

OBJECTIVES: To use wrist-worn accelerometers (Axivity AX3) to establish normative physical activity (PA) and acceptability data for the high-risk elderly preoperative population, to assess whether PA could be modified by a prehabilitation intervention as part of routine care, to assess any correlation between accelerometer-measured PA and self-reported PA and to assess the acceptability of wearing wrist-worn accelerometers in this population. STUDY DESIGN: Prospective, observational, pilot study. SETTING: Single National Health Service Hospital. PARTICIPANTS: Frail patients≥65 years awaiting major surgery referred to a multidisciplinary preoperative clinic at which they received a routine intervention aimed at improving their PA. 35 patients were recruited. Average age 79.9 years (SD=5.6). PRIMARY OUTCOMES: Normative PA data measured as a mean daily Euclidean norm minus one (ENMO) in milli-gravitational units (mg). SECONDARY OUTCOMES: Measure PA levels (mg) following a routine preoperative intervention. Determine correlation between patient-reported PA (measured using the Physical Activity Scale for the Elderly) and accelerometer-measured PA (mg). Assess acceptability of wearing a wrist-worn accelerometer measured using Visual Analogue Scale (VAS) questionnaire and device wear time (hours). RESULTS: Median baseline daily PA was 14.3 mg (IQR 9.75-22.04) with an improvement in PA detected following the intervention (median ENMO post intervention 20.91 mg (IQR 14.83-27.53), p=0.022). There was no significant correlation between accelerometer-measured and self-reported PA (baseline ρ=0.162 (p=0.4), post intervention ρ=-0.144 (p=0.5)). We found high acceptability ratings (median score of 10/10 on VAS, IQR 8-10) and wear-time compliance (163.2 hours (IQR 150-167.5) preintervention and 166.1 hours (IQR 162.5-167) post intervention). CONCLUSIONS: Accelerometery is acceptable to this population and increases in PA levels measured following an unoptimised routine clinical intervention which indicates that health behavioural change interventions may be successful during the preoperative period. Accelerometers may therefore be a useful tool to design and validate interventions for improving PA in this setting. TRIAL REGISTRATION NUMBER: NCT03737903

Development and Validation of Lifestyle-Based Models to Predict Incidence of the Most Common Potentially Preventable Cancers.

BACKGROUND: Most risk models for cancer are either specific to individual cancers or include complex or predominantly non-modifiable risk factors. METHODS: We developed lifestyle-based models for the five cancers for which the most cases are potentially preventable through lifestyle change in the UK (lung, colorectal, bladder, kidney, and esophageal for men and breast, lung, colorectal, endometrial, and kidney for women). We selected lifestyle risk factors from the European Code against Cancer and obtained estimates of relative risks from meta-analyses of observational studies. We used mean values for risk factors from nationally representative samples and mean 10-year estimated absolute risks from routinely available sources. We then assessed the performance of the models in 23,768 participants in the EPIC-Norfolk cohort who had no history of the five selected cancers at baseline. RESULTS: In men, the combined risk model showed good discrimination [AUC, 0.71; 95% confidence interval (CI), 0.69-0.73] and calibration. Discrimination was lower in women (AUC, 0.59; 95% CI, 0.57-0.61), but calibration was good. In both sexes, the individual models for lung cancer had the highest AUCs (0.83; 95% CI, 0.80-0.85 for men and 0.82; 95% CI, 0.76-0.87 for women). The lowest AUCs were for breast cancer in women and kidney cancer in men. CONCLUSIONS: The discrimination and calibration of the models are both reasonable, with the discrimination for individual cancers comparable or better than many other published risk models. IMPACT: These models could be used to demonstrate the potential impact of lifestyle change on risk of cancer to promote behavior change

Screening for type 2 diabetes is feasible, acceptable, but associated with increased short-term anxiety: a randomised controlled trial in British general practice.

BACKGROUND: To assess the feasibility and uptake of a diabetes screening programme; to examine the effects of invitation to diabetes screening on anxiety, self-rated health and illness perceptions. METHODS: Randomised controlled trial in two general practices in Cambridgeshire. Individuals aged 40-69 without known diabetes were identified as being at high risk of having undiagnosed type 2 diabetes using patient records and a validated risk score (n = 1,280). 355 individuals were randomised in a 2 to 1 ratio into non-invited (n = 238) and invited (n = 116) groups. A stepwise screening programme confirmed the presence or absence of diabetes. Six weeks after the last contact (either test or invitation), a questionnaire was sent to all participants, including non-attenders and those who were not originally invited. Outcome measures included attendance, anxiety (short-form Spielberger State Anxiety Inventory-STAI), self-rated health and diabetes illness perceptions. RESULTS: 95 people (82% of those invited) attended for the initial capillary blood test. Six individuals were diagnosed with diabetes. Invited participants were more anxious than those not invited (37.6 vs. 34.1 STAI, p-value = 0.015), and those diagnosed with diabetes were considerably more anxious than those classified free of diabetes (46.7 vs. 37.0 STAI, p-value = 0.031). Non-attenders had a higher mean treatment control sub-scale (3.87 vs. 3.56, p-value = 0.016) and a lower mean emotional representation sub-scale (1.81 vs. 2.68, p-value = 0.001) than attenders. No differences in the other five illness perception sub-scales or self-rated health were found. CONCLUSION: Screening for type 2 diabetes in primary care is feasible but may be associated with higher levels of short-term anxiety among invited compared with non-invited participants.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

The impact of individualised cardiovascular disease (CVD) risk estimates and lifestyle advice on physical activity in individuals at high risk of CVD: a pilot 2 x 2 factorial understanding risk trial.

BACKGROUND: There is currently much interest in encouraging individuals to increase physical activity in order to reduce CVD risk. This study has been designed to determine if personalised CVD risk appreciation can increase physical activity in adults at high risk of CVD. METHODS/DESIGN: In a 2 x 2 factorial design participants are allocated at random to a personalised 10-year CVD risk estimate or numerical CVD risk factor values (systolic blood pressure, LDL cholesterol and fasting glucose) and, simultaneously, to receive a brief lifestyle advice intervention targeting physical activity, diet and smoking cessation or not. We aim to recruit 200 participants from Oxfordshire primary care practices. Eligibility criteria include adults age 40-70 years, estimated 10-year CVD risk > or =20%, ability to read and write English, no known CVD and no physical disability or other condition reducing the ability to walk. Primary outcome is physical activity measured by ActiGraph accelerometer with biochemical, anthropometrical and psychological measures as additional outcomes. Primary analysis is between group physical activity differences at one month powered to detect a difference of 30,000 total counts per day of physical activity between the groups. Additional analyses will seek to further elucidate the relationship between the provision of risk information, and intention to change behaviour and to determine the impact of both interventions on clinical and anthropometrical measures including fasting and 2 hour plasma glucose, fructosamine, serum cotinine, plasma vitamin C, body fat percentage and blood pressure. DISCUSSION: This is a pilot trial seeking to demonstrate in a real world setting, proof of principal that provision of personalised risk information can contribute to behaviour changes aimed at reducing CVD risk. This study will increase our understanding of the links between the provision of risk information and behaviour change and if successful, could be used in clinical practice with little or no modification.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Interventions to promote cycling: systematic review

Objectives To determine what interventions are effective in promoting cycling, the size of the effects of interventions, and evidence of any associated benefits on overall physical activity or anthropometric measures

The cross-sectional association between snacking behaviour and measures of adiposity: the Fenland Study, UK.

Unhealthy dietary behaviours may contribute to obesity along with energy imbalance. Both positive and null associations of snacking and BMI have been reported, but the association between snacking and total adiposity or pattern of fat deposition remains unevaluated. The objective of this study was to investigate the associations between snacking frequency and detailed adiposity measurements. A total of 10 092 adults residing in Cambridgeshire, England, self-completed eating pattern snacking frequency, FFQ and physical activity questionnaires. Measurements included anthropometry, body composition using dual-energy X-ray absorptiometry scan and ultrasound and assessment of physical activity energy expenditure using heart rate and movement sensing. Linear regression analyses were conducted adjusted for age, socio-demographics, dietary quality, energy intake, PAEE and screen time by sex and BMI status. Among normal-weight individuals (BMI<25 kg/m2), each additional snack was inversely associated with obesity measures: lower total body fat in men and women (-0·41 (95 % CI -0·74, -0·07) %, -0·41 (-0·67, -0·15) %, respectively) and waist circumference (-0·52 (-0·90, -0·14) cm) in men. In contrast, among the overweight/obese (BMI≥25 kg/m2), there were positive associations: higher waist circumference (0·80 (0·34, 0·28) cm) and subcutaneous fat (0·06 (0·01, 0·110) cm) in women and waist circumference (0·37 (0·00, 0·73) cm) in men. Comparing intakes of snack-type foods showed that participants with BMI≥25 kg/m2 had higher intakes of crisps, sweets, chocolates and ice-creams and lower intakes of yoghurt and nuts compared with normal-weight participants. Adjusting for these foods in a model that included a BMI-snacking interaction term attenuated all the associations to null. Snacking frequency may be associated with higher or lower adiposity, with the direction of association being differential by BMI status and dependent on snack food choice. Improving snack choices could contribute to anti-obesity public health interventions.The Fenland Study is funded by the Wellcome Trust and the Medical Research Council. Support from Medical Research Council programmes MC_UU_12015/1 and MC_UU_12015/5 is acknowledged.This is the final version of the article. It first appeared from British Journal of Nutrition via http://dx.doi.org/10.1017/S000711451500269