A data-driven, meaningful, easy to interpret, standardised accelerometer outcome variable for global surveillance

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Objectives: Our aim is to demonstrate how a data-driven accelerometer metric, the acceleration above which a person’s most active minutes are accumulated, can a) quantify the prevalence of meeting current physical activity guidelines for global surveillance and b) moving forward, could inform accelerometer-driven physical activity guidelines. Unlike cut-point methods, the metric is population-independent (e.g. age) and potentially comparable across datasets. Design: Cross-sectional, secondary data analysis. Methods: Analyses were carried out on five datasets using wrist-worn accelerometers: children (N=145), adolescent girls (N=1669), office workers (N=114), pre- (N=1218) and post- (N=1316) menopausal women, and adults with type 2 diabetes (N=475). Open-source software (GGIR) was used to generate the magnitude of acceleration above which a person’s most active 60, 30 and 2 minutes are accumulated: M60ACC; M30ACC and M2ACC, respectively. Results: The proportion of participants with M60ACC (children) and M30ACC (adults) values higher than accelerations representative of brisk walking (i.e., moderate-to-vigorous physical activity) ranged from 17-68% in children and 15%-81% in adults, tending to decline with age. The proportion of pre-and postmenopausal women with M2ACC values meeting thresholds for bone health ranged from 6-13%. Conclusions: These metrics can be used for global surveillance of physical activity, including assessing prevalence of meeting current physical activity guidelines. As accelerometer and corresponding health data accumulate it will be possible to interpret the metrics relative to age- and sex- specific norms and derive evidence-based physical activity guidelines directly from accelerometer data for use in future global surveillance. This is where the potential advantages of these metrics lie

Interventions outside the workplace for reducing sedentary behaviour in adults under 60 years of age

Background Adults spend a majority of their time outside the workplace being sedentary. Large amounts of sedentary behaviour increase the risk of type 2 diabetes, cardiovascular disease, and both all‐cause and cardiovascular disease mortality. Objectives Primary • To assess effects on sedentary time of non‐occupational interventions for reducing sedentary behaviour in adults under 60 years of age Secondary • To describe other health effects and adverse events or unintended consequences of these interventions • To determine whether specific components of interventions are associated with changes in sedentary behaviour • To identify if there are any differential effects of interventions based on health inequalities (e.g. age, sex, income, employment) Search methods We searched CENTRAL, MEDLINE, Embase, Cochrane Database of Systematic Reviews, CINAHL, PsycINFO, SportDiscus, and ClinicalTrials.gov on 14 April 2020. We checked references of included studies, conducted forward citation searching, and contacted authors in the field to identify additional studies. Selection criteria We included randomised controlled trials (RCTs) and cluster RCTs of interventions outside the workplace for community‐dwelling adults aged 18 to 59 years. We included studies only when the intervention had a specific aim or component to change sedentary behaviour. Data collection and analysis Two review authors independently screened titles/abstracts and full‐text articles for study eligibility. Two review authors independently extracted data and assessed risk of bias. We contacted trial authors for additional information or data when required. We examined the following primary outcomes: device‐measured sedentary time, self‐report sitting time, self‐report TV viewing time, and breaks in sedentary time. Main results We included 13 trials involving 1770 participants, all undertaken in high‐income countries. Ten were RCTs and three were cluster RCTs. The mean age of study participants ranged from 20 to 41 years. A majority of participants were female. All interventions were delivered at the individual level. Intervention components included personal monitoring devices, information or education, counselling, and prompts to reduce sedentary behaviour. We judged no study to be at low risk of bias across all domains. Seven studies were at high risk of bias for blinding of outcome assessment due to use of self‐report outcomes measures. Primary outcomes Interventions outside the workplace probably show little or no difference in device‐measured sedentary time in the short term (mean difference (MD) ‐8.36 min/d, 95% confidence interval (CI) ‐27.12 to 10.40; 4 studies; I² = 0%; moderate‐certainty evidence). We are uncertain whether interventions reduce device‐measured sedentary time in the medium term (MD ‐51.37 min/d, 95% CI ‐126.34 to 23.59; 3 studies; I² = 84%; very low‐certainty evidence) We are uncertain whether interventions outside the workplace reduce self‐report sitting time in the short term (MD ‐64.12 min/d, 95% CI ‐260.91 to 132.67; I² = 86%; very low‐certainty evidence). Interventions outside the workplace may show little or no difference in self‐report TV viewing time in the medium term (MD ‐12.45 min/d, 95% CI ‐50.40 to 25.49; 2 studies; I² = 86%; low‐certainty evidence) or in the long term (MD 0.30 min/d, 95% CI ‐0.63 to 1.23; 2 studies; I² = 0%; low‐certainty evidence). It was not possible to pool the five studies that reported breaks in sedentary time given the variation in definitions used. Secondary outcomes Interventions outside the workplace probably have little or no difference on body mass index in the medium term (MD ‐0.25 kg/m², 95% CI ‐0.48 to ‐0.01; 3 studies; I² = 0%; moderate‐certainty evidence). Interventions may have little or no difference in waist circumference in the medium term (MD ‐2.04 cm, 95% CI ‐9.06 to 4.98; 2 studies; I² = 65%; low‐certainty evidence). Interventions probably have little or no difference on glucose in the short term (MD ‐0.18 mmol/L, 95% CI ‐0.30 to ‐0.06; 2 studies; I² = 0%; moderate‐certainty evidence) and medium term (MD ‐0.08 mmol/L, 95% CI ‐0.21 to 0.05; 2 studies, I² = 0%; moderate‐certainty evidence) Interventions outside the workplace may have little or no difference in device‐measured MVPA in the short term (MD 1.99 min/d, 95% CI ‐4.27 to 8.25; 4 studies; I² = 23%; low‐certainty evidence). We are uncertain whether interventions improve device‐measured MVPA in the medium term (MD 6.59 min/d, 95% CI ‐7.35 to 20.53; 3 studies; I² = 70%; very low‐certainty evidence). We are uncertain whether interventions outside the workplace improve self‐reported light‐intensity PA in the short‐term (MD 156.32 min/d, 95% CI 34.34 to 278.31; 2 studies; I² = 79%; very low‐certainty evidence). Interventions may have little or no difference on step count in the short‐term (MD 226.90 steps/day, 95% CI ‐519.78 to 973.59; 3 studies; I² = 0%; low‐certainty evidence) No data on adverse events or symptoms were reported in the included studies. Authors' conclusions Interventions outside the workplace to reduce sedentary behaviour probably lead to little or no difference in device‐measured sedentary time in the short term, and we are uncertain if they reduce device‐measured sedentary time in the medium term. We are uncertain whether interventions outside the workplace reduce self‐reported sitting time in the short term. Interventions outside the workplace may result in little or no difference in self‐report TV viewing time in the medium or long term. The certainty of evidence is moderate to very low, mainly due to concerns about risk of bias, inconsistent findings, and imprecise results. Future studies should be of longer duration; should recruit participants from varying age, socioeconomic, or ethnic groups; and should gather quality of life, cost‐effectiveness, and adverse event data. We strongly recommend that standard methods of data preparation and analysis are adopted to allow comparison of the effects of interventions to reduce sedentary behaviour

Diagnostic Accuracy of Dehydroepiandrosterone Sulfate and Corticotropin in Autonomous Cortisol Secretion

Autonomous cortisol secretion (ACS) affects up to 50% of patients with adrenal adenomas. Despite the limited evidence, clinical guidelines recommend measurement of serum concentrations of dehydroepiandrosterone-sulfate (DHEA-S) and corticotropin (ACTH) to aid in the diagnosis of ACS. Our objective was to determine the accuracy of serum concentrations of DHEA-S and ACTH in diagnosing ACS. We conducted a retrospective single center study of adults with adrenal adenoma evaluated between 2000−2020. Main outcome measure was diagnostic accuracy of DHEA-S and ACTH. ACS was defined as post-dexamethasone cortisol >1.8 mcg/dL. Of 468 patients, ACS was diagnosed in 256 (55%) patients with a median post-DST cortisol of 3.45 mcg/dL (range, 1.9–32.7). Patients with ACS demonstrated lower serum concentrations of DHEA-S (35 vs. 87.3 mcg/dL, p < 0.0001) and ACTH (8.3 vs. 16 pg/mL, p < 0.0001) compared to patients with non-functioning adrenal tumors (NFAT). Serum DHEA-S concentration <40 mcg/dL diagnosed ACS with 84% specificity and 81% PPV, while serum ACTH concentration <10 pg/mL diagnosed ACS with 75% specificity and 78% PPV. The combination of serum concentrations of DHEA-S <40 mcg/dL and ACTH <10 pg/mL diagnosed ACS with the highest accuracy with 92% specificity and 87% PPV. Serum concentrations of DHEA-S and ACTH provide additional value in diagnosing ACS