Daily step-count and change in waist circumference during a workplace pedometer program

Background: The health benefit associated with a daily step-count target within pedometer pro- grams is unclear. The aim of this study was to determine if the daily step-count attained during a four month pedometer-based workplace health program was associated with change in waist circumference (WC).

Methods: 762 Australian adults enrolled in a workplace pedometer pro- gram were recruited from ten workplaces in 2008. At the end of the program (four months), 436 participants were eligible for the current analysis. Data included demographics, perceived physical activity change during the program, measured WC at baseline and follow-up, and reported daily pedometer step-counts throughout the program. The association between daily step count and change in WC was examined using linear re- gression.

Results: WC improved by an average of –1.61cm (95% CI: –2.13, –1.09) by the end of the program. There was no relationship between daily step-count and the degree of change in WC. However, among participants reporting an in- crease in physical activity during the program a relationship between daily step count and change in WC was observed, such that those who un-dertook on average 10,000 steps or more per day improved their WC by –1.38cm (95%CI: –2.14, –0.63) more than those who did not achieve an average of 10,000 steps per day. Similarly, among individuals not meeting WC guidelines at baseline a greater daily step count was associ-ated with a greater decrease in WC.

Conclusions: Within a workplace pedometer program, reported daily step count was not associated with greater reductions in WC. However, it was a useful in-dicator of potential health benefits in those who increased their level of physical activity during the program. Pedometer programs need to com- municate clearly the importance of both a step goal and improvement in step count to manage participant expectations about improvements in health markers.

Increasing body weight and risk of limitations in activities of daily living: a systematic review and meta-analysis

This study examined the relationship between normal weight, overweight and obesity class I and II+, and the risk of disability, which is defined as impairment in activities of daily living (ADL). Systematic searching of the literature identified eight cross-sectional studies and four longitudinal studies that were comparable for meta-analysis. An additional four cross-sectional studies and one longitudinal study were included for qualitative review. Results from the meta-analysis of cross-sectional studies revealed a graded increase in the risk of ADL limitations from overweight (1.04, 95% confidence interval [CI] 1.00-1.08), class I obesity (1.16, 95% CI 1.11-1.21) and class II+ obesity (1.76, 95% CI 1.28-2.41), relative to normal weight. Meta-analyses of longitudinal studies revealed a similar graded relationship; however, the magnitude of this relationship was slightly greater for all body mass index categories. Qualitative analysis of studies that met the inclusion criteria but were not compatible for meta-analysis supported the pooled results. No studies identified met all of the pre-defined quality criteria, and subgroup analysis was inhibited due to insufficient comparable studies. We conclude that increasing body weight increases the risk of disability in a graded manner, but also emphasize the need for additional studies using contemporary longitudinal cohorts with large numbers of obese class III individuals, a range of ages and with measured height and weight, and incident ADL questions.

Forecasting daily patient outflow from a ward having no real-time clinical data

OBJECTIVE: Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data.

METHODS: We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous variables (ARMAX), (3) k-nearest neighbor regression, (4) random forest regression, and (5) support vector regression. Although the autoregressive integrated moving average model relied on past 3-month discharges, nearest neighbor forecasting used median of similar discharges in the past in estimating next-day discharge. In addition, the ARMAX model used the day of the week and number of patients currently in ward as exogenous variables. For the random forest and support vector regression models, we designed a predictor set of 20 patient features and 88 ward-level features.

RESULTS: Our data consisted of 12,141 patient visits over 1826 days. Forecasting quality was measured using mean forecast error, mean absolute error, symmetric mean absolute percentage error, and root mean square error. When compared with a moving average prediction model, all 5 models demonstrated superior performance with the random forests achieving 22.7% improvement in mean absolute error, for all days in the year 2014.

CONCLUSIONS: In the absence of clinical information, our study recommends using patient-level and ward-level data in predicting next-day discharges. Random forest and support vector regression models are able to use all available features from such data, resulting in superior performance over traditional autoregressive methods. An intelligent estimate of available beds in wards plays a crucial role in relieving access block in emergency departments.