Predicting daily physical activity in a lifestyle intervention program

The growing number of people adopting a sedentary lifestyle these days creates a serious need for effective physical activity promotion programs. Often, these programs monitor activity, provide feedback about activity and offer coaching to increase activity. Some programs rely on a human coach who creates an activity goal that is tailored to the characteristics of a participant. Throughout the program, the coach motivates the participant to reach his personal goal or adapt the goal, if needed. Both the timing and the content of the coaching are important for the coaching. Insights on the near future state on, for instance, behaviour and motivation of a participant can be helpful to realize an effective proactive coaching style that is personalized in terms of timing and content. As a first step towards providing these insights to a coach, this chapter discusses results of a study on predicting daily physical activity level (PAL) data from past data of participants in a lifestyle intervention program. A mobile body-worn activity monitor with a built-in triaxial accelerometer was used to record PAL data of a participant for a period of 13 weeks. Predicting future PAL data for all days in a given period was done by employing autoregressive integrated moving average (ARIMA) models on the PAL data from days in the period before. By using a newly proposed categorized-ARIMA (CARIMA) prediction method, we achieved a large reduction in computation time without a significant loss in prediction accuracy in comparison with traditional ARIMA models. In CARIMA, PAL data are categorized as stationary, trend or seasonal data by assessing their autocorrelation functions. Then, an ARIMA model that is most appropriate to these three categories is automatically selected based on an objective penalty function criterion. The results show that our CARIMA method performs well in terms of PAL prediction accuracy (~9% mean absolute percentage error), model parsimony and robustness

Added value of frailty and social support in predicting risk of 30-day unplanned re-admission or death for patients with heart failure: an analysis from OPERA-HF

Background: Models for predicting the outcome of patients hospitalized for heart failure (HF) rarely take a holistic view. We assessed the ability of measures of frailty and social support in addition to demographic, clinical, imaging and laboratory variables to predict short-term outcome for patients discharged after a hospitalization for HF. Methods: OPERA-HF is a prospective observational cohort, enrolling patients hospitalized for HF in a single center in Hull, UK. Variables were combined in a logistic regression model after multiple imputation of missing data to predict the composite outcome of death or readmission at 30 days. Comparisons were made to a model using clinical variables alone. The discriminative performance of each model was internally validated with bootstrap re-sampling. Results: 1094 patients were included (mean age 77 [interquartile range 68–83] years; 40% women; 56% with moderate to severe left ventricular systolic dysfunction) of whom 213 (19%) had an unplanned re-admission and 60 (5%) died within 30 days. For the composite outcome, a model containing clinical variables alone had an area under the receiver-operating characteristic curve (AUC) of 0.68 [95% CI 0.64–0.72]. Adding marital status, support from family and measures of physical frailty increased the AUC (p < 0.05) to 0.70 [95% CI 0.66–0.74]. Conclusions: Measures of physical frailty and social support improve prediction of 30-day outcome after an admission for HF but predicting near-term events remains imperfect. Further external validation and improvement of the model is required

The effect of automated oxygen control on clinical outcomes in preterm infants: a pre- and post-implementation cohort study

Several studies demonstrated an increase in time spent within target range when automated oxygen control (AOC) is used. However the effect on clinical outcome remains unclear. We compared clinical outcomes of preterm infants born before and after implementation of AOC as standard of care. In a retrospective pre-post implementation cohort study of outcomes for infants of 24-29 weeks gestational age receiving respiratory support before (2012-2015) and after (2015-2018) implementation of AOC as standard of care were compared. Outcomes of interest were mortality and complications of prematurity, number of ventilation days, and length of stay in the Neonatal Intensive Care Unit (NICU). A total of 588 infants were included (293 pre- vs 295 in the post-implementation cohort), with similar gestational age (27.8 weeks pre- vs 27.6 weeks post-implementation), birth weight (1033 grams vs 1035 grams) and other baseline characteristics. Mortality and rate of prematurity complications were not different between the groups. Length of stay in NICU was not different, but duration of invasive ventilation was shorter in infants who received AOC (6.4 +/- 10.1 vs 4.7 +/- 8.3, p = 0.029). Conclusion: In this pre-post comparison, the implementation of AOC did not lead to a change in mortality or morbidity during admission.What is Known:Prolonged and intermittent oxygen saturation deviations are associated with mortality and prematurity-related morbidities.Automated oxygen controllers can increase the time spent within oxygen saturation target range.What is New:Implementation of automated oxygen control as standard of care did not lead to a change in mortality or morbidity during admission.In the period after implementation of automated oxygen control, there was a shift toward more non-invasive ventilation.Developmen

Comparison of two devices for automated oxygen control in preterm infants: a randomised crossover trial

Objective To compare the effect of two different automated oxygen control devices on target range (TR) time and occurrence of hypoxaemic and hyperoxaemic episodes.Design Randomised cross-over study.Setting Tertiary level neonatal unit in the Netherlands.Patients Preterm infants (n=15) born between 24+0 and 29+6 days of gestation, receiving invasive or non-invasive respiratory support with oxygen saturation (SpO(2)) TR of 91%-95%. Median gestational age 26 weeks and 4 days (IQR 25 weeks 3days-27 weeks 6 days) and postnatal age 19 (IQR 17-24) days.Interventions Inspired oxygen concentration was titrated by the OxyGenie controller (SLE6000 ventilator) and the CLIO2 controller (AVEA ventilator) for 24 hours each, in a random sequence, with the respiratory support mode kept constant.Main outcome measures Time spent within set SpO(2) TR (91%-95% with supplemental oxygen and 91%-100% without supplemental oxygen).Results Time spent within the SpO(2) TR was higher during OxyGenie control (80.2 (72.6-82.4)% vs 68.5 (56.7-79.3)%, p<0.005). Less time was spent above TR while in supplemental oxygen (6.3 (5.1-9.9)% vs 15.9 (11.5-30.7)%, p<0.005) but more time spent below TR during OxyGenie control (14.7 (11.8%-17.2%) vs 9.3 (8.2-12.6)%, p<0.05). There was no significant difference in time with SpO(2) <80% (0.5 (0.1-1.0)% vs 0.2 (0.1-0.4)%, p=0.061). Long-lasting SpO(2) deviations occurred less frequently during OxyGenie control.Conclusions The OxyGenie control algorithm was more effective in keeping the oxygen saturation within TR and preventing hyperoxaemia and equally effective in preventing hypoxaemia (SpO(2) <80%), although at the cost of a small increase in mild hypoxaemia.Developmen