Associations between fruit and vegetable consumption and depressive symptoms: evidence from a national Canadian longitudinal survey

BACKGROUND: Several cross-sectional studies have demonstrated associations between diet quality, including fruit and vegetable consumption, and mental health. However, research examining these associations longitudinally, while accounting for related lifestyle factors (eg, smoking, physical activity) is scarce. METHODS: This study used data from the National Population Health Survey (NPHS), a large, national longitudinal survey of Canadians. The sample included 8353 participants aged 18 and older. Every 2 years from 2002/2003 to 2010/2011, participants completed self-reports of daily fruit and vegetable consumption, physical activity, smoking and symptoms of depression and psychological distress. Using generalised estimating equations, we modelled the associations between fruit and vegetable consumption at each timepoint and depression at the next timepoint, adjusting for relevant covariates. RESULTS: Fruit and vegetable consumption at each cycle was inversely associated with next-cycle depression (β=-0.03, 95% CI -0.05 to -0.01, p<0.01) and psychological distress (β=-0.03, 95% CI -0.05 to -0.02, p<0.0001). However, once models were adjusted for other health-related factors, these associations were attenuated (β=-0.01, 95% CI -0.04 to 0.02, p=0.55; β=-0.00, 95% CI -0.03 to 0.02, p=0.78 for models predicting depression and distress, respectively). CONCLUSIONS: These findings suggest that relations between fruit and vegetable intake, other health-related behaviours and depression are complex. Behaviours such as smoking and physical activity may have a more important impact on depression than fruit and vegetable intake. Randomised control trials of diet are necessary to disentangle the effects of multiple health behaviours on mental health.

Online information seeking by patients with bipolar disorder: results from an international multisite survey

BACKGROUND: Information seeking is an important coping mechanism for dealing with chronic illness. Despite a growing number of mental health websites, there is little understanding of how patients with bipolar disorder use the Internet to seek information.

METHODS: A 39 question, paper-based, anonymous survey, translated into 12 languages, was completed by 1222 patients in 17 countries as a convenience sample between March 2014 and January 2016. All patients had a diagnosis of bipolar disorder from a psychiatrist. Data were analyzed using descriptive statistics and generalized estimating equations to account for correlated data.

RESULTS: 976 (81 % of 1212 valid responses) of the patients used the Internet, and of these 750 (77 %) looked for information on bipolar disorder. When looking online for information, 89 % used a computer rather than a smartphone, and 79 % started with a general search engine. The primary reasons for searching were drug side effects (51 %), to learn anonymously (43 %), and for help coping (39 %). About 1/3 rated their search skills as expert, and 2/3 as basic or intermediate. 59 % preferred a website on mental illness and 33 % preferred Wikipedia. Only 20 % read or participated in online support groups. Most patients (62 %) searched a couple times a year. Online information seeking helped about 2/3 to cope (41 % of the entire sample). About 2/3 did not discuss Internet findings with their doctor.

CONCLUSION: Online information seeking helps many patients to cope although alternative information sources remain important. Most patients do not discuss Internet findings with their doctor, and concern remains about the quality of online information especially related to prescription drugs. Patients may not rate search skills accurately, and may not understand limitations of online privacy. More patient education about online information searching is needed and physicians should recommend a few high quality websites.

Estimating the changes in energy flux that characterize the rise in obesity prevalence

Background: The daily energy imbalance gap associated with the current population weight gain in the obesity epidemic is relatively small. However, the substantially higher body weights of populations that have accumulated over several years are associated with a substantially higher total energy expenditure (TEE) and total energy intake (TEI), or energy flux (EnFlux = TEE = TEI).
Objective: The objective was to develop an equation relating EnFlux to body weight in adults for estimating the rise in EnFlux associated with the obesity epidemic.
Design: Multicenter, cross-sectional data for TEE from doubly labeled water studies in 1399 adults aged 5.9 ± 18.8 y (mean ± SD) were analyzed in linear regression models with natural log (ln) weight as the dependent variable and ln EnFlux as the independent variable, adjusted for height, age, and sex. These equations were compared with those for children and applied to population trends in weight gain.
Results: ln EnFlux was positively related to ln weight (β = 0.71; 95% CI: 0.66, 0.76; R2 = 0.52), adjusted for height, age, and sex. This slope was significantly steeper than that previously described for children (β = 0.45; 95% CI: 0.38, 0.51).
Conclusions: This relation suggests that substantial increases in TEI have driven the increases in body weight over the past 3 decades. Adults have a higher proportional weight gain than children for the same proportional increase in energy intake, mostly because of a higher fat content of the weight being gained. The obesity epidemic will not be reversed without large reductions in energy intake, increases in physical activity, or both.

Estimating the total energy demand for supra-maximal exercise using the VO2-power regression from an incremental exercise test

The VO_2-power regression and estimated total energy demand for a 6-minute supramaximal exercise test was predicted from a continuous incremental exercise test. Sub-maximal VO_2- power co-ordinates were established from the last 40 seconds(s) of 150-second exercise stages. The precision of the estimated total energy demand was determined using the 95% confidence interval (95% CI) of the estimated total energy demand. The linearity of the individual VO_2-power regression equations was determined using Pearson's correlation coefficient. The mean 95% CI of the estimated total energy demand was 5.9±2.5 mL O₂ Eq•^-1kg•min^-1, and the mean correlation coefficient was 0.9942±0.0042. The current study contends that the sub-maximal VO_2-power co-ordinates from a continuous incremental exercise test can be used to estimate supra-maximal energy demand without compromising the precision of the accumulated oxygen deficit (AOD) method.

Estimating process capability index Cpm using a bootstrap sequential sampling procedure

Construction of a confidence interval for process capability index CPM is often based on a normal approximation with fixed sample size. In this article, we describe a different approach in constructing a fixed-width confidence interval for process capability index CPM with a preassigned accuracy by using a combination of bootstrap and sequential sampling schemes. The optimal sample size required to achieve a preassigned confidence level is obtained using both two-stage and modified two-stage sequential procedures. The procedure developed is also validated using an extensive simulation study.

Optimal sensor separation for AoA based localization via linear sensor array

This paper investigates the linear separation requirements for Angle-of-Arrival (AoA) sensors, in order to achieve the optimal performance in estimating the position of a target from multiple and typically noisy sensor measurements. We analyse the sensor-target geometry in terms of the Cramer-Rao inequality and the corresponding Fisher information matrix, in order to characterize localization performance with respect to the linear spacial distribution. Here we consider a situation where one sensor is fixed and the rest are free to be positioned in a linear array.

Optimal fault tolerant Jacobian matrix generators for redundant manipulators

The design of locally optimal fault-tolerant manipulators has been previously addressed via adding constraints on the bases of a desired null space to the design constraints of the manipulators. Then by algebraic or numeric solution of the design equations, the optimal Jacobian matrix is obtained. In this study, an optimal fault-tolerant Jacobian matrix generator is introduced from geometric properties instead of the null space properties. The proposed generator provides equally fault-tolerant Jacobian matrices in R3 that are optimally fault tolerant for one or two locked joint failures. It is shown that the proposed optimal Jacobian matrices are directly obtained via regular pyramids. The geometric approach and zonotopes are used as a novel tool for determining relative manipulability in the context of fault-tolerant robotics and for bringing geometric insight into the design of optimal fault-tolerant manipulators.

Optimal sensor placement in linear arrays : part I - AoA based localization

In this paper, we examine the optimal linear separation requirements for AoA sensors, in order to achieve the best performance in estimating the position of a target subjected to noisy measurements. Cramer-Rao inequality and the corresponding Fisher information matrix are used to analyze the sensor-target geometry, in order to characterize localization performance with respect to the linear spacial distribution of sensors.

Direct method for optimal power management in hybrid electric vehicles

Hybrid electric vehicles are powered by an electric system and an internal combustion engine. The components of a hybrid electric vehicle need to be coordinated in an optimal manner to deliver the desired performance. This paper presents an approach based on direct method for optimal power management in hybrid electric vehicles with inequality constraints. The approach consists of reducing the optimal control problem to a set of algebraic equations by approximating the state variable which is the energy of electric storage, and the control variable which is the power of fuel consumption. This approximation uses orthogonal functions with unknown coefficients. In addition, the inequality constraints are converted to equal constraints. The advantage of the developed method is that its computational complexity is less than that of dynamic and non-linear programming approaches. Also, to use dynamic or non-linear programming, the problem should be discretized resulting in the loss of optimization accuracy. The propsed method, on the other hand, does not require the discretization of the problem producing more accurate results. An example is solved to demonstrate the accuracy of the proposed approach. The results of Haar wavelets, and Chebyshev and Legendre polynomials are presented and discussed. © 2011 The Korean Society of Automotive Engineers and Springer-Verlag Berlin Heidelberg.

Optimal sensor placement in range based localization for linear arrays

This paper investigates the linear separation requirements for range sensors in order to achieve the optimal performance in estimating the position of a target from multiple and typically noisy sensor measurements. We analyze the sensor-target geometry in terms of the Cramer-Rao inequality and the corresponding Fisher information matrix, in order to characterize localization performance with respect to the linear special distribution.

Individual calibration for estimating free-living walking speed using the MTI monitor

Purpose : This study was conducted to devise a new individual calibration method to enhance MTI accelerometer estimation of free-living level walking speed.

Method : Five female and five male middle-aged adults walked 400 m at 3.5, 4.5, and 5.5 km·h-1, and 800 m at 6.5 km·h-1 on an outdoor track, following a continuous protocol. Lap speed was controlled by a global positioning system (GPS) monitor. MTI counts-to-speed calibration equations were derived for each trial, for each subject for four such trials with each of four MTI, for each subject for the average MTI, and for the pooled data. Standard errors of the estimate (SEE) with and without individual calibration were compared. To assess accuracy of prediction of free-living walking speed, subjects also completed a self-paced, "brisk" 3-km walk wearing one of the four MTI, and differences between actual and predicted walking speed with and without individual calibration were examined.

Results : Correlations between MTI counts and walking speed were 0.90 without individual calibration, 0.98 with individual calibration for the average MTI, and 0.99 with individual calibration for a specific MTI. The SEE (mean ± SD) was 0.58 ± 0.30 km·h-1 without individual calibration, 0.19 ± 0.09 km·h-1 with individual calibration for the average MTI monitor, and 0.16 ± 0.08 km·h-1 with individual calibration for a specific MTI monitor. The difference between actual and predicted walking speed on the "brisk" 3-km walk was 0.06 ± 0.25 km·h-1 using individual calibration and 0.28 ± 0.63 km·h-1 without individual calibration (for specific accelerometers).

Conclusion : MTI accuracy in predicting walking speed without individual calibration might be sufficient for population-based studies but not for intervention trials. This individual calibration method will substantially increase precision of walking speed predicted from MTI counts.

Estimating the intensity of ward admission and its effect on emergency department access block

Emergency department access block is an urgent problem faced by many public hospitals today. When access block occurs, patients in need of acute care cannot access inpatient wards within an optimal time frame. A widely held belief is that access block is the end product of a long causal chain, which involves poor discharge planning, insufficient bed capacity, and inadequate admission intensity to the wards. This paper studies the last link of the causal chain-the effect of admission intensity on access block, using data from a metropolitan hospital in Australia. We applied several modern statistical methods to analyze the data. First, we modeled the admission events as a nonhomogeneous Poisson process and estimated time-varying admission intensity with penalized regression splines. Next, we established a functional linear model to investigate the effect of the time-varying admission intensity on emergency department access block. Finally, we used functional principal component analysis to explore the variation in the daily time-varying admission intensities. The analyses suggest that improving admission practice during off-peak hours may have most impact on reducing the number of ED access blocks.

Estimation and extrapolation of optimal treatment and testing strategies

We review recent developments in the estimation of an optimal treatment strategy or regime from longitudinal data collected in an observational study. We also propose novel methods for using the data obtained from an observational database in one health-care system to determine the optimal treatment regime for biologically similar subjects in a second health-care system when, for cultural, logistical, or financial reasons, the two health-care systems differ (and will continue to differ) in the frequency of, and reasons for, both laboratory tests and physician visits. Finally, we propose a novel method for estimating the optimal timing of expensive and/or painful diagnostic or prognostic tests. Diagnostic or prognostic tests are only useful in so far as they help a physician to determine the optimal dosing strategy, by providing information on both the current health state and the prognosis of a patient because, in contrast to drug therapies, these tests have no direct causal effect on disease progression. Our new method explicitly incorporates this no direct effect restriction.

Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, Part I: main content

Dynamic treatment regimes are set rules for sequential decision making based on patient covariate history. Observational studies are well suited for the investigation of the effects of dynamic treatment regimes because of the variability in treatment decisions found in them. This variability exists because different physicians make different decisions in the face of similar patient histories. In this article we describe an approach to estimate the optimal dynamic treatment regime among a set of enforceable regimes. This set is comprised by regimes defined by simple rules based on a subset of past information. The regimes in the set are indexed by a Euclidean vector. The optimal regime is the one that maximizes the expected counterfactual utility over all regimes in the set. We discuss assumptions under which it is possible to identify the optimal regime from observational longitudinal data. Murphy et al. (2001) developed efficient augmented inverse probability weighted estimators of the expected utility of one fixed regime. Our methods are based on an extension of the marginal structural mean model of Robins (1998, 1999) which incorporate the estimation ideas of Murphy et al. (2001). Our models, which we call dynamic regime marginal structural mean models, are specially suitable for estimating the optimal treatment regime in a moderately small class of enforceable regimes of interest. We consider both parametric and semiparametric dynamic regime marginal structural models. We discuss locally efficient, double-robust estimation of the model parameters and of the index of the optimal treatment regime in the set. In a companion paper in this issue of the journal we provide proofs of the main results.