Bayesian hierarchical vector autoregressive models for patient-level predictive modeling

Predicting health outcomes from longitudinal health histories is of central importance to healthcare. Observational healthcare databases such as patient diary databases provide a rich resource for patient-level predictive modeling. In this paper, we propose a Bayesian hierarchical vector autoregressive (VAR) model to predict medical and psychological conditions using multivariate time series data. Compared to the existing patient-specific predictive VAR models, our model demonstrated higher accuracy in predicting future observations in terms of both point and interval estimates due to the pooling effect of the hierarchical model specification. In addition, by adopting an elastic-net prior, our model offers greater interpretability about the associations between variables of interest on both the population level and the patient level, as well as between-patient heterogeneity. We apply the model to two examples: 1) predicting substance use craving, negative affect and tobacco use among college students, and 2) predicting functional somatic symptoms and psychological discomforts

A systematic review and qualitative synthesis of weight management interventions for people with spinal cord injury

People with spinal cord injury (SCI) are at greater risk of developing obesity and related co-morbidities than those without SCI. The objectives of this systematic review were to examine the effectiveness of weight management interventions for people with SCI and to synthesize the experiences of people involved with SCI weight management (e.g., SCI healthcare professionals and caregivers). Five databases were searched (up to July 31, 2023) and 5,491 potentially eligible articles were identified. Following screening, 22 articles were included, comprising 562 adults. There was considerable heterogeneity in study design and weight loss interventions included behavioral nutritional and exercise education sessions, recalling food diaries, exercise interventions, and pharmaceuticals. The mean percentage change of the pooled body mass data equated to −4.0 ± 2.3%, with a range from −0.5 to −7.6%. In addition, 38% of the individuals with SCI who completed a weight loss intervention (N = 262) had a ≥5% reduction in body weight. Collectively, although on average the included interventions led to moderate weight loss, the finding that just over a third of individuals achieved clinically meaningful 5% weight loss suggests that available interventions for this population may need to be improved

Erratum to: Is self-weighing an effective tool for weight loss: A systematic literature review and meta-analysis [Int J Behav Nutr Phys Act, 12, (2015), 104]

BACKGROUND: There is a need to identify effective behavioural strategies for weight loss. Self-weighing may be one such strategy. PURPOSE: To examine the effectiveness of self-weighing for weight loss. METHODS: A systematic review and meta-analysis of randomised controlled trials that included self-weighing as an isolated intervention or as a component within an intervention. We used sub groups to analyse differences in frequency of weighing instruction (daily and weekly) and also whether including accountability affected weight loss. RESULTS: Only one study examined self-weighing as a single strategy and there was no evidence it was effective (-0.5 kg 95 % CI -1.3 to 0.3). Four trials added self-weighing/self-regulation techniques to multi-component programmes and resulted in a significant difference of -1.7 kg (95 % CI -2.6 to -0.8). Fifteen trials comparing multi-component interventions including self-weighing compared with no intervention or minimal control resulted in a significant mean difference of -3.4 kg (95 % CI -4.2 to -2.6). There was no significant difference in the interventions with weekly or daily weighing. In trials which included accountability there was significantly greater weight loss (p = 0.03). CONCLUSIONS: There is a lack of evidence of whether advising self-weighing without other intervention components is effective. Adding self-weighing to a behavioural weight loss programme may improve weight loss. Behavioural weight loss programmes that include self-weighing are more effective than minimal interventions. Accountability may improve the effectiveness of interventions that include self-weighing. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12966-015-0267-4) contains supplementary material, which is available to authorized users

Is weight cycling associated with adverse health outcomes? A cohort study

Evidence about the health effects of weight cycling is not consistent, with some studies suggesting it is harmful for health. Here we investigated whether weight cycling was associated with weight change and mental health outcomes in 10,428 participants in the mid-age cohort of The Australian Longitudinal Study of Women's Health (ALSWH) over 12 years. In 1998 the women were asked how many times they had ever intentionally lost at least 5 kg and how many times had they regained this amount. Women were categorised into four weight pattern groups: frequent weight cyclers (FWC, three or more weight cycles), low frequency weight cyclers (LFWC, one or two weight cycles), non-weight cyclers (NWC), and weight loss only (WL). We used generalised linear modelling to investigate relationships between weight pattern group, weight change and mental health outcomes. In 1998, 15% of the women were FWC, 24% LFWC, 46% NWC and 15% were WL. Weight change was similar across weight pattern groups in women with obesity, however healthy weight and overweight FWC gained more weight than women who did not weight cycle. We found no difference in overall mental health scores between groups, but both LFWC and FWC had higher odds of depressive symptoms (adjusted OR 1.5, 95%CI: 1.1 to 1.9 and 1.7, 95%CI: 1.1 to 2.4, respectively) than NWC. Our results suggest that, although weight cycling is not associated with greater weight gain in women with obesity, it may increase depressive symptoms