Parenting and child externalizing behaviors: Are the associations specific or diffuse?

Building upon the link between inadequate parenting and child noncompliance, aggression, and oppositionality, behavioral parent training has been identified as a well-established treatment for externalizing problems in children. Much less empirical attention has been devoted to examining whether inadequate parenting and, in turn, behavioral parent training programs, have specific effects on child externalizing problems or more diffuse effects on both internalizing and externalizing problems. As an initial attempt to examine the specificity of parenting and childhood externalizing problems, this review examines prior research on the association of three parenting behaviors (parental warmth, hostility, and control) with child externalizing versus internalizing problems. Notably, findings revealed relatively little evidence for the specificity of parenting and child externalizing behaviors in the general parenting literature or in the family context of parent depression. Clinical implications and directions for future research are discussed

Parent Depression and Child Anxiety: An Overview of the Literature with Clinical Implications

The association of parental depression with child anxiety has received relatively little attention in the literature. In this paper we initially present several reasons for examining this relationship. We then summarize the empirical support for a link between these two variables. Finally, we discuss directions for future research and clinical implications of an association of parental depression with child anxiety

Randomized controlled trial of a family cognitive-behavioral preventive intervention for children of depressed parents.

A family cognitive-behavioral preventive intervention for parents with a history of depression and their 9–15-year-old children was compared with a self-study written information condition in a randomized clinical trial (n = 111 families). Outcomes were assessed at postintervention (2 months), after completion of 4 monthly booster sessions (6 months), and at 12-month follow-up. Children were assessed by child reports on depressive symptoms, internalizing problems, and externalizing problems; by parent reports on internalizing and externalizing problems; and by child and parent reports on a standardized diagnostic interview. Parent depressive symptoms and parent episodes of major depression also were assessed. Evidence emerged for significant differences favoring the family group intervention on both child and parent outcomes; strongest effects for child outcomes were found at the 12-month assessment with medium effect sizes on most measures. Implications for the prevention of adverse outcomes in children of depressed parents are highlighted

Voxel‐level Classification of Prostate Cancer on Magnetic Resonance Imaging: Improving Accuracy Using Four‐Compartment

BackgroundDiffusion magnetic resonance imaging (MRI) is integral to detection of prostate cancer (PCa), but conventional apparent diffusion coefficient (ADC) cannot capture the complexity of prostate tissues and tends to yield noisy images that do not distinctly highlight cancer. A four-compartment restriction spectrum imaging (RSI4 ) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI4 -C1 , yielded greatest tumor conspicuity.PurposeTo evaluate the slowest diffusion compartment of a four-compartment spectrum imaging model (RSI4 -C1 ) as a quantitative voxel-level classifier of PCa.Study typeRetrospective.SubjectsForty-six men who underwent an extended MRI acquisition protocol for suspected PCa. Twenty-three men had benign prostates, and the other 23 men had PCa.Field strength/sequenceA 3 T, multishell diffusion-weighted and axial T2-weighted sequences.AssessmentHigh-confidence cancer voxels were delineated by expert consensus, using imaging data and biopsy results. The entire prostate was considered benign in patients with no detectable cancer. Diffusion images were used to calculate RSI4 -C1 and conventional ADC. Classifier images were also generated.Statistical testsVoxel-level discrimination of PCa from benign prostate tissue was assessed via receiver operating characteristic (ROC) curves generated by bootstrapping with patient-level case resampling. RSI4 -C1 was compared to conventional ADC for two metrics: area under the ROC curve (AUC) and false-positive rate for a sensitivity of 90% (FPR90 ). Statistical significance was assessed using bootstrap difference with two-sided α = 0.05.ResultsRSI4 -C1 outperformed conventional ADC, with greater AUC (mean 0.977 [95% CI: 0.951-0.991] vs. 0.922 [0.878-0.948]) and lower FPR90 (0.032 [0.009-0.082] vs. 0.201 [0.132-0.290]). These improvements were statistically significant (P < 0.05).Data conclusionRSI4 -C1 yielded a quantitative, voxel-level classifier of PCa that was superior to conventional ADC. RSI classifier images with a low false-positive rate might improve PCa detection and facilitate clinical applications like targeted biopsy and treatment planning.Evidence level3 TECHNICAL EFFICACY: Stage 2

Voxel-level Classification of Prostate Cancer on Magnetic Resonance Imaging: Improving Accuracy Using Four-Compartment Restriction Spectrum Imaging.

BackgroundDiffusion magnetic resonance imaging (MRI) is integral to detection of prostate cancer (PCa), but conventional apparent diffusion coefficient (ADC) cannot capture the complexity of prostate tissues and tends to yield noisy images that do not distinctly highlight cancer. A four-compartment restriction spectrum imaging (RSI4 ) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI4 -C1 , yielded greatest tumor conspicuity.PurposeTo evaluate the slowest diffusion compartment of a four-compartment spectrum imaging model (RSI4 -C1 ) as a quantitative voxel-level classifier of PCa.Study typeRetrospective.SubjectsForty-six men who underwent an extended MRI acquisition protocol for suspected PCa. Twenty-three men had benign prostates, and the other 23 men had PCa.Field strength/sequenceA 3 T, multishell diffusion-weighted and axial T2-weighted sequences.AssessmentHigh-confidence cancer voxels were delineated by expert consensus, using imaging data and biopsy results. The entire prostate was considered benign in patients with no detectable cancer. Diffusion images were used to calculate RSI4 -C1 and conventional ADC. Classifier images were also generated.Statistical testsVoxel-level discrimination of PCa from benign prostate tissue was assessed via receiver operating characteristic (ROC) curves generated by bootstrapping with patient-level case resampling. RSI4 -C1 was compared to conventional ADC for two metrics: area under the ROC curve (AUC) and false-positive rate for a sensitivity of 90% (FPR90 ). Statistical significance was assessed using bootstrap difference with two-sided α = 0.05.ResultsRSI4 -C1 outperformed conventional ADC, with greater AUC (mean 0.977 [95% CI: 0.951-0.991] vs. 0.922 [0.878-0.948]) and lower FPR90 (0.032 [0.009-0.082] vs. 0.201 [0.132-0.290]). These improvements were statistically significant (P < 0.05).Data conclusionRSI4 -C1 yielded a quantitative, voxel-level classifier of PCa that was superior to conventional ADC. RSI classifier images with a low false-positive rate might improve PCa detection and facilitate clinical applications like targeted biopsy and treatment planning.Evidence level3 TECHNICAL EFFICACY: Stage 2

Measuring nature contact: a narrative review

While many studies suggest evidence for the health benefits of nature, there is currently no standardized method to measure time spent in nature or nature contact, nor agreement on how best to define nature contact in research. The purpose of this review is to summarize how nature contact has been measured in recent health research and provide insight into current metrics of exposure to nature at individual and population scales. The most common methods include surrounding greenness, questionnaires, and global positioning systems (GPS) tracking. Several national-level surveys exist, though these are limited by their cross-sectional design, often measuring only a single component of time spent in nature, and poor links to measures of health. In future research, exposure assessment combining the quantifying (e.g., time spent in nature and frequency of visits to nature) and qualifying (e.g., greenness by the normalized difference of vegetation index (NDVI) and ratings on perception by individuals) aspects of current methods and leveraging innovative methods (e.g., experience sampling methods, ecological momentary assessment) will provide a more comprehensive understanding of the health effects of nature exposure and inform health policy and urban planning