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Physical activity protects from incident anxiety: A meta-analysis of prospective cohort studies
(2019)
Background Prospective cohorts have suggested that physical activity (PA) can decrease the risk of incident anxiety. However, no meta-analysis has been conducted. Aims To examine the prospective relationship between PA and incident anxiety and explore potential moderators. Methods Searches were conducted on major databases from inception to October 10, 2018 for prospective studies (at least 1 year of follow-up) that calculated the odds ratio (OR) of incident anxiety in people with high PA against people with low PA. Methodological quality was assessed using the Newcastle-Ottawa Scale (NOS). A random-effects meta-analysis was conducted and heterogeneity was explored using subgroup and meta-regression analysis. Results Across 14 cohorts of 13 unique prospective studies (N = 75,831, median males = 50.1%) followed for 357,424 person-years, people with high self-reported PA (versus low PA) were at reduced odds of developing anxiety (adjusted odds ratio [AOR] = 0.74; 95% confidence level [95% CI] = 0.62, 0.88; crude OR = 0.80; 95% CI = 0.69, 0.92). High self-reported PA was protective against the emergence of agoraphobia (AOR = 0.42; 95% CI = 0.18, 0.98) and posttraumatic stress disorder (AOR = 0.57; 95% CI = 0.39, 0.85). The protective effects for anxiety were evident in Asia (AOR = 0.31; 95% CI = 0.10, 0.96) and Europe (AOR = 0.82; 95% CI = 0.69, 0.97); for children/adolescents (AOR = 0.52; 95% CI = 0.29, 0.90) and adults (AOR = 0.81; 95% CI = 0.69, 0.95). Results remained robust when adjusting for confounding factors. Overall study quality was moderate to high (mean NOS = 6.7 out of 9). Conclusion Evidence supports the notion that self-reported PA can confer protection against the emergence of anxiety regardless of demographic factors. In particular, higher PA levels protects from agoraphobia and posttraumatic disorder.
Background: Population-specificity of exploratory dietary patterns limits their generalizability in investigations with type 2 diabetes incidence.
Objective: The aim of this study was to derive country-specific exploratory dietary patterns, investigate their association with type 2 diabetes incidence, and replicate diabetes-associated dietary patterns in other countries.
Methods: Dietary intake data were used, assessed by country-specific questionnaires at baseline of 11,183 incident diabetes cases and 14,694 subcohort members (mean age 52.9 y) from 8 countries, nested within the European Prospective Investigation into Cancer and Nutrition study (mean follow-up time 6.9 y). Exploratory dietary patterns were derived by principal component analysis. HRs for incident type 2 diabetes were calculated by Prentice-weighted Cox proportional hazard regression models. Diabetes-associated dietary patterns were simplified or replicated to be applicable in other countries. A meta-analysis across all countries evaluated the generalizability of the diabetes-association.
Results: Two dietary patterns per country/UK-center, of which overall 3 dietary patterns were diabetes-associated, were identified. A risk-lowering French dietary pattern was not confirmed across other countries: pooled HRFrance per 1 SD: 1.00; 95% CI: 0.90, 1.10. Risk-increasing dietary patterns, derived in Spain and UK-Norfolk, were confirmed, but only the latter statistically significantly: HRSpain: 1.09; 95% CI: 0.97, 1.22 and HRUK-Norfolk: 1.12; 95% CI: 1.04, 1.20. Respectively, this dietary pattern was characterized by relatively high intakes of potatoes, processed meat, vegetable oils, sugar, cake and cookies, and tea. Conclusions: Only few country/center-specific dietary patterns (3 of 18) were statistically significantly associated with diabetes incidence in this multicountry European study population. One pattern, whose association with diabetes was confirmed across other countries, showed overlaps in the food groups potatoes and processed meat with identified diabetes-associated dietary patterns from other studies. The study demonstrates that replication of associations of exploratory patterns with health outcomes is feasible and a necessary step to overcome population-specificity in associations from such analyses.
Evaluation of technology-based interventions for informal caregivers of patients with dementia
(2019)
Objective: The aim of this study was to estimate the efficacy of technology-based interventions for informal caregivers of people with dementia (PWD). Methods: PubMed, PsycINFO, and Cochrane Library databases were searched in August 2018, with no restrictions in language or publication date. Two independent reviewers identified 33 eligible randomized controlled trials (RCTs) conducting a technology-based intervention for informal carers of PWD. Meta-analyses for the outcome measures caregiver depression and caregiver burden were conducted with subgroup analyses according to mode of delivery (telephone, computer/web-based, combined interventions). To assess methodologic quality, the Cochrane risk-of-bias assessment was rated. Results: Meta-analyses revealed a small but significant postintervention effect of technology-based interventions for caregiver depression and caregiver burden. Combined interventions showed the strongest effects. Conclusion: Technology-based interventions have the potential to support informal caregivers of PWD. Because of advantages such as high flexibility and availability, technology-based interventions provide a promising alternative compared with "traditional services," e.g., those for people living in rural areas. More high-quality RCTs for specific caregiver groups are needed.
Research synthesis on simple yet general hypotheses and ideas is challenging in scientific disciplines studying highly context-dependent systems such as medical, social, and biological sciences. This study shows that machine learning, equation-free statistical modeling of artificial intelligence, is a promising synthesis tool for discovering novel patterns and the source of controversy in a general hypothesis. We apply a decision tree algorithm, assuming that evidence from various contexts can be adequately integrated in a hierarchically nested structure. As a case study, we analyzed 163 articles that studied a prominent hypothesis in invasion biology, the enemy release hypothesis. We explored if any of the nine attributes that classify each study can differentiate conclusions as classification problem. Results corroborated that machine learning can be useful for research synthesis, as the algorithm could detect patterns that had been already focused in previous narrative reviews. Compared with the previous synthesis study that assessed the same evidence collection based on experts' judgement, the algorithm has newly proposed that the studies focusing on Asian regions mostly supported the hypothesis, suggesting that more detailed investigations in these regions can enhance our understanding of the hypothesis. We suggest that machine learning algorithms can be a promising synthesis tool especially where studies (a) reformulate a general hypothesis from different perspectives, (b) use different methods or variables, or (c) report insufficient information for conducting meta-analyses.
This study examined the effectiveness of psychological interventions for severe health anxiety (SHA) regarding somatic symptoms (SS) and health anxiety (HA). The databases Web of Science, EBSCO, and CENTRAL were searched on May 15, 2019, May 16, 2019, and August 5, 2019, respectively. Eighteen randomized controlled trials (N = 2,050) met the inclusion criteria (i.e., hypochondriasis, illness anxiety disorder or somatic symptom disorder with elevated HA being assessed with validated interviews: use of standardized outcome measures). Two reviewers independently evaluated the studies' risk of bias using the Revised Cochrane Risk-of-Bias Tool for randomized trials (RoB-2) tool. Overall, psychological interventions were significantly more effective than waitlist, treatment-as-usual, or placebo post-treatment (g(SS) = 0.70, g(HA) = 1.11) and at follow-up (g(SS) = 0.33, g(HA)= 0.70). CBT outperformed other psychological interventions or pharmacotherapy for HA post- treatment (Hedge's g(HA) = 0.81). The number of sessions did not significantly predict the effect sizes. In sum, psychological interventions were effective for SHA, but the generalizability of the results for SS is limited, because only two high-quatity trials contributed to the comparison.
Research synthesis on simple yet general hypotheses and ideas is challenging in scientific disciplines studying highly context-dependent systems such as medical, social, and biological sciences. This study shows that machine learning, equation-free statistical modeling of artificial intelligence, is a promising synthesis tool for discovering novel patterns and the source of controversy in a general hypothesis. We apply a decision tree algorithm, assuming that evidence from various contexts can be adequately integrated in a hierarchically nested structure. As a case study, we analyzed 163 articles that studied a prominent hypothesis in invasion biology, the enemy release hypothesis. We explored if any of the nine attributes that classify each study can differentiate conclusions as classification problem. Results corroborated that machine learning can be useful for research synthesis, as the algorithm could detect patterns that had been already focused in previous narrative reviews. Compared with the previous synthesis study that assessed the same evidence collection based on experts' judgement, the algorithm has newly proposed that the studies focusing on Asian regions mostly supported the hypothesis, suggesting that more detailed investigations in these regions can enhance our understanding of the hypothesis. We suggest that machine learning algorithms can be a promising synthesis tool especially where studies (a) reformulate a general hypothesis from different perspectives, (b) use different methods or variables, or (c) report insufficient information for conducting meta-analyses.
This systematic review investigated how successful children/adolescents with poor literacy skills learn a foreign language compared with their peers with typical literacy skills. Moreover, we explored whether specific characteristics related to participants, foreign language instruction, and assessment moderated scores on foreign language tests in this population. Overall, 16 studies with a total of 968 participants (poor reader/spellers:n = 404; control participants:n = 564) met eligibility criteria. Only studies focusing on English as a foreign language were available. Available data allowed for meta-analyses on 10 different measures of foreign language attainment. In addition to standard mean differences (SMDs), we computed natural logarithms of the ratio of coefficients of variation (CVRs) to capture individual variability between participant groups. Significant between-study heterogeneity, which could not be explained by moderator analyses, limited the interpretation of results. Although children/adolescents with poor literacy skills on average showed lower scores on foreign language phonological awareness, letter knowledge, and reading comprehension measures, their performance varied significantly more than that of control participants. Thus, it remains unclear to what extent group differences between the foreign language scores of children/adolescents with poor and typical literacy skills are representative of individual poor readers/spellers. Taken together, our results indicate that foreign language skills in children/adolescents with poor literacy skills are highly variable. We discuss the limitations of past research that can guide future steps toward a better understanding of individual differences in foreign language attainment of children/adolescents with poor literacy skills.
This systematic review investigated how successful children/adolescents with poor literacy skills learn a foreign language compared with their peers with typical literacy skills. Moreover, we explored whether specific characteristics related to participants, foreign language instruction, and assessment moderated scores on foreign language tests in this population. Overall, 16 studies with a total of 968 participants (poor reader/spellers:n = 404; control participants:n = 564) met eligibility criteria. Only studies focusing on English as a foreign language were available. Available data allowed for meta-analyses on 10 different measures of foreign language attainment. In addition to standard mean differences (SMDs), we computed natural logarithms of the ratio of coefficients of variation (CVRs) to capture individual variability between participant groups. Significant between-study heterogeneity, which could not be explained by moderator analyses, limited the interpretation of results. Although children/adolescents with poor literacy skills on average showed lower scores on foreign language phonological awareness, letter knowledge, and reading comprehension measures, their performance varied significantly more than that of control participants. Thus, it remains unclear to what extent group differences between the foreign language scores of children/adolescents with poor and typical literacy skills are representative of individual poor readers/spellers. Taken together, our results indicate that foreign language skills in children/adolescents with poor literacy skills are highly variable. We discuss the limitations of past research that can guide future steps toward a better understanding of individual differences in foreign language attainment of children/adolescents with poor literacy skills.
The large literature that aims to find evidence of climate migration delivers mixed findings. This meta-regression analysis i) summarizes direct links between adverse climatic events and migration, ii) maps patterns of climate migration, and iii) explains the variation in outcomes. Using a set of limited dependent variable models, we meta-analyze thus-far the most comprehensive sample of 3,625 estimates from 116 original studies and produce novel insights on climate migration. We find that extremely high temperatures and drying conditions increase migration. We do not find a significant effect of sudden-onset events. Climate migration is most likely to emerge due to contemporaneous events, to originate in rural areas and to take place in middle-income countries, internally, to cities. The likelihood to become trapped in affected areas is higher for women and in low-income countries, particularly in Africa. We uniquely quantify how pitfalls typical for the broader empirical climate impact literature affect climate migration findings. We also find evidence of different publication biases.
Descriptive analyses of socially important or theoretically interesting phenomena and trends are a vital component of research in the behavioral, social, economic, and health sciences.
Such analyses yield reliable results when using representative individual participant data (IPD) from studies with complex survey designs, including educational large-scale assessments (ELSAs) or social, health, and economic survey and panel studies. The meta-analytic integration of these results offers unique and novel research opportunities to provide strong empirical evidence of the consistency and generalizability of important phenomena and trends.
Using ELSAs as an example, this tutorial offers methodological guidance on how to use the two-stage approach to IPD meta-analysis to account for the statistical challenges of complex survey designs (e.g., sampling weights, clustered and missing IPD), first, to conduct descriptive analyses (Stage 1), and second, to integrate results with three-level meta-analytic and meta-regression models to take into account dependencies among effect sizes (Stage 2).
The two-stage approach is illustrated with IPD on reading achievement from the Programme for International Student Assessment (PISA). We demonstrate how to analyze and integrate standardized mean differences (e.g., gender differences), correlations (e.g., with students' socioeconomic status [SES]), and interactions between individual characteristics at the participant level (e.g., the interaction between gender and SES) across several PISA cycles.
All the datafiles and R scripts we used are available online. Because complex social, health, or economic survey and panel studies share many methodological features with ELSAs, the guidance offered in this tutorial is also helpful for synthesizing research evidence from these studies.