Megan Nuspl

Behavioral Programs for Type 2 Diabetes Mellitus: A Systematic Review and Network Meta-analysis

In 2012, 29.1 million Americans had diabetes with costs of

Behavioral Programs for Type 1 Diabetes Mellitus: A Systematic Review and Meta-analysis

245 billion (1), representing 11% of the total U.S. health care expenditure (2). Although tight glycemic control may reduce the risk for microvascular complications in type 2 diabetes mellitus (T2DM) (3), behavioral and pharmacologic management of body weight, blood pressure, and cholesterol levels are often needed to reduce the risk for mortality and macrovascular complications. Moreover, other patient-centered outcomes, such as diabetes-related distress and depression, are important to address (4). Health care experts recommend that anyone with diabetes adopt and adhere to multiple self-care behaviors, including healthy eating, being active, monitoring, taking medication, problem-solving, healthy coping, and reducing risks (5). Approaches to support behavior change include diabetes self-management education (DSME) with or without an added support (clinical, behavioral, psychosocial, or educational) phase, and lifestyle programs. Because knowledge acquisition insufficiently promotes behavioral changes (6), recommendations for DSME have shifted from traditional didactic educational services to more patient-centered methodologies that incorporate interaction, problem-solving, and other behavioral approaches. Although evidence shows that diabetes-specific behavioral interventions can be effective, which combination of program components and delivery mechanisms is most effective is unclear (711). We conducted a network meta-analysis to identify factors related to program components and delivery mechanisms that moderate the effectiveness of multicomponent behavioral programs for T2DM. Methods Key informants, a technical expert panel, and public commentary informed our methods. A protocol and a peer- and public-reviewed technical report were produced for the Agency for Healthcare Research and Quality (AHRQ) and are available online (www.ahrq.gov/research/findings/evidence-based-reports/). Data Sources and Searches A research librarian searched the following bibliographic databases from 1993 to January 2015: Ovid MEDLINE (Appendix Table 1) and Ovid MEDLINE In-Process & Other Non-Indexed Citations, Cochrane Central Register of Controlled Trials via the Cochrane Library, EMBASE via Ovid, CINAHL Plus with Full Text via EBSCOhost, PsycINFO via Ovid, Scopus, and PubMed via the National Center for Biotechnology Information Databases. We reviewed the reference lists of relevant systematic reviews and of all included studies. We also searched ClinicalTrials.gov and the World Health Organization International Clinical Trials Registry Platform, relevant conference proceedings (2011 through 2014), and the U.S. Federal Register. Appendix Table 1. Search Strategy for MEDLINE* Study Selection We included studies conducted in highly developed countries published in English after 1993 (because medical management for diabetes intensified after this time). We included randomized, controlled trials done in community or outpatient health settings and involving adults that compared a behavioral program with usual care (medical management provided to all participants), an active control (intervention not meeting our definition of behavioral program), or another behavioral program (comparative effectiveness study). A behavioral program was a multicomponent, diabetes-specific program that included repeated interactions with trained individuals over at least 4 weeks, and that consisted of DSME using a behavioral approach or another program format including at least a structured dietary or physical activity intervention with another component (Appendix). We excluded abstracts and studies in which the intervention was a disease or care management program (for example, with active adjustment of diabetes-related medications) (12) or a quality improvement program incorporating strategies targeting health systems or providers (13). Other exclusion criteria included studies 1) focusing on patients with newly diagnosed (1 year) disease; 2) with no outcome of interest to this review (for example, only C-reactive protein), or in which the only difference between the study groups was a factor outside of the reviews scope (for example, low- vs. high-fat diet); and 3) in which 25% or more of the participants had type 1 diabetes mellitus (unless results were reported for those with T2DM). Two reviewers independently screened all titles and abstracts, and the full text of any publication marked for inclusion was retrieved. Two reviewers independently assessed the full texts by using a priori inclusion criteria and a standard form. We resolved disagreements by consensus or consultation with a third reviewer. Data Extraction and Quality Assessment One reviewer extracted data by using a structured form created in the Systematic Review Data Repository (available at http://srdr.ahrq.gov/) (14); a second reviewer verified data. Two reviewers independently applied the Cochrane risk of bias tool (15). Discrepancies were resolved through discussion. Data Synthesis and Analysis With input from technical experts, we categorized behavioral programs by various component and delivery factors (Table). We separated DSME and DSME plus support, in recognition that the support phase of the latter was often of lower intensity (less frequent contacts) and focused on different content, such as psychosocial support. Table. Categorization of Program Components and Delivery Factors To serve as an overview of program effectiveness and help interpret our primary analysis of program moderation, we performed pairwise meta-analyses by using the HartungKnappSidikJonkman random-effects model (16, 17) for multiple behavioral, clinical, and health outcomes, as well as health care utilization and program acceptability (the full report is available at www.ahrq.gov/research/findings/evidence-based-reports/). We defined thresholds for clinical importance where there was guidance: For hemoglobin A1c (HbA1c), we used a reduction of at least 0.4% (for example, 7.6% vs. 8.0%) (18); for quality-of-life measures and other patient-reported outcomes, we used a conservative value of one-half SD (19, 20). We then conducted a network meta-analysis that allowed simultaneous evaluation of a suite of comparisons and considered both direct and indirect evidence while preserving the within-study randomization. To assure the transitivity within the network, we categorized all behavioral programs and comparators into nodes. The nodes for behavioral programs were formed on the basis of different combinations of variables in our program categorization (Appendix Table 2); we identified all plausible nodes differing by only one variable (for example, a level within the intensity category) and then filled the nodes with the applicable interventions on the basis of our coding. The nodes for the comparator groups were categorized as usual care, active non-DSME control (education interventions not meeting our criteria), and active other control (for example, stand-alone dietary or physical activity interventions). Appendix Table 2. Characteristics of Studies of Behavioral Programs for T2DM Appendix Table 2Continued Appendix Table 2Continued Appendix Table 2Continued Appendix Table 2Continued Appendix Table 2Continued Appendix Table 2Continued Appendix Table 2Continued Appendix Table 2Continued The analysis was conducted by using a Bayesian network model to compare all interventions simultaneously and to use all available information on treatment effects in a single analysis (21, 22). These methods ensure that correlation in multigroup trials is preserved. Mean differences (MDs) were modeled using noninformative prior distributions. A normal prior distribution with mean 0 and large variance (10000) was used for each of the trial means, whereas their between study variance had a uniform prior with range 0 to 2. These priors were checked for influence with sensitivity analyses. Markov chain Monte Carlo simulations using WinBugs software were performed to obtain simultaneous estimates of all interventions compared with placebo, as well as estimates of which interventions were the best. A burn-in sample of 20000 iterations was followed by 300000 iterations used to compute estimates. A sensitivity analysis that thinned the amount of used data to every 10th iteration was also conducted to check for proper chain convergence. The analysis was checked for consistency by contrasting direct and indirect estimates in each triangular and quadratic loop by using the methods described by Veroniki and colleagues (23). Results are presented as estimates of the treatment effects (MD) relative to usual care, with 95% credible intervals. To examine different population subgroups, we conducted subgroup analyses of the pairwise meta-analysis results for HbA1c at longest follow-up in comparison with usual care and active controls; subgroups were defined on the basis of study-level baseline HbA1c (<7% vs. 7%), age (<65 vs. 65 years), and ethnicity (75 vs. <75% nonwhite), according to categories that were defined a priori. For baseline HbA1c level and age, we performed subgroup analyses of the network meta-analysis; the analysis was rerun for studies having a mean baseline HbA1c level of 7% or greater and for those with a mean participant age younger than 65 years. For subgroups based on race/ethnicity, the number of trials in either subgroup was insufficient to perform a network meta-analysis. Role of the Funding Source This project was funded under contract 290-2012-000131 from the AHRQ, U.S. Department of Health and Human Services. Staff at AHRQ participated in development of the scope of the work and reviewed drafts of the manuscript. Approval by AHRQ was required before the manuscript could be submitted for publication, but the authors are solely responsible for its content and the decision to submit for publication. AHRQ staff did not participate in the conduct of the review,

Grey literature in systematic reviews: a cross-sectional study of the contribution of non-English reports, unpublished studies and dissertations to the results of meta-analyses in child-relevant reviews

Type 1 diabetes mellitus (T1DM), one of the most common chronic diseases in childhood and adolescence, is increasing in prevalence in the United States (1). The landmark DCCT (Diabetes Control and Complications Trial) and its related longitudinal study (EDIC [Epidemiology of Diabetes Interventions and Complications]) found that intensive glycemic control prevents development and progression of micro- and macrovascular complications (2, 3) and death (4). However, the intervention was initiated early (duration of T1DM <3 years for prevention group) in relatively young (mean age, 27 years), healthy patients. A meta-analysis of 12 trials of intensive control in diverse patient populations confirmed only a reduction in development of microvascular complications. Authors of that analysis stressed that benefits may apply only for interventions initiated early and should be weighed against risks for severe hypoglycemia (5). Factors other than glycemic control appear necessary to improve outcomes. For instance, intensive lowering of blood pressure has reduced major cardiovascular events by 11% (6). In addition, findings from 2 large cross-national studies support interventions to address other outcomes of importance for patients, such as diabetes-related distress (7). All patients with diabetes are encouraged to adopt and adhere to many self-care behaviors (8, 9). This is particularly challenging for those with T1DM, who require lifelong insulin therapy and therefore should undertake rigorous self-monitoring and regulation of blood glucose levels through frequent adjustments to insulin dose, diet, and physical activity (10). Approaches for supporting patients to change several behaviors include diabetes self-management education (DSME) with or without added support (11) and lifestyle programs (12). Because knowledge acquisition alone is insufficient for behavioral changes (13, 14), the focus for DSME has shifted from traditional didactic approaches to more patient-centered methods that incorporate interaction, problem-solving, and other behavioral approaches and techniques (11, 1517). Moreover, programs need to be tailored to the needs of the target population, such as developmental milestones in children or unique personal challenges during adolescence or adulthood (18). Few systematic reviews on education and training in T1DM have been conducted over the past decade (1921). Most reviews assessed only the effects on glycemic control, included highly didactic interventions, or reviewed interventions conducted outside the health care setting (such as summer camps) (1923). All focused on children and adolescents. When calculated, effect sizes demonstrated very modest improvement at longest follow-up (21, 23). An updated evaluationone that focuses on programs incorporating behavioral approaches and targeting several behaviorsis required to determine whether shifts in practice have translated into better outcomes for patients of all ages with T1DM. Anticipating high diversity in program content and delivery mechanisms, our evaluation also explores effect modification by program factors. Methods With assistance from key informants, a technical expert panel, and public commentary, we developed and followed a standard protocol. A peer- and public-reviewed technical report with additional details is available online on the Agency of Healthcare Research and Qualitys (AHRQs) Effective Healthcare Web site (24). Data Sources and Searches Our librarian searched the following bibliographic databases from 1993 to 15 January 2015: Ovid MEDLINE and Ovid MEDLINE In-Process & Other Non-Indexed Citations, Cochrane Central Register of Controlled Trials via Cochrane Library, EMBASE via Ovid, CINAHL Plus with Full Text via EBSCOhost, PsycINFO via Ovid, and PubMed (2014 only) via the National Center for Biotechnology Information Databases (MEDLINE strategy is presented in Appendix Table 1). On 3 June 2015, we updated the search in MEDLINE. We reviewed the reference lists of relevant systematic reviews and all included studies, searched ClinicalTrials.gov and the World Health Organization International Clinical Trials Registry Platform, and searched relevant conference proceedings (2011 through 2014) and the U.S. Federal Register. Appendix Table 1. Search Strategy for MEDLINE* Study Selection We included studies that were conducted in highly developed countries (25) and were published in English after 1993 (reflecting intensification of medical management based on the DCCT) (2). We included prospective comparative studies (that is, randomized, controlled trials [RCTs]; nonrandomized controlled trials; prospective cohort studies; controlled beforeafter studies) that enrolled participants of any age and compared a behavioral program with usual care (that is, medical management provided to all participants), an active control (intervention beyond usual care but not meeting our definition of a behavioral program), or another behavioral program. A behavioral program was operationally defined as a multicomponent, diabetes-specific program with repeated interactions by trained individuals, a duration of 4 weeks or longer, and DSME that entailed a behavioral approach or another program format that included at least a structured dietary or physical activity intervention with another component (Appendix Table 2). Appendix Table 2. Operational Definitions of Behavioral Program and Comparators We excluded studies in which the intervention was a disease or care management program (for example, those with active adjustment of diabetes-related medications) (26) or other quality improvement programs targeting health systems or providers (27). Studies were also excluded if they 1) focused on newly diagnosed (1 year) patients, 2) focused on psychological counseling or treatment without explicitly targeting several diabetes self-care behaviors, 3) had no outcomes of interest to this review (for example, reporting on only insulin sensitivity), 4) had study groups that differed only by a factor outside the reviews scope (for example, low- vs. high-fat diet), and 5) included a study sample in which 25% of participants had type 2 diabetes (unless results were reported for T1DM). Two reviewers independently screened all titles and abstracts. We retrieved the full text of any publications marked for inclusion by either reviewer. Two reviewers independently assessed the full texts using a priori inclusion criteria and a standard form. We resolved disagreements by consensus or by consulting another team member. Data Extraction and Quality Assessment One reviewer extracted data by using a structured form created in the Systematic Review Data Repository (http://srdr.ahrq.gov/) (28); a second reviewer verified all data. Two reviewers independently assessed methodological quality. Discrepancies were resolved through discussion. We used the Cochrane Risk of Bias tool (29) for RCTs and nonrandomized controlled trials and used the NewcastleOttawa Scale (30) for prospective cohort studies and controlled beforeafter studies. Data Synthesis and Analysis Characteristics of included studies are presented in summary tables. Our key outcomes were glycemic control (that is, glycosylated hemoglobin [HbA1c]); quality of life; development of micro- and macrovascular complications; all-cause mortality; adherence to diabetes self-management behaviors; and changes in body composition, physical activity, or dietary or nutrient intake. Secondary outcomes included episodes of severe hypo- or hyperglycemia, depression, anxiety, control of blood pressure and lipids, health care utilization, and program acceptability (via participant attrition). Harms included activity-related injury. We defined thresholds for clinical importance when the literature provided guidance; for HbA1c we used a between-group difference of 0.4percentage point change (for example, 7.6% vs. 8.0%) (31); for patient-reported outcomes represented by continuous data, we used a one-half SD based on the mean SD from the pooled studies (32, 33). With input from our technical experts, we categorized the behavioral programs by various component and delivery factors (Appendix Table 3). Programs not classified as DSME or DSME with added support (both incorporating education or training on several diabetes self-care behaviors) were considered lifestyle because they generally consisted of structured dietary and physical activity interventions. Appendix Table 3. Categorization of Program Components and Delivery Factors When possible we used (or computed) change from baseline values. If SDs were not given, they were computed from P values, 95% CIs, z statistics, or t statistics or were estimated from upper-bound P values, ranges, interquartile ranges, or (as a last resort) imputation using the largest reported SD from the other studies in the same meta-analysis. When computing SDs for change from baseline values, we assumed a correlation of 0.5; we conducted post hoc sensitivity analyses using correlations of 0.25 and 0.75. We pooled results for all ages and for subgroups based on age (that is, youth [aged 18 years] and their families, young adults [aged 19 to 30 years], adults [aged 31 to 64 years], and older adults (aged 65 years]) when there was more than 1 trial in each age category. We used the HartungKnappSidikJonkman random-effects model (34, 35) using Stata 11.2 (Stata Corp.) and Excel 2010 (Microsoft) software. We calculated weighted mean differences (MDs) or standardized mean differences (SMDs), as appropriate, with corresponding 95% CIs. We analyzed outcomes at the end of intervention to 1-month follow-up (EOI), and at 1 to no more than 6 months (6-month), more than 6 to 12 months (12-month), more than 12 to 24 months (12-month), and more than 24 months (24-month) after the intervention. If a study included more than 1 follow-up time point in each stratum, we used the longer follow-up. We did not include observational stud

Exercise for the prevention and treatment of low back, pelvic girdle and lumbopelvic pain during pregnancy: a systematic review and meta-analysis

BackgroundSystematic reviews (SRs) are an important source of information about healthcare interventions. A key component of a well-conducted SR is a comprehensive literature search. There is limited evidence on the contribution of non-English reports, unpublished studies, and dissertations and their impact on results of meta-analyses.MethodsOur sample included SRs from three Cochrane Review Groups: Acute Respiratory Infections (ARI), Infectious Diseases (ID), Developmental Psychosocial and Learning Problems (DPLP) (n = 129). Outcomes included: 1) proportion of reviews that searched for and included each study type; 2) proportion of relevant studies represented by each study type; and 3) impact on results and conclusions of the primary meta-analysis for each study type.ResultsMost SRs searched for non-English studies; however, these were included in only 12% of reviews and represented less than 5% of included studies. There was a change in results in only four reviews (total sample = 129); in two cases the change did not have an impact on the statistical or clinical significance of results. Most SRs searched for unpublished studies but the majority did not include these (only 6%) and they represented 2% of included studies. In most cases the impact of including unpublished studies was small; a substantial impact was observed in one case that relied solely on unpublished data. Few reviews in ARI (9%) and ID (3%) searched for dissertations compared to 65% in DPLP. Overall, dissertations were included in only nine SRs and represented less than 2% of included studies. In the majority of cases the change in results was negligible or small; in the case where a large change was noted, the estimate was more conservative without dissertations.ConclusionsThe majority of SRs searched for non-English and unpublished studies; however, these represented a small proportion of included studies and rarely impacted the results and conclusions of the review. Inclusion of these study types may have an impact in situations where there are few relevant studies, or where there are questionable vested interests in the published literature. We found substantial variation in whether SRs searched for dissertations; in most reviews that included dissertations, these had little impact on results.

Prenatal exercise for the prevention of gestational diabetes mellitus and hypertensive disorders of pregnancy: a systematic review and meta-analysis

Objective The purpose of this review was to investigate the relationship between prenatal exercise, and low back (LBP), pelvic girdle (PGP) and lumbopelvic (LBPP) pain. Design Systematic review with random effects meta-analysis and meta-regression. Data sources Online databases were searched up to 6 January 2017. Study eligibility criteria Studies of all designs were eligible (except case studies and reviews) if they were published in English, Spanish or French, and contained information on the population (pregnant women without contraindication to exercise), intervention (subjective or objective measures of frequency, intensity, duration, volume or type of exercise, alone [“exercise-only”] or in combination with other intervention components [eg, dietary; “exercise + co-intervention”]), comparator (no exercise or different frequency, intensity, duration, volume and type of exercise) and outcome (prevalence and symptom severity of LBP, PGP and LBPP). Results The analyses included data from 32 studies (n=52 297 pregnant women). ‘Very low’ to ‘moderate’ quality evidence from 13 randomised controlled trials (RCTs) showed prenatal exercise did not reduce the odds of suffering from LBP, PGP and LBPP either in pregnancy or the postpartum period. However, ‘very low’ to ‘moderate’ quality evidence from 15 RCTs identified lower pain severity during pregnancy and the early postpartum period in women who exercised during pregnancy (standardised mean difference −1.03, 95% CI −1.58, –0.48) compared with those who did not exercise. These findings were supported by ‘very low’ quality evidence from other study designs. Conclusion Compared with not exercising, prenatal exercise decreased the severity of LBP, PGP or LBPP during and following pregnancy but did not decrease the odds of any of these conditions at any time point.

Prenatal exercise (including but not limited to pelvic floor muscle training) and urinary incontinence during and following pregnancy: a systematic review and meta-analysis

Objective Gestational diabetes mellitus (GDM), gestational hypertension (GH) and pre-eclampsia (PE) are associated with short and long-term health issues for mother and child; prevention of these complications is critically important. This study aimed to perform a systematic review and meta-analysis of the relationships between prenatal exercise and GDM, GH and PE. Design Systematic review with random effects meta-analysis and meta-regression. Data sources Online databases were searched up to 6 January 2017. Study eligibility criteria Studies of all designs were included (except case studies) if published in English, Spanish or French, and contained information on the Population (pregnant women without contraindication to exercise), Intervention (subjective or objective measures of frequency, intensity, duration, volume or type of exercise, alone [“exercise-only”] or in combination with other intervention components [e.g., dietary; “exercise + co-intervention”]), Comparator (no exercise or different frequency, intensity, duration, volume and type of exercise) and Outcomes (GDM, GH, PE). Results A total of 106 studies (n=273 182) were included. ‘Moderate’ to ‘high’-quality evidence from randomised controlled trials revealed that exercise-only interventions, but not exercise+cointerventions, reduced odds of GDM (n=6934; OR 0.62, 95% CI 0.52 to 0.75), GH (n=5316; OR 0.61, 95% CI 0.43 to 0.85) and PE (n=3322; OR 0.59, 95% CI 0.37 to 0.9) compared with no exercise. To achieve at least a 25% reduction in the odds of developing GDM, PE and GH, pregnant women need to accumulate at least 600 MET-min/week of moderate-intensity exercise (eg, 140 min of brisk walking, water aerobics, stationary cycling or resistance training). Summary/conclusions In conclusion, exercise-only interventions were effective at lowering the odds of developing GDM, GH and PE.

Impact of prenatal exercise on both prenatal and postnatal anxiety and depressive symptoms: a systematic review and meta-analysis.

Objective To examine the relationships between prenatal physical activity and prenatal and postnatal urinary incontinence (UI). Design Systematic review with random effects meta-analysis and meta-regression. Data sources Online databases were searched up to 6 January 2017. Study eligibility criteria Studies of all designs were included (except case studies) if they were published in English, Spanish or French and contained information on the Population (pregnant women without contraindication to exercise), Intervention (subjective or objective measures of frequency, intensity, duration, volume or type of exercise, alone [“exercise-only”] or in combination with other intervention components [e.g., dietary; “exercise + co-intervention”]), Comparator (no exercise or different frequency, intensity, duration, volume and type of exercise) and Outcome (prenatal or postnatal UI). Results 24 studies (n=15 982 women) were included. ‘Low’ to ‘moderate’ quality evidence revealed prenatal pelvic floor muscle training (PFMT) with or without aerobic exercise decreased the odds of UI in pregnancy (15 randomised controlled trials (RCTs), n=2764 women; OR 0.50, 95% CI 0.37 to 0.68, I2=60%) and in the postpartum period (10 RCTs, n=1682 women; OR 0.63, 95% CI 0.51, 0.79, I2=0%). When we analysed the data by whether women were continent or incontinent prior to the intervention, exercise was beneficial at preventing the development of UI in women with continence, but not effective in treating UI in women with incontinence. There was ‘low’ quality evidence that prenatal exercise had a moderate effect in the reduction of UI symptom severity during (five RCTs, standard mean difference (SMD) −0.54, 95% CI −0.88 to –0.20, I2=64%) and following pregnancy (three RCTs, ‘moderate’ quality evidence; SMD −0.54, 95% CI −0.87 to –0.22, I2=24%). Conclusion Prenatal exercise including PFMT reduced the odds and symptom severity of prenatal and postnatal UI. This was the case for women who were continent before the intervention. Among women who were incontinent during pregnancy, exercise training was not therapeutic.

Effectiveness of exercise interventions in the prevention of excessive gestational weight gain and postpartum weight retention: a systematic review and meta-analysis

Objective To examine the influence of prenatal exercise on depression and anxiety during pregnancy and the postpartum period. Design Systematic review with random effects meta-analysis and meta-regression. Data sources Online databases were searched up to 6 January 2017. Study eligibility criteria Studies of all designs were included (except case studies) if they were published in English, Spanish or French and contained information on the Population (pregnant women without contraindication to exercise), Intervention (subjective or objective measures of frequency, intensity, duration, volume or type of exercise), Comparator (no exercise or different frequency, intensity, duration, volume and type of exercise) and Outcome (prenatal or postnatal depression or anxiety). Results A total of 52 studies (n=131 406) were included. ‘Moderate’ quality evidence from randomised controlled trials (RCTs) revealed that exercise-only interventions, but not exercise+cointerventions, reduced the severity of prenatal depressive symptoms (13 RCTs, n=1076; standardised mean difference: −0.38, 95% CI −0.51 to –0.25, I2=10%) and the odds of prenatal depression by 67% (5 RCTs, n=683; OR: 0.33, 95% CI 0.21 to 0.53, I2=0%) compared with no exercise. Prenatal exercise did not alter the odds of postpartum depression or the severity of depressive symptoms, nor anxiety or anxiety symptoms during or following pregnancy. To achieve at least a moderate effect size in the reduction of the severity of prenatal depressive symptoms, pregnant women needed to accumulate at least 644 MET-min/week of exercise (eg, 150 min of moderate intensity exercise, such as brisk walking, water aerobics, stationary cycling, resistance training). Summary/Conclusions Prenatal exercise reduced the odds and severity of prenatal depression.

Safe Care for Pediatric Patients: A Scoping Review Across Multiple Health Care Settings

Objective Gestational weight gain (GWG) has been identified as a critical modifier of maternal and fetal health. This systematic review and meta-analysis aimed to examine the relationship between prenatal exercise, GWG and postpartum weight retention (PPWR). Design Systematic review with random effects meta-analysis and meta-regression. Online databases were searched up to 6 January 2017. Study eligibility criteria Studies of all designs in English, Spanish or French were eligible (except case studies and reviews) if they contained information on the population (pregnant women without contraindication to exercise), intervention (frequency, intensity, duration, volume or type of exercise, alone [“exercise-only”] or in combination with other intervention components [eg, dietary; “exercise + co-intervention”]), comparator (no exercise or different frequency, intensity, duration, volume or type of exercise) and outcomes (GWG, excessive GWG (EGWG), inadequate GWG (IGWG) or PPWR). Results Eighty-four unique studies (n=21 530) were included. ‘Low’ to ‘moderate’ quality evidence from randomised controlled trials (RCTs) showed that exercise-only interventions decreased total GWG (n=5819; −0.9 kg, 95% CI −1.23 to –0.57 kg, I2=52%) and PPWR (n=420; −0.92 kg, 95% CI −1.84 to 0.00 kg, I2=0%) and reduced the odds of EGWG (n=3519; OR 0.68, 95% CI 0.57 to 0.80, I2=12%) compared with no exercise. ‘High’ quality evidence indicated higher odds of IGWG with prenatal exercise-only (n=1628; OR 1.32, 95% CI 1.04 to 1.67, I2=0%) compared with no exercise. Conclusions Prenatal exercise reduced the odds of EGWG and PPWR but increased the risk of IGWG. However, the latter result should be interpreted with caution because it was based on a limited number of studies (five RCTs).

Impact of prenatal exercise on neonatal and childhood outcomes: a systematic review and meta-analysis

Children are particularly vulnerable to patient safety concerns due to pediatric-specific and general health care challenges. This scoping review identifies and describes the vulnerabilities of those aged 0 to 18 years to iatrogenic harm in various health care settings. Six databases were searched from 1991 to 2012. Primary studies were categorized using predetermined groupings. Categories were tallied and descriptive statistics were employed. A total of 388 primary studies exploring interventions that improved patient safety, deficiencies, or errors leading to safety concerns were included. The most common issues were medication (189 studies, 48.7%) and general medical (81 studies, 20.9%) errors. Sixty studies (15.5%) evaluated or described patient safety interventions, 206 studies (53.1%) addressed health care systems and technologies, 17 studies (4.4%) addressed caregiver perspectives and 20 studies (5.2%) discussed analytic models for patient safety. Further work is needed to ensure consistency of definitions in patient safety research to facilitate comparison and collation of results.