BACKGROUND: The evidence on prophylactic use of negative pressure wound therapy on primary closed incisional wounds (iNPWT) for the prevention of surgical site infections (SSI) is confusing and ambiguous. Implementation in daily practice is impaired by inconsistent recommendations in current international guidelines and published meta-analyses. More recently, multiple new randomised controlled trials (RCTs) have been published. We aimed to provide an overview of all meta-analyses and their characteristics; to conduct a new and up-to-date systematic review and meta-analysis and Grading of Recommendations Assessment, Development and Evaluation (GRADE) assessment; and to explore the additive value of new RCTs with a trial sequential analysis (TSA).METHODS: PubMed, Embase and Cochrane CENTRAL databases were searched from database inception to October 24, 2022. We identified existing meta-analyses covering all surgical specialties and RCTs studying the effect of iNPWT compared with standard dressings in all types of surgery on the incidence of SSI, wound dehiscence, reoperation, seroma, hematoma, mortality, readmission rate, skin blistering, skin necrosis, pain, and adverse effects of the intervention. We calculated relative risks (RR) with corresponding 95% confidence intervals (CI) using a Mantel-Haenszel random-effects model. We assessed publication bias with a comparison-adjusted funnel plot. TSA was used to assess the risk of random error. The certainty of evidence was evaluated using the Cochrane Risk of Bias-2 (RoB2) tool and GRADE approach. This study is registered with PROSPERO, CRD42022312995.FINDINGS: We identified eight previously published general meta-analyses investigating iNPWT and compared their results to present meta-analysis. For the updated systematic review, 57 RCTs with 13,744 patients were included in the quantitative analysis for SSI, yielding a RR of 0.67 (95% CI: 0.59-0.76, I 2 = 21%) for iNPWT compared with standard dressing. Certainty of evidence was high. Compared with previous meta-analyses, the RR stabilised, and the confidence interval narrowed. In the TSA, the cumulative Z-curve crossed the trial sequential monitoring boundary for benefit, confirming the robustness of the summary effect estimate from the meta-analysis. INTERPRETATION: In this up-to-date meta-analysis, GRADE assessment shows high-certainty evidence that iNPWT is effective in reducing SSI, and uncertainty is less than in previous meta-analyses. TSA indicated that further trials are unlikely to change the effect estimate for the outcome SSI; therefore, if future research is to be conducted on iNPWT, it is crucial to consider what the findings will contribute to the existing robust evidence.FUNDING: Dutch Association for Quality Funds Medical Specialists.
Abstract: Background: Chronic obstructive pulmonary disease (COPD) and asthma have a high prevalence and disease burden. Blended self-management interventions, which combine eHealth with face-to-face interventions, can help reduce the disease burden. Objective: This systematic review and meta-analysis aims to examine the effectiveness of blended self-management interventions on health-related effectiveness and process outcomes for people with COPD or asthma. Methods: PubMed, Web of Science, COCHRANE Library, Emcare, and Embase were searched in December 2018 and updated in November 2020. Study quality was assessed using the Cochrane risk of bias (ROB) 2 tool and the Grading of Recommendations, Assessment, Development, and Evaluation. Results: A total of 15 COPD and 7 asthma randomized controlled trials were included in this study. The meta-analysis of COPD studies found that the blended intervention showed a small improvement in exercise capacity (standardized mean difference [SMD] 0.48; 95% CI 0.10-0.85) and a significant improvement in the quality of life (QoL; SMD 0.81; 95% CI 0.11-1.51). Blended intervention also reduced the admission rate (relative ratio [RR] 0.61; 95% CI 0.38-0.97). In the COPD systematic review, regarding the exacerbation frequency, both studies found that the intervention reduced exacerbation frequency (RR 0.38; 95% CI 0.26-0.56). A large effect was found on BMI (d=0.81; 95% CI 0.25-1.34); however, the effect was inconclusive because only 1 study was included. Regarding medication adherence, 2 of 3 studies found a moderate effect (d=0.73; 95% CI 0.50-0.96), and 1 study reported a mixed effect. Regarding self-management ability, 1 study reported a large effect (d=1.15; 95% CI 0.66-1.62), and no effect was reported in that study. No effect was found on other process outcomes. The meta-analysis of asthma studies found that blended intervention had a small improvement in lung function (SMD 0.40; 95% CI 0.18-0.62) and QoL (SMD 0.36; 95% CI 0.21-0.50) and a moderate improvement in asthma control (SMD 0.67; 95% CI 0.40-0.93). A large effect was found on BMI (d=1.42; 95% CI 0.28-2.42) and exercise capacity (d=1.50; 95% CI 0.35-2.50); however, 1 study was included per outcome. There was no effect on other outcomes. Furthermore, the majority of the 22 studies showed some concerns about the ROB, and the quality of evidence varied. Conclusions: In patients with COPD, the blended self-management interventions had mixed effects on health-related outcomes, with the strongest evidence found for exercise capacity, QoL, and admission rate. Furthermore, the review suggested that the interventions resulted in small effects on lung function and QoL and a moderate effect on asthma control in patients with asthma. There is some evidence for the effectiveness of blended self-management interventions for patients with COPD and asthma; however, more research is needed. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42019119894; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=119894
This case study illustrates the sequential process of the joint and individual knowledge elaboration in a computer-supported collaborative learning (CSCL) environment. The case comprised an Internet-based physics problem-solving platform. Six Dutch secondary school students (three males, three females) participated in the three-week experiment. They were paired based on self-selection. Each dyad was asked to collaborate on eight moderately structured problems concerning Newtonian mechanics. Their online interactions, including their textual and pictorial messages, were categorized and sequentially plotted. The three dyads showed three different collaboration patterns in terms of joint and individual knowledge elaboration.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
