Dataset Information

Quantifying the Difference between Active and Passive Control Groups in Cognitive Interventions Using two Meta-Analytical Approaches.

ABSTRACT: Despite promising reports of broad cognitive benefit in studies of cognitive training, it has been argued that the reliance of many studies on no-intervention control groups (passive controls) make these reports difficult to interpret because placebo effects cannot be ruled out. Although researchers have recently been trying to incorporate more active controls, in which participants engage in an alternate intervention, previous work has been contentious as to whether this actually yields meaningfully different results. To better understand the influence of passive and active control groups on cognitive interventions, we conducted two meta-analyses to estimate their relative effect sizes. While the first one broadly surveyed the literature by compiling data from 34 meta-analyses, the second one synthesized data from 42 empirical studies that simultaneously employed both types of controls. Both analyses showed no meaningful performance difference between passive and active controls, suggesting that current active control placebo paradigms might not be appropriately designed to reliably capture these non-specific effects or that these effects are minimal in this literature.

SUBMITTER: Au J

PROVIDER: S-EPMC8320766 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Publications

Quantifying the Difference between Active and Passive Control Groups in Cognitive Interventions Using two Meta-Analytical Approaches.

Au Jacky J Gibson Benjamin C BC Bunarjo Kimberly K Buschkuehl Martin M Jaeggi Susanne M SM

Journal of cognitive enhancement : towards the integration of theory and practice 20200129 2

Despite promising reports of broad cognitive benefit in studies of cognitive training, it has been argued that the reliance of many studies on no-intervention control groups (passive controls) make these reports difficult to interpret because placebo effects cannot be ruled out. Although researchers have recently been trying to incorporate more active controls, in which participants engage in an alternate intervention, previous work has been contentious as to whether this actually yields meaning ...[more]

PMID: 34337311

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Project description:The interface between field biology and technology is energizing the collection of vast quantities of environmental data. Passive acoustic monitoring, the use of unattended recording devices to capture environmental sound, is an example where technological advances have facilitated an influx of data that routinely exceeds the capacity for analysis. Computational advances, particularly the integration of machine learning approaches, will support data extraction efforts. However, the analysis and interpretation of these data will require parallel growth in conceptual and technical approaches for data analysis. Here, we use a large hand-annotated dataset to showcase analysis approaches that will become increasingly useful as datasets grow and data extraction can be partially automated.We propose and demonstrate seven technical approaches for analyzing bioacoustic data. These include the following: (1) generating species lists and descriptions of vocal variation, (2) assessing how abiotic factors (e.g., rain and wind) impact vocalization rates, (3) testing for differences in community vocalization activity across sites and habitat types, (4) quantifying the phenology of vocal activity, (5) testing for spatiotemporal correlations in vocalizations within species, (6) among species, and (7) using rarefaction analysis to quantify diversity and optimize bioacoustic sampling.To demonstrate these approaches, we sampled in 2016 and 2018 and used hand annotations of 129,866 bird vocalizations from two forests in New Hampshire, USA, including sites in the Hubbard Brook Experiment Forest where bioacoustic data could be integrated with more than 50 years of observer-based avian studies. Acoustic monitoring revealed differences in community patterns in vocalization activity between forests of different ages, as well as between nearby similar watersheds. Of numerous environmental variables that were evaluated, background noise was most clearly related to vocalization rates. The songbird community included one cluster of species where vocalization rates declined as ambient noise increased and another cluster where vocalization rates declined over the nesting season. In some common species, the number of vocalizations produced per day was correlated at scales of up to 15 km. Rarefaction analyses showed that adding sampling sites increased species detections more than adding sampling days.Although our analyses used hand-annotated data, the methods will extend readily to large-scale automated detection of vocalization events. Such data are likely to become increasingly available as autonomous recording units become more advanced, affordable, and power efficient. Passive acoustic monitoring with human or automated identification at the species level offers growing potential to complement observer-based studies of avian ecology.

Project description:BackgroundIn Afghanistan, improving TB case detection remains challenging. In 2014, only half of the estimated incident TB cases were notified, and notifications have decreased since peaking in 2007. Active case finding has been increasingly considered to improve TB case notifications. While access to health services has improved in Afghanistan, it remains poor and many people seeking health services won't receive proper care.MethodsFrom October 2011 through December 2012 we conducted three separate case finding strategies in six provinces of Afghanistan and measured impact on TB case notification. Systematically screening cough among attendees at 47 health facilities, active household contact investigation of smear-positive index TB patients, and active screening at 15 camps for internally displaced people were conducted. We collected both intervention yield and official quarterly notification data. Additional TB notifications were calculated by comparing numbers of cases notified during the intervention with those notified before the intervention, then adjusting for secular trends in notification.ResultsWe screened 2,022,127 people for TB symptoms during the intervention, tested 59,838 with smear microscopy and detected 5,046 people with smear-positive TB. Most cases (81.7%, 4,125) were identified in health facilities while nearly 20% were found through active case finding. A 56% increase in smear-positive TB notifications was observed between the baseline and intervention periods among the 47 health facilities, where cases detected by all three strategies were notified.DiscussionWhile most people with TB are likely to be identified through health facility screening, there are many people who remain without a proper diagnosis if outreach is not attempted. This is especially true in places like Afghanistan where access to general services is poor. Targeted active case finding can improve the number of people who are detected and treated for TB and can push towards the targets of the Stop TB Global Plan and End TB Strategy.

Dataset Information

Quantifying the Difference between Active and Passive Control Groups in Cognitive Interventions Using two Meta-Analytical Approaches.

Publications

Quantifying the Difference between Active and Passive Control Groups in Cognitive Interventions Using two Meta-Analytical Approaches.

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