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Background: There is increasing interest at the clinical level and at the policy level in the use of behavioral interventions - specifically Cognitive Training (CT), Physical Exercise (PE), and Nutrition (NU) to improve brain health and cognitive function in older adults.

Problem: Direct comparisons of the relative efficacy of various classes of behavioral interventions are challenging because meta-analyses typically focus on a single type (CT, PE, or NU) and use different inclusion criteria and analytical methods. Clinicians and policy-makers lack comprehensive comparable data to recommend interventions to patients and to define overall clinical guidelines.

Objective: Develop an open-source database of randomized controlled trials of CT, PE, and NU interventions with a common set of inclusion criteria and evaluate the relative efficacy of various classes of interventions with meta-analysis.

Search CLASSES: We reviewed the top 50 meta-analyses for each intervention type. The search identified 853 publications, of which 576 were excluded. 247 publications comprising 309 clinical trials were included in the analysis. A detailed description of the search strategy is provided on the accompanying website.

DROP QR
CODE
HERE

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The Influence of Control Class on Intervention Efficacy

• Cognitive Training: Net effect sizes are numerically higher in trials with active controls (0.33) than passive controls (0.23).

• Physical Exercise: Net effect sizes are significantly lower ($p=0.0237$) in trials with active controls (0.14) than passive controls (0.29).

• Nutrition: Net effect sizes are similar in trials with active (0.18) and passive (0.14) controls.

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.attr("text-anchor", "start") .text((function(d) { if ((targets == "all") && (trains == "all")) { return "all targets and all train conditions"} else if ((targets == "all") && (trains != "all")){ return "cond1"} else if ((targets != "all") && (trains == "all")){ return "cond2"} else{ return "cond3"} })); // circles svg.selectAll('circle') .data(selectedData) .enter() .append('circle') .sort((a, b) => d3.descending(a.totalhour, b.totalhour)) .attr("stroke", "black") .attr('cx', d => x(d.x)) .attr('cy', d => y(d.y)) .attr("r", function(d) { return Math.sqrt(area(d.totalhour));}) .attr('opacity', 1) .attr('fill', d => color(d.training)) // When "mouseover" event triggers, show the tooltip .on('mouseover', function(d) { // show the tooltip on mouse over tip.show(d, this); // when the bar is mouse-overed, we slightly decrease opacity of the circle. d3.select(this).attr("stroke-width", 2) d3.select(this).attr("stroke", "black") d3.select(this).style('opacity', 0.7); }) .on('mouseout', 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\n Author: ${d.Author}
\n Year: ${d.x}
\n Net Effect: ${d.y}
\n Training: ${d.training}
\n Types: ${d.inter}
\n Target: ${d.target}
\n Num Intervention: ${d.nt}
\n Num Control: ${d.nc}
\n Total Train Hour: ${d.totalhour}
\n Mean Age:${d.age}
\n Mean Education:${d.edu}\n

`)","pinned":false,"hidden":false,"mode":"ojs"},{"id":283,"value":"import {select} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W32","stdlib":"1","titling":"auto"}