PANO

https://vosslab.shinyapps.io/intervention-outcomes/

About

The Physical Activity Neurocognitive Outcomes (PANO) application is a tool for visualizing results from intervention studies examining how exercise training affects cognitive outcomes. Currently the intervention database includes results from 100 individual papers that were gathered from the 12 peer-reviewed meta-analyses listed below. Our application aims to provide a tool for visualizing all of these results with user-defined filters to focus on the age-groups, intervention type, covariates, or outcomes of interest.

Meta-analyses from which individual papers gathered (alphabetical order):

1. Barha, C. K., Davis, J. C., Falck, R. S., Nagamatsu, L. S., & Liu-Ambrose, T. (2017). Sex differences in exercise efficacy to improve cognition: A systematic review and meta-analysis of randomized controlled trials in older humans. Front Neuroendocrinol, 46, 71-85. doi:10.1016/j.yfrne.2017.04.002
2. Colcombe, S., & Kramer, A. (2003). Fitness effects on the cognitive function of older adults: a meta-analytic study. Psychol Sci, 14(2), 125-130.
3. Falck, R. S., Davis, J. C., Best, J. R., Crockett, R. A., & Liu-Ambrose, T. (2019). Impact of exercise training on physical and cognitive function among older adults: a systematic review and meta-analysis. Neurobiol Aging, 79, 119-130. doi:10.1016/j.neurobiolaging.2019.03.007
4. Forbes, D., Forbes, S. C., Blake, C. M., Thiessen, E. J., & Forbes, S. (2015). Exercise programs for people with dementia. Cochrane Database Syst Rev(4), CD006489. doi:10.1002/14651858.CD006489.pub4
5. Heyn, P., Abreu, B., & Ottenbacher, K. (2004). The effects of exercise training on elderly persons with cognitive impairment and dementia: a meta-analysis. Arch Phys Med Rehabil, 85(10), 1694-1704.
6. Kane, R. L., Butler, M., Fink, H. A., Brasure, M., Davila, H., Desai, P., . . . Barclay, T. (2017). AHRQ Comparative Effectiveness Reviews. In Interventions to Prevent Age-Related Cognitive Decline, Mild Cognitive Impairment, and Clinical Alzheimer's-Type Dementia. Rockville (MD): Agency for Healthcare Research and Quality (US).
7. Law, L., Barnett, F., Yau, M., & Gray, M. (2014). Effects of combined cognitive and exercise interventions on cognition in older adults with and without cognitive impairment: a systematic review. Ageing Res Rev, 15, 61-75. doi:10.1016/j.arr.2014.02.008
8. Northey, J. M., Cherbuin, N., Pumpa, K. L., Smee, D. J., & Rattray, B. (2018). Exercise interventions for cognitive function in adults older than 50: a systematic review with meta-analysis. Br J Sports Med, 52(3), 154-160. doi:10.1136/bjsports-2016-096587
9. Panza, G. A., Taylor, B. A., MacDonald, H. V., Johnson, B. T., Zaleski, A. L., Livingston, J., . . . Pescatello, L. S. (2018). Can Exercise Improve Cognitive Symptoms of Alzheimer's Disease? A Meta-Analysis. J Am Geriatr Soc. doi:10.1111/jgs.15241
10. Roig, M., Nordbrandt, S., Geertsen, S. S., & Nielsen, J. B. (2013). The effects of cardiovascular exercise on human memory: a review with meta-analysis. Neurosci Biobehav Rev, 37(8), 1645-1666. doi:10.1016/j.neubiorev.2013.06.012
11. Smith, P. J., Blumenthal, J. A., Hoffman, B. M., Cooper, H., Strauman, T. A., Welsh-Bohmer, K., . . . Sherwood, A. (2010). Aerobic exercise and neurocognitive performance: a meta-analytic review of randomized controlled trials. Psychosom Med, 72(3), 239-252. doi:10.1097/PSY.0b013e3181d14633
12. Young, J., Angevaren, M., & Rusted…, J. (2015). Aerobic exercise to improve cognitive function in older people without known cognitive impairment. Cochrane Database Syst Rev, 4, CD005381. doi:10.1002/14651858.CD005381.pub4

Why have the PANO application if we can read each meta-analysis? Published meta-analyses are static and have limited comparability with each other due to differences in inclusion criteria, how effect sizes are computed, and how cognitive tasks are grouped into higher-level cognitive constructs. To address these issues, PANO provides a tool for interactively visualizing results at the task-level across studies with user-defined inclusion criteria upon an updated database of studies. The database can be visualized based simply on number of present studies or based on LinkType where outcomes are coded relative to the hypothesized effect (negative/null/positive). With LinkType, the app can visualize a conservative estimate of patterns for strong effects, and identify gaps in the literature where few to no studies exist.

General usage and examples

The default plot is a bar graph showing the variable StudyName on the x-axis with frequency of occurance on the y-axis. Categorical variables can be plotted on the x-axis with plot types of bar heatmap or box. Once plot type is chosen, choose what is plotted on the x-axis as your Categorical Value. For example, to look at intervention effects for different training manipulations, choose IndependentVariable as your x-axis, and filter values of this variable to include by clicking the blank field below the variable in the table and choosing levels from the drop-down menu. To then summarize counts based on levels of another categorical variable, choose Split Current Values and select the relevant categorical variable.

Plotting categorical variables: With the default Plot type of bar, plot frequency of different intervention types, and split count by ordinal variable PercFemaleCategorical. Include only studies have coded outcomes by selecting Y for the Include variable in the results database. The plot indicates that about 150 of the effects reported from Aerobic Training interventions are from studies with a majority of women in the sample. Note this plot counts effects reported across studies, so two effects from one study counts as frequency of two.
Using plot type heatmap, I can further split these counts by whether the results were in negative, null, or positive with respect to the hypothesized effect. The plot shows there were no negative results, and the majority of both null and positive effects have majority women in the sample. We can also see that it's uknown what the effect is for a Dance intervention with majority men in the sample.

Plotting numerical variables: Use scatter plot to show InterventionDuration (weeks) on the x-axis and TotalSampleSize on y-axis. Overall, longer studies have larger sample sizes. The plot type histogram can be helpful for seeing the distribution of a numerical variable across studies, such as intervention duration shown here:

Using heatmaps to visualize patterns and gaps: The app is designed to emphasize visualization of the strongest patterns in the literature and gaps in knowledge. The heatmap plot type can be very helpful for this purpose. For example, suppose I want to visualize episodic memory outcomes for aerobic training interventions of different durations. To do this, I would filter variable levels of interest in the table as: Include=Y, IndependentVariable = AerobicTraining, and select neuropsychological tasks testing episodic memory as the allowable DependentVariable. I can then select InterventionDurationCategorical as my Categorical Value X, and DependentVariable as my Categorical Value Y, and split current values by LinkType to get the following plot:

For issues/suggestions, please open an issue on Github here