The health benefits of Snacktivity in currently inactive individuals

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# The health benefits of Snacktivity in currently inactive individuals
## Jonah Thomas
### Presented on 15-06-2021
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###### Last updated on 2021-06-15
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# Who Am I?
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### Background
* First came to loughborough in 2015 to study Sport, Exercise and Health Science BSc

* MSc in Exercise as Medicine

* Research assistant in Digital Health Technologies

* Second year PhD student within Snacktivity 
]

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### Research interests: 
- Measurement of physical activity and sedentary behaviour

- Digital Health Technologies

- All things data science
]
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# Contents

- Rationale for Snacktivity

- Study plan

- Accelerometer-measured physical activity datasets: A cross-country scoping review

- The effects of activity snacks on cardiometabolic health outcomes: a harmonised compositional isotemporal substitution                data-analysis
  
  - The acute effect of activity snacks on glucose control in currently inactive individuals: a wild experiment and comparison to         background activity
  
  - The longer term affects of Snacktivity on cardiometabolic health outcomes
  
- An open offer...

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# Rationale

- The evidence suggests **the volume of physical activity** is more important than how this activity was accumulated when considering   health outcomes

- This led to changes in the four widest reaching physical activity guidelines allowing individuals to achieve **150 minutes per week   of MVPA in bouts of any duration**

- Therefore, for those that are not currently meeting the physical activity guidelines, short bouts could be promoted as a means to     increase the amount of MVPA they perform each week

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# Some activity snacks?

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![dog stairs gif](dog_stairs.gif)
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![dog squating](squating.gif)
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# Study 0: Accelerometer-measured physical activity datasets: A cross-country scoping review

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# Rationale

* Accelerometers have been used frequently to quantify movement behaviours over the last 2 decades

* This has led to a large number of datasets collecting a range of health outcomes and physical activity metrics

* However, there is currently no central library that has cataloged the available datasets

* This is in spite of a highly successfully harmonised dataset of similar data in children <a href="https://www.mrc-epid.cam.ac.uk/research/studies/icad/"> (ICAD) </a> and several notable harmonised data analyses: <a href="https://www.repository.cam.ac.uk/bitstream/handle/1810/255142/Ekelund_et_al-2016-The_Lancet-AM.pdf?sequence=1"> Ulf Ekelund </a>and more recent <a href="https://bjsm.bmj.com/content/bjsports/early/2021/05/09/bjsports-2020-102345.full.pdf">Sebastien Chastin </a>

* Therefore, we will perform a scoping review to identify datasets that could be harmonised (both for our harmonised data analysis)     and to serve as a library for other researchers.

* It will also update the review conducted by <a href=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4731236/pdf/mss-47-2129.pdf >     Katrien Wjindaele </a> published in 2015

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# Aims/Objectives:

* To inform the harmonised data analysis by highlighting the studies that have collected data as well as inform the ease of harmonisation of these studies

* To create a library of datasets to serve as a resource to other academics who may be interested in performing secondary analysis or   data harmonisation

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# Search strategy

* Peer-reviewed  online  literature  databases: Medline and Medline in-progress, EMBASE, CINAHL, Sport Discus and Central

* Grey literature sources: Trials Registries, Open Grey and Conference Proceedings Citation Index-Science (CPCI-S)

* Experts in the field will also be contacted to contribute “emerging” datasets

* Forward  and  backward  searching

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# Selection criteria

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# The first screening

## Full-text: 2284

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# Selection criteria (updated)

<table class="table table-hover" style="margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;"> Inclusion </th>
   <th style="text-align:left;"> Exclusion </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Observational or trials based study design </td>
   <td style="text-align:left;"> Non-human population </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Adult population </td>
   <td style="text-align:left;"> Only measure sleep </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Measure physical activity using an 
  accelerometric device </td>
   <td style="text-align:left;">  </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Published in any language </td>
   <td style="text-align:left;">  </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;color: #2324320 !important;background-color: #009BC9 !important;"> Sample size greater than 400 </td>
   <td style="text-align:left;font-weight: bold;color: #2324320 !important;background-color: #009BC9 !important;">  </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;color: #2324320 !important;background-color: #009BC9 !important;"> Measured at least one cardiometabolic health
  outcome </td>
   <td style="text-align:left;font-weight: bold;color: #2324320 !important;background-color: #009BC9 !important;">  </td>
  </tr>
</tbody>
</table>

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# The second screening

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# (Draft) Results

![](climb_15_06_2021_files/figure-html/datasets-paper-1.png)

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# Demographics

<table class="table table-hover" style="margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:right;"> Average number of participants </th>
   <th style="text-align:right;"> Total number of participants </th>
   <th style="text-align:right;"> Average age of participants </th>
   <th style="text-align:right;"> Percent male </th>
   <th style="text-align:right;"> Percent female </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:right;"> 3555 </td>
   <td style="text-align:right;"> 860289 </td>
   <td style="text-align:right;"> 56 </td>
   <td style="text-align:right;"> 46 </td>
   <td style="text-align:right;"> 55 </td>
  </tr>
</tbody>
</table>

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# Populations

![](climb_15_06_2021_files/figure-html/populations-1.png)

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# Papers by Year

![](climb_15_06_2021_files/figure-html/papers-year-1.png)

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# Datasets Identified
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# Study 1: The effects of activity snacks on cardiometabolic health outcomes: a harmonised compositional isotemporal substitution data-analysis

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# Rationale

* Current evidence from epidemiological studies show the **benefits of short bouts of MVPA** on cardiometabolic health outcomes

* Many of these studies use **homogeneous datasets**, and often the same dataset (NHANES 2003-2006)
  * Due to cultural differences, some populations may be performing a greater number of activity snacks 
  * The health benefits of activity snacks may differ based on demographic characteristics that are rarely examined (ethnicity,           physical activity profile)

* Therefore, we will perform a **harmonised data analysis** on a diverse pool of studies and will perform compositional isotemporal substitution analysis to assess the health benefit of **substituting sedentary time and bouted MVPA with activity snacks**

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# Aims/Objectives:

* To determine whether substituting sedentary time with activity snacks results in an improvement in cardiometabolic health outcomes

* To determine whether substituting bouted MVPA with activity snacks results in any changes in cardiometabolic health outcomes
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![](sitting.gif)
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![](walking.gif)
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# What is compositional isotemporal substitution analysis (cISM)

* Examining multivariate data that forms part of a finite whole

* Multiple variables that when combined will reach a known whole 
  
  * sleep + sedentary time + LPA + MVPA = 24 hours
* By treating time-use as part of a whole, we can model the expected effect of replacing one behaviour with another 
  * We plan to model substituting sedentary time and bouted MVPA with activity snacks

* For more information, <a href="https://journals.sagepub.com/doi/pdf/10.1177/0962280217737805">Dumuid et al </a> provide an excellent summary of the origins and current directions of cISM analysis.

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# Next steps?

* Finish data extraction
  * Should be finalised in the next few weeks

* Begin write up and produce first draft

* Prioritise which datasets to seek access to for the harmonised data analysis

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# Study 2 and 3: Interventions exploring activity snacks

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# Study 2: The acute benefits of activity snacks on cardiometabolic health outcomes

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# Rationale

* Several studies have examined the acute benefit of breaking up prolonged sedentary time with short bouts of physical activity on **glucose control** 
  * MVPA as short as 32 seconds can produce a **beneficial acute response**
  
* However, even the most sedentary individuals (unless infirm) do not sit continually for 8-12 hours a day

* Whilst the laboratory setting is beneficial for **highly controlled conditions**, it is not representative of how an individual goes about their normal life

* Therefore, we will examine the acute benefit of activity snacks on glucose control during individual's usual daily routine

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# Purposeful vs incidental activity snacks

Taken from <a href="https://link.springer.com/content/pdf/10.1007/s40279-020-01368-8.pdf"> Stamatakis et al </a>

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# Aims/Objectives

* To determine whether the Snacktivity intervention leads to individuals performing a **greater number of activity snacks** when compared to their **usual routine**  
*	To assess whether performing **purposeful activity snacks produce an additional health benefit** when compared to an individual’s usual routine 
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*	To determine whether purposeful activity snacks produce a different acute cardiometabolic response to activity snacks performed as part of an individual’s daily routine

*	To assess participants perception of non-purposeful activity snacks and determine whether they are the similar to the perception they have of purposeful activity snacks

*	To assess whether movement differences exist between purposeful vs non-purposeful snacks and could these be utilised to refine the definition of an activity snack

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# Study 3: The longer term impact of activity snacks on cardiometabolic health outcomes

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# Rationale

* Previous cross-sectional studies highlight that performing bouts of **MVPA less than 10 minutes in duration** are associated with **improved cardiometabolic health outcomes** including BMI, HDL and LDL cholesterol and blood glucose.

* However, few studies have looked to assess whether if increasing inactive individuals physical activity by utilising short bouts of MVPA will lead to longer term improvements in cardiometabolic health

* The main Snacktivity trial is not collecting a comprehensive battery of health outcomes

* Therefore, we will look to determine whether the **Snacktivity paradigm** increases the number of activity snacks an individual performs and whether this increase is associated **improvements in cardiometabolic health**

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<img src="long_intervention_plan.png" width="300px" height="625px" />

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# Aims/Objectives

* To determine if the Snacktivity intervention leads to individuals performing a greater number of activity snacks

* To determine whether performing a greater number of activity snacks leads to changes in markers of **cardiometabolic health**

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# What next?

* Finish data extraction and begin write-up of the scoping review

* Begin acquiring datasets for the harmonised data analysis

* Finalise protocols for the intervention studies and begin submitting ethics for both intervention studies

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# An open offer...

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# I might be able to help ... 
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### I am experienced with: 
* R 
* SQL
* Federated data analysis systems 
* Python (a little...hopefully more soon)
]
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### I am interested in:
* Data modeling
* Software development
* Data visualisation
* Anything a bit out there, different or challenging
]

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# Data Analysis and Visualisation

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# Shiny Apps

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# Shiny Apps

<img src="snackapp_sankey.png" width="1920" style="display: block; margin: auto;" />
For more examples visit <a href="https://shiny.rstudio.com/gallery/"> the Shiny Apps Gallery </a>

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# FormR
* A survey framework that allows the creation and management of highly complex survey designs 
  * Entirely open-source (Free!) and coded in R meaning anything you can do in R (which in my opinion is endless!) can be used in a survey
  
<img src="formr_screenshot.png" width="600px" height="325px" style="display: block; margin: auto;" />

* More information can be found on the <a href=https://formr.org/> FormR Website </a>
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# Automatically Generated Reports

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# Automatically Generated Reports

<img src="report_output.png" width="400px" height="400px" style="display: block; margin: auto;" />
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# Want to see more?

* Find my blog at <a href="https://jonahthomas.netlify.app/"> https://jonahthomas.netlify.app/</a> or feel free to drop me an email at <a href="mailto:j.j.c.thomas@lboro.ac.uk"> j.j.c.thomas@lboro.ac.uk</a>

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# Any Questions?

Full slide deck is available on <a href="https://jonahthomas.netlify.app/"> my blog</a>

![](friends_questions.gif)