Background
Update
Outcome and mediation measurements
Variable | Type | Units/categories | Comments |
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Primary outcomes
| |||
Accelerometer assessed mean time per day spent doing moderate/vigorous physical activity (MVPA) | Continuous | Minutes | |
Accelerometer assessed mean time per day spent in sedentary activity | Continuous | Minutes | |
Self-reported (validated questionnaire) consumption of servings of fruit and vegetables | Count | Servings | Will be treated as a continuous variable |
Secondary outcomes
| |||
Self-reported (validated questionnaire) mean time spent screen-viewing on a week day | Continuous | Minutes | |
Self-reported (validated questionnaire) mean time spent screen-viewing on a Saturday | Continuous | Minutes | |
Self-reported (validated questionnaire) consumption of servings of snacks | Count | Servings | Will be treated as a continuous variable |
Self-reported (validated questionnaire) consumption of servings of high fat food | Count | Servings | Will be treated as a continuous variable |
Self-reported (validated questionnaire) consumption of servings of high energy drinks | Count | Servings | Will be treated as a continuous variable |
Body mass index (BMI) | Continuous | z(SD)-score | Age and gender standardised |
Waist circumference (WC) | Continuous | z(SD)-score | Age and gender standardised |
General overweight/obesity | Binary | No | Derived from BMI using IOTF thresholds |
Yes | |||
Central overweight/obesity | Binary | No | Derived from WC using IDF criteria |
Yes | |||
Potential mediators to be explored in secondary analyses
| |||
Self-reported (validated questionnaire) physical activity self-efficacy | Score in whole numbers | Range 26-130 | Will be treated as a continuous variable |
Self-reported (validated questionnaire) fruit and veg consumption self-efficacy | Score in whole numbers | Range 21-105 | Will be treated as continuous variable |
Child-reported (validated questionnaire) perceived maternal logistic support for physical activity | Score in whole numbers | Range 3-12 | Will be treated as continuous variable |
Child-reported (validated questionnaire) perceived paternal logistic support for physical activity | Score in whole numbers | Range 3-12 | Will be treated as continuous variable |
Child-reported (validated questionnaire) perceived maternal modelling of physical activity | Score in whole numbers | Range 5-20 | Will be treated as a continuous variable |
Child-reported (validated questionnaire) perceived paternal modelling of physical activity | Score in whole numbers | Range 5-20 | Will be treated as a continuous variable |
Child-reported (validated questionnaire) perceived maternal limitation of sedentary behaviour* | Score in whole numbers | Range 4-16 | Will be treated as a continuous variable |
Child-reported (validated questionnaire) perceived paternal limitation of sedentary behaviour* | Score in whole numbers | Range 4-16 | Will be treated as a continuous variable |
Child-reported (validated questionnaire) perceived parental modelling for healthy eating fruit and vegetable consumption$
| Score in whole numbers | Range 12-48 | Will be treated as a continuous variable |
Child’s knowledge test related to intervention | Score in whole numbers | Range 0-9 | Will be treated as a continuous variable |
Detailed analysis plan
Quality checking, cleaning data and deriving variables
Accelerometer data
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The epoch (time) length of recorded bouts of data
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What period of records of consecutive zero movement/counts are taken to indicate a participant has removed the accelerometer (these periods are removed from the calculation of hours wear per day)
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Number of hours per day that are considered to provide valid wake-time wear for derivation of outcomes
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Number of days that the accelerometer should be worn to provide valid total wear time for derivation of outcomes
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The thresholds of counts per minute of activity that are used to define MVPA and sedentary behaviour
Criteria that will be used in AFLY5 for deriving accelerometer outcome measurements
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Data collected in 10-s epochs
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A period of ≥60 min of consecutive 0 counts assumed to be non-wear and these periods removed from the measurement of wear-time
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≥ 8 h per day to be considered to have valid wear-time for a given day
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≥ 3 valid days in total
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MVPA defined as ≥2,296 counts per minute
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Sedentary behaviour defined as 0 to 100 counts per minute
Cleaning/QC of the accelerometer derived variables will include
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Normal plots, histograms and scatter plots will be used to identify potentially implausible measurements.
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Scatter plots will compare for each variable its baseline and follow-up value and also will compare different variables measured at the same time point that would be expected to be moderately to strongly correlated; time spent in MVPA to time spent in sedentary behaviour, time spent in MVPA to weight and time spent in sedentary behaviour to weight.
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Values that appear outside of the main distribution in the majority of participants (i.e. outliers) on normal plots and histograms will be assumed to be correct if the scatter plots show consistency – e.g. lying close to the main ‘line’ of positive association for the same variable measured at baseline and again at follow-up or the inverse association of time in MVPA with time in sedentary behaviour and close to the main ‘line’ of inverse association of time spent in MVPA with weight.
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Outliers that deviate from the ‘line’ of the scatter plots by 2 SD or more on either axis will be considered implausible.
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For implausible values, the original data will be checked in the accelerometer software to make sure criteria have been applied correctly and any errors will be corrected.
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For remaining implausible values a variable that indicates ‘possible implausible value of X’ (where X is the name of the variable that has a possible implausible value) will be derived.
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In the main effectiveness analyses we will complete analyses with all participants (including where they have a possible implausible value) included and again with participants excluded for analyses with a given outcome if their value for that outcome has been marked as possibly implausible.
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If removal of participants with possible implausible values results in a change of a magnitude that for that outcome would affect the interpretation/conclusion for that outcome then both sets of results will be reported; otherwise only the results with all included irrespective of ‘implausible value’ status will be reported.
Diet data
Initial cleaning
Coding of diet data
Final cleaning
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Exploring the distribution of the diet score (number of servings) for each of the four types of food used as an outcome in our study (fruit and vegetables, snacks, high fat foods and high energy drinks) using bar charts.
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Checking implausibly high values (for any of these scores it is possible for a child to eat no portions on a day, whereas very high values are more likely to be implausible).
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A priori we consider implausibly high values > 8 portions/day for any single outcome.
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Any possible implausible values will be checked by going back to the original questionnaire responses and coding for that questionnaire, with corrections made as appropriate.
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For remaining implausible values a variable that indicates ‘possible implausible value of X’ (where X is the name of the variable that has a possible implausible value) will be derived.
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In the main effectiveness analyses we will complete analyses with all participants (including where they have a possible implausible value) included and again with participants excluded for analyses with a given outcome if their value for that outcome has been marked as possibly implausible.
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If removal of participants with possible implausible values results in a change of a magnitude that for that outcome would affect the interpretation/conclusion for that outcome then both sets of results will be reported; otherwise only the results with all included irrespective of ‘implausible value’ status will be reported.
Screen viewing data
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Normal plots, histograms and scatter plots will be used to identify potentially implausible measurements.
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Scatter plots will compare self-reported time spent screen-viewing at baseline to the same at follow-up and will also compare self-reported time spent screen-viewing at both time points on weekdays to that on Saturdays and also both to time spent in sedentary behaviour based on the accelerometer data.
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Values that appear outside of the main distribution in the majority of participants (i.e. outliers) on normal plots and histograms will be assumed to be correct if the scatter plots show consistency.
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Outliers that deviate from the ‘line’ of the scatter plots by 2 SD or more on either axis will be considered implausible.
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For implausible values, the original data will be checked on the completed questionnaires and any transcription errors corrected.
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For remaining implausible values a variable that indicates ‘possible implausible value of X’ (where X is the name of the variable that has a possible implausible value) will be derived.
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In the main effectiveness analyses we will complete analyses with all participants (including where they have a possible implausible value) included and again with participants excluded for analyses with a given outcome if their value for that outcome has been marked as possibly implausible.
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If removal of participants with possible implausible values results in a change of a magnitude that for self-reported screen viewing would affect the interpretation/conclusion for that outcome then both sets of results will be reported; otherwise only the results with all included irrespective of ‘implausible value’ status will be reported.
Anthropometric data
Cleaning of data
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Normal plots, histograms and scatter plots will be used to identify potentially implausible measurements.
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Scatter plots will compare each measure at baseline to the equivalent measure at follow-up and will also compare weight to height, weight to waist and height to waist at each time point. Values that appear outside of the main distribution in the majority of participants (i.e. outliers) on normal plots and histograms will be assumed to be correct if the scatter plots show consistency.
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Outliers that deviate from the ‘line’ of the scatter plots by 2 SD or more on either axis will be considered implausible.
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The original data collection sheets for these values will be checked and if these show that the data have been incorrectly entered in the database this will be corrected.
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For remaining implausible values a variable that indicates ‘possible implausible value of X’ (where X is the name of the variable that has a possible implausible value) will be derived.
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In the main effectiveness analyses we will complete analyses with all participants (including where they have a possible implausible value) included and again with participants excluded for analyses with a given outcome if their value for that outcome has been marked as possibly implausible.
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If removal of participants with possible implausible values results in a change of a magnitude that for that outcome would affect the interpretation/conclusion for that outcome then both sets of results will be reported; otherwise only the results with all included irrespective of ‘implausible value’ status will be reported.
Derivation of variables
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International Obesity Task Force (IOTF) age- (in 6 months) and gender-specific thresholds for overweight and obesity derived from BMI in children (general overweight/obesity)[10].
Self-efficacy variables
Initial cleaning – dealing with missing data and deriving score
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Any participant missing three or more items will be identified with a variable (derived variable indicating ‘high level of missing data for X’, where X is the specific self-efficacy measure affected)
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For all participants (irrespective of how many items are missing) a final score that takes account of missing data will be generated as follows:WhereIo = all observed itemsNo = number of observed itemsNm = number of missing itemsi.e. the score is the total sum of all observed scores plus the sum of missing scores with missing scores replaced with the mean of observed scores. So for example for a child who has completed 22 items out of the 26 for physical activity efficacy and has a sum of these 22 completed items of 78 the final score will be 78 + (4 × (78 ÷ 22)) = 81.5
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In the secondary analyses when we are exploring the role of these self-efficacy variables as mediators we will complete analyses with all participants [including where they have a ‘high’ level of missing (defined as above – missing three or more items) for the self-efficacy variable being considered] included and again with participants excluded for a given analysis if they have a high level of missing for the self-efficacy variable.
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If removal of participants with ‘high levels of item missing data’ for a given self-efficacy variable results in a change of a magnitude that would affect the interpretation/conclusion for that mediator (or for its effect on an outcome) then both sets of results will be reported; otherwise only the results with all included irrespective of ‘high levels of item missing data’ status will be reported.
Final cleaning
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Normal plots, histograms and scatter plots will be used to identify potentially implausible measurements.
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Scatter plots will compare self-efficacy variables at baseline and follow-up and will also compare the following within each time point; physical activity and fruit and vegetable self-efficacy (which we would expect to be positively associated), physical activity self-efficacy with accelerometer-assessed time spent in MVPA and fruit and vegetable self-efficacy with total portions of fruit and vegetables consumed.
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Values that appear outside of the main distribution in the majority of participants (i.e. outliers) on normal plots and histograms will be assumed to be correct if the scatter plots show consistency.
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Outliers that clearly deviate from the ‘line’ of scatter plots will be considered implausible.
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The original data collection sheets for these values will be checked and if these show that the data have been incorrectly entered in the database this will be corrected.
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For remaining implausible values a variable that suggests ‘possible implausible value’ will be derived.
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In the main effectiveness analyses we will complete analyses with all participants (including where they have a possible implausible value) included and again with participants excluded for a given analysis if they have an implausible indicator for a particular outcome.
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As with the high levels of item missing data analyses we will compare the effect of the intervention on each self-efficacy mediator variable with and without those with ‘possible implausible values’ removed. If removal changes the size of the effect by an amount that would change the interpretation/conclusion of the results analyses with and without these participants removed will be presented; otherwise only those with the participants included.
Parental support variables
Initial cleaning – dealing with missing data and deriving score
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Generate a mean score for each child irrespective of the number of items completed (e.g. if a child has completed all 3 of the logistic modelling items their score will be the sum of each score divided by 3; if they have completed only 2 their score will be the sum of the 2 divided by 2)
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Generate a variable that indicates some missing data for any of the scores (i.e. whether the child has 1 or more items missing for any of the physical activity/sedentary parental support scores they will be indicated as having some missing).
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In the main mediator analyses all participants will be included for any given physical activity/sedentary behaviour parental support score irrespective of whether they had some missing data or not. The analyses will then be repeated with those who had some missing data excluded from analyses with that particular score.
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If removal of participants with ‘high levels of item missing data’ for a given parental support variable results in a change of a magnitude that would affect the interpretation/conclusion for that mediator (or for its effect on an outcome) then both sets of results will be reported; otherwise only the results with all included irrespective of ‘high levels of item missing data’ status will be reported.
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For the parental modelling of fruit and vegetable consumption the number of items and range of potential scores is relatively large and we will approach item missing data in this variable in the same way as that for the self-efficacy variables described above, i.e.:
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Any participant missing three or more items for the parental modelling of fruit and vegetable score will be identified with a variable (derived variable indicating ‘high’ level of missing data).
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For all participants (irrespective of how many items are missing) a final score that takes account of missing data will be generated as follows:
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In the main mediator analyses we will complete analyses with all participants (including where they have a ‘high’ level of missing for the parental modelling of fruit and vegetable variable) included and again with participants excluded if they have high levels of missing for this variable.
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If removal of participants with ‘high levels of item missing data’ for the parental modelling of fruit and vegetables variable results in a change of a magnitude that would affect the interpretation/conclusion for this mediator (or for its effect on an outcome) then both sets of results will be reported; otherwise only the results with all included irrespective of ‘high levels of item missing data’ status will be reported.
Final cleaning of all parental support scores
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Means, median, SD, IQR and full range,together with normal plots and histograms will be examined. As with the physical activity/sedentary behaviour scores any values outside the possible range must be an error in summing/generating the final score and will therefore be checked by looking at the Stata code used for doing this.
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To check for implausible values within the range expected relationships between variables will be checked by looking at scatter plots between each of the variables when measured at baseline and at follow-up and also between variables at the same time point as follows: physical activity/sedentary behaviour parental support and the fruit and vegetable support scores, using scatter plots. Where these suggest unlikely values for any participant (deviation from the scatter predicted line of association of more than 2 SD on either axes) the data entered values for each item will be compared against the original questionnaires and any entry errors corrected.
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For remaining unlikely values after these checks a variable that indicates ‘possible implausible value’ will be derived.
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In the main effectiveness analyses we will complete analyses with all participants (including where they have a possible implausible value) included and again with participants excluded for a given analysis if they have an implausible indicator for a particular outcome.
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As with the high levels of item missing data analyses we will compare the effect of the intervention on each mediator variable with and without those with ‘possible implausible values’ removed. If removal changes the size of the effect by an amount that would importantly influence the interpretation or conclusion of results both sets of results will be presented; otherwise only the ones with no exclusion.
Child’s knowledge
Effectiveness and mediation analyses
Objective | Main methods | Analysts and timing* | Journal submit* | |
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Determine the effect of the AFLY5 intervention on primary outcomes | ITT analysis | DAL | Feb –March 2013 | Aug 2013 |
Multivariable multi-level linear regression (continuously measured outcomes), with adjustment for baseline variables | LH | |||
RRK | ||||
TJP | ||||
Determine the effect of the AFLY5 intervention on secondary outcomes | ITT analysis | DAL | Feb – March 2013 | Aug 2013 |
Multivariable multi-level linear regression (continuously measured outcomes) and multivariable multi-level logistic regression (for the two binary – general and central overweight/obese – outcomes), with adjustment for baseline variables | LH | |||
RRK | ||||
TJP | ||||
Complete secondary analyses to determine effect in those who completed the intervention as intended | Per-protocol analysis, excluding children from the intervention schools in which fewer than 11 lessons were taught. Multivariable multi-level linear or logistic regression (as above), with adjustment for baseline variables | DAL | Mar – Apr 2013 | Aug 2013 |
LH | ||||
TJP | ||||
Sensitivity analyses to determine whether any effect of the intervention on primary outcomes based on accelerometer data vary by weekend or weekday | ITT analysis | DAL | Mar – Apr 2013 | Aug 2013 |
Multivariable multi-level linear regression (continuously measured outcomes), with adjustment for baseline variables | LH | |||
TJP | ||||
Additional secondary analyses
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Complete secondary analyses explore whether associations differ by gender and area deprivation | Stratified analyses (by gender and separately by school area deprivation) | DAL | Mar – Apr 2013 | Not for journal but will be reported to funder (see below) |
LH | ||||
ITT analysis | TJP | |||
Multivariable multi-level linear or logistic regression (as above), with adjustment for baseline variables. | ||||
Test of interaction between gender × intervention and deprivation × intervention |
Comparison of baseline characteristics and extent of missing follow-up data between intervention and control groups
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Continuous variables that we anticipate will have approximately normal distributions (likely to include age, accelerometer time spent in MVPA, time spent in sedentary behaviour, BMI z-scores, WC z-scores) will be presented as means and standard deviations (SD).
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Continuous variables/scores that we anticipate will not have an approximate normal distribution (likely to include self-reported time spent screen viewing, self-efficacy scores for both physical activity and fruit and vegetables, parental modelling scores for fruit and vegetables) will be presented as medians and interquartile ranges (IQR).
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Binary/categorical variables (general overweight/obesity, central overweight/obesity, school involvement in other health promoting activities and school area deprivation) will be presented as number (N) and percentage (%).
Dealing with missing data
Missing baseline data
Intention-to-treat analyses and dealing with missing follow-up outcome data
Dealing with missing data | Assumptions | Implications/rationale | |
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Main
a
| All participants will be included if they have the particular outcome being assessed measured at the follow-up. | Data are MAR | The number included in these main analyses will differ for each outcome e.g. based on comments above regarding likely levels of missing data for each specific outcome measure it is possible that fewer participants will contribute to accelerometer outcomes than questionnaire outcomes |
An indicator variable (indicating whether baseline data are missing or not for each outcome) together with allocation of a ‘temporary’ value to those with baseline missing data, will be used to deal with missing baseline data[22] | |||
S1 | Similar to above but participants are only included for each measurement if they have both baseline and follow-up data observed for each outcome | As above | Numbers will differ for each outcome. |
Allows assessment of whether those with missing baseline data differ in terms of the trial effect compared with those who do not have missing baseline data | |||
S2 | Similar to above but participants are only included if they have both baseline and follow-up data of all three primary outcomes | As above | For the three primary outcomes numbers will be the same numbers may differ for each secondary outcome. |
Allows assessment of whether any apparent differences in effect for the three primary outcomes are due to differs between these outcomes in missing data mechanisms | |||
S3 | Similar to the main analyses but for any child with a missing follow-up measure the child is allocated a value that is 10% ‘healthier’ for a given outcome than all participants with observed data (irrespective of randomised group). This will be done by calculating the 10% value of the mean or median follow-up measure for each outcome and then adding or subtracting (depending on whether healthier levels are higher or lower for the particular outcome) this value to the outcome mean or median; this final value will then be imputed to the outcome value for every child with missing follow-up data. | Those with missing outcome data on average behave in a relatively healthy way. | Numbers will be the same for all outcomes. |
Allows assessment of the possibility that missing data may be more likely to occur in families from higher SEP who may have missing data because of moving from state to private education. And to assess whether this form of missing data biases our assessment of the trial effect. | |||
This will also test whether selection bias occurs as a result of limiting analyses only to those with the required wear-time for the accelerometer based outcomes (this outcome is likely to have more missing data than other outcomes). As these analyses include all recruited participants. | |||
S4 | Similar to the main analyses but for any child with a missing follow-up measure the child is allocated a value that is 10% ‘less healthy’ for a given outcome than all participants with observed data (irrespective of randomised group). This will be done by calculating the 10% value of the mean or median follow-up measure for each outcome and then adding or subtracting (depending on whether less healthy levels are higher or lower for the particular outcome) this value to the outcome mean or median; this final value will then be imputed to the outcome value for every child with missing follow-up data | Those with missing data on average behave in less healthy ways than those who do not have missing data through mechanisms that are not captured by observed data | Numbers will be the same for all outcomes. |
Allows assessment of the possibility that missing data may be more likely to occur in families from lower SEP and who may have missing data because of being more dysfunctional and perhaps having to care for a relative at home or having higher rates of truancy. And to assess whether this form of missing data biases our assessment of the trial effect. | |||
This will also test whether selection bias occurs as a result of limiting analyses only to those with the required wear-time for the accelerometer-based outcomes (this outcome is likely to have more missing data than other outcomes). As these analyses include all recruited participants |
Effect analyses
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Use normal plots and histograms to assess normality of the follow-up measure of the outcome. If variables are approximately normally distributed they will be used as they are (i.e. with no transformation). If they are clearly non-normal we will explore transforming them to improve normality of the residuals in the regression models. The choice of whether or not to transform variables, and if so which transformation to use, will be decided by considering: (1) the distribution of the variable, (2) the distribution of residuals from regression models, (3) the ease of interpreting results following any given transformation compared with no transformation and (4) whether main results/conclusions are influenced by the transformation or not. From our pilot and feasibility studies for this trial and considerable experience with the outcome measurements that are used in this trial, we anticipate that all outcomes will be either approximately normally distributed or right (positively) skewed. For right skewed variables that result in markedly non-normal residuals in regression models we would use a natural log transformation and compare results with and without this transformation. If the overall conclusion is not altered by whether the variable is transformed or not, we would use the untransformed (easier to interpret) version. Where variables have been log-transformed, the resulting coefficients will be converted to differences in means on a % scale.
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Use multilevel multivariable linear regression to determine the difference in means between participants from schools allocated to the intervention and those allocated to control (reference group = control schools) whilst taking account of clustering (non-independence) amongst children from the same school.
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The analyses will include adjustment for the following baseline and stratifying covariables: age, gender, the baseline measure of the outcome being analysed (i.e. for the effect of the intervention on time spent in MVPA we will include baseline MVPA in the model and so on), school involvement in other health promoting activities and school area deprivation.
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The model for the main effect of the intervention on the continuously measured outcomes is
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WhereY ijp is the outcome for participant p = 1…………………m, in the j th school j = 1 …………………..60 in intervention group i = 1, 2β0 is the intercept, i.e. the outcome amongst those in intervention schools with the lowest level of all continuously measured covariables, the reference category for all categorical covariables and in school coded 1β1 is the treatment effect (i.e. the mean difference in outcome comparing pupils from intervention schools to those form control schools) having adjusted for baseline characteristics and taken account of non-independence amongst children from the same school
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β2 to β6 are the adjusted associations of the baseline and stratifying covariables X2ijp to X6ijp with the outcome [i.e. age, gender, baseline measure of the outcome (X4ijp ), school involvement in other health promoting activities and school area deprivation]β7 is the association of the indicator variable X7ijp , indicating missing baseline measure of the outcome, with the follow-up outcome.β8 is the interaction coefficient for the interaction of the missing baseline indicator variable with the baseline measure of the outcome (X7ijp *X4ijp )C ij is the school level effect for the school j th school in intervention group iand C ij ~ N(0, σ2 A)Є ijp is the residual of the outcome for participant p from the j th school in intervention group iand Є ijp ~ N(0, σ2 W)and C ij and Є ijp are independent of each other.
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The approach will be broadly similar to that above described for continuously measured outcomes.
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A multilevel multivariable logistic regression model will be used to calculate the odds ratio of binary outcomes children in intervention schools to those in control schools (reference category), whilst taking account of clustering within schools.
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Baseline covariables identical to those listed above will be included.
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Thus, the model for binary outcomes is
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Whereπ ijp is the probability that participant p = 1…………………m, in the j th school j = 1 …………………..60 in intervention group i = 1, 2 is overweight or obeseβ0 is the intercept, i.e. the probability of normal weight amongst those in intervention schools with the lowest level of all continuously measured covariables, the reference category for all categorical covariables and in school coded 1β1 is the treatment effect (i.e. the log odds of each binary outcome comparing pupils from intervention schools to those form control schools) having adjusted for baseline and stratifying covariables (as above) and taken account of non-independence amongst children from the same schoolβ2 to β6 are the adjusted associations of the baseline and stratifying covariables X2ijp to X6ijp with the outcome (ie. age, gender, baseline measure of the outcome, school involvement in other health promoting activities and school area deprivation)β7 is the association of the indicator variable X7ijp , indicating missing baseline measure of the outcome, with the follow-up outcome.β8 is the interaction coefficient for the interaction of the missing baseline indicator variable with the baseline measure of the outcome (X7ijp *X4ijp )C ij is the school level effect for the school j th school in intervention group iЄ ijp is the residual of the outcome for participant p from the j th school in intervention group i
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Secondary per-protocol analyses to determine effect in those who completed the intervention as intended
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Including all children from control schools and only those children from intervention schools in which at least 70% of the lessons had been taught (i.e. at least 11 of the 16 lessons were taught).
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Children from schools that were randomised to the intervention but in which fewer than 11 lessons were taught will be excluded from these analyses.
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Teacher-completed logs will be used to determine how many of the lessons have been taught. Relevant data from these logs for completing the per-protocol analyses have been provided for 28 of the 30 schools. We will continue to try to obtain the other two, but it is possible we will not do so. In which case, we will undertake two secondary per-protocol analyses: one in which those who fail to return their logs are excluded (in effect treated as if they have taught fewer than 11 lessons) and one in which they are included (equivalent to assuming they have taught 11 or more of the lessons).
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Once children from schools that completed fewer than 70% of the lessons have been excluded the per-protocol analysis will be identical to the main analyses assessing the effectiveness of the intervention on the primary and secondary outcomes as described above, except that sensitivity analyses related to assumptions about missing data will not be completed, i.e. these secondary per-protocol analyses will only be conducted using the main analyses approach described above and in Table 3. Additional file1: Table S7 illustrates how these results will be presented.
Sensitivity analyses to see if any effects on accelerometer assessed time spent in MVPA or sedentary behaviour vary by weekend or weekday
Additional sensitivity analyses to explore whether there is any evidence that the intervention effect differs by gender and area deprivation
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Repeating all of the main effectiveness analyses with primary and secondary outcomes as described above (main analysis only, see Table 3 above) separately in females and males, presenting the point estimates and their 95% CI in each subgroup.
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Undertaking an analysis that includes all participants (irrespective of gender) and includes an interaction term between gender and randomised group for each outcome. Presenting the interaction coefficient with its 95% confidence interval, as an indication of the precision with which this interaction can be detected in this trial and also presenting the p-value for the interaction effect.
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Repeating all of the main effectiveness analyses with primary and secondary outcomes as described above (main analysis only, see Table 3 above) separately in thirds (low, mid, high) of the school area deprivation score, presenting the point estimates and their 95%CI in each subgroup.
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Undertaking an analysis that includes all participants (irrespective of school area deprivation) and includes an interaction term between school area deprivation and randomised group for each outcome. Presenting the interaction coefficient with its 95% confidence interval, as an indication of the precision with which this interaction can be detected in this trial and also presenting the p-value for the interaction effect.
Mediation analyses: to examine the extent to which any immediate effect of the intervention is mediated by measurements of the pathways through which AFLY5 is theorised to work
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First, determine the effect of the intervention on each of the ten measured mediators (see Table 1 above).
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Each of these mediators will be treated as a continuously measured variable and in the first stage we will explore the differences in mean scores of each mediator comparing children in the intervention to those in the control schools.
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Distributions of the mediators will be explored and procedures for transforming any that are non-normal will be the same as those used for the continuously measured primary and secondary outcomes, as described in Section 2.2 above.
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Multilevel multivariable linear regression will be used to examine the effect of the intervention on each mediator using the same approach as that used for the continuously measured primary and secondary outcomes, as described in Section 2.2 above.
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In these analyses we will include the following baseline and stratified covariables: age, gender, the baseline measure of each mediator, whether the school is involved in other health promoting activities and school level deprivation. Note: knowledge was not assessed at baseline so there is no baseline measure of this.
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Only the main approach for dealing with missing data (see Table 3 above) for any one of the mediators will be undertaken.
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As discussed in the section on cleaning these variables we will conduct analyses with and without those who have high levels (see above for definition) of item missingness within scales. Thus, for these analyses we will do one main analysis and one sensitivity analysis (with those who have high levels of item missing data for any mediator removed).
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In these analyses we will correct for multiple testing by adding the ten mediators to the nine secondary outcomes and assuming (two-sided) statistically significant effects with p ≤ 0.003 (0.05 ÷ 19). In journals we will present p-values multiplied by 19 to help interpretation.
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Second, we will explore whether mediators explain the effect of the intervention on outcomes. This second stage will only be conducted if: (1) the intervention has been shown to effect one or more of the primary outcomes (Section 2.2 above) and (2) the intervention has been shown to effect one or more of the mediators relevant to a primary outcome that the intervention has affected (first stage of mediation analyses described above).
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If the two criteria above are fulfilled we will complete multilevel multivariable linear regression exactly as described above in Section 2.2 for the specific outcome fulfilling these criteria. We will then repeat that analysis with any relevant mediator added and compare the effect of the intervention on the outcome before and after adjustment for the mediator.
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Each mediator that has been shown to be affected by the intervention, and that is relevant to an outcome that has also been affected by the intervention, will be added as a single covariable. In addition relevant mediators will then be added simultaneously in one final mediation model. For example, if the intervention is shown to increase time spent in MVPA, to increase knowledge relevant to the aims of the intervention and to increase child self-efficacy for physical activity (but has no impact on parental support for physical activity), we would complete the multilevel linear regression model with time spent in MVPA exactly as describe in Section 2.2 above. We would then repeat that analysis adding the following additional covariables: (1) knowledge score; (2) self-efficacy for physical activity score; (3) both knowledge and self-efficacy for physical activity score.
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A relative reduction (change towards the null) of the initial effect of the intervention on outcome (i.e. before addition of any mediators as covariables) of 10% or more will be considered to indicate some evidence of mediation.
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The% relative reduction in the initial trial effect on any of the primary outcomes will be recorded (see Additional file1: Table S11) in all cases that fulfil the criteria for these analyses.
Effectiveness analyses 2: the effect of the AFLY5 intervention on long-term outcomes (assessed ~12 months after the end of the intervention)
Objective | Main methods | Analysts and timing* | Journal submit | |
---|---|---|---|---|
Determine the effect of the AFLY5 intervention on primary outcomes assessed 12 months after the end of the intervention | ITT analysis | DAL LH TJP | Dec 2013 – Feb 2014 | May 2014 |
Multivariable multi-level linear regression (continuously measured outcomes), with adjustment for baseline variables. | ||||
Determine the effect of the AFLY5 intervention on secondary outcomes assessed 12 months after the end of the intervention | ITT analysis | DAL | Dec 2013 – Feb 2014 | May 2014 |
Multivariable multi-level linear (continuously measured outcomes) or logistic (binary) regression, with adjustment for baseline variables. | LH | |||
TJP | ||||
Determine the effect of the AFLY5 intervention on change in primary outcomes between the baseline and the longer-term follow-up, including examining whether change in outcome between baseline and immediate follow-up differs from change in outcome between immediate and long-term follow-up. | ITT analysis | DAL | Dec 2013 – Feb 2014 | May 2014 |
Multivariable multi-level repeat measures linear regression, with adjustment for baseline variables. | LH | |||
TJP | ||||
Determine the effect of the AFLY5 intervention on change in secondary outcomes between the baseline and the longer-term follow-up, including examining whether change in outcome between baseline and immediate follow-up differs from change in outcome between immediate and long-term follow-up | ITT analysis | DAL | Dec 2013 –Feb 2014 | May 2014 |
Multivariable multi-level repeat measures linear regression (continuously measured outcomes) and multivariable multi-level logistic regression (binary outcomes), with adjustment for baseline variables | LH TJP |
Analyses of difference in means and odds of outcomes at long-term follow-up between randomised groups
Analyses of difference in change in outcomes between baseline and long-term follow-up between randomised groups
Economic evaluation analyses
Primary analysis
Resource | How it will be measured | How it will be valued |
---|---|---|
Global organisation of training
| ||
CH staff time organising training, including organising training materials and briefing the trainers1
| TS: number of hours | Salary scales |
CH staff attendance at training day1
| TS: number of hours | Salary scales |
Trainers fee1
| Fee per session | Fee as given |
Venue cost1
| Cost per hour | University finance |
Trainers subsistence cost1
| From expense sheets | Cost as given |
Refreshments1
| From invoices | Cost as given |
School-specific organisation of training
| ||
CH staff time organising training2
| TS: number of hours | Salary scales |
CH staff time on phone calls2
| TS: number of phone calls* average length of phone call (in min) | Salary scales |
School staff time on phone calls2
| TS: number of phone calls* average length of phone call (in min) | Salary scales |
Phone calls2
| TS: number of phone calls | BT |
Teachers time attending training day2
| Cost of supply teachers | Cost given by schools |
Travel costs2
| TDES: Car: mileage | University reimbursement |
Bus/train/taxi: fare | ||
Child care costs2
| TDES | Cost given by teachers |
Informal costs: | TDES: difference between normal travel time to work and travel to training day | Average wage rate from labour force survey |
Extra time spent travelling to training day2
| ||
Global delivery of intervention
| ||
Time spent producing teaching and homework materials1
| TS: number of hours | Salary scales |
Cost of consumables1
| TS | Cost as given |
CH staff time in meetings in relation to delivering the intervention1
| TS: number of hours | Salary scales |
School-specific delivery of intervention
| ||
Time spent delivering materials to schools2
| TS: number of hours | Salary scales |
Travel costs of delivering materials to schools2
| Travel claim forms | University reimbursement |
CH staff time corresponding with schools in relation to delivery of the intervention2
| TS: number of hours | Salary scales |
Phone calls2
| TS: number of phone calls | BT |
School staff time on phone calls2
| TS: number of phone calls* average length of phone call (in min) | Salary scales |
Teachers’ time in preparation of AFL5 lessons2
| TL: number of minutes | Salary scales |
The opportunity cost of teaching the AFL5 lessons2
| TL: the AFL5 lesson time in min. Who taught the AFL5 lesson? The lesson it displaced. Who would have taught the displaced lesson? | The AFL5 lesson time (min)*pro rata salary scale of teacher delivering session minus the AFL5 lesson time (min)*pro rata salary scale of teacher who would have taught displaced lesson |
Consumables used 2
| TL | Cost as given |
Secondary analysis
Resources used | How will it be measured | How will it be valued |
---|---|---|
Parental time spent on relevant homework | Parent questionnaire (min) | Average wage rate from labour force survey |
Household spend on food | Parent questionnaire: cost per week | Cost given, adjusted for household members |
Cost of out of school activities | Cost per week | Cost given, termly costs will be converted to weekly costs |
Parental time spent on child activities | Parental questionnaire: (hours per week) | Average wage rate from labour force survey |
NHS resource use for exercise related injuries | Number of visits/nights in hospital (parental questionnaire) | National reference costs |
Private health service resource use for exercise-related injuries | Number of visits | Using available web based sources |
Paid time off work because of exercise related injuries | Number of days | Average wage rate from labour force survey |
Unpaid time off work because of exercise related injuries | Number of days | Average wage rate from labour force survey |