Time in activity intensities

Prior to examining the PA and PWB relationships, time in sedentary, light, moderate, vigorous and MVPA intensities were calculated for each set of cut points. It was expected that time in intensities would vary depending upon the cut-points employed, results showed this to be the case. Time in sedentary activity revealed only two significant differences; appearing when the Stone et al. (2009) and Puyau et al. (2002) cut-points were applied; these were the only cut-points to deviate from the standard <100 counts∙min^-1, a threshold that appears to be widely accepted as the most appropriate when classifying sedentary activity (Trost et al., 2011; Yildirim et al., 2011). This point was also noted by Fischer et al. (2012), who examined the classification of observed sedentary behaviours using three of the cut-points employed in the present study (<100, <300 & < 800) and the Reilly cut points (<1100). The authors reported that for observed behaviours, accelerometer output in the 75^th percentile of counts∙min^-1 only exceeded the lowest cut-points during short periods in sedentary activity, yet did not exceed the <300 threshold.

Furthermore, they noted that using the higher cut-points, for example (<800 count·min^-1) would lead to an over estimation of time in sedentary, as none of

132 the observed behaviours exceeded 300 counts∙min^-1. Though during a free play activity, counts in the 25^th percentile generally fell below 800 counts∙min^-1, demonstrating that intensities above those classed as sedentary would be captured with the use of this high cut-point. For the remaining intensities in the present study, significant differences were apparent between all cut-points, with the exception being at moderate intensity, for which no differences were found between ST and PU cut-points.

As anticipated, the cut-points with the highest lower boundary for any given intensity resulted in the lowest time accrued in that intensity; for example in light activity, the PU threshold was highest (≥800 counts∙min^-1), consequently the time reported in light activity whilst using these cut-points was the lowest (67 minutes); for moderate activity MT cut-point was highest (≥3581 counts∙min^-1) resulting in 17.9 minutes accumulated at this intensity; while PU again resulted in the lowest time in vigorous activity (3 minutes) due to the highest lower boundary (>8200 counts∙min^-1). For MVPA, Mattocks cut-points (as above) revealed the shortest time (25 minutes). These results are similar to those found by Loprinzi et al, (2012), who noted that lowest estimates of MVPA were achieved when employing MTs cut-points, with the highest recorded time in MVPA apparent with the application of the FR equation, as was apparent in the present study.

These results serve to reinforce the findings of previous studies (Reilly et al., 2008; McClure et al., 2009; Stone et al., 2009; Fischer et al., 2012), whilst the examination of a wider variety of cut-points allows the issue to be more thoroughly examined and further highlights the impact that cut-point application has on classification of PA. Interestingly, on average, participants failed to achieve the recommended guidelines of ≥ 60 minutes daily of MVPA with the application of all cut-points except those derived from the Freedson equation.

4.4.2. PA and PWB relationships 4.4.2.1 Magnitude and significance

Correlational analysis revealed relationships between time in sedentary behaviour and measures of depression, and between time spent in light activity

133 and perceptions of sport competence for multiple cut-points; both of which were weak and in a negative direction. For the sedentary and depression correlation, the strength of relationship remained consistent across the majority of cut-points, which was to be expected as four of the cut-points had the same sedentary threshold (<100 counts∙min^-1). When the slightly higher ST sedentary cut-points were employed, the magnitude of the relationship marginally decreased, still, the correlation did not differ significantly from those observed with the four other points. Additionally, with the application of the PU cut-points, this relationship became non-significant, this is not surprising as the high sedentary/ light boundary apparent in the PU cut-points allows for greater misclassification of light activities as sedentary, diluting the relationship observed with the application of all other cut-points.

Similar results were also seen for the significant light/sport competence relationship, with no significant differences in the strength of relationships occurring between the cut-points. As with the results discussed above, these significant relationships between light and sports competence were not apparent across all cut-points; in fact, relationships were attenuated and became non-significant with the application of F/T, MT and PU cut-points. Once again, the activities included within the light activity boundaries for these cut-points may explain the attenuation of the relationships. MT has the highest upper-bound threshold for light activity, potentially allowing moderate activity to be misclassified as light. Additionally, PU not only has a high lower bound threshold (800 counts∙min^-1) but also a high upper bound threshold (3200 counts∙min^-1), therefore capturing less light activity and potentially more moderate activity.

When examining the non-significant relationships between activity intensity and PWB variables, large differences in magnitude can be seen. For example, time in MVPA classified using the ST revealed a non-significant relationship with perceptions of physical strength (r = .239, p =.055), whereas the same relationship using F/T to define MVPA revealed an r value of .053. Additionally, the direction of some relationships was also found to change with the application of various cut-points; the relationships between MVPA and sport competence became negative with the use of the F/T equation.

134 Relationships between PA intensities and PWB variables were shown to alter in magnitude, and become non-significant with the application of different cut-points; it is thought that these differences can be attributed to how intensities are classified and the extent to which those classifications are accurate. As determination of intensity depends on the counts∙min^-1 at the upper and lower boundaries, those sets of cut-points with wide ranging boundaries will capture a broader variety of activity, classifying them into one category (e.g. light), potentially grouping more intense activities into a lower level. Whereas those with narrow range of counts∙min^-1 may be classifying activities of lower intensity as higher than they actually are. An example from the present study has already been noted above with the PU upper and lower bound cut-points for light activity, which could have resulted in the non-significant relationship apparent between light and sports competence.

The potential for this misclassification is thought to occur mostly between the light and moderate boundaries (Kim et al. 2012), which may explain why significant relationships became non-significant with the application of some cut-points and why other relationships varied in their magnitude (regardless of significance); the inclusion of more intense activities through high upper limit boundaries has the potential to dilute the relationships.

Trost et al. (2011) examined the classification accuracy of five of the six cut-points examined in the present study, the authors found that for light activity, the PU cut-points had an extremely low sensitivity and area under the curve (AUC) values (11.8 & 0.43, respectively) demonstrating a poor classification accuracy;

evidence for this was also found by Alhassan et al. (2012). The sensitivity, specificity and AUC for the MT light cut-points were somewhat higher (58.8, 68.4 & 0.64 respectively). Furthermore, the authors noted that both PU and MT also had poor classification accuracy for moderate activity. The aspects of the light-moderate boundaries described in the Trost et al. (2011) study may serve as an explanation as to why significant relationships between light activity and perceptions of sports competence were not observed when these cut-points (PU & MT) were employed in the present study. Similar misclassification potential may explain the variation in magnitude of non-significant relationships between PA and PWB variables across cut-points. Interestingly, Trost et al.

135 (2011) also reported good classification accuracy for the EV and F/T cut-points, which exhibited different relationships in the present study, with the F/T defined light activity and perceptions of sport competence being non – significant, yet the same relationship defined by EV demonstrated the strongest relationship.

For light activity Trost et al. (2011) reported slightly better classification accuracy for EV than F/T (AUC = .70 & .69 respectively) indicating that the accuracy of the EV cut-points allows for the appropriate detection of the light / sports competence relationship.

Findings from the present study provide partial support for findings of Stone et al. (2009), who examined the impact of cut-points upon PA and physiological health related variables. The authors reported no difference in relationships across multiple cut-points, yet one relationship became non-significant with the application of one set of cut-points (ArteAcc; Ekelund et al. 2005), concluding that cut-points did not have an effect upon relationships. The findings of the present study echo those of Stone et al. (2009) yet can be concluded in a different manner; relationships were only apparent with the application of certain cut-points and that strength of relationships varied depending upon cut-point application demonstrating the high importance of cut-points when establishing relationships with health variables and disagreeing with the conclusion of Stone et al. (2009). The findings of the present study, do however support those of Atkin et al (2013) concerning sedentary behaviour and metabolic risk, discussed earlier, yet the present study highlights the importance of classification accuracy to a greater extent than Atkin and colleagues. Yet in the present study, relationships were attenuated with the application of high cut-points rather than strengthened. These finding may indicate that relationships in the present study were diluted with misclassification of activity, whereas the misclassification in the Atkin study may have been so great that rather than diluting effects of one intensity, the results actually represented a completely separate relationship.

4.4.2.2 Direction of relationships

An interesting finding from the present study was the direction of the relationship between time spent sedentary and depression. Time in sedentary was negatively related to levels of depression; the less time children spend sedentary, the more likely they are to be depressed. These findings are contrary

136 to the finding of previous studies that have used both subjective and objective measures of PA. Parfitt et al., (2009) found that very light activity (classed as <

1.9 METS) had a positive relationship with depression (r = .345). In the present study, sedentary activity was classed as < 1.5 METS, therefore encompassing the majority of Parfitt et al. (2009) classification of very light activity. It follows then, that the magnitude and direction of the relationships should be similar, especially considering the higher sedentary boundaries used with two cut-points, meaning that light activity would likely be included when these cut-points were used. Though other studies using self-reported sedentary time have also demonstrated positive relationships with depression (e.g. Sund, et al., 2011), discord within the literatures is apparent. Page et al. (2010) reported that higher levels of television viewing and computer use (popular sedentary activities) were related to greater psychological difficulties in children, yet the authors also noted that objectively measured sedentary time was inversely related to psychological difficulties. Furthermore Johnson et al (2008) reported an inverse relationship between objectively measured sedentary time and depressive symptoms; the authors attributed this to a statistical anomaly.

It is important to note that the context of sedentary behaviour is unknown, it may be that some sedentary activities, (e.g., reading) have a positive impact on PWB and others, (e.g., television viewing) a negative impact, a concept which has yet to be examined (Page et al., 2010). Additionally, certain sedentary activities are often attractive to children (Parfitt et al., 2009) and may increase levels of PWB, offering a potential explanation for the results found in the present study.

The relationship between time spent in light activity and perceptions of sport competence was also found to be significant, this time, in the expected negative direction; the more time children spent in light activity, the lower their perceptions of their sport competence. The results of the present study expand upon those of previous literature using objective measurement (Parfitt et al., 2009), which found a weak, non-significant relationship between these variables. These relationships may occur as perceptions of sports competence will be influenced through participation in sports activities which predominantly fall into a higher intensity categories, so if children are spend large amount of

137 time in light activity, it may be indicative of lack of involvement with sporting activities, therefore developing low levels of sport competence; this notion is speculative however and findings from the present study do not provide the necessary evidence to draw this conclusion, especially considering that no relationships are apparent between the psychological construct and moderate, vigorous or MVPA intensities. Findings from previous studies however demonstrate a weak to moderate, yet significant relationship between objectively measured vigorous physical activity and perceptions of sports competence (Parfitt et al., 2009) and between self-reported MVPA and perceptions of sports competence (Raudsepp et al., 2002).

Following this, it should be noted that the present study revealed no significant relationships for moderate, vigorous or MVPA and psychological well-being variables; though relationships for both perceptions of strength and physical conditioning with moderate intensity activity defined by a number of cut-points approached significance. These findings are in accordance with Parfitt et al. (2009) who also failed to demonstrate relationships between moderate activity and PWB variables, though the authors did find a relationship between vigorous activity and other PWB variables, namely perceptions of physical conditioning, sports competence and anxiety. However the authors did not examine the relationships with MVPA. For the present study, those relationships approaching significance may have become significant with the inclusion of more participants, yet this may have further demonstrated the influence of cut-point application on finding significant relationships with health variables.

4.4.2.3 Partial correlations

It was expected that relationships would alter once BMI Z-score was controlled for, due to its relationship with both psychological health (Duncan, Al-Nakeeb, Nevill & Jones, 2006) and physical activity (Rowlands, et al., 2000).

However, contrary to previous findings of (Parfitt et al., 2009), controlling for BMI Z-score did not significantly alter the relationships observed, with some relationships remaining the same as the zero-order correlations. Only one relationship became non-significant when controlling for BMI z-score; Treuth defined light activity and sports competence, the relationship was only

138 attenuated by .001, resulting in a p values of .053. Based on previous findings of Fairclough et al. (2012), it would be expected that partialling out the effects of BMI would substantially reduce the relationship between PA and specific self-perceptions, as normal weight children reported more positive associations with self-perceptions than overweight and obese children. The results of the present study demonstrate that BMI Z-score has little influence upon the relationships between PA intensities and PWB and will therefore not be considered with future studies of this thesis.