accelerometer defined intensity levels
4.2.5. Data Analysis
Accelerometer data was processed using Kinesoft software (Version 3.3.62;
Kinesoft). A series of cut-points, consistent with those used in Trost et al.’s (2011) comparison study, with the addition of Stone et al. (2009) cut-points, were applied to the data in order to assess the impact of intensity classification upon the relationship between PA intensity and PWB. Table 1 details the specific cut-points by author. These widely applied cut-points do not all use the same MET definition of moderate intensity activity. Puyau and Freedson use a 3 MET definition, whereas Mattocks provide cut-points for a 3 and 4 MET definition, for the present study only the 4 MET Mattocks values have been employed, representing the choices made in the wider literature, where the 4 MET cut-point is often employed (e.g. Tobias, Steer, Mattocks, Riddoch & Ness, 2007) and the 3 MET Mattocks cut-point seldom used.
Table 4.1. Published cut-points for activity intensities established with ActiGraph models 7164 and GT1M.
Activity intensity cut-points
Author Sedentary Light Moderate Vigorous
Evenson et al. 2008 ≤ 100 > 100 ≥ 2296 ≥ 4012
Stone et al. 2009 ≤ 300 > 300 ≥ 2910 ≥ 5010
Freedson / Trost1
Mattocks et al. 2007 ≤ 100 > 100 ≥ 3581 ≥ 6130
Puyau et al. 2002 < 800 ≥ 800 ≥ 3200 ≥ 8200
Treuth et al. 2004 ≤ 100 > 100 ≥ 3000 > 5200
Time spent in activity intensities was computed for each set of cut-points presented in table 4.1. Repeated measures ANOVAs were then used to assess
1 . Freedson / Trost equation. METs = 2.757 + (0.0015 x counts per minute) – (0.08957 x age) – 0.000038 x counts per minute x age)
125 whether differences in time spent in each activity were present depending upon the cut point employed. Pairwise comparisons using a Bonferroni correction were undertaken to establish where any differences lay. In accordance with the findings of Parfitt et al. (2009) zero-order and partial correlations, controlling for BMI Z-score, were undertaken to assess the relationships between time spent at each activity intensity (categorised by the above cut-points) and PWB. Any relationships observed between a psychological variable and multiple cut-points were tested for significant differences using Meng, Rosenthal & Rubin’s (1992) method of comparing correlated correlation coefficients (see appendix 4.1. p 331 for worked example).
4.3 Results
4.3.1. Descriptives
Means and standard deviations for psychological well-being, are presented in table 4.2.
Table 4.2. Means SD for PWB variables. Possible ranges of scores for each scale are given in brackets.
Psychological variables Means SD
Depression (CDI; 0 -52) 6.8 5.6
Trait anxiety (STAIC; 20 - 60) 30.8 6.5
Self-perceptions
Global self-worth (GSW; 1 - 4) 3.4 0.6
Physical self-worth (PSW; 1 - 4) 3.1 0.5
Sport competence (1 - 4) 3.0 0.6
Attractive Body (1 - 4) 2.9 0.6
Physical Strength (1 - 4) 2.7 0.5
Physical conditioning (1 - 4) 3.1 0.5
4.3.2. ANOVAs
A series of repeated measures ANOVAs revealed significant differences for time spent in activity intensities dependent upon the cut-points applied.
Differences were apparent for time in sedentary (F(5,240) = 1328.83, p < 0.001), light activity intensity (FGG(1.631,78.30) = 1094.01, p < 0.001), moderate activity intensity (FGG(1.413, 67.82) = 801.42, p <0.001), and vigorous activity intensity
126
Figure 4.1. Significant differences between times spent in each activity intensity when classified by different points * Significantly different from all other cut-points; # significantly different from all other cut-points except others marked by
#
127 4.3.3. Correlations
No significant relationships were apparent between mean counts per day ( ̅=
313254 61279) and psychological well-being variables. However, the relationships between time spent in different physical activity intensities and psychological variables (depression, anxiety and physical self-perceptions) differed slightly depending on the cut-points employed. Zero-order and partial correlations, controlling for BMI Z-score are presented in tables 4.3 – 4.7. No significant relationships were apparent when using Puyau cut-points to define PA intensity.
Table 4.3. Zero-order and partial correlations between cut-point defined sedentary behaviour and PWB variables (n = 49)
*p < 0.05
The relationship between PU cut-point defined sedentary and depression approached significance for both zero order and partial correlations (p = .07 &
.071 respectively).
2 CDI, depression; STAIC, Anxiety; SC, sports competences; AB, Attractive body; PS, Physical Strength;
PC, physical conditioning; PSW, physical self-worth; GSW, Global self -worth
3 Ev, Evenson points ; ST, Stone points; F/T, Freedson / Trost equation; MT, Mattocks cut-points; PU, Puyau cut-cut-points; TR, Treuth cut-points
Cut-points CDI STAIC SC AB PS PC PSW GSW2
EV3 Zero-order -.304* -.075 .130 .215 .141 .057 .237 .221
Partial -.305* -.076 .130 .215 .145 .058 .222 .237
ST Zero-order -.291* -.072 .103 .204 .129 .047 .217 .211
Partial -.293* -.075 .103 .203 .135 .047 .213 .217
F/T Zero-order -.304* -.075 .130 .215 .141 .057 .237 .211
Partial -.305* -.076 .130 .215 .145 .058 .222 .237
MT Zero-order -.304* -.075 .130 .215 .141 .057 .237 .221
Partial -.305* -.076 .130 .215 .145 .058 .222 .237
PU Zero-order -.261 -.063 .057 .177 .108 .028 .181 .192
Partial -.263 -.066 .057 .177 .114 .028 .194 .181
TR Zero-order -.304* -.075 .130 .215 .141 .057 .237 .221
Partial -.305* -.076 .130 .215 .145 .058 .222 .237
128 Table 4.4. Zero-order and partial correlations between cut-point defined light activity and PWB variables (n = 49)
*p < 0.05
The relationship between Mattock’s cut-point defined light activity and sports competence approached significance for both zero-order (p=0.069) and partial correlations (p=0.073). Furthermore, although only the zero-order correlation between light activity and sports competence was significant when using the Treuth cut-points, the partial correlation approached significance (p=0.053).
Cut-points CDI STAIC SC AB PS PC PSW GSW
EV Zero-order .180 .063 -.300* -.150 -.135 -.128 -.204 -.068
Partial .178 .058 -.299* -.151 -.131 -.128 -.204 -.065
ST Zero-order .205 .063 -.285* -.148 -.121 -.105 -.189 -.033
Partial .204 .062 -.285* -.149 -.122 -.105 -.189 -.032
F/T Zero-order .117 .019 -.236 -.081 -.108 -.069 -.168 -.067
Partial .114 .011 -.234 -.082 -.101 -.070 -.168 -.063
MT Zero-order .174 .052 -.262 -.112 -.091 -.073 -.161 -.015
Partial .172 .046 -.261 -.112 -.084 -.073 -.161 -.012
PU Zero-order .232 .069 -.244 -.129 -.094 -.060 -.153 .023
Partial .233 .071 -.245 -.129 -.098 -.060 -.153 .023
TR Zero-order .181 .054 -.283* -.133 -.115 -.101 -.184 -.037
Partial .179 .049 -.282 -.133 -.110 -.101 -.184 -.034
129 Table 4.5. Zero-order and partial correlations between cut-point defined moderate activity and PWB variables (n = 49)
*p < 0.05
For relationships between moderate activity and psychological well-being variables, a number of relationships approached significance; relationships with perceptions of physical strength approached significance for zero-order and partial correlations when the Stone (p=0.065 & p=0.051 respectively) and Treuth (p=0.069 & 0.053 respectively) cut-points were used. Similar findings were apparent between perceptions of physical conditioning and moderate activity defined by Evenson (Zero-order, p= 0.076; partial, p= 0.079) and Stone (Zero-order, p=0.066; Partial, p = 0.069) cut-points.
Cut points CDI STAIC SC AB PS PC PSW GSW
EV Zero-order .045 -.012 .084 .146 .207 .256 .154 .234
Partial .043 -.018 .086 .145 .223 .256 .154 .239
ST Zero-order .036 .030 .088 .134 .266 .264 .160 .147
Partial .034 .024 .090 .133 .284 .264 .161 .151
FR Zero-order .208 .066 -.215 -.098 -.031 -.022 -.099 .063
Partial .208 .064 -.214 -.098 -.028 -.022 -.099 .066
MT Zero-order .077 .048 .020 .022 .208 .178 .071 .020
Partial .074 .042 .022 .022 .226 .178 .072 .024
PU Zero-order .041 .014 .064 .024 .214 .208 .077 .042
Partial .034 .000 .069 .023 .243 .208 .078 .051
TR Zero-order .038 .036 .077 .120 .262 .254 .151 .129
Partial .035 .030 .080 .120 .281 .254 .152 .134
130 Table 4.6. Zero-order and partial correlations for cut-point defined vigorous activity and PWB variables. (n = 49)
*p < 0.05
Table 4.7. Zero-order and partial correlations for cut-point defined MVPA and PWB variables. (n = 49)
*p < 0.05
Relationships between cut-point defined time in MVPA and perception of physical strength approached significance when controlling for BMI Z-score when Evenson (p = .062), Stone (p=.055), Puyau (p=.066) and Treuth (p=.058) cut-points were employed.
Cut-points CDI STAIC SC AB PS PC PSW GSW
EV Zero-order -.033 -.025 .080 -.037 .181 .177 .005 -.019
Partial -.045 -.051 .089 -.039 .223 .187 .007 -.005
ST Zero-order -.079 -.058 .102 -.074 .129 .149 -.038 -.042
Partial -.095 -.092 .113 -.076 .179 .150 -.036 -.025
FR Zero-order -.011 .016 .026 -.061 .158 .124 -.012 -.018
Partial -.023 -.008 .033 -.062 .197 .124 -.011 -.005
MT Zero-order -.158 -.095 .162 -.050 .141 .171 -.029 -.018
Partial -.178 -.134 .176 -.053 .197 .173 -.026 .002
PU Zero-order -.237 -.106 .190 .010 .179 .201 -.005 .022
Partial -.260 -.148 .205 .008 .239 .203 -.003 .043
TR Zero-order -.093 -.067 .115 -.071 .125 .149 -.036 -.040
Partial -.110 -.102 .126 -.074 .176 .150 -.034 -.023
Cut-points CDI STAIC SC AB PS PC PWS GSW
EV Zero-order .003 -.023 .100 .056 .235 .259 .088 .117
Partial -.006 -.044 .106 .055 .271 .260 .090 .129
ST Zero-order -.021 -.014 .112 .043 .239 .249 .080 .069
Partial -.032 -.036 .120 .042 .279 .250 .082 .082
FR Zero-order .142 .054 -.140 -.098 .053 .043 -.076 .037
Partial .137 .042 -.136 -.099 .073 .043 -.075 .044
MT Zero-order -.025 -.014 .092 -.010 .207 .202 .034 .005
Partial -.036 -.036 .099 -.011 .246 .202 .036 .017
PU Zero-order -.026 -.015 .103 .023 .227 .228 .064 .041
Partial -.037 -.038 .111 .022 .267 .229 .066 .054
TR Zero-order -.025 -.013 .111 .039 .236 .244 .078 .062
Partial -.036 -.035 .119 .038 .276 .245 .080 .074
131 Comparison between significant relationships occurring between a PWB variables and PA defined by multiple cut-points revealed no significant differences between relationships dependent on which cut-points were employed (p > 0.05).
4.4. Discussion
The aim of the present study was to assess whether relationships between physical activity intensities and psychological well-being varied with the employment of different accelerometer cut-points. Significant differences were observed for time spent in activity intensities when defined by various published cut-points. Those relationships apparent between time in activity intensities and psychological well-being across multiple cut-points did not differ from each other. Interestingly though, the application of some cut-points resulted in the attenuation of several relationships, with some becoming non-significant with the application of certain cut-points.