Doctoral Degrees (Exercise and Sport Sciences)
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Item Open Access A conceptual framework to improve the reporting quality of strength training exercise descriptors in anterior cruciate ligament reconstruction rehabilitation programs(University of the Free State, 2023) Vlok, Arnold; Coetzee, F. F.𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 Muscle weakness after anterior cruciate ligament reconstruction (ACLR) is persistent and associated with abnormal biomechanics, poor knee function, new knee injury and development of osteoarthritis. The proposed drivers of persistent muscle weakness after ACLR are changes in muscle morphology, atrophy-inducing cytokines in the knee joint, and neurological alterations on a cortical and spinal level. The most accessible approach to target muscle weakness is various types of strength training exercises. However, another explanation for persistent weakness after ACLR rehabilitation could be that programs are not following the best practice for strength training. Failure to improve muscle strength after ACLR could be caused by faulty programming of exercise descriptors (e.g., exercise type, frequency, load). 𝐀𝐢𝐦 The main aim of this study was to develop a conceptual framework to improve the reporting quality of strength training exercise descriptors in ACLR rehabilitation programs. 𝗠𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝘆 The study was conducted in three stages, including a Scoping Review, focussing on which strength training exercise descriptors are reported in ACLR research after ACLR surgery, and comparing the current standards of reporting ACLR strength training exercise descriptors to international best practice strength training guidelines. The modified e-Delphi survey was utilised to formulate a conceptual rehabilitation framework for ACLR. The last stage included validating the preliminary ACLR conceptual framework that included a core outcome set (COS) of strength training exercise descriptors for reporting after ACLR. 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 𝗮𝗻𝗱 𝗱𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻 We extracted data on 117 exercises from 41 studies. A median of seven of the 19 possible exercise descriptors were reported (range 3-16). Reporting of specific exercise descriptors varied across studies from 93% (name of the strength training exercise) to 5% (exercise aim). On average, 46%, 35%, and 43% of the exercise descriptors included in the ACSM, CERT, and Toigo and Boutellier guidelines were reported, respectively. The e-Delphi results from 27 ACLR experts regarding the 21-exercise descriptor definition was 100% consensus agreement (>80% agreement), also 100% consensus agreement on a COS of strength training exercise descriptors (). However, very low consensus agreement on exercise dosages prescribed in ACLR strengthening programs. The validation meeting consisted of four panellists that validated the preliminary ACLR conceptual framework and proposed to re-organise the 13 COS of exercise descriptors into levels of importance regarding the frequency of reporting. 𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻 The proposed ACLR conceptual framework for researchers and clinicians provided a platform for the reporting of strength training rehabilitation after ACLR. Improving the reporting quality of strength training exercise descriptors, definitions, and exercise dosages for ACLR rehabilitation programs can aid in the transfer of ACLR rehabilitation research towards private practice. Therefore, enabling clinicians to implement evidence-based strength training exercise configurations.Item Open Access Developmental coordination disorder in children: assessment, identification and intervention(University of the Free State, 2020-11) Du Plessis, Aletta Margaretha (Alretha); De Milander, M.; Coetzee, F. F.𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻: Developmental coordination disorder (DCD) is a motor skill disorder that affects children worldwide, with various prevalence rates reported in the literature. Approximately 60% of children in South Africa (SA) come from low socio-economic (SE) environments. It is, therefore, essential to determine the prevalence of possible DCD in these environments. Although various screening tools are available for identifying possible DCD, teachers' ability to use the Movement Assessment Battery for Children-2 (MABC-2) Checklist has not been established. Furthermore, children with DCD and possible DCD will continue to experience motor difficulties if motor intervention is not provided. A motor intervention guideline for children with DCD in SA in the field of Kinderkinetics has not been established. 𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲𝘀: The first objective was to determine the prevalence of possible DCD in Grade 1 (Gr. 1) learners in a low SE environment in Mangaung, SA, using the MABC-2 Performance Test. Secondly, the study aimed to establish teachers' ability to identify Gr. 1 learners with possible DCD in low SE environments using the MABC-2 Checklist. Finally, an e-Delphi survey was used to develop a motor intervention framework as a guideline for Kinderkineticists to help children with DCD or possible DCD within the South African context. 𝗠𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝘆: Two hundred and forty-two (N=242; 51.2% boys, 48.8% girls) Gr. 1 learners, 6–8-year-old from a low SE environment (quintile 1–3 schools) in Mangaung Metro, Motheo District, Free State (FS) Province, participated in study objective one. Possible DCD prevalence was determined using the MABC-2 Performance Test. For the second objective, the study was conducted in the same environment. Gr. 1 learners 6–8-year-old (N=200; 49.5% boys, 50.5% girls) and 29 female class teachers of the Gr.1 learners participated in the study. The convergent validity of the MABC-2 Performance Test and Checklist completed by teachers was determined. Lastly, for objective three, 29 Kinderkineticists in SA with expert experience participated in a three-round online e-Delphi survey by answering questions related to motor intervention for children with possible DCD. 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: The results of objective one showed that the prevalence of possible DCD found in the Gr. 1 learners was 9.9%. The gender results indicated a possible DCD prevalence of 10.5% for boys and 9.3% for girls. No statistically significant difference between the boys and girls was found (p=0.94). The results concerning objective two indicated that the movement specialists identified more learners (90%) in the non-DCD group (> 15th percentile) than the teachers (54%). The teachers wrongfully identified 46% of the learners with possible DCD, who were not identified with possible DCD according to the movement specialists. The movement specialists identified 10% of the learners with possible DCD. Only a slight agreement ((k=0.17) was found between the MABC-2 Performance Test and Checklist when the ≤ 15th percentile was used as a cut-off score. The sensitivity was 85% and the specificity 58%. In the e-Delphi survey, consensus (80%) was reached on 51/89 questions in round one, 89/144 for round two, and 12/30 in round three. A motor intervention framework was developed using the feedback of each round from the participants where consensus was reached. 𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻: The prevalence of possible DCD in low SE environments in Mangaung of Gr. 1 learners was higher than the worldwide estimated prevalence of DCD (5–6%). It is recommended that when teachers use the MABC-2 Checklist, the Performance Test should be performed in conjunction with the Checklist to obtain the most reliable results. A motor intervention framework was developed as a first draft to use as a guideline by Kinderkineticists, focusing on intervention planning, goal-setting, intervention approaches, intervention apparatus, intervention delivery mode, additional role players, settings, dosage, and evaluation.Item Open Access Effect of short-term macronutrient manipulation on endurance capacity of long-distance runners(University of the Free State, 2020-11) Deacon, Lizl; Coetzee, F. F.; Coetzee, B.; Du Toit, W. C.Introduction: The influence of specific nutrition programmes on optimal endurance performance enjoys wide interest. However, limited knowledge in this regard accentuates the need for further research on optimal nutrition for individual endurance performance optimisation. Objectives: (i) To investigate differences in the effects of a short-term (48-hour) highcarbohydrate (high-CHO) versus a high-FAT diet on indirect respiratory indices of long-distance runners, namely maximal oxygen consumption (V̇ O₂max), oxygen consumption (V̇ O₂), carbon dioxide output (V̇ CO₂), respiratory exchange ratio (RER), minute ventilation (V̇ E), and substrate utilisation (CHO oxidation and fat oxidation), as well as on physiological and perceptual measurements such as time to exhaustion, absolute (W) and relative power output (W/kg) and work output (kJ), during a treadmill graded exercise test (GXT) to exhaustion. (ii) To determine certain threshold points that occurred during the GXT, including ventilatory threshold 1 (VT1), ventilatory threshold 2 (VT2), lactate lhreshold (LT), peak oxygen uptake (V̇ O₂peak) and maximal oxygen consumption (V̇ O₂max) after the high-CHO and high-FAT trials, respectively. (iii) To explore individual preferential fuel source use over a short-term period to enhance performance. Methods: This was a randomised controlled cross-over trial assessing the effects of a 48-hour high-CHO (67%CHO, 17%fat, 16%Prot) or 48-hour high-FAT (65%fat, 21%CHO, 14%prot) diet amongst 24 well-trained male endurance runners. After each 48-hour diet period and an overnight fast, the participants completed a GXT consisting of 3-minute stages with 1 km/h increments until exhaustion. The two dietary treatment periods were parted by a two-week washout period. The study treatments were compared with respect to the various measurements using ANOVA with diet, participant and period as fixed effects. From these ANOVAs, the mean values for each study treatment (high-FAT and high-CHO diets) were calculated, including a point estimate and 95% confidence interval (CI) for the mean difference "high-FAT – highCHO", the p-value associated with a test of the null-hypothesis of no difference between treatment means, and the effect size calculated as the ratio of the point estimate of the mean difference divided by the residual standard deviation from the ANOVA. Results: No statistically significant differences were observed between the diets with regard to any of the indirect indices measured [V̇ O₂max, V̇ O₂, V̇ CO₂, RER and V̇ E and carbohydrate oxidation (CHOox) and fat oxidation (FATox] as well as LT. Furthermore, no statistically significant differences were observed with regard to the physiological and perceptual responses (RPE, HR, time to exhaustion, work and absolute and relative power output). Moderate effect sizes were observed for V̇ O₂ at VT1 (d = 0.58) and at VT2 (d = 0.41), and for V̇ O₂max at VT1 (d = 0.61) and VT2 (d = 0.47). Otherwise, moderate effect sizes were observed for speed at VT1 (d = 0.48) and HR at V̇ O₂max (d = 0.41). For fat contribution, moderate effect sizes were observed at both VT1 (d = 0.40) and VT2 (d = 0.43), and a medium effect size at V̇ O₂max (d = 0.56). Conclusion: No statistically significant differences were seen between the effects of the short-term high-CHO and high-FAT diets on any of the respiratory and other indices measured in endurance runners during a GTX to exhaustion. However, some moderate effects sizes observed for some of the indices either favouring high-CHO or high-FAT depending on the individual, suggest that further research is justified, possibly involving longer-term diets.Item Open Access Positional match statistics in Currie Cup and Super Rugby competitions between winning and losing teams(University of the Free State, 2016-11) Schoeman, Riaan; Coetzee, F. F.English: Background Rugby union (here after referred to as rugby), as most other team sports, is becoming more aware of statistics as a reliable method to evaluate players and match variables during match play. This non-invasive evaluation method provides coaches and conditioning coaching with much needed information regarding player attendance to match situations and the successful execution of these match situations. Winning and losing teams from all levels of competitions use statistics to not only evaluate the team’s performance, but to determine which variables might be responsible for the outcome of the game. It is accepted that teams from a winning side might perform better in certain areas of play than losing teams, and players from higher levels of participation can execute certain skills more effectively. Previous research has been conducted on various teams from different participation levels on the physiological differences, mental toughness and match variables. The increased professionalism of rugby players may also indicate an increased ability of players from one season to the next. The ability of players will also vary from one position to the next and may be approximately exposed to certain match variables. Aims The first aim of this study was to determine the tackle and collision count for Super Rugby players during the 2013 competition. The second was to analyse the passing and kicking statistics that discriminate between winning and losing teams during the 2014 Super Rugby season. Thirdly, the study attempted to differentiate between the Super Rugby competition and the Currie Cup competition according to the occurrence of match activities and lastly to evaluate the evolution of the Super Rugby competition from 2011 to 2015 by the use of regression statistics. Method Sample The first aim consisted of conducting an analysis of 1,900 players from 30 games played during the 2013 Super Rugby competition. Two games from each of the participating franchises were used and selected in regards to number of matches available and balance of the sample. The second aim included an analysis of 1298 players from the 2013 Super Rugby season, whilst the third aim involved 1800 players with n=900 players from Super Rugby and n=900 players from the Currie Cup competition. Furthermore, aim 4 consisted of 4500 players and included n=900 from each of the Super Rugby seasons from 2011 to 2015. Measuring instruments Data was supplied by the Cheetahs Super Rugby Franchise, Bloemfontein, South Africa, using the Verusco TryMaker Pro. Verusco has provided Super Rugby teams with TryMaker Pro since the year 2000. TryMaker Pro is the most advanced analysis system custom-made for rugby, and it is the preferred system for the professional teams using Verusco. The Verusco coding centre codes all the games for registered teams and delivers high-detail, high-speed analysis within hours of the game having been played. Data analysis All data were captured in Microsoft Excel 2007 and subsequently converted into an SAS data set. For aim 1 the following analysis was done: The GLIMMIX procedure of the SAS Version 9.22 statistical software package was used for further statistical analysis (SAS, 2009). Means and standard deviations were used for numerical data. Individual tackle counts for each position, team and game were analysed using a generalised linear mixed model (GLIMM) with position and team as fixed effects, the natural logarithm of individual time played in minutes as offset, and position-by-team and game-by-team interaction terms as random effects. Regarding the fitted random effects, it seemed reasonable to allow for correlation between tackle counts for a specific individual across several games (modelled by the position-by-team random effect), and for correlation between tackle counts across players in a given team and game (modelled by the team-by-game random effect). Furthermore, the GLIMM was specified with Poisson error distribution and the natural logarithm as link function. Individual collision counts for each position, team and game were analysed in the same manner. In both cases – tackle counts and collision counts – the model fitted the data well and there was no evidence of residual over-dispersion. Based on the GLIMM, the mean rate of tackles and mean rate of collisions per 80 minutes (that is, normalised to a full-length rugby game) were estimated for each playing position, with 95% confidence intervals (CIs) of the mean rates. Similarly, in order to compare the mean rates of tackles and collisions between different playing positions, rate ratios (that is, the ratio of tackle and collision rates between playing positions) were estimated, with 95% CIs for the rate ratios. Aim 2 included the following statistical analysis: Means and standard deviations were used for numerical data. Individual tackle counts for each position, team and game were analysed using a generalised linear mixed model (GLIMM) with position and team as fixed effects, the natural logarithm of individual time played in minutes as offset, and position-by-team and game-by-team interaction terms as random effects. Regarding the fitted random effects, it seemed reasonable to allow for correlation between tackle counts for a specific individual across several games (modelled by the position-by-team random effect), and for correlation between tackle counts across players in a specific team and game (modelled by the team-by-game random effect). Furthermore, the GLIMM was specified with Poisson error distribution and the natural logarithm as link function. Team rates for passing and kicking were analysed in the same manner. In both cases, passing and kicking rates, the model fitted the data well and there was no evidence of residual over-dispersion. Based the GLIMM, the mean rate of passing and mean rate of kicking per 80 min were estimated for each team, with 95% confidence intervals (CIs) of the mean rates. Aim 3 consisted of each count variable (number of lineouts, scrums, rucks, mauls etc.) to be analysed using a generalised linear mixed model (GLIMM) with season (2011 versus 2015) as fixed effect, and both winning team and losing team as random effect. (The fitting of the variables winning team and losing team as random effects allowed for correlation between the counts in question for a given team across several games.) Furthermore, the GLIMM was specified with Poisson error distribution and the natural logarithm as link function; residual over-dispersion was allowed for in the model. Based on the GLIMM, the mean rates of lineouts, scrums, rucks, mauls etc. per game were estimated for the 2011 and 2015 seasons. Similarly, in order to compare the mean rates between the 2011 and 2015 seasons, ratios of lineout rates etc. between the 2015 and 2011 seasons were estimated, together with 95% CIs for the rate ratios. The above analyses were carried out separately for the data of the winning teams, for the data of the losing teams, and for the data of two teams involved in each game combined (that is, for the game). The analysis was carried out using SAS procedure GLIMMIX (SAS, 2013). Aim 4 used descriptive statistics for the count and percentage data calculated for the 2011 to 2015 seasons. Descriptive statistics were calculated per season for the winning teams, for the losing teams, and for the two teams involved in each game combined (that is, for the total count per game). Each count variable (number of lineouts, scrums, rucks, mauls etc.) was analysed using a generalised linear mixed model (GLIMM) with Season (2011 versus 2015) as fixed effect, and both winning team and losing team as random effect. (The fitting of the variables winning team and losing team as random effects allowed for correlation between the counts in question for a given team across several games.) Furthermore, the GLIMM was specified with Poisson error distribution and the natural logarithm as link function; residual over-dispersion was allowed for in the model. Based on the GLIMM, the mean rates of lineouts, scrums, rucks, mauls etc. per game were estimated for the 2011 and 2015 seasons. Similarly, in order to compare the mean rates between the 2011 and 2015 seasons, rate ratios, that is, ratios of lineout rates etc. between the 2015 and 2011 seasons were estimated, together with 95% CIs for the rate ratios. The above analyses were carried out separately for the data of the winning teams, for the data of the losing teams, and for the data of two teams involved in each game combined (that is, for the game). Percentage territory and percentage possession of the winning team in each game were analysed using a linear mixed model with Season as fixed effect, and both Winning Team and Losing Team as random effects. Based on the linear mixed model, the mean percentage territory (and possession) was estimated for each season, together with a 95% CI for the mean percentage. Similarly, in order to compare the mean percentage between the 2011 and 2015 seasons, mean differences, that is, differences of mean percentage territory and possession between the 2015 and 2011 seasons were estimated, together with 95% CIs for the mean differences. The analysis was carried out using SAS procedure MIXED (see SAS, 2013). Results The results from aim one underlined the importance of specific demands on the various playing positions regarding the tackles and collisions sustained by Super Rugby players. Clearly, loose forwards (6: = 16.65 tackles/80 min; 7: = 17.30 tackles/80 min; 8: = 14.68 tackles/80 min) had the highest tackling rates, followed by the locks (4: = 13.74 tackles/80 min; 5: = 14.07 tackles/80 min). Amongst the backs, the inside centre (12: = 12.89 tackles/80 min) was the player with the highest tackling rates, followed by the outside centre (13: = 9.96 tackles/80 min). The results showed that the open-side flanker (7) had the highest tackle rate of all playing positions (17.30 tackles/80 min). The open-side flank (7) was involved in the most collisions (50.91), followed by the blind-side flank (6), loosehead lock (4) and eighthman (8), with collision rates of 46.08, 44.81 and 43.03 respectively, per 80 minutes collision count per game. The results showed significant differences between positional groups for tackles, except for the front row players and the second row (1, 2, 3 vs 4, 5; p=0.0715 to p=0.6324). Within a positional group, namely the backline players, the tackling rate of the inside centre differed significantly from the tackling rate of the other backline players (9 vs 12, p=0.0029; 10 vs 12, p=0.0045; and 12 vs 13, p=0.0100). Aim two indicated that losing teams tend to pass the ball more (157.41) than winning teams (127.02). The results illustrated a significant difference between winning teams and losing teams regarding total passes, bad passes, and good passes (p=<0.05). Winning teams tend to kick the ball more (25.77) than losing teams (20.23). Results indicated a significant difference between winning teams and losing teams regarding total kicks, long kicks, short kicks, and kicking metres (p=<0.05). Winning teams kicked more long kicks (18.55) than losing teams (14.19). Winning teams also used the short kick (7.22) more effectively and more often than losing teams (6.04). Losing teams gain a mean total of 660.01m per game in comparison to winning teams who gain 901.4m per game. In the third aim it was discovered that, when the two competitions are compared, it is evident that only two variables can be distinguished. The mauls and tackles missed are the only two variables that show remarkable difference, with 3.23 mauls and 8.9 tackles missed per game more in Currie Cup competition than the Super Rugby. The results of this study underline the importance of measuring and analysing specific performance indicators on a regular basis as these performance indicators can increase or decrease as the level of competition change. The greatest increase occurred with rucking, as this variable increased from 139.63 in Currie Cup to 143.13 in Super Rugby. Super Rugby teams lose fewer lineouts, and have less missed tackles, while Currie Cup teams utilise mauls more as an offensive weapon. Aim 4 identified playing time, lineouts lost, scrums, scrums lost, tackles and penalties decreased from 2011 to 2015, while lineouts, mauls and the number of missed tackles increased. The results of this study underline the importance of measuring and analysing specific performance indicators on a regular basis as these performance indicators can increase or decrease in a short time frame. From 2011 to 2015 winning teams consistently lost fewer lineouts than losing teams, even with an overall increase in the number of lineouts per game. The study indicates a slight decrease in the number of tackles, but still supports the fact that winning teams have higher tackle rates than losing teams. Conclusions The results of the study show that there are significant differences between individual playing positions within the same positional group with regard to tackling and collision rates sustained during match play. The study confirms that losing teams pass more than winning teams and that winning teams kick more than losing teams during match play. The study also discovered a greater distance gained through kicks by winning teams. The higher or lower numbers of performance indicators performed by teams over competitions emphasise the different physiological demands for teams. The study concluded that playing time, lineouts lost, scrums, scrums lost, tackles and penalties decreased from 2011 to 2015, while lineouts, mauls and the number of missed tackles increased. The findings may be important for future research as they indicate a constant shift in statistics and outcomes of teams over seasons within a particular competition.Item Open Access Morphological and skill-related fitness components as possible predictors of injuries in elite female field hockey players(University of the Free State, 2014) Naicker, Marlene; Coetzee, F. F.Introduction: The incidence of injury in female field hockey players is high, but there is little data on the physical demands of the game or the injury risk factors. Objective: To establish an athletic profile of elite female field hockey players and to determine if morphological or skill-related factors measured in the pre-season can predict injury in the in-season. Methods: Thirty female field hockey players comprising the South African national field hockey team underwent pre-season testing. These tests included anthropometry, balance, flexibility (sit and reach test), explosive power (vertical jump test), upper and lower body strength (bench and leg press), core strength, speed (10 m, 40 m and repeated sprint test with and without a hockey stick), agility (Illinois test) and isokinetic testing of the ankle. Also included was a questionnaire to collect information on demographic data, elite-level experience, playing surface, footwear and injury history. Injuries in training and matches were recorded prospectively in the subsequent season using an injury profile sheet. Players reporting an injury were contacted to collect data regarding injury circumstances. Univariate and multivariate regression analyses were used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for ±1 standard deviation of change. Results: A total of 87 injuries were recorded with ligament and muscle injury the most frequent. The highest incidence of injury was the ankle joint followed by the hamstring muscles and lower back respectively. Univariate analyses showed that ankle dorsiflexion strength was a very strong predictor of ankle injuries (p=0.0002), and that ankle dorsiflexion deficit (p=0.0267) and eversion deficit (p=0.0035) were significantly good predictors of ankle injury. All balance indices, i.e. anterior/posterior (p=0.0465), medial/lateral (p<0.0001) and overall (p<0.0001), constituted the other pre-season performance measures showing significant potential to predict ankle injury. For lower leg injuries, univariate associations were found with ankle inversion deficit (p=0.0253), eversion deficit (p=0.0379) and anterior/posterior balance index (p=0.0441). Conclusion: Dorsiflexion strength and all balance indices were strong predictors of ankle injury while ankle inversion deficit, eversion deficit and anterior/posterior balance were associated with lower leg injuries in elite female field hockey players.Item Open Access Variables contributing to satisfaction in wildlife tourism(University of the Free State, 2007-09) Moreri-Toteng, Amanda Bobeo; Bloemhoff, J.; Saayman, M.The study was undertaken to identify and evaluate variables that contribute to wildlife tourist satisfaction. Clark et al. (1999) argue that the hospitality and tourism industries are still relatively under researched. Therefore, this research is particularly important because it focuses on wildlife tourist satisfaction as opposed to customer satisfaction in general. According to Teye and Leclerc (1998), satisfaction is vital for ensuring sustainability of the tourism industry. Similarly, Bramwell (1998) argues that tourist destinations should offer exceptional and satisfying products and services in order to retain and attract more tourists. The study was conducted at the Chobe National Park (CNP), Botswana’s largest and most popular national park. CNP is popular for its abundant and diverse wild species. Following the arguments on the importance of wildlife tourist satisfaction the study sought to establish how wildlife tourists’ experiences impact on their overall satisfaction. The study also assessed the extent to which Chobe National Park contributes to wildlife tourist satisfaction in relation to the identified variables. The convenience sampling method was applied and the success of the pilot study indicated the usability of the research instrument. The research utilised the SERV-PERVAL scale (Petrick 2000). The scale was developed to assess service quality and perceived value. SERV-PERVAL measures quality as a measure of the supplier’s performance. The measurement of quality is crucial because quality is argued to be the best predictor of perceived value. Data was collected by the use of a structured self-completion questionnaire. The questionnaire was divided into three sections: demographic data, the SERV-PERVAL scale to assess questions on service quality, perceived value and satisfaction. The third section was a combination of a Likert scale and open-ended questions gathering information on expectations and motivations. The descriptive method of analysis, with tables and figures, was applied. The level of significance between variables was determined through the use of the correlation analysis, and the multiple regression model was utilised to investigate the contribution of variables to wildlife tourist satisfaction. The conclusion derived from the literature reviewed is that the concept satisfaction is core in the wildlife tourism industry because it involves feelings of wildlife tourists after experiencing wildlife tourism services. The literature has positively associated and it emphasised the importance of several concepts to wildlife tourist satisfaction. These concepts are: service quality, price and value for money, tourist experience and expectations. While the results of the survey condoned the significance of service quality, price, value for money and tourist experience to wildlife tourist satisfaction, they also indicated and emphasised the importance of wildlife-related variables. These are: safety measures from attack by wild animals, availability and diversity of wild species, condition of vegetation in the wildlife area and accessibility. It is through the use of these variables that wildlife tourists evaluate their experiences and rate their satisfaction levels. Some of the results are, however, in conflict with two arguments found in the literature. Firstly the results contradicted the argument that wildlife tourists assess their satisfaction on the basis of whether or not their initial expectations were met. Some tourists indicated they had a satisfactory experience and yet they did not have prior expectations before they travelled to CNP. As a result, tourists’ expectations were found not to be one of the critical variables that contribute to wildlife tourist satisfaction. Secondly, despite the argument that one benefit of tourist satisfaction is the revisits by satisfied tourists, satisfied wildlife tourists in this study indicated they were satisfied with their experience but would not re-visit CNP, mainly because they had other commitments.Item Open Access A perceptual-motor intervention programme for grade 1-learners with developmental coordination disorder(University of the Free State, 2015-02) De Milander, Monique; Coetzee, F. F.; Venter, A.English: Background Developmental coordination disorder (DCD) is recognised as one of the most common developmental dysfunctions during childhood. Developmental coordination disorder is diagnosed in children who experience significant difficulties in motor learning and in the performance of functional motor tasks that are critical for success in their daily lives. However, one of the major concerns regarding children with DCD is that they are often not formally diagnosed, but rather described by their parents and teachers as lazy or awkward. In an attempt to identify children with DCD, several research tools, such as questionnaires for screening purposes and norm-referenced tests to measure the degree of movement difficulties, can be used. Even though children will not outgrow this disorder as previously believed, children can be helped by means of various interventions. Aims The first aim of this study was to determine the prevalence of DCD among Grade 1 children in Bloemfontein. The second aim was to establish the ability of parents to identify Grade 1 children with DCD at home; in addition the third aim was to establish the ability of teachers in identifying Grade 1 children with DCD in the classroom. The fourth aim was to explore the influence of DCD on learning related skills. Aim five and six was to determine if the application of a perceptual-motor intervention as well as a sport stacking intervention will significantly improve the motor proficiency status of Grade 1 children identified with DCD independently. Method Participants For the purpose of aim 1, 559 participants’ between the ages of 5 and 8 years took part in this study. There were n=321 girls and n=238 boys. Aim 2 include 410 participants and consisted of n=226 girls and n=184 boys, whilst aim 3 had 506 participants and there were n=289 girls and n=217 boys. Furthermore, aim 4 had 347 participants including n=190 girls and n=157 boys. Aim 5 and 6, which relates to the two interventions used in this study was as follows. Seventy six (76) participants took part in the perceptual-motor intervention. The group consisted of girls (n=34) and boys (n=42) classified with DCD. The intervention had a pre-test/post-test experimental design (n=36) with a control group (n=40). With reference to the sport stacking intervention, 18 children between the ages of 6 and 7 years took part in this study. The group consisted of girls (n=6) and boys (n=12) classified with DCD. This intervention also had a pre-test/post-test experimental design (n=10) with a control group (n=8). Measuring instruments The instrument used to assess the participants motor proficiency levels and to identify symptoms of DCD was the Movement Assessment Battery for Children-2 (MABC-2 Test). This test includes manual dexterity, balance as well as aiming and catching, in addition the three sub-tests constitute a total test score. In order to determine if parents possess the ability to identify symptoms of DCD at home the Developmental Coordination Disorder Questionnaire ’07 (DCDQ’07) was used. With the purpose of determining if teachers possess the ability to identify DCD in the classroom the Movement Assessment Battery for Children-2 Checklist (MABC-C) was used. It is designed to identify primary school children likely to have movement difficulties. The Aptitude Test for School Beginners (ASB) was administered by qualified teachers to all participating children in the first two months of the school year. A requirement of the ASB is that it must be presented and completed in a child’s mother tongue. The ASB is a norm-based instrument and consists of eight sub-items, which include perception, spatial skills, reasoning, numerical skills, gestalt, coordination, memory and verbal comprehension. Each sub-item is evaluated by means of a standard score out of five. An evaluation score of 1 is regarded as below average and an evaluation score of 5 as above average. The aim of the ASB is to obtain a differentiated picture of certain aptitudes of grade 1 children. Data analysis Analysis of the data was done by a biostatistician using Statistical Analysis Software Version 9.1.3. Descriptive statistics, namely frequencies and percentages, were calculated for categorical data. Medians and percentiles were calculated for numerical data. Median differences were tested by calculating p-values using the signed-rank test. The Chi-square statistics were used to test for proportion differences. This was used to determine the prevalence of DCD (article 1), as well as for learning related skills and DCD (article 4) and for the sport stacking intervention (article 6). Furthermore, data analysis was performed using the Statistical Package for the Social Sciences (SPSS) for Windows (SPSS version 16.0), in order to determine if parents and teachers possess the ability to identify children with DCD. The convergent validity of the classification of motor problems (no motor difficulties or motor difficulties) using the MABC-2 Test and the classification of motor difficulties (no motor difficulties or motor difficulties) by the parents of the participants using the DCDQ’07 and the teachers using the MABC-C, the kappa (k-) coefficient was used. Finally, the Mann-Whitney-U test was used to compare differences between the experimental- and control group with reference to the perceptual-motor intervention for children with DCD (article 5). Probability level of 0.05 or less was taken to indicate statistical significance. Results The results of aim 1 revealed the prevalence of DCD amongst Grade 1 learners in Bloemfontein is estimated to be 15%. The results also indicate that boys have a significantly higher (p=0.050) prevalence of DCD although marginally when compared to their female counterparts. Aim 2 indicated a 15% convergent validity between the MABC-2 Test and the DCDQ’07, similar results were obtained for aim 3, indicating a 11% convergent validity between the MABC-2 Test and the MABC-C. Therefore, it can be argued that parents using the DCDQ’07 and teachers using the MABC-2 could not identify children with DCD at home or in the classroom. The results in aim 4 indicated the prevalence of DCD to be 12%. Additionally, DCD had a significant effect (p=0.050) on five of the eight learning-related subtypes, namely reasoning, numerical skills, gestalt, coordination and memory. Furthermore, the results of aim 5 indicated that a perceptual-motor intervention only improved balance as a sub-test of the MABC-2 Test. Interesting to note is that children taking part in Physical Education classes presented by the teachers also prove to be beneficial. In contrast, aim 6 (sport stacking intervention for DCD) showed that the intervention had a significant effect (p=0.050) on two of the three sub-tests, namely manual dexterity, balance, as well as the total test score. This suggests that sport stacking can be used as an effective intervention programme for children with DCD. Conclusions The results revealed that the school age children in the current study had a higher incidence of DCD (15%) compared to the findings reported in the literature (5-6%). This information is important, and indicates that appropriate screening tools should be used to identify children earlier. Unfortunately the reliability of the MABC-C and the DCDQ’07 completed by parents and teachers to identify children with DCD was found to be low. Therefore it is recommended that specific norms should be developed for South African children. Furthermore, the results revealed that children with DCD do struggle with learning related skills. This knowledge enables teachers to address the specific needs of children with DCD. It can be concluded that perceptual-motor interventions have more often than not positive effects on children with DCD; however it is recommended that a combination of the bottom-up approach and top-down approach should be used for optimal results.