Mainly by electronic pedometer vibration sensors and electronic counter. People walking up and down the center of gravity should be a little bit. Displacement of the waist and down to the most obvious, step counter so the most suitable for hanging on the belt. The so-called vibration sensor is actually a balance weight in the upper and lower vibration balance is upset to make a contact to appear on / off action by the electronic counter to record and display the main function is complete, the other calorie consumption, distance to complete conversion by the circuit .
Paper presented at Walk21-VI “Everyday Walking Culture”, The 6th International Conference on Walking in the 21st Century, September 22-23 2005, Zurich, Switzerland www.walk21.ch www.walk21.com Are pedometers useful motivational tools for increasing walking in sedentary adults? Graham Baker, BSc (Hons): University of Strathclyde Professor Nanette Mutrie, DPE, M.Ed, PhD, FBASES: University of Strathclyde Contact details Graham Baker PhD Candidate PESOE Building Strathclyde University (Jordanhill Campus) Glasgow G13 1PP UK firstname.lastname@example.org Abstract Walking helps people achieve the recommended accumulation of 30 minutes moderate activity daily. It is accessible to both genders, young and old, and is capable of overcoming many barriers such as time or expense. The aim of this study was to investigate the effectiveness of pedometers, in conjunction with goal setting programmes (designed to accumulate 30 minutes of walking at least 5 days of the week), in providing motivation for walking. The study used the transtheoretical model of behaviour change (TTM) as a framework for behaviour change. Seventy-one participants (54 women and 17 men, aged 42±11 years; range 18-61 years) wore a sealed pedometer for 7 days to establish baseline step counts. Participants were randomly assigned to; pedometer intervention (n=23, pedometer open for feedback plus 4-week goal setting programme in steps), minutes goal setting intervention (n=24, 4-week goal setting programme in minutes) or control group (n=24, no action for 4 weeks). Questionnaires based on the 4 components of the TTM (self-efficacy, decisional balance, process of change, stage of change) and a 7-day recall of physical activity (PA) were completed at baseline and week 4. A one-way ANOVA was performed between the groups for step-count at baseline, week 4 and step-count difference (week 4 minus baseline). No significant difference was found between the groups at baseline in terms of step-count. However at week 4 (p=0.044) and for step-count difference (p=0.000) significant differences were found. A one sample t-test identified that the pedometer intervention group significantly increased step count from baseline to week 4 (mean increase of 20,186 steps, p<0.001). Both other groups displayed no significant difference. 1- sample Wilcoxon tests showed both the pedometer intervention group (p=0.012) and minutes goal setting group (p=0.002) significantly increased their total 7-day recall of PA. The control group reported no significant difference. For all groups (at baseline and at week 4), the processes of self-liberation and self re-evaluation received the highest frequency of use scores. Wilcoxon analysis found the minutes goal setting group significantly increased their use of counter conditioning (p=0.022), while the control group significantly increased their use of stimulus control (p=0.007). 69% of participants considered themselves inactive at baseline, decreasing significantly (p<0.05) to 45% at 4 weeks. Neither self-efficacy or decisional balance displayed any change over time for any group, or differed significantly between groups. The combination of pedometer feedback and a goal-setting programme appears to provide additional short-term motivation for those wishing to increase walking over a goal-setting programme alone. Further research needs to examine possible long-term motivational effects of pedometers. The application of the transtheoretical model within active living interventions should be examined with specific attention to the questionnaires. Biographies Graham Baker Graham Baker has a Faculty of Education PhD scholarship at the University of Strathclyde and is investigating the motivational effect of pedometers in sedentary individuals. Previously Graham achieved a first class Honours degree in Physiology and Sports Science at the University of Glasgow. Professor Nanette Mutrie Nanette Mutrie is Professor of Exercise and Sport Psychology at the University of Strathclyde and is also a visiting professor at the MRC Social and Public Health Sciences Unit at the University of Glasgow. She is an Accredited Sport and Exercise Psychologist with the British Association of Sport and Exercise Science (BASES). Are pedometers useful motivational tools for increasing walking in sedentary adults? Graham Baker, BSc (Hons): University of Strathclyde Professor Nanette Mutrie, DPE, M.Ed, PhD, FBASES: University of Strathclyde Introduction In 1995 the American College of Sports Medicine issued new physical activity guidelines which shifted from the traditional cardiovascular fitness message and towards “active living recommendations” (Pate et al, 1995). The new guidelines state that attaining 30 minutes of accumulated moderately intense physical activity is sufficient to achieve the associated health benefits of physical activity, such as decreased mortality and morbidity. Moderately intense physical activity is defined as activity performed at 3-6 MET’s which is the equivalent of walking 3-4mph for most healthy adults (Pate et al, 1995). There is now an accumulation of evidence which supports the active living recommendations, including the US Surgeon General’s report, (US Department of Health and Human Services, 1996), and walking as a way to achieve the recommendations. Walking has been increasingly promoted as a viable tool for increasing ones’ physical activity given its’ numerous and varying benefits. Walking has many associated health benefits, with the possibility of being used as a weight management technique. It can be employed all year round, carries little risk and is perhaps the one activity that is available to almost all individuals, (Morris and Hardman, 1997). Walking also overcomes many of the barriers associated with more structured forms of exercise such as time and expense. Previous walking interventions have been successful in both raising awareness and promoting short-term behaviour change, however the challenge still remains to provide walking interventions that can bring long-term adherence to walking. Pedometers have recently been promoted in the media as effective tools to increase walking, with the target of achieving 10,000 steps/day being the most popular proposal. Pedometers are well established as a form of objectively measuring walking through step-counts, and have been proven to be both valid and reliable (Tudor-Locke et al, 2002; Crouter et al, 2003; Schneider et al, 2004). Traditionally pedometers were sealed to provide objective measurement. However recent studies designed to increase walking have employed the pedometer open for feedback (Sugiura et al, 2003; Talbot et al, 2003; Chan et al, 2004). This has led to the suggestion that pedometer feedback, by allowing the individual to monitor the step-count and set goals, may be used as a motivational tool. The majority of recent pedometer research has focused on identifying the best way to implement the pedometer in terms of step-goals and targets. The popular 10,000steps/day target is deemed inappropriate, as is any universal step-goal due to the variations noted in different sub- populations, (Tudor-Locke and Myers, 2001). Research from Welk et al, (2000), Wilde et al, (2001) and others has indicated that future walking programmes should be based on personalized step-goals. With the overall aim of achieving the accumulated 30 minutes of activity a day by adding 3,100-4,000 steps onto initial baseline step-counts. However, there is in fact little empirical evidence to support the claim of pedometers as motivational tools. Marshall and Ferney, (2003) have shown that having access to pedometer feedback can result in significantly increased walking compared to those who wore a sealed pedometer. Rooney et al, (2003) has also suggested that the mere presence of a pedometer may increase walking. A study from Mutrie et al, (2004) attempted to establish whether using a pedometer for feedback combined with a goal setting programme provided additional motivation over use of only a goal setting programme. Both groups significantly increased their walking over 4-weeks regardless of whether the pedometer was open for feedback or sealed. Further, Mutrie et al also established that the increase in step-counts was only evident in the short-term and after 52-weeks walking levels had returned to baseline. Contrasting to these studies Eastep et al, (2004) found no increase in walking levels either in those who wore a pedometer open for feedback, or a sealed pedometer. However, Easteps’ study failed to implement a goal setting strategy which has been suggested as vital to the success of pedometers, (Tudor-Locke and Myers, 2001). The Transtheoretical Model (TTM) for behaviour change is a theoretical framework for how behaviour change occurs. It consists of 4 basic constructs and has been adapted to deal with exercise behaviours. The 4 constructs are: stage of change – 5 distinct stages of an individuals’ readiness to cease inactivity; processes of change – 10 processes which represent an individuals behaviours; emotions when becoming active; self-efficacy – the scale of confidence about becoming more active and decisional balance – weighing of the pros and cons in becoming more active. A review from Adams and White, (2002) suggest that physical activity interventions based around the TTM can be effective in encouraging short-term physical activity adoption. However, the long-term effects of the TTM are limited. It has also been suggested the relevance of the TTM relating to the active living message is not established and that the reliability of measurement needs to be improved (Marshall and Biddle, 2001). Aim The aim of this study is to investigate the effectiveness of pedometers, in conjunction with goal- setting programme in providing additional motivation for walking compared with a goal-setting programme only using the transtheoretical model as a framework for behaviour change. Methods Participants Individuals (n=71) were volunteers who responded to adverts (posters, newsletters and emails) and all displayed an interest in improving physical activity levels by increasing walking. Instruments & Questionnaires Participants used the Omron HJ-104 Step-O-Meter due to its’ 7-day memory capability and automatic daily reset function. All participants completed the Physical Activity Readiness Questionnaire (Thomas et al, 1992) to ensure they were physically able to participate. The 4 constructs of the transtheoretical model (TTM) were measured using standard questionnaires. The stage of exercise behaviour change questionnaire was employed (Marcus et al, 1992b). Questionnaires developed by Marcus and Simkin (1994) to measure decisional balance and self- efficacy were employed. Processes of exercise behaviour change were measured by the standard 40-item instrument commonly used for exercise behaviour, (Marcus et al, 1992a). A 7-day recall of physical activity was obtained using The Scottish Physical Activity Questionnaire (SPAQ) (Lowther et al, 1999). Procedures Participants were met at the initial meeting and completed the questionnaires based on the 4 constructs of the TTM. Participants were then instructed to wear a sealed pedometer for 7 days to establish baseline step-counts. Participants were instructed not to alter normal walking patterns and to wear the pedometer continuously apart from when sleeping, showering or engaging in any form of structured physical activity or sport. At 7 days baseline step-counts were obtained and all participants completed the 7-day recall of physical activity SPAQ. Participants were then randomly assigned to either the pedometer intervention group, the minutes intervention group or the control group using allocation by sequentially ordered envelopes. Participants in the pedometer intervention group (n=23) wore a pedometer, open for feedback, for 28 days during which they followed a goal-setting programme based on steps (see Table 1). Given that 1,000 steps is roughly equivalent to 10 minutes moderate walking (Sidman, 2002), participants were instructed to walk 3,000 extra steps on at least 5 days of the week in order to meet active living recommendations. At 28 days participants were met and their previous 7-day step-count recorded. Participants also completed the 7-day recall of physical activity and questionnaires based on the 4 basic constructs of the TTM. Participants in the minutes intervention group (n=24) followed a corresponding goal-setting programme based in minutes for 28 days (see Table 1). At 21 days they were met and asked to wear a sealed pedometer for the final 7 days of the walking programme. At 28 days participants were met and their previous 7-day step-count recorded, at which time they also completed the 7- day recall of physical activity and the questionnaires based on the 4 basic constructs of the TTM. Participants in the control group (n=24) were instructed to follow their normal walking patterns for 28 days. At 21 days participants were met and asked to wear a sealed pedometer for the final 7 days. At 28 days participants were met and their previous 7-day step-count recorded, at which time the 7-day recall of physical activity was completed as were the questionnaires based on the 4 basic constructs of the TTM. Table 1: Graduated weekly step-counts targets for both intervention groups over the 4-week study Week Pedometer Intervention Targets Minutes Intervention Targets 1 1500 extra steps at least 3 days of 15 extra minutes at least 3 days of the the week week 2 1500 extra steps at least 5 days of 15 extra minutes at least 5 days of the the week week 3 3000 extra steps at least 3 days of 30 extra minutes at least 3 days of the the week week 4 3000 extra steps at least 5 days of 30 extra minutes at least 5 days of the the week week Results Participants Participants (n=71) who completed the 4-week programme had an average age of 42.41±10.95 years with a range of 18-61 years. There were no significant differences between groups in terms of age. The participants consisted of 76% (n=54) women and 24% (n=17) men. Step-counts A one-way ANOVA was performed between the groups for step-count at baseline, week 4 and step-count difference (week 4 minus baseline). No significant difference was found between the groups at baseline in terms of step-count. However at week 4 (p=0.044) and for step-count difference (p<0.001) significant differences were found. Follow-up Bonferroni comparisons failed to identify the exact location of the difference at week 4, however Bonferroni comparisons identified that the change from baseline to week 4 between the pedometer intervention group and the control group was significant (p<0.001). One-sample t-tests performed on each group identified that from baseline to week 4 the minutes intervention group and the control group did not significantly alter their step-count. The pedometer intervention group significantly increased their step-count from baseline to week 4, by a mean increase of 20,186 steps (p<0.001, CI: 12194, 28178). Figure 1 displays the mean step-count over time for each group. 85000 80000 75000 te s 70000 S p 65000 60000 55000 50000 Baseline Week 4 Tim e Peodmeter Intervention Minutes Intervention Control Figure 1: Mean step-counts for each group at baseline and week 4 7-day recall of physical activity Kruskal Wallis tests identified that there were no significant differences between groups in terms of total minutes recalled either at baseline or week 4. Wilcoxon within group tests identified that in terms of total minutes of recalled physical activity the pedometer intervention group (p=0.012) significantly increased the total minutes recalled from baseline to week 4 by 336 minutes. The minutes intervention group significantly (p=0.002) increased the total minutes recalled from baseline to week 4 by 466 minutes. The control group displayed no significant increase in total minutes recalled from baseline to week 4. The total minutes were then broken down into minutes recalled in leisure time and minutes recalled in occupational time. Kruskal Wallis tests identified that there were no significant differences between groups at either baseline or week 4 in terms of either leisure or occupational minutes recalled. Wilcoxon within group tests identified that no group significantly altered their recall of occupational minutes between baseline and week 4. However, Wilcoxon tests identified that both the pedometer intervention group (p=0.001) by a mean increase of 316 minutes and the minutes intervention group (p=0.001) by a mean increase of 403 minutes significantly increased their recall of leisure minutes from baseline to week 4. The control group displayed no significant difference. Stage of Change Chi-squared analysis determined that there were no significant differences between the groups at baseline or at week 4 in terms of stage of change. For analysis it was decided to combine the stages of pre-contemplation, contemplation and preparation to establish those who were inactive, and combine the stages of action and maintenance to establish those who were active. At baseline 69% of all participants considered themselves inactive, which fell to 45% of all participants at week 4. This movement of 17 participants from being inactive at baseline to active at week 4 was statistically significant (p<0.005). Self-efficacy Self-efficacy was scored on a 7-point Likert scale where 1 = extremely unconfident and 7 = extremely confident. A median score of 6 was found for each group at both baseline and week 4. Kruskal Wallis analysis identified that there were no significant differences between groups in terms of reported self-efficacy either at baseline or week 4. Wilcoxon within group tests identified that no group altered their reported self-efficacy from baseline to week 4. Decisional Balance Decisional Balance was scored on a 5-point Likert scale with a minimum score of 3 and a maximum score of 15 obtainable for both pros and cons. Median scores for each group are displayed in Table 2. Table 2: Median scores for pros and cons by group at each time-point Pedometer Minutes Control Intervention Intervention Baseline 4-Week Baseline 4-Week Baseline 4-Week Median 12.00 13.00 11.50 11.50 12.00 11.50 Pros Median 5.00 6.00 5.00 5.00 5.00 6.00 Cons Kruskal Wallis tests identified that there were no significant differences between groups either for pros or cons at baseline or week 4. Wilcoxon within group tests identified that there were no significant changes from baseline to week 4 for either pros or cons in any group. Processes of Exercise Behaviour Change The process of change questionnaire contains 40 questions, the 10 processes covered equally with 4 questions each. A Likert scale, of 1-5, is employed for each question, used as a rating scale for the frequency of each process used, 1 being “used never”, and 5 being “used repeatedly”. By addition of the 4 questions, a score can be established for each process, with 4 being the minimum score and 20 the maximum score possible. Kruskal Wallis analysis of each process at baseline established that 9 of the 10 processes were not significantly different. A significant difference in process use between the groups was found at baseline for the process of self-liberation (p=0.023). Follow-up Mann Whitney tests established that the pedometer group reported a significantly lower use of self-liberation than the control group. Kruskal Wallis analysis of each process at week 4 established that there were no significant differences in process use for any process between groups. Table 3 displays the ranked median process use at week 4 for each group. A similar table is found for process use at baseline with the processes of self-liberation and self re-evaluation being the most frequently used processes and the processes of helping relationships and stimulus control being the least frequently used processes. Table 3: Ranked median process use by group at week 4 Process Pedometer Minutes Control Intervention Intervention Self Liberation 14.0 14.5 14.0 Self Re-evaluation 14.0 13.5 13.0 Counter Conditioning 11.0 14.0 11.0 Consciousness Raising 11.0 11.0 10.0 Reinforcement 9.0 10.5 10.5 Management Environmental Re- 9.0 11.0 9.0 evaluation Social Liberation 9.0 10.0 9.0 Dramatic Relief 8.0 8.0 9.0 Helping Relationships 6.0 10.5 7.5 Stimulus Control 7.0 6.5 8.0 Wilcoxon tests were performed on each group to determine any change in process use from baseline to week 4. The pedometer intervention group displayed no significant differences in process use from baseline to week 4. Wilcoxon analysis found that the minutes intervention group significantly increased their use of counter-conditioning (p=0.022) from baseline to week 4, and the control group significantly increased their use of stimulus control (p=0.007) from baseline to week 4. Discussion The results from this study suggest there is a short-term additional motivational effect of using a pedometer compared with using only a goal-setting programme. The pedometer intervention group was the only group to significantly increase their step-count from baseline to week 4. Despite the minutes intervention group receiving corresponding targets they failed to significantly increase their step-count. This would indicate that the feedback received from the pedometer allowed the pedometer intervention group to monitor their walking levels to a greater degree. Indeed, a significantly higher percentage of the pedometer intervention group (77%) achieved their 4 week walking goals, compared with the minutes intervention group (33%) and control group (17%). This is further supported by qualitative data from participants in the pedometer intervention group who suggested that the pedometer allowed them to easily set short-term goals and provided motivation to achieve these. The minutes intervention group also suggested that having the pedometer would have helped them achieve their goals. These results concur with previous research such as Rooney et al, (2003) which suggest that pedometers can provide a short-term motivational effect. Whereas Mutrie et al, (2004) suggests that a goal setting programme alone can provide the same motivational effect as those also employing pedometer feedback, these results would suggest that it is the pedometer feedback which is imperative to increase walking. This could be explained by the participants in Mutrie et al, (2004) who followed a goal setting programme alone, actually wore a sealed pedometer at all times, and it could be the mere presence of the pedometer which accounted for the increase in walking in that group. Both the pedometer intervention and the minutes intervention group significantly increased their recalled physical activity, by significantly recalling a greater number of leisure minutes spent being physically active. No group altered their recalled minutes being physically active at work which suggests that it is difficult to incorporate walking during working hours due to the nature of the participants’ occupation. The decisional balance and self-efficacy results indicate that walking is seen as an activity which most people can participate comfortably and successfully in. The high levels of self- efficacy reported throughout the study would suggest that the active living recommendations are seen as realistic and achievable. Despite this the minutes intervention group did not achieve the recommendations by week 4 indicating that a successful intervention, such as the pedometer, is still necessary to help people achieve these. There may be a suggestion that these questionnaires do not fully explore an individuals’ self-efficacy and decisional balance with respect to walking, and there perhaps needs to be re-development or refining of the questionnaires. Current theory regarding the processes of exercise behaviour change is that experiential processes are more commonly used during the early stages of change. However, 69% of all participants considered themselves inactive at baseline yet certain behavioural processes such as self-liberation and counter-conditioning were used just as frequently as experiential processes. This would agree with previous research which has suggested that experiential and behavioural and experiential processes may not operate according to current theory with regards to physical activity (Marshall and Biddle, 2001). Marcus et al, (1992a) suggested that all processes of change might be important at all stages of change. Our results indicate that this may indeed be the case, and further qualitative data from participants indicated that several of the questions were either not relevant or simply confusing. Indeed Pate et al, (1995) suggested a need to refine the process questionnaire when dealing with the active living message. Conclusion These results agree with previous research that pedometers can provide a short-term motivational effect for people wishing to increase walking. This study helps to establish a knowledge base that highlights and identify this effect. The results also identify aspects of the transtheoretical model that need to be further examined for future use with active living interventions. This study further emphasizes the need for research into the long-term effect of pedometer motivation and adherence. Acknowledgements 1. Walking the Way to Health Initiative – for the supply of the Omron HJ-104 Step-O- Meters 2. Ashley Gillies, Louise Galloway, Sabina McDonald, Paul MacDonald, Jessica Tyrrell for help with data collection. References Chan, C.B., Ryan, D.A.J. & Tudor-Locke. C. (2004). Health benefits of a pedometer-based physical activity intervention in sedentary workers. Preventive Medicine, 39, 1215-1222 Crouter, S.E., Schneider, P.L., Karabulut, M. & Bassett. D.R. Jr. (2003). Validity of 10 electronic pedometers for measuring steps, distance, and energy cost. Medicine & Science in Sports & Exercise 35(8), 1455-60 Eastep, E., Beveridge, S., Eisenman, P., Ransdell, L. & Shultz, B. (2004). 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