Papers by Anouk Middelweerd
Journal of Rehabilitation Medicine, 2014
Objective: To compare daily stride rate activity, daily exercise intensity, and heart rate intens... more Objective: To compare daily stride rate activity, daily exercise intensity, and heart rate intensity of stride rate in children with cerebral palsy with that of typically developing children. Methods: Forty-three children with cerebral palsy, walking without (Gross Motor Function Classification System (GMFCS) I and II) or with (GMFCS III) an aid and 27 typically developing children (age range 7-14 years) wore a StepWatch TM activity monitor and a heart rate monitor. Time spent and mean heart rate reserve at each stride rate activity level and time spent in each mean heart rate reserve zone was compared. Results: Daily stride rate activity was lower in children with cerebral palsy (39%, 49% and 79% in GMFCS I, II and III, respectively) compared with typically developing children (p < 0.05), while there were no differences in time spent at different mean heart rate reserve zones. Mean heart rate reserve at all stride rate activity levels was not different between typically developing children, GMFCS I and II, while mean heart rate reserve was higher for GFMCS III at stride rates < 30 strides/min (p < 0.05). Conclusion: Stride rate activity levels reflect the effort of walking, in children with cerebral palsy who are walking without aids, similar to that of typically developing children, whereas children with cerebral palsy using walking aids show higher effort of walking. Despite a lower stride rate activity in cerebral palsy, daily exercise intensity seems comparable, indicating that the StepWatch TM monitor and the heart rate monitor measure different aspects of physical activity.
The international journal of behavioral nutrition and physical activity, 2014
In May 2013, the iTunes and Google Play stores contained 23,490 and 17,756 smartphone application... more In May 2013, the iTunes and Google Play stores contained 23,490 and 17,756 smartphone applications (apps) categorized as Health and Fitness, respectively. The quality of these apps, in terms of applying established health behavior change techniques, remains unclear. The study sample was identified through systematic searches in iTunes and Google Play. Search terms were based on Boolean logic and included AND combinations for physical activity, healthy lifestyle, exercise, fitness, coach, assistant, motivation, and support. Sixty-four apps were downloaded, reviewed, and rated based on the taxonomy of behavior change techniques used in the interventions. Mean and ranges were calculated for the number of observed behavior change techniques. Using nonparametric tests, we compared the number of techniques observed in free and paid apps and in iTunes and Google Play. On average, the reviewed apps included 5 behavior change techniques (range 2-8). Techniques such as self-monitoring, provid...
Journal of Rehabilitation Medicine, 2014
Objective: To compare daily stride rate activity, daily exercise intensity, and heart rate intens... more Objective: To compare daily stride rate activity, daily exercise intensity, and heart rate intensity of stride rate in children with cerebral palsy with that of typically developing children. Methods: Forty-three children with cerebral palsy, walking without (Gross Motor Function Classification System (GMFCS) I and II) or with (GMFCS III) an aid and 27 typically developing children (age range 7-14 years) wore a StepWatch TM activity monitor and a heart rate monitor. Time spent and mean heart rate reserve at each stride rate activity level and time spent in each mean heart rate reserve zone was compared. Results: Daily stride rate activity was lower in children with cerebral palsy (39%, 49% and 79% in GMFCS I, II and III, respectively) compared with typically developing children (p < 0.05), while there were no differences in time spent at different mean heart rate reserve zones. Mean heart rate reserve at all stride rate activity levels was not different between typically developing children, GMFCS I and II, while mean heart rate reserve was higher for GFMCS III at stride rates < 30 strides/min (p < 0.05). Conclusion: Stride rate activity levels reflect the effort of walking, in children with cerebral palsy who are walking without aids, similar to that of typically developing children, whereas children with cerebral palsy using walking aids show higher effort of walking. Despite a lower stride rate activity in cerebral palsy, daily exercise intensity seems comparable, indicating that the StepWatch TM monitor and the heart rate monitor measure different aspects of physical activity.
IEEE Internet Computing, 2015
The international journal of behavioral nutrition and physical activity, 2015
The transition from adolescence to early adulthood is a critical period in which there is a decli... more The transition from adolescence to early adulthood is a critical period in which there is a decline in physical activity (PA). College and university students make up a large segment of this age group. Smartphones may be used to promote and support PA. The purpose of this qualitative study was to explore Dutch students' preferences regarding a PA application (PA app) for smartphones. Thirty Dutch students (aged 18-25 years) used a PA app for three weeks and subsequently attended a focus group discussion (k = 5). To streamline the discussion, a discussion guide was developed covering seven main topics, including general app usage, usage and appreciation of the PA app, appreciation of and preferences for its features and the sharing of PA accomplishments through social media. The discussions were audio and video recorded, transcribed and analysed according to conventional content analysis. The participants, aged 21 ± 2 years, were primarily female (67%). Several themes emerged: ap...
Background
In May 2013, the iTunes and Google Play stores contained 23,490 and 17,756 smartphone ... more Background
In May 2013, the iTunes and Google Play stores contained 23,490 and 17,756 smartphone applications (apps) categorized as Health and Fitness, respectively. The quality of these apps, in terms of applying established health behavior change techniques, remains unclear.
Methods
The study sample was identified through systematic searches in iTunes and Google Play. Search terms were based on Boolean logic and included AND combinations for physical activity, healthy lifestyle, exercise, fitness, coach, assistant, motivation, and support. Sixty-four apps were downloaded, reviewed, and rated based on the taxonomy of behavior change techniques used in the interventions. Mean and ranges were calculated for the number of observed behavior change techniques. Using nonparametric tests, we compared the number of techniques observed in free and paid apps and in iTunes and Google Play.
Results
On average, the reviewed apps included 5 behavior change techniques (range 2–8). Techniques such as self-monitoring, providing feedback on performance, and goal-setting were used most frequently, whereas some techniques such as motivational interviewing, stress management, relapse prevention, self-talk, role models, and prompted barrier identification were not. No differences in the number of behavior change techniques between free and paid apps, or between the app stores were found.
Conclusions
The present study demonstrated that apps promoting physical activity applied an average of 5 out of 23 possible behavior change techniques. This number was not different for paid and free apps or between app stores. The most frequently used behavior change techniques in apps were similar to those most frequently used
in other types of physical activity promotion interventions.
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Papers by Anouk Middelweerd
In May 2013, the iTunes and Google Play stores contained 23,490 and 17,756 smartphone applications (apps) categorized as Health and Fitness, respectively. The quality of these apps, in terms of applying established health behavior change techniques, remains unclear.
Methods
The study sample was identified through systematic searches in iTunes and Google Play. Search terms were based on Boolean logic and included AND combinations for physical activity, healthy lifestyle, exercise, fitness, coach, assistant, motivation, and support. Sixty-four apps were downloaded, reviewed, and rated based on the taxonomy of behavior change techniques used in the interventions. Mean and ranges were calculated for the number of observed behavior change techniques. Using nonparametric tests, we compared the number of techniques observed in free and paid apps and in iTunes and Google Play.
Results
On average, the reviewed apps included 5 behavior change techniques (range 2–8). Techniques such as self-monitoring, providing feedback on performance, and goal-setting were used most frequently, whereas some techniques such as motivational interviewing, stress management, relapse prevention, self-talk, role models, and prompted barrier identification were not. No differences in the number of behavior change techniques between free and paid apps, or between the app stores were found.
Conclusions
The present study demonstrated that apps promoting physical activity applied an average of 5 out of 23 possible behavior change techniques. This number was not different for paid and free apps or between app stores. The most frequently used behavior change techniques in apps were similar to those most frequently used
in other types of physical activity promotion interventions.
In May 2013, the iTunes and Google Play stores contained 23,490 and 17,756 smartphone applications (apps) categorized as Health and Fitness, respectively. The quality of these apps, in terms of applying established health behavior change techniques, remains unclear.
Methods
The study sample was identified through systematic searches in iTunes and Google Play. Search terms were based on Boolean logic and included AND combinations for physical activity, healthy lifestyle, exercise, fitness, coach, assistant, motivation, and support. Sixty-four apps were downloaded, reviewed, and rated based on the taxonomy of behavior change techniques used in the interventions. Mean and ranges were calculated for the number of observed behavior change techniques. Using nonparametric tests, we compared the number of techniques observed in free and paid apps and in iTunes and Google Play.
Results
On average, the reviewed apps included 5 behavior change techniques (range 2–8). Techniques such as self-monitoring, providing feedback on performance, and goal-setting were used most frequently, whereas some techniques such as motivational interviewing, stress management, relapse prevention, self-talk, role models, and prompted barrier identification were not. No differences in the number of behavior change techniques between free and paid apps, or between the app stores were found.
Conclusions
The present study demonstrated that apps promoting physical activity applied an average of 5 out of 23 possible behavior change techniques. This number was not different for paid and free apps or between app stores. The most frequently used behavior change techniques in apps were similar to those most frequently used
in other types of physical activity promotion interventions.