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      Using Smartphone Technology to Monitor Physical Activity in the 10,000 Steps Program: A Matched Case–Control Trial

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          Abstract

          Background

          Website-delivered physical activity interventions are successful in producing short-term behavior change. However, problems with engagement and retention of participants in these programs prevent long-term behavior change. New ways of accessing online content (eg, via smartphones) may enhance engagement in these interventions, which in turn may improve the effectiveness of the programs.

          Objective

          To measure the potential of a newly developed smartphone application to improve health behaviors in existing members of a website-delivered physical activity program (10,000 Steps, Australia). The aims of the study were to (1) examine the effect of the smartphone application on self-monitoring and self-reported physical activity levels, (2) measure the perceived usefulness and usability of the application, and (3) examine the relationship between the perceived usefulness and usability of the application and its actual use.

          Methods

          All participants were existing members of the 10,000 Steps program. We recruited the intervention group (n = 50) via email and instructed them to install the application on their smartphone and use it for 3 months. Participants in this group were able to log their steps by using either the smartphone application or the 10,000 Steps website. Following the study, the intervention group completed an online questionnaire assessing perceived usability and usefulness of the smartphone application. We selected control group participants (n = 150), matched for age, gender, level of self-monitoring, preintervention physical activity level, and length of membership in the 10,000 Steps program, after the intervention was completed. We collected website and smartphone usage statistics during the entire intervention period.

          Results

          Over the study period (90 days), the intervention group logged steps on an average of 62 days, compared with 41 days in the matched group. Intervention participants used the application 71.22% (2210/3103) of the time to log their steps. Logistic regression analyses revealed that use of the application was associated with an increased likelihood to log steps daily during the intervention period compared with those not using the application (odds ratio 3.56, 95% confidence interval 1.72–7.39). Additionally, use of the application was associated with an increased likelihood to log greater than 10,000 steps on each entry (odds ratio 20.64, 95% confidence interval 9.19–46.39). Linear regression analysis revealed a nonsignificant relationship between perceived usability ( r = .216, P = .21) and usefulness ( r = .229, P = .17) of the application and frequency of logging steps in the intervention group.

          Conclusion

          Using a smartphone application as an additional delivery method to a website-delivered physical activity intervention may assist in maintaining participant engagement and behavior change. However, due to study design limitations, these outcomes should be interpreted with caution. More research, using larger samples and longer follow-up periods, is needed to replicate the findings of this study.

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          Most cited references47

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          World Health Organization.

          Ala Alwan (2007)
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            The Effectiveness of Web-Based vs. Non-Web-Based Interventions: A Meta-Analysis of Behavioral Change Outcomes

            Background A primary focus of self-care interventions for chronic illness is the encouragement of an individual's behavior change necessitating knowledge sharing, education, and understanding of the condition. The use of the Internet to deliver Web-based interventions to patients is increasing rapidly. In a 7-year period (1996 to 2003), there was a 12-fold increase in MEDLINE citations for “Web-based therapies.” The use and effectiveness of Web-based interventions to encourage an individual's change in behavior compared to non-Web-based interventions have not been substantially reviewed. Objective This meta-analysis was undertaken to provide further information on patient/client knowledge and behavioral change outcomes after Web-based interventions as compared to outcomes seen after implementation of non-Web-based interventions. Methods The MEDLINE, CINAHL, Cochrane Library, EMBASE, ERIC, and PSYCHInfo databases were searched for relevant citations between the years 1996 and 2003. Identified articles were retrieved, reviewed, and assessed according to established criteria for quality and inclusion/exclusion in the study. Twenty-two articles were deemed appropriate for the study and selected for analysis. Effect sizes were calculated to ascertain a standardized difference between the intervention (Web-based) and control (non-Web-based) groups by applying the appropriate meta-analytic technique. Homogeneity analysis, forest plot review, and sensitivity analyses were performed to ascertain the comparability of the studies. Results Aggregation of participant data revealed a total of 11,754 participants (5,841 women and 5,729 men). The average age of participants was 41.5 years. In those studies reporting attrition rates, the average drop out rate was 21% for both the intervention and control groups. For the five Web-based studies that reported usage statistics, time spent/session/person ranged from 4.5 to 45 minutes. Session logons/person/week ranged from 2.6 logons/person over 32 weeks to 1008 logons/person over 36 weeks. The intervention designs included one-time Web-participant health outcome studies compared to non-Web participant health outcomes, self-paced interventions, and longitudinal, repeated measure intervention studies. Longitudinal studies ranged from 3 weeks to 78 weeks in duration. The effect sizes for the studied outcomes ranged from -.01 to .75. Broad variability in the focus of the studied outcomes precluded the calculation of an overall effect size for the compared outcome variables in the Web-based compared to the non-Web-based interventions. Homogeneity statistic estimation also revealed widely differing study parameters (Qw16 = 49.993, P ≤ .001). There was no significant difference between study length and effect size. Sixteen of the 17 studied effect outcomes revealed improved knowledge and/or improved behavioral outcomes for participants using the Web-based interventions. Five studies provided group information to compare the validity of Web-based vs. non-Web-based instruments using one-time cross-sectional studies. These studies revealed effect sizes ranging from -.25 to +.29. Homogeneity statistic estimation again revealed widely differing study parameters (Qw4 = 18.238, P ≤ .001). Conclusions The effect size comparisons in the use of Web-based interventions compared to non-Web-based interventions showed an improvement in outcomes for individuals using Web-based interventions to achieve the specified knowledge and/or behavior change for the studied outcome variables. These outcomes included increased exercise time, increased knowledge of nutritional status, increased knowledge of asthma treatment, increased participation in healthcare, slower health decline, improved body shape perception, and 18-month weight loss maintenance.
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              How many steps/day are enough? Preliminary pedometer indices for public health.

              Pedometers are simple and inexpensive body-worn motion sensors that are readily being used by researchers and practitioners to assess and motivate physical activity behaviours. Pedometer-determined physical activity indices are needed to guide their efforts. Therefore, the purpose of this article is to review the rationale and evidence for general pedometer-based indices for research and practice purposes. Specifically, we evaluate popular recommendations for steps/day and attempt to translate existing physical activity guidelines into steps/day equivalents. Also, we appraise the fragmented evidence currently available from associations derived from cross-sectional studies and a limited number of interventions that have documented improvements (primarily in body composition and/or blood pressure) with increased steps/day.A value of 10000 steps/day is gaining popularity with the media and in practice and can be traced to Japanese walking clubs and a business slogan 30+ years ago. 10000 steps/day appears to be a reasonable estimate of daily activity for apparently healthy adults and studies are emerging documenting the health benefits of attaining similar levels. Preliminary evidence suggests that a goal of 10000 steps/day may not be sustainable for some groups, including older adults and those living with chronic diseases. Another concern about using 10000 steps/day as a universal step goal is that it is probably too low for children, an important target population in the war against obesity. Other approaches to pedometer-determined physical activity recommendations that are showing promise of health benefit and individual sustainability have been based on incremental improvements relative to baseline values. Based on currently available evidence, we propose the following preliminary indices be used to classify pedometer-determined physical activity in healthy adults: (i). or=10000 steps/day indicates the point that should be used to classify individuals as 'active'. Individuals who take >12500 steps/day are likely to be classified as 'highly active'.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J. Med. Internet Res
                JMIR
                Journal of Medical Internet Research
                Gunther Eysenbach (JMIR Publications Inc., Toronto, Canada )
                1439-4456
                1438-8871
                Mar-Apr 2012
                20 April 2012
                : 14
                : 2
                : e55
                Affiliations
                [1] 1simpleCentre for Physical Activity Studies simpleInstitute for Health and Social Sciences Research simpleCQUniversity North RockhamptonAustralia
                [2] 2simpleFaculty of Physical Education and Recreation simpleUniversity of Alberta Edmonton, ABCanada
                Article
                v14i2e55
                10.2196/jmir.1950
                3376516
                22522112
                6b1028ae-9669-4168-b395-aee587ace2b2
                ©Morwenna Kirwan, Mitch J Duncan, Corneel Vandelanotte, W Kerry Mummery. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 20.04.2012.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 27 September 2011
                : 18 October 2011
                : 08 March 2012
                : 08 March 2012
                Categories
                Original Paper

                Medicine
                smartphone,health behavior,physical activity,matched case-control study,intervention
                Medicine
                smartphone, health behavior, physical activity, matched case-control study, intervention

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