+1 Recommend
0 collections
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Adherence and retention to the self-managed community-based Step Into Health program in Qatar (2012–2019)


      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.



          Investigate adherence and retention to the “Step Into Health (SIH)” initiative ( www.stepintohealth.qa [website access only available from within the State of Qatar]), a Qatari self-managed community-based health program, from 2012 to 2019.


          Participants (16,711; 16–80 years; 37% females, 34% Qatari) used a pedometer or smartphone application (app) to measure step count. Absolute adherence (ADH) and retention (RET) were calculated, with ADH (%) the ratio between number of days data and SIH enrollment length (RET). Linear Mixed Models identified differences in ADH between RET groups, main effects (i.e., sex, device, age, BMI, nationality) and interaction effects for ADH (RET entered as a covariate).


          Average ADH and RET to SIH (irrespective of sex, age, device and BMI) was 50% (±31%), and 16% (±20%), respectively, with ADH differing significantly between RET groups ( F = 460.2, p < 0.001). RET (as a covariate) revealed a significant main effect for device ( F = 12.00, p < 0.001) and age ( F = 4.31, p = 0.001) on ADH observed. There was a significant association between RET and sex ( p < 0.001), device ( p < 0.001), and age groups 16–25 y ( p < 0.001), and 26–35 y ( p < 0.001). There were no significant main effects for sex or BMI on ADH, and no interaction effects ( p ≥ 0.21) observed.


          Follow-up data (e.g., interviews, focus groups, etc.) determining why differences in ADH and RET are observed appears prudent. To convert those that lapsed and/or abandoned SIH/PA into committed long-term PA adherers. This would be a first step to develop targeted public health promotions and initiatives to enhance health outcomes at a population level.

          Related collections

          Most cited references49

          • Record: found
          • Abstract: found
          • Article: not found

          Physical activity in the United States measured by accelerometer.

          To describe physical activity levels of children (6-11 yr), adolescents (12-19 yr), and adults (20+ yr), using objective data obtained with accelerometers from a representative sample of the U.S. population. These results were obtained from the 2003-2004 National Health and Nutritional Examination Survey (NHANES), a cross-sectional study of a complex, multistage probability sample of the civilian, noninstitutionalized U.S. population in the United States. Data are described from 6329 participants who provided at least 1 d of accelerometer data and from 4867 participants who provided four or more days of accelerometer data. Males are more physically active than females. Physical activity declines dramatically across age groups between childhood and adolescence and continues to decline with age. For example, 42% of children ages 6-11 yr obtain the recommended 60 min x d(-1) of physical activity, whereas only 8% of adolescents achieve this goal. Among adults, adherence to the recommendation to obtain 30 min x d(-1) of physical activity is less than 5%. Objective and subjective measures of physical activity give qualitatively similar results regarding gender and age patterns of activity. However, adherence to physical activity recommendations according to accelerometer-measured activity is substantially lower than according to self-report. Great care must be taken when interpreting self-reported physical activity in clinical practice, public health program design and evaluation, and epidemiological research.
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Behavior Change Techniques Implemented in Electronic Lifestyle Activity Monitors: A Systematic Content Analysis

            Background Electronic activity monitors (such as those manufactured by Fitbit, Jawbone, and Nike) improve on standard pedometers by providing automated feedback and interactive behavior change tools via mobile device or personal computer. These monitors are commercially popular and show promise for use in public health interventions. However, little is known about the content of their feedback applications and how individual monitors may differ from one another. Objective The purpose of this study was to describe the behavior change techniques implemented in commercially available electronic activity monitors. Methods Electronic activity monitors (N=13) were systematically identified and tested by 3 trained coders for at least 1 week each. All monitors measured lifestyle physical activity and provided feedback via an app (computer or mobile). Coding was based on a hierarchical list of 93 behavior change techniques. Further coding of potentially effective techniques and adherence to theory-based recommendations were based on findings from meta-analyses and meta-regressions in the research literature. Results All monitors provided tools for self-monitoring, feedback, and environmental change by definition. The next most prevalent techniques (13 out of 13 monitors) were goal-setting and emphasizing discrepancy between current and goal behavior. Review of behavioral goals, social support, social comparison, prompts/cues, rewards, and a focus on past success were found in more than half of the systems. The monitors included a range of 5-10 of 14 total techniques identified from the research literature as potentially effective. Most of the monitors included goal-setting, self-monitoring, and feedback content that closely matched recommendations from social cognitive theory. Conclusions Electronic activity monitors contain a wide range of behavior change techniques typically used in clinical behavioral interventions. Thus, the monitors may represent a medium by which these interventions could be translated for widespread use. This technology has broad applications for use in clinical, public health, and rehabilitation settings.
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study

              Background Physical activity has not been objectively measured in prospective cohorts with sufficiently large numbers to reliably detect associations with multiple health outcomes. Technological advances now make this possible. We describe the methods used to collect and analyse accelerometer measured physical activity in over 100,000 participants of the UK Biobank study, and report variation by age, sex, day, time of day, and season. Methods Participants were approached by email to wear a wrist-worn accelerometer for seven days that was posted to them. Physical activity information was extracted from 100Hz raw triaxial acceleration data after calibration, removal of gravity and sensor noise, and identification of wear / non-wear episodes. We report age- and sex-specific wear-time compliance and accelerometer measured physical activity, overall and by hour-of-day, week-weekend day and season. Results 103,712 datasets were received (44.8% response), with a median wear-time of 6.9 days (IQR:6.5–7.0). 96,600 participants (93.3%) provided valid data for physical activity analyses. Vector magnitude, a proxy for overall physical activity, was 7.5% (2.35mg) lower per decade of age (Cohen’s d = 0.9). Women had a higher vector magnitude than men, apart from those aged 45-54yrs. There were major differences in vector magnitude by time of day (d = 0.66). Vector magnitude differences between week and weekend days (d = 0.12 for men, d = 0.09 for women) and between seasons (d = 0.27 for men, d = 0.15 for women) were small. Conclusions It is feasible to collect and analyse objective physical activity data in large studies. The summary measure of overall physical activity is lower in older participants and age-related differences in activity are most prominent in the afternoon and evening. This work lays the foundation for studies of physical activity and its health consequences. Our summary variables are part of the UK Biobank dataset and can be used by researchers as exposures, confounding factors or outcome variables in future analyses.

                Author and article information

                Front Public Health
                Front Public Health
                Front. Public Health
                Frontiers in Public Health
                Frontiers Media S.A.
                15 September 2022
                : 10
                [1] 1Department of Physical Education, College of Education, Qatar University , Doha, Qatar
                [2] 2World Innovation Summit for Health, Qatar Foundation , Doha, Qatar
                [3] 3Aspetar Orthopedic and Sports Medicine Hospital , Doha, Qatar
                Author notes

                Edited by: Sathish Thirunavukkarasu, Emory University, United States

                Reviewed by: Elezebeth Mathews, Central University of Kerala, India; Uma V. Sankar, MVR Cancer Centre and Research Institute, India

                *Correspondence: Bryna C. R. Chrismas bchrismas@ 123456qu.edu.qa

                This article was submitted to Public Health Education and Promotion, a section of the journal Frontiers in Public Health

                Copyright © 2022 Chrismas, Majed, Al-Mohannadi and Sayegh.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Page count
                Figures: 3, Tables: 2, Equations: 0, References: 52, Pages: 10, Words: 6487
                Funded by: Qatar National Library, doi 10.13039/100019779;
                Public Health
                Original Research

                physical activity,public health,wearable technology,pedometer,smartphone application,walking


                Comment on this article