Publications by authors named "Stephanie Schoeppe"

42 Publications

Sedentary behaviour research in adults: A scoping review of systematic reviews and meta-analyses.

J Sports Sci 2021 May 19:1-13. Epub 2021 May 19.

Appleton Institute, School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, Australia.

Sedentary behaviour research is rapidly growing. Scoping reviews are important to inform policy and practice.The aim of this scoping a review was to map the available evidence from systematic reviews and meta-analyses of sedentary behaviour research on adults (≥18 years), within the phases of the behavioural epidemiology framework, and to identify bibliometric parameters of studies included in this review. Nine bibliographic databases were searched. Studies were screened and relevant information (e.g., general information, inclusion criteria, findings and reporting quality) was extracted independently by two authors. In total, 108 systematic reviews and/or meta-analyses of sedentary behaviour research in adults (≥18 years) were included. Most papers (91.7%) were published between 2010 and 2020. Studies on the relationship of sedentary behaviour with health (53.7%) and interventions to reduce sedentary behaviour (25.9%) were most common. Forty-five (41.7%) studies reported quality assessment with categorization, and 887 out of 1268 (70%) included primary studies were categorized having moderate-to-high quality. Sedentary behaviour research on adults (≥18 years) has grown exponentially in the last decade and demonstrates strength in several stages of the behavioural epidemiology framework. However, more research should focus on the measurement, prevalence/epidemiology and determinants of sedentary behaviour, to better inform policy development.
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http://dx.doi.org/10.1080/02640414.2021.1928382DOI Listing
May 2021

Quality, Features, and Presence of Behavior Change Techniques in Mobile Apps Designed to Improve Physical Activity in Pregnant Women: Systematic Search and Content Analysis.

JMIR Mhealth Uhealth 2021 04 7;9(4):e23649. Epub 2021 Apr 7.

School of Health, Medical and Applied Sciences, CQUniversity, Rockhampton, Australia.

Background: Physical activity during pregnancy is associated with several health benefits for the mother and child. However, very few women participate in regular physical activity during pregnancy. eHealth platforms (internet and mobile apps) have become an important information source for pregnant women. Although the use of pregnancy-related apps has significantly increased among pregnant women, very little is known about their theoretical underpinnings, including their utilization of behavior change techniques (BCTs). This is despite research suggesting that inclusion of BCTs in eHealth interventions are important for promoting healthy behaviors, including physical activity.

Objective: The aim of this study was to conduct a systematic search and content analysis of app quality, features, and the presence of BCTs in apps designed to promote physical activity among pregnant women.

Methods: A systematic search in the Australian App Store and Google Play store using search terms relating to exercise and pregnancy was performed. App quality and features were assessed using the 19-item Mobile App Rating Scale (MARS), and a taxonomy of BCTs was used to determine the presence of BCTs (26 items). BCTs previously demonstrating efficacy in behavior changes during pregnancy were also identified from a literature review. Spearman correlations were used to investigate the relationships between app quality, app features, and number of BCTs identified.

Results: Nineteen exercise apps were deemed eligible for this review and they were accessed via Google Play (n=13) or App Store (n=6). The MARS overall quality scores indicated moderate app quality (mean 3.5 [SD 0.52]). Functionality was the highest scoring MARS domain (mean 4.2 [SD 0.5]), followed by aesthetics (mean 3.7 [SD 0.6]) and information quality (mean 3.16 [SD 0.42]). Subjective app quality (mean 2.54 [SD 0.64]) and likelihood for behavioral impact (mean 2.5 [SD 0.6]) were the lowest scoring MARS domains. All 19 apps were found to incorporate at least two BCTs (mean 4.74, SD 2.51; range 2-10). However, only 11 apps included BCTs that previously demonstrated efficacy for behavior change during pregnancy, the most common being provide opportunities for social comparison (n=8) and prompt self-monitoring of behavior (n=7). There was a significant positive correlation between the number of BCTs with engagement and aesthetics scores, but the number of BCTs was not significantly correlated with functionality, information quality, total MARS quality, or subjective quality.

Conclusions: Our findings showed that apps designed to promote physical activity among pregnant women were functional and aesthetically pleasing, with overall moderate quality. However, the incorporation of BCTs was low, with limited prevalence of BCTs previously demonstrating efficacy in behavior change during pregnancy. Future app development should identify and adopt factors that enhance and encourage user engagement, including the use of BCTs, especially those that have demonstrated efficacy for promoting physical activity behavior change among pregnant women.
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http://dx.doi.org/10.2196/23649DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8060865PMC
April 2021

Examining moderators of the effectiveness of a web- and video-based computer-tailored physical activity intervention.

Prev Med Rep 2021 Jun 23;22:101336. Epub 2021 Feb 23.

Central Queensland University, School of Health, Medical and Applied Sciences, Appleton Institute, Physical Activity Research Group, Rockhampton, Australia.

Understanding for whom behaviour change interventions work is important, however there is a lack of studies examining potential moderators in such interventions. This study investigated potential moderators on the effectiveness of a computer-tailored intervention to increase physical activity among Australian adults. People who had <150 min of moderate-vigorous physical activity (MVPA) a week, able to speak and read English, aged ≥18 years, lived in Australia, and had internet access were eligible to participate. Participants recruited through social media, emails, and third-party databases, were randomly assigned to either the control (n = 167) or intervention groups (n = 334). Physical activity was measured objectively by ActiGraph GT3X and also by self-report at baseline and three months. Three-way interaction terms were tested to identify moderators (i.e., demographic characteristics, BMI, and perceived neighbourhood walkability). The results showed that the three-way interaction was marginally significant for sex on accelerometer measured MVPA/week (p = 0.061) and steps/day (p = 0.047). The intervention appeared to be more effective for women compared to men. No significant three-way interactions were found for the other potential moderators. Strategies to improve levels of personalisation may be needed so that physical activity interventions can be better tailored to different subgroups, especially sex, and therefore improve intervention effectiveness.
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http://dx.doi.org/10.1016/j.pmedr.2021.101336DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7937773PMC
June 2021

Are web-based personally tailored physical activity videos more effective than personally tailored text-based interventions? Results from the three-arm randomised controlled TaylorActive trial.

Br J Sports Med 2021 Mar 3;55(6):336-343. Epub 2020 Nov 3.

Priority Research Centre for Physical Activity and Nutrition, School of Education, The University of Newcastle, Callaghan, New South Wales, Australia.

Objectives: Some online, personally tailored, text-based physical activity interventions have proven effective. However, people tend to 'skim' and 'scan' web-based text rather than thoroughly read their contents. In contrast, online videos are more engaging and popular. We examined whether web-based personally tailored physical activity videos were more effective in promoting physical activity than personally tailored text and generic information.

Methods: 501 adults were randomised into a video-tailored intervention, text-tailored intervention or control. Over a 3-month period, intervention groups received access to eight sessions of web-based personally tailored physical activity advice. Only the delivery method differed between intervention groups: tailored video versus tailored text. The primary outcome was 7-day ActiGraph-GT3X+ measured moderate-to-vigorous physical activity (MVPA) assessed at 0, 3 and 9 months. Secondary outcomes included self-reported MVPA and website engagement. Differences were examined using generalised linear mixed models with intention-to-treat and multiple imputation.

Results: Accelerometer-assessed MVPA increased 23% in the control (1.23 (1.06, 1.43)), 12% in the text-tailored (1.12 (0.95, 1.32)) and 28% in the video-tailored (1.28 (1.06, 1.53)) groups at the 3-month follow-up only, though there were no significant between-group differences. Both text-tailored (1.77 (1.37, 2.28]) and video-tailored (1.37 (1.04, 1.79)) groups significantly increased self-reported MVPA more than the control group at 3 months only, but there were no differences between video-tailored and text-tailored groups. The video-tailored group spent significantly more time on the website compared with text-tailored participants (90 vs 77 min, p=0.02).

Conclusions: The personally tailored videos were not more effective than personally tailored text in increasing MVPA. The findings from this study conflict with pilot study outcomes and previous literature. Process evaluation and mediation analyses will provide further insights.

Trial Registration Number: ACTRN12615000057583.
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http://dx.doi.org/10.1136/bjsports-2020-102521DOI Listing
March 2021

Validation of the Mobile Application Rating Scale (MARS).

PLoS One 2020 2;15(11):e0241480. Epub 2020 Nov 2.

Department of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, University Ulm, Ulm, Germany.

Background: Mobile health apps (MHA) have the potential to improve health care. The commercial MHA market is rapidly growing, but the content and quality of available MHA are unknown. Instruments for the assessment of the quality and content of MHA are highly needed. The Mobile Application Rating Scale (MARS) is one of the most widely used tools to evaluate the quality of MHA. Only few validation studies investigated its metric quality. No study has evaluated the construct validity and concurrent validity.

Objective: This study evaluates the construct validity, concurrent validity, reliability, and objectivity, of the MARS.

Methods: Data was pooled from 15 international app quality reviews to evaluate the metric properties of the MARS. The MARS measures app quality across four dimensions: engagement, functionality, aesthetics and information quality. Construct validity was evaluated by assessing related competing confirmatory models by confirmatory factor analysis (CFA). Non-centrality (RMSEA), incremental (CFI, TLI) and residual (SRMR) fit indices were used to evaluate the goodness of fit. As a measure of concurrent validity, the correlations to another quality assessment tool (ENLIGHT) were investigated. Reliability was determined using Omega. Objectivity was assessed by intra-class correlation.

Results: In total, MARS ratings from 1,299 MHA covering 15 different health domains were included. Confirmatory factor analysis confirmed a bifactor model with a general factor and a factor for each dimension (RMSEA = 0.074, TLI = 0.922, CFI = 0.940, SRMR = 0.059). Reliability was good to excellent (Omega 0.79 to 0.93). Objectivity was high (ICC = 0.82). MARS correlated with ENLIGHT (ps<.05).

Conclusion: The metric evaluation of the MARS demonstrated its suitability for the quality assessment. As such, the MARS could be used to make the quality of MHA transparent to health care stakeholders and patients. Future studies could extend the present findings by investigating the re-test reliability and predictive validity of the MARS.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0241480PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7605637PMC
December 2020

Effects of an Activity Tracker and App Intervention to Increase Physical Activity in Whole Families-The Step It Up Family Feasibility Study.

Int J Environ Res Public Health 2020 10 20;17(20). Epub 2020 Oct 20.

Physical Activity Research Group, School of Health, Medical and Applied Sciences, Appleton Institute, Central Queensland University, Building 77, Bruce Highway, Rockhampton, QLD 4702, Australia.

(1) Background: Interventions using activity trackers and smartphone apps have demonstrated their ability to increase physical activity in children and adults. However, they have not been tested in whole families. Further, few family-centered interventions have actively involved both parents and assessed physical activity effects separately for children, mothers and fathers. Objective: To examine the feasibility and short-term effects of an activity tracker and app intervention to increase physical activity in the whole family (children, mothers and fathers). (2) Methods: This was a single-arm feasibility study with pre-post intervention measures. Between 2017-2018, 40 families (58 children aged 6-10 years, 39 mothers, 33 fathers) participated in the 6-week program in Queensland, Australia. Using commercial activity trackers combined with apps (Garmin Vivofit Jr for children, Vivofit 3 for adults; Garmin Australasia Pty Ltd., Sydney, Australia), the intervention included individual and family-level goal-setting, self-monitoring, performance feedback, family step challenges, family social support and modelling, weekly motivational text messages and an introductory session. Parent surveys were used to assess physical activity effects measured as pre-post intervention changes in moderate-to-vigorous physical activity (MVPA) in children, mothers and fathers. Objective Garmin activity tracker data was recorded to assess physical activity levels (steps, active minutes) during the intervention. (3) Results: Thirty-eight families completed the post intervention survey (95% retention). At post intervention, MVPA had increased in children by 58 min/day (boys: 54 min/day, girls: 62 min/day; all < 0.001). In mothers, MVPA increased by 27 min/day ( < 0.001) and in fathers, it increased by 31 min/day ( < 0.001). The percentage of children meeting Australia's physical activity guidelines for children (≥60 MVPA min/day) increased from 34% to 89% ( < 0.001). The percentage of mothers and fathers meeting Australia's physical activity guidelines for adults (≥150 MVPA min/week) increased from 8% to 57% ( < 0.001) in mothers and from 21% to 68% ( < 0.001) in fathers. The percentage of families with 'at least one child and both parents' meeting the physical activity guidelines increased from 0% to 41% ( < 0.001). Objective activity tracker data recorded during the intervention showed that the mean () number of active minutes per day in children was 82.1 (17.1). Further, the mean () steps per day was 9590.7 (2425.3) in children, 7397.5 (1954.2) in mothers and 8161.7 (3370.3) in fathers. (4) Conclusions: Acknowledging the uncontrolled study design, the large pre-post changes in MVPA and rather high step counts recorded during the intervention suggest that an activity tracker and app intervention can increase physical activity in whole families. The program warrants further efficacy testing in a larger, randomized controlled trial.
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http://dx.doi.org/10.3390/ijerph17207655DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7588994PMC
October 2020

Efficacy of a computer-tailored web-based physical activity intervention using Fitbits for older adults: a randomised controlled trial protocol.

BMJ Open 2019 12 23;9(12):e033305. Epub 2019 Dec 23.

School of Health, Medical and Applied Sciences, Appleton Institute, Physical Activity Research Group, CQUniversity, Rockhampton, Queensland, Australia.

Introduction: Physical activity is an integral part of healthy ageing, yet the majority of older adults 65+ years are not sufficiently active. Web-based physical activity interventions hold much promise to reach older adults. Preliminary evidence suggests that web-based interventions with tailored advice and Fitbits may be well suited for older adults.

Methods And Analysis: This study aims to test the effectiveness of 'Active for Life', a 12-week computer-tailored web-based physical activity intervention using Fitbits for older adults. We will recruit 300 participants who will be randomly assigned to one of three trial arms: (1) web-based physical activity intervention with tailored advice only, (2) web-based physical activity intervention with tailored advice and Fitbit or (3) a wait-list control. The primary outcome, objective moderate to vigorous physical activity (MVPA) and secondary outcomes of objective sedentary behaviour, objective sleep, quality of life, social support, physical function and satisfaction with life will be assessed at baseline and week 12. The secondary outcomes of self-reported physical activity, sitting time and sleep will be assessed at baseline, week 6, 12 and 24. Website usability and participant satisfaction will be assessed at week 12 and website usage and intervention fidelity will be assessed from week 1 to 24. Intention-to-treat linear mixed model analyses will be used to test for group (tailoring only, tailoring +Fitbit, control) differences on changes in the main outcome, MVPA and secondary outcomes. Generalised linear models will be used to compare intervention groups (tailoring only, tailoring +Fitbit) on website usability, participant satisfaction, website usage and intervention fidelity.

Ethics And Dissemination: The study has received ethics approval from the Central Queensland University Human Research Ethics Committee (H16/12-321). Study outcomes will be disseminated through peer-reviewed publications and academic conferences and used to inform improvements and dissemination of a tailored, web-based physical activity intervention for adults 65+ years.

Trial Registration Number: Australian and New Zealand Clinical Trials Registry Number: ACTRN12618000646246.
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http://dx.doi.org/10.1136/bmjopen-2019-033305DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7008447PMC
December 2019

A focus group study of older adults' perceptions and preferences towards web-based physical activity interventions.

Inform Health Soc Care 2020 Sep 6;45(3):273-281. Epub 2019 Nov 6.

School of Health, Medical and Applied Sciences, Central Queensland University , Rockhampton, Australia.

Objective: To explore older adults' perceptions and preferences for web-based physical activity interventions.

Participants: Adults 65+ years were recruited via telephoning randomly selected households in Central Queensland, Australia.

Methods: Six focus groups were conducted with a total of 46 adults 65+ years. Data were analyzed by qualitative content analysis.

Results: This group of older adults liked websites that have links to information and included instructional videos and disliked websites that were hard to navigate. Many participants did not express an initial interest in web-based physical activity programs. The most common reason was that they did not have a computer or adequate internet connection. Some participants said they would be interested if it included a structured exercise program. When asked about preferences for web-based physical activity programs, this group preferred them to be simple and not cluttered, to include personalized advice, to include reminder check-ins and the ability to review goals after illness or injury. The most common preference for personalized advice in web-based interventions was that the information needs to be tailored to their existing injuries and illnesses.

Conclusion: The findings from this study will inform the design of future web-based interventions specifically tailored to the needs of older people.
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http://dx.doi.org/10.1080/17538157.2019.1656210DOI Listing
September 2020

Should I sit or stand: likelihood of adherence to messages about reducing sitting time.

BMC Public Health 2019 Jul 3;19(1):871. Epub 2019 Jul 3.

Physical Activity Research Group, Appleton Institute, School of Health, Medical and Applied Sciences, Central Queensland University, Building 7, Bruce Hwy, Rockhampton, QLD, 4701, Australia.

Background: High population levels of sitting is contributing to high rates of chronic health problems. Therefore, the aim of this study was to identify the sitting time messages with the greatest potential to reduce sitting behaviour, as well as identify how this may differ according to demographic, behavioural and psychosocial characteristics.

Methods: Australian adults (N = 1460) were asked to report the likelihood that they would adhere to seven messages promoting reduced sitting time and two messages promoting increased physical activity (from 'not at all likely' to 'very likely'). Ordinal regression models were used to compare messages on the likelihood of adherence and whether likelihood of adherence differed as a function of demographic, psychosocial and behavioural characteristics.

Results: Likelihood of adherence was highest for the messages, 'Stand and take a break from sitting as frequently as you can' (83% respectively) and 'Avoid sitting for more than 10 hours during the entire day' (82%) and was significantly lower for the message, 'Sit as little as possible on all days of the week' (46%) compared to all other messages.

Conclusions: To increase likelihood of adherence messages should be specific, achievable and promote healthy alternatives to sitting (e.g. standing). Messages promoting standing as a healthy alternative to sitting may be more likely to engage people with high sitting behaviour and messages promoting physical activity may be more likely to engage males and retired adults.
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http://dx.doi.org/10.1186/s12889-019-7189-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6610814PMC
July 2019

The Effectiveness of a Web-Based Computer-Tailored Physical Activity Intervention Using Fitbit Activity Trackers: Randomized Trial.

J Med Internet Res 2018 12 18;20(12):e11321. Epub 2018 Dec 18.

Physical Activity Research Group, Appleton Institute, School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, Australia.

Background: Web-based interventions that provide personalized physical activity advice have demonstrated good effectiveness but rely on self-reported measures of physical activity, which are prone to overreporting, potentially reducing the accuracy and effectiveness of the advice provided.

Objective: This study aimed to examine whether the effectiveness of a Web-based computer-tailored intervention could be improved by integrating Fitbit activity trackers.

Methods: Participants received the 3-month TaylorActive intervention, which included 8 modules of theory-based, personally tailored physical activity advice and action planning. Participants were randomized to receive the same intervention either with or without Fitbit tracker integration. All intervention materials were delivered on the Web, and there was no face-to-face contact at any time point. Changes in physical activity (Active Australia Survey), sitting time (Workforce Sitting Questionnaire), and body mass index (BMI) were assessed 1 and 3 months post baseline. Advice acceptability, website usability, and module completion were also assessed.

Results: A total of 243 Australian adults participated. Linear mixed model analyses showed a significant increase in total weekly physical activity (adjusted mean increase=163.2; 95% CI 52.0-274.5; P=.004) and moderate-to-vigorous physical activity (adjusted mean increase=78.6; 95% CI 24.4-131.9; P=.004) in the Fitbit group compared with the non-Fitbit group at the 3-month follow-up. The sitting time and BMI decreased more in the Fitbit group, but no significant group × time interaction effects were found. The physical activity advice acceptability and the website usability were consistently rated higher by participants in the Fitbit group. Non-Fitbit group participants completed 2.9 (SD 2.5) modules, and Fitbit group participants completed 4.4 (SD 3.1) modules.

Conclusions: Integrating physical activity trackers into a Web-based computer-tailored intervention significantly increased intervention effectiveness.

Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12616001555448; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=371793 (Archived by WebCite at http://www.webcitation.org/73ioTxQX2).
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http://dx.doi.org/10.2196/11321DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6315269PMC
December 2018

How are different levels of knowledge about physical activity associated with physical activity behaviour in Australian adults?

PLoS One 2018 28;13(11):e0207003. Epub 2018 Nov 28.

Physical Activity Research Group, Appleton Institute, School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, QLD, Australia.

People with knowledge of the benefits of physical activity tend to be more active; however, such knowledge is typically operationalized as a basic understanding that physical activity is 'good' for health. Therefore, the aim of this study was to investigate whether there are differences in how detailed a person's knowledge is about the benefits of physical activity. Participants (N = 615) completed an online survey to measure their current physical activity behaviour, as well as their level of knowledge of the benefits and risks of physical (in)activity. The majority of participants (99.6%) strongly agreed that physical activity is good for health, however on average, participants only identified 13.8 out of 22 diseases associated with physical inactivity and over half of participants (55.6%) could not identify how much physical activity is recommended for health benefits. Furthermore, 45% of the participants overestimated, 9% underestimated and 27% did not know the increased risk of disease resulting from inactivity as indicated by the Australian Department of Health. Participants were significantly more active when they correctly identified more diseases associated with physical inactivity and when they overestimated the risks associated with inactivity. Therefore, health promotion initiatives should increase knowledge of the types of diseases associated with inactivity. Low knowledge of physical activity guidelines suggest they should be promoted more, as this knowledge provides guidance on frequency, types and duration of physical activity needed for health.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0207003PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6261553PMC
April 2019

Physical Activity Attitudes, Preferences, and Experiences of Regionally-Based Australia Adults Aged 65 Years and Older.

J Aging Phys Act 2019 08 1;27(4):446-451. Epub 2019 Aug 1.

An understanding of physical activity attitudes, preferences, and experiences in older adults is important for informing interventions. Focus groups were conducted with 46 regionally-based Australian adults aged 65 years and older, who were not currently meeting activity recommendations. Content analysis revealed that participants mainly engaged in incidental activities such as gardening and household chores rather than planned exercise; however, leisure-time walking was also mentioned frequently. Although participants valued the physical and mental health benefits of physical activity, they reported being restricted by poor physical health, extreme weather, and fear of injury. Participants were interested in exercise groups and physical activity programs tailored to their existing physical health. The majority of participants reported preferring to be active with others. The findings from this study are useful in for informing future interventions specifically tailored to the needs of older adults in Australia.
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http://dx.doi.org/10.1123/japa.2017-0426DOI Listing
August 2019

Physical Activity and Outdoor Play of Children in Public Playgrounds-Do Gender and Social Environment Matter?

Int J Environ Res Public Health 2018 06 28;15(7). Epub 2018 Jun 28.

Faculty of Statistics, Technische Universität Dortmund, 44221 Dortmund, Germany.

Background: Few studies have delved into the relationship of the social environment with children’s physical activity and outdoor play in public playgrounds by considering gender differences. The aim of the present study was to examine gender differences and the relationship of the social environment with children’s physical activity and outdoor play in public playgrounds.

Methods: A quantitative, observational study was conducted at ten playgrounds in one district of a middle-sized town in Germany. The social environment, physical activity levels, and outdoor play were measured using a modified version of the System for Observing Play and Leisure Activity in Youth.

Results: In total, 266 observations of children (117 girls/149 boys) between four and 12 years old were used in this analysis. Significant gender differences were found in relation to activity types, but not in moderate-to-vigorous physical activity (MVPA). The presence of active children was the main explanatory variable for MVPA. In the models stratified by gender, the presence of opposite-sex children was a significant negative predictor of MVPA in girls but not in boys.

Conclusions: The presence of active children contributes to children’s physical activity levels in public playgrounds. Girls’ physical activity seems to be suppressed in the presence of boys.
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http://dx.doi.org/10.3390/ijerph15071356DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6069007PMC
June 2018

Ten-year physical activity trends by location in Queensland.

Aust J Rural Health 2018 Apr 19. Epub 2018 Apr 19.

Physical Activity Research Group, Appleton Institute, School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, Queensland, Australia.

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http://dx.doi.org/10.1111/ajr.12415DOI Listing
April 2018

Do singles or couples live healthier lifestyles? Trends in Queensland between 2005-2014.

PLoS One 2018 28;13(2):e0192584. Epub 2018 Feb 28.

Physical Activity Research Group, School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, Queensland, Australia.

Objectives: To compare the frequency of and trends in healthy lifestyle factors between singles and couples.

Methods: Cross-sectional data from annual surveys conducted from 2005-2014 were used. The pooled sample included 15,001 Australian adults (mean age: 52.9 years, 50% male, 74% couples) who participated in the annual Queensland Social Survey via computer-assisted telephone interviews. Relationship status was dichotomised into single and couple. Binary logistic regression was used to assess associations between relationship status, and the frequency of and trends in healthy lifestyle factors.

Results: Compared to singles, couples were significantly more likely to be a non-smoker (OR = 1.82), and meet recommendations for limited fast food (OR = 1.12), alcohol consumption (OR = 1.27) and fruit and vegetable intake (OR = 1.24). Fruit and vegetable intake was not significantly associated with relationship status after adjusting for the other healthy lifestyle factors. Conversely, couples were significantly less likely to be within a normal weight range (OR = 0.81). In both singles and couples, the trend data revealed significant declines in the rates of normal weight (singles: OR = 0.97, couples: OR = 0.97) and viewing TV for less than 14 hours per week (singles: OR = 0.85, couples: OR = 0.84), whilst non-smoking rates significantly increased (singles: OR = 1.12, couples: OR = 1.03). The BMI trend was no longer significant when adjusting for health behaviours. Further, in couples, rates of meeting recommendations for physical activity and fruit/vegetable consumption significantly decreased (OR = 0.97 and OR = 0.95, respectively), as did rates of eating no fast food (OR = 0.96). These trends were not significant when adjusting for the other healthy lifestyle factors. In singles, rates of meeting alcohol recommendations significantly increased (OR = 1.08).

Conclusions: Health behaviour interventions are needed in both singles and couples, but relationship status needs to be considered in interventions targeting alcohol, fast food, smoking and BMI. Further research is needed to understand why health behaviours differ by relationship status in order to further improve interventions.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0192584PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5830314PMC
April 2018

Recovery without autonomy: Progress forward or more of the same for mental health service users?

Int J Ment Health Nurs 2018 Oct 15;27(5):1459-1469. Epub 2018 Feb 15.

School of Nursing, Midwifery and Social Sciences, Central Queensland University, Rockhampton, Queensland, Australia.

In Western nations, the Recovery approach has become a widely accepted philosophy and treatment concept in mental health. Yet, community understanding of the Recovery approach remains largely unexplored. This study aimed to investigate (i) people's awareness of the principles underpinning the Recovery approach in mental health, and (ii) the treatment approaches people consider most important, and whether these align with the Recovery approach. To achieve these aims, a random sample of 1217 Australian adults participated in the National Social Survey (QSS) via telephone interview. People's experience with mental health services, the importance they place on various treatment approaches, and their awareness of principles underpinning the Recovery approach were assessed. Analyses were conducted using descriptive statistics. Most participants (94%) agreed that 'regardless of the severity of symptoms experienced and/or the mental illness diagnosis, being diagnosed with a mental illness means there is always hope for a meaningful life'. Moreover, most participants considered treatments in line with the Recovery approach as important. However, few participants (35%) agreed with the principle that 'after diagnosis, the person themselves should direct the long-term management of their mental illness, rather than a medical professional'. Australian people were to some extent aware of the principles underpinning the Recovery Approach, particularly with regard to hope, ability to live a meaningful life, and the importance of support from family, friends, and others living with mental illness. Nonetheless, autonomy was not highly prioritized, with the prevailing view that management of mental illness should be directed by the medical profession.
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http://dx.doi.org/10.1111/inm.12446DOI Listing
October 2018

Age differences in physical activity intentions and implementation intention preferences.

J Behav Med 2018 06 7;41(3):406-415. Epub 2017 Nov 7.

Physical Activity Research Group, Appleton Institute, School of Health, Medical and Applied Sciences, Central Queensland University, Building 77, Bruce Highway, Rockhampton, QLD, 4702, Australia.

This study aimed to examine older adults' physical activity intentions and preferred implementation intentions, and how intentions and preferred implementation intentions differ between older, middle aged and younger adults. A cross-sectional Australian wide telephone survey of 1217 respondents was conducted in 2016. Multiple and ordinal regression analyses were conducted to compare intentions and preferred implementation intentions between older (65 +), middle aged (45-64) and younger adults (< 45). A higher percentage of older adults had no intentions to engage in regular physical activity within the next 6 months (60%) compared to younger adults (25%). Older adults' most popular preferences included being active at least once a day and for 30 min or less and were more likely to prefer more frequent and shorter sessions compared to younger adults. Both older and middle aged adults were more likely to prefer slower paced physical activity compared to younger adults who preferred fast paced physical activity. Physical activity interventions for older adults should address the high percentage of older adults with no intentions and public health campaigns for older adults should promote 30 min daily sessions of slow paced activity.
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http://dx.doi.org/10.1007/s10865-017-9899-yDOI Listing
June 2018

Sitting Time in Adults 65 Years and Over: Behavior, Knowledge, and Intentions to Change.

J Aging Phys Act 2018 04 12;26(2):276-283. Epub 2018 Apr 12.

This study examined sitting time, knowledge, and intentions to change sitting time in older adults. An online survey was completed by 494 Australians aged 65+. Average daily sitting was high (9.0 hr). Daily sitting time was the highest during TV (3.3 hr), computer (2.1 hr), and leisure (1.7 hr). A regression analysis demonstrated that women were more knowledgeable about the health risks of sitting compared to men. The percentage of older adults intending to sit less were the highest for TV (24%), leisure (24%), and computer (19%) sitting time. Regression analyses demonstrated that intentions varied by gender (for TV sitting), education (leisure and work sitting), body mass index (computer, leisure, and transport sitting), and physical activity (TV, computer, and leisure sitting). Interventions should target older adults' TV, computer, and leisure time sitting, with a focus on intentions in older males and older adults with low education, those who are active, and those with a normal weight.
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http://dx.doi.org/10.1123/japa.2016-0304DOI Listing
April 2018

Activity Trackers Implement Different Behavior Change Techniques for Activity, Sleep, and Sedentary Behaviors.

Interact J Med Res 2017 Aug 14;6(2):e13. Epub 2017 Aug 14.

School of Science and Health, Western Sydney University, Campbelltown, Australia.

Background: Several studies have examined how the implementation of behavior change techniques (BCTs) varies between different activity trackers. However, activity trackers frequently allow tracking of activity, sleep, and sedentary behaviors; yet, it is unknown how the implementation of BCTs differs between these behaviors.

Objective: The aim of this study was to assess the number and type of BCTs that are implemented by wearable activity trackers (self-monitoring systems) in relation to activity, sleep, and sedentary behaviors and to determine whether the number and type of BCTs differ between behaviors.

Methods: Three self-monitoring systems (Fitbit [Charge HR], Garmin [Vivosmart], and Jawbone [UP3]) were each used for a 1-week period in August 2015. Each self-monitoring system was used by two of the authors (MJD and BM) concurrently. The Coventry, Aberdeen, and London-Refined (CALO-RE) taxonomy was used to assess the implementation of 40 BCTs in relation to activity, sleep, and sedentary behaviors. Discrepancies in ratings were resolved by discussion, and interrater agreement in the number of BCTs implemented was assessed using kappa statistics.

Results: Interrater agreement ranged from 0.64 to 1.00. From a possible range of 40 BCTs, the number of BCTs present for activity ranged from 19 (Garmin) to 33 (Jawbone), from 4 (Garmin) to 29 (Jawbone) for sleep, and 0 (Fitbit) to 10 (Garmin) for sedentary behavior. The average number of BCTs implemented was greatest for activity (n=26) and smaller for sleep (n=14) and sedentary behavior (n=6).

Conclusions: The number and type of BCTs implemented varied between each of the systems and between activity, sleep, and sedentary behaviors. This provides an indication of the potential of these systems to change these behaviors, but the long-term effectiveness of these systems to change activity, sleep, and sedentary behaviors remains unknown.
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http://dx.doi.org/10.2196/ijmr.6685DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5575434PMC
August 2017

A Test of How Australian Adults Allocate Time for Physical Activity.

Behav Med 2019 Jan-Mar;45(1):1-6. Epub 2017 Sep 13.

a Physical Activity Research Group, School of Health, Medical, and Applied Sciences , Central Queensland University , Rockhampton , QLD , Australia.

The most common reported barrier to physical activity is a lack of sufficient time. Just like most resources in economics are finite, so too is time within a day. We utilized a time-utility model to better understand how people are allocating time for physical activity. Additionally, we tested whether the allocation of physical activity time impacts people's perception of "lack of time" as a barrier for physical activity or their likelihood of being sufficiently physical active. Australian adults (N = 725 participants, 54% men) reported their time use throughout their day, perceived lack of time as a barrier to activity, and physical activity. Cluster analysis and χ-tests were used to test the study research questions. People tended to either be entirely inactive (29%) or active while doing either leisure (18%), occupation (18%), transport (14%), or household (22%) activities. Those who were active during their leisure or transport time were most likely to be sufficiently active. There were no significant differences among clusters in how much people perceived that lack of time was a physical activity barrier. The commonly reported barrier of not having enough time to be active might be a fallacy. Although a lack of time is a commonly reported barrier of physical activity, these findings bring to light that increasing physical activity behavior is not as simple as adding more time to the day.
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http://dx.doi.org/10.1080/08964289.2017.1361902DOI Listing
August 2019

Apps to improve diet, physical activity and sedentary behaviour in children and adolescents: a review of quality, features and behaviour change techniques.

Int J Behav Nutr Phys Act 2017 06 24;14(1):83. Epub 2017 Jun 24.

School of Health, Medical and Applied Sciences, Physical Activity Research Group, Central Queensland University, Bruce Highway, Rockhampton, QLD, 4702, Australia.

Background: The number of commercial apps to improve health behaviours in children is growing rapidly. While this provides opportunities for promoting health, the content and quality of apps targeting children and adolescents is largely unexplored. This review systematically evaluated the content and quality of apps to improve diet, physical activity and sedentary behaviour in children and adolescents, and examined relationships of app quality ratings with number of app features and behaviour change techniques (BCTs) used.

Methods: Systematic literature searches were conducted in iTunes and Google Play stores between May-November 2016. Apps were included if they targeted children or adolescents, focused on improving diet, physical activity and/or sedentary behaviour, had a user rating of at least 4+ based on at least 20 ratings, and were available in English. App inclusion, downloading and user-testing for quality assessment and content analysis were conducted independently by two reviewers. Spearman correlations were used to examine relationships between app quality, and number of technical app features and BCTs included.

Results: Twenty-five apps were included targeting diet (n = 12), physical activity (n = 18) and sedentary behaviour (n = 7). On a 5-point Mobile App Rating Scale (MARS), overall app quality was moderate (total MARS score: 3.6). Functionality was the highest scoring domain (mean: 4.1, SD: 0.6), followed by aesthetics (mean: 3.8, SD: 0.8), and lower scoring for engagement (mean: 3.6, SD: 0.7) and information quality (mean: 2.8, SD: 0.8). On average, 6 BCTs were identified per app (range: 1-14); the most frequently used BCTs were providing 'instructions' (n = 19), 'general encouragement' (n = 18), 'contingent rewards' (n = 17), and 'feedback on performance' (n = 13). App quality ratings correlated positively with numbers of technical app features (rho = 0.42, p < 0.05) and BCTs included (rho = 0.54, p < 0.01).

Conclusions: Popular commercial apps to improve diet, physical activity and sedentary behaviour in children and adolescents had moderate quality overall, scored higher in terms of functionality. Most apps incorporated some BCTs and higher quality apps included more app features and BCTs. Future app development should identify factors that promote users' app engagement, be tailored to specific population groups, and be informed by health behaviour theories.
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http://dx.doi.org/10.1186/s12966-017-0538-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5483249PMC
June 2017

8-year trends in physical activity, nutrition, TV viewing time, smoking, alcohol and BMI: A comparison of younger and older Queensland adults.

PLoS One 2017 1;12(3):e0172510. Epub 2017 Mar 1.

Physical Activity Research Group, School of Medical, Health and Applied Sciences, Central Queensland University, Rockhampton, Queensland, Australia.

Lifestyle behaviours significantly contribute to high levels of chronic disease in older adults. The aims of the study were to compare the prevalence and the prevalence trends of health behaviours (physical activity, fruit and vegetable consumption, fast food consumption, TV viewing, smoking and alcohol consumption), BMI and a summary health behaviour indicator score in older (65+ years) versus younger adults (18-65 years). The self-report outcomes were assessed through the Queensland Social Survey annually between 2007-2014 (n = 12,552). Regression analyses were conducted to compare the proportion of older versus younger adults engaging in health behaviours and of healthy weight in all years combined and examine trends in the proportion of younger and older adults engaging in health behaviours and of healthy weight over time. Older adults were more likely to meet recommended intakes of fruit and vegetable (OR = 1.43, 95%CI = 1.23-1.67), not consume fast food (OR = 2.54, 95%CI = 2.25-2.86) and be non-smokers (OR = 3.02, 95%CI = 2.53-3.60) in comparison to younger adults. Conversely, older adults were less likely to meet the physical activity recommendations (OR = 0.86, 95%CI = 0.78-0.95) and watch less than 14 hours of TV per week (OR = 0.65, 95%CI = 0.58-0.74). Overall, older adults were more likely to report engaging in 3, or at least 4 out of 5 healthy behaviours. The proportion of both older and younger adults meeting the physical activity recommendations (OR = 0.97, 95%CI = 0.95-0.98 and OR = 0.94, 95%CI = 0.91-0.97 respectively), watching less than 14 hours of TV per week (OR = 0.96, 95%CI = 0.94-0.99 and OR = 0.94, 95%CI = 0.90-0.99 respectively) and who were a healthy weight (OR = 0.95, 95%CI = 0.92-0.99 and OR = 0.96, 95%CI = 0.94-0.98 respectively) decreased over time. The proportion of older adults meeting the fruit and vegetable recommendations (OR = 0.90, 95%CI = 0.84-0.96) and not consuming fast food (OR = 0.94, 95%CI = 0.88-0.99) decreased over time. Although older adults meet more health behaviours than younger adults, the decreasing prevalence of healthy nutrition behaviours in this age group needs to be addressed.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0172510PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5332140PMC
August 2017

Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: a systematic review.

Int J Behav Nutr Phys Act 2016 Dec 7;13(1):127. Epub 2016 Dec 7.

Central Queensland University, School of Health, Medical and Applied Sciences, Physical Activity Research Group, Building 77, Bruce Highway, Rockhampton, QLD 4702, Australia.

Background: Health and fitness applications (apps) have gained popularity in interventions to improve diet, physical activity and sedentary behaviours but their efficacy is unclear. This systematic review examined the efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour in children and adults.

Methods: Systematic literature searches were conducted in five databases to identify papers published between 2006 and 2016. Studies were included if they used a smartphone app in an intervention to improve diet, physical activity and/or sedentary behaviour for prevention. Interventions could be stand-alone interventions using an app only, or multi-component interventions including an app as one of several intervention components. Outcomes measured were changes in the health behaviours and related health outcomes (i.e., fitness, body weight, blood pressure, glucose, cholesterol, quality of life). Study inclusion and methodological quality were independently assessed by two reviewers.

Results: Twenty-seven studies were included, most were randomised controlled trials (n = 19; 70%). Twenty-three studies targeted adults (17 showed significant health improvements) and four studies targeted children (two demonstrated significant health improvements). Twenty-one studies targeted physical activity (14 showed significant health improvements), 13 studies targeted diet (seven showed significant health improvements) and five studies targeted sedentary behaviour (two showed significant health improvements). More studies (n = 12; 63%) of those reporting significant effects detected between-group improvements in the health behaviour or related health outcomes, whilst fewer studies (n = 8; 42%) reported significant within-group improvements. A larger proportion of multi-component interventions (8 out of 13; 62%) showed significant between-group improvements compared to stand-alone app interventions (5 out of 14; 36%). Eleven studies reported app usage statistics, and three of them demonstrated that higher app usage was associated with improved health outcomes.

Conclusions: This review provided modest evidence that app-based interventions to improve diet, physical activity and sedentary behaviours can be effective. Multi-component interventions appear to be more effective than stand-alone app interventions, however, this remains to be confirmed in controlled trials. Future research is needed on the optimal number and combination of app features, behaviour change techniques, and level of participant contact needed to maximise user engagement and intervention efficacy.
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http://dx.doi.org/10.1186/s12966-016-0454-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5142356PMC
December 2016

Impact of increasing social media use on sitting time and body mass index.

Health Promot J Austr 2017 08;28(2):91-95

Physical Activity Research Group, School of Human, Health and Social Sciences, Building 18, Central Queensland University, Rockhampton, Qld 4702, Australia.

Issue addressed Sedentary behaviours, in particular sitting, increases the risk of cardiovascular disease, type 2 diabetes, obesity and poorer mental health status. In Australia, 70% of adults sit for more than 8h per day. The use of social media applications (e.g. Facebook, Twitter, and Instagram) is on the rise; however, no studies have explored the association of social media use with sitting time and body mass index (BMI). Methods Cross-sectional self-report data on demographics, BMI and sitting time were collected from 1140 participants in the 2013 Queensland Social Survey. Generalised linear models were used to estimate associations of a social media score calculated from social media use, perceived importance of social media, and number of social media contacts with sitting time and BMI. Results Participants with a high social media score had significantly greater sitting times while using a computer in leisure time and significantly greater total sitting time on non-workdays. However, no associations were found between social media score and sitting to view TV, use motorised transport, work or participate in other leisure activities; or total workday, total sitting time or BMI. Conclusions These results indicate that social media use is associated with increased sitting time while using a computer, and total sitting time on non-workdays. So what? The rise in social media use may have a negative impact on health by contributing to computer sitting and total sitting time on non-workdays. Future longitudinal research with a representative sample and objective sitting measures is needed to confirm findings.
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http://dx.doi.org/10.1071/HE16026DOI Listing
August 2017

The effectiveness of e-& mHealth interventions to promote physical activity and healthy diets in developing countries: A systematic review.

Int J Behav Nutr Phys Act 2016 10 10;13(1):109. Epub 2016 Oct 10.

Physical Activity Research Group, School of Human, Health and Social Sciences, Central Queensland University, Building 77, Bruce Highway, Rockhampton, QLD 4702, Australia.

Background: Promoting physical activity and healthy eating is important to combat the unprecedented rise in NCDs in many developing countries. Using modern information-and communication technologies to deliver physical activity and diet interventions is particularly promising considering the increased proliferation of such technologies in many developing countries. The objective of this systematic review is to investigate the effectiveness of e-& mHealth interventions to promote physical activity and healthy diets in developing countries.

Methods: Major databases and grey literature sources were searched to retrieve studies that quantitatively examined the effectiveness of e-& mHealth interventions on physical activity and diet outcomes in developing countries. Additional studies were retrieved through citation alerts and scientific social media allowing study inclusion until August 2016. The CONSORT checklist was used to assess the risk of bias of the included studies.

Results: A total of 15 studies conducted in 13 developing countries in Europe, Africa, Latin-and South America and Asia were included in the review. The majority of studies enrolled adults who were healthy or at risk of diabetes or hypertension. The average intervention length was 6.4 months, and text messages and the Internet were the most frequently used intervention delivery channels. Risk of bias across the studies was moderate (55.7 % of the criteria fulfilled). Eleven studies reported significant positive effects of an e-& mHealth intervention on physical activity and/or diet behaviour. Respectively, 50 % and 70 % of the interventions were effective in promoting physical activity and healthy diets.

Conclusions: The majority of studies demonstrated that e-& mHealth interventions were effective in promoting physical activity and healthy diets in developing countries. Future interventions should use more rigorous study designs, investigate the cost-effectiveness and reach of interventions, and focus on emerging technologies, such as smart phone apps and wearable activity trackers.

Trial Registration: The review protocol can be retrieved from the PROSPERO database (Registration ID: CRD42015029240 ).
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http://dx.doi.org/10.1186/s12966-016-0434-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5057225PMC
October 2016

Automatic Evaluation Stimuli - The Most Frequently Used Words to Describe Physical Activity and the Pleasantness of Physical Activity.

Front Psychol 2016 23;7:1277. Epub 2016 Aug 23.

Physical Activity Research Group, School of Human, Health, and Social Sciences, Central Queensland University, RockhamptonQLD, Australia; Health Psychology and Behavioural Medicine Research Group, Faculty of Health Sciences, School of Psychology and Speech Pathology, Curtin University, PerthWA, Australia.

Physical activity is partially regulated by non-conscious processes including automatic evaluations - the spontaneous affective reactions we have to physical activity that lead us to approach or avoid physical activity opportunities. A sound understanding of which words best represent the concepts of physical activity and pleasantness (as associated with physical activity) is needed to improve the measurement of automatic evaluations and related constructs (e.g., automatic self-schemas, attentional biases). The first aim of this study was to establish population-level evidence of the most common word stimuli for physical activity and pleasantness. Given that response latency measures have been applied to assess automatic evaluations of physical activity and exercise, the second aim was to determine whether people use the same behavior and pleasant descriptors for physical activity and exercise. Australian adults (N = 1,318; 54.3% women; 48.9% aged 55 years or older) were randomly assigned to one of two groups, through a computer-generated 1:1 ratio allocation, to be asked to list either five behaviors and pleasant descriptors of physical activity (n = 686) or of exercise (n = 632). The words were independently coded twice as to whether they were novel words or the same as another (i.e., same stem or same meaning). Intercoder reliability varied between moderate and strong (agreement = 50.1 to 97.8%; κ = 0.48 to 0.82). A list of the 20 most common behavior and pleasantness words were established based on how many people reported them, weighted by the ranking (1-5) people gave them. The words people described as physical activity were mostly the same as those people used to describe exercise. The most common behavior words were 'walking,' 'running,' 'swimming,' 'bike riding,' and 'gardening'; and the most common pleasant descriptor words were 'relaxing,' 'happiness,' 'enjoyment,' 'exhilarating,' 'exhausting,' and 'good.' These sets of stimuli can be utilized as resources for response latency measurement tasks of automatic evaluations and for tools to enhance automatic evaluations of physical activity in evaluative conditioning tasks.
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http://dx.doi.org/10.3389/fpsyg.2016.01277DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4994326PMC
September 2016

Is preference for mHealth intervention delivery platform associated with delivery platform familiarity?

BMC Public Health 2016 07 22;16:619. Epub 2016 Jul 22.

Physical Activity Research Group, School of Human, Health and Social Sciences, Building 18, Central Queensland University, Rockhampton, QLD, 4702, Australia.

Background: The aim of this paper was to ascertain whether greater familiarity with a smartphone or tablet was associated with participants' preferred mobile delivery modality for eHealth interventions.

Methods: Data from 1865 people who participated in the Australian Health and Social Science panel study were included into two multinomial logistic regression analyses in which preference for smartphone and tablet delivery for general or personalised eHealth interventions were regressed onto device familiarity and the covariates of sex, age and education.

Results: People were more likely to prefer both general and personalised eHealth interventions presented on tablets if they reported high or moderate tablet familiarity (compared to low familiarity) and people were more likely to prefer both general and personalised eHealth interventions presented on smartphones if they reported high or moderate smartphone familiarity, were younger, and had university education (compared to completing high school or less).

Conclusion: People prefer receiving eHealth interventions on the mobile devices they are most familiar with. These findings have important implications that should be considered when developing eHealth interventions, and demonstrates that eHealth interventions should be delivered using multiple platforms simultaneously to optimally cater for as many people as possible.
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http://dx.doi.org/10.1186/s12889-016-3316-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4957352PMC
July 2016

Interest and preferences for using advanced physical activity tracking devices: results of a national cross-sectional survey.

BMJ Open 2016 07 7;6(7):e011243. Epub 2016 Jul 7.

Physical Activity Research Group, School of Human, Health and Social Sciences, Central Queensland University, Rockhampton, Queensland, Australia.

Objectives: Pedometers are an effective self-monitoring tool to increase users' physical activity. However, a range of advanced trackers that measure physical activity 24 hours per day have emerged (eg, Fitbit). The current study aims to determine people's current use, interest and preferences for advanced trackers.

Design And Participants: A cross-sectional national telephone survey was conducted in Australia with 1349 respondents.

Outcome Measures: Regression analyses were used to determine whether tracker interest and use, and use of advanced trackers over pedometers is a function of demographics. Preferences for tracker features and reasons for not wanting to wear a tracker are also presented.

Results: Over one-third of participants (35%) had used a tracker, and 16% are interested in using one. Multinomial regression (n=1257) revealed that the use of trackers was lower in males (OR=0.48, 95% CI 0.36 to 0.65), non-working participants (OR=0.43, 95% CI 0.30 to 0.61), participants with lower education (OR=0.52, 95% CI 0.38 to 0.72) and inactive participants (OR=0.52, 95% CI 0.39 to 0.70). Interest in using a tracker was higher in younger participants (OR=1.73, 95% CI 1.15 to 2.58). The most frequently used tracker was a pedometer (59%). Logistic regression (n=445) revealed that use of advanced trackers compared with pedometers was higher in males (OR=1.67, 95% CI 1.01 to 2.79) and younger participants (OR=2.96, 95% CI 1.71 to 5.13), and lower in inactive participants (OR=0.35, 95% CI 0.19 to 0.63). Over half of current or interested tracker users (53%) prefer to wear it on their wrist, 31% considered counting steps the most important function and 30% regarded accuracy as the most important characteristic. The main reasons for not wanting to use a tracker were, 'I don't think it would help me' (39%), and 'I don't want to increase my activity' (47%).

Conclusions: Activity trackers are a promising tool to engage people in self-monitoring a physical activity. Trackers used in physical activity interventions should align with the preferences of target groups, and should be able to be worn on the wrist, measure steps and be accurate.
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http://dx.doi.org/10.1136/bmjopen-2016-011243DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4947799PMC
July 2016

Examining the Correlates of Online Health Information-Seeking Behavior Among Men Compared With Women.

Am J Mens Health 2018 09 18;12(5):1358-1367. Epub 2016 May 18.

1 University of Adelaide, Adelaide, South Australia, Australia.

This study aimed to identify and compare the demographic, health behavior, health status, and social media use correlates of online health-seeking behaviors among men and women. Cross-sectional self-report data were collected from 1,289 Australian adults participating in the Queensland Social Survey. Logistic regression analyses were used to identify the correlates of online health information seeking for men and women. Differences in the strength of the relation of these correlates were tested using equality of regression coefficient tests. For both genders, the two strongest correlates were social media use (men: odds ratio [ OR] = 2.57, 95% confidence interval [CI: 1.78, 3.71]; women: OR = 2.93, 95% CI [1.92, 4.45]) and having a university education (men: OR = 3.63, 95% CI [2.37, 5.56]; women: OR = 2.74, 95% CI [1.66, 4.51]). Not being a smoker and being of younger age were also associated with online health information seeking for both men and women. Reporting poor health and the presence of two chronic diseases were positively associated with online health seeking for women only. Correlates of help seeking online among men and women were generally similar, with exception of health status. Results suggest that similar groups of men and women are likely to access health information online for primary prevention purposes, and additionally that women experiencing poor health are more likely to seek health information online than women who are relatively well. These findings are useful for analyzing the potential reach of online health initiatives targeting both men and women.
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http://dx.doi.org/10.1177/1557988316650625DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6142140PMC
September 2018