The moderating effect of social support on the effectiveness of a web-based, computer-tailored physical activity intervention for older adults

This study aimed to assess the moderating effect of social support on the effectiveness of a web-based, computer-tailored physical activity intervention for older adults. In the Active for Life trial, 243 inactive adults aged 65+ years were randomised into: (1) tailoring + Fitbit (n = 78), (2) tailoring-only (n = 96) or (3) control (n = 69). For the current study, participants were categorised as having higher (n = 146) or lower (n = 97) social support based on the Duke Social Support Index (DSSI_10). Moderate-to-vigorous physical activity (MVPA) was measured through accelerometers at baseline and post-intervention. A linear mixed model analysis demonstrated that among participants with lower social support, the tailoring + Fitbit participants, but not the tailoring only participants increased their MVPA more than the control. Among participants with higher social support, no differences in MVPA changes were observed between groups. Web-based computer-tailored interventions with Fitbit integration may be more effective in older adults with lower levels of social support.

Exploring Older Adults' Experiences of a Home-Based, Technology-Driven Balance Training Exercise Program Designed to Reduce Fall Risk: A Qualitative Research Study Within a Randomized Controlled Trial

BACKGROUND AND PURPOSE

With an aging population, falls have become an increasing public health concern. While face-to-face exercise programs have demonstrated efficacy in reducing falls, their effectiveness is hampered by low participation and adherence. Digital technologies are a novel and potentially effective method for delivering tailored fall prevention exercise programs to older adults. In addition, they may increase the reach, uptake, and sustainability of fall prevention programs. Therefore, understanding older adults' experiences of using technology-driven methods is essential. This study explored the user experience of StandingTall , a home-based fall prevention program delivered through a tablet computer.

METHODS

Fifty participants were recruited using purposive sampling, from a larger randomized controlled trial. Participants were selected to ensure maximum variability with respect to age, gender, experience with technology, and adherence to the program. Participants undertook a one-on-one structured interview. We followed an iterative approach to develop themes.

RESULTS AND DISCUSSION

Eight themes were identified. These fall under 2 categories: user experience and program design. Participants found StandingTall enjoyable, and while its flexible delivery facilitated exercise, some participants found the technology challenging. Some participants expressed frustration with technological literacy, but most demonstrated an ability to overcome these challenges, and learn a new skill. Older adults who engaged in a technology-driven fall prevention program found it enjoyable, with the flexibility provided by the online delivery central to this experience. While the overall experience was positive, participants expressed mixed feelings about key design features. The embedded behavior change strategies were not considered motivating by most participants. Furthermore, some older adults associated the illustrated characters with gender-based stereotypes and negative views of aging, which can impact on motivation and preventive behavior.

CONCLUSION

This study found digital technologies are an effective and enjoyable method for delivering a fall prevention program. This study highlights that older adults are interested in learning how to engage successfully with novel technologies.

Engagement, acceptability, usability and satisfaction with Active for Life, a computer-tailored web-based physical activity intervention using Fitbits in older adults

BACKGROUND

Preliminary evidence suggests that web-based physical activity interventions with tailored advice and Fitbit integration are effective and may be well suited to older adults. Therefore, this study aimed to examine the engagement, acceptability, usability, and satisfaction with 'Active for Life,' a web-based physical activity intervention providing computer-tailored physical activity advice to older adults.

METHODS

Inactive older adults (n = 243) were randomly assigned into 3 groups: 1) tailoring + Fitbit, 2) tailoring only, or 3) a wait-list control. The tailoring + Fitbit group and the tailoring-only group received 6 modules of computer-tailored physical activity advice over 12 weeks. The advice was informed by objective Fitbit data in the tailoring + Fitbit group and self-reported physical activity in the tailoring-only group. This study examined the engagement, acceptability, usability, and satisfaction of Active for Life in intervention participants (tailoring + Fitbit n = 78, tailoring only n = 96). Wait-list participants were not included. Engagement (Module completion, time on site) were objectively recorded through the intervention website. Acceptability (7-point Likert scale), usability (System Usability Scale), and satisfaction (open-ended questions) were assessed using an online survey at post intervention. ANOVA and Chi square analyses were conducted to compare outcomes between intervention groups and content analysis was used to analyse program satisfaction.

RESULTS

At post-intervention (week 12), study attrition was 28% (22/78) in the Fitbit + tailoring group and 39% (37/96) in the tailoring-only group. Engagement and acceptability were good in both groups, however there were no group differences (module completions: tailoring + Fitbit: 4.72 ± 2.04, Tailoring-only: 4.23 ± 2.25 out of 6 modules, p = .14, time on site: tailoring + Fitbit: 103.46 ± 70.63, Tailoring-only: 96.90 ± 76.37 min in total, p = .56, and acceptability of the advice: tailoring + Fitbit: 5.62 ± 0.89, Tailoring-only: 5.75 ± 0.75 out of 7, p = .41). Intervention usability was modest but significantly higher in the tailoring + Fitbit group (tailoring + Fitbit: 64.55 ± 13.59, Tailoring-only: 57.04 ± 2.58 out of 100, p = .003). Participants reported that Active for Life helped motivate them, held them accountable, improved their awareness of how active they were and helped them to become more active. Conversely, many participants felt as though they would prefer personal contact, more detailed tailoring and more survey response options.

CONCLUSIONS

This study supports web-based physical activity interventions with computer-tailored advice and Fitbit integration as engaging and acceptable in older adults.

TRIAL REGISTRATION

Australian and New Zealand Clinical Trials Registry: ACTRN12618000646246. Registered April 23 2018, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374901.

Depression, anxiety, stress, and physical activity of Australian adults during COVID-19: A combined longitudinal and repeated cross-sectional study

Background

The COVID-19 pandemic has led to a worsening of mental health and health behaviors. While physical activity is positively associated mental health, there is limited understanding of how mental health and physical activity evolve throughout the COVID-19 pandemic. This study aimed to examine changes in depression, anxiety and stress and physical activity, and associations between depression, anxiety, and stress with physical activity in Australian adults across three-time points during the COVID-19 pandemic.

Materials and methods

This study collected both longitudinal and cross-sectional data at three-time points during the COVID-19 pandemic in Australia (i.e., April, July/August, and December 2020). Australians aged 18 years and over were invited to complete online surveys hosted on Qualtrics survey platform. Linear mixed models with random subject effect and general linear models were used to analyze the longitudinal and repeated cross-sectional data respectively.

Results

The number of participants in cross-sectional surveys and longitudinal surveys was 1,877 and 849, respectively. There was an overall reduction between time 2 vs. time 3 in depression (d = 1.03, 95% CI = 0.20, 1.85), anxiety (d = 0.57, 95% CI = 0.02, 1.12), and stress (d = 1.13, 95% CI = 0.21, 2.04) scores but no significant differences in physical activity across three-time points. On average, participants who met the physical activity guidelines had lower depression (d = −2.08, 95% CI = −2.90, −1.26), anxiety (d = −0.88, 95% CI = −1.41, −0.34), and stress (d = −1.35, 95% CI = −2.13, −0.56) scores compared to those not meeting the guidelines.

Conclusion

In the context of the ongoing COVID-19 pandemic, both governments and service providers should continue to provide the public with timely mental health support and promote the benefits of physical activity, as a cost-effective strategy to improve mental health and wellbeing.

The Effectiveness of a Computer-Tailored Web-Based Physical Activity Intervention Using Fitbit Activity Trackers in Older Adults (Active for Life): Randomized Controlled Trial

BACKGROUND

Physical activity is an integral part of healthy aging; yet, most adults aged ≥65 years are not sufficiently active. Preliminary evidence suggests that web-based interventions with computer-tailored advice and Fitbit activity trackers may be well suited for older adults.

OBJECTIVE

The aim of this study was to examine the effectiveness of Active for Life, a 12-week web-based physical activity intervention with 6 web-based modules of computer-tailored advice to increase physical activity in older Australians.

METHODS

Participants were recruited both through the web and offline and were randomly assigned to 1 of 3 trial arms: tailoring+Fitbit, tailoring only, or a wait-list control. The computer-tailored advice was based on either participants' Fitbit data (tailoring+Fitbit participants) or self-reported physical activity (tailoring-only participants). The main outcome was change in wrist-worn accelerometer (ActiGraph GT9X)-measured moderate to vigorous physical activity (MVPA) from baseline to after the intervention (week 12). The secondary outcomes were change in self-reported physical activity measured by means of the Active Australia Survey at the midintervention point (6 weeks), after the intervention (week 12), and at follow-up (week 24). Participants had a face-to-face meeting at baseline for a demonstration of the intervention and at baseline and week 12 to return the accelerometers. Generalized linear mixed model analyses were conducted with a γ distribution and log link to compare MVPA and self-reported physical activity changes over time within each trial arm and between each of the trial arms.

RESULTS

A total of 243 participants were randomly assigned to tailoring+Fitbit (n=78, 32.1%), tailoring only (n=96, 39.5%), and wait-list control (n=69, 28.4%). Attrition was 28.8% (70/243) at 6 weeks, 31.7% (77/243) at 12 weeks, and 35.4% (86/243) at 24 weeks. No significant overall time by group interaction was observed for MVPA (P=.05). There were no significant within-group changes for MVPA over time in the tailoring+Fitbit group (+3%, 95% CI -24% to 40%) or the tailoring-only group (-4%, 95% CI -24% to 30%); however, a significant decline was seen in the control group (-35%, 95% CI -52% to -11%). The tailoring+Fitbit group participants increased their MVPA 59% (95% CI 6%-138%) more than those in the control group. A significant time by group interaction was observed for self-reported physical activity (P=.02). All groups increased their self-reported physical activity from baseline to week 6, week 12, and week 24, and this increase was greater in the tailoring+Fitbit group than in the control group at 6 weeks (+61%, 95% CI 11%-133%).

CONCLUSIONS

A computer-tailored physical activity intervention with Fitbit integration resulted in improved MVPA outcomes in comparison with a control group in older adults.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry ACTRN12618000646246; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12618000646246.

The association of resilience with depression, anxiety, stress and physical activity during the COVID-19 pandemic

Background

COVID-19 has resulted in substantial global upheaval. Resilience is important in protecting wellbeing, however few studies have investigated changes in resilience over time, and associations between resilience with depression, anxiety, stress, and physical activity during the COVID-19 pandemic.

Methods

Online surveys were conducted to collect both longitudinal and cross-sectional data at three time points during 2020. Australian adults aged 18 years and over were invited to complete the online surveys. Measures include the six-item Brief Resilience Scale, the 21-item Depression, Anxiety and Stress Scale, and the Active Australia Survey which have eight items identifying the duration and frequency of walking, and moderate and vigorous physical activities (MVPA), over the past 7 days. General linear mixed models and general linear models were used in the analysis.

Results

In the longitudinal sample, adjusted differences (aDif) in resilience scores did not significantly change over time (time 2 vs. time 1 [aDif = − 0.02, 95% CI = − 0.08, 0.03], and time 3 vs. time 1 [aDif = < 0.01, 95% CI = − 0.07, 0.06]). On average, those engaging in at least 150 min of MVPA per week (aDif = 0.10, 95% CI = 0.04, 0.16), and having depression (aDif = 0.40, 95% CI = 0.33), anxiety (aDif = 0.34, 95% CI = 0.26, 0.41), and stress scores (aDif = 0.30, 95% CI = 0.23, 0.37) within the normal range had significantly higher resilience scores. The association between resilience and physical activity was independent of depression, anxiety, and stress levels. All results were similar for the cross-sectional sample.

Conclusions

Resilience scores did not change significantly during the COVID-19 pandemic. However, there were significant associations between resilience with physical activity and psychological distress. This research helps inform future interventions to enhance or nurture resilience, particularly targeted at people identified as at risk of psychological distress.

How are COVID-19 knowledge and concern associated with practising preventive behaviours in Australian adults?

OBJECTIVE

This study investigated the association between COVID-19 prevention knowledge and concern and practising preventive behaviour in Australian adults.

METHODS

Using an online survey, knowledge of Australian COVID-19 guidelines, concerns about pandemic impact, the practice of preventive behaviours, and sociodemographic variables (i.e. age, gender, information source) were measured. Bivariate analysis and linear regression models were used.

RESULTS

A total of 1,491 participants (age 50.5 ±14.9 years, 32.3% males) completed the survey. Higher knowledge and concern scores were associated with a higher practice of preventive behaviour scores (βs:0.47 & 0.08 respectively, p<0.001). Older adults (>65 years) and women had higher knowledge and practice scores compared to their counterparts. Being younger (<45 years) and male were associated with a lower practice score (βs:-0.88 & -2.52, respectively, p<0.001). Referring to public and government sources as primary sources of information was associated with a higher practice score (β:1.21, p<0.001).

CONCLUSIONS

Government-run campaigns appear to be effective in promoting preventive practices and achieving a high knowledge of COVID-19 guidelines in Australian adults. Implications for public health: Public health strategies are required to promote the practice of preventive behaviour for COVID-19 (or future pandemics), especially among men and younger adults using social media, given their wide use of these sources.

Willingness to Vaccinate against COVID-19 Declines in Australia, Except in Lockdown Areas

This study investigates changes in willingness to vaccinate against COVID-19 and the effect of the extended restrictions in metropolitan Victoria on this change. Longitudinal and repeated cross-sectional data were collected from online surveys distributed in April, between July and August, and December 2020. Australian adults who were ≥18 years old were recruited through email lists, social media networks, and paid Facebook advertisement. Willingness to vaccinate against COVID-19 was self-reported. The results showed that participants were more willing to vaccinate if the vaccine was safe at survey 1 (longitudinal: adjusted OR (aOR) = 1.88, 95%CI = 1.38, 2.56; cross-sectional: aOR = 3.73, 95%CI = 2.55, 5.45) and survey 2 (longitudinal: aOR = 1.54, 95%CI = 1.19, 2.00; cross-sectional: aOR = 2.48, 1.67, 3.67), compared to survey 3. The change in willingness to vaccinate if the vaccine was safe and effective was not significant for those in Metropolitan Victoria; but was for those living in other Australian locations at survey 1 (OR = 2.13, 95%CI = 1.64, 2.76) and survey 2 (OR = 1.62, 95%CI = 1.30, 2.01), compared to survey 3. Willingness to vaccinate even if a vaccine had not been proven safe decreased at survey 3 (OR = 2.02, 95%CI = 1.14, 3.57) for those living in Metropolitan Victoria. In conclusion willingness to vaccinate against COVID-19 decreased over time among Australians, except for those living in metropolitan Victoria, where an additional strict and prolonged lockdown was implemented around the time of survey 2. Either the experience of the lockdown, or the presence of the COVID-19 virus itself had a positive influence on participants' willingness to vaccinate, even if such a vaccine was not yet proven to be safe and effective.

Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic

Anti-vaccination attitudes have been an issue since the development of the first vaccines. The increasing use of social media as a source of health information may contribute to vaccine hesitancy due to anti-vaccination content widely available on social media, including Twitter. Being able to identify anti-vaccination tweets could provide useful information for formulating strategies to reduce anti-vaccination sentiments among different groups. This study aims to evaluate the performance of different natural language processing models to identify anti-vaccination tweets that were published during the COVID-19 pandemic. We compared the performance of the bidirectional encoder representations from transformers (BERT) and the bidirectional long short-term memory networks with pre-trained GLoVe embeddings (Bi-LSTM) with classic machine learning methods including support vector machine (SVM) and naïve Bayes (NB). The results show that performance on the test set of the BERT model was: accuracy = 91.6%, precision = 93.4%, recall = 97.6%, F1 score = 95.5%, and AUC = 84.7%. Bi-LSTM model performance showed: accuracy = 89.8%, precision = 44.0%, recall = 47.2%, F1 score = 45.5%, and AUC = 85.8%. SVM with linear kernel performed at: accuracy = 92.3%, Precision = 19.5%, Recall = 78.6%, F1 score = 31.2%, and AUC = 85.6%. Complement NB demonstrated: accuracy = 88.8%, precision = 23.0%, recall = 32.8%, F1 score = 27.1%, and AUC = 62.7%. In conclusion, the BERT models outperformed the Bi-LSTM, SVM, and NB models in this task. Moreover, the BERT model achieved excellent performance and can be used to identify anti-vaccination tweets in future studies.

As the Pandemic Progresses, How Does Willingness to Vaccinate against COVID-19 Evolve?

Controversy around the safety and efficacy of COVID-19 vaccines may lead to low vaccination rates. Survey data were collected in April and August 2020 from a total of 2343 Australian adults. A quarter (n = 575, 24%) completed both surveys. A generalized linear mixed model analysis was conducted to determine whether willingness to vaccinate changed in the repeated sample, and a multinominal logistic regression was conducted in all participants to determine whether willingness to vaccinate was associated with demographics, chronic disease, or media use. Willingness to vaccinate slightly decreased between April (87%) and August (85%) but this was not significant. Willingness to vaccinate was lower in people with a certificate or diploma (79%) compared to those with a Bachelor degree (87%), p < 0.01 and lower in infrequent users of traditional media (78%) compared to frequent users of traditional media (89%), p < 0.001. Women were more likely to be unsure if they would be willing to vaccinate (10%) compared to men (7%), p < 0.01. There were no associations between willingness to vaccinate and age, chronic disease, or social media use. Promotion of a COVID-19 vaccine should consider targeting women, and people with a certificate or diploma, via non-traditional media channels.

Depression, Anxiety and Stress during COVID-19: Associations with Changes in Physical Activity, Sleep, Tobacco and Alcohol Use in Australian Adults

The novel coronavirus (COVID-19) has enforced dramatic changes to daily living including economic and health impacts. Evidence for the impact of these changes on our physical and mental health and health behaviors is limited. We examined the associations between psychological distress and changes in selected health behaviors since the onset of COVID-19 in Australia. An online survey was distributed in April 2020 and included measures of depression, anxiety, stress, physical activity, sleep, alcohol intake and cigarette smoking. The survey was completed by 1491 adults (mean age 50.5 ± 14.9 years, 67% female). Negative change was reported for physical activity (48.9%), sleep (40.7%), alcohol (26.6%) and smoking (6.9%) since the onset of the COVID-19 pandemic. Significantly higher scores in one or more psychological distress states were found for females, and those not in a relationship, in the lowest income category, aged 18-45 years, or with a chronic illness. Negative changes in physical activity, sleep, smoking and alcohol intake were associated with higher depression, anxiety and stress symptoms. Health-promotion strategies directed at adopting or maintaining positive health-related behaviors should be utilized to address increases in psychological distress during the pandemic. Ongoing evaluation of the impact of lifestyle changes associated with the pandemic is needed.

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

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.

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

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.

Electronic supplementary material

The online version of this article (10.1186/s12889-019-7189-z) contains supplementary material, which is available to authorized users.

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

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).

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

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.

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

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.

The effectiveness of a web 2.0 physical activity intervention in older adults - a randomised controlled trial

BACKGROUND

Interactive web-based physical activity interventions using Web 2.0 features (e.g., social networking) have the potential to improve engagement and effectiveness compared to static Web 1.0 interventions. However, older adults may engage with Web 2.0 interventions differently than younger adults. The aims of this study were to determine whether an interaction between intervention (Web 2.0 and Web 1.0) and age group (<55y and ≥55y) exists for website usage and to determine whether an interaction between intervention (Web 2.0, Web 1.0 and logbook) and age group (<55y and ≥55y) exists for intervention effectiveness (changes in physical activity).

METHODS

As part of the WALK 2.0 trial, 504 Australian adults were randomly assigned to receive either a paper logbook (n = 171), a Web 1.0 (n = 165) or a Web 2.0 (n = 168) physical activity intervention. Moderate to vigorous physical activity was measured using ActiGraph monitors at baseline 3, 12 and 18 months. Website usage statistics including time on site, number of log-ins and number of step entries were also recorded. Generalised linear and intention-to-treat linear mixed models were used to test interactions between intervention and age groups (<55y and ≥55y) for website usage and moderate to vigorous physical activity changes.

RESULTS

Time on site was higher for the Web 2.0 compared to the Web 1.0 intervention from baseline to 3 months, and this difference was significantly greater in the older group (OR = 1.47, 95%CI = 1.01-2.14, p = .047). Participants in the Web 2.0 group increased their activity more than the logbook group at 3 months, and this difference was significantly greater in the older group (moderate to vigorous physical activity adjusted mean difference = 13.74, 95%CI = 1.08-26.40 min per day, p = .03). No intervention by age interactions were observed for Web 1.0 and logbook groups.

CONCLUSIONS

Results partially support the use of Web 2.0 features to improve adults over 55 s' engagement in and behaviour changes from web-based physical activity interventions.

TRIAL REGISTRATION

ACTRN ACTRN12611000157976 , Registered 7 March 2011.

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

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.