Abstract
Objective:
The promotion of walking could be a feasible population-level physical activity strategy because it requires little planning, is low cost and can be done year-round across settings. Community, nonprofit organisations offer one means by which to help increase walking through community programmes. The US Cooperative State Research, Education, and Extension Service has a history that spans more than a century and is known for quality in the delivery of educational programmes to help improve the lives of people in communities across the USA. The purpose of this study was to evaluate the effectiveness, feasibility and acceptability of the Get WalkIN’ intervention – an initiative supported by this programme – from the perspectives of both programme participants and county extension educators.
Methods:
Participants were recruited from 15 county extension sites in the Midwest region of the USA. Intervention emails targeted self-efficacy, social support, goal-setting and benefits/barriers to walking. To assess the perceptions of feasibility and acceptability of the intervention, participants and extension educators were asked to respond to a series of Likert-type scale and open-ended questions. Self-reported physical activity was assessed using the Godin Leisure-Time Physical Activity Questionnaire.
Results:
On average, participants and extension educators agreed that the programme was easy to use and would consider either recommending the programme to a friend or implementing the programme again within the community. Post-intervention, 69.1% of respondents were classified as sufficiently active compared to 60.5% pre-intervention.
Conclusion:
The use of the county-based US Cooperative State Research, Education, and Extension Service is an effective option for health promotion programming. Furthermore, a theory-based, email-mediated intervention is a valuable strategy as an independent and convenient way to facilitate increase in physical activity.
Introduction
Maintaining a physically active lifestyle is crucial for achieving and sustaining health and quality of life (US Department of Health and Human Services, 2015). The US national physical activity guidelines recommend adults achieve at least 150 minutes of moderate-intensity physical activity each week (Department of Health and Human Services, 2008). Although the promotion of an active lifestyle has been a public health priority for decades (US Department of Health and Human Services, 2010), national surveillance still indicates that approximately 80% of US adults do not meet physical activity guidelines (Centers for Disease Control and Prevention, 2012a). Furthermore, the World Health Organization (2017) recognises inactivity as the fourth leading risk factor for global mortality. Regular physical activity lowers the risks and symptoms of multiple chronic conditions, including cardiovascular disease, stroke, diabetes and obesity (Department of Health and Human Services, 2008).
Background
Brisk walking is a particularly appropriate physical activity promotion strategy. Starting a walking routine does not require special equipment, is of low cost with a low rate of injury and can be done year-round in various settings (US Department of Health and Human Services, 2015). In 2016, the US Department of Health & Human Services released Step It Up! The Surgeon General’s Call to Action to Promote Walking and Walkable Communities (US Department of Health and Human Services, 2015). In this report, one of the five strategic goals includes promotion of programmes to support walking in communities. In addition, volunteer and nonprofit organisations are recognised as playing a valuable role in helping to increase walking through community programmes which share information about the benefits of walking (US Department of Health and Human Services, 2015). Effective community-wide intervention strategies are needed to increase and maintain physical activity across populations.
One nonprofit network that could help increase walking through community-based programmes is the US Cooperative State Research, Education, and Extension Service, hereafter referred to as Cooperative Extension Service. The Cooperative Extension Service is operated through land-grant institutions, designated colleges and universities across the USA that receive federal, state and local support to advance research and education. It has a history that spans more than a century and originated to help strengthen and meet agricultural needs by educating farmers on advancements in science and technology (United States Department of Agriculture, 2018). Over time, the service has grown rapidly, expanding from an agriculture focus to include services such as nutrition education, food safety training and youth leadership development. The fastest growing sector of needs that Cooperative Extension is called upon to address is related to improving the health and well-being of populations. Cooperative Extension depends on the expertise and research from universities and colleges to provide vital, practical solutions, information and programmes for both urban and rural communities nationwide.
In Indiana, the Cooperative Extension Service works in each of the state’s 92 counties. County-based extension educators provide a variety of educational programmes to meet their community’s needs and priorities, with more focus in recent years on health-oriented programmes. Extension educators have the benefit of living and working in the community they serve, which allows them to build trust and relationships with residents as well as establish and grow partnerships with other community-based organisations. Educators’ connection to the state’s land-grant university, Purdue University, provides credibility to programmes offered and draws upon the reputation and research of its faculty.
Through a partnership with the Cooperative Extension Service, we explored the use of an email-mediated walking programme. Internet-mediated physical activity interventions have the potential to reach a large number of people with lower costs compared to in-person intervention delivery (Van den Berg et al., 2007). By using email, programme participants can have flexibility about when and where they choose to interact and receive intervention information (Napolitano and Marcus, 2002). Previous reviews on the effectiveness of Internet-mediated physical interventions have indicated email as a promising intervention delivery mode (Marcus et al., 2000, 2009; Van den Berg et al., 2007).
Purpose
The purpose of this study was to evaluate the effectiveness, feasibility and acceptability of the Get WalkIN’ intervention from the perspectives of both programme participants and county-based extension educators. In addition, the use of the Cooperative Extension Service as a delivery mechanism for this intervention was to be examined.
Methods
Theoretical framework
A description of the theoretical framework for this intervention is published elsewhere (Richards et al., 2015, 2016). Briefly, the Get WalkIN’ intervention aligns with social cognitive theory which proposes that behaviour is influenced through reciprocal interactions between personal factors, environmental influences and behavioural attributes (Bandura, 1997). The main construct, self-efficacy, refers to an individual’s confidence in his or her ability to perform a behaviour while overcoming barriers and exerting control over the behaviour through self-regulation and goal-setting (Bandura, 1997). Self-efficacy directly and indirectly influences physical activity behaviour through other theoretical constructs such as social support (Figure 1).
Theoretical framework.
Intervention procedure and structure
Get WalkIN’ was tailored from an existing email-based walking intervention which has been described in detail elsewhere (Richards et al., 2016). Prior to intervention implementation, a team of Extension staff, including extension educators and a health and wellness specialist, worked with the lead researcher (E.A.R.) to tailor intervention content for programme use. This team then developed an intervention toolkit to assist with the successful delivery of the walking intervention in counties across the state. The toolkit included marketing materials for recruiting participants for the walking programme, a programme timeline, resources for engaging with special populations (such as those audiences traditionally served by Extension such as farmers) and 16 pre-developed email messages to send to participants, which constituted the intervention. While extension educators were instructed not to delete or modify any of the content for the email messages, they did have the ability to include additional county-specific information or announcements that may have been of interest to participants.
The walking programme lasted 12 weeks, with email messages being sent twice weekly for the first 4 weeks and then weekly for the next 8 weeks. These messages targeted principles of self-efficacy, social support and goal-setting (as described in the social cognitive framework) with the intention of increasing walking behaviours. In summer 2016, all participants received the intervention emails. As this intervention had already been shown to be effective in a randomised design (Richards et al., 2016), a control group was not used in this study. It is important to note that survey completion was not required to participate in this community-based programme. Procedures were approved by the Purdue University Committee on the Use of Human Research Subjects.
Measures
Measurement of variables occurred at baseline, immediately post-intervention (12 weeks) and at 3 months post-intervention using standardised online questionnaires. Sociodemographic characteristics included age, gender, marital status, household income, race, ethnicity and education and were assessed at baseline only.
Intervention evaluation: participants
To assess the perceptions of the acceptability of the intervention by participants, ten 5-point Likert-type scale questions (1 = strongly disagree; 5 = strongly agree) were used. Questions asked about the ease of reading emails, the frequency of emails, the credibility of the emails and the encouragement provided by the email content. In addition, participants were also asked how often he or she read the emails and three additional open-ended questions regarding specific thoughts on email content (i.e. helpful, unnecessary).
Intervention evaluation: extension educators
To assess the perceptions of feasibility of the intervention by county-based extension educators, nine open-ended questions were asked. Questions assessed perceptions of the intervention delivery training and instructions and challenges with recruitment and with programme participation. Educators were also asked to report what they liked best and least about the programme and suggestions for future programme implementation.
Self-reported health and physical activity
Self-reported health measures included number of poor physical health days and poor mental health days in the past 30 days using the questions from the Behavioural Risk Factor Surveillance System (Centers for Disease Control and Prevention, 2012a). Body mass index (BMI) was calculated based on self-reported height and weight using the following formula: weight (lbs)/[height (in)]2 × 703 (Centers for Disease Control and Prevention, 2015). Participants were classified as overweight if BMI was 25.0–29.9 and obese if BMI was ≥30.0 (Centers for Disease Control and Prevention, 2012b).
Self-reported physical activity during the past 7 days was assessed with six items from the Godin Leisure-Time Physical Activity Questionnaire (Godin and Shephard, 1985). Participants were asked to report how many times on average they participate in mild, moderate and strenuous activity in a typical week. Each frequency score was multiplied by a corresponding metabolic equivalent (MET) value (i.e. frequency of mild physical activity × 3, frequency of moderate physical activity × 5 and frequency of strenuous physical activity × 9) to calculate an activity score. Individuals reporting activity scores ≥24 were classified as active, activity scores 14–23 were classified as insufficiently active and individuals reporting activity scores ≤13 were classified as inactive (Godin and Shephard, 1985).
Theoretical constructs
Theoretical constructs were measured using existing measures with demonstrated reliability and validity and adapted to be specific to walking (Sallis et al., 1987, 1988). All measures demonstrated acceptable levels of internal consistency reliability (α > .70). Measures included self-efficacy for walking which was measured with two Likert-type scale subscales: making time (5 items) and resisting relapse (4 items) (Sallis et al., 1988). Social support for walking items assessed perceived social interactions and activities aimed at supporting walking received from family and friends (4 items) (Sallis et al., 1987). Mean scores were computed across all items in each subscale.
Participants and recruitment
The study was conducted in 15 counties in Indiana, USA. While the counties were geographically diverse in location across the state, nine of the counties are classified as metropolitan counties, based on the population size of their metro areas, with the rest being classified as non-metro (United States Department of Agriculture, 2016). In spring 2016, participants were recruited by extension educators working in these counties. Each educator was given the goal of recruiting 15 participants from their county to provide a general representation of the population served by the programme.
Extension educators were provided with tailored recruitment material which included social media messages, news releases, newspaper articles and flyers. While the extension educators were given a variety of methods for programme recruitment, the most common method was using pre-existing email listservs and/or recruiting at current extension programme offerings. In addition, newsletter and newspaper articles were utilised in several counties. There were no limiting inclusion criteria for participating in the programme. Initially, 311 community members expressed interest in the programme. Participants were then sent an introductory email which included a link to an online baseline survey. One week after the initial email, a reminder email was sent to all participants who had not yet completed the survey.
While 311 participants initially signed up for the walking programme, 177 participants took the baseline survey. On average, participants were middle aged (54.3 ± 12.6 years), non-Hispanic White (95.4%), females (94.0%). In total, 54% of participants had at least a 2-year college degree, and 50% of the participants reported a household income of at least US$70,000 per year. In all, 8% of participants were single, 9% were divorced and 75% were married or partnered. For comparison, 79.6% of Indiana adults are non-Hispanic White, 87.8% are high school graduates and the median household income is US$49,255 (US Census Bureau, 2016).
Data analysis
Descriptive statistics were used to summarise participant characteristics and outcome measures, including feasibility and usability data. Mean values and standard errors were calculated for continuous variables and frequencies and percentages for categorical variables. Chi-square and two-sample t-tests were used to assess differences between baseline and post-intervention assessments. To examine the relationship between changes in theoretical constructs and changes in weekly phyiscal activity scores pre–post intervention, simple linear regression was conducted. Due to multicollinearity between theoretical constructs, it was not appropriate to include all the theoretical constructs in one model for a multivariate analysis. Data were analysed using SAS 9.3 (SAS Institute Inc., 2009), and statistical significance was set at p < .05.
Findings
Intervention evaluation: participants
Participants, on average, agreed that the intervention emails were easy to read (mean = 4.3 ± 0.1) and easy to understand (mean = 4.4 ± 0.1; see Table 1). Participants also reported that the frequency of emails was acceptable (mean = 4.3 ± 0.1) and that the receipt of the emails encouraged an increase in walking (mean = 4.0 ± 0.1). Most participants reported reading the email messages. In all, 63% of participants reported always reading the intervention emails, 25% reported reading the emails quite often, 10% reported reading the emails sometimes and 2% reported rarely reading the emails.
Mean values and standard errors of intervention acceptability survey.
SE: standard error.
When specifically asked what features of the intervention emails participants felt were most helpful, five participants stated that all of the material was helpful and five participants stated the weekly tips on how to incorporate more walking were most helpful. In addition, 15 participants stated that the emails themselves provided encouragement and reminders to increase walking. Furthermore, while two participants reported the web links embedded in the emails were helpful, two other participants found the web links burdensome and mentioned the link as an area for improvement. Also, two participants reported the embedded links to track (self-monitor) his or her progress was most helpful, while three additional participants stated it would be helpful to ask them to specifically monitor their walking behaviour and ask them to report their progress to help foster personal accountability.
Participants also identified areas of the emails which needed improvement. Three participants specifically stated that the email layout was not appealing and suggested the emails be more colourful and visually interesting. Importantly, all but one reporting participant indicated they would recommend the intervention to a friend. The participant who stated he or she would not recommend the programme suggested that the intervention content was too basic and that an accountability aspect needed to be added.
Intervention evaluation: extension educators
Overall, all responding (n = 9) extension educators reported that the programme training was adequate. All educators believed the pre-developed email messages were helpful with easy to follow instructions with the caveat of message formatting. Several educators indicated that the message template was cumbersome to transfer into their email system. Educators also agreed that the tailored recruitment materials were sufficient, but one educator did state additional materials such as pre-written ‘tweets’ would have been helpful. Two educators stated it was a struggle not meeting with participants face-to-face, as the majority of educational programmes offered through extension are traditionally delivered in this fashion.
Overall, however, the educators indicated the email format was a strength of this programme. This format eliminated all barriers of having to attend a programme in-person and allowed participants flexibility to engage with the information delivered, thus making the programme more appealing to more individuals. Other programme strengths identified by the educators included the low amount of time and labour expended for programme implementation. Identified challenges to the programme included lack of participant feedback and contact. Future suggestions by the educators included an initial face-to-face orientation, including some type of email conversation with participants or pairing the email programme with an in-person walking club.
Self-reported health and physical activity
Overall, participants were classified as obese with an average BMI of 30.4 ± 8.0. For comparison, 67.2% of Indiana residents are estimated to be overweight or obese (Centers for Disease Control and Prevention, 2016).
At baseline, participants reported an average of 3.4 ± 6.4 poor physical health days in the past 30 days and 4.8 ± 7.2 poor mental health days in the past 30 days. In terms of baseline physical activity, the average activity score was 36.1 ± 26.6 with 15.2% (n = 27) of participants classified as inactive, 24.3% (n = 43) classified as insufficiently active and 60.5% (n = 107) classified as sufficiently active (see Table 2). For comparison, an estimated 28.4% of Indiana adults met physical activity guidelines in 2015 (Centers for Disease Control and Prevention, 2016).
Mean values and standard errors of physical activity and theoretical constructs at baseline and post-intervention.
SE: standard error.
p < .01.
Immediately post-intervention, the average activity score significantly increased to 50.8 ± 26.2 (p < .01). However, the percent of inactive participants also increased to 23.5% (n = 16), while the percent of participants classified as insufficient active significantly decreased to 7.4% (n = 5) and the percent of participants classified as sufficiently active significantly increased to 69.1% (n = 47; p < .01).
Theoretical constructs
While there was an increase in the theoretical constructs of self-efficacy and social support, pre–post changes were not statistically significant (see Table 2). In the linear regression model, changes in theoretical constructs significantly impacted changes in activity scores. Changes in both subscales of self-efficacy, making time (β = 10.3 ± 94.1; p = .01) and resisting relapse (β = 9.7 ± 4.7; p = .04) significantly positively impacted changes in activity score. In addition, social support also positively impacted changes in activity score (β = 8.5 ± 3.3; p = .01).
Discussion
The primary purpose of this study was to evaluate the effectiveness, feasibility and acceptability of the Get WalkIN’ intervention from the perspective of both participants and county-based extension educators. Results of this programme evaluation indicate that a simple theory-based, email-mediated intervention was effective at increasing physical activity in a community population. The effectiveness of email-mediated interventions in increasing physical activity has been supported by other studies as well (Marcus et al., 2009; Plotnikoff et al., 2010). Findings were consistent with the previous version of this walking intervention which demonstrated efficacy in increasing walking and maintaining this increase at 12 months (Richards et al., 2016).
However, there are several study limitations worth noting. The participants recruited in this pilot study were generally active at the start with 60.5% being classified as sufficiently active at baseline. In addition, 95.4% of participants were non-Hispanic White in a population of 79.6% non-Hispanic Whites, and 50% reported a household income of at least US$70,000 compared to a median household income of US$49,255. This suggests that the intervention attracted those of better socio-economic advantage, who were more physically active, which limits the generalisability to a broader audience. To address this, our future implementation of this programme involves targeting low-income, limited resource and minority populations.
Furthermore, as this was a community programme, survey completion was not a requirement for programme participation. This attributed to an attrition of 177 participants completing baseline surveys and 68 participants completing post-programme follow-up. Therefore, reporting bias may be seen in the results. Demographic characteristics were only assessed at baseline, so we are unable to compare demographic differences between responders and non-responders. However, there was consistency seen in programme evaluation feedback from both the participants and the educators. In addition, monitoring the number of emails viewed by participants could further strengthen the findings of this study as it is currently unknown whether each participant received the full dose of the intervention. Also, physical activity intervention research to date has been unable to determine the needed intervention dose or duration to achieve and maintain behaviour change. It is possible that more frequent emails or receiving emails for longer than 3 months would intensify the dose–response effect. In addition, this study relied on self-reported physical activity behaviour which is prone to self-report, social desirability, testing and recall bias. These biases may be seen in the overall high percentage of participants classified as active at baseline. However, the survey items used to assess physical activity have been extensively tested and are shown to be reliable and valid measures (Godin and Shephard, 1985). Objective assessments of physical activity such as the use of accelerometers, pedometers or smart phone–based fitness apps should be considered in future studies (Strath et al., 2013; Van den Berg et al., 2007).
Importantly, there are also strengths of this study worth noting. First, the intervention messages were created based on a well-studied health behaviour theory – social cognitive theory (Maddux, 1995) which has been tested in previous trials (Richards et al., 2016). In the past, physical activity interventions have not consistently utlised health behaviour theories in intervention development and evaluation (Marcus et al., 2006; Rhodes and Pfaeffli, 2010). There is significant research that shows health behaviour change interventions are more likely to be successful if they are based on a clear understanding of the targeted health behaviour and influencing factors (Richards et al., 2016). In addition, the email delivery mechanism for this intervention is easily transferable across settings and implementation costs are low. The community buy-in for implementing this intervention is high as evidenced by a recent state-wide launch of the programme.
Conclusion
Approaches are needed that effectively support both increasing physical activity behaviour and maintaining physical activity behaviour change.
The use of Cooperative Extension Service is a valuable option for health promotion programme delivery. The Cooperative Extension Service has a strong infrastructure established and is operational in states across the USA, including many county-based staff who provide educational programmes. These staff are experienced in delivering educational programmes, many of whom seek to help individuals change behaviours to be healthier and improve their quality of life. The Extension Service also has a vast number of community partnerships and a strong network of individuals and families served through its programmes and initiatives.
Furthermore, theory-based, email-mediated interventions can be a valuable strategy as an independent and convenient way to facilitate an increase in physical activity (Marcus et al., 2009). The email format of this intervention allows convenience, flexibility and independence in physical activity behaviour change. In conclusion, providing programme planners with information and training on creating and delivering theory-based, email-mediated physical activity promotion is a promising avenue to health behaviour change.
