Using gender-based analyses to understand physical inactivity among women in Yellowstone County, Montana

Evaluation and Program Planning 51 (2015) 45–52

Contents lists available at ScienceDirect

Evaluation and Program Planning journal homepage: www.elsevier.com/locate/evalprogplan

Using gender-based analyses to understand physical inactivity among women in Yellowstone County, Montana Diane K. Duin a,*, Amanda L. Golbeck b, April Ennis Keippel c, Elizabeth Ciemins d, Hillary Hanson e, Tracy Neary c, Heather Fink f a

Montana State University-Billings, College of Allied Health Professions, 1500 University Drive, Billings, MT 59101, United States University of Montana, School of Public and Community Health Sciences, 32 Campus Drive, Missoula, MT 59812, United States St. Vincent Healthcare, 1233 N 30th Street, Billings, MT 59101, United States d Billings Clinic, Center for Clinical Translational Research, 2800 10th Avenue N, PO Box 37000, Billings, MT 59107, United States e Flathead City-County Health Department, 1035 1st Avenue West, Kalispell, MT 59901, United States f Riverstone Health, Community Health Improvement Coordinator, 123 S 27th Street, Billings, MT 59101, United States b c

A R T I C L E I N F O

A B S T R A C T

Article history: Available online 9 December 2014

Physical inactivity contributes to many health problems. Gender, the socially constructed roles and activities deemed appropriate for men and women, is an important factor in women’s physical inactivity. To better understand how gender influences participation in leisure-time physical activity, a gender analysis was conducted using sex-disaggregated data from a county-wide health assessment phone survey and a qualitative analysis of focus group transcripts. From this gender analysis, several gender-based constraints emerged, including women’s roles as caregivers, which left little time or energy for physical activity, women’s leisure time activities and hobbies, which were less active than men’s hobbies, and expectations for women’s appearance that made them uncomfortable sweating in front of strangers. Gender-based opportunities included women’s enjoyment of activity as a social connection, less rigid gender roles for younger women, and a sense of responsibility to set a good example for their families. The gender analysis was used to gain a deeper understanding of gender-based constraints and opportunities related to physical activity. This understanding is being used in the next step of our research to develop a gender-specific intervention to promote physical activity in women that addresses the underlying causes of physical inactivity through accommodation or transformation of those gender norms. ß 2014 Elsevier Ltd. All rights reserved.

Keywords: Physical inactivity Gender analysis Gender and health promotion Gender framework

1. Introduction The World Health Organization’s Manual for Integrating Gender into Reproductive Health and HIV Programs(2009) defines gender as ‘‘the socially constructed roles, behaviors, activities, and attributes that a given society considers appropriate for men and women (p. 8).’’ When examining determinants of health and designing community health interventions, it is important to take into consideration the surrounding culture and context in which health occurs, and this includes gender.

* Corresponding author. Tel.: +1 406 896 5833. E-mail addresses: [email protected] (D.K. Duin), [email protected] (A.L. Golbeck), [email protected] (A.E. Keippel), [email protected] (E. Ciemins), hhanson@flathead.mt.gov (H. Hanson), [email protected] (T. Neary), [email protected] (H. Fink). http://dx.doi.org/10.1016/j.evalprogplan.2014.12.006 0149-7189/ß 2014 Elsevier Ltd. All rights reserved.

Many of the factors that affect health have a basis in societal biases that perpetuate stereotypes, gender roles, or discriminatory policies that adversely affect individuals’ lives. In a genderbased approach, data are analyzed with the awareness that the biases and gender roles into which we are socialized also need to be examined (Brittle & Bird, 2007; Khosla & Barth, 2008; Parker, 1993). ‘‘While theories about gender have been well developed and debated in the social sciences for at least two decades, the notion of gender as being distinct from sex is still a relatively new concept in medical discourse and research’’ (Johnson, Greaves, & Repta, 2007, p. 1). Much of the existing gender-based literature focuses largely on traditional women’s issues such as reproductive health or gender-based violence (Inner Spaces Outer Faces Initiative, 2007; Parker, 1993; Commonwealth Secretariat and Maritime Centre of Excellence for Women’s Health, 2002; Interagency Gender Working Group, 2009; Greene, 2012; Ostlin, Eckermann, Mishra, Knowane, & Wallstam, 2006). This article aims to illustrate that broader community health issues, such as

46

D.K. Duin et al. / Evaluation and Program Planning 51 (2015) 45–52

physical activity, may also benefit from gender-based program planning and analysis. Regular physical activity is associated with enhanced health, including reduced risk of cardiovascular disease, stroke, type 2 diabetes, cancers, osteoporosis, depression, and fall-related injuries (Warburton, Nicol, & Bredin, 2006; U.S. Public Health Service, 1996; World Health Organization, 2007, 2008, 2010). Though most individuals are aware of the benefits of physical activity, most men and women in the United States do not engage in the 2008 Physical Activity Guidelines for Americans. These Physical Activity Guidelines (U.S. Department of Health and Human Services, 2008) include at least 150 min a week of moderate-intensity or 74 min a week of vigorous-intensity aerobic physical activity or an equivalent combination of moderate- and vigorous-intensity aerobic activity. Furthermore, when data are examined by sex, health disparities emerge with women reporting significantly less leisure-time physical activity than men (Adachi-Mejia et al., 2010; U.S. Public Health Service, 1996; Doldren & Webb, 2013; Ransdell, Vener, & Sell, 2004; U.S. Department of Health and Human Services, 2000). The authors seek to demonstrate how data garnered from the framework of gender-based analysis using the Gender Analysis Framework of the Liverpool School of Tropical Medicine [hereafter referred to as Gender Analysis Framework] (Gender and Health Group, Liverpool School of Tropical Medicine, 1999) can be used to gain a deeper understanding of the barriers to, and opportunities for, physical activity that can be used to develop a gender-based health intervention to increase leisure-time physical activity for women. 2. Background The Alliance, an affiliated partnership of two tertiary care hospitals (Billings Clinic and St. Vincent Healthcare) and Yellowstone County’s health department (RiverStone Health), founded the Healthy By Design Coalition, a cross-sector coalition working in Yellowstone County, Montana. The mission of the Coalition is to create a community that embraces a culture of health and wellbeing. The Coalition previously focused community health solutions on the community at-large without using a gender lens or without taking gender into account. Health solutions for the community were based on a 2006 Community Health Assessment and generalized to the entire population, which, as determined through identified disparities based on sex, may not adequately address the needs of women and girls. As a result, the Healthy By Design Coalition’s Women and Children Subcommittee was formed. The Subcommittee represents various agencies serving women and children in Yellowstone County, including two hospitals, the health department, Montana State University-Billings, YMCA, Big Brothers/Big Sisters, and Montana Amateur Sports. The study was initiated in 2011 by the Subcommittee with funding support from the U.S. Department of Health and Human Services Office on Women’s Health. 3. Methods The Gender Analysis Framework (Gender and Health Group, Liverpool School of Tropical Medicine, 1999) was used to conduct the gender analysis. The framework consists of three parts: (1) identifying who gets ill, including when and where; (2) identifying factors affecting who gets ill; and (3) identifying factors affecting responses to poor health. The established framework was translated into a focus on wellness instead of illness. The steps to conduct the gender analysis were adapted to include examination of sex-disaggregated data related to wellness behaviors. Factors that had the potential to influence engagement in physical

activity were examined. The World Health Organization publication, ‘‘Gender Analysis in Health: A Review of Selected Tools,’’ examined 17 widely used gender tools and noted a limitation of the framework, ‘‘while the guidelines provide tools for analysis, they do not address, in concrete terms, how one might actually institutionalize the use of such tools’’ (p. 58). However, the broad scope of the analysis tools allowed the adaptation of the framework for a gender-based analysis of wellness behaviors, which was used in the present intervention. Three parts of the Framework were identified: Part 1: patterns of ill health, identifying who gets ill, when and where; Part 2: factors affecting who gets ill; and Part 3: factors affecting responses to ill health. The steps associated with each part of the Framework included the following:  Step A: Examine sex-disaggregated quantitative data—identify existing patterns for women’s health.  Step B: Examine why the identified patterns exist—identify factors affecting who gets ill, including socially constructed roles, behaviors, activities, and attributes.  Step C: Examine how the identified gender factors either constrain or support women’s health. 3.1. Step A: Examination of sex-disaggregated quantitative data The Alliance sponsored a Community Health Assessment (CHA) in Yellowstone County, Montana, in 2011. The purpose of the 2011 CHA was to identify primary health-related issues that affect county residents. Professional Research Consultants (PRC), a marketing research organization providing services exclusively for the healthcare industry, was contracted to conduct the CHA. PRC developed the final survey instrument, the sampling plan, administered the CHA survey, and conducted the data collection and data analysis. The study population for the CHA included all residents of Yellowstone County in Montana. The county was defined by zip code (www.usps.com). The survey instrument used for the CHA was based largely on the Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS) and included customized questions addressing gaps in indicator data relative to health promotion and disease prevention objectives and other recognized health issues. The final survey instrument was similar to the previous 2006 Community Health Assessment (CHA) used in the region, allowing for data trending over time. The survey consisted of 201 questions that were largely nationally normed by asking the same question in other communities contracted with PRC. Questions not nationally normed were specific to the geographic region and identical to questions on the 2006 CHA, which was also conducted in Yellowstone County (, p.6). The survey was administered by telephone to a random sample of 400 individuals aged 18 and older in Yellowstone County. A total of 213 men and 187 women participated in the phone survey. Telephone interviews took approximately 20 min. To accurately represent the population studied, PRC randomly sampled the population and adjusted the results of the random sample to match the geographic distribution and demographic characteristics of Yellowstone County (post-stratification) to minimize any naturally occurring bias. Data from the CHA were analyzed to determine health issues for the community at large. After the community-wide analysis, data from the 2011 CHA were segmented by sex and analyzed to determine the nature and extent of sex-based disparities (Appendix A)1. 1 Complete findings from the 2011 Community Health Assessment are available upon request to the authors.

D.K. Duin et al. / Evaluation and Program Planning 51 (2015) 45–52

3.2. Step B: Examination of why the identified patterns exist We conducted focus groups to gain an understanding of the identified sex-based disparities. Working with members of the Healthy Women and Children Subcommittee, specific populations were targeted for participation in the focus groups. The specific populations targeted included low-income women, teenagers, business women, new mothers, low-income seniors, active seniors, social service providers, migrant Hispanic women, and American Indian women. In addition, men were targeted to form a separate focus group for a complete gender-based perspective. Focus group participants were invited by Subcommittee members to participate or had been previous participants in focus groups conducted in conjunction with the 2006 or 2011 CHA. Thirteen focus groups of between 4 and 12 participants were conducted, utilizing a focus group guide (Table 1). The high school student groups represented two class sessions for students serving as mentors in the Big Brothers/Big Sisters program. The professional working women’s group represented women serving on an advisory board for a women’s health program. The health providers group represented nurses, clinic managers, and care coordinators from various health-focused organizations in the community. Social service providers were selected from agencies that provided services to low-income or vulnerable populations in the community. The low-income senior group consisted of seniors serving in the Foster Grandparent Program. The low-income women’s group consisted of women enrolled in a class for low-income women at The Salvation Army. The low-income mother’s group consisted of women served by the local prenatal clinic for low-income women. The active senior group was drawn from hospital volunteers. The American Indian women’s group consisted of young American Indian women. The men’s group consisted of professional working men. The Hispanic women’s group consisted of clients served by the Montana Migrant Council clinic. Focus group sessions were offered in English and Spanish. A bilingual staff member of the Montana Migrant Council translated both the focus group questions and responses for those participants who only spoke Spanish. Focus groups followed a standard format. The focus group facilitator stated a significant 2011 Community Health Assessment finding, ‘‘In our survey, women reported being less physically active than men,’’ and asked the focus group participants to provide their insights as to ‘‘why.’’ Follow-up prompts were based on the Gender Analysis Framework (Gender and Health Group, Liverpool School of Tropical Medicine, 1999). Participants were

Table 1 Focus groups’ characteristics. Focus group

Target population

1 2 3

High school students High school students Professional working women Social service providers Health providers Social service providers Low-income women Low-income seniors Low-income mothers Active seniors Hispanic women Men American Indian women

4 5 6 7 8 9 10 11 12 13

Total

Number of women

Number of men

Number of participants

11 6 4

1 5 0

12 11 4

6 4 11 7 36 5 10 6 0 4

1 0 1 0 3 0 2 0 11 0

7 4 12 7 39 5 12 6 11 4

110

24

134

47

asked prompts such as ‘‘What kind of role do you think the environment would have?’’ Or ‘‘How might gender norms impact physical activity rates?’’ Focus groups were conducted between January 28, 2011, and April 6, 2011. Focus group sessions averaged approximately 40 min. Two staff members conducted the sessions, with one staff member present at all sessions, either as facilitator or as note taker. All sessions were electronically recorded, transcribed verbatim, and analyzed using inductive analysis procedures outlined by Patton (2002) and Thomas (2006). Transcription text was coded to represent the domains of the Gender Analysis Framework, and text supporting similar themes was grouped together. Themes within each of the framework domains were recorded with supporting quotes from participants. Validation of themes was completed by a graduate student at Montana State University Billings who conducted an independent qualitative analysis of the transcripts. The results of the independent analysis correlated with the original inductive analysis (Seale & Siverman, 1997). 3.3. Step C: Examination of how these gender factors constrain or support women’s health Identified themes within the Gender Analysis Framework were further segmented by project staff into categories of Gender-Based Constraints or Gender-Based Opportunities. Themes could be identified as both constraint and opportunity if the gender-based expectation of behavior or activity could lead to both poor health in one aspect and improved health in another aspect. 4. Results 4.1. Step A: Examination of sex-disaggregated quantitative data The CHA sex-disaggregated data revealed two noted findings related to women’s physical activity levels (Table 2). These findings concluded that significantly more women in comparison with men reported being limited in activity due to physical, mental, or emotional problems. Significantly more women than men reported a lack of leisure-time physical activity. Finally, although not statistically significant, but still considered in the context of developing an intervention focused on safety, more women than men reported being prevented from being active because they felt unsafe due to traffic or crime. 4.2. Step B: Examination of why the identified patterns exist Analysis of transcripts from the focus groups revealed several themes related to physical activity. Some of these themes related to gender-based roles, activities, and responsibilities, especially those related to women in caregiving roles. 4.2.1. Caregiving responsibilities Caregiving was a major theme. Women discussed their motivation for physical exercise in terms of their responsibilities. One informant stated, ‘‘We’re busy taking care of others rather than ourselves and the physical exercise comes as an aside.’’ A professional woman agreed, stating, ‘‘I thought I was unique with my mother being Japanese, you know it’s very important that you wait on your husband, the kids, you come last. But I’ve realized pretty much everyone is like that.’’ Another woman said, ‘‘Get the kids, get to the grocery store, and do all those other things. Sometimes I think we take the back seat a lot to running to softball, baseball, the grocery store, get the laundry done. At the end, it’s 9 o’clock, 10 o’clock, bedtime and it’s like well, I could go to the gym at like 11 o’clock, right?’’ A low-income woman noted, ‘‘We weren’t taught to take care of ourselves. Like as a child, I had seven brothers

48

D.K. Duin et al. / Evaluation and Program Planning 51 (2015) 45–52

Table 2 Quantitative data results: physical activity level differences between women and men, 2011 Yellowstone County Community Health Assessment. Variable

Women

Men

p-Value

Limitation of activity due to physical, mental or emotional problems Lack of leisure-time physical activity Prevention of activity because felt unsafe due to traffic or crime

30.9% (n = 194)

20.1% (n = 206)

0.018

27.2% (n = 193) 8.9% (n = 191)

17.4% (n = 205) 3.9% (n = 205)

0.018

and sisters, and I was the most responsible one. They’d start sticking those children off on me to take care of and somehow or another I missed my whole development in there. I went from being born and out of my own diapers to changing diapers.’’ Differences were noted according to age and individuals’ caregiving status. A young woman suggested, ‘‘I think that’s dependent on age and relationship. I mean like I’m single and live by myself, so I don’t really have responsibility for anyone but myself. I’m finding that I can go to the gym for an hour and a half at night if I need to, and I do, have that time. But I would not say that about the majority of people that have a spouse or someone else they have to look after.’’ A male informant who served as the primary caregiver in his family noted, ‘‘To me, working out was very, very selfish. I did that for me, made me feel good. As soon as kiddos came along, I don’t allow myself to be that selfish. I feel guilty when I take that time away to go do something just for me.’’ A young mother said, ‘‘Even though we’re more modern and stuff, men consider when they watch the kids they’re babysitting. You know, ‘I have to babysit tonight because my wife has to go and workout’ or whatever. Where[as] the children to the mom, they’re an extension of us. For at least 18 years, they’re a part of us and they’re our responsibility for the most part.’’ 4.2.2. Leisure-time activities In addition to the influence of caregiving roles and responsibilities, several informants noted a difference in gender-based leisure-time activities. A senior woman noted she liked to ‘‘sit and watch TV, or crochet or something, I work with my hands, but not my legs.’’ A senior male respondent agreed stating, ‘‘Most men, when they retire, they pick a hobby. They stay active, and women have less physical hobbies, knitting and things like that. Maybe it’s just the difference in their hobbies that keep the man maybe more active than the woman.’’ Another female participant noted, ‘‘Women might be in the home taking care of the children in the home. . . The man is more likely to be taking care of the yard, walking the dog or whatever those things he may classify as physical activity. While the woman might not classify four loads of laundry as physical activity, maybe it really should be.’’ A professional woman said, ‘‘I work long hours, so when it comes to taking care of myself, it just feels good to sit and be quiet for a little while.’’ An older woman in poverty noted, ‘‘We tend to be in front of the TV or computer screen, or whatever the case may be, so we’re not getting the physical exercise that we once did just tending the garden, cleaning the yard.’’ A Hispanic participant mentioned ‘‘watching Spanish soap operas’’ as an activity that prevented her from wanting to engage in physical activity. A social service provider working with youth noted, ‘‘A lot of the girls are on TV and computers, and texting. Texting always comes up, ‘I like to text,’ ‘that’s what I do,’ ‘that’s a main activity.’’ 4.2.3. Appearance expectations Additional themes emerged related to gender-based expectations for appearance. One young woman said, ‘‘There’s a huge expectation for you to put on your cute gym clothes and go to the gym and look really cute while you’re doing all these, you know things.’’ Another woman related, ‘‘It may be the vanity in me

coming out, but let’s say you have a spare hour and that’s all you have. That’s enough time to work out. But for me, when I work out, I work out hard. I get dirty and sweaty and then I have to get ready. If I only have an hour, that’s not enough time to do both. Where for my husband, he can shower and look like he did in 5 min. For me, it takes at least 45 min to look presentable and not smell when I get back to work.’’ Appearance was mentioned by an older woman in poverty when she explained why she didn’t use her gym membership, ‘‘You ain’t getting this fat old woman in no bathing suit if I think a man’s going to be there.’’ The pressure to maintain an expected appearance was mentioned by a female social service provider as a reason her clients might avoid physical activity, ‘‘I think it could possibly have something to with body image. I think sometimes women, if they’re down on themselves, and if they feel like they can’t go out and run in the cute little spandex leotard that my neighbor runs in, that ‘I’m not going to do it cause I don’t want people to see me not looking perfect.’’ An American Indian woman noted the difference in experience expectations between men and women, ‘‘The guys can get away with it. They run or jog on their lunch break, get sweaty and messy and go back to work. I can do a 45 min workout. Take 15 min to shower and prepare again, but it just seems to be too much effort to do.’’ A professional woman also noted the barrier of appearance expectations related to exercise on a lunch break stating, ‘‘I mean, if I didn’t have to shower and do hair and get back to work, be presentable, I could on a lunch hour.’’ Several female informants also mentioned gender-based appearance expectations for men as a factor in their increased activity levels, ‘‘For men, I think it’s more of a competition, more muscle and looking better.’’ One senior woman noted, ‘‘I think many of us were raised in a time where boys’ sports were okay. Girls’ sports, maybe you got a little gym suit and you ran around the gym in high school, but there were not organized girls’ sports in school. At least not where I went to school, but my nieces were all in organized girls’ sports in school. So they have just a generational thing.’’ Another woman noted that women might not be accounting for what would actually be physical activity, stating, ‘‘They’re not accounting for what is actually physical activity, running from here to there to there. That’s part of our job, we’re not thinking of it as exercise.’’ 4.2.4. Environment and safety Environmental factors and personal safety also emerged as key themes. In our men’s focus group it was noted, ‘‘I think, especially with outdoor activities, the safety factor is huge. I like to run, I like to ride my bike on the trail system that goes from 27th all the way to the Heights, but there’s no way I would let most of my family go there by themselves and do anything in some of those areas even though it’s a nice trail. Jogging anywhere outside, my impression is, for good reason many times, the women don’t feel as comfortable or as safe recreating outside.’’ A female respondent shared similar sentiments, ‘‘I live up by the bike trail and there’s lots of spots that are really dangerous and very concealed from anyone else’s eyes and there’s a lot of weird people down there.’’ A young low income mother noted, ‘‘women may not feel as safe outdoors exercising the same range of hours that men might feel comfortable being outdoors just exercising.’’ An older woman in poverty stated, ‘‘I live near the park and it’s all winos out in the alley and stuff.’’

D.K. Duin et al. / Evaluation and Program Planning 51 (2015) 45–52

4.2.5. Resources Resources emerged as a theme, especially for women in poverty and women raising children on their own. As a service provider explained, ‘‘I don’t know how many of our families, not only do they have one job, they have two jobs and they’re going to school too. My gosh. They barely have time to stop and breathe, and I think that they’re just busy people. When you’re busy like that, being able to take advantage of recreational opportunities is just not on the radar.’’ An informant agreed, ‘‘not being able to afford a gym membership when you’re paying your bills’’ is a low priority. ‘‘A $25 gym membership for each of you or $70 for a family at the Y is a luxury when you’re making minimum wage, like in our status, and that’s my electric bill. We’ll be awful skinny because we’ll shiver a lot to be able to afford those things. Well, even kids sports too, there are scholarships for those things, but they’re incredibly expensive for the equipment to try to encourage those healthy things. Very expensive, equipment, outfits.’’ 4.2.6. Power Power, as a theme, emerged in relation to activities, responsibilities, and in the determination of the division of labor. A young professional woman said, ‘‘I know that if I don’t go home and take care of stuff, I know my husband’s not going to. And it’s not that he’s necessarily expecting me to, or demanding that I do it, but I want it to get done, so I have to do it. Even though I’m working all day, again, not because my husband necessarily expects it, that just usually seems to fall more on my plate than it does his. . .Maybe that’s just enabling him by allowing it to be that way. Then, when I’m busy taking the kids to and from their sports, he says, ‘well, maybe I’ll just go to the gym then because there’s nothing else to do.’ Maybe I should say, ‘well you go take the kids to their sports and I’ll go to the gym.’’ A teenage girl noted, ‘‘I see the women are usually the ones that give up more things. I mean not in all cases, but they give up more things to raise a family or keep the family.’’ An older woman speaking of her abusive ex-husband said, ‘‘He was trained by his daddy that women are to do the housework and feed the men and women and children come last. The man is number one.’’ A young woman explained how she began negotiating a more equitable division of tasks with her husband, ‘‘I remember when I was little, I can’t remember my dad ever staying home when I was sick. My mom was the one that always stayed home when I was sick. My husband and I do a pretty good job of sharing that, but at first I felt like it was my responsibility because that’s what I saw growing up. So I just assumed that responsibility, but then I have to work too. We both work full-time, so I thought ‘why am I doing a full-time job and taking care of all this stuff?’ So, I said, ‘you know what, Cole was sick last week and I stayed home. Ryan’s sick this week, you’re staying home.’ We’ve got to take turns. I think that in a way that’s not seen as nagging. You’ve got to encourage your husband or significant other to share in some of that and not let them get away with it.’’ Several respondents mentioned the cultural norms of Montana as a power issue affecting physical activity. One social service provider described the power domain in these terms, ‘‘I find Montana is a good old boy state, not only from a professional realm of being a woman who is in resource development trying to get money from others. Anything. You go to the gym and the majority of the people are men. You go to the park and the people walking their dogs are men. You know the female’s at home watching the kids.’’ A woman in poverty expressed a similar thought, ‘‘I still think that Montana is still truly the good old boy state. I think that the hierarchy of male versus female plays a part. I think the women still in a lot of ways feel inferior.’’ 4.2.7. Social support Several participants mentioned social support as an important factor in engaging in physical activity. One female suggested a way

49

to increase physical activity was ‘‘making things a social event as opposed to whatever else. I am not a runner, I despise running, I will do any other exercise except running, but I go to the Women’s Run every year with my girlfriends not to run, but to be with my friends because it’s a social event. Apparently, you know, 5,000 other women feel the same way.’’ Another female, when discussing a previous gym membership said, ‘‘I just felt like when I was at the BAC, you belonged to something. You felt good.’’ A senior female described, ‘‘I’m part of a group who, weather permitting, we reserve an hour to walk every morning. We’re spurred on because somebody doesn’t want to be left alone. So if you think, ‘oh, I don’t want to go,’ then someone says, ‘I’ll meet you at the corner.’ So you are inspired when there’s a group to walk with you.’’ A senior male said, ‘‘I know my wife and my daughter won’t go anywhere unless [they have] somebody. If you put somebody with her, she works out like all heck. But if she’s alone, she doesn’t want to; she can’t find the time.’’ A Hispanic woman said that she didn’t have a friend to walk with, so she only walked from work to home but would have walked more if she had someone to walk with. An active senior woman said that ‘‘social interaction with the exercise keeps you going.’’ 4.3. Step C: Examination of how these gender factors constrain or support women’s health The qualitative themes were further segmented into GenderBased Constraints or Gender-Based Opportunities depending on whether the theme would lead to poor health or improved health (Table 3). Themes such as caregiving responsibilities were identified as both constraint and opportunity. For example, the gender-based expectation of caregiving behaviors meant women often prioritized taking care of others ahead of themselves. This could lead to poor health; however, it was also an opportunity because the desire to set a good example as women cared for others could lead to their improved health. 5. Discussion This study, which used a gender-based analysis to gain an understanding of gender-based constraints and opportunities related to physical inactivity, was used in the next stage of our research as a foundation for developing an intervention to increase physical activity for women in Yellowstone County, Montana. Recognizing the root causes of gender inequities in health is crucial when designing health system responses. When planning and implementing health promotion and disease prevention strategies, gender is an issue that is often neglected (Ostlin et al., 2006; Doyal, 2001; Greaves, 2009). Many health-promotion interventions are gender blind and may not respond to women’s health needs. Ostlin et al. (2006) suggest recognizing gender inequalities as crucial in the design of health promotion strategies and that without such a perspective ‘‘their effectiveness may be jeopardized, and inequities in health between men and women are likely to increase’’ (p. 33). Though some countries such as Canada (Status of Women Canada, 2001; Clow, Pederson, Haworth-Brockman, & Bernier, 2009) have taken steps to integrate gender perspectives into policy and practice, the approach is relatively new, especially in other developed countries. Gender-based analysis is more often used in developing countries as part of international aid efforts (World Health Organization, 2011; Interagency Gender Working Group, 2009). Gender-based analyses have been primarily used in the study of health issues such as sexual and reproductive health or gender-based violence (World Health Organization, 2002; World Health Organization, 2011; Inner Spaces Outer Faces Initiative, 2007; Parker, 1993; Commonwealth Secretariat and Maritime Centre of Excellence for Women’s Health, 2002; Greene, 2012;

50

D.K. Duin et al. / Evaluation and Program Planning 51 (2015) 45–52

Table 3 Qualitative data results—physical activity constraints and opportunities (n = 134). Key gender themes—physical activity

Gender-based constraints

Gender-based opportunities

Environment Activities/responsibilities

Safety and crime concerns, especially related to outdoor recreation Caregiving roles are placed above self-care Household chores are the primary responsibility of women, even when both men and women work full-time Less physical hobbies and occupations for women

Gender norms

Appearance expectations, including hair and makeup and not sweating, are barriers for women’s exercise, especially during lunch breaks

Access and control of resources

Working multiple jobs makes physical activity a low priority Childcare access and associated expenses Transportation barriers, especially for women with small children Expectations for responsibilities Lack of negotiation over division of responsibilities

Enjoyment of outdoor activities, especially walking Less differences noted by younger generations in the physicality of activities or jobs for men and women Caregiving role leads to a desire to set a good example for those closest including children Caring for pets leads to activity Women may not be counting their household and caregiving activities as physical activity Women enjoy social activities and spending time with family and friends Men’s appearance expectations to be muscular lead to an emphasis on physical activity Gyms with free childcare

Power and decision making

Interagency Gender Working Group, 2009; Ostlin et al., 2007). The authors suggest broader community health issues not generally viewed as gender-based, such as physical activity, may also benefit from gender-based program planning and analysis. Through the use of the Gender Analysis Framework, an ongoing health intervention to address a lack of leisure-time physical activity for women was developed. As noted by Ransdell et al. (2004), as women’s role expectations (wife, mother, caretaker, and employee) increase, the level of physical activity declines. By creating an intervention which focuses on gender-based opportunities and minimizes gender-based constraints, those underlying genderbased disparities related to physical activity will be addressed. These gender relations, roles, and identities are relevant to achieving program objectives; without the analysis of how gender norms and inequities undermine health, programs and interventions will fail to address those important social contexts. The gender analysis had several limitations. The Gender Analysis Framework of the Liverpool School of Tropical Medicine (Gender and Health Group, 1999) relies heavily on the use of sexdisaggregated data to determine patterns of disparity. However, gender is more than a categorical variable, but measures such as gender identity and gender equitable beliefs were not included in the surveys or focus groups. Focus groups were convenience samples, which may not accurately represent the views of those demographic groups. For example, the high-school student focus groups consisted of junior and senior students who were selected to serve as mentors for youth. Their experiences may not have accurately reflected the experiences of all high school students. Some focus groups consisted of mixed-sex participants, which may have limited participants’ willingness to discuss gender constraints. In addition, focus group questions focused on the sexdisaggregated Community Health Assessment results specific to women, which limited the scope of the discussion and excluded gender identity. 6. Lessons learned Because gender is socially constructed, the biases and gender roles into which individuals are socialized often occur at the subconscious level. To analyze gender’s role, it was important to utilize a framework for discussion. Utilizing the Gender Analysis Framework and including the areas of Environment, Activities and Responsibilities, Gender Norms, Access and Control over

Negotiation of shared responsibilities and creating more equitable household chore arrangements

Resources, and Power and Decision Making allowed for that discussion and analysis. The gender analysis may have been limited by not utilizing additional constructs or frameworks for the analysis such as the Gender Analysis Matrix (Parker, 1993) or the Harvard Gender Roles Framework (Overholt, Anderson, Cloud, & Austin, 1985). However, through use of the Gender Analysis Framework to examine the ‘‘why’’ behind the sex disaggregated data in the CHA, socially constructed norms will be a central consideration in developing an intervention to increase physical activity for women in Yellowstone County, Montana, thereby bolstering chances for success in addressing the underlying gender-based disparities that undermine physical activity. Acknowledgements We wish to thank the HHS/Office on Women’s Health, (Grant CCEHW111023-01-01) for supporting this research.

Appendix A. Sex-disaggregated data and potential areas of focus A.1. Women report a lack of leisure-time physical activity Male

Female

Overall

During the past month, other than your regular job, did you participate in any physical activities or exercises, such as running, calisthenics, golf, gardening, or walking for exercise? Yes 82.6% 72.8% 77.6% No 17.4% 27.2% 22.4% n 194 206 400 Vigorous physical activity (20+ minutes/3+ times per week) Yes 38.1% 29.0% No 61.9% 71.0% n 192 200

33.5% 66.5% 392

Moderate physical activity (30+ minutes/5+ times per week) Yes 30.6% 22.4% No 69.4% 77.6% n 192 204

26.4% 73.6% 396

Meets physical activity recommendations Yes 50.4% No 49.6% n 191

47.2% 52.8% 394

44.2% 55.8% 203

D.K. Duin et al. / Evaluation and Program Planning 51 (2015) 45–52

A.2. Women report being more limited in activity due to physical, mental or emotional problems Male

Female

Overall

The following questions are about health problems or impairments you may have. Are you limited in any way in any activities because of physical, mental or emotional problems? Yes 20.1% 30.9% 25.7% No 79.9% 69.1% 74.3% n 193 205 398 What is the major impairment or health problem that limits you? Other impairment/problem 30.5% 26.5% Back or neck problem 20.1% 13.0% 17.3% 14.4% Arthritis/rheumatism Eye/vision problem 9.0% 1.6% Fractures, bone/joint injury 8.5% 8.4% Heart problem 6.8% 8.1% Depression, anxiety, emotional problem 3.8% 1.7% Lung/breathing problem 2.7% 2.9% Diabetes 1.2% 0.0% 0.0% 23.5% Walking problem n 39 60

28.1% 15.7% 15.5% 4.5% 8.4% 7.6% 2.5% 2.8% 0.5% 14.3% 99

A.3. Women report higher chronic depression Male Major depression diagnosed by a doctor Yes 10.2% No 89.8% n 94

Female

Overall

16.0% 84.0% 206

13.2% 86.8% 400

Have you had two years or more in your life when you felt depressed or sad most days, even if you felt okay sometimes? Yes 17.8% 31.8% 25.0% No 82.2% 68.2% 75.0% n 191 204 395

A.4. Women report higher percentages of chronic pain Male

Female

Overall

Do you experience any type of pain on an ongoing basis? Yes 28.0% 35.1% No 72.0% 64.9% n 193 206

31.7% 68.3% 399

Do you regularly take a prescription medication for your pain? Yes 23.4% 34.7% No 76.6% 65.3% n 53 72

29.9% 70.1% 125

Do you WISH you could regularly take a prescription medication for your pain? Yes 38.4% 20.7% 28.9% No 61.6% 79.3% 71.1% n 41 47 88 Sciatica or chronic back or neck pain Yes 16.6% No 83.4% n 194

23.1% 76.9% 206

20.0% 80.0% 400

Arthritis or rheumatism Yes 19.6% No 80.4% n 194

25.6% 74.4% 206

22.7% 77.3% 400

A.5. Women report being prevented from being active because they felt unsafe due to traffic or crime Male

Female

Overall

In the past 12 months, has there been a time when you wanted to be more physically active outdoors, but you were not because things like traffic or crime made you feel unsafe? Yes 3.9% 8.9% 6.5% No 96.1% 91.1% 93.5% n 191 205 396

51

References Adachi-Mejia, A., Drake, K., Mackenzie, T., Titus-Ernstoff, L., Longacre, M., Hendricks, K., et al. (2010). Perceived intrinsic barriers to physical activity among rural mothers. Journal of Women’s Health, 19(12), 2197–2202. Brittle, C., & Bird, C. (2007). Literature review on effective sex-and gender-based systems/ models of care. Uncommon Insights LLC Retrieved October 19, 2011, from hhttp:// www.womenshealth.gov/archive/owh/multidisciplinary/reports/GenderBasedMedicine/FinalOWHReport.pdfi. Clow, B., Pederson, A., Haworth-Brockman, M., & Bernier, J. (2009). Rising to the challenge: Sex-and gender-based analysis for health planning, policy and research in Canada.. Halifax, NS, Canada:: Atlantic Centre of Excellence for Women’s Health. Commonwealth Secretariat Maritime Centre of Excellence for Women’s Health (2002). Gender mainstreaming in HIV/AIDS taking a multisectoral approach. London: Commonwealth Secretariet, ISBN: 0850926556. Doldren, M., & Webb, F. (2013). Facilitators of and barriers to healthy eating and physical activity for black women: A focus group study in Florida, USA. Critical Public Health, 1(23), 32–38. Doyal, L. (2001). Sex, gender and health: The need for a new approach. British Medical Journal, 323, 1061–1063. Gender and Health Group, Liverpool School of Tropical Medicine (1999). Guidelines for the analysis of gender and health. United Kingdom: Liverpool School of Tropical Medicine and Department for International Development Retrieved November 29 2010 from Liverpool School of Tropical Medicine hhttp://www.lstmliverpool.ac.uk/ research/departments/international-public-health/gender-and-health-group/ chapter-3i. Greaves, L. (2009). Women, gender and health research. In P. Armstrong & J. Deadman (Eds.), Women’s health: Intersections of policy, research and practice (pp. 3–20). Toronto, ON, Canada: Women’s Press. Greene, M. (2012). A practical guide for conducting and managing gender assessments in the health sector. Washginton, DC: Population Reference Bureau. IGWG and USAID (2009, August). A manual for integrating gender into reproductive health and HIV programs: From commitment to action (2nd ed.). IGWG and USAID Retrieved November 20, 2011, from hhttp://www.prb.org/igwg_media/manualintegrgendr09_eng.pdfi. Inner Spaces Outer Faces Initiative (2007). ISOFI Toolkit: Tools for learning and action on gender and sexuality. Atlanta, GA; Washington, DC: Cooperative for Assistance and Relief Everywhere, Inc, and International Center for Research on Women. Interagency Gender Working Group (2009). Gender perspectives improve reproductive health outcomes: New evidence. Washington, DC: Population Reference Bureau. Johnson, J., Greaves, L., & Repta, R. (2007). Better science with sex and gender: A primer for health research. Vancouver, BC, Canada: Women’s Health Research Network. Khosla, P., & Barth, B. (2008). Gender in local government: A sourcebook for trainers (HS/ 993/08 E). Nairobi, Kenya: United Nations Human Settlements Programme. Ostlin, P., Eckermann, E., Mishra, U., Knowane, M., & Wallstam, E. (2006). Gender and health promotion: A multisectorall policy approach. Health Promotion International, 21(S1), 24–35. Overholt, C., Anderson, M., Cloud, K., & Austin, J. (1985). Women in development: A framework for project analysis. In C. Overholt, M. B. Anderson, K. Cloud, & J. E. Austin (Eds.), Gender roles in development projects. Connecticut: Kumarian Press. Parker, R. (1993). Another point of view: A manual on gender analysis training for grassroots workers. New York, NY: UNIFEM. Patton, M. (2002). Qualitative research & evaluation methods. Thousand Oaks, CA: Sage. Professional Research Consultants Inc. (2010). 2010 PRC community health report: Yellowstone county Montana. Nebraska: Omaha. Available from hhttp:// www.healthybydesignyellowstone.org/wp-content/uploads/2010-PRC-Community-Health-Assessment-REPORT-Yellowstone-County-Montana1.pdfi. Ransdell, L., Vener, J., & Sell, K. (2004). International perspectives: The influence of gender on lifetime physical activity participation. The Journal of the Royal Society for the Promotion of Health, 124(1), 12–13. Seale, C., & Siverman, D. (1997). Ensuring rigour in qualitative research. European Journal of Public Health, 7(4), 379–384. Status of Women Canada (2001). Canadian experience in gender mainstreaming. Ottawa, ON: Gender-Based Analysis Directorate. Thomas, D. (2006). A general inductive approach for analyzing qualitative evaluation data. American Journal of Evaluation, 27(2), 237–246. U.S. Department of Health and Human Services (2000). Healthy people 2010 women’s and men’s health: A comparison of select indicators. Washington, D.C. U.S. Government Printing Office. U.S. Department of Health and Human Services (2008). 2008 Physical activity guidelines for Americans. (OD PHP Publication U0036). Retrieved from hhttp://www.health.gov/paguidelines/pdf/paguide.pdfi. U.S. Public Health Service, Office of the Surgeon General (1996). Physical activity and health: A report of the Surgeon General. U.S. Public Health Service, Office of the Surgeon General (S/N 017-023-00196-5). Retrieved from hhttp://profiles.nlm.nih.gov/ps/retrieve/ResourceMetadata/NNBBHBi. Warburton, D., Nicol, C., & Bredin, S. (2006). Health benefits of physical activity: the evidence. Canadian Medical Association Journal, 174(6), 801–809. World Health Organization (2002). Gender analysis in health: A review of selected tools. Geneva: WHO Press, ISBN: 9241590408. Wold Health Organization (2007). A Guide for population-based approaches to increasing levels of physical activity: Implementation of the WHO global strategy on diet, physical activity and health. Geneva: WHO Press, ISBN: 9241595175. World Health Organization (2008). WHO global strategy on diet, physical activity and health: A framework to monitor and evaluate implementation. Geneva: WHO Press, ISBN: 9789241597302.

52

D.K. Duin et al. / Evaluation and Program Planning 51 (2015) 45–52

World Health Organization (2010). Global recommendations on physical activity for health. Geneva: WHO Press, ISBN: 9789241599979. World Health Organization (2011). Gender mainstreaming in WHO: What is next?.: Report of the midterm review of WHO gender strategy Geneva: WHO Press, ISBN: 9789241502337. Diane K. Duin, Ph.D., is the Dean of the College of Allied Health Professions at Montana State University Billings, and has a Ph.D. in Rural Sociology. In addition to her research interests in issues involving aging, she also serves as a Fellow for the Commission on Accreditation of Healthcare Management Education, is a member of the Board of Directors for RiverStone Health Clinic, Billings, MT, a member of Building and Supporting Montana’s Healthcare Workforce Group, serves as a member of the Review Board for the Journal of Health Administration Education, and the editorial board for HAP/ AUPHA Publishing. Amanda L. Golbeck is Professor of Biostatistics at The University of Montana. She earned a M.A. in anthropology, M.A. in statistics and Ph.D. in biostatistics from The University of California, Berkeley. She has had executive education in educational management, leadership and negotiation from Harvard University and AASCU. She also has an outstanding Alumni Award from Grinnell College and a special Appreciation Award from the Kansas Board of Regents. Dr. Golbeck is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute. She currently serves as Past President of the Caucus for Women in Statistics. April Ennis Keippel is a Grant Project Coordinator for St. Vincent Healthcare in Billings, MT. She coordinates the Office on Women’s Health’s Coalition for a Healthier Community grant-funded project. She received her M.A. in organizational communications from the University of Montana. Ms. Keippel currently serves as co-lead for Yellowstone County’s Healthy By Design Health Equity Subcommittee. Elizabeth Ciemins is a Health Services Research Scientist and Research Director of the Center for Clinical Translational Research at Billings Clinic in Billings, Montana and part time faculty at the University of Montana, Missoula and Montana State University, Billings. Dr. Ciemins received a PhD in Health Services and Policy Analysis from University of California, Berkeley and an MPH in Population/Family Health and MA in African Studies at University of California, Los Angeles. She also served as a Health

Educator/Nutritionist in Niger, West Africa, where she worked with traditional birth attendants to improve the health of women and children.

Hillary Hanson is the Deputy Health Officer the Flathead City-County Health Department located in Kalispell, MT where she plays a leadership role in the community health, health promotion and environmental health programs. Prior to coming to Flathead, Ms. Hanson served as the Director of Population Health Services and Deputy Health Officer for the local health department in Yellowstone County. Ms. Hanson received her M.S. in applied statistics from the Purdue University and an M.P.H. in public health leadership from the University of North Carolina. Ms. Hanson serves in various leadership roles, including serving on the National Association for County and City Health Officials Healthy People 2020 work group and as the Past President for the Montana Public Health Association.

Tracy Neary is Director of Mission Outreach & Community Benefit at St. Vincent Healthcare in Billings, Montana. Tracy oversees the organization’s community benefit processes from needs assessment to strategic planning to execution of strategies and program evaluation. She received her M.S. in Public Relations after undergraduate work in Organizational Communication and serves as an adjunct faculty member at Montana State University, Billings. Tracy was honored by the Catholic Health Association in 2012 as one of Tomorrow’s Leaders of the Catholic Healthcare Ministry, for outstanding leadership, community service and passionate commitment to the mission of Catholic Health Care.

Heather R. Fink, MA, has worked for social and health service organizations in the areas of public relations, communications, program management, administration, capacity building, fundraising, grant writing and grant management for the past 15 years. She co-authored an article for the journal Rural Roads entitled ‘‘Transforming Rural Communities: The faith health connection’’ in 2004. Ms. Fink holds a Master’s Degree in Integrated Corporate Communications and has served as a Federal Grant Reviewer for the Administration for Children and Families. She most recently authored and managed grants for St. Vincent Healthcare Foundation, a philanthropic organization supporting St. Vincent Healthcare. Currently, Ms. Fink serves as the Community Health Improvement Coordinator for the Alliance: RiverStone Health, Billings Clinic, St. Vincent Healthcare in Billings, Montana