introduction - uleth.ca web viewthe prevalence of adult obesity has reached an alarming level in the...

Running head: Social marketing Physical Activity Campaigns in Adults

Effectiveness of Social Marketing Campaigns to Promote Physical Activity in Adults:

A Systematic Review

Yuan Xia

A Thesis Proposal

Submitted to the School of Graduate Studies of the University of Lethbridge

In Partial Fulfillment of the Requirements for the Degree

Master of Science (MSc) in Management

Faculty of Management

University of Lethbridge

© Yuan Xia, 2013

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Abstract

Obesity has become a severe public health issue and its consequences lead to severe

health disorders and economic hardships. Reduced levels of physical activity is a major

contributor to the epidemic and as a result promoting physical activity is an important

strategy in the battle to prevent obesity and promote public health. Social marketing

framework as a behavior change tool is compatible with the psychological states of the

target audience (adults, in our case) and what managers of a physical activity intervention

want to achieve. However, it is not known whether social marketing strategy promotes

physical activity. The present study will perform a systematic review of published studies

to assess effectiveness of physical activity interventions that employed a social marketing

framework and provide recommendations to social change managers to improve future

interventions.

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Table of Contents

Introduction................................................................................................................4

Literature Review.......................................................................................................6Increasing Prevalence and Trends of Adult Obesity in the World.....................................6Impacts of Obesity..............................................................................................................9Causes of Obesity..............................................................................................................11The Role of Physical Activity in Obesity Prevention and Long-term Weight Loss Maintenance......................................................................................................................17How Much Physical Activity Is Enough for Body-Weight Regulation?............................21Target Audience of Physical Activity Interventions..........................................................23Why Social Marketing......................................................................................................25Social Marketing Benchmarks..........................................................................................29

Behavior...............................................................................................................................29Customer Orientation............................................................................................................31Theory..................................................................................................................................32Insight...................................................................................................................................33Exchange..............................................................................................................................34Competition..........................................................................................................................35Segmentation........................................................................................................................36Marketing Mix......................................................................................................................38Funding and Partnership.......................................................................................................40

Evaluating Effectiveness of A Physical Activity Intervention...........................................40

Objectives of the Study.............................................................................................42

Method......................................................................................................................42Key Words and Inclusion Criteria....................................................................................43Article Data Extraction.....................................................................................................44Data Analysis....................................................................................................................44

Contributions............................................................................................................45

Timeline....................................................................................................................45

Budget.......................................................................................................................45

References.................................................................................................................47

Appendix 1 Data Extraction Form............................................................................57

Appendix 2 Data Extraction Codebook....................................................................59

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Introduction

Obesity epidemic has become a worldwide health issue that needs attention from

governments and nongovernmental organizations. The prevalence of adult obesity has

reached an alarming level in the world (Flegal et al., 2012; Tjepkema, 2005; Berghofer et

al., 2008; Ma et al., 2005). Obesity not only leads to other severe health disorders, such

as resistance and hyperinsulinemia, type-2 diabetes, hypertension, dyslipidemia, coronary

heart disease, gallbladder disease, cancer, and early mortality (Pi-Sunyer, 2002), but also

creates enormous economic burden for both government health systems and individuals

(Withrow and Alter, 2010).

Existing literature suggests that physical activity has a significant role in reversing

obesity epidemic and maintaining general health. Researchers observed that motorized

transportation (Bassett, et al., 2008; Bell et al., 2002), low level of leisure-time physical

activity (Abu-Omar and Rutten, 2008), and high level of “screen time” (Banks et al.,

2010) are associated with obesity. A dose-response effect of physical activity on weight

loss and long-term weight maintenance is found through randomized, controlled trials

(Jeffery et al., 2003; Wing and Phelan, 2005). Because of the decreasing level of physical

activity (Brownson, 2004) and the significant role of physical activity in weight loss,

weight control, and general health, it is recommended that adults should practice 30

minutes of moderate exercise 5 times per week to maintain general health (World Health

Organization), 60-90 minutes of moderate exercise per day to prevent weight regain

(Saris et al., 2003), and 45-60 minutes of moderate exercise per day to prevent transition

from overweight to obese (Saris et al., 2003).

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Social marketing is “a process that applies marketing principles and techniques to

create, communicate, and deliver value in order to influence target audience behaviors

that benefit society (public health, safety, the environment, and communities) as well as

the target audience” (Kotler and Lee, 2008, p. 7). Since social marketing frame is

compatible with what managers in a physical activity campaign want to achieve, it seems

reasonable to put social marketing efforts to promote physical activity. While some

amount of research has been conducted to review effectiveness of social marketing

programs promoting health behaviors (Stead et al., 2007; McDermott et al., 2005), past

studies have not assessed effectiveness of social marketing efforts independently, so the

field remains generally under-researched.

The purpose of the present study is to systematically review evidence on efforts to

promote physical activity among adults and to assess these studies within a social

marketing framework. We believe that the present study will provide recommendations to

improve future physical activity campaigns and to contribute to social marketing theory.

The rest of the proposal report is presented as follows. The report will first discuss

the extent of obesity epidemic around the world, and later consequences and causes of

obesity. Among causes of obesity, physical activity plays a central role. As a result, the

report will focus on efforts to understand the role of physical activity in obesity

prevention and weight control and discuss recommendations by health experts to observe

adequate levels of physical activity. Later the report will discuss levels of audience

members to target, argue why social marketing should be employed as a social change

tool to promote physical activity, and what social marketing principles should be

employed to conduct an effective social marketing campaign. In the final pages, the

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report delves into methodological issues and ends by discussing activities to be carried

out after proposal defense.

Literature Review

Increasing Prevalence and Trends of Adult Obesity in the World

Body mass index, an index to detect weight balance, is calculated as weight in

kilograms divided by height in meters squared (kg/m2). Usually, people whose BMI is

between 25.0 and 29.9 are categorized as overweight, and people whose BMI is more

than 30.0 are categorized as obese (Flegal et al., 2012).

Adult obesity is a public health challenge in Canada and the United States. In the

United States, obesity in adult population has been significantly increasing since 1980s

and reached an alarming level by 2010. To get the idea of prevalence and trends of

obesity at the population level, we used data from a series of National Health and

Nutrition Examination Surveys (NHNES) carried out by the National Center for Health

Statistics (NCHS). Figure 1 shows time trends in age-adjusted prevalence (%) of obesity

(BMI≥30.0 kg/m2 and 35.0≤BMI≤39.9 kg/m2) in adults (age 20-74 years old for

NHANES I-III and age≥20 years old for NHANES 2009-2010) from 1971-2010. There is

no significant change of prevalence in obesity among American adults in the first ten

years. At some time between NHANES II (1976-1980) and NHANES III (1988-1994),

American adult obesity population started to increase sharply from 13.2% to 19.6%. By

2010 when the latest NHANES was released, more than 44% of American adult

population was obese. Obesity prevalence in 2010 is more than twice as high as the

number in 1994. Furthermore, those numbers do not include data from pre-obesity group

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(25.0≤BMI≤30.0 kg/m2). According to the latest NHANES, by 2010, at least 68.8% of

American adults aged 20 or older was overweight and obese (Flegal et al., 2012).

All Adults Men Women0

5

10

15

20

25

30

35

40

45

50

USAPr

eval

ence

(%) o

f Obe

sity

Figure 1. The data are from NHANES (1971-1974, 1976-1980, 1988-1994, 1999-

2010) (Flegal et al., 1998; Flegal et al., 2012)

In Canada, although the absolute prevalence of obesity among adults is lower than

that of the U.S., the trend is upwards since 1978. We used data from Canada Health

Survey (1978-1979), Canadian Heart Health Surveys (1986-1992), and Canadian

Community Health Survey (2004). Figure 2 shows the trend in adults (aged 18 to 74

years old) in Canada from 1978 to 2004. During the first 15 years of the time span, the

rate in adult obesity slightly increased from 13.7% to 14.6%. By 2004 when the Canadian

Community Health Survey was released, the obesity rate among adults in Canada had

reached 23.1%, almost 10% higher than the number in 1978-1979. Compared to the

trends of adult obesity in the U.S., rates of obesity among adults in Canada are

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significantly lower, and the increase in adult obesity in Canada is much slower. However,

it is a worrying trend that the graph continues to show upward trend.

1978-1989 1986-1992 20040

5

10

15

20

25

CanadaO

besit

y Ra

tes

Figure 2. The data are from Canada Health Survey (1978-1979), Canadian Heart

Health Surveys (1986-1992), and Canadian Community Health Survey (2004)

(Tjepkema, 2005)

The same upward trend of obesity is also observed in other countries in the world

such as European cities and China. In Europe, the prevalence of obesity in male adults

ranged from 4.0% to 28.3% and in female adults from 6.2% to 36.5% (Berghofer et al.,

2008). Generally speaking, the adult obesity rates of western or northern regions are

lower than the rates of central, eastern, and southern regions (Berghofer et al., 2008). The

rates of male adult obesity (BMI≥25.0) are high in Spain, Italy, Cyprus, Czech R., and

Poland, and the rates of female adult obesity (BMI≥25.0) are high in Spain, Italy,

Romania, Czech R., and Poland (Berghofer et al., 2008). In central, eastern, and southern

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Europe, the prevalence of adult obesity has reached a concerning level. Since Chinese

tend to have a higher percentage of body fat than westerners under the same BMI value

(Deurenberg-Yap and Deurenberg, 2003), people with BMI≥24.0 and BMI≥28.0 are

categorized as overweight and obesity for Chinese population (Chen, 2008). Under this

classification of overweight and obesity, in 2002, the prevalence of overweight and

obesity of Chinese adults is 7.1% and 22.8%, respectively (Chen, 2008). From 1992 to

2002, prevalence of obesity and overweight in Chinese adults aged 18 year old and over

has increased by 40.7% and 97.2%, respectively (Ma et al., 2005). In summary, obesity

has become a public health issue in many parts of the world. As a global health and

clinical issue, obesity, as well as its consequences, has reached a level that deserves

attention from governments and nongovernmental organizations.

Impacts of Obesity

Obesity leads to several health risks (Pi-Sunyer, 2002; Thompson et al., 1999).

From a pathophysiological perspective, Pi-Sunyer (2002) proposed several disorders

associated with obesity that lead to higher mortality and morbidity, including insulin

resistance and hyperinsulinemia, type-2 diabetes, hypertension, dyslipidemia, coronary

heart disease, gallbladder disease, cancer, and early mortality. Thompson and colleagues

(1999) employed a dynamic model to analyze relationship between BMI and five obesity-

related diseases: hypertension, hypercholesterolemia, type-2 diabetes mellitus, coronary

heart disease, and stoke. Their findings show that the estimated risk (%) of all the five

diseases has a positive association with BMI for both men and women aged 35-64 years

old. That is, obese individuals have a greater chance to have the five diseases than their

leaner peers. For example, among women aged 55 to 64 years old, obese individuals

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(18.4%) have three times higher chance to suffer from type-2 diabetes mellitus than their

leaner peers (5.6%). Furthermore, Thompson and colleagues (1999) suggested that this

estimate of risk of related diseases tend to be conservative because other diseases

associated with obesity, such as gallbladder disease, were not included in the model. In

other words, obesity may have more influential power on health issues in reality than

what is reported in the literature.

Obesity also results in economic hardships for both society and individuals. Other

than the direct costs of healthcare and treatment, obesity is also responsible for indirect

costs such as significant productivity loss, transportation costs and human capital

accumulation costs (Hammond and Levine, 2010). By studying articles published

between 1990 and 2009, Withrow and Alter (2010) found that the direct costs of obesity

impose a heavy burden for both health systems and obese individuals. According to their

estimate, obesity is responsible for between 0.7% and 2.8% of a country’s total healthcare

expenditures. Obese individuals spend approximately 30% more in medical costs than

individuals with a normal body weight (Withrow and Alter’s, 2010).

Further, developing countries might suffer the obesity burden more than developed

countries in the future. Kelly and colleagues (2008) pointed out that although obesity and

overweight is more common in economically developed countries than in economically

developing countries (prevalence of overweight and obesity is 35.2% versus 19.6% and

20.3% versus 6.7%, respectively), developing countries may result in more absolute

numbers of overweight and obese population because of their large population. Given the

underdeveloped status of economy of those countries, obesity might place an even

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heavier burden on the government and health system of economically developing

countries in the future.

Causes of Obesity

From an energy balance perspective, body-weight regulation is achieved by

systems that adjust or match energy balance (energy intake versus energy expenditure)

(Martinez, 2000; Hill, 2006; Dietz, 2004). When energy intake exceeds energy

expenditure, it results in a positive energy balance and unhealthy body-weight gain.

Researchers (Martinez, 2000; Hill, 2006; Dietz, 2004) believe that the interaction

between genetic factors, dietary patterns, physical activity, and environment factors

influence energy balance and body-weight maintenance.

The biological and genetic factors have played a significant role in global obesity

epidemic. According to Hill (2006), our biological system strongly favors weight gain

over weight loss. Furthermore, humans prefer sweet-tasting foods and perhaps high-

energy dense foods (Drewnowski, 1998), and humans do not seem to have a strong

biological drive to promote energy expenditure (Hill & Peters, 1998). Martinez (2000)

also suggested that some people are more genetically susceptible to obesity than others.

However, genetic and biological factors should not be solely held responsible for the

increasing prevalence of obesity. According to Martinez (2000), the worldwide increase

of obesity is also triggered by the presence of other factors such as unfavorable

environment (e.g. easy access to energy-, fat-dense, and high-sugar foods) and behavioral

changes in physical activity.

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According to Martinez (2000), a low level of activity-related energy expenditure

plays a major role in the increasing prevalence of obesity. By studying U.S. national

surveys, such as Behavioral Risk Factor Surveillance System, from 1950s to 2000s,

Brownson (2004) and colleagues concluded that the overall levels of physical activity of

Americans have been decreasing due to a host of factors such as technological advances

(motorized transportation, conveniently available services, and digital entertaining

products), urban sprawl and sedentary activities. Of the total time that people are awake,

the time spent carrying out all the activities (work-related activity, transportation, and

those carried out at home) are declining. Brownson et al.’s estimation of levels of

physical activity of Americans tends to be conservative because they only studied

sedentary activities during leisure time. Sedentary behaviors are also work- and

transportation-related. Due to technological advances, many occupations do not require

much physical activity anymore. More and more people rely heavily on motorized

transportation instead of walking and cycling. Despite this limitation, Brownson et al.’s

study gives us a general idea of Americans’ decreasing level of physical activity.

In general, researchers (Sturm, 2004; Brownson et al., 2004; Sallis and Glanz,

2009) categorize physical activity into four domains: leisure-time/recreation activity,

occupation-related activity, transportation activity, and home production/domestic

activity. Of those domain-specific activities, transportation and leisure-time/recreation are

found to have strong association with obesity prevalence while occupation and domestic

activity is remotely associated (Ball et al., 2001; Abu-Omar & Rutten, 2008).

Active transportation is a predictor of low rates of obesity. To study the relationship

between the transportation behavior and rates of obesity, Bassett and colleagues (2008)

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analyzed data on transportation and rates of obesity from national surveys and published

studies released between 1994 and 2006 and based on European, North American, and

Australian populations. In their study, active transportation was defined as the percentage

of trips taken by walking, bicycling, and public transit, and obesity was defined as

BMI≥30 kg*m-2. They observed much lower levels of active transportation in North

America (12% and 19% in America and Canada, respectively) and Australia (14%) than

the levels in European cities. The most transportation-active countries are Latvia (67%),

Switzerland (62%), and Netherlands (52%). On the other hand, the prevalence of obesity

based on measured weight and height in North America (34.3% and 22.7% for America

and Canada, respectively) and Australia (20.8%) is much higher than the rates of

European countries except for Great Britain. Based on Bassett et al.’s (2008) analysis,

one deduces a relationship between active transportation and the prevalence of obesity

based on energy expenditure.

Bassett and colleagues (2008) suggested that the observed trend between active

transportation and obesity rates could be explained by the contribution of active

transportation in energy expenditure. Bassett and colleagues (2008) calculated and

compiled data on walking and bicycling distance and calories burnt by Europeans and

Americans in 2000. Europeans walked and bicycled a total average of 569 kilometer per

year, three times as much as the Americans’ total average of 180 kilometer. In 2000,

Europeans burnt between 48 and 83 calories per person per day on active transportation,

comparing to Americans burning 20 calories per person per day.

Bell et al.’s study (2002) supports the influence of active transportation on low

prevalence of obesity using longitudinal data of Chinese population. Bell and colleagues

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(2002) used cross-sectional data (1997) from Chinese adults aged 20 to 55 years to

explore relationship between current obesity status and vehicle ownership and cohort data

(1989 to 1997) from adults aged from 20 to 45 years to study the impact of vehicle

acquisition on the potential odds of becoming obese. In Bell et al.’s (2002) study, obesity

was defined as BMI≥25 kg/m2 based on body weight status adjusted for Asian

populations as proposed by World Health Organization (2000). Non-motorized

transportation included bicycles and tricycles, and motorized transportation included

motorcycles and cars. Bell and colleagues (2002) found a positive relationship between

ownership of motorized transportation and prevalence of obesity.

Specially, obesity rates of people who owned motorized vehicles were 70% higher

among men and 85% higher among women than those who did not own a motorized

vehicle. To further substantiate the effect of transportation shift from non-motorized ones

to motorized ones on the odds of becoming obese, Bell and colleagues (2002) studied the

link between motorized vehicle acquisition and weight gain. Their findings suggest that

acquiring motorized transportation is associated with body-weight gain among men. Men

with a motorized vehicle during 1989 to 1997 attained 1.82 kg weight while those

without lost 0.57 kg. Bell et al. (2002) concluded that ownership and acquisition of

motorized transportation is a significant predictor of obesity for Chinese population.

Low level of leisure-time physical activity (LTPA) is associated with body-weight

gain and adult obesity (Abu-Omar and Rutten, 2008), although gender and ethnic

differences exist (Seo & Li, 2010; Ball et al., 2001; Li et al., 2010). Abu-Omar and

Rutten (2008) conducted a study to learn relationship between leisure-time physical

activity and obesity in a national population level. They recruited participants from 27

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member states of the European Union and from Croatia, Turkey, and Cyprus North in

2005. In their study, obesity was defined as BMI≥30.0 kg/m2, and total volumes of

physical activity were calculated with metabolic equivalents (MET-min/week). The

participants were 15 years older and were recruited from a face-to-face interview. The

findings suggest that the level of LTPA is inversely associated with the risk of being

obese for both males and females in European countries. Other studies recruiting national

or state level of samples also observed the inverse association between LTPA and adult

obesity, although the results favor women and Caucasian population (Seo & Li, 2010;

Ball et al., 2001; Li et al., 2010).

According to Wagner et al. (2001), regular moderate levels of leisure-time physical

activity is negatively associated with BMI and waist circumference independent of high

intensity physical activity. Wagner and colleagues (2001) conducted multiple regression

analysis on a 5-year longitudinal study among middle-aged European men aged between

50 and 59 years old, free of coronary heart disease. Wagner and colleagues (2001)

observed a significant inverse association between regular moderate level of LTPA and

BMI and waist circumference. Compared to participants in sedentary group, participants

who performed more than 9 MET hour/week of low-to-moderate-intensity recreational

activity had 0.33kg/m2 and 0.90 cm lower levels in mean BMIs and waist circumference,

respectively (Wagner et al., 2001).

Environmental factors influence obesity issue by influencing behavioral factors,

such as levels of physical activity. Sallis and Glanz (2009) groups environments into a)

physical activity environments that support people to be physically active, such as parks,

sidewalks, trails, schools, workplaces, playgrounds, child care settings, and private

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recreation facilities, and b) common settings for sedentary behaviors, such as homes,

workplaces, sports venues, schools, and driver-friendly roadways. In Bauman and Bull’s

(2007) review, several studies found that people who live in a neighborhood with easy

access to various recreation facilities are more physically active.

In Saelens and Handy’s review (2008), they found a trend that recreation walking is

positively related to pedestrian infrastructure and aesthetics. Kaczynski and Henderson

(2007) reviewed studies on relationship between physical activity levels and built

environment, such as parks and recreation facilities. The review found that increased

physical activity is associated with proximity to parks or recreation settings. Past

literature thus suggests that easy access to recreation facilities and better pedestrian

infrastructures motivates recreational physical activity and activity-related energy

expenditure.

Sedentary behaviors, such as “screen time” watching TV and using computer, are a

major contributor to total energy expenditure and thus obesity (Brownson et al., 2004;

Banks et al., 2010). Banks and colleagues (2010) conducted a cross-sectional analysis in

adults aged 45 years and older to study the relationship between sedentary behaviors and

obesity. In their study, Banks and colleagues (2010) combined TV and computer usage

into “screen time”, and calculated “total daily time spent sitting”. Banks et al.’s findings

suggest that sedentary behavior has positive association with the risk of obesity. The risk

of being obese grows with increase in screen time. That is, people whose screen time was

less than 1 hour had 15% risk of being obese while people whose screen time was more

than 8 hours had 27.6% risk of being obese.

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For other sedentary behaviors, the association between total sitting time and obesity

is less dramatic than the trend between screen time and obesity. However, the positive

association between total sitting time and the risk of being obese is clear. That is, people

who sat for less than an hour had 18.3% risk of obesity while people who sat more than 8

hours had 24.6% risk of obesity. Furthermore, sedentary behaviors have an independent

effect on obesity, which means the effect of physical activity on reducing risk of obesity

may be compromised by a sedentary lifestyle. The findings of Banks et al.’s study (2010)

show that the association of physical activity and risk of obesity is influenced by screen

time. Interestingly, within high-level physical activity group, individuals whose screen

time was more than 8 hours had a much higher risk of being obese than those whose

screen time was less than one hour did. Also, the most active individuals who had highest

level of screen time had almost the same risk of obesity (1.72, 95% CI 1.46, 2.03) as the

least active ones with lowest level of screen time (2.05, 95% CI 1.71, 2.45).

Although genetic and environmental factors influence obesity epidemic, evidence

of association between physical activity and obesity highlights the need to change

people’s behavior (e.g. active transportation, regular leisure-time physical activity and

less sedentary behaviors) and make them more physically active.

The Role of Physical Activity in Obesity Prevention and Long-term Weight Loss

Maintenance

According to Catenacci and Wyatt (2007), physical activity that produces energy

deficit of 500-1,000 kcal per day will initiate substantial weight loss. With cross-sectional

data and longitudinal data, previous studies have observed a trend that low risk of obesity

is accompanied by increasing levels of physical activity (See Causes of Obesity).

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However, observational studies cannot provide causality of the association between

physical activity and obesity. Randomized, controlled trials (RCTs) give us a more clear

idea of the dose-response effect of physical activity on obesity than observational studies

do.

Catenacci and Wyatt (2007) reviewed prospective, randomized, controlled trials

with an intervention of at least more than 4 months. All the studies identified in their

review were published after 1997. The Centers for Disease Control and Prevention and

the American College of Sports Medicine issued a public health recommendation in 1995

(Pate et al., 1995). Reviewing studies building on the updated guideline gives us new

insights on physical activity. Catenacci and Wyatt (2007) identified 16 randomized,

controlled trials, which compared participants’ weight loss in a group prescribed physical

activity with weight loss in a control group prescribed no physical activity intervention.

By reviewing the findings of RCTs, they found that in most of the trials (11 out of 16),

there was significant weight loss in physical activity groups as compared to the control

groups. However, the amount of weight loss was modest. Most of the trials reported

weight loss of 1-3 kg in exercise groups.

In Klem and colleagues’ (1997) long-term weight loss maintenance study,

participants reported a 30.00 (±15.49) kg of weight loss and 10.57 (±5.23) kg/m2 of BMI

decrease. Compared to the data from Klem et al.’s study, weight loss reported in the

RCTs in Catenacci and Wyatt’s review is not substantial. However, Catenacci and Wyatt

(2007) pointed out that the prescribed level of physical activity in those trials was not

enough to result in an energy deficit. Of the 16 randomized, controlled trials, 11 trials

prescribed physical activity of 60-180 minutes per week. Compared to caloric restriction

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programs that usually produce an energy deficit of 500-1,000 kcal per day, the prescribed

level of physical activity in those trials cannot burn enough calories to result in a large

energy deficit to initiate substantial weight loss (Catenacci and Wyatt, 2007).

Catenacci and Wyatt’s argument on the dose-response effect of high levels of

physical activity on substantial weight loss is supported by Jeffery et al.’s (2003) study.

The study compared weight loss in two treatment groups with prescribed physical activity

of 1000 kcal/week energy expenditure (standard behavior therapy) and 2500 kcal/week

(high physical activity) energy expenditure during 18 months. The energy expenditure

was not significantly different between the two treatment groups at the baseline.

Participants in high physical activity group gradually increased their energy expenditure

to 2500 kcal/week at the end of the sixth month and maintained this level of energy

expenditure till the end of the trial.

Jeffery and colleagues (2003) reported that mean cumulative weight loss in high

physical activity groups were 9.0 ± 7.1, 8.5 ± 7.9, and 6.7 ± 8.1kg, respectively at 6, 12,

and 18 month while the corresponding weight losses in standard behavior therapy were

8.1 ± 7.4, 6.1 ± 8.8, and 4.1 ± 7.3 kg, respectively. Of the results, weight losses in 12

month and 18 month between the two treatment groups were significantly different.

Weight loss in high physical activity group reported in Jeffery et al.’s (2003) is greater

than the results of trials reviewed in Catenacci and Wyatt’s (2007) study (0.1 kg to 5.2 kg

in the exercise groups) and the result from previous review (Wing, 1999). It seems that a

higher prescribed level of physical activity contributes to more weight loss.

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High level of physical activity is a predictor of long-term weight loss maintenance

(Wing & Phelan, 2005; Baak et al., 2003). Losing body-weight is not the end of

combatting obesity epidemic; on the contrary, long-term weight regulation is also

important to reversing the prevalence of obesity, especially for the people who were

obese and have lost substantial body-weight lately. To learn how to successfully maintain

long-term weight loss, Wing and Hill (Klem et al., 1997) established the National Weight

Control Registry in 1994. The registry included more than 4,000 adults (aged 18 or

above) who had lost at least13.6 kg and kept it off for at least one year. Wing and Phelan

(2005) documented registered members’ behaviors related to long-term maintenance of

weight loss. Engaging in high levels of physical activity was one of the most commonly

reported strategies to maintain weight loss (The other strategies were low-calorie and

low-fat diet, regular breakfast, regular self-monitoring of weight, maintaining a consistent

eating pattern, and catching signs of weight regain before it gets out of control). Women

in the registry reported an average of 2,545 kcal/week in physical activity, and men

reported an average of 3,293 kcal/week. According to Wing and Phelan (2005), those

levels of physical activity equal to about 1 hour/day of moderate-intensity activity, such

as brisk walking. Other reported activities included weight lifting, cycling, and aerobics.

The association between physical activity and long-term weight loss maintenance is

also supported by Baak et al.’s (2003) prospective study with European samples. Baak et

al. (2003) found that leisure-time physical activity is one of the three most important

determinants of long-term maintenance of weight loss. Their study included two phases.

The first phase focused on losing weight for the first six months. Participants who were

aged 17-53 and had a BMI of 30-45 were treated for obesity with combination of

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sibutramine, dietary restriction and advice on exercise and behavior in the first phase.

According to James et al. (2000), sibutramine is “a tertiary amine that has been shown to

induce dose-dependent weight loss and to enhance the effects of a low-calorie diet for up

to a year” (p. 2119). The next 18 months were the second phase to evaluate long-term

weight loss maintenance. Variables of body weight, dietary intake, and physical activity

were measured at baseline, 6, 12, 18 (except physical activity), and 24 months. The

results of multiple regression analysis showed that LTPS is one of the three factors that

significantly influences weight maintenance. Higher levels of LTPA, along with

sibutramine treatment and greater initial body weight, explained 20% of the variation in

weight maintenance. Interestingly, dietary factors, age, and sex did not significantly

influence weight maintenance in their case.

How Much Physical Activity Is Enough for Body-Weight Regulation?

Before we discuss how to promote physical activity, a central question that needs

to be addressed is how much physical activity is enough to manage healthy weight.

According to Saris and colleagues (2003), two factors determine its answer: whom to

target to and what physical activity goals are. Past literature has identified three groups:

people who have been obese (BMI≥30.0 kg/m2), people who have lost substantial body-

weight, and people who have a normal (BMI≤25.0 kg/m2) or overweight body-weight

(25.0≤BMI≤30.0). The corresponding PA goals should be treatment of obesity aiming by

achieving substantial weight loss, long-term weight maintenance to prevent weight

regain, and obesity prevention.

Most nations base their PA guidelines with the goal to improve and maintain

general health and cardiorespiratory fitness. Warburton and colleagues (2007) reviewed

21


15 physical activity national guidelines for adults since 1975 and 22 consensus statements

about health outcomes of physical activity in different countries and regions, including

the United States, Canada, Australia, Switzerland, Philippines, New Zealand, UK, and

European Union. Almost all the consensus statements and guidelines identified in their

review converge on the fact that adults should accumulate at least 30 minutes of

moderate-intensity physical activity per day, on at least 5 days, preferable all days, of a

week. According to World Health Organization’s recommendation on levels of physical

activity for adults aged 18 - 64 years, this amount and intensity of physical activity

recommended in most national guidelines helps reduce the risk of cardiovascular disease,

diabetes, colon cancer and breast cancer.

Saris et al. (2003) suggested that 60-90 minutes of moderate intensity activity or

lesser amounts of intensive activity per day would help formerly obese individuals

prevent weight regain and that 45-60 minutes of moderate intensity activity per day are

required to prevent the transition to overweight or obesity. Despite lack of details, it

seems that the amounts and intensity of physical activity for general health recommended

in most national guidelines in Warburton et al.’s (2007) review is not enough for obesity

prevention and long-term weight maintenance. Of the 15 national physical activity

guidelines in Warburton et al.’s review, only two guidelines (UK and World Health

Organization) mentioned obesity prevention or weight control. Of the 22 consensus

statements about health benefits of physical activity identified in Warburton et al.’s

(2007) review, only one consensus specifically addresses obesity issue, but it did not

specify details on how much physical activity is enough to deal with obesity.

International recommendation for physical activity released from World Health

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Organization suggests that more activity is required for more health benefits, but it does

not give details on how much more on weight loss and control. National physical activity

guideline released in UK suggested that 45-60 minutes of moderate intensity exercise per

day are required for obesity prevention (Department of Health, 2004).

A meeting was held in Bangkok in 2002 to specifically address the issue of

physical activity level that can potentially prevent weight gain and regain. All the

participants at the meeting agreed that for formerly obese adults, 60 minutes of moderate

intensity of activity, such as walking and cycling, is necessary to prevent weight regain,

and 80-90 minutes are preferable (Saris et al., 2003). Also, it seems that 45-60 minutes of

moderate intensity activity per day is likely to help prevent the transition from

overweight or obesity (Saris et al., 2003).

In short, to answer the question raised at the beginning of the sector, there is

consensus in the literature that at least 30 minutes of moderate-intensity physical activity

5 days, preferably on all days, of a week is recommended to maintain general health.

More physical activity will bring more health benefits. Forty-five to sixty minutes of

moderate intensity activity per day is likely to prevent becoming overweight or obese.

For individuals who were obese and have lost substantial weight lately, at least 60

minutes of moderate intensity of activity, preferably 80-90 minutes, is recommended to

prevent weight regain.

Target Audience of Physical Activity Interventions

Selecting target audience is a key step in the social marketing planning process. In

social marketing, all decisions are audience-centric. In other words, campaign strategy

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and evaluation is influenced by the selection and understanding of the audience group. In

the present study, we choose to target adults aged between 18 and 60 years old. This

group includes college population (18-24 years old), young adults (25-35 years old),

middle age adults (35-50 years old), and adults (50-60 years old) before their early senior

life. However, this audience group is just one of the many groups one could target in the

physical activity promotion efforts.

“Downstream” individuals refer to the ones who are physically inactive, and who

are at risk of certain consequences (e.g. obesity and related health issues) because of the

undesired behavior. Social marketers target downstream individuals because downstream

individuals can make the issue go away by adopting a desired behavior. Campaigns with

a downstream approach target individuals such as residents in a neighborhood, students

in a university, associates in a workplace, and so on. In the present study, we choose

studies targeting downstream individuals because interventions can bring direct benefits

by attempting to influence people who are having the issue.

Three age groups are identified from the existing literature to whom physical

activity is usually promoted: children, adults, and seniors. We believe that adult

population fits the present study the best. Here is why. Adult exercise pattern has an

important influence on quality of people’s senior life. Although physical activity prevents

people from severe health disorders, it might be too late to promote PA to people who

have already suffered the disorders. We believe that by targeting adults, social marketing

campaigns can influence not only adults but also future seniors. On the other hand,

childhood obesity requires incorporation of multiple parties, such as schools, parents, and

policy makers (Andreasen, 2006). For example, parents’ exercise habit may play an

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important role in children’s beliefs of physical activity and physical activity pattern. It is

logical for social marketers to target upstream or midstream members instead of children

(e.g. participACTION campaign) (Craig et al., 2009) when promoting physical activity

among children. To be brief, we believe that promoting PA to adults will be an efficient

way of promoting physical activity among the broadest sections of the society. Thus, we

will review studies targeting adults.

Why Social Marketing

Social change managers can employ several approaches to influence individual

health behavior. Social marketing is one of them. We will briefly discuss the various

behavior change options and argue in this section how social marketing is an appropriate

tool to promote physical activity among adults.

According to Rothschild (1999), social change managers can employ three

strategies to influence individual behavior: education, marketing, and law. Education

relies on sending messages to inform or/and persuade target audience to adopt desired

behavior voluntarily but does not provide direct or and immediate benefits (Rothschild,

1999). Marketing offers benefits and reduces barriers toward the desired behavior by

providing opportunity in the environment and incentives (Rothschild, 1999). Law

promotes desired behavior in a non-voluntary by using coercion or by threatening to be

punished for noncompliance (Rothschild, 1999).

To explain why and when to apply social marketing, Rothschild (1999) proposed a

model to manage health behavior in terms of target audience’s motivation, opportunity,

and ability. Rothschild (1999) suggests that individuals will be motivated to behave when

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they can discern that the desired behavior will serve their self-interest. In the MOA

model, law is appropriate when targets lack motivation (e.g. college students have to

attain a certain amount of credits from physical education classes to apply for

graduation). Opportunity is environmental mechanism that facilitates individuals to act

(Rothschild, 1999). Rothschild (1999) suggests that opportunity can be created through

social marketing programs or indirectly through law. Social marketers provide

opportunity by delivering products or service. For example, to motivate individuals to use

stairs, a social marketing campaign can persuade university to close elevators for certain

period of a day so that people have to use stairs.

Law imposes behaviors and penalizes individuals if they do not practice them. For

instance, government can withhold funding to the schools that do not provide students

enough time to be physically active. In the MOA model, ability is defined as perceived

individual skill or proficiency to carry out desired behavior, such as overcoming a well-

formed or addictive habit or confronting peer pressure. Rothschild (1999) suggests that

ability can be taught through educational programs and imparted through marketing by

reinforcing a newly developed skill. Rothschild’s (1999) MOA model suggests that

marketing is a powerful tool to develop opportunity or/and ability when individuals are

motivated and that marketing is helpful to develop motivation in combination with law.

The stages of change model (Prochaska & DiClemente, 1983) suggests that

awareness is also an important factor for individuals to adopt health behaviors. Within

stages of change model, there are five phases for individuals to complete a behavioral

change: pre-contemplation, contemplation, preparation, action and maintenance.

According to Ronda and colleagues (2001), people in pre-contemplation are not

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considering increasing their physical activity within six months, and people in

contemplation are. A significant factor that proceeds people from pre-contemplation to

contemplation is awareness of personal risk behavior (Ronda et al., 2001; van Sluijs et

al., 2007). Ronda et al.’s study shows that participants who were not aware of their

physical inactivity had less intention to increase their level of physical activity than those

who rated their physical activity as low. Rothschild (1999) also suggests that people who

perceive benefits of a desired behavior are more motivated to behave. The previous

literature suggests that when individuals are premature to adopt a desired behavior,

increasing their awareness of benefits of a desired behavior or/and consequences of the

corresponding unhealthy behavior may help them increase intention to behave.

Other than motivation, opportunity, ability, and awareness, current usage and level

of competitions is strong indicator of which tool(s) (education, marketing, and law) to

apply (Rothschild, 1999). Current usage may involve breaking a well-formed habit that is

addictive. In such situations, education is not sufficient. More powerful tools such as

marketing can help individual develop ability to break a problematic behavior pattern.

The more competitive the current behaviors are, the more powerful tools should be

applied to manage health behaviors. Sedentary behaviors are a strong competing behavior

for being physically active. For example, many people prefer elevator than stairs even for

a short distance. Many of them might have known the benefits of taking stairs and being

active, but they continue to use elevator. In this case, the competing behavior is so strong

that educational programs are less likely to influence individuals’ behavior. Marketing

should be applied to help people be physically activity.

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Perceived barrier has been identified as a reliable predictor of physical activity

from the existing literature, and consistent evidence shows that barriers are negatively

correlated with physical activity behavior (Schwetschenau et al., 2008). Lack of time,

care-giving duties, lack of motivation, and lack of energy are identified as the major

barriers to physical activity for adults from existing literature (Toscos, Consolvo, and

McDonald, 2011). Lack of time is the top reported barrier (Toscos, Consolvo, and

McDonald, 2011). It is highly correlated to women who have heavy care-giving duties of

children, aged parents, or other family members (Toscos, Consolvo, and McDonald,

2011). Lack of time is also a most cited barrier to follow healthy dietary and exercise

habits for college students (Silliman et al., 2004).

Fear of potential injury or poor health is most reported in the studies targeting

older populations (Costell et al., 2011; Moschny et al., 2011). For employees who are

provided on-site corporate fitness center, internal barriers (e.g. embarrassment to workout

around colleagues) and external barriers (e.g. inadequate equipment) are found to be

negatively associated with physical activity (Silliman et al., 2004). To be brief, perceived

barriers to physical activity can be grouped into environmental factors (e.g. inadequate

recreational facilities in the neighborhood or equipment provided by gyms), motional

factors (e.g. lack of motion and lack of willpower), health issue (e.g. fear of potential

exercise injury), personal factors (e.g. embarrassment to exercise around people), and

other priorities (e.g. lack of time or other duties). To overcome such barriers, marketing is

necessary.

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Social Marketing Benchmarks

Since the present study proposes assessing effectiveness of social marketing

framework to promote physical activity, we will delve deeper into the social marketing

concepts. We will utilize these concepts, especially the social marketing benchmarks

proposed by UK’s National Social Marketing Centre (NSMC, 2006), to identify studies

during literature search. These benchmarks will help us identify social marketing

programs in the literature search and distinguish them from other behavior management

efforts (e.g. communication interventions). Social marketing benchmarks also provide a

consistent approach to reviewing and evaluating studies and campaigns (NSMC, 2006).

According to NSMC (NSMC, 2006), a social marketing interventions aiming to influence

individuals’ behavior should possess eight elements: behavior, customer orientation,

theory, insight, exchange, competition, segmentation, and marketing mix. Each

component is described below.

Behavior.

“The intervention is focused on influencing specific behaviors, not just

knowledge, attitudes and beliefs

Clear, specific, measurable and time-bound behavioral goals have been set, with

baselines and key indicators established” (NSMC, 2006)

Behavioral change is the key objective of a social marketing campaign, although

social marketers use education to influence targets’ knowledge, attitudes and beliefs to

support the behavioral change. According to Kotler and Lee (2008), there are three types

of objectives that are associated with a social marketing campaign: behavior objective

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(something marketers want target audience to act), knowledge objectives (something

marketers want target audience to know), and belief objectives (something that marketers

want target audience to feel or believe). However, a social marketing campaign always

has a behavior objective, although sometimes a social marketing campaign has

knowledge or/and belief objectives to support a behavior objective.

When the targets are not aware of an issue or don’t possess the requisite knowledge

or skill to perform a desired behavior, combined efforts of educational programs and

marketing programs should be applied. In Rothschild’s (1999) MOA model, there are two

situations that marketers need to bring education before they launch marketing campaign:

lack of ability and lack of motivation. Education can deliver facts on attractive

alternatives, information on how to perform the desired behavior, information on location

of goods or services and so on (Kotler & Lee, 2008). The other situation is that the targets

might not realize or not think they have a problem, so they do not have a motivation to

behave. Education programs can provide information on benefits of a desired behavior,

on consequences of an undesired behavior and so on (Kotler & Lee, 2008). In this case,

education may raise awareness of an issue, change targets’ belief of the issue, and

provide educational information on how to perform a desired behavior. However,

changes in knowledge, attitudes and beliefs will not necessarily lead to behavior change.

Social marketing goals establish a desired level of behavioral change in percentage

or numbers as a result of social marketing program efforts (Kotler & Lee, 2008). To

establish a proper social marketing goal, marketers need to know the current level of a

behavior engagement (baseline data). Social marketing goals, especially behavior goals,

should be doable so that target audience feels they can make progress step by step and

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will not be frustrated. Also, social marketing goals should be measurable so that target

audience can determine if they have performed the desired behavior.

Customer Orientation.

Involves the target audience and local community, rather than treating them as

research subjects

Gains key stakeholder understanding and feeds it into strategy

Uses a range of research analyses at every step of strategy development and

delivery process and combines data from different sources (qualitative and

quantitative) (NSMC, 2006)

Formative research is a key element to study target audience in customer

orientation. Formative research can help social marketers understand target audience’s

needs and wants and make effective and efficient marketing strategy to promote desired

behavior to target audience. Kotler and Lee defined formative research as “research used

to help form strategies, especially to select and understand target audiences and develop

draft marketing strategies.”(2008, p. 75).

Other than formative research, pretest and monitor test is also powerful tools to

study target audience (Kotler and Lee, 2008; Andreasen, 2002). Pretest research refers to

a study that test alternative strategies and tactics with target audience before launching

the campaign. It helps campaign managers to ensure that there is no major defect of

potential executions and to fine-tone possible approaches to get to target audiences

effectively (Kotler and Lee, 2008). Monitor research “provides ongoing measurement of

program outputs and outcomes” (Kotler and Lee, 2008, p.76). More importantly, monitor

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research helps campaign managers decide if correction and altering and increased

resources are needed to fulfill campaign objectives (Kotler and Lee, 2008).

Theory.

“The theory, or theories used, are identified after conducting the customer

orientation research

Appropriate behavioral theory is clearly used to inform and guide the methods

mix

Theoretical assumptions are tested as part of the intervention pre-testing” (NSMC,

2006)

Health behavior theories and models help social marketers deepen their

understanding of how their target audience changes behaviors (Kotler and Lee, 2008;

Andreasen, 2002; Dunton et al., 2010). The most widely applied behavior theories and

models in public health include health belief model, theory of reasoned action/theory of

planned behavior, social cognitive theory, transtheoretical model/stages of change

model, and social norms theory. When considered at a broader level, various behavior

theories suggest that a person ought to possess following attributes to perform a desired

behavior (Fishbein, 1995; Kotler & Lee, 2011, pp. 201):

The person intends to carry out the desired behavior;

Few environmental constraints in regards to carrying out the desired

behavior exist in the person’s life;

The person believes he/she ‘can’ perform the desired behavior;

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The person anticipates that the outcome of performing the behavior will be

beneficial;

The person perceives social pressure to behave desirably;

The person has positive emotions to perform the desired behavior;

Insight.

“A deep understanding of what moves and motivates the target audience,

including who and what influence the targeted behavior

Insight is generated from customer orientation work

Identifies emotional barriers (such as fear of testing positive for a disease) as

well as physical barriers (such as service opening hours)

Uses insight to develop an attractive exchange and suitable methods mix”

(NSMC, 2006)

To get deeper understanding of their target audience, social marketers need to know

target audience’s perceived benefits and perceived barriers of the behavior they promote

(Kotler & Lee, 2008). Barriers are something that prevents people from behaving and

what costs for people to behave (Kotler & Lee, 2008). Barriers include internal ones and

external ones (McKenzie-Mohr). Internal barriers refer to individuals lacking the skill or

knowledge needed to perform a behavior, and external barriers refer to the environmental

factors that need to be changed to made individuals perform a behavior more

conveniently. For example, in physical activity context, inactive people may not know

that they are having a problem of being physically inactive. External barriers may include

not having recreational facilities in the neighborhood, not having lanes for bicycles in the

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community, or expensive gym membership. Benefits are something that target audience

wants or needs and thus that they value the most (Kotler & Lee, 2008). By offering

benefits that interest target audience, social marketing campaigns are likely to motivate

target audience to adopt a promoted behavior. Numerous health outcomes are a major

benefit to individuals being physically active.

Exchange.

“Clear and comprehensive analyses of the perceived/actual costs versus

perceived/actual benefits

Considers what the target audience values: offers incentives and rewards,

based on customer orientation and insight findings

Replaces benefits the audience derives from the problem behavior and

competition

The exchange offered is clearly linked to “price” in the methods mix”

(NSMC, 2006)

Exchange is a key concept of marketing. Traditional concepts of exchange in

economics and commercial marketing also apply in social marketing context: each party

believes that the potential exchange is beneficial (Bagozzi, 1975; Kotler, 1972); there

might be more than two parties involved (Bagozzi, 1978); transactions involve not only

tangible goods and financial payments but also intangible or symbolic products and non

financial payments (e.g. time and efforts) (Kotler & Levy, 1969).

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Peattie and Peattie (2003) gave two specific cases of exchange in social marketing

context. One possibility of exchange is that social marketers provide information,

products, or incentives to audience in exchange for audience adopting desirable

behaviors. The other possibility of exchange in social marketing is that audience changes

behaviors to get psychological benefits of peace of mind or satisfaction in exchange.

Kotler and Lee (2008) suggest there are three factors that need to be considered when an

exchange happens: barriers, benefits, and competition. Voluntary exchange takes place

when target market believes they can get as much or more than they pay (Kotler, 1972).

Thus, in social marketing context, to facilitate a voluntary exchange and, eventually, a

behavioral change, marketers should manage to provide benefits that will help target

audience overcome barriers and benefits offered by competition.

Competition.

“Addresses direct and external factors that compete for the audience’s time

and attention

Develops strategies to minimize the impact of competition, clearly linked to

the exchange offered

Forms alliances with or learns from the competing factors to develop the

methods mix” (NSMC, 2006)

When conducting audience research, social marketers need to identify competition.

According to Kotler and Lee (2008), competition is alternative behaviors that target

audience prefers, may be tempted to do, or is currently doing, rather than adopts the one

that social marketers promote. Competition also refers to organizations or groups who

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promote or sell the competing behavior (e.g. video game industry versus physical

activity). In many cases, social marketing programs compete with individual’s lethargy,

habit, and inclination to “do nothing” (Andreasen, 2002; Kotler and Lee, 2008).

Understanding competition is important to form exchange and marketing mix

strategy. McKenzie-Mohr and Smith (1999) proposed a framework to change the ratio of

benefits to barriers so that the desired behavior will be more attractive to target markets:

“a) increase the benefits of the target behavior; b) decrease the barriers (and/ or costs) to

the target behavior; c) decrease the benefits of the competing behavior(s); and d) increase

the barriers (and/or costs) of the competing behaviors”. Kotler and Lee (2008) also

suggested that marketers should make social marketing products and service more

accessible or make access to the competition more difficult and unpleasant.

Segmentation.

“Segmentation is drawn from customer orientation and insight

Does not only rely on traditional demographic, geographic or epidemiological

targeting

Draws on behavioral and psychographic data

Identify the size of your segment or segments

Segments are prioritized and selected based on clear criteria, such as size and

readiness to change

Interventions in the methods mix are directly tailored to specific audience

segments” (NSMC, 2006)

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Segmentation is to divide a broad relevant population into smaller groups that

require unique but similar strategies to change behavior (Kotler & Lee, 2008). There are

three reasons to segment markets. First, audience segmentation can increase campaign

effectiveness. Marketers expect campaign outcomes will be greater by segmenting

markets. For example, a significantly greater percentage of target group is persuaded to

do regular physical activity. Second, audience segmentation can increase campaign

efficiencies. By segmenting markets, marketers expect that a certain amount of outputs

(resources expended) will result in larger outcomes. Finally, audience segmentation will

give marketers input on resource allocation and developing marketing strategies.

Segmentation process can be done through segmentation variables and behavior

models (Kotler & Lee, 2008). The most widely used segmentation variable is

demographic factors (age, gender, family size, income, occupation, education, religion,

and generation) because of their easy availability and predictable power of market needs,

wants, barriers, and behaviors. Other segmentation variables include geographic factors

(world, region, or country, country or region, city or metro size, density, climate, etc.),

psychographic factors (social class, lifestyle, and personality), and behavioral factors

(occasions, benefits, user status, usage rate, loyalty status, readiness stage, and attitude

toward product). Choosing target markets needs to be based on priority of segments

(Kotler & Lee, 2008). Organizations that implement physical activity intervention face

limited resources and efforts, so they need to choose the markets that need the

intervention the most.

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Marketing Mix.

“Uses all elements of the marketing mix (product, prices, place and

promotion) and/or primary intervention methods (inform, educate, support,

design and control)

Promotion is used to “sell” the product, price, place and benefits to the target

audience, not just to communicate a message

Takes full account of existing interventions in order to avoid duplication

Creates a new brand, or leverages existing brands appropriate to the target

audience

Methods and approaches are financially and practically sustainable” (NSMC,

2006)

Developing a good positioning statement will help form strategy of 4Ps (Kotler &

Lee, 2008). Positioning refers to “the act of designing the organization’s actual and

perceived offering in such a way that it lands on and occupies a distinctive place in the

mind of the target market-where you want it to be” (Kotler & Lee, 2008, p. 185). With

understanding of target market from audience research, positioning statements create an

audience-oriented value proposition, which gives target market a convincing reason why

they should “buy” the product from you instead of your competitors (organization or

groups sell or encourage competing behavior).

Social marketing emphasizes use of all four elements of marketing mix-product,

price, place, and promotion-to form campaign strategy (Kotler & Lee, 2008). Kotler and

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Lee (2011) proposed that there are three levels of product in social marketing context:

core product, i.e., benefits of a desired behavior, actual product, i.e., tangible objects and

services provided to facilitate a behavioral change, and augmented product, i.e., any

additional tangible goods or services.

In social marketing context, price management refers to identifying monetary and

nonmonetary costs that target audience associates with adopting a desired behavior

(Kotler & Lee, 2008), and reducing those using various strategies. Monetary costs are the

tangible objects and services related to adopting a desired behavior (e.g. buying sport

gear to be physically active). Nonmonetary costs may include input of time, effort, and

energy to perform a desired behavior, perceived or experienced psychological risks and

losses, and physical discomforts while performing a desired behavior.

Place is “where and when the target market will perform the desired behavior,

acquire any related tangible objects, and receive any associated services.” (Kotler & Lee,

2008, p. 247). Individuals value convenience nowadays. Easy access to the campaign

resources will be an asset for marketers.

Promotion is the persuasive communications tool designed and delivered to

motivate the target audience of a social marketing campaign to take actions (Kotler &

Lee, 2008). Promotion strategy includes making communication strategy and choosing

communication channels. For a communication strategy, marketers need to decide key

message(s) of a campaign (what is expected from target audience to do, know and

believe), messengers, people who deliver campaign message or who endorse the

campaign, and what to say in the campaign and how to say it.

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Funding and Partnership

National Social Marketing Centre does not include funding and partnership into

social marketing benchmarks. However, efforts to bring funding and to build partnership

helps bring great resources to a social marketing campaign and positively impacts

campaign strategy. Social marketer can find additional resources and support from

government agencies, nonprofit organizations/foundations, adverting and media partners,

coalitions, and private businesses (Kotler & Lee, 2008).

Evaluating Effectiveness of A Physical Activity Intervention

To review the effectiveness of social marketing campaigns, it is necessary to

conduct evaluation research of social marketing campaigns. According to Kotler and Lee

(2008), depending on different purpose of evaluation, measures of evaluation of social

marketing campaigns fit in one or more of the three categories: output/process measures,

outcomes measures, and impact measures. The present study is to review effectiveness of

social marketing campaigns promoting physical activity, and social marketing

emphasizes behavioral change. Thus, the present study will be focusing on reviewing

outcomes measures and impact measures.

Outcome measures assess target audience response to the efforts of a social

marketing campaign (Kotler & Lee, 2008). These measures are built on the campaign

goals, the specific measurable results that a social marketing program wants its target

audience to achieve. Kotler and Lee (2008) proposed that to evaluate a social marketing

campaign, 9 types of change should be measured:

1. Changes in behavior, including changes in percentage or numbers;

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2. Changes in behavior intent;

3. Changes in knowledge, including changes in awareness of important facts,

information, and recommendations;

4. Changes in beliefs, such as changes attitude, opinions, and values;

5. Responses to campaign elements, such as participation rates;

6. Campaign awareness, which provides feedback on the degree to which the

campaign is notices and recalled;

7. Customer satisfaction levels, which will give marketers insight on analyzing

data and future practice;

8. Partnerships and contributions created, which might be associated with

positive responses to the campaign;

9. Policy changes, which may be appropriate for campaigns targeting

“upstream” individuals who are in government agencies.

The most rigorous, costly, and controversial measure of evaluation of social

marketing campaigns is to measure impact that behavioral change a campaign achieves

(Kotler & Lee, 2008). Marketers may need more time to measure impact of a campaign

because target audience needs time to respond. Also, measuring impact of a social

marketing campaign needs rigorous methodology, such as pre-post design, control-

experiment design, control variables, and so on.

It is hard to determine if a social marketing campaign is successful. Many studies

report statistically significant difference of an outcome measure (e.g. levels of

recreational physical activity) between baseline and post-campaign test. If there is a

significant dose-response effect of a campaign on an outcome behavior, most researchers

41


consider the campaign successful or effective. However, Kotler and Lee (2008) also

pointed out situations without behavior change within changes of stage model. We think

moving target audience from an earlier stage to later stage should also be considered as

successful efforts. Also, behavior adherence should be another factor to determine

effectiveness of a campaign. However, it is hard to determine how long is long enough.

For instance, a person has been physically active for a year. Is one year enough to say

that the person has transferred from maintenance stage to termination? We cannot find

answers to the question from the literature.

Objectives of the Study

As far as we know, no study in the past has systematically reviewed effectiveness

of physical activity promotion efforts using social marketing framework. Although

researchers believe that the more social marketing benchmarks are applied, the more a

campaign will be successful, there is no empirical evidence to support this assertion. To

fill this gap, we present our hypothesis:

The number of social marketing benchmarks applied in a campaign will be

positively associated with success of the campaign outcome.

Method

We will carry out a systematic literature search of studies reporting effectiveness of

social marketing campaigns to promote physical activity in adults listed in five electronic

databases (PubMed, Medline via OVID, Business Complete Source, Web of Science, and

PsycINFO) published between January 1997 and April 2013. In 1995 the Centers for

42


Disease Control and Prevention (CDC) and the American College of Sports Medicine

(ACSM) issued a physical activity recommendation “Every US adults should accumulate

30 minutes or more of moderate-intensity physical activity on most, preferably all, days

of the week” (Pate et al., 1995), which has an impact on latter studies. Thus, we will

focus on reviewing studies built on the recommendation in 1995.

Key Words and Inclusion Criteria

Combinations of key words include: “physical activity/physical exercise/physical

fitness” AND “social marketing”, “active living/active life” AND “social marketing”,

“physical activity campaign/intervention/promotion”, “physical exercise

campaign/intervention/promotion”, “physical fitness campaign/intervention/promotion”,

“active living campaign/intervention/promotion”, and “active life

campaign/intervention/promotion”.

We will restrict our review to English language published studies, applying the

following inclusion criteria:

Campaigns targeting adults (aged 18 and 60 years old),

Campaigns that the main component or one of the components was aimed

to promote physical activity through behavior change,

Campaigns that employed at least two activity of “marketing mix” (we

will exclude educational campaigns),

Campaigns that reported statistical analyses of an outcome measure

related to physical activity (self-reported or objectively measured), and

Articles published between 1997 and 2013.

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Article Data Extraction

Data extraction form and codebook are adapted from the ones in Mah et al.’s

(2008). See Appendix 1 and 2 for the extraction form and codebook respectively. Articles

will be analyzed mainly for the presence of social marketing benchmarks. Social

marketing benchmarks were discussed in the literature review section.

Other information extracted from the articles reviewed will include whether the

campaign was self-identified as a marketing campaign; campaign outcome; marketing

strategy (campaign description, behavior positioning, and campaign duration); sampling;

setting; individual characteristics; study design; measures; data analysis methodology;

study results.

Once articles are identified, they will be reviewed by two coders. According to

Lipsey and Wilson (2001), an amount of twenty to fifty studies should be coded

independently between coders to generate coding reliability. In the present study, if the

literature search results in less than fifty studies, coders will code independently all the

studies. Coding discrepancies will be discussed between coders through meetings after

the first round of data extraction. If coders cannot settle the discrepancies, disagreement

will be consulted with the thesis supervisor. The process of discussion will continue until

the coders reach an intercoder reliability of 90%.

Data Analysis

The strategy of data analysis applied in the present study is adapted from Mah et

al.’s study (2008). Campaign outcome will be categorized into either improvement

(statistically significant improvement of outcome measure) or no improvement (no

44


significant improvement or significantly worsening trend). We will use chi-square test to

assess the statistical significance of association between the number of marketing

benchmarks applied & campaign outcome (improvement & no improvement).

Additionally, we will analyze association of campaign outcomes with each benchmark.

Contributions

The present study will provide evidence of effectiveness and appropriateness of

social marketing campaigns in physical activity context. The result of the study will not

only give a general idea of current social marketing practice in physical activity but also

give insights for improving future efforts to promote physical activity.

Timeline

Proposal Defense May 2013

Data Extraction and Analysis May-July 2013

Thesis Write-Up August-October 2013

Thesis Defense Application October 2013

Thesis Defense December 2013

Budget

The study will hire a graduate student as a second coder for data extraction. The

anticipated rate of the second coder is 15 per hour plus benefits. The total cost of the

45


study depends on the hours spent on data extraction, so it cannot be known at this point.

The expenses of the study would be covered by thesis funds provided by MSc (Mgt)

program at the University of Lethbridge. The remainder would be covered by personal

funds.

46


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Appendix 1 Data Extraction Form

Paper ID#Reviewer’s NameFull CitationAims/ObjectivesDid this study self-identify as social marketing interventions? Yes __No

Did the author(s) attempt to change physical activity behavior? If yes, describe the behavior.

__Yes __No

If yes, what was the behavioral outcome of the intervention?

Behavioral outcome parameter:Behavioral outcomes and their significance:

Other outcomes of the intervention

__Awareness__Attitude/Belief__Behavior Intention__Other (please specify)

Success rate on other outcomes Please provide details

Check presence of the following social marketing concepts:

Audience Research other than Evaluation Research (Check all that apply): Primary formative research Secondary formative research Pretest research Monitoring research__Segmentation __intentional list target audiences:__Was the campaign self-funded? If no, list the funding agencies:__Partners other than the funding agencies (e.g. governor agencies and foundations)__Exchange of value__Marketing mix: __product (tangible or intangible) __price __place __promotion__Behavioral Competition


Behavioral theory (behavior theory or model explicitly stated) __Yes __No

Marketing StrategyIntervention(s)Behavior Positioning:Duration:

Sample__Selective __Representative Please specify the sampling design:

Sample SizeCity, CountryIndividual CharacteristicsSettingStudy DesignData Analysis MethodsStudy ResultsMeasuresConclusionComments

58

Appendix 2 Data Extraction Codebook

Paper ID#: number every identified paper

Reviewer’s Name: Put this on every form-Yuan Xia/another coder’s name

Full Citation: Follow APA style

Examples: Koren, G., Koren, T., Gladstones, J. (1996). Mild maternal drinking and pregnancy outcomes: Perceived versus true risks. Clinica Chimica Acta, 246, 155-162

Aims/Objectives: Overall aims and objectives of the paper (see if the paper abstract has the info), e.g. update and clarify the 1995 recommendation on the types and amounts of physical activity needed by healthy adults to improve and maintain health.

Did this study self-identify as social marketing interventions?

Did the authors regard the intervention discussed in the paper as a social marketing one?

Did the author(s) attempt to change physical activity behavior? Describe the behavior.

Did the intervention achieve a change in physical activity behavior, and what was the behavior, e.g. to urge audience to achieve the recommended level and correct kind of physical activities.

If yes, what was the behavioral outcome of the intervention?

Behavioral outcomes parameter: e.g. 30% target audience individuals maintained recommended levels and nature of physical activity for a period of six months after intervention.

Behavioral outcomes and their significance: success rate and statistical significance of behavioral outcome variables, e.g. compliance increased from 20% to 30%, and the difference was significant.

Other outcomes of the intervention

Awareness: A change in knowledge in benefits from recommended levels and nature of physical activity or consequences of not conducting the recommended levels and nature of physical activity.


Attitude/Belief: A change in beliefs or perceptions towards physical activity or related behaviors promoted by the intervention, e.g. self-efficacy towards achieving recommended level and nature of physical activities.

Behavior Intention: Future intention to adopt the favorable behavior, e.g. intention to achieve recommended level and nature of physical activities.

Other: Anything that did not fit above, e.g. media exposure levels.

Success rate on other outcomes

See if the intervention achieved an improved and statistically significant performance of other outcome variables, e.g. the levels of knowledge of the recommended level of physical activity intensity increased from 30% to 40%, and improvement was significant.

Social Marketing Concepts (Mah, Tam, & Deshpande, 2008)

Audience Research (Other than evaluation research): Primary formative research, secondary formative research, pretesting research, or monitoring research. Check all that apply.

Segmentation: Did the intervention tailor to fit a segment? Was the attempt of segmenting intentional? If it was, what was the target audience? (e.g. seniors, healthy adults, etc.)

Funding Source: Was the intervention self-funded? If not, who were the funding agencies?

Partners: Who were the partners? Were funding agencies part of the partnership team?

Exchange: If the intervention encouraged target group to adopt the favorable behavior by offering benefits and/or reducing costs (barriers), e.g. offering discount to people who register in a specific physical activity program.

Marketing mix: If the intervention used a 4P strategy. If it did, which Ps was used? (e.g. distributing fliers providing knowledge of recommended level and nature of physical actives)

Behavioral Competition: Did the article acknowledge competing behavior (e.g. watching TV at home) and competitors (groups and organizations, e.g. commercial companies or media promoting competing behavior)? Did the strategy make an attempt to achieve competitive advantage (e.g. promoting unique benefits of doing physical activities, such as improving mental health and mood)?

60


Behavioral Theory: If the research stated behavior theory or model explicitly, e.g. Stages of Change, such as pre-contemplation, contemplation, preparation, action, and maintenance.

Marketing Strategy:

Intervention(s): describe intervention, e.g. short or intensive motivational interviewing with or without financial incentive (30 vouchers entitling free access to leisure facilities) (Harland, Jane, et al. "The Newcastle exercise project: a randomized controlled trial of methods to promote physical activity in primary care." Bmj 319.7213 (1999): 828-832.).

Behavior positioning: what was the primary platform on which the desired behavior was promoted? (e.g. positioning moderate physical activities such as taking stairs instead of taking an elevator as something that is easy to fit in daily routine)

Duration: duration of the intervention

Sample: Was the sample selective or representative? Provide details on the sampling design: e.g. convenience sampling, judgment sampling, snowball sampling, simple random, systematic random, cluster, etc.

Sample Size: N=

City, Country: In which city (cities) and country (countries) was (were) the intervention conducted?

Individual Characteristics: target audience profile and inclusion and exclusion criteria (e.g. healthy adults who have not been smoking for at least one year)

Setting: Where was the intervention conducted? (e.g. individual, family, or community)

Study Design: e.g. before/after, after-only, RCT, Solomon, between-subject, basic/factorial, etc.

Data Analysis Methods: Which statistical method (e.g. t-test, ANCOVA, regression, factor analysis, SEM, etc.) and computer programs (e.g. SPSS, SAS, etc.) was employed to analyze data?

Study Results: What were the major findings?

Measures: variables that were measured in the study (e.g. amounts of physical activity, physical activity intensity, etc.)

Conclusion: Overall conclusions of the paper (see if the paper abstract has the info)

Comments:

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Running head: Social marketing Physical Activity Campaigns in Adults 62

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