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. 2020 Dec 3;36(4):989–999. doi: 10.1093/heapro/daaa134

How to recruit inactive residents for lifestyle interventions: participants’ characteristics based on various recruitment strategies

Eva Smit 1, Karlijn Leenaars 2, Annemarie Wagemakers 3, Koos van der Velden 1, Gerard Molleman 1,4,✉
PMCID: PMC8521843  PMID: 33270846

Summary

Care Sport Connectors (CSCs) have been appointed to create a connection between primary care and physical activity (PA) sectors to stimulate inactive residents into becoming physically active. Adequate recruitment strategies are necessary to reach the intended target group in order to foster the sustainability of lifestyle interventions. The objective of this study is to explore PA behavior and health characteristics of the target group reached by CSCs and if these characteristics differ between participants when grouped based on how they were recruited. Participants from lifestyle interventions were included between September 2014 and April 2016 using a purposive sampling method. Participants were recruited through CSCs via public relations (n = 135), a personal letter (n = 136), or a referral (n = 98) and compared based on their PA level, health-related quality of life, motivation, self-efficacy, morbidity and health-related fitness. Scores were analyzed with a multi-level (mixed model) analysis measured before the intervention. The three groups were different in PA level (p = 0.002). The outcomes regarding health-related quality of life, motivation, and number of somatic disorders were also significantly different for the three groups, except for the categories of mental health (p = 0.145) and self-efficacy (p = 0.464). For all dimensions, the referral group scored the least favorable. The investment in time and money for an active recruitment strategy like referrals is worthwhile because it provides CSCs the opportunity to reach people who are inactive and at risk of chronic disease. Future studies are necessary to reveal the effect on PA levels and health in the long-term.

Keywords: health promotion, primary care, physical activity, recruitment strategy, care sport connector

INTRODUCTION

Physical activity (PA) is an important determinant of health and a preventive factor for the development of many chronic diseases (U.S. Department of Health and Human Services, 1996; Balady et al., 2000; Penedo and Dahn, 2005; Reiner et al., 2013). Although PA can be seen as an individual behavior, it is also determined by social and environmental factors (Bauman et al., 2012; Dollman, 2018). Therefore, it is important to promote PA using an integrated approach wherein policy can play a necessary and stimulating role (McLeroy et al., 1988; Bull et al., 2004). For that reason, in the Netherlands, the Ministry of Health, Welfare, and Sports introduced Neighborhood Sport Coaches (in Dutch: Buurtsportcoaches) in 2012. Neighborhood Sport Coaches have the task of making a connection between the sport or PA sector and other health-related sectors to stimulate residents to become physically active.

A few of these Neighborhood Sport Coaches, the so-called Care Sport Connectors (CSCs), focus on the connection between the primary care and PA sectors. Previous results showed that CSCs have an important role in strengthening intersectoral collaboration (Smit et al., 2020) and that CSCs collaborate with professionals from the primary care, PA and welfare sectors (Leenaars et al., 2016b; 2017). The general idea is professionals in these sectors collaborate and implement lifestyle interventions, which are directed toward specific target groups. Eventually, these target groups self-manage their PA. When the target groups become more physically active, health outcomes improve. This is supported by review studies, showing that lifestyle interventions can be (modestly) effective in changing behavior (Kirk et al., 2012; Lv et al., 2017; Bergeron et al., 2019; Grimmett et al., 2019).

The way CSCs are employed is different in each municipality because there is no blueprint for CSC implementation. CSCs are working either within the PA sector or across multiple sectors with an integrated approach. The organization where the CSC professional is appointed has a major influence on the way in which the partners in the network interact and work together (Leenaars et al., 2017). In the case of an integrated approach, primary care professionals mostly fulfill the role of referrer, where other professionals arrange the activity and ensure that the necessary preconditions are arranged to be able to carry out the interventions. In networks from CSCs who worked from the PA sector, primary care professionals help to implement CSCs’ activities by fulfilling the role of instructor or arranging a fittest (Leenaars et al., 2017). An integrated approach seems the most promising when it comes to organizing, supporting and implementing different kinds of activities for various target groups. This prospect may be decisive for the success of the lifestyle interventions because the recruitment of an insufficient number of participants is a well-known pitfall (Boutaugh, 2003; Peck et al., 2008; Deakin University, 2012; Leenaars et al., 2016a).

Optimal recruitment strategies are necessary to reach the intended target group (i.e. inactive adults with or at risk of a chronic disease) in order to foster the sustainability of lifestyle interventions and to decrease health inequalities by reaching persons with the highest burden of illness. Previous studies (Peck et al., 2008; Carroll et al., 2011; Deakin University, 2012; Jiwa et al., 2012; Bonevski et al., 2014; Leemrijse et al., 2015) gave insight into various recruitment strategies to reach hard-to-reach target groups, such as partnering, advertising channels, incentives, and referrals. However, due to inadequate reporting and evaluation, the mechanism for achieving effective recruitment are unclear, especially for hard-to-reach groups (Cooke and Jones, 2017). There is a need for the comparison of different recruitment strategies regarding the representativeness of reached target groups (Glasgow et al., 1999; UyBico et al., 2007; Treweek et al., 2010; Jiwa et al., 2012) instead of only a reached percentage. It would be useful to describe socio-demographic, health-related, or clinical characteristics of the reached population and, if this is a representation of the target population (Glasgow et al., 1999; UyBico et al., 2007), to determine if a recruitment strategy is effective and successful. Therefore, this study focuses on the reached target group of lifestyle interventions whereby a CSC is involved. The objective of this paper is to reveal the PA behavior and health characteristics of the reached target groups of CSCs and if these characteristics differ between various recruitment strategies (i.e. public relations, personal letters and referrals). The information from this explorative study is helpful for CSCs and other health promotion professionals to be able to choose a suitable recruitment strategy to reach their intended target group of inactive adults with or at risk of a chronic disease.

METHODS

This explorative study is part of the larger project titled, ‘Connecting Care, Sport, and PA’, which is a multiple case evaluation study of 14 CSCs conducted in 9 municipalities spread over the Netherlands. The aim of that study is to get more insight into the function of CSCs (Smit et al., 2015). The project consists of two trajectories: The first trajectory focuses on the intermediary target groups, specifically CSCs and professionals active in the primary care and PA sectors who implement lifestyle programs. The CSCs are expected to form health alliances by connecting professionals from different sectors and sustain the collaboration in these alliances. The second trajectory, which provides the input for this study, concentrates on health and PA behavior changes of the participants of the lifestyle programs. Here, adults who participate in the lifestyle programs organized by professionals from the alliances of the first trajectory are the focus.

Participants

Participant recruitment occurred between September 2014 and April 2016. A purposive sampling method was used: participants were recruited if they took part in a lifestyle intervention, sports activity, or PA whereby a CSC was involved. CSCs indicated when a new lifestyle intervention or activity started and if it was possible to include participants in our study. The participants were divided into three groups based on the way they were approached to take part in the intervention or activity. The first group, the public relations (PR) group, consisted of participants who were approached through PR activities, such as flyers, social media, receiving the contact information of a CSC and word of mouth. The second group, the personal letter group, was recruited by a personal letter from a CSC. The home addresses were obtained from the population register of the municipality on the basis of age criteria. The third group, the referral group, consisted of participants who were directed by a professional (primary care, welfare or otherwise) to take part in an intervention or activity. The measurements for this study started 1 or 2 weeks before the start of the intervention.

Outcome measures

The PA level of a participant was measured via the Short Questionnaire to Assess Health-enhancing PA (SQUASH) (Ooijendijk et al., 2007). SQUASH measures the amount of PA, by asking the mean time spent per day and the number of days per week that they carry out the following activities: commuting, PA at work or school, domestic work and leisure time. Scores were converted according to the PA guideline and the PA variable was dichotomized in order to decide if a participant met the guideline of 30 min of moderate to vigorous PA at least five times a week (Kemper et al., 2000). Participants received a booklet with five questionnaires, the SQUASH, the health-related quality of life questionnaire (Rand 36) (Van der Zee and Sanderman, 1993), motivation (Breq-2) (Markland and Tobin, 2004), self-efficacy (SEE) (Resnick and Jenkins, 2000) on a five-point Likert scale and personal information questionnaire (age, gender, somatic disorders and to which recruitment strategy they responded).

In addition, participants did a fitness test to test their health-related fitness. The components of our fitness test are based on the Toronto Model (Bouchard and Shephard, 1994), whereby health-related fitness is divided into the following five factors: morphological, muscular strength and endurance, motor, cardio-respiratory fitness, and metabolic fitness. For each factor, suitable tests were selected based on their ability to test functional fitness. The tests were not too burdensome for older adults and people with (or with an increased risk of) a chronic disease and practical feasibility for the execution of the fitness test. Morphological factors, mass, height, fat percentage (measured using the Omron Body Fat Monitor BF306) and waist circumference were recorded respectively to the nearest 0.1 kg, 0.5 cm, 0.1% and 0.5 cm. Only mass and height were used to calculate body mass index (BMI). Shoulder, back and leg flexibility was tested using the Straight Leg Raise test (Rikli and Jones, 1999), the Back Scratch test (Rikli and Jones, 1999) and the Modified Schober test (Macrae and Wright, 1969), respectively. For each test, participants made three attempts. For the Modified Schober test and Straight Leg Raise test, the mean score was recorded. For the Back scratch test, the best attempt was recorded. The metabolic components blood pressure (measured while seated), cholesterol and glucose were measured using the Omron Omron M5-1 and the Accutrend Plus meter, in line with the protocol of punctures (Vilans, 2012). Muscular strength and endurance were measured with the Arm Curl, the Chair Stand test, and the Hand Grip Strength test (Rikli and Jones, 1999). For the latter, the JAMAR Hand Dynamometer was used, and participants had two attempts. Of the two attempts, the maximum score was noted. The Up and Go test (Rikli and Jones, 1999) was used to test agility, speed and dynamic balance for the motor functioning component. Scores were noted to the nearest 0.1 s. For the measurement of cardio-respiratory fitness, two tests were used: the 6-min Walk test (Wasserman et al., 1987) and the Astrand test. The walk test was used for older adults (50+) and immigrants who are not used to cycling. To compare both scores, we used norm equations (Wasserman et al., 1987; Beekman et al., 2014; Myers et al., 2017) to reveal the deviation from the norm for each participant as a percentage. Percentages from both norms were transformed to a z score.

All components of the functional fitness test were described in a protocol to make sure that each test leader performed the test identically. Each test leader was instructed by ES (first author), who was on site at 80% of the fitness tests. When she was absent, an instructed physiotherapist took over. After the fitness tests, all participants received the questionnaire and had three weeks to fill in the questionnaire. A reminder was sent after two weeks. All participants provided informed consent and agreed to participate voluntarily.

Statistical analysis

Due to the hierarchical structure of our study, in which participants were clustered by each CSC, we used a multi-level (mixed model) analysis to test if characteristics of participants differ among the three groups that are demarcated by how they were recruited. For both linear and logistic models, we fit the data to a model with a random intercept and all other variables fixed. All outcomes except PA were treated as continuous and thus were outcomes in linear models. PA, treated as a dichotomous variable was modeled with a logistic regression. Outcome measures, except the score for PA behavior, were corrected for age and gender. Medication-related variables (i.e. blood pressure, cholesterol and blood glucose level) were also adjusted for associated medication use. The medical use variable was dichotomized into yes or no. A p-value of <0.05 was considered to be statistically significant based on a two-sided test. Sidak post hoc analysis was used to test for significant differences among the three groups. All analyses were performed using SPSS version 22.

RESULTS

In the end, 369 participants were included in our study. Thirty-three participants were excluded due to an unknown recruitment strategy. This exclusion was partly selective, as the population that was excluded were immigrants who found it hard to fill in the questionnaire due to language issues. We compared the groups, and they were not significantly different for BMI, blood pressure, strength and flexibility. After a year of recruitment-activities, it became noticeable that inclusion numbers were unequally distributed among the three groups. Therefore, we included three more CSCs, of which two recruited participants through the referral of professionals and the other mainly through PR. In total, 17 CSCs from 12 municipalities were included in our study. The participants of our study were recruited as follows: 98 participants through referral, 135 through PR and 136 through a personal letter. To get an insight into the number of included participants per CSC, we added a table in Supplementary Appendix S1. We also present the total reach of each CSC and their own perception as a percentage of which recruitment strategy they use to recruit participants. The numbers of reached participants are the self-reported numbers obtained in half yearly interviews with the CSCs (8) and give a global impression of the reach and recruitment strategy of each CSC.

Of the total sample, 71.3% were women, and the mean age was 61.8 years (SD = 14.11). The groups did not differ significantly in gender and age. The three groups had significantly different PA behavior (p = 0.002). The referred group, with 32.7%, had the least percentage of participants who met the PA guidelines compared with 45.2% and 60.3% of the PR and personal letter group, respectively. Unlike mental health (p = 0.145) and self-efficacy (p = 0.464), the other outcomes regarding health-related quality of life, motivation and the number of somatic disorders were also significantly different (p < 0.05) among the three groups. For all dimensions, the referral group scored least favorable.

The scores concerning health-related fitness were significantly different for waist circumference (p = 0.012); shoulder flexibility (p = 0.004); leg strength (p = 0.003); arm strength (p ≤ 0.001); speed, agility and balance (p ≤ 0.001); and endurance (p ≤ 0.004). Table 1 gives an overview of the scores for each group and the corresponding p-value.

Table 1:

Scores for physical activity behavior, health-related quality of life, motivation, self-efficacy, and health-related fitness per recruitment strategy (means and standard deviation)

PR Personal letter Referral p-value
Women, n (%) 111 (82.2%) 81 (59.6%) 71 (72.4%) 0.428
Age (years) 59.8 ± 14.9 67.9 ± 7.6 53.5 ± 16.9 0.821
Meets physical activity guideline n (%)a 61 (45.2%) 82 (60.3%) 32 (32.7%)b 0.002
Health-related quality of life (0–100)c
 Physical functioning 71.3 ± 25.0 79 ± 19.0 62.1b ± 23.9
 Social functioning 77.9 ± 23.6 85.4 ± 19.5 70.9 ± 24.2
 Role limitation physical 75.7 ± 37.3 81.8 ± 32.0 50.7b ± 44.8
 Role limitation emotional 80.2 ± 37.2 88.2 ± 27.3 67.1 ± 40.5
 Mental health 72.5 ± 18.6 76.9 ± 17.2 68.3 ± 19.4
 Vitality 62.5 ± 19.0 67.7 ± 18.3 53.3d ± 20.0
 Pain 73.2 ± 25.5 78.5 ± 20.4 62.5 ± 24.9
 General health 62.6 ± 15.7 65 ± 16.7 49.7b ± 16.9
 Health change 50.4 ± 17.7 51.3 ± 16.2 40.5b ± 18.6 0.005
Motivation (−25–19)c 9.0 ± 5.1 8.9 ± 5.3 6.5e ± 5.2 0.027
Self-efficacy (1–5)c 3.2 ± 0.8 3.1 ± 0.7 2.9 ± 0.7 0.464
Number of somatic diseasesf 1.5 ± 1.8 1.6 ± 1.5 2.4b ± 2.1 0.007
Body mass index (kg/m2)f 29.7 ± 5.4 27.7 ± 4.9 32.5 ± 6.9 0.050
Waist circumference (cm) f 97.1 ± 12.2 96.7 ± 12.9 107.3d ± 19.2 0.012
Fat percentage (%)f 38.0 ± 7.5 33.8 ± 8.4 40.4 ± 9.2 0.174
Glucose (mmol/l)f 5.7 ± 1.8 5.9 ± 1.6 5.7 ± 1.7 0.705
Cholesterol (mmol/l)f 5.5 ± 1.0 5.8 ± 1.0 5.2 ± 1.0 0.198
Blood pressuref
 Diastolic (mmHg) 84.9 ± 10,1 87.3 ± 10.0 82.9 ± 11.2 0.199
 Systolic (mmHg) 135.9 ± 18.5 145.7 ± 17.9 135.3 ± 17.7 0.149
Shoulder flexibility (cm) 8.7 ± 10.0 8.3b ± 9.2 11.8 ± 11.2 0.004
Leg flexibility (degrees) 81.5 ± 14.7 74.9 ± 15.3 83.9 ± 15.0 0.834
Back flexibility (cm) 20.8 ± 2.3 20.3 ± 1.6 20.5 ± 1.9 0.333
Leg strength (n) 12.8 ± 4.1 13.7 ± 3.2 11.8b ± 3.8 0.003
Arm strength (n) 15.4b ± 4.6 17.7b ± 4.1 15.2b ± 5.9 <0.001
Hand grip strength (kg) 59 ± 18.3 65.4 ± 20.0 59.5 ± 23.2 0.124
Speed. agility and balance (sec)f 7.3 ± 2.6 5.8b ± 1.5 7.4 ± 2.0 <0.001
Endurance (z) −0.3 ± 0.9 0.5 ± 0.8 −0.4d ± 1.0 0.003
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The data represents the means of each score with the corresponding standard deviation. Scores were adjusted for age, gender, and medication for medication related variables (blood pressure, glucose, cholesterol).

a

Guideline of 30 min of moderate to vigorous PA, at least five times a week (Kemper et al. 2000).

b

Significantly different from the other two recruitment strategy groups.

c

Total possible score on the scale.

d

Significantly different from the personal letter group.

e

Significantly different from the PR group.

f

A lower score represents a higher level of fitness.

Post hoc analysis showed that the referral group was significantly different from the other two groups for PA behavior, physical functioning, role limitation due to physical health, general health, health change, the number of somatic diseases, leg strength and arm strength. The referral group was significantly different than the PR group for motivation and differed from the personal letter group for vitality; waist circumference; shoulder flexibility; and speed, agility and balance. The results of the post hoc analysis are presented in Table 2.

Table 2:

Post hoc analysis for the three recruitment strategies

Recruitment strategy n (%) Odds ratio (95% CI) p-value
Meets physical activity guideline (n) PR (reference) 61 (45.2%)
Personal letter 82 (60.3%) 2.0 (0.1–1.3) 0.026
Referral 32 (32.7%) 0.6 (0.3–1.1) 0.113
Meana (95% CI) Mean difference (95% CI)
 p-value
PR Personal letter Referral PR Personal letter Referral
Physical functioning PR 69.6 (62.1–77.1) — — — — — —
Personal letter 77.8 (67.2–88.3) 8.1 (−5.6–21.9) — — 0.373 — —
Referral 60.2 (52.6–67.9) −9.4 (−18.4 - −0.3) −17.5 (−30.9 - −4.1) — 0.039 0.007 —
Social functioning PR 76.7 (70.4–83.1) — — — — — —
Personal letter 82.4 (72.0–92.0) 5.7 (−7.3–18.7) — — 0.565 — —
Referral 70.1 (63.5–76.8) −6.6 (−15.5–2.3) −12.3 (−24.7–0.2) — 0.210 0.054 —
Role limitation physical PR 69.4 (56.0–80.7) — — — — — —
Personal letter 80.2 (63.4–97.0) 10.8 (−11.8–33.5) — — 0.511 — —
Referral 49.4 (47.6–61.3) −20.0 (−35.3 - −4.6) −30.8 (−52.7 - −8.9) — 0/006 0.004 —
Role limitation emotional PR 73.9 (62.5–85.3) — — — — — —
Personal letter 86.1 (69.5–102.6) 12.2 (−9.4–33.8) — — 0.400 — —
Referral 64.9 (52.9–76.8) −9.0 (−23.5–5.4) −21.2 (−42.5–0.0) — 0.348 0.051 —
Vitality PR 61.7 (56.5–66.8) — — — — — —
Personal letter 65.2 (56.9–73.4) 3.5 (−7.7–14.6) — — 0.745 — —
Referral 54.8 (49.4–60.1) −6.9 (−14.3–0.5) −10.4 (−20.7 - −0.0) — 0.078 0.049 —
Pain PR 71.2 (64.3–78.1) — — — — — —
Personal letter 75.6 (65.7–85.6) 4.4 (−9.3–18.1) — — 0.788 — —
Referral 62.5 (55.3–69.8) −8.7 (−18.3–0.9) −13.1 (−26.5–0.3) — 0.087 0.056 —
General health PR 62.3 (58.6–66.0) — — — — — —
Personal letter 65.0 (57.9–72.1) 2.7 (−6.3–11.6) — — 0.703 — —
Referral 49.6 (45.4–53.9) −12.7 (−19.1 - −6.3) −15.3 (−23.3 - −7.4) — 0.000 0/001 —
Health change PR 49.6 (45.3–54.0) — — — — — —
Personal letter 51.5 (45.6–57.5) 1.9 (−7.0–10.8) — — 0.916 — —
Referral 40.5 (35.7–45.4) −9.1 (−16.2 - −2.0) −11.0 (−20.0 - −2.1) — 0.007 0.012 —
Motivation PR 9.1 (7.8–10.5) — — — — — —
Personal letter 8.7 (6.8–10.6) −0.5 (−3.2–2.3) — — 0/953 — —
Referral 6.8 (5.3–8.2) −2.4 (−4.4 - −0.3) −1.9 (−4.6–0.8) — 0.017 0.237 —
Number of somatic diseases (n)b PR 1.7 (1.4–2.1) — — — — — —
Personal letter 1.6 (0.5–2.7) −0.1 (−1.2–0.9) — — 0.925 — —
Referral 2.9 (2.5–3.3) 1.2 (0.6–1.8) 1.3 (0.5–2.2) — <0.001 0.004 —
Waist circumference (cm) b PR 100.5 (95.5–105.4) — — — — — —
Personal letter 95.9 (89.0–102.7) −4.6 (−13.6–4.4) — — 0.513 — —
Referral 105.6 (100.5–110.6) 5.1 (−0.6–10.8) 9.7 (0.9–18.5) — 0.093 0.026 —
Shoulder flexibility (cm) PR 11.4 (8.7–14.1) — — — — —
Personal letter 6.0 (2.2–9.8) −5.4 (−10.7 - −0.7) — — 0.046 — —
Referral 13.4 (10.5–16.2) 2.0 (−1.9 - −5.8) 7.3 (2.1–12.6) — 0.524 0.004 —
Leg strength (n) PR 12.7 (11.5–13.9) — — — — — —
Personal letter 14.1 (12.5–15.8) 1.4 (−0.8–3.6) — — 0.297 — —
Referral 11.3 (10.1–12.5) −1.4 (−2.9–0.4) −2.9 (−5.0 - −0.7) — 0.060 0.005 —
Arm strength (n) PR 15.0 (13.4–16.7) — — — — — —
Personal letter 18.3 (16.0–20.5) 3.3 (0.4–6.1) — — 0.020 — —
Referral 13.4 (11.8–15.1) −1.6 (−3.3–0.2) −4.8 (−7.6 - −2.1) — 0.089 <0.001 —
Speed, agility and balance (sec)b PR 7.5 (6.9–8.1) — — — — — —
Personal letter 5.6 (4.7–6.5) −1.9 (−3.0 - −0.8) — — <0.001 — —
Referral 7.9 (7.2–8.5) 0.4 (−0.4–1.1) 2.3 (1.2–3.3) — 0.559 <0.001 —
Endurance (z) PR −0.3 (−0.6–0.1) — — — — — —
Personal letter 0.2 (−0.2–0.7) 0.5 (−0.7–1.1) — — 0.100 — —
Referral −0.5 (−0.9 - −0.2) −0.3 (−0.6–0.1) −0.8 (−1.4 - −0.2) — 0.250 0.003 —
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Scores were adjusted for age, gender, and medication for medication related variables (blood pressure, glucose, cholesterol).

a

Estimated means.

b

A lower score represents a higher level of fitness.

DISCUSSION

The purpose of this study is to explore if the different recruitment strategies (i.e. PR, personal letters and referrals) would reach similar participants for lifestyle interventions and if these participants are representative of the CSCs’ target group (i.e. inactive adults with or at risk of a chronic disease). Our results show that recruited participants differ across the three recruitment strategies. The PR group can be described as a group that was highly motivated to become more physically active. They scored for health-related quality of life within the normal range and were moderately fit. The personal letter group, recruited through a fitness test, experienced an overall good health-related quality of life and was in general fit. The referral group was less motivated to become physically active, experienced a lower health-related quality of life and was less fit compared to the other two groups. Overall, we can conclude that the group with participants who were referred by a professional score less auspiciously than the other two groups, and represented the desired target group most. Although each recruitment strategy reached participants belonging to the desired target group, the referral group reached the most inactive adults with or at risk for a chronic disease.

Previous studies showed that PA levels, fitness and health-related quality of life decrease with age (Van der Zee and Sanderman, 1993; Rikli and Jones, 1999; Nelson et al., 2007; Hallal et al., 2012; RIVM, 2014). Therefore, it is striking that participants recruited by referral were the youngest, least likely to be meeting PA guidelines, least fit, and reported the lowest levels of health-related quality of life. However, (perceived) health is a strong predictor of and has a positive correlation with PA levels and fitness (Suni et al., 1998; Malmberg et al., 2002; de Hollander et al., 2015). From this point of view, it is reasonable that participants who were referred scored lower on PA level and fitness because they also suffer from more somatic diseases and experience a lower health-related quality of life. The percentage of participants who received referrals that meet the PA guidelines (32%) is lower than the Dutch mean of 44% for people with lower experienced health (de Hollander et al. 2015). Whereas the percentage of the PR group who meet the PA guideline (45%) is lower to the Dutch mean of 55%, the percentage of the personal letter group who meet that criteria is 60%, which is higher but not as high as that of healthy older adults in the Netherlands, of whom 84% meets the PA guideline (de Hollander et al., 2015).

We can make a distinction between active and passive recruitment. Active recruitment refers to having direct interaction with potential participants via person-to-person contact (Estabrooks et al., 2017). Passive recruitment involves no direct interaction and can be in the form of flyers and websites, for example (Estabrooks et al., 2017). Passive recruitment strategies, such as PR and personal letters, combined with the use of the population register of a municipality reach a greater amount of people with less effort. Previous studies showed that passive recruitment strategies were more cost efficient (Lee et al., 1997; Estabrooks et al., 2017) and had a high percentage of enrollment in a lifestyle intervention after screening (Lee et al., 1997). In the case of passive recruitment, the assumption is made that people decide themselves if they belong to the addressed target group. This assumption holds if we look at the enrollment rates. However, our study and previous studies (Lee et al., 1997; Cooke and Jones, 2017; Estabrooks et al., 2017) showed that active recruitment strategies such as referral reach more vulnerable and representative participants according to the defined target group or participants with a higher BMI (Yancey et al., 2001). People who were refereed had a lower BMI at the start of the lifestyle intervention, and this difference should be taken into account with the offered lifestyle intervention. Passive recruitment strategies more often reach participants with a higher socio economic status and who are more physically active (Martinson et al. 2010; Cooke and Jones, 2017). Therefore, several authors recommend that strategies should be clearly designed to target specific populations, e.g. by targeting specific demographic characteristics such as ethnicity (Cooke and Jones, 2017; Lion et al., 2019) and that a combination of passive and active recruitment strategies, including digital tools (e.g. e-mail, websites) should be used in promoting PA (Bergeron et al., 2019).

New professions, like CSCs, will be assessed mostly on reach; therefore, it is understandable to choose less time-consuming passive recruitment strategies that have a higher reach. However, health inequalities will become bigger if we do not reach the most vulnerable residents (i.e. inactive persons with or at risk of a chronic disease). One can argue that each recruitment strategy will reach a certain number of vulnerable residents, but this reasoning will have consequences for lifestyle interventions. Participants have various PA levels, clinical characteristics and motivation. The referral group had the lowest motivation for becoming physically active and was less fit compared to the other groups. Participants from the PR group had the highest motivation to become physically active. The personal letter group was most fit. Participants who are less fit may not feel comfortable in a group-based PA program or cannot handle the intensity of the activity, which will lead to drop-outs (Sabiston et al., 2009; Penn et al., 2013; Kluge et al., 2014). Therefore, it would be a good investment to focus on an active recruitment strategy and the way in which partners in the network interact and work together. This tactic takes time, and CSCs are dependent on other professionals to execute an active recruitment strategy. For that reason, CSCs need support to create a sustainable collaboration structure and should be assessed rather on the type of participants they reach. Furthermore, it would be helpful if CSCs are assigned in advance a specific target group instead of being given a general role description, which was what was available at the time of this study. A previous study revealed that an integrated approach for the embedding of the CSC function, in which CSCs collaborate with primary health care professionals, is helpful for establishing a referral system with primary care professionals (Leenaars et al., 2017).

Future studies should reveal if CSCs who use an integrated approach reach a higher proportion of representatives of the target group. In addition, future studies should focus on reasons why fewer participants enroll in a lifestyle intervention after active recruitment and what are more tailored recruitment strategies. Alongside this, process evaluation is necessary, wherein drop-outs, regular assessment of the fitness of new recruits and maintenance are monitored in the long-term.

Strengths and limitations

This study provides an in-depth insight into the reach of CSCs, to get an idea of the representativeness of their reach regarding the defined target population (i.e. inactive adults with or at risk of a chronic disease). Our results are helpful for CSCs and their supervisors to determine which recruitment strategy is suitable for their context. However, there are a few limitations that we have to consider for the interpretation of these results. Our sample was dependent on practical issues, so we cannot guarantee representativeness, a matter which is also visible in the non-proportionate delivery of participants per CSC. However, a full check was hard to establish because CSCs do not monitor their recruitment adequately. Not all participants who were reached were included in the monitor, and not all included participants in the monitor started with a lifestyle intervention. On the other hand, our study represents a real practical situation and thereof a good exploration. However, an explorative study does not test or confirm a hypothesis. For an explorative study, it is enough to present effect and variation sizes. P-values and a 95% confidence interval are very informative and provided in this study because it gives the possibility to compare the explorative results with other results.

CSCs offered different lifestyle activities, sports and PA. These activities were mentioned within the recruitment strategy and could have influenced the choice to participate or not. However, almost all activities were a combination of various sports and were offered in a group setting. An exception is the personal letter group, who were offered specific sports activities, in addition to the various sports offered in a group setting.

We used various measurements for this study captured with a self-administered questionnaire. The chosen questionnaires are commonly used and extensively tested for validity and reliability (Van der Zee and Sanderman, 1993; Resnick and Jenkins, 2000; Markland and Tobin, 2004; Ooijendijk et al., 2007). A limitation is that each questionnaire or even item might be over- or under-estimated by the respondent. Another remark must be made about the endurance test. We had to use two different kinds of tests due to the variation in participants. We used norm equations (Wasserman et al., 1987; Beekman et al., 2014; Myers et al., 2017) to determine how participants’ scores deviate from the norm as a percentage. These norm scores were used to compare participants in the different groups. These norms are set for healthy adults and give the opportunity to demonstrate their prognostic characteristics across healthy and diseased populations. And for this study, it gave an insight into the representativeness of the reached participants regarding the defined target population.

CONCLUSION

With this study, we meet the need for comparison of different recruitment strategies regarding the representativeness of reached target groups. Our explorative study showed that recruitment strategies reach different target groups. Participants who received referrals from a professional were least likely to be meeting PA guidelines and reported the lowest levels of health-related fitness. The investment in time and money for an active recruitment strategy is worthwhile because it gives the opportunity to reach people who are inactive and at risk for a chronic disease. Future studies are necessary to reveal long-term benefits of active recruitment into PA interventions on PA and health.

SUPPLEMENTARY MATERIAL

Supplementary material is available at Health Promotion International online.

Supplementary Material

daaa134_Supplementary_Appendix_1
Click here for additional data file. (15.8KB, docx)

ACKNOWLEDGMENTS

We thank our partners, i.e. Dutch Institute for Sports and Physical activity (NISB), Association of Sports and Municipalities (VSG), Royal Dutch Society for Physical Therapy (KNGF) and Regional Support Structures (ROS), Dutch Olympic Comity * Dutch Sport Federation (NOC*NSF) for their helpful input and help to approach the professionals.

FUNDING/TRIAL REGISTRATION

The study is funded by ZonMw, the Dutch Organisation for health research and healthcare innovation (project number 525001002) and registered in the Dutch Trialregister NTR4986 on 14 December 2014.

ETHICS APPROVAL

This study has been approved by the Medical Ethical Review Committee: CMO Regio Arnhem-Nijmegen (file number 2013–492).

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Associated Data

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Supplementary Materials

daaa134_Supplementary_Appendix_1
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