A concept mapping approach to explore the perceived facilitating factors in shifting sedentary behavior into (more) physical activity: perspectives of healthcare professionals versus sedentary and/or inactive adults

Abstract

Purpose

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Healthcare professionals (HCPs) such as Physical Activity on Prescription (PAP)-coaches and physiotherapists might play a crucial role in activating sedentary and/or inactive adults, which in turn might benefit their health. We aimed to explore the perceived facilitating factors to shift sedentary behavior (SB) into more physical activity (PA) comparing two perspectives 1) sedentary and/or inactive adults and 2) HCPs in their role to activate these adults.

Methods

A mixed method approach, i.e. concept mapping, was used to explore the facilitating factors among 1) HCPs (n = 10) and 2) adults (n = 40). During a brainstorm, perceived facilitating factors were gathered in response to one seeding statement ‘what help(s) you/your clients to shift SB into (more) PA?’. Thereafter, all answers were sorted by relatedness into different piles and each answer was rated on effectiveness, feasibility, changeability and enjoyment (5-point Likert scale). Data analysis was done via RCMap.

Results

Clusters identified by both HCPs and adults included ‘social networks’, ‘everyday activities as opportunities for PA’, ‘awareness of PA benefits’, ‘motivation regarding PA’, ‘integration of PA into daily routines’, ‘goal-setting to facilitate PA’, ‘environmental facilities’, ‘financial resources’, and ‘technology, digital tools and external tools to facilitate PA’. Each group created unique clusters such as factors related to the ‘work environment’ by adults and factors related to ‘tailored support to facilitate PA’ by HCPs. The average rating for each cluster was higher for HCPs compared to adults for effectiveness (4/5 vs 3.5/5), feasibility (3.8/5 vs 3.1/5), and changeability (3.7/5 vs 3.2/5).

Conclusions

HCPs and sedentary and/or inactive adults identified factors at the intra- and interpersonal level which were rated as feasible, effective and changeable, as well as factors at the responsibility of external stakeholders such as employers or policy makers. These results highlight that future interventions aimed at shifting SB into PA include a multilevel challenge.

Background

As each 24-h period is a complex interplay of movement behaviors, i.e. physical activity (PA), sedentary behavior (SB) and sleep, it is essential to consider how people allocate their time within one day [1, 2]. Observational studies suggest that adults experience favorable health effects when they allocate more time from SB into PA, most prominent for moderate-to-vigorous PA (MVPA), and with a smaller magnitude for light-intensity PA (LPA) [1]. Yet, many adults find it challenging to incorporate more PA into their routines, as evidenced by the low prevalence of adults meeting the 24-h movement behavior guidelines established by the Canadian Society of Exercise Physiology [3]. Data of the National Health and Nutrition Examination Survey (NHANES) showed that only 41.5% of the United States adult population met the PA recommendation of 150 min of MVPA per week, while only 35.3% adhered to the SB guideline of a maximum of 8 h of sedentary time per day [3]. Therefore, there is a growing need to explore how individuals can effectively facilitate less SB and more PA into their daily routines without compromising other essential aspects of their day, like adequate sleep duration [4].

Healthcare professionals (HCPs) might be key actors in facilitating this behavior change by helping adults to change their time spent in SB into higher levels of PA. Inspired by the European Physical Activity on Prescription (EUPAP) model, the Flemish Institute of Healthy Living (www.gezondleven.be), in collaboration with the government, introduced ‘Physical Activity on Prescription’ (PAP)-coaches in Belgium in 2018 [5]. A degree in Physiotherapy or Movement and Sport Sciences is required to be a PAP-coach [6]. The aim of PAP-coaches is to support people towards increasing their motivation to sit less and move more during the day, including the exploration of low threshold PA opportunities in the neighborhood [5]. Results from an evaluation report from the Flemish Institute of Healthy Living showed an increase of 50 min/week of total PA after 6 months of coaching among adults [6]. Moreover, findings from a systematic review evaluating the Swedish PAP model included 6 studies, with three out of five RCTs and one cohort study showing a significant increase in PA levels after 6 to 18 months of PAP coaching compared to control groups receiving usual care/group sessions/written information on PA [7]. Nevertheless, there is currently a lack of research including the perspectives of PAP-coaches on how to decrease SB and increase PA in sedentary and/or inactive adults.

In order to develop intervention components aimed at replacing time spent in SB into (more) PA, it is essential to explore the facilitating factors of this behavioral transition. Research on explanatory variables of less SB and more PA from both qualitative (e.g. interviews) and quantitative (e.g. questionnaires) research designs already exists. For example, studies revealed different facilitating factors to reduce SB or increase PA in adults with a different weight category (normal weight versus overweight/obesity) including intrapersonal factors (e.g. self-efficacy), interpersonal factors (e.g. social networks) and environmental factors (e.g. physical environment features) [8,9,10]. Other research designs using mixed methods such as concept mapping try to combine the strengths of qualitative research (e.g. capturing the perceptions of the population of interest on a seeding statement) with quantitative techniques (e.g. sort the answers into clusters, rate each answer on e.g. effectiveness) [11]. A European concept map created by researchers and policy experts identified key clusters of perceived factors facilitating PA, including intrapersonal context and well-being, family and socioeconomic status, policy and provision, cultural context and media, social support and modeling, and supportive environment [12]. Additionally, this mixed method has been applied in several other studies. One study explored the factors influencing older adults’ outdoor walking including facilitating factors such as sidewalks, crosswalks and neighborhood features [13, 14]. Another study focused on the identification of determinants of SB among a general population resulting in clusters with facilitating factors such as physical health and wellbeing, social and cultural context and built and natural environment [13, 14]. Additionally, another study using a concept mapping design explored the predictors of relapse in weight loss maintenance behaviors (including PA as well as diet) by both HCPs supervising adults in weight loss programs and adults who had regained weight [15]. Clusters of predictors or relapse included factors such as decreased motivation, a non-supportive physical environment, goal disengagement, lack of social support, obstructing beliefs about lifestyle changes, and perceived financial barriers [15].

Until now facilitating factors of PA comparing the perspective of HCPs (with the main focus on PAP-coaches) and sedentary and/or inactive adults have not been investigated. Therefore, this paper aims to explore the differences and similarities in factors facilitating changing SB into (more) PA as perceived by (1) HCPs focusing on facilitating adults’ PA and (2) sedentary and/or inactive adults. Examining both perspectives on facilitating factors will help to identify meaningful themes to inform the development of targeted interventions that promote sustained PA and decreased SB in the long term. Moreover, by using a mixed methods approach such as concept mapping, insights can be gained on how facilitating factors are quantitatively scored on indicators such as effectiveness, feasibility, changeability or enjoyment.

Methods

This study employs a cross-sectional mixed-method design using concept mapping in two participant groups: 1) HCPs, and 2) sedentary and/or inactive adults. First, this method section gives a description of the recruitment strategy and eligibility criteria, followed by a description of sociodemographic information which was provided by the participants via an online questionnaire. Next, the concept mapping approach is discussed explaining the four phases: 1) Preparation phase, 2) Brainstorm phase, 3) Sorting and rating phase and 4) Analysis phase.

The ethical committee of Ghent University Hospital approved the study (ONZ-2023–0185), and written informed consent was obtained from all participants prior to the study. Participants were recruited and participated in the present concept mapping study between November 2023 and July 2024.

Participants: recruitment and eligibility criteria

HCPs

Flyers with information about the study and a QR code were distributed via social media. Additionally, 184 HCPs were contacted via mail or phone including PAP-coaches (registered on the website of the Flemish Institute of healthy living) and physiotherapists without PAP-accreditation. Physiotherapists without PAP-accreditation were additionally recruited due to a low response rate of the PAP-coaches. Importantly, the physiotherapists without PAP-accreditation needed a main expertise in increasing PA in adults with sedentary and/or inactive lifestyles to be eligible for this study, which was questioned by a phone call before study participation.

Sedentary and/or inactive adults

Recruitment was conducted through convenience sampling, using flyers containing study information and a QR code. The flyer was distributed via the HCPs included in this study, social media and emailed to adults who had previously agreed to be contacted about studies from our research team. Due to recruitment difficulties, the flyer was additionally handed out in public areas to reach as many potential participants as possible. Adults aged 25 to 64 years were eligible if they were employed at least part-time (0.5 FTE) and subjectively reported non-compliance with the SB and/or PA guidelines [16]. As the reasons for a sedentary and/or inactive lifestyle might differ between working and non-working individuals, we focused on working individuals only [16]. Subjectively reporting compliance with the SB and/or PA guidelines was questioned by first providing information about the content of the guidelines followed by a yes/no question. Adults engaging in less than 150 min of MVPA a week were considered as non-compliant with the PA guidelines and are referred to in this study as inactive adults. Adults engaging in more than 8 h of SB including 3 h or more per day of recreational screen time were considered as non-compliant with the SB guidelines and referred to as sedentary adults [2]. Individuals with significant physical (e.g. amputations), cognitive (e.g. dementia), medical (e.g. hospitalization, cancer treatment) or other (e.g. night shift work, currently involved in other studies, participation in post-surgery weight loss programs, being on a waitlist for weight loss surgery, currently pregnant or being pregnant within the past year) conditions affecting daily activities were excluded from study participation.

Sociodemographic information

For all participants, sociodemographic information was self-reported via an online survey prior to the concept mapping: sex (male/female), age, height, weight, marital status (i.e. living alone, not having a partner/living together with a partner/living alone and having a partner), living together with children (yes/no), educational level (i.e. low (until secondary education)/ mid (college)/ high (university)), net family income (i.e. < 2000 euro/ ≥ 2000 euro per month), smoking status (non-smoker/ex-smoker/smoker), environmental (urban/rural), seated job (sedentary job/mix of sedentary and light physically active job/physically heavy job).

Among the group of sedentary and/or inactive adults, additional questions asked whether they were currently in, or had previously joined, a trajectory with a PAP-coach or if they knew what this was. If they followed/had followed a PAP-trajectory, questions were asked about the reason why they followed this trajectory, and they were asked to give a score between 0 (not satisfied at all) and 10 (very satisfied) about their satisfaction of their PAP-trajectory. Additional questions regarding HCPs were questions about their profession and the number of years they had spent working to promote PA among adults in their professional role.

Concept mapping approach

Concept mapping involves four structured phases to ensure comprehensive data collection and organization [17], and these phases were led by a researcher (IW) in the present study. It begins with the 1) Preparation phase, which includes formulation of the seeding statement by the researchers and recruitment of the participants [17]. The 2) Brainstorming phase starts with providing the seeding statement in advance to the participants [17]. This enables them to prepare a preliminary list of answers at home followed by bringing this list to the subsequent brainstorming session [17]. In this session, participants review and expand their prepared lists individually [17]. Following this, a group brainstorming session takes place where participants share their answers one by one. During the sharing of answers, participants are encouraged to write down additional answers inspired by others. If there are overlapping answers, the researcher facilitates clarification and helps the group to reach a consensus. Theoretical data saturation is considered to be achieved when no new answers emerged [18]. The finalized list of unique answers forms the basis for the next phase. In the 3) Sorting and rating phase, participants individually sort the unique answers into piles and provide descriptive names for each pile [17]. In the present study, this process was facilitated through a website, where the researcher was available during online sessions to address questions if necessary. In general, guidelines exist for sorting: (1) each pile must contain at least two answers, (2) piles named ‘other’ are not allowed, and (3) a minimum of three piles is required. Multi-criteria rating considers the rating of each answer on different scales [17]. In the present study, participants (both HCPs and adults) rated each answer on a 5-point Likert scale for effectiveness, feasibility and changeability. Effectiveness referred ‘What is your opinion on how effective this item could be in promoting more physical activity for your clients (HCPs)/ for you (Adults)? Does this item work for your clients (HCPs)/ for you (Adults)?’. Feasibility referred to ‘How feasible do you think it is to implement this item in the life of your clients (HCPs)/ your life (Adults)?’. Changeability referred to ‘How changeable do your clients (HCPs)/ do you (Adults) perceive this item? Do your clients (HCPs) /Do you (Adults) perceive this as difficult or easy to change?’. The group of adults additionally scored the answers on their perceived enjoyment of each answer i.e. ‘How enjoyable would you find it to incorporate this item in your daily life?’. The output from the sorting and rating phase was generated as an Excel file listing piles with accompanying answers for each participant and ratings for each answer per participant. Finally, the 4) Analysis phase used the Excel file generated in Phase 3 and was processed by two researchers (IW and MDC). This part is described in the data analysis section [19].

Concept mapping process with HCPs

For the HCPs, the Preparation phase, Brainstorm phase, Sorting and rating phase and Analysis phase of the concept mapping approach were implemented as outlined above (i.e., ‘concept mapping approach’). The seeding statement regarding HCPs was formulated in Dutch as follows: ‘Wat faciliteert/helpt jouw cliënten om hun sedentair gedrag om te ruilen in (meer) beweging? Denk hierbij aan zowel lichte fysieke activiteit als matige tot intense fysieke activiteit als ook aan verschillende contexten waar je actief kan zijn zoals transport, vrije tijd, werk en huishouden’. The English translation is as follows: ‘What facilitates/helps your clients to change their sedentary behavior into (more) physical activity? Think about both light physical activity and moderate to vigorous physical activity, and also try to think as broad as possible and think of contexts like transport, leisure time, work, and household activities.’ The Brainstorm phase was conducted via an online group session, with all answers recorded in a shared Excel sheet displayed on the screen.

Concept mapping process with sedentary and/or inactive adults

Due to challenges in recruiting adults, the proposed four-step process was slightly adapted for this group. Deviations from the original approach were related to the Preparation phase and the Brainstorm phase. The Preparation phase includes a similar seeding statement as for the HCPs which is formulated in Dutch as follows: ‘Wat faciliteert/helpt jou om jouw sedentair gedrag om te ruilen in (meer) beweging? Denk hierbij aan zowel lichte fysieke activiteit als matige tot intense fysieke activiteit als ook aan verschillende contexten waar je actief kan zijn zoals transport, vrije tijd, werk en huishouden’. The English translation is as follows: What facilitates/helps you to change your sedentary behavior into (more) physical activity? Think about both light physical activity and moderate to vigorous physical activity, and also try to think as broad as possible and think of contexts like transport, leisure time, work, and household activities.’ Due to difficulties in recruiting adults who were willing to join the online Brainstorm phase, the researchers decided to change the format of the online group brainstorm session into an individual brainstorm facilitated by an online template. To encourage detailed individual brainstorming, five €20 coupons were raffled among participants who contributed at least five brainstorming ideas. This online template first checked the eligibility criteria such as age range, employment status, condition affecting daily activities and compliance with SB and/or PA guidelines (for details see ‘recruitment and eligibility criteria’). In case of non-eligibility, participants received a message that participation was discontinued meaning that no access was provided to the online template. Two researchers (IW and MDC) reviewed all answers from the online individual brainstorming phase to ensure clarity and uniqueness, combining them in a final list of unique answers. A total of 100 adults completed the online individual screening, with response patterns indicating that theoretical data saturation was reached after approximately 60 participants [18]. After completing the online individual brainstorm, participants were asked about their willingness to join the next phase (i.e. the Sorting and rating phase). The Sorting and rating phase and analysis phase of the concept mapping approach were implemented as outlined above (i.e. ‘concept mapping approach’).

Data analysis (phase 4)

The free and open-source software RCMap for R Software, described in Bar & Mentch (2017) [19], was used to analyze the results of the sorting and rating for each participant. This analysis was done separately for the HCPs and adults. Within this software, a matrix was created based on how the participant sorted their answers. In this matrix, each cell (i, j) included a 1 if the participant placed answer i and j in the same pile, and a 0 if they placed them in different piles [19]. The matrices of all the participants were added together and then divided by the number of participants [19]. This resulted in a distance matrix quantifying the distances between all answers [19]. The Multidimensional Scaling (MDS) Algorithm was used to visualize similarity or distance between answers in a dataset [17, 19]. This algorithm reconverts the multiple k-dimensions into two dimensions by trying to maintain the original distances to minimize errors [17, 19]. After this, the distances between answers were shown in a two-dimensional graph, where answers that were sorted together often appeared closer to each other [17, 19]. A split-half analysis was used to estimate the consistency of the 2D by checking how much the map varies when a random subsample of the sorters is used [17, 19]. The mean correlation between the split halves (using 20 random splits) was r = 0.6 for adults and r = 0.3 for HCPs, suggesting a good and moderate reliability, respectively.

Next, RCMap tries to identify participants who may distort the results by generating warning messages when a participant sorted more than one-third of the answers into a single pile as well as by using a leave-one out analysis. The leave-one-out analysis evaluates the influence of each participant on the overall distances calculated during the MDS process by measuring how much the exclusion of a single participant changes the configuration. Moreover, given the large number of answers in both the HCPs and adults group (125 answers by HCPs and 143 answers by adults), we expected more than five clusters for a meaningful classification and therefore decided to exclude the influential participants from the data analysis. Upon reviewing the leave-one-out analysis as well as the individual participants who sorted more than one-third of the answers into a single pile, 18 influential participants were identified. All these participants had also created less than five clusters so were excluded from the analysis (adults (n = 18), HCPs (n = 0)).

Next, piles of answers that captured more or less general themes were clustered by using Ward’s algorithm [19]. The most optimal number of clusters was evaluated by two researchers (IW and MDC) by checking the within cluster sum of squares and the average silhouette width [17, 19]. These metrics determined that the ideal number of clusters was 10 for both HCPs and adults. As it is recommended to explore clusters both below and above the statistically optimal number to identify the best fit, two researchers (IW and MDC) individually evaluated a range of 8 to 12 clusters for both groups, considering the recommended number of 10 clusters as a reference. They checked for each number of clusters (8 to 12 clusters) whether the answers within each cluster were similar in meaning and whether each cluster was conceptually distinct from the others. Thereafter, they agreed on the most optimal number of clusters, followed by naming the clusters, based on the pile-names provided by the individual participants for both the HCPs and adults. The final number of clusters for both HCPs and adults were separately plotted by the RCMap software on a concept map showing 1) answers closer together tend to be more similar and 2) clusters closer together tend to be conceptually more similar.

Last, the average effectiveness (HCP and adults), feasibility (HCP and adults), changeability (HCP and adults), enjoyment (adults) of each cluster was calculated based on the ratings of all individuals answers within these clusters. The average cluster rating was plotted as a bar chart, for effectiveness, feasibility, changeability and enjoyment. Moreover, this average cluster rating was plotted in bivariate plots, also called GoZones, to show the rating of statements based on combinations of rating of two indicators (feasibility, effectiveness, changeability or enjoyment). These plots are divided in four quadrants where the upper right quadrant indicated answers scoring the highest (referred as the mean of all raters for the specific indicator) on two indicators and the left lower quadrant indicated the statements with the worst scoring on the combination of the rating on two indicators. GoZones were created for all possible combinations of ratings (effectiveness, feasibility, changeability, and enjoyment) in both adults and HCPs, except for the combinations with enjoyment ratings, which are available only for adults. More information on the GoZones can be found in Additional file 4.

Results

Participant characteristics

In total 10 HCPs completed the online brainstorm group session. Nine of these HCPs completed phase 3 ‘Sorting and rating’ providing both valid sorting and rating data. The 10 HCPs consisted of 70% females (n = 7), had a mean age of 36.8 ± 11.4 years and a mean body mass index (BMI) of 22.5 ± 2.4 kg/m² (Table 1). Two PAP-coaches had a degree in movement and sport sciences, five PAP-coaches had a degree in physiotherapy, and three had a degree in physiotherapy but no PAP-coach accreditation. On average, HCPs had 6.1 ± 4.3 years of professional experience.

Table 1 Socio-demographic characteristics of HCPs and adults

A total of 100 adults (mean age 43.1 ± 12.3 years; 76% females) participated in the online individual brainstorm of which 40 adults agreed to participate in phase 3 ‘Sorting and rating’. Of these 40 adults, 22 (55%) provided valid ‘sorting of answers into piles’ data where 38 (95%) adults provided ratings for each of the answers on effectiveness, feasibility, changeability, and enjoyment. The 40 adults included in the sorting and rating phase consisted of 60% females (n = 24), had a mean age of 47.7 ± 11.0 years, and a mean BMI of 26.3 ± 4.8 kg/m² (Table 1). Thirty-six adults (90%) had a mid to high educational level. The majority of adults (n = 27, 67.5%) reported having a sedentary job, while 12 adults (30%) reported a job involving a mix of SB and LPA (e.g., teaching). Only one adult (2.5%) reported having a physically active job. Two adults (5%) had previously completed a PAP-trajectory, rating their satisfaction with the program as very high (8/10). Additionally, 32 adults (80%) reported being unaware of what PAP-coaches were, and six adults (15%) knew about PAP-coaches but expressed no interest in participating in such a trajectory.

Clusters of HCPs’ perceived factors facilitating PA

HCPs generated a total of 125 perceived facilitating factors that were represented in the optimal number of 10 clusters. Table 2 presents the 10 clusters including the cluster names, example answers and mean ratings for effectiveness, feasibility and changeability for each cluster. Figure 1 shows the concept mapping plot for HCPs.

Table 2 Summary of clusters of HPCs’ perceived factors facilitating PA and their corresponding ratings

Fig. 1

Concept map for HCPs

This figure shows the different clusters. The black number represents the number of each answer (see Additional file 1), where the blue numbers represents the cluster number referring to Table 2. Clusters closer together tend be conceptually more similar. For example, Cluster 3 ‘Social networks facilitating PA’ and Cluster 5 ‘Personalized support by healthcare professionals to facilitate PA’ show more conceptually similar concepts. Red: Cluster 1 ‘Everyday activities as opportunities for facilitating PA’. Blue: Cluster 2 ‘Awareness and education of the benefits regarding PA facilitation’. Green: Cluster 3 ‘Social networks facilitating PA’. Purple: Cluster 4 ‘Digital and external tools facilitating PA’. Orange: Cluster 5 ‘Personalized support by healthcare professionals to facilitate PA’. Brown: Cluster 6 ‘Tips to increase habitual PA’. Pink Cluster 7 ‘Enjoyment and practical solutions to facilitate PA’ Grey: Cluster 8 ‘Positive mindset building regarding PA by awareness and education’. Flashy purple: Cluster 9 ‘Motivation and goal setting to facilitate PA’. Flashy pink: Cluster 10 ‘Environmental facilities, financial support and online tools to facilitate PA’

Cluster 6 ‘Tips to increase habitual PA’ scored the highest for both effectiveness (4.3 ± 0.6) and changeability (4.0 ± 0.7), whereas cluster 2, ‘Awareness and education of the benefits regarding PA’ scored highest on feasibility (4.2 ± 0.7) (Table 2). The lowest effectiveness score was assigned to cluster 4 ‘Digital and external tools to facilitate PA’ with a score of 3.5 (± 0.8). Additionally, cluster 10, ‘Environmental facilities, financial support and online tools to facilitate PA’ received the lowest scores for both feasibility (3.4 ± 0.9) and changeability (3.4 ± 1.1) (Table 2). The list of perceived facilitating factors with matching rating scores for each factor can be found in Additional file 1. Visualizations of these results (in bar plots) are available in Additional file 3.

Clusters of adults ‘ perceived factors facilitating PA

Adults generated a total of 143 perceived facilitating factors in the individual brainstorming phase, which were grouped in the optimal number of 10 clusters. Table 3 presents these 10 clusters including the cluster names, example answers and mean ratings for effectiveness, feasibility, changeability and enjoyment. Figure 2 presents the concept mapping plot, showing the different sizes and locations of the clusters.

Table 3 Summary of clusters of adults’ perceived factors facilitating PA and their corresponding ratings

Fig. 2

Concept map plot for sedentary and/or inactive adults

This figure shows the different clusters. The black number represents the number of the answers (see Additional file 2), where the blue numbers represents the cluster number referring to Table 3. Clusters closer together tend be conceptually more similar. For example, Cluster 3 ‘Awareness of the benefits & motivation regarding PA facilitation’, Cluster 4 ‘Everyday activities as opportunities for facilitation pA’ and Cluster 6 ‘Daily routines and goal setting facilitating PA’ are closer together and therefore considered to be conceptually similar. Red: Cluster 1 ‘Work-life balance allowing time for PA’. Blue: Cluster 2 ‘Social networks facilitating PA’. Green: Cluster 3 ‘Awareness of the benefits & motivation regarding PA facilitation’. Purple: Cluster 4 ‘Everyday activities as opportunities for facilitating PA’. Orange: Cluster 5 ‘Home and outdoor ideas to facilitate PA’. Brown: Cluster 6 ‘Daily routines and goal setting facilitating PA’. Pink Cluster 7 ‘Work environment and workplace initiatives facilitating PA’. Grey: Cluster 8 ‘Environmental facilities and financial support facilitating PA’. Flashy purple: Cluster 9 ‘Digital tools and technology facilitating PA’. Flashy pink: Cluster 10 ‘Organizational and stress management to facilitate PA’

Adults provided the highest effectiveness rating for cluster 6, ‘Daily routines and goal setting facilitating PA ‘ (3.8 ± 1.1), the highest feasibility and changeability for cluster 9 ‘Digital tools and technology facilitating PA’ (3.9 ± 0.9; 3.7 ± 1.0 respectively), and the highest enjoyment rating for cluster 10 ‘Organizational and stress management to facilitate PA’ (3.8 ± 0.9) (Table 3). Cluster 7, ‘Work environment and workplace initiatives facilitating PA’ received the lowest effectiveness (3.2 ± 1.3) and enjoyment score (3.2 ± 1.2), whereas cluster 1, ‘Work-life balance allowing time for PA scored the lowest on feasibility and changeability (2.9 ± 1.1; 2.6 ± 1.0 respectively) (Table 3). The list of perceived facilitating factors with matching rating scores for each factor can be found in Additional file 2. Visualizations of these results (in bar plots) are available in Additional file 3.

Similarities and differences in perceived factors facilitating PA between HCPs and adults

Similar perceived facilitating factors were found within the following clusters for both groups: ‘Everyday activities as opportunities for facilitating PA’ and ‘Social network facilitating PA’. The cluster ‘Everyday activities as opportunities for facilitating PA’ (Cluster 1, HCPs; Cluster 4, adults) included answers such as: stimulation of active transport for short distances; take the stair instead of the elevator; walking during lunchbreak. The cluster ‘Social networks facilitating PA’ (Cluster 3, HCPs; Cluster 2, adults) mentioned factors such as the relevance of engaging in PA with other people, such as a buddy to be physically active with, group lessons, or being guided by a HCP.

Other similarities for the perceived facilitating factors of HCPs and adults were found between the clusters ‘Digital and external tools facilitating PA’ (Cluster 4, HCPs) ‘Environmental and online tools facilitating PA’ (Cluster 10, HCPs) and ‘Digital tools and technology facilitating PA’ (Cluster 9, adults). Examples of similar perceived facilitating factors between groups include a tracking system such as an app to gain insights into PA and SB, real time feedback on behaviors and reminders to engage in PA.

Both HCPs and adults identified perceived facilitating factors regarding affordable PA options and attractive outdoor PA opportunities, and clustered them in slightly different clusters such as ‘Environmental facilities, financial support and online tools facilitating PA’ (Cluster 10, HCPs) and ‘Environmental facilities and financial support facilitating PA’ (Cluster 8, adults).

The perceived facilitating factors in the HCPs’ cluster ‘Motivation and goal setting to facilitate PA’ (Cluster 9, HCPs), and cluster ‘Awareness and education of the benefits regarding PA facilitation’ (Cluster 2, HCPs) were overlapping with clusters identified by the sedentary and/or inactive adults such as the cluster ‘Daily routine and goal setting to facilitate PA’ (Cluster 6, adults) and the cluster ‘Awareness of the benefits & motivation regarding PA facilitation’ (Cluster 3, adults). Similar factors reported by both groups include: setting daily PA goals with a focus on small achievable goals, raising awareness of the positive effects of being more physically active and create a reward system for yourself.

Alongside the similarities, there were notable differences in the clusters identified between HCPs and adults. Unique clusters formulated by the HCPs focused on positive mindset building regarding PA, exploring enjoyment of PA, practical solutions to facilitate PA, tips to increase habitual PA, and personalized support provided by a HCP. However, adults mentioned similar individual factors related to these clusters mentioned by the HCPs, but grouped them within broad clusters (Cluster 2, 3, 8, adults). Examples of similar factors between the two groups were: trying out different types of sports to explore what you like, making PA enjoyable, exercise programs or coaching by an external persons, awareness why you want to be more active. Next, unique clusters mentioned by adults were related to work-life balance, work environment, home environment, and organizational and stress management. Nevertheless, HCPs mentioned similar individual work- and home-related factors but sorted them in broader clusters (Cluster 4, 7, 10 HCPs). Examples of similar factors between both groups include: practical adjustments within the home environment to stimulate more PA, a work environment encouraging PA such as providing standing desks, nudges to take the stairs, and walking meetings. Figure 3 presents the similarities and differences in clusters between HCPs and adults.

Fig. 3

Comparison of clusters between HCPs and adults

The circles represent the clusters for the sedentary and/or inactive adults (bold line) and the HCPs (dotted line). The intersection includes the identical clusters. The overlapping but not fully identical clusters are presented within the boxes. The clusters are listed in a random order. Cluster numbers are presented in between brackets

Discussion

This concept mapping study is the first to explore the perceived facilitating factors regarding the transition from SB to (more) PA from the perspective of HCPs (both PAP-coaches and physiotherapists without PAP-accreditation) and adults who self-reported themselves as sedentary and/or inactive. The most optimal number of clusters for each group was 10, of which the clusters ‘social networks facilitating PA’ and ‘everyday activities as opportunities to facilitate PA’ were identified by both groups. Several other clusters partially overlapped, including factors such as awareness of PA benefits, daily routines, goal setting, environmental facilities, financial support, and digital tools. Unique to the adults' group were clusters related to work-life balance, the work environment, the home environment, organization, and stress management. Conversely, clusters focusing on personalized support from HCPs, enjoyment, and fostering a positive mindset regarding PA were distinctive to the HCPs. Similar to other concept mapping studies, a score of 3 out of 5 on the Likert scale was considered relevant across the various dimensions [20]. Among HCPs, all clusters received a score above 3 for all different rating dimensions, i.e. effectiveness, feasibility and changeability. For adults, most clusters scored above 3, with some exceptions. For example ‘work-life balance allowing time for PA’ and ‘work environment and workplace initiatives facilitating PA’, scored lower than 3 on feasibility and changeability. ‘Environmental facilities and financial support facilitating PA’, scored lower than 3 on changeability. Among adults, the most effective cluster was ‘daily routines and goal setting facilitating PA.’ The most feasible and changeable clusters were ‘home and outdoor ideas to facilitate PA’ and ‘digital tools and technology facilitating PA’. Additionally, the most enjoyable factors for adults were associated with ‘home and outdoor ideas’ and ‘organizational and stress management to facilitate PA’. For HCPs, the most effective and changeable cluster was ‘tips to increase habitual PA’. The highest feasibility scores were attributed to ‘awareness and education of the benefits regarding PA facilitation’.

Some clusters with facilitating factors identified in the present study partially aligned with other studies using both qualitative and quantitative methods [21, 22]. Goal setting, social influence, availability of PA stimulating environmental factors and resources, beliefs of PA benefits, and enjoyment were mentioned as influencing factors of PA among middle aged to older adults as well as adults in clinical practice who suffered from illnesses that their general practitioner believed could be positively impacted by PA [21, 22]. Other similar facilitating factors were found in research exploring the factors of engaging in PA when guided by a PAP-coaches [23]. These were linked with the feeling of having access to activities, having a balanced work-life situation, having social support regarding PA, finding activities that encourage continuation of PA, the desire to improve your health condition, tailoring activities to individuals’ capacity and account for earlier experiences of PA [23]. Despite these similarities, only two of the 40 adults in the present study completed a PAP-trajectory, six adults knew about the existence of PAP but expressed no interest and the remaining 32 adults reported being unaware of what the PAP-trajectory entails.

Ratings of effectiveness, feasibility, and changeability were generally higher in HCPs compared to adults. This might be explained by a difference in perspective between the two groups as they reflect all facilitating factors within their own framework of expertise or abilities. HCPs included in this study (PAP-coaches (n = 7) and physiotherapists without PAP-accreditation (n = 3)) were familiar with stimulating behavior change in both adults who are or are not ready for behavior change. As a result, their broader professional experience and typical clients may have influenced how they rated the statements based on what they perceive as successful [15, 16]. HCPs are trained to view change as a gradual, modifiable process, and may be more optimistic about what is effective, feasible and changeable. In contrast, adults may base their evaluations more heavily on personal experience, perceived barriers, and past struggles with increasing PA, potentially leading to more reserved ratings. Therefore, the HCPs’ higher scores may reflect both their broader behavioral change experience and their potential professional bias. Moreover, previous research has shown that adults with greater preparedness and confidence in their readiness to change achieved higher levels of PA after following the PAP-trajectory compared to those who were not yet ready to change [16]. This might imply that the factors influencing behavior change may differ between adults who are prepared to make changes and those who are not yet contemplating such changes.However, the readiness to change behaviors was not assessed in the present study.

The perceived facilitating factors mentioned in this study present the different socio-ecological levels, suggesting a multilevel challenge for supporting these facilitating factors [24]. First, the intrapersonal level was represented by clusters such as ‘awareness of benefits and motivation regarding PA facilitation’, ‘motivation and goal setting facilitating PA’, ‘awareness and education of the benefits regarding PA facilitation’, and ‘positive mindset building regarding PA by awareness and education’. At the interpersonal level, clusters such as ‘social networks facilitating PA’ and ‘personalized support by healthcare professionals to facilitate PA’ could be identified. The environmental level contains both the micro environment represented by the cluster ‘work environment and workplace initiatives facilitating PA’ and the macro environment which is represented by the cluster ‘environmental facilities and financial support facilitating PA’ including factors such as green and attractive neighborhood, or sport facilities in the neighborhood. These findings are in line with a European concept map presenting facilitating factors of PA created by a top-down approach consulting researchers and policy experts. They emphasized clusters with facilitating factors of PA on different socio-ecological levels such as intrapersonal context and well-being (intrapersonal level), social support and supportive environments to facilitate PA (interpersonal level) [12]. These policy experts and researchers additionally identified a broader perspective within the socio-ecological model, focusing on policy-level factors aligned with their expertise including policy frameworks (e.g. city planning, leisure activity subsidy, financial measures and regulation of PA and sports), cultural contexts and media influence (e.g. advertisement, social media, cultural traditions) [12]. In the present study, clusters more situated on the micro and macro environmental and policy level received a lower rating on feasibility and changeability. This might be explained by the fact that the mentioned facilitating factors are often beyond the participants' control to modify. Examples of factors within these clusters include employer financial support for PA (e.g. time, equipment, or sponsored PA activities), the implementation of universal bike-sharing systems in Belgium, accessible and attractive green spaces, and affordable local sports facilities. Despite their lower scores on feasibility and changeability, the effectiveness ratings were higher for both groups (3.1–3.4).

Approximately 67.5% of the adults recruited in this study were classified as overweight or obese which is a population often characterized by higher SB levels and reduced PA levels [25]. A systematic review of qualitative studies exploring the PA and SB perceptions of overweight and obese adults revealed similar factors facilitating PA such as social interaction, goal setting, perceived health benefits, and having more time available [25]. Moreover, the review showed that technology was often identified as a barrier to PA enhancement. This might be in line with results of the present study as digital tools and technology were (compared to own rated clusters) scored by the adults as rather lower effective and enjoyable. Conversely, the scores on feasibility and changeability were rather higher compared to their own rated clusters [25].

Practical implications

Integrating the perspectives of both HCPs and sedentary and/or inactive adults as well as examining the differences and similarities in clusters of perceived factors facilitating PA offers valuable insights for enhancing PA-coaching. HCPs can benefit from understanding adults' framework, enabling them to incorporate their perspectives into their daily practice [16]. Some of the factors (e.g. set individual goals, awareness raising on the benefits of PA, create an action plan to deal with barriers related to PA) can be addressed through behavior change techniques focusing on socio-cognitive variables within the intrapersonal level whereas other factors regarding the different socio-ecological levels (e.g. universal biking system, PA facilities in the neighborhood, PA equipment at the workplace) are partially within the control of other stakeholders such as employers or policy makers [26, 27]. Additionally, given that the working population spends a significant portion of their time at work, it presents an opportunity to implement initiatives (both individual and environmental modification) that support increased PA [27, 28].

Moreover, as a similar cluster between HCPs and sedentary and/or inactive adults referred to ‘everyday activities as opportunities for PA’, as well as a adults’ cluster referring to ‘work-life balance allowing time for PA’, a new concept such as Vigorous Intermittent Lifestyle Physical Activity (VILPA) could be a promising and time-efficient approach to increasing PA levels [10]. Unlike traditional forms of MVPA that often require dedicated time, preparation, or access to facilities, VILPA consists of brief, vigorous bursts of incidental PA lasting 1–2 min, integrated seamlessly into daily routines such as running up the stairs, brisk walking to work, walking uphill, playing actively with children, vigorous household work, lifting groceries. Recent epidemiological research has demonstrated that even these short, intense bouts can yield significant health benefits such as a decrease in all-cause mortality and cardiovascular risk factors [10]. VILPA’s practicality and effectiveness make it an appealing strategy for promoting health in time-constrained populations, potentially addressing common barriers such as lack of time, convenience, and accessibility. Future research should focus on understanding the preferences of sedentary and/or inactive adults for integrating VILPA into their daily routines.

Strengths and limitations

The concept mapping technique uniquely combines qualitative and quantitative methods, making it an ideal approach for gaining comprehensive insights into the perspectives of both HCPs and sedentary and/or inactive adults on facilitating less SB and more PA. This dual perspective integrates insights from adults who self-reported them as sedentary and/or inactive, offering expertise on their own behaviors, as well as from HCPs, whose daily practice provides valuable understanding of strategies to support and activate this population. Moreover, this is the first study including Belgian PAP-coaches, a group whose primary role is to support individuals toward increased PA [5, 7]. Their inclusion provides practice-based insights into the perceived effectiveness, feasibility and changeability of facilitating factors to shift SB into (more) PA. These findings can inform PAP-coaches as well as other professionals with similar responsibilities. Additionally, the findings can guide intervention development where PAP-coaches may serve as key-stakeholders in the co-development and implementation process of the intervention. Next, we adhered to the statistical clustering output by the software RCMap, refraining from moving statements between clusters to achieve a better fit. While some concept mapping research permits such adjustments, we argue that following the statistical method is more appropriate in this context, as we were unable to reassign statements in consultation with the participants. A limitation is that we were unable to objectively measure participants' PA and SB using accelerometers. Therefore, we asked participants to self-report about complying with both PA and/or SB guidelines which is less optimal compared to an objective measurement [2]. Another limitation was the recruitment challenge among HCPs. Only 10 HCPs participated, of which 9 HCPs completed both the group brainstorm and sorting and rating phases. Similarly, recruitment was challenging among the adult participants as we initially aimed to organize group brainstorm sessions with a similar format as for the HCPs. As recruitment remained low, we changed the strategy of the group brainstorm phase into an individual brainstorm via an online questionnaire allowing participants to join at the best suitable time of the day. This individual brainstorm technique is less appropriate compared to the group brainstorming techniques, which typically encourages participants to generate novel ideas about facilitating factors during the group session. Next a more diverse sample regarding sex and educational level would be appropriate as in the study there was a slight overrepresentation of women (60% adults) and almost 90% had a high educational level. Last, this paper focused on a working population where almost all adults reported to have a sedentary job. As many clusters referred to job related factors such as work-life balance and PA at the work environment, it may be of interest to focus in future research on a sedentary and/or inactive adult population which is non-employed.

Conclusion

According to HCPs including PAP-coaches and physiotherapists as sedentary and/or inactive adults, clusters with factors to facilitate the transition from SB to PA were mentioned according to the different socio-ecological levels. Similarities between the two groups included clusters with facilitating factors as social networks, everyday activities as opportunities for facilitating PA, awareness of PA benefits, motivation regarding PA facilitation, integration of PA into daily routines, goal-setting to facilitate PA, environmental facilities, financial resources, and technology, digital tools and external tools to facilitate PA. Moreover, each group created unique clusters such as factors related to the work environment mentioned by the sedentary and/or inactive adults and factors related to tailored support to facilitate PA by HCPs mentioned by the HCPs. Both groups highlighted factors within the intra- and interpersonal level that were rated as effective, feasible and changeable, as well as environmental factors at the responsibility of external stakeholders such as employers or policy makers which were perceived as less feasible and changeable. These results highlight that future interventions aimed at shifting SB into PA will need to address a multilevel challenge for supporting these PA facilitating factors.