This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.
Low dietary intake of fruits and vegetables and physical inactivity are 2 modifiable risk factors for cardiovascular disease. Fruit and vegetable gardening can provide access to fresh produce, and many gardening activities are considered moderate physical activity. This makes gardening interventions a potential strategy for cardiovascular disease risk reduction. Previously developed gardening interventions have relied on in-person delivery models, which limit scalability and reach.
The purpose of this study was to ascertain participant insight on intervention components and topics of interest to inform a digitally delivered, gardening-focused, multiple health behavior change intervention.
A web-based survey was delivered via Amazon Mechanical Turk (MTurk), including quantitative and open-ended questions. Eligible participants were aged ≥20 years, could read and write in English, were US residents, and had at least a 98% MTurk task approval rating. A multilevel screening process was used to identify and exclude respondents with response inattention, poor language fluency, or suspected automated web robots (bots). Participants were asked about their interest in gardening programming, their preferences for intervention delivery modalities (1-hour expert lectures, a series of brief <5-minute videos, or in-person meetings), and what information is needed to teach new gardeners. Comparisons were made between never gardeners (NG) and ever gardeners (EG) in order to examine differences in perceptions based on prior experience. Quantitative data were summarized, and differences between groups were tested using chi-square tests. Qualitative data were coded and organized into intervention functions based on the Behavior Change Wheel.
A total of 465 participants were included (n=212, 45.6% NG and n=253, 54.4% EG). There was a high level of program interest overall (n=355, 76.3%), though interest was higher in EG (142/212, 67% NG; 213/253, 84.2% EG;
In a sample of potential web-based learners, participants were interested in a digitally delivered gardening program. They preferred brief videos for content delivery and suggested content topics that encompassed how to garden from planting to harvesting and cooking. The next step in this line of work is to identify target behavior change techniques and pilot test the intervention to assess participant acceptability and preliminary efficacy.
Unhealthy lifestyle behaviors such as low fruit and vegetable intake and low levels of physical activity are associated with cardiovascular disease (CVD), the leading cause of premature death in the United States [
Food gardening (herein referred to as gardening) has drawn the attention of public health advocates given the many investigations that demonstrate its general health benefits [
The vast majority of published studies that teach gardening skills to adults have relied on synchronous, in-person delivery models [
While there are many potential benefits for gardening and using digital intervention delivery modes, there is a need in the larger body of behavioral eHealth literature to document the intervention development process and to integrate behavioral theory [
In this study, we begin at phase Ia of the ORBIT model and solicit input from a pool of potential web-based learners to gain insight on perceived needs and candidate intervention components. We included both never gardeners (NG) and ever gardeners (EG) to identify how perceptions of needs may differ based on prior experience. We used quantitative and open-ended survey questions to ascertain participant preferences for intervention delivery, topics of interest, and potential information resources that they would like to see included in a future intervention. Next, we categorized the participant comments using the COM-B (capability, opportunity, motivation, behavior) framework and intervention functions from the Behavior Change Wheel (eg, education, training, enablement) [
Participants were Amazon Mechanical Turk (MTurk) workers who were recruited to complete human intelligence tasks (HITs) through the MTurk site in 7 batches between November 2020 and January 2021. Participants were required to be aged 20 years or older, read and write in English as their primary language, live in the United States, and have at least a 98% task approval rating from completing previous tasks. If eligible, participants were provided the survey link and were required to reach the end of the survey in order to receive a completion code. The code was the product of their Research Electronic Data Capture (REDCap; Vanderbilt University)–generated participant ID multiplied by a random number (ie, 117) and was thus unique to each participant. In addition, precautions were taken to ensure that the same worker did not take the survey multiple times, whereby workers were marked with a custom qualification through the Amazon platform, which then restricted potential workers in future batches to only those who had not previously completed the survey. Once the survey was completed, participants were further screened through a 2-level process for response inattention, thoroughness, English language fluency, and automated web robot (bot) responses [
Prior to approving the HIT, survey responses underwent a first-level review and were only approved for compensation if (1) the worker provided the study team with a valid completion code that was associated with the correct participant ID, (2) the participant provided at least one coherent response to the open-ended questions (eg, responses were not nonsensical, were broadly relevant to the question, and words were in the appropriate order), and (3) the open-ended responses were not identical to a sentence published on the internet that was identified through a Google search (indicating a bot response).
Once the HITs were approved, a second-level screening occurred, which included checking for consistent responses using the following criteria: (1) age and gender demographic information was complete, and (2) open-ended responses to gardening-related questions were rereviewed based on criteria 2 and 3 from the first-level screening.
Participants were grouped according to self-reported experience with gardening based on the following question: “Have you ever participated in vegetable gardening in your lifetime? Yes or no.” Those who responded “no” were labeled as NGs, and all others were considered EGs. Participants completed basic demographic questions including age, race (self-selected), Hispanic or Latino ethnicity (yes or no), sex (self-selected, male or female), and education level. Participants were asked if they had received Supplemental Nutrition Assistance in the past year (yes or no) and answered the 6-item US Department of Agriculture (USDA) food security screening questionnaire [
Health-related characteristics were reported using questions from the Behavioral Risk Factor Surveillance System Survey (BRFSS) [
We asked 6 questions about participant perceptions of homegrown fruits and vegetables and garden space availability. Perceptions of homegrown foods were assessed with 4 items: “Do you think homegrown fruits and vegetables [taste different; differ in cost; differ in quality; differ in appearance] compared to store bought fruits and vegetables? [Yes or no].” If yes, participants were further asked to choose one of 2 options: “I think homegrown fruits and vegetables [taste better or taste worse; cost less or cost more; are higher quality or are lower quality; have a better appearance or have a worse appearance] than store-bought fruits and vegetables.” Questions about garden space included a root question asking: “Where you live now, do you have any space outside where you can grow fruits, vegetables, and herbs? [yes or no]” and “Is there space for you to grow plants in pots? [yes or no]” with responses to describe this outdoor space as “in pots on a patio or deck, in pots on a balcony, or in pots on the ground.” Responses were categorized as no outside space versus any.
Three cooking-related constructs were assessed, including food agency, cooking skills, and food skills [
To assess interest in participating in a gardening program, participants were asked the following question using a 3-option choice format used in previous clinical studies to gauge interest in future programs [
Participants were asked to complete 3 open-ended questions to understand what information they would consider important in a gardening program, including: (1) “If we were going to create a gardening program, what kinds of things would you like to see included?”; (2) “What information do you think you (or someone who is new to gardening) would need to start a vegetable garden on your (their) own?”; and (3) “What types of information sources would you most want [were helpful for you] to learn about how to start a vegetable garden?” One additional question also asked what sources of information participants would use to learn about gardening.
We began the analysis of the qualitative open-ended questions with 2 investigators (SV and MWZ) independently reviewing all the participant responses. Using the constant comparative method, we identified words or phrases with like meaning and unitized these phrases with codes [
This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Penn State University Institutional Review Board (protocol #15352, approved June 14, 2020). All participants were provided a written summary explanation of the research and completed informed consent prior to providing study data. All study data were collected using REDCap [
A total of 808 MTurk workers started the survey and provided a completion code. Of them, 91 were excluded based on the first level screening review. During the second level review, an additional 250 were excluded owing to a combination of missing demographics (n=244) and inconsistent responses (n=8), leaving an analytic sample of 465 participants (212 NG and 253 EG). Participants resided in 43 states within the continental US. The sociodemographic characteristics, health-related outcomes, perceptions of home-grown fruits and vegetables, and cooking skills of the sample overall and by group are presented in
Overall, 72.3% (n=336/465) of participants had outdoor space available for gardening (either in containers or in the ground) although this varied by gardening group (NG: 120/212, 56.6%; EG: 216/253, 85.4%;
Sociodemographic and health-related characteristics of study participants overall and comparisons between Never Gardeners (NG) and Ever Gardeners (EG).a
Characteristic or measure | Overall (N=465) | NG (N=212) | EG (N=253) | ||||||
|
|||||||||
|
Age (years), mean (SD) | 40.2 (11.8) | 36.9 (9.8) | 43.0 (12.7) | <.001 | ||||
|
Female, n (%) | 219 (47.2) | 92 (43.4) | 127 (50.2) | .14 | ||||
|
White, n (%) | 371 (79.8) | 161 (75.9) | 210 (83.0) | .06 | ||||
|
Hispanic or Latino ethnicity, n (%) | 38 (8.2) | 22 (10.4) | 16 (6.3) | .11 | ||||
|
Married or partnered, n (%) | 316 (68.0) | 144 (67.9) | 172 (68.0) | .99 | ||||
|
Bachelor’s degree or higher, n (%) | 315 (67.7) | 148 (69.8) | 167 (66.0) | .38 | ||||
|
Low or very low food security, n (%) | 205 (43.9) | 102 (48.1) | 101 (39.9) | .08 | ||||
|
Received SNAPc (past year), n (%) | 151 (32.5) | 64 (30.2) | 87 (34.4) | .34 | ||||
|
|
.001 | |||||||
|
|
Urban | 201 (43.2) | 100 (47.2) | 101 (39.9) |
|
|||
|
|
Suburban | 194 (41.7) | 94 (44.3) | 100 (39.5) |
|
|||
|
|
Rural | 70 (15.1) | 18 (8.5) | 52 (20.1) |
|
|||
|
|||||||||
|
In good health or better | 428 (92.0) | 196 (92.5) | 232 (91.7) | .77 | ||||
|
BMI≥25 (kg/m2) | 207 (48.6) | 107 (53.8) | 100 (44.1) | .045 | ||||
|
Current smoker | 101 (21.7) | 42 (19.8) | 59 (23.3) | .36 | ||||
|
Prediabetes or diabetes | 45 (9.7) | 16 (7.6) | 29 (11.5) | .16 | ||||
|
High cholesterol | 62 (13.3) | 26 (12.3) | 36 (14.2) | .54 | ||||
|
History of coronary artery disease | 16 (3.4) | 6 (2.8) | 10 (4.0) | .51 | ||||
|
History of heart attack or angina | 14 (3.0) | 6 (2.8) | 8 (3.2) | .84 | ||||
|
History of stroke | 19 (4.1) | 9 (4.3) | 10 (4.0) | .87 | ||||
|
History of deep vein thrombosis | 20 (4.3) | 9 (4.3) | 11 (4.4) | .96 | ||||
|
Family history of early CVDd | 78 (16.8) | 29 (13.7) | 49 (19.4) | .10 | ||||
|
With at least 1 risk factor for CVD | 342 (73.6) | 156 (73.6) | 186 (73.5) | .99 | ||||
|
|||||||||
|
Home-grown taste better | 340 (72.8) | 123 (58.2) | 215 (85.0) | <.001 | ||||
|
Home-grown cost less | 300 (64.2) | 118 (55.7) | 182 (71.9) | .001 | ||||
|
Home-grown F&V are higher quality | 324 (69.4) | 117 (55.2) | 205 (81.0) | <.001 | ||||
|
Home-grown F&V have a better appearance | 188 (40.3) | 63 (29.7) | 123 (48.6) | <.001 | ||||
|
|||||||||
|
|
12.2 (2.4) | 11.7 (2.3) | 12.7 (2.5) | <.001 | ||||
|
|
Cooking attitudes | 4.0 (1.0) | 3.8 (0.96) | 4.2 (1.00) | <.001 | |||
|
|
Structural skills | 3.1 (1.0) | 2.9 (0.92) | 3.1 (1.01) | .02 | |||
|
|
Functional skills | 5.1 (1.0) | 4.8 (1.06) | 5.3 (0.89) | <.001 | |||
|
Mean cooking skills score | 73.7 (15.6) | 68.8 (17.1) | 77.8 (13.0) | <.001 | ||||
|
Mean food skills score | 102 (18.7) | 96.4 (19.9) | 106.7 (16.1) | <.001 |
aNG reported 0 years of gardening experience, EG reported 1 or more years of gardening experience.
bChi-square tests and independent
cSNAP: supplemental nutrition assistance program.
dCVD: cardiovascular disease.
eF&V: fruits and vegetables.
The frequency and ranking of the participants' comments from the qualitative, open-ended questions, organized by intervention function and group are presented in
Frequency of participant comments ranked by the percentage of total comments for Never Gardeners (NG) and Ever Gardeners (EG) organized by the capability, opportunity, motivation, and behavior (COM-B) model, and linked to the Behavior Change Wheel intervention functions from Michie et al [
Participant response summary and illustrative comments | NG,a Rank | NG, n (%) | EG,a Rank | EG, n (%) | |
|
|||||
|
1 | 85 (26.0) | 1 | 93 (18.1) | |
|
2 | 55 (16.8) | 2 | 91 (17.7) | |
|
3 | 35 (10.7) | 3 | 82 (15.9) | |
|
4 | 28 (8.6) | 4 | 51 (9.9) | |
|
5 | 21 (6.4) | 7 | 31 (6.0) | |
|
7 | 18 (5.5) | 12 | 10 (1.9) | |
|
8 | 16 (4.9) | 6 | 35 (6.8) | |
|
9 | 13 (4.0) | 5 | 44 (8.5) | |
|
11 | 9 (2.8) | 10 | 15 (2.9) | |
|
|||||
|
14 | 3 (1.0) | 14 | 0 (0) | |
|
|||||
|
6 | 19 (5.8) | 9 | 21 (4.1) | |
|
|||||
|
10 | 12 (3.7) | 8 | 25 (4.9) | |
|
|||||
|
12 | 9 (2.8) | 11 | 13 (2.5) | |
|
13 | 4 (1.2) | 13 | 4 (0.8) |
This ORBIT phase Ia study solicited input from potential web-based learners to inform the development of a future digitally delivered, MHBC gardening intervention. The majority of participants expressed interest in the intervention topic area and indicated that a web-based format using a series of brief videos would be preferable over webinar-based lectures or in-person models. Participants also suggested the inclusion of 6 different intervention functions based on the COM-B framework from Michie et al [
Capability involves an individual’s knowledge or skills and can be described as the psychological or physical capacity to engage in a particular behavior [
The participants identified cooking knowledge and skills as areas of interest and this is encouraging since preparing garden produce is a necessary prerequisite to consuming it. Other investigators have found that both gardening and cooking are independently and positively associated with higher-quality diets [
Opportunity in the COM-B model includes the factors that lie outside of the individual and can encompass both physical and social aspects [
When considering space limitations, it is tempting to assume that gardeners are less likely live in urban settings. However, in a representative sample of US adults, Kegler et al [
In this study, both NG and EG identified small-space gardening as a topic of interest. However, it was not clear from their responses how small these spaces might be (ie, a pot of thyme on a windowsill or a network of pots on a balcony). Given that our interest is in the potential for gardening to improve CVD outcomes, future work is needed to understand these comments and to examine if small-space gardening can provide meaningful amounts of fruits and vegetables or physical activity.
Finally, motivation in the COM-B model includes internal processes that energize and direct behavior [
In this study, EG commented on the personal satisfaction that arises from gardening by stating “how nice it is” when you “get to pick the vegetables.” They also valued garden produce and were significantly more likely to rate garden produce higher than store-bought produce for characteristics such as cost, flavor, and quality. Taken together, these findings suggest that a future gardening intervention could be positioned to appeal to non-health–related values; an approach that has been called a stealth intervention [
After education and training, the next largest proportion of participant suggestions was related to environmental restructuring of the social environment including social support, group interaction, discussions, and feedback. These comments raise the question as to whether a digitally delivered intervention needs to include in-person components or whether digital person-to-person interactions are adequate. Santarossa et al [
This study has limitations and strengths. Previous investigators have noted some concerns about the use of the MTurk platform for survey distribution [
Finally, our results are based on the acceptability of a hypothetical intervention. It is possible that what participants want in an intervention may not be practically feasible to deliver, may not fit what is known by behavioral scientists to optimize behavior change, or may not confer cardiovascular benefits (ie, small space gardening). In addition, gardening is complex and relies not only on individual behavior change, but is also dependent on the external environment (eg, soil conditions, weather, and pests) that may be outside the control of an individual or what an intervention can address. Future work is needed to develop an intervention that balances what can practically be delivered with fidelity in a digital environment, the recommendations made by the participants in this report, and behavior change strategies known to be effective.
In this ORBIT Phase 1a study, potential web-based learners were interested in a digitally delivered gardening program, they preferred brief videos for content delivery (<5 minutes), and they suggested content topics that encompassed how to garden from planting to harvesting and cooking. Participant comments and health behavior theory support incorporating opportunities for gardening knowledge and skill development while also fostering social support and highlighting the enjoyable and personally satisfying aspects of gardening. The next step in this line of work is to identify target behavior change techniques, develop an intervention delivery strategy that engages these targets, and conduct pilot testing to assess participant acceptability and preliminary efficacy of the newly developed intervention.
Behavioral Risk Factor Surveillance System Survey
capability, opportunity, motivation, behavior
cardiovascular disease
ever gardeners
human intelligence tasks
multiple health behavior change
Mechanical Turk
never gardeners
Obesity-Related Behavioral Intervention Trials
US Department of Agriculture
The authors would like to thank the study participants as well as Young Ho and Crystal Lovelace who contributed to processing the data and managing the MTurk platform. This study was funded by the Penn State College of Medicine, Office of Faculty Development. SV is funded by the National Center for Advancing Translational Sciences, Grant KL2 TR002015 and Grant UL1 TR002014. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
The data sets generated during or analyzed during this study are available from the corresponding author upon reasonable request.
None declared.