One significant factor in facilitating students’ career intentions and persistence in STEM  fields is targeting their interests and motivation before eighth grade. To reach students at this critical stage, a design-based afterschool STEM program, titled Studio STEM, was implemented to foster motivation and engagement in STEM topics and activities. The purpose of this study is twofold: (a) to investigate how Studio STEM affected students’ beliefs about science and whether these beliefs differed from their peers who did not participate in the program, and (b) to examine a case study of one Studio STEM implementation to investigate elements of the curriculum that motivated students to engage in the program.After completing two Studio STEM programs, participants’ ratings of their values for science and science competence were higher than those of non-participants. In addition, the Studio STEM participants’ motivational beliefs about science and intentions to pursue a college degree were more resilient over time than their peers. We also found that students could be motivated in a voluntary afterschool program (Studio STEM) in which they grappled with STEM concepts and activities, and could verbalize specific program elements that motivated them.Through this study, we found that students could be motivated in Studio STEM and that the experience had a positive impact on their perceptions about science as a field. Importantly, Studio STEM appeared to halt the decline in these students’ motivational beliefs about science that typically occurs during the middle school years, indicating that afterschool programs can be one way to help students maintain their motivation in science. Studying the program features that the students found motivating may help educators to make connections between research and theory, and their classroom instruction to motivate their students. Studio STEM, was implemented to foster students’ motivation and engagement in STEM topics and activities. Studio STEM used design-based activities in an afterschool program to provide instruction in STEM topics and concepts, with a focus on science and engineering. Initial studies of this program  worked with students who learned skills and facts as they progressed through the process of solving a problem using a design approach. All of the problems involved a “Save the Animals” theme, which was intended to interest middle school youth by showing them how their energy-related behaviors could affect animals all over the world. For example, using electricity produced by burning coal can increase the levels of carbon dioxide in the atmosphere, which can have effects on animals  is useful for their short- or long-term goals, or in the real world Jones, . To helpThe success component of the MUSIC model includes teaching strategies that help students believe that they can succeed if they put forth the appropriate effort Jones, . TeacherThe interest component of the MUSIC model pertains to instructional strategies that stimulate interest in the academic activity, content, or domain Jones, . InteresInstructional strategies related to the caring component of the MUSIC model are aimed at creating a learning environment in which students feel that their instructors and classmates care about their learning and general well-being Jones, . InstrucWe collected two convenience samples. The first sample, which was used for the quantitative analyses addressing RQ1, included both Studio STEM participants and non-participants from two rural, low-income (Title I) K-7 schools in Southwest Virginia. The schools were located in the same rural county. We collected longitudinal quantitative data from 102 fifth-, sixth-, and seventh-grade students who were not enrolled in Studio STEM . We also collected longitudinal quantitative data from 19 fifth-, sixth-, and seventh-grade students who completed both the Save the Penguins curriculum and the Save the Seabirds curriculum between the years 2012 and 2013 . To collect this sample, we surveyed all of the fifth-, sixth-, and seventh-grade students who were in attendance at the schools on the days that the survey was administered. We only included students in this sample who were present at both data collection points (December 2011 and May 2013), which we describe in more detail in another section.The second sample was used for RQ2 and included students who participated in Studio STEM at one of the K-7 schools. We collected qualitative interview data and quantitative self-report data about Studio STEM from 14 students who were enrolled in one Save the Seabirds unit during the spring 2013 semester . This sample represents an 87.5% response rate from that Studio STEM group, and includes all students present during the Studio STEM session on the day of data collection.Week 1: To provide a rationale for the program, the students watched a presentation about oil rigs and their effects on animals and the environment, and then watched a video about how solar cells work. Then, the students began working on their storyboards, which detailed their experiences and learning throughout the program.Week 2: The students watched and listened to a presentation on the basics of solar cells and how they work. Then, they completed a Studio STEM web-quest activity, and discussed the videos on the web-quest with their partners. Finally, in pairs, they examined real solar cells by studying their electrical energy using multi-meters, and then examined how solar cells work by studying them in parallel.Week 3: The students completed the multi-meter activity with their partners and then reviewed the concept of force. Then, they worked with their partners using a motor, gears, solar panels or batteries, a cup, and some string to design a device that could pull up a cup of cubes, and measured the number of cubes they could pull up at one time.Week 4: The students first reviewed what they learned so far in the program , and then worked with their partners to finish using a motor, gears, solar panel(s), and some string to pull up a cup of cubes; however, this time they used different sized gears. When finished, they researched the word “friction” in the computer lab.Week 5 (data collection day): The students reviewed the previous week’s activity, the purpose of the program, and the science and engineering concepts they had learned thus far. Next, there was a presentation about friction, what it is, and what it does. Then, the teacher showed the students what happens to a toy car when the wheels are wrapped with either sand paper (high friction) or wax paper (low friction), and raced the cars on the floor of the classroom. Next, the students started designing their own solar cars with their partners. When they completed their designs, they used a lamp to see if the car moved. If it did not move, they re-designed.Week 6: The students continued to design and re-design their solar powered cars, and tested their cars’ movement with handheld lamps.The students participated in the Save the Penguins and Save the Seabirds curricula for 90 minutes after school once a week for a duration of 6 weeks; however, two groups of students participated in Save the Seabirds over 12 weeks, which afforded them more time to complete the activities. To answer RQ2, we examined how the Save the Seabirds curriculum affected students’ motivation and engagement. Therefore, we provide more details about the specific implementation of this curriculum in the following outline of the topics and activities.We triangulated qualitative and quantitative data regarding students’ beliefs about Studio STEM to develop a deeper understanding of their motivation and engagement. In addition, we collected and analyzed data regarding Studio STEM students’ beliefs about science in general. We posit that our sample size, although limited, is sufficient due to our concentration on qualitative methods . In other words, 121 students completed the questionnaire at both time points. Any student who did not complete the questionnaire fully at both time points was not included in our analyses. This questionnaire assessed students’ intentions to attend college and their expectancy and value beliefs  in the two schools who were present when data were collected during school hours completed a Science Questionnaire in December 2011 and May 2013 . Our measure of science utility value assessed the extent to which the students perceived that science was useful for their futures and present lives, and is based on the measure Hulleman et al. (α = .72). We measured science competence perceptions  with three items that Eccles and her colleagues have often used, and that have been found to be both valid ; “How good at science are you?” ; “How have you been doing in science this year?” .We measured the students’ intentions to attend college using one item (“I plan on attending college”). Due to conceptual similarities between  Eccles, , our mea Eccles,  altered  Eccles,  to asses Eccles,  and relin et al.  found to® Model of Academic Motivation Inventory . The MUSIC Inventory measures students’ perceptions of the five MUSIC model components , and we adapted it to reflect the students’ experiences in Studio STEM by replacing “science class,” for example, with “Studio STEM activities.” See Table We measured students’ perceptions about their experiences during the six Studio STEM sessions using the middle/high school version of the MUSICα = .84, .84, .86, .84), and has an adequate Cronbach’s alpha value in the present study . The interviews lasted approximately 10 to 20 min each, and followed a structured protocol including two to five questions for each of the five MUSIC model components. The objective of these questions was to reveal the students’ perceptions of (or lack thereof) the MUSIC model components experienced during the Studio STEM sessions. To help students remember what they did during the program, we began each interview by showing them a listed summary of the presentations, activities, and demonstrations that occurred during each of the sessions. Then, we asked the students to think about those sessions in which they were present as they responded to our questions.n = 19) who completed the school-wide measure of their motivational beliefs about science at two time points, which provided both “before” Studio STEM (December 2011) and “after” Studio STEM (May 2013) scores. We gathered their responses to the questionnaire the semester prior to participating in the first Studio STEM program (Save the Penguins) and then their responses immediately after completing the second curriculum program (Save the Seabirds). We limited our sample to 19 by selecting only those students who participated in Studio STEM programs during the same time period at the same two schools, and in which both before and after scores relating to their overall science beliefs were available. In addition, we collected data from 102 of their peers who did not participate in Studio STEM. Although all present fifth-, sixth-, and seventh-grade students at both schools completed the Science Questionnaire each year, our sample was limited to 102 students because only those who completed the questionnaire fully at the same two time points (December 2011 and May 2013) were included in the study. For example, the seventh-grade students in December 2011 were not surveyed in May 2013 because they were eighth-grade students at that time and, thus, they attended a different school. We only surveyed students who were the Studio STEM participants’ peers in terms of grade level and presence at the schools.To investigate RQ1, “To what extent do students’ motivational beliefs about science change as a result of participating in Studio STEM?”, we specifically examined students’ motivational beliefs about science and school, including their plans to attend college, science attainment value, science interest value, utility value for science, and science competence beliefs. Included in our analyses were a group of Studio STEM students (t tests to compare the Studio STEM participants and non-participants’ responses at each time point (December 2011 and May 2013), and (2) we examined changes in the students’ perceptions over time by computing separate paired-samples t tests for the Studio STEM participants and again for the non-participants. We used a significance level (α) of .05 for all t tests.We completed these analyses in two main stages: (1) we ran two independent samples n = 14, 87.5% response rate for this Studio STEM unit). We computed descriptive statistics for the questionnaire data and transcribed the audio-recorded interviews verbatim. We approached the analysis of the interview responses in four main stages. First, our interviews were structured to form five a priori categories related to the five MUSIC model components (five categories). Second, informed by Glaser and Strauss’s  and their classmates who were not involved in the program (n = 102), we compared their reported science perceptions at the same two time points (December 2011 and May 2013). We first examined differences between their science beliefs at a time point prior to their involvement in Studio STEM (December 2011). Independent samples t tests revealed that the Studio STEM students started with significantly higher science attainment value than their peers, while all other perceptions were statistically similar  = 2.10, p = .039), science interest value (t(101) = 2.29, p = .024), and science utility value (t(101) = 3.33, p = .001) significantly decreased over time  = 1.81, p = .073), and only their science competence beliefs increased over time, although the change was minor (t(101) = −0.542, p = .589). Conversely, we found that the Studio STEM participants’ motivational beliefs did not significantly decrease over time; in fact, unlike their peers, their reported beliefs maintained in terms of college plans (t(18) = −1.302, p = .209), attainment value (t(18) = 0.587, p = .564), interest value (t(18) = −0.150, p = .882), and utility value (t(18) = −0.228, p = .882), and, further, the Studio STEM participants reported significantly higher science competence beliefs (t(18) = −2.22, p = .040) after participating in Save the Seabirds  reported that they were empowered in the program , found the activities and content useful , felt that they could be successful , found the tasks to be interesting and enjoyable , and perceived that others in Studio STEM cared about their successes and personal well-being . In addition, they reported that they put forth a high amount of effort in Studio STEM . Overall, these values ranged from 5.02 to 5.57 on a 6-point Likert-type scale (for which 5 = agree and 6 = strongly agree), indicating that the students were highly motivated to engage in Studio STEM.The students (n = 14) about their perceptions of each of the five components of the MUSIC model to better understand how these perceptions influenced their motivation in Studio STEM. Based on the interview responses, we developed a total of 98 codes that we discuss in the next sections. We present our results in the order of the MUSIC model acronym. Of these 98 codes, the majority (88.1%) were descriptions of positive/motivating program features. The percentages cited in the following sections are out of the total number of coded responses  for each category.Research and theory posit that students often engage in and put forth effort during learning tasks when they perceive that they are empowered, the content is useful, they can be successful with effort, the content is interesting, and others in the learning environment are caring Jones, . Thus, wTo study students’ perceived empowerment, we asked them to describe the choices they perceived during Studio STEM and two themes emerged from their responses: the students perceived that they (a) had some choice over how to work on the activities (86%) and (b) had choice regarding when to participate during the sessions (14%).The activity choices the students mentioned tended to focus on their use of specific types, quantities, and sizes of materials during the activities , as well as how they designed and assembled their solar powered car and the device that lifted a basket of cubes . For example, one student noted that he had choice over “how heavy or light the car is.” Another student explained that she had “a lot” of choice noting that, “We got to pick how we got to make our car, what design we wanted, where to put the motor, where to make the wheels go, which wheels we wanted. Entirely up to us.” In addition, some students explained that they had some choices regarding when and how they would participate during the presentations and activities, and when working on EDMODO, a Facebook-like social learning community application that is designed for K-12 students, teachers, and parents to interact in a secure environment.We asked students to describe what they had done in Studio STEM that (1) was useful to their lives currently, and (2) what was useful for their futures. Three themes emerged: (a) learning useful information about specific science and engineering topics, and learning concepts that are (b) useful for school and professional success, and (c) useful for doing something outside of school. In addition, two students (7% of the coded excerpts) believed that they would not have any future need for the information they learned in Studio STEM.Several responses (19%) described specific topics that the students considered useful for their present lives, including learning about gears, motors, solar cells, and friction. However, even though they stated that specific topics and concepts were important, they did not always explain why or how. For example, one student said, “Friction is kind of useful because in some cases you might need to know when friction is too much or too little. Maybe when you’re in science class or something, it’s just a little difficult [to know when].” At the same time, others more readily related the learning material to real world uses . Many responses (41%) indicated that the students believed they learned information that was either useful for their current science class, or would be useful for pursuing a career in science or engineering in the future. In particular, several females specifically expressed interest in STEM careers. One female student contested female stereotypes: “Even though boys think that girls can’t be engineers because they think they are too girly and everything, I think that girls can be engineers so this is a good way of learning how to be an engineer.” Regardless of their expressed intentions, it is difficult to know if these students’ goals were influenced by the Studio STEM curriculum or by weekly interactions with male and female undergraduate science and engineering students who volunteered as facilitators. Finally, multiple responses (33%) also indicated that topics learned in Studio STEM were useful for some tasks outside of school (now and in the future), such as repairing cars and fixing technological devices. For example, one student explained, “We’ve learned how to build cars and how they would look, and how to repair them.”We asked students what made them feel like they could be successful in Studio STEM, and three themes emerged: (a) others were helpful during Studio STEM, (b) they felt successful during specific activities and presentations, and (c) they already felt like they were good at or enjoyed related topics or activities. Also, six students (23% of the coded excerpts) explained that there were moments when they did not feel successful during Studio STEM, including at times when building the model car and when working with motors to lift a basket of cubes.Some of the more common responses (35%) suggested that the support systems built in to Studio STEM were important to the students’ perceived success, including support from the facilitators  and other students in the program . Other responses (31%) described certain activities and topics during which the students felt successful, including when they worked on the solar cars, solar panels, motors, pulling up a basket with cubes, and measuring voltage and amps with multi-meters. Finally, three students explained that they felt successful because they were very interested in science and held high science competence beliefs, or had been successful on similar tasks in the past. For example, when asked to describe what made him feel successful, one student responded that he already felt confident on task components: “I like building Legos. I’m good at building Legos and just working with other people.”We asked the students to explain what was interesting about the presentations and activities (two questions), as well as what was boring about the presentations and activities (two follow-up questions). Five themes emerged from their responses about what was interesting: (a) the structure of the program and daily activities, (b) certain presentations and demonstrations, (c) specific hands-on activities, (d) working with others, and (e) feeling successful and learning new things during the activities. In addition, we found three themes in their responses about boring aspects: (a) when it was difficult or challenging, (b) when information was reviewed, and (c) some activities, presentations, and topics.You got to build and you got to re-create, and then figure out your mistakes, and then do it again. If you did not know it [before], you thought, “oh, I get it now,” and then you are glad that you accomplished something that you have never done before.Responses (16%) suggested that some students found several structural aspects of Studio STEM to be interesting, such as the teaching methods, the structure of the presentations, and that they had many opportunities to experiment by themselves and participate in hands-on activities . In addition, they listed many things they considered to be interesting, including multiple presentations and presentation topics , and activities . They also explained that working with other people was interesting (8% of responses) and, in particular, collaborating with their partners on the activities. For example, one student described the benefits of teamwork during one activity: “When we worked with our partner using the motor, it was fun because we both got to intertwine our ideas all together and see how they worked.” Finally, some responses (5%) equated interest to learning and success, and explained that it was interesting when they learned new things and felt confident in succeeding. For instance, one student described how her interest related to her sense of accomplishment and learning:The students also described several aspects of Studio STEM that they considered boring, with more responses concerning the presentations being boring than the activities. Two excerpts (3%) explained that sometimes it was boring when the presentations or activities were difficult or confusing, suggesting a connection between success beliefs and perceived interest . Inversely, five additional responses (8%) suggested that the presentations and activities were boring when the students felt like the material was reviewed too extensively . Finally, several responses (11%) indicated that some topics and tasks were boring. Most of the responses concerned the presentations . One student attributed the problem to the instructional methods: “Sometimes it would get a little boring because they just didn’t explain it in a very exciting way, it was just very dull.” Further, two students said that an activity was boring (working on the computers and creating a device to lift a basket).We asked students to talk about both the facilitators’ caring and the other students’ caring, and seven themes emerged from their responses. Three themes pertained to the facilitators: (a) they helped the students be successful, (b) they communicated that they cared about the students and their successes, and (c) their choice to develop and participate in Studio STEM communicated caring. No students mentioned anything negative about the facilitators’ caring. Another three themes describe their perceptions about the other students’ caring: (a) partnerships between students were trusting and helpful, (b) they were helpful and wanted others to be successful, (c) they showed respect towards and interest in others’ work. In addition, one student indicated that the other students were not interested in others’ work.Many students agreed that the facilitators were helpful (38% of the responses). They explained how the facilitators helped them when they were stuck, confused, or missed something, as well as used teaching methods that helped them to understand the content. The facilitators also asked them how they were progressing on the activities and encouraged them to keep trying. For example, one student explained, “They kept me going. If I had trouble they’d always help me a lot. They’d say, “You should try another thing.” [Once] I tried using a tire as a gear [laughs], I tried pulling it up with a tire!” The students described facilitator behaviors like making encouraging statements, being funny, and communicating that they wanted to students to persevere. The second theme pertains to how the facilitators communicated that they wanted the students to be successful in Studio STEM and cared about their personal well-being (9%). The facilitators showed that they not only wanted to help the students when they were struggling but also that they genuinely desired student success, and enjoyed their job. The third theme concerned the facilitators’ choices to participate in Studio STEM and the reasons why they were motivated to develop the program (4% of responses). The fact that Studio STEM was developed communicated caring to one student: “Well first of all, they wouldn’t even have this program if they didn’t care.” Another student thought that they cared because they chose to be facilitators in Studio STEM and to educate students.In the first student caring theme, several responses (6%) described effective and helpful partnerships. The students explained that their partners helped each other and worked well together to solve the problems. In addition, a majority of the students’ responses about their peers’ caring (34%) indicated that others in the program were also invested in their successes. They explained that the other students helped when they struggled, such as offering specific suggestions, allowing struggling students to look at their designs, and communicating when a specific tactic did not work for them . Finally, responses (6%) in the third theme indicate that there were positive levels of respect and interest among the students .The primary objective of this study was to investigate the effects of an afterschool science and engineering program, Studio STEM, on upper-elementary and middle grades students’ beliefs about science, and their motivation and engagement in the program. Our discussion of these findings centers first on the students’ overall science beliefs, including changes noted over time, and then their motivation in Studio STEM.Overall, our findings suggest that Studio STEM had a positive impact on the participants’ motivational beliefs about science and plans to go to college. The Studio STEM students held fairly similar perceptions to their peers before they participated in the program, with the exception of the level of importance they attributed to science as a part of their self-identities. We posit that their higher science attainment value is consistent with students who were recommended for a voluntary afterschool program by their teachers, and who agreed to participate. Following participation in two Studio STEM programs (Save the Penguins and then Save the Seabirds), the Studio STEM students maintained their increased attainment value, and, furthermore, they reported that they found science more interesting and useful, and were more competent in their science abilities than their peers. Because Studio STEM students reported these higher values than non-participants, participation in Studio STEM likely facilitated their development of these motivational beliefs.N = 913) provided evidence of this decline in motivation beliefs over time , students did not experience a reduction in identification. The program was set up to provide regular models of those who have persisted in STEM fields, including daily college student volunteers who were undergraduate STEM majors. According to social cognitive theory . These data offer several examples of how students were able to make choices and engage in student-centered learning. The choices the students described were primarily meaningful and relevant to the overall course or specific lesson, and sometimes the program more generally, which provided them with some authentic and significant control. Previous research indicates that providing choices , and teachers who hold high expectations and encourage students to seek challenges, as the students we interviewed described, can facilitate the development of positive success beliefs . In all, the students were able to communicate many specific program elements they found to be motivating, which served to explain their highly positive responses to the questionnaire items. However, we also asked about and received their recommendations for program improvement. Of these responses, most students agreed on one point: that it was boring when information was reviewed extensively or the teacher belabored initial instruction, such as when students felt that they already knew the information well enough and were ready to move forward. Another weakness prevalent in their responses pertains to the elements of direct instruction in Studio STEM. Both of these points indicate that the presentations and instructional methods may be key areas to target for improvement, which also aligns with the results of a previous study wherein we investigated the motivation of a different Studio STEM Save the Seabirds group  the sample size and (b) the selection of participants into the program. First, our sample was somewhat limited in the number of students who participated. However, we posit that our sample is sufficient due to our concentration on qualitative methods .A second limitation concerns possible selection bias in Studio STEM participation. Studio STEM program selection was based on teacher recruitment and recommendations. Furthermore, the program was voluntary in nature. Because of these procedures, it is possible that the students who participated in Studio STEM were more likely to be motivated and persist in STEM fields. We addressed this concern by running independent samples Studio STEM largely had a positive impact on the participants’ motivation and engagement in the program-based science and engineering activities, as well as their motivational beliefs about science and intentions to pursue a college education. Although students cited some negative aspects of the program and activities, their perceived motivation and interview responses were consistently and mostly positive. Through this study, we found that students could be motivated in a voluntary afterschool program in which they grappled with STEM concepts and activities, and that the experience had a positive impact on their perceptions about science as a field. Furthermore, the students were able to verbalize specific program elements that they found to be motivating. Studying the program features that the students found motivating may help educators to make connections between scientific research and theory, and their classroom instruction to motivate their students. Finally, Studio STEM appeared to halt the decline in these students’ motivational beliefs about science that typically occurs during the middle school years. This finding indicates that afterschool programs can be one way to help students maintain their motivation in science.


Māori, Pacific, Indian, and New Zealand European pre-school children’s caregivers’ views on determinants of childhood obesity are needed to inform strategies that will reduce disparities in prevalence. Nineteen focus groups were conducted to explore the relative influence of factors contributing to body weight in children. Predetermined and participant-suggested factors were ranked. Discussion data were inductively analysed. The cost of healthy foods was the highest ranked factor across all groups. Ranked similarly were ease of access to takeaways and lack of time for food preparation. Cultural factors followed by screen time induced sedentariness in children and lack of time to ensure children exercised was next. Participant-raised factors included lack of familial, social, and health promotion support, and others’ behaviour and attitudes negatively impacting what children ate. All groups rejected stereotyping that blamed culture for higher obesity rates. Compared to the Māori and NZ European groups, the Pacific Island and Indian participants spoke of losing culture, missing extended family support, and not having access to culturally appropriate nutrition education or social support and services. Public health policies need to mitigate the negative effects of economic deprivation on food insecurity. Complementary interventions that increase access to healthier meal choices more often are needed. Reducing the prevalence of childhood obesity is complicated by disparities among ethnicities. In the 2015/2016 year, 14.9% of 4-year-old children in New Zealand were obese, but there were marked ethnic differences in obesity prevalence .In addition to maternal and early life factors  a multitExisting interventions for preventing early childhood obesity have not been as effective as anticipated. Education has been identified as key to helping parents address childhood obesity . While eThe aim of this study was to better understand how parents and caregivers of 6-month-old to 5-year-old children view the relative influence of a range of factors that might contribute to pre-school children becoming overweight. We were also interested to determine if these views differed across the main ethnic groups in New Zealand, especially in groups who have proportionately higher rates of childhood obesity.www.abetterstart.nz) programme of research aiming to increase our understanding of how parents and caregivers view growth in infants/toddlers (0–2 years) and pre-schoolers (3–5 years) and how these views influence feeding practices. This paper reports on one focus group activity that was used to facilitate discussion of factors that might influence the weight of children aged 6 months to 5 years. Thematic analysis of other focus group content which included in-depth exploration of Māori focus group findings and Pacific, Indian, and New Zealand European parents and carers is presented elsewhere  the atmosphere is very different—single family, single child—not like in India where we have family, cousins, granny, and big support there.”Lack of support was related to a lack of time, but there were several cards that were more specifically about lack of support per se. A Māori group, an Indian group, and the Tongan groups independently created a card for “lack of support”. Explanations given included that the people around the parents might not be supportive, or that no support was available due to isolation. For example, an Indian group talked about being deprived of parental and other family support due to migrating to NZ as a single family, as opposed to an extended family unit:“Preparation is hard. ‘Cause you know what, sometimes you’re doing all the cooking and you’re pretty much too tired to eat after, ‘cause you’ve done so much prep to prepare all this food. It’s just going to be gone in a matter of minutes.”The Cook Island group said that eating healthy foods requires support from the whole family, that it should not be just “the children doing it, but kind of doing it more together being supportive of each other.” Similarly, they said that providing healthy food needed the whole family to assist in the food preparation because of the time involved, as summarised by one participant:“We’ve got housing conditions and environment… in terms of hygiene and if there’s overcrowding and dampness, that contributes to their weight because if they’re sick all the time they’ll lose weight.”All groups stressed that there were many demands and pressures on parents and families which undermined their ability to feed children healthy foods and ensure the children got sufficient physical exercise. Lack of support for dealing with these other pressures meant that the time, effort, stress, and energy required to plan, prepare, and serve healthy foods was too much for them to cope with. One of these stressors was “housing and environment”:One source of support that could have helped alleviate other pressures  was childcare, with a Māori group creating the card “childcare is expensive”.Loss of culture was a new theme that was derived from a subgroup of cards created across several focus groups. Cards amalgamated under this theme were, in essence, produced by participants who commented on the loss of aspects of their culture. Cards included “lack of access to culturally appropriate information and resources” and “culturally appropriate services” (Indian groups) and “extensive cooking time needed for Indian cooking”, “traditional versus Western foods”, “economic manipulation of Pasifika culture/group psychology”, and “loss of skill and knowledge to grow own food/knowing how to garden”.“like those debt trucks, and even things like liquor stores that you don’t see over at St. Heliers [a high socioeconomic suburb], things like all of the fast food, really fatty food takeaways that you don’t see over in some of the other areas of Auckland, they’re all just here… When you go to [low socioeconomic suburb], the first thing you look at is, you know, all the takeaways are open and Subway’s closed, we don’t even have a choice not to go there… You don’t have the quality shops here for the community itself.”The Pacific fathers group said that their card “economic manipulation of Pasifika culture/group psychology” was about fast food retailers deliberately exploiting positive Pacific values by positioning cheap food outlets in their communities:“In Samoa, all the generations eat the same food . But when we come to NZ, it’s too much food, too much takeaway, too much drink.”As mentioned in a previous section, “traditional versus Western foods” was grouped with other cards that suggested that it was Western influences that were inhibiting the provision of a healthier diet to children. As explanation, a participant said that“So, we can make coleslaw and green tossed salad, but all the other things like couscous and all that kind of stuff—quinoa, we don’t know those kind of stuff so it looks expensive and we’re not exposed. We just know how to put the basics: spinach and cabbage into the corned silverside. That’s the vegetables. That’s how we do vegetables. We don’t really do like, other types of salads that’s out there.”Varying levels of literacy about food and nutrition, along with conflicting information, were identified as barriers to optimal food provisioning. Parents and caregivers mentioned having a limited range of cooking skills, having limited recipe knowledge, and not knowing how to cook different foods, especially different types of salads:“Confusion about information available due to generation gap, cultural gap, system gap, lifestyle, and different beliefs... Conflicting views which can become difficult. Confusion about information available due [to] white culture, from elders back home, and due to generation gaps. System gap—system does not understand our beliefs, practices, boundaries, and other influences in our life and upbringing. Main system assume that we know all as they do.”The NZ European group felt there was a lot of pressure on parents to provide the “right” foods, exercise, and lunch boxes, which in turn created more stress for parents. A participant in the Indian group also spoke about the stress caused by conflicting information:Participants in different groups mentioned a range of ideologies around food, such as “clean eating”, “sustainability”, and “spray free… free range, grass fed”.This was a large multi-ethnic qualitative study involving 180, mainly female, parents and caregivers. Across all the participating ethnic focus groups, the most influential factor perceived to impact on child weight gain was the relatively higher cost of healthy foods, followed by the ease of access to takeaway meals. Extensive discussion through ethnic-specific focus groups yielded many additional relevant factors, not initially part of the ranking exercise, highlighting the richness of data obtained through such techniques. Eating behaviours such as taste preferences and portion sizes were considered major determinants of child weight, as were social influences including peer pressure, the influence of other family members on the diet of children, and how the parents themselves ate. Parents identified several non-food-related issues that placed additional demands and stresses on families that in turn undermined their ability to provide a healthy lifestyle for their children, such as the housing environment, the expense of childcare, and inability to have a good work/life balance. Finally, issues with health or food literacy and the array of conflicting information about food and health from difference sources made it difficult to know what other options there were for feeding children healthy foods.The association of diet disparities with socioeconomic status is well established ,22. ChilThe three lowest ranked influencing factors overall—lack of exercise, poor sleep, and cost of sport—might have been relegated to the bottom of the ranking due to poor wording used on the cards. Some participants questioned how relevant these were given the age range being focused on. For example, children aged five years and under do not typically participate in sport or exercise. Thus, in hindsight, these two cards could have been reframed as insufficient physical activity. Poor sleep was not perceived to be a problem, except among Indian families. Similar to our findings, screen time was also not identified as an important determinant of excess weight in children among Alaskan Natives in the Children’s Healthy Living project .The cards that presented statements such as “culture encourages eating”, which was established early on in New Zealand obesity literature and became a popular belief ,28, wereThis study only investigated the relative importance of perceived determinants of childhood obesity. Other parental concerns about their children’s growth and health were not considered. In their study of Australian kindergarten children, Hickie et al. 2013) found that only 12% of parents were concerned about their child becoming obese 013 found. There wOne strength of the study was the use of a parallel method involving ethnic-specific focus groups. Having ethnically matched facilitators who encouraged culturally specific views to be expressed likely mitigated a number of biases, such as social desirability bias and culture bias that favour dominant-culture perspectives , resultiAnother strength of this study was the involvement of grandparents and other caregivers in our focus groups, which added to the diversity of views presented.One limitation was that fathers and other male caregivers were under-represented. In addition, the majority of our participants lived in Auckland, New Zealand’s largest city. While one focus group was conducted in Northland, most participants lived in urban areas. Thus, the results may not be generalisable to families living in other regions, and particularly to those living in small towns and rural areas.Moreover, because this study relied on ranking, there is a risk that this method might have exaggerated the difference between the perceived influences of the various factors. Nonetheless, a strength of the ranking approach is that factors are assessed relative to each other. The advantage is that more of the context is considered when ranking, as opposed to rating individual factors on a scale and in isolation. This is relevant because a related study showed that parents and caregivers weigh multiple interacting factors when considering what food to provide to their children throughout each day . The autUnaffordable healthy food, time scarcity, and convenient access to fast and affordable meal choices close to home present challenges for designing interventions to reduce childhood obesity. While parents/caregivers of young children want to fulfil their parenting role and intend to feed their children well, the competing demands on their time, tiredness from work, and lack of support for food preparation or childcare means that they often felt that they had little choice but to buy convenient readymade meals from takeaway food shops close to home.Notably, the ethnic groups in our study described different needs. For example, Indian participants reported a lack of culturally appropriate information, while the Pacific groups reported a need for education on the wider range of foods available in New Zealand  and how to cook them.As recommended by Glover et al. , FialkowIt is important to identify solutions that do not unintentionally worsen the barriers to healthier eating by increasing the time involved and/or the cost of preparing meals or stigmatisation and guilt resulting from feeling like the food being provided is judged to be “wrong”. Interventions that focus on education risk stereotyping parents as deficient, and these interventions will have little salience to parents who are simply unable to provide healthier food due to financial constraints . Public


Social media provides a complementary source of information for public health surveillance. The dominate data source for this type of monitoring is the microblogging platform Twitter, which is convenient due to the free availability of public data. Less is known about the utility of other social media platforms, despite their popularity.This work aims to characterize the health topics that are prominently discussed in the image-sharing platform Instagram, as a step toward understanding how this data might be used for public health research.The study uses a topic modeling approach to discover topics in a dataset of 96,426 Instagram posts containing hashtags related to health. We use a polylingual topic model, initially developed for datasets in different natural languages, to model different modalities of data: hashtags, caption words, and image tags automatically extracted using a computer vision tool.We identified 47 health-related topics in the data (kappa=.77), covering ten broad categories: acute illness, alternative medicine, chronic illness and pain, diet, exercise, health care & medicine, mental health, musculoskeletal health and dermatology, sleep, and substance use. The most prevalent topics were related to diet  and exercise .A large and diverse set of health topics are discussed in Instagram. The extracted image tags were generally too coarse and noisy to be used for identifying posts but were in some cases accurate for identifying images relevant to studying diet and substance use. Instagram shows potential as a source of public health information, though limitations in data collection and metadata availability may limit its use in comparison to platforms like Twitter. Social media can provide a vast source of insight into a wide variety of applications in public health monitoring and surveillance . The bulWhile Twitter has strengths as a data source, its dominance in research relative to other public platforms has been explained as a matter of convenience: Twitter provides free APIs to obtain large volumes of random or targeted samples of data . HoweverMost prior health research using image-sharing platforms has focused on lifestyle issues, such as diet and substance use. Mejova et al  analyzedMany of these studies focused on the text features  of the image posts, and some conducted a manual content analysis of the images themselves. Two of the studies cited above-used computer vision—a type of artificial intelligence that can automatically analyze the content of images—to perform automatic identification of certain types of images. Pless et al  built imThis study seeks to characterize the health content shared on Instagram, the most popular image-sharing site, toward the goal of identifying potential areas of research that may benefit from this type of data source. In particular, we consider the following research questions: (1) what health topics are prominently shared in Instagram, and (2) what are the characteristics of those topics, specifically the types of images associated with the topics? This study is related to exploratory topic analyses of other platforms for health research ,25, withThis study was reviewed by the University of Colorado Boulder Institutional Review Board, which determined that it does not constitute human subjects research. However, given that publicly posted images may still be considered sensitive material by the users, we took steps to preserve privacy. We did not download any images as part of our study. Instead, we collected the URL pointers to images hosted on Instagram, which were processed by an external computer vision application programming interface (API). Our data collection only contains the abstract features extracted from the images.While Instagram provides APIs for specific applications, Instagram does not provide an API to collect public data , in contWe iteratively queried the search engine for 269 general health-related keywords that were used in previous work to obtain a general collection of health-related tweets . The keyInstagram only allows searching for hashtags rather than free text, so we treated each keyword as a hashtag . We repeated these 269 queries continuously from September 29, 2016, through October 25, 2016, attempting to simulate a “streaming” collection as with Twitter, and obtained 174,517 posts. We did not download the same post more than once as measured by a unique post identifier. However, if the same content was shared in multiple posts , these posts would be considered separate in the dataset.Each post includes an image, a set of hashtags, and an optional free text caption. We used langid.py to identify and remove posts containing non-English captions . We alsoWe extracted “tag” features from each image using Microsoft’s Computer Vision API . This seOnce extracted, we treated the image tags as an additional type of text, along with captions and hashtags. In the final collection, there are 96,426 posts with a nonempty list of hashtags for an average of 15.2 tags per post. There were 95,208 posts  with nonempty captions for an average of 21.6-word tokens per caption. There were also 76,407 posts  with at least 1 image tag for an average of 3.7 tags per image. The dataset is shown in We use probabilistic topic models  to charaA topic model is a statistical model with many latent variables and parameters that can be inferred by fitting the model to data. In this model, each “topic” has a probability distribution over words, estimated from data, and topics are usually represented by presenting the 10-20 most probable words in the topic. Additionally, each document has a probability distribution over topics, which can be used to characterize the topic composition of a document and to identify documents that describe particular topics.θd in each document d, and the distribution over words ϕk for each topic k) are estimated to fit the observed data . The estimated parameters are often interpretable to people, and the words associated with each topic cluster can be used to assign a concept to the topic [Topic models take documents, represented as vectors of word counts, as input. The model parameters , and to decide on a final label.When comparing whether the 2 annotators thought a topic was coherent, as opposed to the “unknown” label, the annotators agreed on 124/150 (82.7%) of the topics (Cohen kappa=.62). When comparing whether the 2 annotators thought a topic was related to health, the agreement was 136/150 (90.7%) with Cohen kappa=.77.Additionally, we grouped the topics into coarse-grained categories, to make the results easier to summarize. One annotator created a grouping of the topics and then iterated with feedback from the other annotator. Categories are not mutually exclusive; topics could be assigned to more than 1 category.k in each document d, θdk. To get the overall prevalence of each topic, we calculate the average proportion, where D is the number of documents: To estimate the prevalence of the coarser categories, we simply define each category’s prevalence of the sum of its topic proportions, where Cj is the set of topics in category j: To summarize the degree to which 2 topics have a tendency to occur together in documents, we calculate the Pearson correlation of the θdk values for all pairs of topics. Most of the topic pairs with high correlations were similar topics in the same category. To discover less obvious topic co-occurrences, we focus only on topic pairs from different categories.The topic model gives the probability of each topic Of all the topics, 93/150 (62%) were identified as coherent, with 47/150 (31%) related to health and 46/150 (31%) not related to health. The 47 health topics were grouped into 10 high-level categories: acute illness, alternative medicine, chronic illness and pain, diet, exercise, health care & medicine, mental health, musculoskeletal health and dermatology, sleep, and substance use.Examples of health topics are shown in Comparing the different modalities, hashtags tend to contain words specific to the topic , while caption words give indications of the context of the posts . In some cases, the caption words include first-person  and informal  language that might be observed in personal conversations. Other topics include caption words consistent with advertising .Meat topic contains the image tags “meat” and “barbeque,” Desserts contains “dessert” and “chocolate,” Alcohol contains “alcohol” and “beer,” and Caffeine contains “coffee” and “coffee cup.” A small number of other topics are also associated with image tags that are directly related to the topic: Sleep includes “pillow” and “bed,” and Dental health includes “toothbrush” and “mouth.”Examining the image tags, there are often not many tags that are directly related to the health topics, with the exception of topics related to food and beverage, which usually had explicit image descriptors of the corresponding food. For example, the Mental health topic suggest positive imagery  while the tags of the Suicide & self-harm topic suggest negative imagery . The Cancer topic contains imagery associated specifically with breast cancer awareness, with the tag “pink” appearing as an image tag, as well as in the top hashtags and caption words.While not explicitly about the health topic, some topics contain image tags that convey other characteristics of the imagery. For example, the image tags of the Chronic illness, Diabetes, Gym/fitness, Health care, Nursing, Hospitalization, Mental health, Skin health, Tanning, Cosmetic surgery, Dental health). Other topics have the top tag of “person” , “group” , “woman” , and “man” . The image tags “swimsuit” and “underwear” are especially common in many of the exercise and fitness topics. This matches an observation in a previous study of fitness images on Instagram which found that “most images contained posed individuals with some degree of objectification” [A common theme across topics is that images frequently contain people. The image tag “posing” is the top tag associated with 11 topics , and 6 topics that contain a female-associated tag  but no male tag . Only 1 topic  included both genders in the top 10 image tags .Gender associations can also be observed in the image features. There are four topics that contain a male-associated image tag  but no female tag  were labeled as coherent by the annotators. Of the topics with the lowest probabilities, only 15/25 (60%) were labeled as coherent. The variability in average probabilities was low; the values ranged from .005 to .012.Vitamins & supplements topic, which co-occurs with a variety of other health topics. Inspiration and Poetry & quotes) tend to co-occur with mental health and exercise topics, and topics about religion and spirituality tend to co-occur with certain health topics, like Alternative medicine and Yoga.Cancer topic contains the image tags “group” and “crowd;” it appears these many posts in this topic are about cancer awareness events. In some cases, image tags were the defining characteristics that distinguished clusters that were otherwise very similar, which suggests that images are informative beyond the hashtags and captions to conduct content analyses of Instagram posts.The topic model results show a large and diverse set of health topics are discussed in Instagram. Qualitatively, we find that the top hashtags tend to be the best descriptors of topics, while caption words give some indication of what kind of messages are associated with the topics, such as whether they are more informational or conversational. The extracted image tags are generally much less coherent, though they do help characterize the types of images that are associated with each topic. For example, many of the topics related to pain contain images of animals, perhaps because users post cheerful images in response to pain. The tag “posing” appears in some topics, suggesting these posts may be informational rather than personal. The Qualitatively, it appears that in most cases the image tags are not specific enough to be useful for directly identifying posts relevant to a specific health application. However, tags of food and beverages appear to be fairly specific and accurate, suggesting that computer vision may help in identifying posts for studies of diet and food consumption. The only previous work we are aware of that used automatically extracted image tags for this purpose is , which fVitamins & supplements topic is less likely to appear in a post in isolation but instead co-occurs with other topics, likely because supplements are discussed in the context in which they are used. Using this data to study nutrition in a population may, therefore, be able to show how nutrition is discussed and applied to specific aspects of health. Some pairs of topics with high correlations may indicate comorbidities, such as Chronic illness and Mental health [We gained additional insights by considering the co-occurrences of health topics in the data. For example, the l health .Co-occurrences with nonhealth topics may give insights into other contexts in which health is discussed. We observed that many health topics frequently co-occur with inspirational topics, such as topics containing poetry and quotes, or topics about nature, as well as topics related to spirituality and religion. These types of posts may give insight into how individuals cope with and support others with, illness and disease.An additional observation in some topics is the use of certain hashtags to identify a specific community of users , such asOne of our methodological contributions was to repurpose an existing tool, the polylingual topic model, for a new task of combining different modalities of data in a topic model. We showed that automatically extracted image tags from a computer vision API can be treated as text tokens in an existing topic model. Beyond topic models, our observations of the results suggest that these extracted image tags are in some cases useful descriptors of images. We suggest that this type of tool can be applied to images for health research more broadly.We observed the same broad set of topics in Instagram that have previously been seen in Twitter , suggestInstagram may complement Twitter as a data source because it has a different demographic distribution. The user base of Instagram is younger, lower income, and more urban compared to Twitter, . These dNot all health topics are discussed widely on Instagram, which may be a limitation of using Instagram. By far the most common topics in Instagram are related to diet and exercise, while topics on acute illness, which would be needed for a task like influenza surveillance, are the least common. This may explain why all prior work we identified using image-sharing platforms for health research was related to lifestyle factors, such as diet (most common) and physical activity. Nonetheless, topics about infectious disease do exist on Instagram, and so it may be worth investigating the utility of contributing this data to an ensemble surveillance system . To the Another limitation of using Instagram is the limited availability of metadata. When crawling Instagram, it is difficult to sample data uniformly across time, as Instagram does not provide a streaming API analogous to Twitter’s widely-used APIs, which would make it difficult to extract the long-term pattern, for example, to validate influenza tracking. Location data also appears to be difficult to obtain when crawling from the web. In our dataset, 46% of posts contained a user-specified location string, but these were not in a standard format, and many of them were names of businesses or other specific locations, without reference to a geographic area. Geolocation from Instagram is less well understood in social media research, as well as inference of other demographic attributes that may be important in public health research. Richer data may be available from certain resellers; for example, Gnip, who is the official seller of Twitter data, also sells Instagram data, which can be searched by either tag or geolocation.In addition to limitations of this data, there are limitations with the topic model methodology. Topic model evaluation is notoriously difficult , though what is being discussed, but it is difficult to describe how the content is presented. For example, the topic model can identify posts that are related to marijuana, but it does not distinguish between personal marijuana use, information about marijuana, or advertisements for cannabis products—distinctions that have been made in prior work using more qualitative methods [Another limitation of topic modeling is that the topics characterize  methods . HoweverWe note that there exist other methods for identifying thematic patterns in text beyond probabilistic topic models that have been used in health research, such as network-based clustering on term co-occurrence graphs . Most suFinally, the grouping of topics into ten overlapping categories is also limited. Some topics were difficult to categorize, and the boundaries of some categories were difficult to define. However, the goal of the categorization is to present the raw results (available from our dataset in This study shows that health is discussed on Instagram in a variety of ways, and there is potential for computer vision techniques to automatically characterize health-related images, which could extend public health surveillance of social media beyond text-based analysis. Our dataset of nearly 100,000 posts is available to allow for the study of specific topics and image tags in more depth. There are pragmatic reasons why this popular platform has been used in research relatively little compared to platforms like Twitter and Facebook, but our results and discussion point to ideas that image-sharing platforms like Instagram may complement other social media data sources in health research.


Participants completed questionnaires assessing social support from, and the smoking behavior of, people in their social environment. They were also tested for biochemically validated continuous abstinence directly after finishing the training and after 12 months. The data were analyzed using mixed-effects logistic regression analyses. Social support from colleagues was positively associated with 12-month quit success (odds ratio (OR) = 1.85, 95% confidence interval (CI) = 1.14–3.00, p = 0.013). Support from a partner was positively associated with short-term quit success . Having a higher proportion of smokers in the social environment was negatively associated with long-term abstinence . Compared to having a non-smoking partner, long-term quit success was negatively associated with having no partner , with having a partner who smokes , and with having a partner who used to smoke . In conclusion, people in a smoker’s social environment, particularly colleagues, were strongly associated with quit success. The workplace may, therefore, be a favorable setting for smoking cessation interventions.The current study investigated whether quit success among employees who participated in a smoking cessation intervention at the workplace was associated with social support from, and the smoking behavior of, people in their environment. Tobacco-smoking employees ( Current smoking cessation therapy focusses mainly on the individual smoker who wants to quit, although smoking is a social behavior that is greatly influenced by a smoker’s social environment ,2,3. SmoLongitudinal studies have shown evidence that smokers with a partner who smokes are less likely to attempt to quit and to quit successfully, while having a non-smoking partner can promote quitting ,7. SmokeApart from the home environment, the workplace can be considered as another social setting where people spend a large part of the day together, interact, and may affect each other’s beliefs and behaviors ,17. FreqAlthough people in the social environment have been shown to influence smoking cessation, it is not entirely clear at what stage of the cessation process social dynamics are particularly influential. Some studies found that positive social support was associated with short-term, but not with long-term quit success, while the absence of negative support such as criticism predicted continuous abstinence ,19,20. HThe current study is a secondary analysis from a cluster randomized trial, in which employees participated together with colleagues in a smoking cessation group training program at their workplace. In the current study, we investigated whether the social influence of different types of people in the environment of the participant, including colleagues who were peer quitters in the group smoking cessation program, was associated with quit success. We also assessed associations of the number of smokers among people in the close circle of the participant  and the smoking status of their partner with successful smoking cessation. Additionally, we investigated whether the extent to which these persons were supportive toward the participant’s quit attempt was related to quit success. We assessed this social influence on short-term quit success directly after the smoking cessation group training program, and on long-term quit success 12 months later.The current study is a secondary analysis of data from a cluster randomized trial (RCT), of which the methods and results have been described in more detail in previous publications ,22. In sThe participants were 604 tobacco-smoking employees aged at least 18 from companies in the Netherlands. Data were collected between March 2016 and March 2018.To assess social influence, the same questionnaire was sent out at baseline, and at four follow-up measurements: directly after the smoking cessation training had ended (two months after baseline), three months, six months and 12 months after the training had ended. In case of no response, participants were called by phone or were sent a text message with a reminder to fill out the questionnaires. Participants were awarded a voucher of €25 for completing the questionnaires. Smoking abstinence was assessed directly after the training and at 12 months after the training by a research assistant who visited the study participants at their workplace or at home. The research assistant used a Smokerlyzer (Bedfont) to measure expired-air carbon monoxide (CO) concentrations.The main outcome variables were continuous abstinence from smoking directly after finishing the smoking cessation program and continuous abstinence 12 months after finishing the program, confirmed by CO measurement with a threshold value of 9 parts per million. . ParticiWe assessed the smoking status of the partner at baseline (T0) and directly after finishing the training (T1), using the question: “Does your partner smoke?”. Response categories were: “yes”, “no”, “I do not have a partner” or “don’t know”. A new variable was created by combining the smoking status of the partner at baseline and at the end of the cessation program. This resulted in the categories “never smoker”, “continued smoking”, and “stopped between T0 and T1”. Participants who answered “I do not have a partner” at both T0 and T1 were categorized as “no partner”. All other combinations of answers were categorized as “other”. “Don’t know” was categorized as missing.Other independent variables were the social support that participants received from colleagues who also participated in the group smoking cessation program, colleagues who did not participate in the cessation program, their partner, and friends and family. These were assessed with the question “How supportive were (colleagues who participated in the training program/other colleagues/partner/friends and family) of your quit attempt in the last two months?” at follow-up directly after the smoking cessation program. Response categories were “don’t know”, “very supportive”, “moderately supportive”, and “a little supportive”. For the analyses, the “very supportive” category was retained. The last two response categories were combined into “not very supportive”, and the “don’t know” category was classified as missing. For the variable “social support from partner”, the response category “I do not have a partner” was combined with “not very supportive” in the multivariable analysis to prevent collinearity effects with the “I do not have a partner” response option in the questions assessing the smoking status of the partner. Additional covariates in the multivariable analysis were measured at baseline and included intervention group (incentive vs. no incentive), nicotine dependence (Fagerström score 0–10) , income p values ≤0.05 as statistically significant. IBM SPSS statistics for Windows (version 25.0)  was used to compute descriptive statistics, RStudio version 1.1.383 MICE  package for multiple imputation, and lme4 package (glmer function) for the mixed-effects logistic regression analysis.Differences between participants lost to follow-up and participants included in the analyses were tested using independent T tests for numerical variables and Chi square tests for categorical variables. To assess the influence of the social environment on smoking abstinence, we performed separate analyses for each individual variable, correcting only for the covariates . For the outcome “smoking abstinence after 12 months”, we included the same covariates and we additionally selected the variables with a P value lower than 0.200 and included these in a multivariable analysis, because the expected that a small number of quitters did not allow including all variables and covariates in the model. We used multivariable mixed-effects logistic regression analysis with a random intercept at company level to adjust for the clustering of participants within a company. Participants with missing data on the outcome “smoking abstinence” were considered as smokers and included in the intention to treat analyses. Other missing data were imputed using all other fixed variables that were included in the model, and values on these variables from other measuring points. We created 50 complete datasets using multiple imputation with a maximum number of 20 iterations and used trace lines to check convergence. We performed a complete case analysis as a sensitivity analysis that only included participants without any missing values. We considered two-sided The mean age of the participants was 45.1 (standard deviation (SD) 10.2) and there were more male than female participants . The mean = 542), participants who did not complete this questionnaire (n = 62) more often had a low income and a higher nicotine dependence, but did not differ significantly in terms of educational level, age, sex, or intervention group. There were no missing data on the outcome “smoking cessation”. Compared to participants who completed the follow-up questionnaire directly after finishing the smoking cessation training (p < 0.001), and also with 12-month quit success . The support of other colleagues (not participating in the smoking cessation program) was not significantly associated with quit success at either time. Having very supportive friends compared to not very supportive friends was not significantly associated with short-term or 12-month quit success. Finally, having a very supportive partner was positively associated with quit success , but only directly after finishing the training and not after 12 months. The results of the separate analyses of the social influence of the social environment that were performed to select variables to include in the multivariable analysis showed effects comparable to the multivariable analysis . This result means that with each extra smoker within someone’s close circle of five people, the odds of continuous smoking cessation after 12 months decreased by approximately 19%. Of the participants who had a partner, most had a partner who had never smoked (58%), 26% had a partner who smoked and 15% had a partner who had quit smoking between baseline and the end of the smoking cessation program. The smoking status of the partner was also associated with quit success. Compared to having a partner that had never smoked, having no partner was significantly and negatively associated with quit success directly after finishing the smoking cessation training . The partner’s smoking status during the quit attempt was also significantly associated with 12-month quit success. Compared to participants who had a partner that had never smoked, all other partner categories  were significantly and negatively associated with successful quitting. The results of the separate analyses of the smoking behavior of persons in the social environment that were performed to select variables to include in the multivariable analysis showed comparable effects to the multivariable analysis  . A largeanalysis .The sensitivity analyses where only complete cases were included showed comparable results to the main analyses of the impact of the smoking behavior of and social support from people in the social environment on short-term as well as long-term smoking cessation success .In the current study, we aimed to investigate whether social support from, and the smoking behavior of colleagues, partners, and family and friends of employees who participated in a workplace smoking cessation training program were associated with short- and long-term quit success.The results of the current study confirm our hypothesis that colleagues have an important influence on smoking cessation success. Not only did most participants (78%) find their colleagues who also participated in the smoking cessation group training very supportive, but also support from these colleagues was significantly associated with quit success. Having a very supportive partner was also positively associated with short-term quit success, while support from friends and family and other colleagues was not significantly associated with quit success. These results suggest that people who are closely involved in the quitting process of the smoker are especially important. An interesting finding was that unlike partner support, social support from colleagues who participated in the training was associated with a higher probability of smoking cessation up to 12 months after finishing the training. Previous research had suggested that social support mainly influences the achievement of abstinence, not the continuation of abstinence in the long term ,19,20. HOur study indicated that the number of other smokers in the social environment has a large effect on the success of people who want to quit smoking. This result confirms the influence of smokers in the social environment found in previous research ,2,20, anAccording to our results and previous research ,8, havinA strength of the current study is that it showed a potentially very effective source of social support that can be used to improve quit success in smokers, namely the support of colleagues who participated in a workplace group smoking cessation program alongside the smoker. Further research should explore whether the positive association of peer support with quit success can be generalized to general population settings. The current study was a post hoc analysis, which implies that research questions and hypotheses were made after the data were collected. Further research is therefore needed to confirm our findings. Another limitation of the current study is that while we investigated the influence of the smoking behavior of the social environment and social support between baseline and the end of the smoking cessation program, we did not assess the influence of changes in these variables within the remainder of the 12-month follow-up period. Furthermore, data on the smoking behavior of partners were self-reported by participants, so they may not be accurate if the participants misclassified their partner’s smoking status. However, it can be argued for the current study that the smoking status of the partner as perceived by the participant is more relevant than the partner’s objective smoking behavior. In addition, in order to determine the smoking status or change in smoking status of the partner between baseline and after finishing the smoking cessation training (a period of eight weeks), limitations in the data forced us to assume that these partners were still the same individuals, while it is possible that some participants changed partners in between. Moreover, because few participants had relapsed into smoking at the follow-up measurement directly after completion of the program, we performed separate analyses to assess the associations of the independent variables with quit success to prevent unreliable results. For the outcome “quit success directly after completion of the program”, we were, therefore, unable to determine whether variables would still be independently associated with quit success if all variables were included in the model. Finally, the amount of missing data that needed to be imputed was rather high for some variables, and there were differences in some variables between participants with and without missing values, which could indicate selection bias. However, the complete case analysis showed conclusions similar to the main analysis.In the current study, social support from colleagues was strongly associated with the long-term quit success of employees who participated in a group smoking cessation training program at the workplace. Whether a quit attempt is successful may also depend on the smoking behavior of the partner and the proportion of smokers in the social environment. The current study implies that it is desirable to include the social environment in the smoking cessation process in order to increase the probability of quit success. The workplace can be a favorable setting for smoking cessation interventions, as colleagues may provide a valuable source of support.