Smoking is more prevalent and persistent among lower socio-economic status (SES) compared with higher-SES groups, and contributes greatly to SES-based health inequities. Few interventions exist that effectively help lower-SES smokers quit. This study evaluated “De StopCoach”, a mobile phone delivered eHealth intervention targeted at lower-SES smokers based on the evidence-based StopAdvisor, in a real-world setting  in The Netherlands in 2019–2020.N = 22), and examined log data from the app (N = 235). For practical reasons, SES of app users was not measured. Qualitative data were analysed using the Framework Approach, with the Consolidated Framework for Implementation Research (CFIR) and Unified Theory of Acceptance and Use of Technology (UTAUT) as theoretical models.We conducted individual semi-structured interviews with project leaders, healthcare professionals, and participating smokers . Key barriers to implementation of the app were limited integration of the app in SCC programs in practice, difficulty experienced by project leaders and healthcare professionals to engage the local community, and barriers to SCC more generally  that prevented healthcare professionals from offering the app to smokers. Quantitative data showed that 48% of app users continued using the app after the preparation phase and pre-quit day, and that 33% of app users had attempted to quit. Both app adherence and quit attempts were more likely if smokers also received SCC from a professional coach. Posthoc analyses suggest that adherence is related to higher likelihood of a quit attempt among participants with and without a professional coach.Smokers, healthcare professionals and project leaders indicated in the interviews that the StopCoach app would work best when combined with SCC. It also appears from app log that app adherence and quit attempts by app users can be facilitated by combining the app with face-to-face SCC. As such, blended care appears promising for helping individual smokers quit, as it combines the best of regular SCC and eHealth. Further research on blended care for lower-SES smokers is needed.The online version contains supplementary material available at 10.1186/s13722-021-00255-5. Despite gradual decline in smoking prevalence in many high-income countries, a substantial proportion of the adult population still smokes . EvidencSmoking is more prevalent and persistent among lower-SES compared with higher-SES groups, and contributes greatly to SES-based health inequities –8. SES ceHealth interventions can help smokers quit more effectively, especially if interventions are personalized, interactive, and include text messages , 21. FroThe implementation of eHealth interventions often fails, with shortage of financial resources being an important barrier . The comWhat is the experience of project leaders and healthcare professionals implementing StopCoach, which barriers and facilitators emerge from the implementation process, and what is the experience of smokers using StopCoach?Is app adherence to StopCoach related to participant , smoking  and initial app usage characteristics ?What percentage of app users undertake a quit attempt?How do users evaluate their progress in quitting?Are quit attempts related to participant, smoking and initial app usage characteristics?This study evaluated StopCoach, a mobile phone delivered eHealth intervention (app) targeted at lower-SES smokers based on StopAdvisor, in a real-world setting. We aimed to implement StopCoach in blended care settings within five municipalities in The Netherlands. The Netherlands were chosen as several organizations in the field indicated that they were in need of a smoking cessation intervention for lower-SES smokers . The proThis real-world study was performed in five municipalities involved in the proof-of-concept implementation project of StopCoach in The Netherlands in 2019–2020. Municipalities were recruited by Pharos. Pharos supports 155 municipalities that participate in the “Healthy in the City” program by the Dutch Ministry of Health, Welfare and Sport in reducing local health disparities. Any interested municipality could take part. Participating municipalities  were located in various regions in The Netherlands with different degrees of urbanisation, with numbers of inhabitants ranging from around 26,000–58,000 in 2019. Each municipality appointed their own project leader, who selected a setting in which to implement the app, and involved local healthcare professionals that could offer the app to smokers as part of a SCC program. Project leaders were instructed to take local health and social services into account, such that healthcare professionals could refer smokers for other problems  if necessary. In three municipalities, the implementation project was embedded in ongoing projects that focused on reducing smoking rates among pregnant women in the region, or promoting health among either the municipality inhabitants or the regional population which contained a relatively large share of lower-SES individuals. StopCoach was a stand-alone project in the two other municipalities, where it was implemented in midwifery practices and existing SCC programs serving the broader population of smokers. Smokers could use the app regardless of their SES.Qualitative data was collected through interviews with five project leaders, seven healthcare professionals and ten participating smokers between November 2019 and February 2020. We aimed to include mostly lower-SES smokers. One project leader also participated as a healthcare professional and was interviewed twice . Healthcare professionals were recruited through project leaders, and smokers were contacted by one of the interviewers after they had indicated that they were open to being interviewed on an information sheet about the project.N = 235) who indicated that they resided in one of the five municipalities involved, and who used the app during the period when the project was running  regardless of how long they had used the application , and how users could have their data removed . This procedure suffices the GDPR and Dutch privacy law requirements for consent.Measures had no missing values, unless indicated otherwise.The app asked participants about the municipality participants resided in , number of cigarettes smoked per day, smoking within 30 min after waking up (yes/no), previous quit attempts ever , and whether they currently had a professional coach for SCC (yes/no). For cigarettes per day, two values over 80 were recoded into 80 in order to correct for inaccurate data entry . ParticiThe app registered whether participants had enabled push-notifications, and had chosen the male/female virtual coach. We calculated duration of app usage (in days) by subtracting the date of last activity from the date of initial activity in the app, and assessed in which step participants showed their last activity within the app, in how many of the steps they had shown activity, and how many activities were registered in the app in total.To answer research question 2, initial qualitative data analysis was performed by JK and KO, and supervised by EM who also performed the final, integrative analytic steps. Analysis took place according to the principles of the framework approach and cross-case analysis , 40. Sepp < 0.05 were then included in a multivariable logistics regression analysis. We performed a sensitivity analysis without the reasons to quit variables, as these variables had missing values and models were using cases with complete data for variables included in the respective model. Descriptive statistics were estimated to answer research questions 3 and 4. For research question 5, we fit univariable and multivariable models as for research question 2, with quit attempt as the dependent variable.For research question 2, a dichotomous adherence variable was constructed, with the non-adherent category including participants who only did the preparation phase, and adherent participants showing activity after the preparation phase. We performed separate univariable logistic regression analyses, with adherence as the dependent variable and participant, smoking and initial app usage characteristic variables  as independent variables. Dummy variables were created for municipality, with the municipality that most app users resided in (Roermond) as the reference category. Independent variables associated with adherence at The CFIR domains ‘Five healthcare professionals were experienced in providing SCC, and two participated in SCC training in order to take part in the project. One project leader was a current smoker, none of the healthcare professionals currently smoked. Six participating smokers reported a lower educational level and two each a middle  or higher educational level . All but one participant (P8) simultaneously participated in a SCC program. At the time of the interview, five participants had quit smoking successfully , three had fallen back to their former number of cigarettes after attempting to quit  or cutting down (P3), and two were smoking less than before ; see Additional file “The ones who couldn’t manage the installation weren’t very much at home on their phones either. (…) Well, that a 71-year-old lady doesn’t remember how her phone works... I’d forgive her, right?”In this study, effort expectancy focuses on the expected ease (or difficulty) associated with using StopCoach. All ten participants downloaded StopCoach, as well as all but one of both the healthcare professionals and project leaders who were interviewed . Interviewees discussed experiences with both installing the app, and using the app. Almost all participants thought the application was easy to install, although some needed help with the installation from other participants in their SCC group or the healthcare professional. In contrast, both healthcare professionals and project leaders indicated that installing the app required too many steps to be taken. All participants started using the app after installation, except for P4 who never used eHealth and believed himself too old for this. Several participants and healthcare professionals also believed that older people may struggle with eHealth, for example P6 said:“I have indeed had problems with all of my groups. That somebody had to sit next to them and help them step by step. A lot of older people don’t know their phone very well or get a bit confused.”In line with this, healthcare professional 3 mentioned:“I’ll ask them in advance. See, if it’s really someone older sitting here. I’ll ask them, ‘Do you do a lot on the computer, do you work with apps?’ If they say yes, then it’s fine. If I’ve got somebody that does not have anything to do with such a thing, I don’t go with that.”Healthcare professional 6 only offered the app to participants whom she perceived to have sufficient digital literacy, and linked this to age as well:“I had to get used to it for a while. The first time I had chosen Suzanne, indeed. And she said, ‘I sent you a message.’ And I would be like, ‘Okay, where?’ Then I went looking in the app. That was the only thing that was unclear about it.”With regard to using the app after installation, results showed that most participants, healthcare professionals and project leaders found StopCoach easy to use, and those who commented on the design and lay-out were positive about this . For example, project leader 4 stated that “it is a clear product, for a clear target group”, and she appreciated “the colours, the lay-out, the use of language and the choice to receive messages, or not”. Many participants did have to learn working with the push notifications containing the messages from the virtual coach, such as P6:This participant seemed to have found out quite quickly by herself, and in general appeared to find the application easy to use. However, she did suggest that perhaps the app could explain where to find the messages. Healthcare professionals believed that the app was suitable for the lower-SES target group, mostly because of the accessible language that was used.“You’re not doing it alone, you’re doing it with your coach. Even if it’s someone virtual. You still have a coach that sends a message every day and says you’re doing well. I think that is very important.”Performance expectancy concerns the expected gains or positive outcomes (or lack thereof) resulting from using StopCoach. Participants initially opened the application daily, then every other day, and then weekly, in line with the app’s decreasing intensity of contact after the quit date. Participants who continued using the app, as well as healthcare professionals, appreciated the descriptive statistics, support provided by the virtual coach, practical information, and tips. The virtual coach’s messages appeared to increase self-efficacy, such as explained by P8:“The first two days were very difficult. But because of that app, I purchased a lot of tomatoes and those washed carrots. So, every time I felt like smoking, I took a carrot or tomato. And that app had another tip for if you wanted to smoke, you had to say: ‘I don’t want to smoke, I want…’ So I was like, ‘I don’t want to smoke, I want a microwave.’ And that really helped me out a lot.”Similarly, P6 stated that the messages reassured her of being on the right track when she did not receive support from her social environment, and she would think “well, at least someone thinks I’m doing well”. Practical information and tips provided by the app were perceived as useful; for example, P8 stated that:As such, this participant found that the practical tips offered by the app helped her to quit smoking successfully. She followed up on the advice to eat something healthy when experiencing an urge to smoke, and she focused on what she wanted instead of smoking .Only one participant called the telephone quit-line associated with the app, but most participants did not feel the need as they simultaneously participated in a face-to-face SCC program. Healthcare professional 5 stated that the quit-line option was good, but that this also was the SCC provider’s responsibility.“After eight weeks, it’s finished at once. All it does afterwards is keep on counting . So, either you have to throw it off your phone, or you could receive another message from Suzanne [virtual coach] every two or three weeks, ‘How’s it going?’ It’s just an abrupt ending.”The app’s limited duration (8 weeks) emerged as its major downside, as was forwarded by a number of participants, healthcare professionals, as well as project leaders. For example, P9 said:“Recently, a woman from one of my groups indicated that she was disappointed that the app stopped after eight weeks. She still needed support and I can imagine that. That’s what many people say in my groups after six meetings as well, ‘What a pity that it’s over now.’ They like the app a lot, but the support is getting less and less and then it’s nothing.”Healthcare professional 3 recognized this from her SCC groups, and underscored that both the app and face-to-face SCC programs are perceived as too limited in duration:Moreover, participants who relapsed stopped using the app. They were bothered by how the app continued to track cigarettes avoided and money saved as if they were still in the process of quitting smoking, because these statistics were incorrect following their relapse. One of these participants did mention that she appreciated the possibility to start over, and all participants who smoked at the time of the interview indicated that they would use Stopcoach for a future quit attempt.The analysis showed several factors in the inner and outer setting. These were thematically organized in factors related to blended care, problems with engaging the community, and barriers to SCC more generally that also appeared to affect the implementation of StopCoach.“Even though I really do like it [StopCoach], I think that for people who want to quit smoking, the app alone is not enough. I don’t think that is going to work. You need it in combination with SCC. I think it complements each other very well then.”Most participants and healthcare professionals, as well as some project leaders, agreed that the app would not suffice as a stand-alone solution for most smokers, but should be combined with regular SCC. However, blended care was hardly achieved in practice. project leader 1/healthcare professional 4 believed that blended care would combine the best of regular SCC and the app:“It could be the biggest pitfall for these people, the stress and the tensions. And in a lot of families, there is so much going on, or there is a divorce, violence, or whatever. And you cannot just take that away.”Similarly, P9 stated that “With the app alone, you won’t quite make it.”, and healthcare professional 5said that “If you find it really difficult to quit, you could just ignore the app. It’s a nice tool, but you really need to see it as an extra tool”. Healthcare professional 5 perceived the ease of ignoring or forgetting about the app as an additional risk for lower-SES smokers. She perceived lower-SES smokers as ‘sensitive to letting things take their course’, perhaps because of other (SES-related) issues that they need to deal with, whereas with the app ‘the smoker has to take action himself’. In addition, several healthcare professionals worried that other problems that lower-SES smokers may experience would hinder using the app as well as quitting smoking more generally, as explained by healthcare professional 3:“I noticed that the app tries to provide personal coaching support. The questions we ask people face-to-face or by phone, those are also covered in the app. That’s very good, I think that’s what’s better about the app [compared to other apps].”Project leader 2 suggested to incorporate a social map of the municipalities’ healthcare services into the app to facilitate referral. Participants as well as healthcare professionals appreciated that the app’s content corresponded with the SCC meetings, which allowed participants to use the app for later reference after a SCC session. Healthcare professional 5 said:“If there were any questions from the group about StopCoach, I would answer them. Well, the counsellor even said, ‘Oh how nice, I’m learning from this too.’ And then she came up with questions about the application too. But that wasn’t my job, so to speak. It might have been nice if she had known the app better than we did before we started.”However, in contrast to the app’s compatibility with existing SCC programs and the shared belief that blended care would work best, both participants and healthcare professionals stated that the app was only introduced and installed during the first meeting of the SCC programs. It was hardly integrated with subsequent SCC sessions, other than some healthcare professionals informing whether participants were using the application. For example, healthcare professional 1 stated that “we didn’t use it [StopCoach] very actively, actually. Erm, we helped to install it”, and healthcare professional 2 said that “we did almost nothing with it in the counselling [program].” Healthcare professionals underscored that “it is up to the participants if they will start using the app” , “if they are interested in having the app on their phone” , suggesting that they did not perceive stimulating use of the app as part of their role. Healthcare professionals also indicated that they needed to familiarize themselves more with the app before being able to offer blended care, which required time as healthcare professionals walked through the app in the same pace as participants when using the app for the first time. Some Healthcare professionals intended to blend the app with their existing SCC program in the future, now that they had a better understanding of the app. In line with these healthcare professional accounts, one participant (P6) recalled:One suggestion was made for further development of the app in order to optimize blended care, that is, healthcare professional 1 desired the possibility of monitoring his clients’ progress in the app.“In our hospital we agreed with the communication department that StopCoach will be promoted through television screens.”Some healthcare professionals mentioned that they received support for implementing the app from the organizations they were working for. For example, healthcare professional 3 explained:“StopCoach is a standard topic at these meetings. […] But most of the time it was only some kind of update about StopCoach, like ‘do we already have the app’ and ‘maybe you could visit the sports clubs as well, instead of only the GP.”However, other healthcare professionals stated that it was difficult to engage their colleagues, or healthcare professionals from other organizations in implementing the app. Project leaders encountered similar problems in engaging municipal organizations. Although StopCoach was incorporated in broader health-promoting projects in some of the municipalities involved, the extent to which this facilitated implementation varied. Project leader 3 for example noted that “the municipality said that they wanted to become a smoke free municipality. […] But it is not top priority”. Similarly, project leader 1/healthcare professional 4 explained that the monthly meetings for the overarching project did not add much for StopCoach:Several Project leaders mentioned that they had experienced difficulty with working around organizations’ hierarchical structures, which led to delays in making decisions. For example, project leader 1/healthcare professional 4 had made arrangements with a non-profit organization for disadvantaged individuals to get involved, but his contact persons were volunteers and could not get their superiors on board, such that he had to find a new setting for implementing the app. Project leader 4 stated that “I had my contact person at the municipality, […] but she had a superior as well”, and “one told me A and the other told me B”. She was able to solve this with the municipality as follows: “We came to the conclusion that it is better to have one spokesperson”. Project leader 3 encountered problems with an organization that initially was involved in the project, but then decided to offer the app to their lower-SES employees on their own. After that, project leader 3 recalled that “they did not return my calls, did not respond to emails”, and like project leader1/healthcare professional 4, project leader 3 decided to find different partners for the project.“They [healthcare professionals] started a course, it was a good one. It was a small group in the beginning. But after the course had ended, that group made a lot of promotion, they really became ambassadors. And they have, there is another course right now, but the group has become bigger, so that has been put away in a good way.”In addition to engaging colleagues and organizations, the importance of engaging smokers from the community was underscored by project leader 5 as well, who explained how ex-smokers became involved in promoting the app:“I am really happy that it has been viewed over 3000 times, but I did not receive any phone call because of that vlog.”However, in other communities it continued to be difficult to involve smokers in using the app, such as described by project leader1/healthcare professional 4 who had incorporated StopCoach in a series of vlogs about health:“I invited them all. Everybody said yes. Because everybody seemed to be positive about this app.” However, several project leaders such as project leader 5 saw that healthcare professionals’ initial enthusiasm decreased:“I noticed that during the pilot the enthusiasm lowered, because it is quite hard to let people…Quitting smoking is something, it is not really acceptable for conversation.”The analysis also showed a number of barriers to SCC more generally that hampered the implementation of StopCoach. Project leaders noted that some healthcare professionals lost enthusiasm to work with the app during the project, because of perceived resistance in smokers to consider or discuss quitting smoking. Project leader 4 was one of the project leaders who initially encountered enthusiasm for the project among their healthcare professionals, which was driven by their positive attitude toward the app: “I think that the integral approach exists, or is being created by healthcare teams. […] Or somebody that is really working in that neighbourhood. […] But that is on a municipality level. I don’t know if they, for example, stay in contact with the medical part. I do not have a clear picture of that, but that is what I think.”Similarly, project leader 2 stated that the midwives that he worked with initially offered StopCoach to all of their pregnant patients, but encountered resistance among pregnant smokers whom they had already advised to quit before. For example, healthcare professional 7 who worked in this municipality stated that pregnant smokers “found it very annoying to talk about the smoking again”. The midwives had therefore decided “fair or not, to focus more on the new patients” (project leader 2) in offering the app. Another more general barrier was lack of clarity on referral options for SCC or for psychosocial and socioeconomic problems more broadly, or even a combined, integral approach. Project leader 4 described her confusion as follows:“I think, because I totally support the product [the app], I could sell it with enthusiasm [to the healthcare professionals], to say so, and that I engaged a lot of people [healthcare professionals] this way.”The analysis showed that implementation was facilitated by healthcare professionals’ and project leaders’ motivation to implement the app, as well as their positive attitudes toward the app. Healthcare professionals’ and project leaders’ positive attitude likely resulted from both positive effort and performance expectancies, and facilitated the implementation, such as explained by project leader 4:Furthermore, project leaders’ experience with managing projects and with the healthcare sector emerged as a facilitating factor.Some project leaders mentioned that the delay in the release of StopCoach caused difficulties during the planning phase, and that is was difficult to schedule preparatory meetings with healthcare professionals. Project leaders appreciated the support by Pharos at the start of, and during, the implementation projects, but most did not make use of the possibility to contact project leaders in other municipalities as they perceived these projects as being too different from their own (project leader 5), or because they believed that they did not need it (project leader 4). Project leader 2 stated that “I can imagine that all project leaders could have learned more from each other, from what we have tried and what barriers we encountered”. Finally, project leader 4 mentioned that “I had no idea about what had already been agreed on, and until the day of today, I do not have that information”, as she had replaced another project leader in her municipality halfway during the implementation project. This project leader also seemed to have difficulty getting a clear picture of how SCC and support for other problems common in people with lower-SES, were organized in the municipality.N = 235) were relatively heavy smokers  and 70% had attempted to quit before. Seventy-seven percent did not have a professional SCC coach but used the app as a stand-alone intervention, and the most cited reason to quit was improving physical condition and energy. With regard to app usage, results showed that 85% of participants had enabled push-notifications and 74% had chosen the female virtual coach. In addition, 48% continued using the app after the preparation phase and pre-quit day, and only 9% completed the final step. In line with this, median duration of app usage was 1 day . Having a professional coach and having enabled push notifications were significantly associated with app adherence.The multivariable model included 217 participants, as 18 participants had missing values for the reasons to quit variables. In a sensitivity analysis, the model was fit without the two reasons variables  evaluated their quit attempt positively , with the proportion of participants for whom quitting was more difficult  decreasing over time. It appears that participants were more inclined to adhere to the app and answer these questions if quitting smoking went well, and reversely that smokers who found it more difficult to quit dropped out.Univariable logistic regression analyses showed that participants were significantly more likely to attempt to quit if they had attempted to quit in the past or had a professional coach, and less likely if they lived in Stadskanaal (vs. Roermond), see Table ps < 0.001). Results were similar when these associations were examined only among those who were adherent, i.e. who showed activity on, and possibly after, their quit day.We explored associations between adherence and quit attempts among those with and without a professional coach. In both groups, Mann–Whitney tests showed that those who attempted to quit had used the app longer, had activity in more steps of the app, and had more registered activities in the app  were more likely to attempt to quit, suggesting that the app facilitated their smoking cessation process. It also seemed that participants who lapsed or relapsed were more likely to stop using the app, given that most participants who kept using the app reported that they had not smoked and that quitting went quite well. The current study design does not allow for assessing whether adherence led to quit attempts or vice versa. The app was perceived as less useful for dealing with lapse or relapse, as indicated by some smokers who had stopped using the app after having resumed smoking. In contrast to StopAdvisor, StopCoach allowed smokers to continue using the app after they had smoked, but the motivational messages, information about lapses, and the possibility to restart the app after a lapse do not seem sufficient to ensure that smokers who had smoked kept using the app. StopCoach was also felt to be less useful for facilitating long-term abstinence, as several participants and healthcare professionals found the app’s 8-weeks duration too short. A main barrier to implementation that emerged from the analysis of ‘Intervention’ factors were difficulties installing the app encountered by smokers with limited digital literacy. This occurred despite elaborate testing with low literate individuals and the general finding that the app was experienced as accessible and understandable, and suggests that developing inclusive eHealth interventions remains is challenging .With regard to ‘Setting’ factors, we found that the implementation was facilitated by good compatibility between the app and existing SCC programs, which allowed for blended care . In addiAn important barrier emerging from the analysis of ‘Setting’ factors was the difficulty experienced by project leaders and healthcare professionals to engage colleagues, organizations, the municipality and smokers in the project. This resulted, at least partially, from suboptimal communication at various levels. As a consequence, much of the work had to be done by a small number of people, which threatens sustainability of the implementation , 42. BarThis study has limitations. First, selection bias likely plays a role in the interview sample for healthcare professionals and participants. Most project leaders stated that it was difficult to enthuse healthcare professionals for working with StopCoach for a longer period of time, but the healthcare professionals included in this study were positive about the project. Relatedly, one municipality was not represented by healthcare professionals, as the implementation process lagged behind and healthcare professionals were not yet involved when the interviews were conducted. Likewise, participating smokers were recruited from three municipalities only. App data furthermore showed that most people using the app did not have a professional helping them quit, whereas all but one of the interview participants participated in SCC and were advised to use the app by their healthcare professional. However, all project leaders were interviewed and provided their views on the implementation process, and we used both qualitative and quantitative data, which reduces risks associated with selection bias and helps provide a representative answer to the research questions. Second, although we assessed a number of smoking characteristics during the installation process of the app, we had to be selective in which variables to measure to prevent putting off app users. As such, the exact proportion of lower-SES smokers in the quantitative sample is unknown as SES was not asked . This also means that we were unable to assess associations between SES and both adherence and quit attempts. With regard to the interviews, a small majority of participating smokers (6/10) had a lower-SES, but the sample also contained a number of middle and higher SES participants, reflecting the real-world nature of this study. Although the study was not designed to study SES-based differences in evaluation of the app, we did not observe these either among our ten participant interviewees. Pending further research, this suggests that StopCoach may be useful for smokers regardless of SES, despite the fact that it was developed for the lower-SES target group. This corresponds with research showing that higher-SES individuals, like people with lower-SES, prefer simple language . FinallyThis real-world study demonstrated that it is possible to develop an accessible and supportive smoking cessation app for lower-SES smokers. Results based on interviews with project leaders, healthcare professionals and smokers suggest that future eHealth interventions can be made useful for lower-SES smokers by providing practical guidance using understandable language and visual material, including feedback on progress made, and incorporating a virtual coach that sends motivational messages which address the user by name. In addition, such interventions ideally last longer than 8 weeks, and provide adequate and tailored support for smokers who lapsed or relapsed. The implementation of an eHealth smoking cessation intervention in local settings proves to be a complex process, in which intervention characteristics and setting characteristics seem to play a key role. Given that the implementation of many eHealth interventions fails, the current study provides important insight into factors that can facilitate and obstruct successful implementation. Blended care appears to be both challenging and promising, as it requires effort on behalf of the healthcare professionals, as well as smokers willing to participate in both regular SCC and an eHealth intervention, but at the same time can increase adherence to the app and facilitate quit attempts. We believe that, for individual smokers, blended care combines the best of both face-to-face and digital coaching in helping smokers quit smoking successfully.Additional file 1. Descriptive statistics app users and app usage.Additional file 2. Intervention “De StopCoach”.Additional file 3. Interview protocols.Additional file 4. Background information interview participants.Additional file 5. Smoking status and evaluation of progress.


Migration is an adaptation strategy to unfavorable conditions and is governed by a complex set of socio-economic and environmental drivers. Here we identified important drivers relatively underrepresented in many migration models—CHanging mindset, Agglomeration, Social ties, and the Environment (CHASE)—and asked: How does the interplay between these drivers influence transient dynamics and long-term outcomes of migration? We addressed this question by developing and analyzing a parsimonious Markov chain model. Our findings suggest that these drivers interact in nonlinear and complex ways. The system exhibits legacy effects, highlighting the importance of including migrants’ changing priorities. The increased characteristic population size of the system counter-intuitively leads to fewer surviving cities, and this effect is mediated by how fast migrants change their mindsets and how strong the social ties are. Strong social ties result in less diverse populations across cities, but this effect is influenced by how many cities remain. To our knowledge, this is the first time that these drivers are incorporated in one coherent, mechanistic, parsimonious model and the effects of their interplay on migration systematically studied. The complex interplay underscores the need to incorporate these drivers into mechanistic migration models and implement such models for real-world cases. Migration has always been part of human history since the times before we settled for more sedentary lifestyles in farming communities and, later, town centers and urban cities. Humans are driven to move by a diverse set of factors—environmental, social, economic, political, and so on—and these factors influence one another. Human movements exert feedback onto these drivers, e.g., effects on the environment, cultural diversity and social tension, restructuring of labor markets, contributions to and burdens on public infrastructure, etc. These are serious consequences for the well-being of people and societies. As such, there is a need to understand migration dynamics and their relations to these different drivers, and it is no surprise that many theories and models have been developed on the topic—not only for humans e.g., –5), but , but 5])dynamics and how it is affected by the interplay between these intertwined drivers and feedback mechanisms, a migration model with more explicit representation of some of these drivers that captures the dynamics and feedback mechanisms of migration is needed.Our initial focus was on modeling migration dynamics in the context of environmental change. It was soon realized, however, that it was not adequate or useful to confine ourselves to “environmentally induced” migration because, as discussed above, the drivers of migration are intertwined , 11. To gravity-like, [dα−; and intervening opportunities, [Many migration models have been proposed, each with different strengths, disadvantages, and applicability, , 12–19 aty-like, –22 whereunities, –26 whereunities, , 28; thiAlso lacking in these models is the explicit representation of social ties. Social ties or social networks can strongly influence a potential migrant’s decision on her destination, , 29–37 sAnother assumption in many existing models is that a migrant’s weighing of different drivers remains unchanged throughout a migration episode. This is not always valid. Consider refugees fleeing from a natural disaster or a conflict : theinterplay between those drivers and feedback mechanisms influence transient dynamics and long-term outcomes of migration? Despite its simplicity, the model yielded a rich array of results (some unexpected) that highlight the importance of clearer and better understanding of how these drivers interact to impact migration.To address the issues described above, we developed in this paper a minimalist model of migration that includes CHanging mindset, Agglomeration, Social ties, and the Environment . In developing this model, we strove to strike the right balance between model simplicity and clearer representation of those drivers of migration. We then conducted numerical experiments with the model’s structure and parameters to investigate how they affect migration outcomes. The scope of this work is to provide insights into the dynamics and interplay between the different aspects of migration included in the model. The analysis enabled us to address the question: How does the L patches, randomly placed on a two-dimensional plane  imposed on the potential migrants: not all who want to migrate can actually do so;βt is the exponent that captures the benefit of agglomeration, which can change in time;γ captures the effect of social ties, specifically how much people value others from the same origin;α is the exponent related to the deterring effect of distance ; andC is the normalization constant to ensure that δ have the maximum value of 1 and that Kj may be different for each city j and change in time. Indeed, making Kj dynamic and/or a function of social and environmental factors will greatly enrich the model, but it will also complicate result interpretation. Given the exploratory nature of this work, we aimed for clarify by keeping Kj the same for all cities and constant in time, leaving the dynamical and heterogeneous Kj for future work.The factor ogether,  e.g., econ model . In prinβt is time-dependant. That is,As discussed earlier, a migrant’s priorities may change during the course of a migration episode. When people escape from a conflict or a natural disaster, they may initially consider only the distance to nearby safer locations and only take into account economic opportunities or social ties in subsequent moves. In order to simulate such changes in priorities, we also explored a version of the model in which the exponent βt starts from zero at time t = 0 and it asymptotically approaches 1 as time goes by, with τβ characterizing the speed at which people’s mindsets change. As βt = 0, Here, the exponent βt).As the migration episode unfolds, people gradually give more weight to, say, economic opportunities and social ties  it means that people from all cities are equally represented in city j.Diversity is an important aspect of a society and it might affect the attractiveness of a region in positive and negative ways –45. To qty index :Sj={1∑xδ = 0.2, α = 1, and To clearly investigate the effects of and the interplay among the selected drivers—namely, the migrants’ changing mindsets, human’s tendency to agglomerate, social ties, and pressure people put on the environment—, we varied only the parameters of these drivers, while keeping the rest constant. For each simulation, a new random map with 10 cities was created . At t ∼ 0, βt ∼ 0 and migration probabilities follow βt increases, and the effects of social ties and economic opportunities become more relevant. When τβ = 60  decreases as Kj of each city increases (K), people want to agglomerate into bigger groups. This mechanism reminds one of how farming and domestication gave rise to the first villages in human history.Recall that ncreases . At firsK and the number of surviving cities in the system is also influenced by the strength of social ties (γ) and how fast migrants change their mindset (τβ). Such interplay is more pronounced for an intermediate range of K, around 7,000 to 12,000 when τβ is small, and becomes weaker as τβ increases . In these cases, migrants are more reluctant to leave their native cities for bigger cities, thereby maintaining more cities in the long run. As τβ is higher, however, the number of surviving cities is less affected by γ. A high value of τβ reflects a scenario where migrants take longer to shift their mindsets to include social ties and economic opportunities in their decision making, thereby reducing the effects of social ties on the resulting number of surviving cities. These results highlight the importance of studying not only the effects of different drivers on migration, but also how they interplay with one another.The relationship between the characteristic population size ncreases . In thisγ = 1 there is no special social ties between people with the same background, resulting in well-mixed cities  versus sudden shocks  in the characteristic population size. The strength of social ties, could also be heterogeneous across cities; indeed, different cultures value these social connections at varying degrees. Depending on data availability, the model should be modified and implemented for real-world case studies, as both a predictive tool and a theoretical lens through which we look at the empirical patterns. Additionally, the model may be adapted to describe migration of other social species; indeed the comparison of findings from applying the model of the same structure to humans and to other social species will lead to deeper insights towards a general theory of migration. Given the rich interplay suggested in this work, we suggest that these modifications be done in small steps such that the effects of each modification are clear.To obtain a clear picture of the effects on migration and the interplay among its drivers, only a few of them were selected and a number of simplifying assumptions were made. Despite its simplicity, the model yielded a rich array of results that highlight the In this study, we set out to address the following question: How does the interplay between the selected drivers—changing mindset, agglomeration, social ties, and the environment—and feedback mechanisms influence transient dynamics and long-term outcomes of migration? We did so by developing and analyzing a parsimonious Markov chain model, dubbed CHASE, that incorporates these drivers. Our model results suggest that these drivers interact in nonlinear and complex ways. Migrants’ changing mindset affects not only the transient period but also long-term outcomes, highlighting the importance of including this feature in migration models. Increased characteristic population size of the system can, perhaps counter-intuitively, lead to fewer surviving cities, and this effect is mediated by how fast migrants change their mindsets and how strong their social ties are. Strong social ties also result in less diverse populations across cities, but this effect is influenced by how many cities remain. The overall results paint a picture of intertwined effects of these drivers on migration dynamics and patterns. To our knowledge, this is the first time that these drivers are incorporated in a mechanistic, parsimonious model and the effects of their interplay on migration is systematically studied. The complex interplay underscores the need for migration models to mechanistically incorporate these drivers. Future research directions include adding more realism into some of the model components: making the characteristic population size of each city dynamical and dependent on other social and environmental variables; making the strength of social ties vary across different cultures; perturbing the system with gradual and sudden changes to compare how the system responds; and modifying and implementing the model for real-world case studies. All in all, we believe the CHASE model constitutes a versatile and novel tool to study migration process in a wide range of scenarios.S1 AppendixComplete time series for 100 independent runs of the model using three different combination of parameters used in (PDF)Click here for additional data file.


When the Fun Stops, Stop, is a prominent ‘responsible gambling’ campaign in the UK, originally funded and delivered by the industry-initiated and funded Senet Group. Since the Senet Group’s dissolution in 2020, the campaign has been overseen by the Betting and Gambling Council (BGC), the main gambling industry trade body. There has been no prior analysis of the activities, ideas and framing adopted by the Senet Group, who claimed to be acting as an industry ‘watchdog’ and oversaw what they characterised as a major public education campaign. We collated written and image-based material related to the Senet Group and its When the Fun Stops, Stop campaign from multiple sources. Guided by Entman’s four functions of framing, we analysed the Senet Group’s framing of the issues it sought to address, particularly harmful gambling, as well as its causes, and the solutions, focusing on the group’s main activity: the delivery of the When the Fun Stops, Stop campaign. We also critically appraised an evaluation of the campaign funded by the Senet Group, using the findings to interrogate the stated claims about the campaign’s effectiveness. The analysis showed that the Senet Group’s framing of the problem, its causes, and proposed responses resemble those adopted by other industries and industry-funded groups. This involves portraying any harms caused by their products as limited to an atypical minority, rejecting upstream determinants of harm, and promoting individually-targeted voluntary measures, all contrary to the evidence of what works in health promotion, and what would characterise a public health approach. Neither the existing evidence base nor the evidence presented by the Senet Group support their claims about the campaign’s effectiveness. These findings add to concerns about industry-funded campaigns in other areas. To minimise conflicts of interest, interventions intended to address gambling-related harms, such as public education campaigns, should be evidence-based and developed, implemented and evaluated completely independent of the industry and industry-funded organisations. The global liberalisation of commercial gambling over the past forty years has been justified by governments and industries as increasing choice for consumers while creating jobs and government revenue , 2. HoweThe philosophy underpinning responsible gambling has been challenged in the academic literature –9. ConceA public health approach is also informed by a growing understanding of the commercial determinants of health. This perspective is informed by a body of literature documenting the strategies adopted by different industries selling potentially harmful products to delay regulation by spreading doubt and placing responsibility for harm onto individuals, including the funding of industry-friendly research and public education campaigns, with the latter predominantly focused on individualised determinants of, and solutions for, the problems associated with their products, including gambling , 17–21. In 2014 four of the UK’s then largest gambling companies  formed the Senet Group “… in response to public concerns on gambling, and gambling advertising in particular” . Its web“… an independent body set up to raise standards in the sector, supporting the Gambling Commission’s work to make services safer and fairer ensuring, in particular, that responsible gambling messages are put to players with frequency and prominence” .When the Fun Stops, Stop , the newly formed industry standards body launched in 2019 . The cam“Although overall gambling has fallen dramatically with the absence of sport and due to the closure of betting shop and casino closures, the BGC’s pledges will come into force immediately to help ensure that the highest safeguards are in place and action is taken to protect anyone betting online who may be more vulnerable as a result of the crisis” .The Week magazine, that the campaign was effective in reducing harm  specific regulatory actions. Often this will be through individual or collective voluntary action, through forms of self-regulation on the part of operators and agreements or partnerships with advertisers, broadcasters and others. The Commitments made in 2015 by Senet Group members and complied with consistently are proof that non-statutory solutions that build on and build-in industry commitment can deliver better, faster and at less cost to the taxpayer and consumer” .Their approach to regulation draws heavily on this ‘industry-as-part-of-the-solution frame’, with Senet Group members stressing the need for industry involvement to achieve optimal outcomes: “The best results are achieved when those who are regulated help inform the shape of that regulation.” (Senet Group Chief Executive in response to release of new gambling advertisement regulations) .The Senet Group’s framing of the campaign sought to portray gambling as normally good (an enjoyable experience), while accepting there can be a risk, as in “…an advertising campaign reminding gamblers that when gambling stops being fun then, they should call it a day” , while aWhen the Fun Stops, Stop. As highlighted above, the tagline is framed as conveying multiple different messages: “…..pause and think about his actions” [Previous industry-funded responsibility campaigns, for example by the alcohol industry, have used vague and ambiguous wording, potentially sending mixed-messages by both apparently promoting the product while simultaneously appearing to warn about consumption , 29, 71.actions” , “…they actions” , and “…Wactions” .Elements of the campaign  adopt a humorous or light-hearted tone that can also convey mixed messages. For example, the GIF “never chase your losses”, tweeted by the Senet Group, contained a moving image of a white-haired fluffy puppy chasing its own tail. When presented as a moving image, the word “FUN” from the campaign tagline appears before the remaining words at various times and is accompanied, and emphasised, by twinkles or flashes. In June 2015, it was announced that “Senet has slightly altered the relative size of the words ‘Fun’ and ‘Stop’ in its yellow advertising strip, ‘When the Fun Stops, Stop’, to bring the two symbols into better balance” . HoweverIn a 2018 BBC Radio 5 live interview, the Senet Group Chair did acknowledge that concerns about the approach adopted by the campaign had been raised, but then countered them by arguing that a different approach is likely to backfire: “It’s a challenge, I mean some people say for example we shouldn’t have the word “When the Fun Stops, Stop” we shouldn’t have the word fun, but if you lecture people you can have the opposite effect” .The Senet Group framed the campaign, its content, and approach, as being consistent with methods of health messaging and awareness-raising known to be effective. In particular, the campaign and surrounding discourse emphasised the effectiveness of humorous content used repeatedly across multiple platforms:“We have found the best results are achieved when the messages are written in informal language and communicated consistently across an assortment of platforms. We do not preach or seek to scare, but rather speak in a way that will encourage gamblers to consider their own habits and to speak to family members and friends if they think they might need help.”  .Their claims did, however, reveal apparent confusion about who they were targeting: “We are clear, however, that the light and conversational tone which has worked with the generality of players with gambling problems is not appropriate in messaging activity that is targeted specifically at problem gamblers.” (Senet Group response to the DCMS 2017/18 consultation)  Yet “SenThe effectiveness of the campaign was often framed in terms of its impacts on awareness and education, assuming causal associations between the campaign and behaviour change. For example, the Senet Group claimed that “…the campaign has helped over a third of regular gamblers control their gambling, and more than two million people have directly quoted ‘when the fun stops, stop’” . This cl“Today, surveys show over 80% of players are aware of the messages and tips in the Senet Group’s responsible gambling campaign and it seems clear these have resulted in millions of players changing their behaviours and millions of others feeling more confident to raise gambling issues with a friend or family member” .Frequent claims were also made about effects on awareness and self-reported behaviour, including “…to approach gambling more responsibly” .The success of their approach is presented, at least implicitly, as self-evident, as in the promotional film for the campaign, where the Senet Group Chair states that “…when the fun stops, stop works because it’s simple, people understand it, they like it and its effective and it changes behaviour” . This asAs emphasised by Entman , what isWe located five campaign evaluation documents from the Senet Group website, covering the period 2015–2017 . Using the AXIS tool for critically appraising the quality of cross-sectional studies , we critOur analysis demonstrated that the Senet Group framed gambling as a leisure activity which is undertaken safely by the majority of gamblers. Gambling was dichotomised: fun (safe) or not fun (unsafe). Those that are harmed, described as a minority, have “lost control”, no longer have “fun”, and are presented as vulnerable in some shared way which is independent of their engagement with gambling. Prescribed solutions were based on industry-led provision of information on responsible use. This framing aligned with the design of their public awareness campaign. The simplistic dichotomy presented by the Senet Group—fun (safe) or not-fun (unsafe)–and now maintained by the BGC through their adoption of the campaign–risks undermining what is a far more complex picture that requires a more nuanced approach similar to that taken to address other harmful, addictive products, and the industries that produce and market them . Yet theWhen the Fun Stops, Stop campaign, do not reflect public health approaches but appear to align closely with what has been found in previous research on industry-funded initiatives, including those falling within the corporate social responsibility (CSR) concept [The Senet Group’s framing of the problem, its causes, and proposed remedies, which underpin the  concept , 77–79.  concept ; (2) fra concept , and (3) concept , 81. In The Senet Group also asserted the need for industry-led interventions, such as public awareness campaigns, even though these have often been found to promote the product or industry in question –30, a reagency, to address significant public health issues are also least likely to be effective, and more likely to deepen inequities [The Senet Group portrayed their campaign as underpinned by research, referring to an evaluation conducted between 2015 and 2017. They claimed that their ideas and campaign were informed by evidence and knowledge of what constitutes effective public health messaging, endorsing the use of humour and asserting that government-led interventions are inferior to using peer-pressure. This is not supported by the evidence on the effectiveness of such approaches. For example, mandated tobacco health warnings contain graphic imagery that leverages disgust and fear , 87, enhequities . Such apequities .By framing the issue as affecting a minority who are weak or prone to addiction compared to a majority comprised of “millions” who enjoy gambling safely, the Senet Group’s framing potentially contributed to the perception of the problem as one of a separate minority group in need of professional help–as opposed to a wider public health issue. This has important implications for public health. A substantial body of evidence exists to show that when individuals are provided with information that creates, even if false, the perception that an activity or product is very common, thereby creating ‘social norms’, then they tend to feel unusual if they are not engaging with it, which in turn influences their behaviour . Such frTo our knowledge, this is the first analysis of the provenance, ideational and evidence base of a major industry-funded gambling awareness campaign. However, there may be additional unpublished data that we could not locate. We also cannot comment on the motivation for adopting certain framings or how effective they have been. Future research could explore the influence industry-supported groups have on policy and public opinion, for example, and how particular framings are adopted by the mass media and in policy discourse. To our knowledge, no independent research assessing the unintended impacts of the campaign has been conducted. This is particularly relevant for children, who may be exposed to the campaign through land-based gambling venues, and mass and social media, for example when using apps . Further“"When the fun stops, stop." Really?…Can you imagine the authorities allowing Big Tobacco to present smoking in the same "fun" way, knowing everything we do today about the dangers of inhaling the fumes from burning tobacco? When the cancer starts, stop?” .Here we have demonstrated that the dominant framing adopted by the Senet Group aligned with industry interests and resembled those employed, for several decades, by other industries which also sell harmful products and those they support as a form of CSR. It reflects wider challenges in the UK and internationally, that is, the mismatch between interventions which the evidence suggests will be effective in preventing gambling harm, and those that are delivered in practice, and the role of industry and industry-funded bodies in the design and delivery of health information and interventions .When the fun stops, stop, the Senet Group continued to assert the success of the campaign and announced that it intended to launch a “next generation” of the campaign [Although the Senet Group, and others, acknowledged the need for new messaging campaigns that go beyond campaign . While dcampaign , our fincampaign , 103, buThe Senet Group’s framing of the problem, its causes, and responses deemed acceptable resemble those adopted by other industries who produce and sell harmful products, and the groups they fund, when seeking to influence policy and issue-framing . Our anaOur findings have implications for gambling-related policy and public health practice, while contributing to a growing body of evidence on the impacts and implications of industry involvement in addressing the issues caused by their products. To minimise conflicts of interest and barriers to progress, public health interventions that seek to address gambling-related harms, such as public education campaigns, should be designed, implemented, and evaluated independent of industry and industry-funded organisations.S1 File. BMJ Open. 2016;6(12):e011458) .(PDF)Click here for additional data file.


During the coronavirus pandemic of 2020, the migrant farmworker came to be deemedas ‘essential’ worker, thereby complicating discourses of ‘illegality’. Thismoment presents an ontological paradox, allowing means for examining thesimultaneity of discursivity and materiality within the migrant farmworker assubject. Reflecting upon the ‘ontological turn,’ this article presents fourlessons of ontology: post-representation, a focus on objects/artifacts,posthumanism and the politicizing of ontology, gained from interrogating theunraveling of discourse about the migrant-other in the time of coronavirus.These lessons coalesce in the entwining of the powers of discourse with thepresence of the tangible, to understand the profound contradictions between asocial structure that doesn’t want the migrant as neighbor and a capitalisteconomy that needs her as laborer. In closing, the article considers howshifting the gaze inward as consumers and then outwards unto our relationalitycan be a political act. New York Times article on the coronavirus crisis of 2020, theheadline stated that “Farmworkers, Mostly Undocumented, Become ‘Essential’ DuringPandemic” , it also heightened awareness about the agriculture industry’smigrant farmworker who labors to produce our food.The pandemic gives us the rare occasion to come face to face with a fundamentalcontradiction of our capitalist society: our demand for migrant labor and inclusion ofmigrants in our economic sphere but exclusion of migrants in our social sphere asneighbors .The balOver the last decade, academic fields have grappled with the “ontological turn,” fromcultural anthropology to technology studies. Social work scholars too have discussedontology see , a discuand discursiverealities”  isneeded in the first place to better situate what is at hand (detailed next) andenvision realities moving forward (discussed in closing).Ontology is usually a matter of philosophical and conceptual abstractions. But ourcurrent moment—the migrant farmworker in the time of Covid-19—offers an opportuneoccasion or case for reflecting on the ontological turn as evidenced in the realworld. This empiricity offers us a moment to “recalibrate the level at whichanalysis takes place,” , a momen4It has perhaps become impossible to dismiss how U.S. political discourse hasincreasingly vilified migrants, especially undocumented migrants. Through aconstructionist ontological lens, this discourse subjectifies the migrant, forgingrealities that result in her detention, separation from her family, suffering, andeven death. But with the pandemic, the federal government was cornered into areversal: a different representation of the migrant farmworker as essential. Thatshift was sudden, occurring as quickly as the new coronavirus spread. And the shiftcould not be more opposing, from the illegal worker who wasdeportable to the essential worker who was heroic . Indeed, for indigenous thinkers, the environment has long been “acommon organizing force” (Jordan’s article continues with labor unions, activists and advocates, farm owners oremployers, government policies and policing, and the migrant herself in Ms. Silva,illustrating the relationality of things, the non/human assemblage of actors,institutions, infrastructures, environments and industries that are necessarilyconnected to the migrant farmworker. Here, we come to our final lessons of ontology:A shift in the migrant-other necessarily entails a shift in the consumer-us and theworlds we inhabit. Shifting the gaze inward as consumers can be a political act, anaction designed to attain a purpose through the use of power. If “the real” isrelational and processual or dynamic, and if we-as-consumers are on the other sideof the migrant farmworker in this relationship and dynamic, then we compose the realand we can make the change happen. Possibility and potential constitute ontology.Amidst the pandemic, during a migrant farmworker advocacy videoconference , food ac


Tangible happiness, Affective happiness and Serene happiness. We noted more differences in the associations with “unhappiness,” for which the two groups share only two clusters: Loss and Everyday problems. A distinct third cluster, Affective unhappiness, emerged for the younger women, whereas older women’s associations are further differentiated into a more complex structure, including two more clusters: Dejection and Apprehension. Additionally, CA shows that in both age groups, self-reported happiness levels do not discriminate which words are associated with happiness and unhappiness. Finally, qualitative content analysis of a questionnaire item investigating how to reach complete happiness suggested that there are three recurring answer types: happiness can be improved through external changes, internal changes, or not at all because complete/permanent happiness does not exist. The study provides a methodological design which, unlike most happiness studies, allows participants the freedom to bring up the meaning of happiness and unhappiness. Thus, the study constitutes a contribution to a more nuanced understanding of happiness.This paper presents results from a study exploring representations of “happiness” and “unhappiness.” Word associations with these concepts were produced by 16–18 and 29–34-year-old women from Finland, the country that the United Nation’s World Happiness Report has ranked the “happiest” in the world. Correspondence Analysis (CA) and Hierarchical Cluster Analysis show that participants in both age groups share three clusters of words associated with “happiness”:  Other researchers . And yet, except for a limited number of studies . On the other hand, there were different ways of understanding happiness: the Chilean group identified happiness with fullness, well-being and enjoying life, while the Italian group found more importance in interpersonal affections.In contrast, in his cross-cultural studies on the social representations of happiness in Italy and Chile, Rodríguez-Araneda  has focuOf direct relevance to the present research is the study by Shin et al.  were parSuch results are intriguing because they challenge the idea of a universal definition of happiness, emphasizing the role of cultural differences as well as the need to explore different ways of understanding the same word.meaning of happiness in particular start with the premise that the nature of this concept is resistant to any definition. Contributing to the difficulties in finding a unanimously accepted definition of happiness is the fact that in many languages this concept does not have a clear opposite. In the English language, for example, the term used most often to indicate the opposite of happiness is ‘unhappiness.’ The prefix ‘un-,’ meaning ‘not,’ is used as an English formative to give negative or opposite force in adjectives and their derivative adverbs and nouns.Most studies on happiness in general and those focusing on the onnellisuus (‘happiness’) cannot be turned into its opposite by adding a prefix. In spite of this linguistic challenge, and the much-debated first-place position of Finland in the WHR notwithstanding, the question of binaries is of great significance when regarding emic approaches to the processes of sense-making of happiness and unhappiness. For example, the latest available statistics regarding mental health in the Finnish population show that the level of depression in the general population is high, and the incidence of depression in women is higher than for men across all age groups  are born between the 1980s and 1990s, and Generation Z, whose members were born in 1995–2015. A comparison between the two generation is compelling for several reasons, but mainly because, having experienced social media and the advent of the internet at different stages of life, these two cohorts privilege different modes of communication and socialization, values, motivations, and attitudes , we suggest that exploring everyday understanding among women is a fruitful starting point. In the present study, we specifically focus on women belonging to two particular age groups: 16–18 and 29–34-year-old women. We have a twofold rationale for selecting these age groups. First, we are striving to represent the two different generations known as  Wiedmer , as it i Wiedmer  and unde Wiedmer . Based o Wiedmer  and Offi Wiedmer , we haveWhile there is no formal agreement on how happiness should be defined and operationalized, most scholars would agree on the societal saliency of this construct. Library shelves are overflowing with self-help books urging readers to follow the “right steps” toward happiness, dozens of mobile device applications promise users to assist them in tracking their happiness and implementing mindfulness in their daily routines, and most parents would probably declare that “they want their children to be happy” when discussing educational goals.increase happiness levels. For example, according to Ojanen  common understandings to communicate about and make sense of the object in question. Thus, we propose that the local understandings of happiness in countries and cultures form (a) social representation(s). That is, making sense of happiness is a kind of commonsense that is elaborated, shared and continually renegotiated in everyday communication occurring between individuals and groups, as well as through media and social media platforms.Social Representations Theory (SRT) acknowledges the coexistence of “scientific” and “commonsense” (or lay theories) and their functions in different fields of knowledge  and 189 adult women (29–34 years old). The two age groups were recruited respectively in 2016 and 2018.The 16–18-year-old group was recruited from nine upper-secondary schools located in the Helsinki region. We selected upper-secondary schools located in various districts of the urban capital region and specializing in different subjects. In Finland, students can choose themselves which upper-secondary school to apply for, regardless of their domicile. Moreover, many upper-secondary schools allow students to focus on certain areas defined by a national program for school specialization .The first author contacted the principals of the selected schools and booked an appointment with the ones that were interested in participating in the study. No compensation was offered and the participants were recruited on a volunteer basis. The participants were then given approximately 45 min to fill in a paper version of the questionnaire.The 29–34-year-old group was recruited through a link to an e-form version of the questionnaire share on Facebook. As radically different as it may seem to recruit a second group of participants online when the first one was given the option to fill in a paper version, we argue that it would be virtually impossible to replicate the data collection system implemented in upper-secondary schools with a group of adults while simultaneously retaining the same variations in terms of geographical location and area of expertise of the participants. The link to the e-form was shared on various closed Facebook groups. These included groups for local communities in different areas of the Helsinki region , parents’ groups linked to different neighborhoods , and women-specific groups . Since most closed Facebook groups are intended for users fulfilling specific requirements , the primary author contacted the admin of each group through Facebook’s private message function, in order to introduce the general aim of the research and ask for permission to share the link to the e-form in the group. Once we obtained permission, we made posts asking those members of the groups fulfilling the participation criteria to fill in the e-form. After participants ticked the box indicating that they had read and understood the informed consent form, they had access to the online questionnaire.onnellisuus (‘happiness’). Subsequently, the task asked participants to share what, in their view, represents onnellisuuden vastakohta (‘opposite of happiness’), by writing down the first five words that first came to their mind when thinking of that. As Finnish language does not have a proper equivalent of the English term ‘unhappiness,’ we considered that forcing on the participants an assigned term to define the concept  would have been antithetical to the basic premises and aims of our study. However, we would like the reader to notice that in the present paper, for the sake of consistency with previous studies and fluency in English language, we use the term ‘unhappiness’ to refer to the ‘opposite of happiness’.We collected the data through a word association task located at the start of a longer questionnaire designed by the first and third authors. The stimulus words, as well as the rest of the questionnaire, invitation, and informed consent form, were presented in Finnish. Participants were asked to write down, without thinking too much, the first five words or ideas that first came to their mind when thinking of the stimulus word Word associations comprise a technique frequently used for the analysis of social representations of topical issues  and, more rarely, short sentences  or words in pairs . Whenever possible, short sentence were reduced to the most similar single word  and words appearing in pairs would be merged with single words, taking into account the first word mentioned by the participant . After the edited lists were organized in both alphabetic and frequency order, we adopted a twofold criterion as guidance in the synonym reduction process: the criterion of a common root word , omat lapset (‘own children’) and lapset (‘children’) were categorized under the word category <children>, which was the most frequent variation of the term. Similarly, the words ilo (‘joy’) and iloinen (‘joyful’) were categorized under <joy>, which again was the most dominant variation of term. This phase was particularly relevant, considering that Finnish is not an Indo-European language but rather a Finno-Ugric language belonging to the Uralic language family. As such, Finnish is an agglutinative language, meaning that words are composed of a string of morphemes representing single grammatical categories.The associations generated were prepared for statistical analysis by the first author following the guidelines set forth by Wagner , p. 7 ofrahaongelmat (‘money problems’) and taloudelliset huolet  were grouped under <money/economic problems>, since there existed a rather wide range of heterogeneous terms used to express the same concept. In this phase, the first author cooperated closely with the third and fourth authors, who are native Finnish speakers, to find a common agreement regarding which minor semantic variations could be retained in order to preserve the integrity of the data.After completing this first selection procedure, the associations were also homogenized according to semantically equivalent terms. For example, N > 10) in the list of one of the age groups but low (or zero) frequency in the other age group were included as well.Finally, the newly compiled lists of associations with the concepts “happiness” and “unhappiness” were separately ranked by frequency in each age group. The two resulting lists were compared between the two age groups and homogenized as follows: for each of the four lists, we selected the 20 words with the highest frequency. After that, we looked at the differences in the top 20 “happiness” words between the two age groups. If a word figuring among the “top 20 words” for “happiness” in the younger group list occurred at least five times in the “happiness” list compiled for the older age group, then the term was selected as a final common shared category. The same rationale was used to inspect the lists in the reverse order (older age group to younger age group), and the entire procedure was then repeated for the two lists of words associated with “unhappiness”. Terms that had a high frequency  associations with ‘happiness’  associations with ‘happiness’ (29–34 years old.); 3) associations with ‘unhappiness’ (16–18 years old); and 4) associations with ‘unhappiness’ (29–34 years old). The associations making up the four lists and their respective frequencies are reported in Table We used the data reported in Table cosine distance) between the word associations, as this is the most indicated metric for measuring variable distances in exploratory settings where the vectors’ magnitude of text data is not relevant. We calculated 3 to 6 clustering solution dendrograms for each of the four word lists and mapped them onto the CA plots  over Dimensions 1 and 2, which emerged from the CA. When calculating the HCA, we considered the cos(θ)  that gave the most meaningful/interpretable results with happiness-related terms: Tangible happiness, contains words expressing concrete aspects of happiness, embodied in the form of people , material assets , activities  and places . Tangible happiness is the repertoire that contains both the highest number of associations and the highest number of differences between age groups. More specifically, the words ‘music,’ ‘sun,’ ‘summer,’ ‘sports’ and ‘warmth’ make up a big part of Tangible happiness for younger women, whereas older women do not mention these terms, and include the term ‘children’ in this repertoire.The first semantic repertoire, Affective happiness contains words that refer to emotional states  as well as physiological  and expressive  responses commonly associated with happiness. The words contained in this repertoire are nearly identical between the age groups, with the exception of the word ‘positivity,’ being mentioned only by younger women and ‘security’ and ‘warmth’ being mentioned only by older women.The repertoire Serene happiness represent long-term desired life standards, ranging from more abstract constructs  to more concrete expressions of states of being . Younger women include also the term ‘health’ in this repertoire, whereas older women include the term ‘calm.’Finally, the words contained in the repertoire The repertoires that emerged when exploring the associations made with “unhappiness” were less structured than their “happiness” counterparts. Moreover, there are clear structural differences between the age groups.Loss, Everyday problems and Affective unhappiness. For the older women, the representations of unhappiness appear much more fragmented and heterogeneous.For the younger women it was possible to identify three repertoires, which we have named Loss, which includes associations with the loss of something positive: sickness is a loss of health, poverty is a loss of wealth, boredom a loss of meaningful/interesting activities and so on. Therefore, this repertoire refers to conditions of deprivation, where the things that are missed create a state of misery.Both age groups share the repertoire Everyday problems, like the first repertoire, is shared between the two age groups, and it reflects a more mundane array of issues which can be considered more transitory and part of everyday challenges.The repertoire Affective unhappiness, which contains terms referring to a “general idea” of unhappiness, including an array of negative emotional states  and related responses . The associations produced by the older women generated a third and a fourth repertoire, which we named respectively Dejection and Apprehension. As suggested by the term, Dejection refers to dejection-based feelings, characterized by a state of depression or tiredness. On the other hand, Apprehension includes associations related to the state of alertness induced by fear, anxiety, stress, etc.Younger women present a clear third repertoire, The self-rated levels of happiness on a scale from 0 to 10 were divided into three categories as follows: low (0–7); medium (8) and high (9–10). These were added to the co-occurrence tables as passive variables when calculating the CA. Figures If your answer to the previous question wasn’t 10, can you think of what would have to change in your life so that you could answer ‘10’?” were analyzed by using qualitative data-driven content analysis. We adopted an inductive analytical approach, beginning with careful readings and re-readings of the material followed by generating data-driven codes. Finally, we searched for broader themes under which to categorize the codes generated while going back and forth between the code list and the original text. The codes and the related themes that emerged are presented in Table The answers to the open-ended question item “Our analysis suggested three recurring answer types: happiness can be improved through external changes, internal changes, or not at all because complete or permanent happiness does not exist. We further elaborate on the significance of these answers in the discussion section below.The main objective of this study was to explore what kind of everyday notions young (16–18-year-old) and adult (29–34-year-old) women in Finland have about happiness and unhappiness. Additionally, we sought to explore how these understandings related to the participants’ self-reported happiness levels and what they thought about reaching the highest possible happiness level.Tangible happiness, Affective happiness and Serene happiness. Although the structure and contents of these repertoires greatly overlap between the two age groups, there are also some interesting differences. For example, in Tangible happiness, we noted that younger women mention words that are intuitively closer to “enjoyment” . On the other hand, when we look at the same semantic repertoire in the data of the older women, these “enjoyment” words are missing, while the word ‘children,’ which did not appear among younger women, clearly has an important focus. This is perhaps not surprising, considering that according to Statistics Finland, 30 is the average age for women in Finland to get their first baby. These variations in the concrete elements associated with happiness between the two age groups corroborate previous Finnish studies; for example, Pessi  antecedents. In other words, this repertoire resonates with the question What makes people happy?The findings of the present study highlighted that the social representations of happiness in both age groups are structured in a very similar way. Three clear semantic repertoires emerged from the HCA: e, Pessi  found thAffective happiness, which is nearly identical between the age groups, includes both notions of positive affects and the way positive feelings are externalized and made concrete. The associations contained in this repertoire align with the “folk prototype” of happiness proposed by Kovecsez  are more noticeable when looking at someone else from the outside.The repertoire of Kövecses , charact” Danesi , p.11. ASerene happiness can be intuitively connected to more stable connotations of happiness, which have a longer, albeit less visible, lifespan compared to Affective happiness. Instead of fleeting emotions, the third semantic repertoire hints at a more harmonic vision of happiness, closer to the idea of fulfilment and satisfaction. Interestingly, younger women include ‘health’ in this third repertoire, whereas for older women, who have probably had a few more years to encounter some health-related problems (either directly or indirectly), ‘health’ is instead part of the Tangible happiness repertoire.Finally, the repertoire Loss and Everyday problems. A distinct third semantic repertoire, which we named Affective unhappiness, emerged for the younger women; it resonated—and, to a certain extent, mirrored—with the content of the semantic repertoire Affective happiness. Thus, we suggest that Affective unhappiness might be the way younger women turn the vagueness elicited by “unhappiness”As regards to ‘unhappiness’, the various repertoires that emerged when were less homogenous than their “happiness” counterparts. In terms of “unhappiness,” the two groups share only two semantic repertoires: Dejection and Apprehension. When thinking or discussing about mental disorders, there is a chance that most laypeople would name something like depression and/or anxiety. Depressive and anxiety disorders are in fact the two main categories of mental disorders, and they are considered “common” because they are highly prevalent in the general population . On the other hand the semantic repertoire Apprehension, which locates unhappiness around notions supposedly derived from the category of anxiety disorders, is characterized by words that suggest overall restlessness .Older women’s associations with “unhappiness” are further differentiated into a more complex structure, which includes two additional semantic repertoires: tion WHO . We deemwhat makes you happy?’) and the meaning of happiness as a concept (‘what is happiness for you?’). Overall, these studies have highlighted cultural differences as well as similarities in conceptualizations of happiness, identifying social relationships as one of the most universally shared component of happiness  for happiness/unhappiness. As mentioned in the introduction, little is known about laypeople’s understanding of happiness. So far, the relatively limited number of studies which have focused on common sense notions of happiness appears to be heterogeneous in respect to the phrasing of the questions employed to explore definitions of happiness, which can range between its antecedents  leading to greater happiness as an example. While the answers in the theme External-circumstance would have mentioned this aspect simply by indicating “more friends” as a solution, answers in the theme Internal-self looked like the following instead:I don’t know [what would have to change so I could reach 10 on a 1–10 happiness scale]. Maybe nothing? But I could learn to find comfort in myself in the (rare) moments in which I feel lonely: learning to be alone.The second theme that emerged from the data, 10 is unattainable, includes answers which, unlike the previous themes, challenge the setting proposed by the researchers, arguing that “10” is a bogus number on the happiness scale considered, which is supposed to be there only to remind us that complete happiness is elusive. The reasons given (indicated through the sub-themes) include the fleeting nature of happiness, which, by default, prevents us from answering “10” at any given time, or, on the other hand, the tendency to believe that there is always “something better around the corner,” as indicated in the following extract:It’s difficult to ever say 10, when you never really know [even when you are happy] how much happier you could still be.To sum up, while most people seem to attribute the possibility of becoming happier to changes which can be either external or internal, a minority thinks that complete happiness is unattainable. While there is empirical evidence showing that people who believe they can influence their level of happiness are in fact happier than those who do not believe they can  to implement such changes. In contrast, considering the highest possible level of happiness as “unattainable,” and thus aligning with representations of happiness as an “elusive” state rather than a goal to reach, might in the end translate into higher levels of self-assessed happiness. This vision of happiness resonates in the famous folk wisdom quote “Happiness is a journey, not a destination,” often seen printed on cards, coffee mugs, fridge magnets or shared on the internet.Interestingly, the third and final theme identified through our analysis, named n Ojanen , we thinThe present study presents a number of limitations. First, although justified because of the higher incidents of depression among women in the Finnish general population, our decision to focus on the way women understand happiness and unhappiness in Finland would have yielded clearer results if compared to social representations of happiness/unhappiness among Finnish men.Generations Y and Z and on ages 18–16 and 29–30 being two relevant life periods for Finnish women, we would like to acknowledge that there are other age groups , which in the future should be included in such studies, as they might be likely to produce different understandings of happiness.Secondly, although we had reasons to select two specific age groups based on Finally, we would like to invite the reader to note that CA is a thoroughly descriptive technique; thus, results showing the relationship within words, as well as the relationships between words and self-reported happiness levels, are not based on inference statistics but on an exploratory design. Future studies could be more creative in coming up with mixed-method designs to move more fluidly between qualitative, non-parametric and parametric approaches.In spite of the limitations discussed above, we believe our findings contribute to a more nuanced understanding of the way laypeople make sense of the abstract concept of happiness.Tangible happiness (what makes us happy), Affective happiness (what happiness looks like), and Serene happiness (what long-lasting happiness is). If, as our findings suggest, the objectification of happiness relies on a common understanding of what happiness is supposed to look like (Affective happiness), we can understand why Finland being reported as the “happiest country in the world” can potentially clash with the discourse of laypeople around Finns being emotionally introverted people. We suggest that this clash constitutes a possible explanation why Finland’s position in the WHR was received at the same time as a “joke” and as something which “makes sense,” as the young woman cited at the beginning of this paper pointed out.First, our findings suggest that happiness is understood in three similar ways among both Finnish young and adult women: Affective happiness which can be mirrored in the objectification of “unhappiness” (Affective unhappiness), at least among younger women, it also seems that there are other semantic repertoires or understandings of happiness which do not find such a perfect semantic match in their unhappiness counterpart. Thus, to answer the question posed in the title of the present paper, happiness and unhappiness do not seem to map onto the figurative heads or tails of the same coin, but rather seem to constitute two separate dimensions, or two different “currencies,” constituting two separate sets of meanings. This finding could be considered a plausible explanation for why the same country could score high in the World Happiness Report and, at the same time, have high depression rates. Based on our results, we argue that releasing survey results on life satisfaction/quality of life using the term ‘happiness’ (such as the WHR), on the one hand, and the assumption that depression rates can be regarded as symmetrical or a continuous opposite of happiness, on the other hand, are not in line with everyday understandings of happiness and unhappiness.Secondly, our findings suggest that the social representations of “happiness” shared by Finnish women are not completely antithetical with their representations of “unhappiness”; more specifically, those of “unhappiness” are more complex and heterogeneous than their “happiness” counterparts. Thus, Tolstoy’s words “All happy families are alike; each unhappy family is unhappy in its own way”—reflecting the idea that happy people share a common set of attributes which lead to happiness while unhappiness yields greater complexity and diversification—seem to hold a kernel of truth when looking at our results. While there is an Finally, while self-rated levels of happiness do not seem to elicit associations to either happiness or unhappiness, we propose that there are different understandings of how (and if) the highest levels of happiness can be reached, namely, a change of external circumstances, a change within the self or, finally, acknowledgement of the idea that reaching the highest degree of happiness at any given time is simply impossible.In conclusion, our study offers a new means to study happiness, starting with the way in which laypeople understand the concept and extending to how they understand its opposite.As lay discourse is understood to be culturally and historically situated, we believe that taking into account emic approaches in cross-cultural studies on well-being could have a number of practical implications.First, delving deeper into cultural differences and similarities of the notion of happiness (and unhappiness) could provide tools for developing new models and approaches to empirically investigate happiness as a universal yet cultural-specific concept. From a methodological perspective, most scaled instruments used in surveys measuring happiness, are in fact designed in academic contexts and directly applied to participants without consideration for local processes of sense-making of happiness and unhappiness, and without critical awareness (Christopher et al. Secondly, in line with Delle Fave et al.  we belie