We calculated descriptive statistics and applied structural coding and content analysis to qualitative data. Results: At least 75% of participating centers secured health care system administrative, clinical, and IT leadership buy-in and support. Six themes emerged from interviews: engaging leadership, access to resources, leveraging federal funding support to build leadership interest, designating champions, identifying training needs, and ensuring staff roles and IT systems support workflows. Conclusions: Buy-in among staff and clinicians is defined by the belief that the TTP is necessary, valuable, and evidence based. Recognizing and securing these dimensions of buy-in can facilitate implementation success, leading to improved cancer outcomes.Background: Delivering evidence-based tobacco dependence treatment in oncology settings improves smoking abstinence and cancer outcomes. Leadership engagement/buy-in is critical for implementation success, but few studies have defined buy-in or described how to secure buy-in for tobacco treatment programs (TTPs) in cancer care. This study examines buy-in during the establishment of tobacco treatment programs at National Cancer Institute (NCI)-designated cancer centers. Methods: We utilized a sequential, explanatory mixed-methods approach to analyze quantitative data and qualitative interviews with program leads in the U.S.-based NCI Moonshot-supported Cancer Center Cessation Initiative ( Approximately 24% of patients with cancer smoke cigarettes at the time of diagnosis . DeliverMultiple factors at the patient, clinician, and health system levels influence the success of tobacco dependence treatment program implementation. Patients diagnosed with cancer may not be ready to quit, want to quit smoking on their own , or be aMitigating such barriers requires obtaining buy-in to successfully implement and integrate TTPs as part of routine cancer care. Buy-in from clinical leadership and oncology clinicians specifically is a critical factor for the implementation success of evidence-based programs in behavioral health and primary care settings ,13. EvidIn 2017, the U.S.-based National Cancer Institute (NCI) initiated the Cancer Center Cessation Initiative (C3I) as part of the Moonshot program with the goal of integrating and enhancing TTPs in routine oncology care . C3I hasIn this mixed-methods sequential explanatory study , we condEvery six months, the C3I Coordinating Center collects data from C3I cancer centers via a web-based Qualtrics survey. Quantitative items assess cancer center and TTP characteristics, staffing, the status of implementation activities, and aggregate data of program reach and effectiveness among patients for the prior 6-month period. The biannual report is described in more detail elsewhere .For the current study, to answer our first research aim , we analyzed 5 items evaluating implementation activities to assess cancer center/health system leadership and clinician and staff buy-in with the following response options: not applicable, not started, in progress, completed, and in maintenance . In addiTo address the second aim of our study , we performed secondary data analysis on interview transcripts of C3I program leads and co-leads. The C3I coordinating center principal investigator (BR) and project scientist (HD), both PhD-level researchers with training in qualitative approaches, conducted in-person interviews with up to 10 program team members at each center between September 2018 and October 2019. The purpose of the 30–60 min interviews was to obtain in-depth perspectives on the TTP and its implementation across C3I centers. The interview guide (available upon request) included 12 main questions with probes to better understand the process, barriers, and facilitators of implementing an evidence-based TTP in oncology care. Interview participants were informed of the purpose of the interviews, which were audio-recorded and professionally transcribed. For this analysis, we included only transcripts from program leads representing the 20 (of 22) Cohort 1 C3I centers that participated in the qualitative interviews and submitted quantitative report data for the July to December 2019 reporting period. One Cohort 1 C3I center did not submit quantitative report data for this reporting period, and one did not participate in qualitative interviews, so both of those centers were excluded from the current analysis.The aim of the qualitative analysis was to understand how program leads described buy-in, how buy-in was obtained, and the ways in which levels of cancer center leadership and clinician buy-in influenced TTP implementation. We utilized a homogenous, purposive sampling approach for this analysis , in whicQuantitative data were analyzed using SPSS (Version 27). Descriptive statistics were calculated for program characteristics (items are listed in n = 30) of program lead/co-lead interviews across 20 C3I centers that submitted data reports for the June–December 2019 reporting period were uploaded into NVivo (2020)  staff were also willing to work with us. Although the time pressures and demands on the EHR staff on the numerable other things that they need to be collecting, I think, puts a little bit of a pressure on this”.-Respondent 10, Center 8Another program leader echoed that gaining this buy-in from cancer center leadership helped expedite this process of further engaging other partners across the health system:“As we were able to get buy-in from the cancer center leadership, the amount of support seemed to move, and the support from the administrative people and from both [hospital] administration and our own departmental administration in being able to do simple things as find the space around the clinic and work out billing issues, all of that moved much faster once we had buy-in from the cancer center leadership, knowing that this was something that was important to the cancer center”.-Respondent 15, Center 11The four centers for whom securing leadership buy-in was not completed described specific challenges regarding turnover throughout the health system that impacted the level of buy-in and the effort required to obtain it, as illustrated by the following program lead:“One of the things that hurt us a lot here is that during the past two years, we have gone through a lot of changes and transition in leadership. So, every time we have a meeting with leadership members, by the time we go back, the [previous leadership is] not there or they have forgotten […] Every time, it was like starting from scratch, and a new approval is required from someone that we never heard of before”.-Respondent 6, Center 5“We need leadership to provide resources. We need institutional support for these two full-time positions and to cover nicotine replacement therapy and to cover the space”.-Respondent 1, Center 1Beyond obtaining verbal support for the TTP, program leads across all 20 centers said leadership buy-in was demonstrated through the allocation of tangible resources, such as staff time—including IT staff time, which is often a shared resource at cancer centers—designated office space within the cancer clinics, and funds for evidence-based treatment that was not reimbursed by health insurance. Program leads saw their own role as asking for what they needed from clinic and cancer center leadership to implement and sustain a successful TTP, even when those asks required significant time and effort. For example, this program lead explained that the“…biggest test of this program going forward is, Do we have enough staff? We’ll find out very quickly. […] For now, we’ll get the main leadership buy-in to go to their faculty meetings, go to their nurse meetings. That’s basically going to be a full-time job for all of probably January and February”.-Respondent 28, Center 9Program leads said that integrating referral systems and the TTP into routine workflows and monitoring its progress required significant IT support. More than a third of program leads indicated that their IT teams prioritized time-sensitive requests from other cancer center programs over those of the TTP, creating delays and impeding progress towards implementation. Program leads said that identifying IT leaders who would advocate for the program was an essential step in obtaining buy-in from those leaders and their teams. A program lead at one center who had engaged the IT team successfully described the cancer center IT director as being:“…like our quarterback in the IT world. […] He just knows who to talk to. They had their annual system-wide IT meeting [IT Director] invited us to present about this program because he noted IT people usually work behind the scenes. They don’t always learn how their work impacts patient care. After the presentation, his words literally were, ‘My inbox is flooded with people wanting to know how they can get involved in this project’”.-Respondent 8, Center 6In 2019, 17 (85%) centers were offering in-person evidence-based counseling, which required designated physical space to deliver that treatment. At most cancer centers, the TTP spanned multiple clinics where space was a limited resource. However, the relationships and buy-in program leads had established with clinic and cancer center leadership helped mitigate space issues, as illustrated by the following program lead:“The director of the ambulatory operations in our cancer clinics controls the space in our hematology-oncology clinics, which is where they have like a rotating schedule of space that [the tobacco treatment specialist] can use. If it becomes a problem, she will reach out very practically and let him know like, ‘Hey, this is what happened today’. […] They’re supporting us in finding that space. […] We have her set up to have a mobile office capability [with a] laptop. [Cancer center director] got her a cellphone for use inhouse”.-Respondent 7, Center 6A total of 17 program leads representing 15 (75%) centers highlighted the critical role that funding and program endorsement from NCI played in bolstering cancer center leadership support and facilitating early implementation success. Of those, over half described specifically leveraging NCI’s backing of the program as a key discussion point in meetings with cancer center leadership, who program leads perceived to be highly motivated to demonstrate progress in NCI priority areas. This respondent explained that achieving implementation success for the TTP was now an institutional priority:“… I’ve been doing this for a really long time and have had a very hard time convincing people that tobacco dependence services are a critical part of a health system’s role. As soon as the NCI spoke up, that changed”.-Respondent 21, Center 15Program leads described NCI funding itself as a net benefit to initiating the implementation of TTPs at their centers, particularly to designate staff time to develop and scale-up successful programs while devising a longer-term strategy for sustainability. Illustrating this concept, this program lead offered: “The NCI being willing to be involved in this and to put in, even if only a small amount of money to get this jump started, is a very big deal. […] Without that, we wouldn’t have seen the program success that has happened in this past year. Now I’m fully confident that we can continue to keep this up and running over the next years to decades”.-Center 8, Respondent 10However, the end of the grant period presented difficulties at some centers, regardless of if they reported having secured leadership buy-in, as staff would need to be funded outside of the grant model on which the program originated. This program lead explained:“We had a long, arduous negotiation with the hospital over making good on their commitment in the proposal to hire a [tobacco treatment specialist]. […] It required the addition of an FTE to their hard budget…to absorb the people who had already been hired and had been paid for four years on grant funds. It’s just, you know, anything they don’t have to, if they can’t get reimbursement and it’s going to cost them money, they’re skeptical”.-Respondent 3, Center 2Yet, the persistent engagement of cancer center leadership enabled program leads to begin conversations about securing funding beyond that provided by NCI. This program lead explained:“We knew we would have to have [cancer center administrative and clinic directors] on board. […] It’s really not hard to sell treating tobacco [dependence] to oncologists, you know, at least in the head-nod version of doing it. Now, when it comes to the wallet version, I’m not so sure”.-Respondent 16, Center 12Between 85 and 95% of centers had trained clinicians and staff in the new clinical workflow and to implement the TTP . Three tIn quantitative surveys, 19 (95%) centers reported having fully or somewhat integrated clinical champions into the TTP; however, respondents at just 12 (60%) centers discussed champions during qualitative interviews. Program leads representing those centers emphasized champions’ expertise in building enthusiasm among their peers for the program, which they accomplished through leading training and education, answering questions, and helping clinicians overcome TTP ambiguity and indifference. A few program leads also asserted that champions played a pivotal role in achieving buy-in from cancer center and clinic-level leadership in addition to that from clinicians and staff. Program leads said champions were designated this role because they were trusted and respected among their peers and leadership, knowledgeable about tobacco dependence treatment, and dedicated to its implementation. They described two ways that champions were identified. At some centers, program leads recognized promise in nurse practitioners or oncologists themselves. For example, this program lead explained:“The cancer clinicians are so consumed by the active treatment, they weren’t considering tobacco as part of treatment. This project is a tremendous opportunity to change that culture […] When this project came up, I said, ‘[champion name], you seem to get it, and you seem to be a perfect person for this sort of project.’ […] There was no question she is someone who was able to have relationships and come as an insider and bring this part in to start changing the culture”.-Respondent 23, Center 16In contrast, at other centers, program leads relied on the relationships they had developed with cancer center leadership to recommend a person who fit the criteria stated above. This respondent described that the cancer center director:“…was really instrumental in identifying a champion for the program. [We] said, ‘You know, who do you recommend?’ And he recommended two of the very best champions in the cancer center, one on the medical side, one on the radiation side, who have been part of this from the beginning”.-Respondent 2, Center 2Regardless of the presence of champions at their centers, program leads emphasized the importance of providing clinicians and staff—and when possible, cancer center leadership—with opportunities for training to encourage buy-in and increase the likelihood, frequency, and quality of TTP implementation. Program leads highlighted four critical areas in which they perceived that clinicians and staff required ongoing education and training. First, they perceived a need for education on the evidence for tobacco dependence treatment effectiveness among patients with cancer. For example, this program lead recalled:“Some of the people that have been here for a very long time have kind of hardened to the idea that smoking is something that they’re going to be able to change in our cancer patient population and in our culture. They’re pessimistic, and it’s just not a priority”.-Respondent 15, Center 11Second, program leads observed that leadership, clinicians, and staff were largely unaware of evidence for the effectiveness of tobacco cessation in improving cancer treatment and survivorship outcomes. This program lead articulated:“One of the other challenges is just getting people to understand the importance of intervening early in a person’s cancer experience. […] They’re still worrying about, ‘Let’s get to survivorship, and then we’ll start talking about this,’ which the evidence doesn’t support that. I mean, they’re less likely to get to survivorship, is what we’ve been pointing out, if we wait until then”.-Respondent 1, Center 1Third, program leads remarked that many providers were skeptical that patients with cancer would participate in tobacco dependence treatment, given these patients’ unique considerations, such as treatment side effects and smoking as means for coping with a cancer diagnosis. Finally, program leads said clinicians and staff required training on how to refer patients and effectively implement the TTP. Training sessions to address these four areas were implemented in a multitude of ways across cancer centers. At one center, for example, program leads utilized weekly in-services among clinicians“…to increase awareness of the program and create a more personalized relationship with the oncologists to be able to get those referrals”.-Respondent 6, Center 5Champions also commonly delivered training, relying on the trust and relationships they had established among peers to garner interest in program implementation. Several program leads described leveraging the C3I network to send staff to training offered at other C3I centers or to develop joint training programs. For example, this program lead asserted:“We were very fortunate in that we paired up with [other C3I center] to create the tobacco treatment specialist program, and now all of our providers go through that program. We provide them specialized training, which we’ve had to develop for medical providers, specialized training for behavioral providers, and then a lot of just hand holding as well”.-Respondent 9, Center 7Program leads were cognizant that clinician and staff buy-in for the TTP was influenced by who was responsible for referring patients, how easy the referral process was to use, and how well the TTP was integrated into their clinical workflows. In 2019, fewer than half of centers were utilizing automatic EHR referrals; most centers utilized either optional EHR or clinician-initiated referrals , which r“I think for us, the biggest problem is the rate at which the oncologists are canceling the [referral]. I think it’s upwards of two-thirds, so we’ve still got a long way to go. And many of them may be new patients or inpatients”.-Respondent 21, Center 15Many program leads said they mitigated this challenge by giving the responsibility for referrals to nurses, medical assistants, or tobacco treatment specialists. Illustrating this theme, this program lead described hiring a full-time nurse practitioner who“…would show the physicians the best practice advisory and orient them to how to prescribe. However, we’d say, ‘If you don’t want to prescribe, we’re happy to, but let’s set up [electronic] links so that we can see these patients’”.-Respondent 30, Center 20Program leads observed that unless the referral process was both perceived as important and easy to use, clinicians would likely not use it. As this program lead reflected:“We don’t have an alert culture. Physicians ignore it anyway. I will admit to that also. You know how to bypass it, and you’ll just ignore it”.-Respondent 23, Center 16Program leads who perceived themselves as the most successful in addressing clinician resistance persistently engaged their cancer centers’ IT leadership and staff and facilitated exchange between IT teams and the clinicians. This program lead explained:“I was surprised how willing and how supportive [the IT staff] were in the process of getting providers engaged in the tobacco treatment program. [They pulled] some of the data on the Quality Oncology Practice Initiative, analyzed the workflows. They even made small tests of change to support implementation”.-Respondent 14, Center 10In this sequential, explanatory mixed-methods study, we utilized data from quantitative reports and qualitative interviews with 30 program leads at 20 cancer centers throughout the U.S. participating in the Cancer Center Cessation Initiative. Centers offered a range of evidence-based tobacco dependence treatment interventions in inpatient and outpatient clinics, including counseling, pharmacotherapy, and referrals to Quitlines. In quantitative reports, 75% or more centers reported having secured multiple levels of leadership buy-in and having trained clinicians/staff in areas relevant to the TTP. Qualitative data further elucidated components of buy-in across levels of cancer center leadership and clinician/staff levels that were critical to ensuring the successful implementation of TTPs in cancer care settings and the implications of securing each component for TTP implementation . These cOur work both corroborates and builds on prior studies that have examined leadership and clinician buy-in for implementing new programs in primary care, behavioral health, and oncology settings. Published work in implementation science has characterized buy-in as providing support and advocacy . In a stPrior work suggests that implementing new programs for cancer patients and survivors requires buy-in characterized by the belief that this population needs the program and would use it . MoreoveProgram leads described the process of obtaining buy-in from multiple levels as iterative in nature. Although it was possible to have leadership buy-in without staff/clinician buy-in and vice versa, program leads who had secured cancer center leadership’s verbal support for the program, received financial resources and office space, and were provided IT support to implement new workflows and program monitoring indicated that securing staff buy-in came more easily. Centers that did not report having secured all three levels of leadership buy-in tended to focus more on obtaining buy-in among staff and clinicians who would implement the program, particularly as some managed leadership turnover. Future research is needed to determine the extent to which buy-in at the leadership and clinician/staff level influenced the longer-term program sustainability, reach, and effectiveness of programs in C3I.Our study is the first to examine buy-in across a diverse network of cancer centers committed to integrating tobacco dependence treatment into routine oncology care. We used both quantitative and qualitative data to characterize and operationalize this concept. Despite its strengths, there are some limitations to this work. No survey item specifically assessed clinician buy-in, nor did our analysis differentiate between clinician and staff buy-in. The two questions regarding training served as proxies for staff and clinician buy-in. This is consistent with prior work that has found that training improves buy-in among clinicians and those implementing or supporting a new program ,31. The Obtaining buy-in from cancer center and clinical leadership is critical to the successful implementation of tobacco dependence treatment into routine oncology care, but buy-in has not been clearly defined or operationalized. Despite 75% or more of the centers reporting having secured all levels of buy-in and training in quantitative reports, nuances emerged in qualitative data, which offered a deeper understanding of the what, how, and why of buy-in. Our findings suggest that buy-in from leadership is defined as verbal support, access to tangible resources , and access to power  that facilitate TTP implementation. Buy-in among staff and clinicians is defined by the belief that the TTP is necessary, valuable, and evidence based; the belief that patients served will utilize the TTP; and self-efficacy and willingness to refer and deliver evidence-based tobacco dependence treatment to patients with cancer who smoke. Securing these dimensions of buy-in are likely to facilitate implementation success, leading to increased rates of cessation and improved short- and long-term tobacco and cancer outcomes for patients.


Based on the social ecological approach, a photovoice study was conducted to explore how neighborhood built and social environments facilitate or hinder college students’ lifestyle behaviors, including physical activity, active transportation, and dietary behavior. A total of 37 college students took photos about neighborhood built and social environments that may affect their physical activity, dietary behavior, and active transportation, and shared their perceptions about how neighborhood built and social environments influence their lifestyle behaviors. Our findings demonstrated that the availability and accessibility of services, school facilities, and home facilities affected physical activity and dietary behaviors among college students. Moreover, the well-developed transportation facilities and networks benefit college students’ active transportation. Environments-based interventions are recommended in future research to better understand the associations between neighborhood built and social environments and lifestyle behaviors in college students.  Social environment disparities were marked as an important factor in restricting the mobility of communities; these disparities have worsened as a result of the current global health catastrophe bought on by the coronavirus . In PipiRegarding the influences of environmental factors on college students’ physical activity, reviews indicated that people’s moderate-to-vigorous physical activity and walking rates were higher in activity spaces that were more walkable, more densely populated, greenery, or had utilitarian services ,16,17. HTransportation behavior is also treated as a predictor of healthy behaviors among college students . Yet, trBeyond the influences of built environments, the social environment has also been demonstrated as a key factor on cultivating healthy lifestyle behaviors among college students . The dimDifferent environments and activity spaces can influence college students’ healthy lifestyle behaviors. In Hong Kong, studies targeting students in a variety of activity spaces, including the social, home, market, transportation, and school environments, have not been conducted to determine their impact on healthy lifestyle behaviors ,30. The Mage = 19.95 SD = 1.10, age range: 18–23) was recruited via distributing leaflets in a public college in Hong Kong to participate in the photovoice study. The photovoice method requires participants to take photographs of the investigating topic and the photography would act as information pieces to guide the in-depth interviews [A convenience sample of 37 college students  Marketing and policy: price/affordability, online shopping, advertisement; (b) Physical environment: availability and accessibility of food stores, supermarkets; healthy or unhealthy options in food outlets, vending tool; (c) Social environment: peer and parental influence, with whom college students eating together. Cultural preferences, such as cooking themselves; (d) School environment: canteen , vending machine, and coffee shop; and (e) Home environment: food availability and diversity at home, family rules on food.Recreational physical activity, including: (a) Marketing and policy: information and provision of community-based physical activity program, affordability of recreational facilities and active clubs, availability of free physical activity programs, fundraising to support physical activity, physical education policy at universities, and after school physical activity programs; (b) Physical environment: sidewalks, walking trails, recreational facilities, distance from home to active places, traffic safety, street lighting, aesthetics of built environment, school active places, trash, street lighting; (c) Social environment: crime safety, social influence of parents and peers; (d) Home environment: sport equipment, indoor activities; and (e) Influences of climate and air pollution.Active transportation, including: (a) Marketing and policy: affordability of public transportation, government financial support of active transportation, convenience of public transportation; (b) Physical environment: sidewalks, walking trails, distance, traffic safety, aesthetics of street, street lighting, walking facilities, and pedestrian sign; (c) Social environment: crime safety, social influence of parents and peers; and (d) Influences of climate and air pollution.Informed consents were firstly obtained from participants after the purpose of this study was explained to them. Participants were informed that they had the freedom to choose what, when, where, and how many photographs to take. Participants were also reminded to seek approval when other persons are involved in the picture. After the initial meeting, participants were allowed to use two weeks’ time to take photos that they felt facilitated or hindered their physical activity and food intake behavior. There was no defined minimum or maximum number of photographs that should be taken, but participants were advised that in the follow-up interview, there would only be time to discuss a handful of photos. Following two-weeks of photo taking time, participants were invited to have an in-depth interview with the fourth author, RZ, to discuss the photographs taken. In the face-to-face in-depth interviews, each participant was asked to select and talk about one or two photographs of each healthy lifestyle behavior that the student they believed are most significant and/or that are their favorited photos. Sampled interview questions are: (a) What do you see here? (b) What is really happening here in this picture? (c) How does this relate to your physical activity or eating habits? (d) How does school/family/society have an influence on your behavior? and (e) The interviewers have at least gone through photos for each of the behaviors . The in-depth interviews were audio recorded with participants’ permission and they lasted from 45 to 60 min. The interviews were conducted in Chinese and the audio recordings were transformed into texts verbatim. Participants were invited to check the accuracy of the interview texts for accuracy.https://www.maxqda.com/new-maxqda-2022) [In line with previous photovoice studies ,36,37, tda-2022) . First, da-2022) .Findings of this photovoice study on the influences of built and social environmental factors on healthy lifestyle behaviors among college students in Hong Kong can be summarized into three main themes: (a) dietary behavior; (b) recreational physical activity; and (c) transportation and physical activity. Direct quotes from participants were presented and acronyms of pseudo names of participants were used. The photos and examples of codes and sub-themes on dietary behaviors among college students are shown in “Because the taste that has been set since childhood, it’s weird to have the sugar-free food. So, whether it is healthy or tasty, I will go for the tasty ones.” (WCY)“In Hong Kong, there’s often publicity for trending restaurants and signature food, so I’m going to queue up to try it. It’s not that I want to go there for dessert, it’s that it’s so famous that I want to go there to take pictures and try it.” (SUM)College campus’ catering environments influence students’ healthy dietary habits. Most students complained that the quality of food in their school cafeteria is bad, for instance, less healthy food selection, heavy oil and salt, and poor hygiene, which force them to dine outside. Meanwhile, facing less choice at the canteen, dining out with classmates and friends has become the common social gathering for students, and there are lots of promotions in different restaurants, therefore they would intake unhealthier foods. They also claimed they do not care about healthy food when having a meal with friends. “The drinks at school are all sodas, sweet and high in sugar. And Ovaltine and other things are also sweet and high in calories, which is not healthy for us.” (WCY)“There is less vegetable choice in our school canteen. Once I ordered meat rice and there was only one green vegetable in it.” (DCY)“I think having a dinner party with friends might make you eat a bit more freely... Because you would be very enjoyable and then order a lot of things, so that can be a barrier to healthy eating.” (WZW)Family dietary habits may also influence college students’ dietary behaviors. Most participants expressed they tend to intake healthier and lighter foods when they eat at home, because their parents knew they might eat unhealthy meals outside, so their parents would cook healthier and more nutritious dishes such as vegetables, fruits, soups, etc. Meanwhile, students would choose to cook easier and healthier foods when they are at home. However, some family members have unhealthy eating habits that would also affect students, for example, order takeaway food and snacking.“The principle of eating in my family is that my mom insistence that we must eat vegetables.” (DHY)“As my father is not in good health, and my mother knows that we all eat fatty food outside, so she would cook lighter at home.” (WWK)“We don’t cook at home; we usually store some noodles and canned goods at home... or takeaway some fast food.” (SUM)For physical environment, it was found that there are lots of fast-food restaurants, such as McDonald’s, KFC, and Pizza Hut, located near their neighborhood. Therefore, lots of students choose these restaurants because they are inexpensive and convenient. “This McDonald’s has put its dessert shop outside on the street, which I think is a big obstacle for our health. It’s quite common to pass by and buy an ice cream especially in hot weather like now. And McDonald’s is also cheaper than the others.” (ZZY)The photos and examples of codes and sub-themes of recreational physical activities among college students are shown in “Yoga mat. I bought it for a long time, I put it at home, and then I can do exercise anytime when I’m free.” (DHY)“These are the two pieces of equipment I have in my home, a yoga mat and a dumbbell. Occasionally, on a whim, I will use a yoga mat to do a little bit of yoga.” (CL)“There are three or four sports fields near my home, including soccer and basketball. I will get out and play when I’m free, because I like sports as well.” (FYX)“The pool in the picture is at my clubhouse. It’s on the fifth floor of our clubhouse. It’s not standard, but it’s not too crowded, and it’s only open to the owners who live there, so it’s quite convenient.” (FYX)In addition to the home environment, the built environment is considered to be the second most intimate location for a college student. Students expressed a higher level of intention to engage in physical activity when school sport facilities are accessible. College students who lived in the dormitory showed a higher frequency of campus gym room usage. Students were also interested in joining recreational activities held by the dormitory or school, for instance, the dormitory hiking group, and the inter-dormitory basketball, or soccer team. In contrast, other sport facilities, such as football courts, basketball courts, or tables tennis centers, were considered as limited; therefore, they might consider the public community sports facilities near their dormitory rather than those at the college. The college campus’ facilities would influence their intention to exercise, and some students might choose to work out during low-peak hours, but time is regarded as unpredictable, resulting in unstable workout schedules.“The schedule of the school’s fitness room collided with my class schedule, so there is no way to do fitness in our free time, although the other fitness room is not full of schedule, but because the big one is not available, so many people are crowded on this side of the small one, the space is very small.” (WZY)“This is the school gym. I have been to rent that field with my classmates to play badminton.” (NPY)“There are regular activities in the dormitory, and my roommate and I occasionally participate in interhall competitions, such as dodgeball and stickball.” (MK)Built environments of campuses facilitated students’ social environment  for engaging in recreational activities. Participants of our study emphasized that they tended to enjoy cycling or hiking during weekends with friends as leisure activities. They would choose to go exercise at the campus sport center together after class as well. However, physical environments of these sporting facilities could also be a barrier to students for doing physical activity. Other than being crowded, the quality of the machine and facilities  and the hygiene of the sporting area would be considered by the students before deciding to go exercise; along with the weather. When the weather is too hot, too shiny, raining, and humid, students will be less likely to participate in physical activity.“Sometimes it will be renovated, you can also see, is rotten, sealed, people can only pass through that side, it is inconvenient.” (YWS)“There will be too many cars if I run here, because there is no running trail near my home, so I can only run near the road, and there are too many cars. Interviewer: Not safe? Interviewee: Yes, there are too many cars, the air quality is low.” (WWY)“Kowloon Bay sports ground, soccer field, stadium, and park are all close to each other, but the surrounding geographical environment is very remote. It is built in a factory area, and it is different from the usual sports ground, so most people need to take a transport bus to get there. Also, there were very few people walking around and the lights were very dim. The Kowloon Bay Sports Complex has only one floor and no gym, so sometimes there is no chance to go to a more remote place for fitness.” (WZY)In their activity spaces, participants of this study also mentioned some positive sides of physical environments that facilitate their physical activity. For example, sport electronic devices, such as smart watches or apps that can record physical activity or walking performances, can help facilitate self-monitoring and self-determination in the pursuit of regular and consistent physical activity.“It’s about a five-minute walk from my house. Then it has all kinds of facilities, a gym, and some courts.” (HWY)“As you can see, there are lots of stools here. Because there are many residential buildings around... the weather will be cool at around seven o’clock in the evening, people will play sports or rest here, and it is very convenient.” (YXY) “Using sport electronic devices allows me to understand what my heart rate is like when I’m doing exercise and how many calories I can burn. It gives me more insight into what kind of a result I can get from this exercise, so I like that.” (WZW)The photos and examples of codes and sub-themes on transportation and physical activities among college students are shown in “Because the subway is more punctual and will not be jammed, the time is easier to control.” (FXY)“It is very dark and dangerous on the way from the dormitory to Kowloon City... Usually, I don’t walk alone on this road.” (HWC)It should be noted that, most of the participants in our study were living near the college campus when the interviews were conducted. Therefore, walking is clearly that first choice. Students tended to walk due to the expensive price of taking public transport, in particular, the minibus near the campus.“Because I have to pay for the whole trip, if I want to take the car to school, I have to pay nearly six dollars; then if it’s a round trip, I have to pay almost 12 dollars a day for the care fare, I think it’s very uneconomical, so I’ll choose to walk.” (DHY)“No, I mainly consider the fare, that is to say, if I travel by public transport every day, the daily fare will be ten dollars; But if I choose to walk, I save this money and use it for other purposes.” (LMF)In addition, the condition and environment of the street were also seen as contributing factors. The participants expressed those roads with a cover , street lights, green spaces, and aesthetically pleasing roads would encourage them to walk as a mode of transportation.Furthermore, apart from walking, cycling is also a form of healthy travel mode. Shared bicycles were evolving in the mainland China community, but not in Hong Kong. Participants expressed that those shared bicycles were expensive, and they found it difficult to use cycling as a travelling mode in Hong Kong. Cycling is not allowed on pedestrian walkways, but it is relatively dangerous to ride on the road. Therefore, cycling cannot be considered an effective healthy mode of transportation in Hong Kong. There were only a few interviewees who indicated that they might use cycling as a transportation mode sometimes. In common with all of the interviewees is that they are all living in the New Territories, like Sha Tin, Hang Hau, where there are cycling tracks that allow them to travel within short distances by bicycle. It is because there are only cycling tracks in the New Territories but not in other areas of Hong Kong.“This is where I would ride my bike when I had to tutor. Yes, because the kid I was tutoring lived in Hang Hau, so it was very close  It’s safe to have a bike lane.” (NPY)Please note that Using the photovoice study approach of photos of everyday life followed by in-depth interviews, the current study has explored the bottom-up perceptions of the healthy lifestyle behaviors among Hong Kong college students. Findings on students’ perceived influences of environmental factors on healthy lifestyle behaviors can be summarized into the main themes of dietary behavior, recreational physical activity, and transportation and physical activity. It seems that Hong Kong college students’ healthy lifestyle behaviors, especially physical activity participation and dietary habits, varied according to the built and social environments. Findings of the current study can be applied into the macro levels of cultural preferences, marketing and policy, the meso levels of physical and social environments, for example, the school and home environments.Findings of this study showed that as college students are in the transition phase from living at home to living alone or with peers, many food choices are deeply involved in this change. Based on the Social Ecological Model , collegeThe important role of social environment on college students’ dietary behaviors was supported in the current study. Young adults’ food intake was also moderated by their identified norm reference group, because they are in a period of life in which desires to be liked and ‘fit in’ to peer groups are high . TherefoFindings of the current study revealed that social support such as family, friend, and peer support has strongly affected college students’ physical activity behaviors. College students received higher levels of esteem support from their family, and companionship support from their friends influenced their physical activity engagement . Photos Findings of the current study further demonstrated that physical environments at home and on campus also influence physical activities of college students. In line with previous review, findings of this study showed that having exercise equipment at home, such as dumbbells and a sport mat, or sports devices, such as an exergaming bike and smart watches, could increase college students’ physical active behaviors . HoweverFindings of the current study showed that, in a high population density metropolis like Hong Kong, the most common transport modes for college students were walking and public transport. This finding is in line with a previous study that revealed Hong Kong adolescents and young people in healthy and sustainable travel modes . AccordiBased on the findings of our study, it is suggested that urban transportation constructors should consider the distance from public transit stops to school, therefore setting an acceptable walking distance from public transit stops to campus can motivate students to walk. Because of the hot climate condition in Hong Kong, it was reported that the threshold for Hong Kong students’ active school transport was 400 m for street greenness and the number of parks surrounding school or 800 m for overall greenness surrounding the school . In addiThe strength of the current study includes using the photovoice approach. This is expected to create a more comprehensive and more dimensional view of Hong Kong college students’ physical, eating, and transportation habits. Participants were able to identify their healthy lifestyle behaviors in different social environments. Nevertheless, the process of taking pictures and recalling the events shown in the pictures has also encouraged Hong Kong college students to become self-aware of their lifestyle behavior and to admit that it is not healthy. In addition to individual responsibility, Hong Kong’s market and environment are considered potential barriers to people’s healthy lifestyle choices. Limitations of the current study should be acknowledged. First, a convenience sample was used in the current study. Future research with representative samples is recommended to better examine how neighborhood built and social environments influence lifestyle behaviors in college students. Second, feedback was difficult to seek from the participants due to graduation and loss of contact, hence the extent of data saturation might be affected. Nevertheless, this study also provides themes of health-related behaviors among young adults. It is suggested that future studies involve more students from different backgrounds and use these themes to explore more health behaviors and their correlation with health among college students. Third, the current study did not conduct a pilot study to test the interview questions or include pre-post questions. Fourth, we did not make field notes during or after the interview, suggesting a lack of contextual information. Future research is recommended to conduct field notes during and after the interview to record contextual information. Lastly, we acknowledge a relatively small sample size was used in the current study. Although a large range of sample size from five to fifty was used in the photovoice research, we have to admit that a large sample size would make the research outcomes more persuasive and influential. Using the photovoice approach, the current study explored the influences of different elements within the home, social, and physical environments on college students’ physical activities, dietary behaviors, and active transportation. Findings indicated that college students’ dietary and exercise habits were highly influenced by the physical environment of college campuses and home and neighborhood food environments. College students’ dietary choices and physical activity levels depend on the affordable accessibility of food environments and exercise facilities at the neighborhoods of their college campuses and homes. Active transportation is common among college students in Hong Kong, with college students who live within walking distance of college campuses and choose to walk. Implications of our study are that future intervention endeavors should focus on promoting positive eating and exercise habits among college students through environments-based interventions and policy making from college administration and local government.


Patient-reported outcomes (PRO) represent a cornerstone in the management of patients with rheumatoid arthritis (RA). However, PRO are currently recorded mainly on paper and only during on-site appointments. Electronic PRO (ePRO) enable continuous remote monitoring and could improve shared decision-making (SDM) and implementation of a treat-to-target (T2T) approach. This study aims to investigate patient and physician experiences, perceived drawbacks and benefits of using an ePRO web-app (ABATON RA) to digitally support SDM and T2T. A qualitative study embedded in a multicenter randomized controlled trial (RCT) consisting of interviews with RA patients and physicians that were subsequently analyzed using deductive-inductive qualitative content analysis. Between August 2021 and May 2022, interviews with ten RA patients and five physicians were completed. Three key themes emerged in the analysis: (i) App user experiences; (ii) perceived drawbacks of app-supported rheumatology care; and (iii) perceived benefits of app-supported rheumatology care. Continuous ePRO collection and a high level of standardization strained some RA patients. Certain ePRO seemed outdated and were hard to understand. Patients and physicians appreciated having an improved overview of disease activity, capturing disease flares and continuous remote monitoring. Paper- and time-saving were associated with using ePRO. Physicians feared to become too focused on ePRO data, stressed the lack of ePRO monitoring reimbursement and app interoperability. For RA patients and physicians, benefits seemed to outweigh observed drawbacks of the digitally supported SDM using ePRO. The software was easy to use and could lead to a better understanding of the individual disease course, resource allocation and treatment of rheumatoid arthritis.The online version contains supplementary material available at 10.1007/s00296-022-05224-y. Rheumatoid arthritis (RA) is a chronic inflammatory disease that requires lifelong medical care. Patient-reported outcomes (PRO) represent a cornerstone in the management of RA patients. This is exemplified by the patient’s global self-assessment (PGA) of disease activity on a 0–100 scale where 100 means maximal activity, being part of the composite gold standard to evaluate disease activity, the Disease Activity Score 28 (DAS-28) , the recElectronic PRO (ePRO) enable continuous remote monitoring and could improve monitoring of disease activity by capturing otherwise overlooked changes of disease activity . Shaw etThe aim of the prospective multicenter AORTA (AbatOn for RheumaToid Arthritis) trial was to investigate the benefit of using an ePRO web-app, ABATON RA, to support shared decision-making (SDM) and treat-to-target (T2T) in RA patients. The aim of this embedded qualitative study was to investigate patient and physician experiences, perceived drawbacks and benefits of using the ePRO web app, ABATON RA, to digitally support SDM and T2T.The participants of this qualitative study were RA patients who were randomized to the intervention group (IG) of the AORTA trial and physicians in rheumatology care.AORTA is a three-armed, partially blinded multicenter randomized controlled trial (RCT) with four visits, each being 3 months apart. The IG patients used the web app ABATON RA to implement ePRO, SDM and T2T. At baseline, rheumatologists presented their patients 6 ePRO to choose from, including RAID , RADAI-5The ABATON RA app is a medical device developed and maintained by ABATON GmbH . All digital administered questionnaires, forms and monitoring instruments were pre-configured. Patients get invited by their local care team via a short messaging system (SMS) invite which contains a personalized link to set a password. Using this password and the mobile phone number patients can theoretically login to their account on any device, as ABATON RA is a web app. Patients were however instructed to use the app on their smartphones only. Patients can set if they want to be reminded of new questionnaires via push notifications or SMS. A reminder logic is implemented to remind the patients 3 consecutive days if they have not filled out the questionnaires and stop as soon as the due questionnaire is completed. Results are immediately available to the patient Fig. a and theTo explore the user experiences with the app, we conducted qualitative phone interviews with IG RA patients from one center  and participating physicians from three German centers  that had used the software for at least 3 months. Participants were selected using purposive sampling , to inclInterviews were conducted by two health services researchers (FM and SM), and one medical student (K.H.) using two analogous open-ended interview guides that were developed to elicit patients’ and physicians’ perspectives on app-supported rheumatology care. The interview guides were developed by F.M., one physician (J.K.) and one app developer (M.G.). The interview guides  included the following three main topics: 1. Study procedure and participants’ experiences; 2. Description of the app and its usability; 3. Impact of the app on rheumatology care. Initial exploratory questions were then specified by follow-up questions. We conducted pilot interviews to test and refine the interview guides. No revisions were necessary. Additional sociodemographic data were collected, including gender, age, diagnosis, education and occupation or medical practice. To reduce the risk of infection and lower participant burden, the interviews were conducted via telephone.The interviews were audio-recorded and transcribed verbatim. Qualitative analysis of the interviews was performed iteratively by F.M. and S.M. based on Kuckartz’s structured qualitative content analysis  using MA. Interviews with patients lasted an average of 28 (8–77) minutes. Most physicians were assistant physicians (n = 4), and female . Interviews lasted an average of 25 (10–47) minutes.From August to December 2021, we conducted qualitative interviews with 10 RA IG patients and from February to May 2022 with five physicians, see Table The analysis followed three key themes: (i) App user experiences; (ii) perceived drawbacks of app-supported rheumatology care; and (iii) perceived benefits of app-supported rheumatology care. The results of the key themes are presented separately for patients and physicians.“I find the app brilliant, because the questions are presented beautifully. Not flashy and colorful, but really neutral.” . Patients reported that they use the app primarily after being reminded that a new questionnaire is available for completion ); or to track their disease progression ). Most patients described the app as helpful in gaining an overview of their own disease activity. Some reported that the use of the app gave them a feeling of support or security, took away fear or had a motivating effect: “The ABATON RA app is such a hold for me, it makes me feel calmer. Because I see that [the disease] is slowing down, it’s working, [the medication] is kicking in and it's great and everything's in the green.” .Patients described the app as easy to use. They highlighted the user interface of the app: “'Is there any difficulty in turning a faucet on and off?' Well, I do not know where there are still faucets nowadays that you have to turn on and off.” . Other patients criticized the high degree of standardization and repetition of the questionnaires, while calling for more specific or differentiated answer options, e.g., to link changes in the disease state and lifestyle changes: “Last time the doctor told me that my score was very good in July. And I was in rehab, but he can't know that. I can't enter it anywhere. It wouldn't be bad if you could simply enter something like that as a patient.” . Finally, patients proposed that users themselves should be able to determine the times at which they are reminded of questionnaires.Yet, most patients reported opportunities for improvement: Some of the questions asked  ) were di“I had a few technical difficulties at the beginning. Those diminished, once you understand a little bit how it works.” . Physicians reported that they use the app to prepare for the consultation, after the consultation, to follow up disease status after medication changes, and most importantly during consultation:“I ultimately rebuild the whole consultation, usually I always ask ‘How was it last week?’. I have a certain pattern and now with ABATON RA it's completely different. You can start by saying ‘Let's take a look at the course of your illness or the last three months.’ and then look at the screen together. So just turning the screen around is something completely new that I've never done before. (…) So it's somehow easier to get into a conversation with the patient and the patients also feel better understood, because often you don't see the symptoms at the doctor's visit. And then the patient can show you: ‘Look over here, two weeks ago I felt bad and then again four weeks ago’. Thus he can also refer to it. So I think the patient also feels better understood if he can show you something, as if he then sits in front of me with a bad conscience and says ‘yes, I'm currently doing well, but three weeks ago I was doing badly, but I can't really show you anything now, like that’.” .Overall, the interviewed physicians described the app as well-structured and easy to use, while some mentioned initial difficulties: “And yes, then the patients simply do not fill out these questionnaires and then mention technical difficulties as an excuse. But then it works again during the consultation. So technical difficulties are often used as a bit of an excuse for not using it.” . This might also be due to the high level of standardization and repetition of the questions: “Many patients complain that it's always the same, but that's exactly the whole purpose of the app, isn't it? And that works super reliably.” . Physicians reported that the use of the app ultimately re-defines the roles in the relationship between patients and their treating medical staff, as those can follow and audit the medical documentation:“In other words, patients do take a look at what you document in the app. And when it comes to medication, for example, you really do have access to the same data. In standard care, patients have no access to our medical documentation. And [with the app] we really do share the same data. So it's just very unusual, because normally you're somehow a bit untouchable. You can document whatever you want, and it's really the first time that ‘I don't just check the patient’ to see whether he's taken his medication or had any vaccinations. Often one is nevertheless in such a control function, but with the app also vice versa; whether I have also documented the whole thing cleanly.” The participating physicians consider the app as an additional aid for most patients to gain better overview of their disease and increase treatment adherence and motivation; while other patients lack motivation to use the app: “But as it is right now, the app is rather for regular communication with the doctor, so that he knows how I am, not bad, but for self-reflection it is not enough for me.” . Therefore, the patient perceived the benefit of the app actually only on the doctor's side: “Yes, it doesn't change the care at all, I think. (…) The doctor looks at it. I believe that he knows everything better. And then I think it's important for the doctor and not for me. He has to explain it to me. That's how I see it.” . One participant described that constant pursuit of one's own disease activity can potentially lead to negative thoughts: “If you dwell too often on your own disease activity, it can be associated with negative thoughts. You may be more likely to get into such a negative vortex. [The app] will keep reminding you of your disease.” . Another drawback reported was that the entered information might be inaccurate due to recall bias: “But ultimately it's like this, you tend to answer from the gut: Yes, I'm fine today. One tends to remember less about how it was five or seven days ago.” . While patients reported that particularly individuals with a smartphone, technical skills, high health literacy and disease knowledge would be suitable to use the app, persons who do not meet these characteristics are left out: “If someone does not work with a smartphone, he has no idea how to do it, he needs guidance.” . This also applies to patients who do not have access to technical devices: “And there are also a lot of people who simply don't have the money. (…) Having a compatible device actually also involves a lot of money” .Patients also reported limitations of app-supported ePRO documentation. For example, due to the high degree of standardization, the app was perceived as too superficial to encourage self-reflection: “Of course, you have to be careful not to become too much of a data junkie and then ignore everything else, right?” . In line with the patient perspective, interviewed physicians reported that ePRO are subjective and prone to error: “You just have to answer the questionnaires honestly. There are certainly some who are only doing it to please me and perhaps don't take the answers seriously.” . The interviewed physicians considered it a limitation that the app was only available for rheumatoid arthritis. Moreover, general limitations of ePRO were pointed out: “The problem, which always exists, is that you can't really differentiate on the basis of the questions and the app, is it rheumatoid arthritis or is it fibromyalgia, which a lot of patients have.” . Another drawback reported was that ePRO app use is not incorporated in the remuneration system for rheumatological care in Germany: “So it would not be feasible at the moment because, of course, if the patients don't visit because they're doing well, we can't earn any money or bill them for anything. And also this monitoring of patient input is currently not yet remunerated, i.e., only minimally.” . In addition, one physician reported that more and more mhealth applications are finding their way into clinical routine, hence integrating them all into everyday practice and electronic health records is a challenge:“That's always my nightmare. Every patient waves his smartphone because he has collected some kind of data, and then I have to compare and evaluate them all. That's my nightmare, of course. That's why I have an interest in making sure that the data is interoperable and can be easily merged, right? (...) Well, that starts with the interface definition. This must somehow be integrated into my practice management system. And then, as I said, the daily work routine is very complex. And I need the information at a glance.” .A central drawback reported by the interviewed physicians was to become very focused on app data and get biased before the appointment, no longer perceiving the patients and their needs holistically: “Because, of course, for patients, using the app at home sometimes feels a bit pointless, when the findings or the individual results are not discussed. In this respect, all patients who have used the system regularly actually respond to this. And they then also demand that you look into the app together and also help them in interpreting the data.” .Finally, as mentioned by patients, physicians reported that patients expect the discussion of the entered ePRO, which may mean extra work: “And I mean, the doctor always immediately sees all the information that I constantly enter as well as how I've been doing in the last few weeks. That is positive, too. You can also prove that.” . According to most patients, app use encourages reflection on their own disease, which is why they described the app to be helpful for other diseases and medical areas as well: Multiple sclerosis, pain management, medication management, as well as other rheumatological diseases. In addition, patients reported that using the app, they save paper and time at the rheumatology ward, which they usually need to fill out paper-based PRO-questionnaires.Major benefits of app-supported rheumatology care reported by the patients are the possibility to continuously monitor their health, to receive a clear overview of the disease progression, as well as, to provide the treating medical staff with better and more comprehensive insights. Patients emphasized that they could use the app to show or even prove their disease activity, specifically deterioration of the health status, to their medical staff: “Frequent documentation, closer monitoring of the clinical patient, that's something on the one hand. Of course, also the agreement of a common therapy goal - by simply being able to define the patient outcome, which is also understandable for the patient. The patient then understands his starting point and perhaps also where he can reach or perhaps where he should stay. And then there is also compliance promotion, patient education involved.” Overall, the benefits of app-supported rheumatology care reported by patients were consistent with those of interviewed physicians:“I rather feel that it leads to a saving of time, because you talk relatively concretely about complaints, relapses that have occurred. Or it's quite clear in the ABATON RA app if everything was just fine. You don't even have to look any further. Because you can just see that in the graph. The patient kept answering these questionnaires.” . Thus the app might ultimately promote a more effective rheumatology care delivery: “Well, it's more effective because I open the program before I call the patient in, I look at the data and I know before the patient comes in whether it's going well or not and whether we probably have to change the therapy or not.” . Physicians emphasized that the app could be used to implement need-adapted rheumatology care: “(…) in such a way that I see mainly patients who just deteriorated. But then to be able to see them more quickly and perhaps also patients who are demonstrably doing well, who perhaps only need to be spoken to briefly by telephone or not at all and simply only once a year. So more flexible patient management.” . Physicians also reported that app-based continuous documentation of ePRO could be helpful in other medical domains, such as multiple sclerosis, heart failure, chronic kidney disease, diabetes, or pre- and post-operative in orthopedics.Furthermore, physicians emphasized that the use of the app can lead to time savings in rheumatology care: In this qualitative study, we explored RA patients’ and physicians’ experiences using a new ePRO web app ABATON RA, including drawbacks, as well as benefits of app-supported rheumatology care. Overall, the results demonstrate the feasibility of digitally supported SDM and T2T, ease of use of the app and the overall dominance of observed benefits.Users appreciated having a better overview of disease activity. Some RA patients perceived the app as supportive for their care, i.e., making disease flares but also disease improvement visible, graphically and in numbers. Similarly, physicians felt better prepared for the appointment and treatment decisions. Compared to traditional paper-based PRO, users reported the potential of time-saving and paper reduction. The high level of repetition and standardization, as well as potentially inaccurate data and difficult-to-understand PRO questions were reported as limitations. Physicians feared to become too focused on ePRO data, stressed the lack of reimbursement and interoperability. Participants stressed that some patients might be left out due to a lack of technical skills and equipment.HCP1 in this study. The potential of saving resources by implementing ePRO is in line with previous studies [HCP 5 feared. As ePRO are purely subjective, additional objective laboratory data could improve a data-driven monitoring approach. Previous studies showed the high interest of patients in self-collection of blood [P3, Navarro-Millán et al. reported that providing patients with social support might enhance PROs collection by helping them overcome barriers with using electronic devices and patients’ reservations about the value of these data [Collection and graphical display of disease activity has been previously identified as a main app function, desired by rheumatic patients and can support care in multiple ways . Capturi studies –24. The  studies , 24, andof blood , 26 and of blood . As repoof blood , one of ese data .This study has some limitations. First, all patients were recruited in a single study site, thus results might not be generalizable. Second, even though all of the physicians interviewed were practicing in rheumatology care, only one physician was a finished-trained rheumatologist; the other four physicians were completing their residency training in rheumatology. Furthermore, participating physicians did not have access to any preliminary results of the AORTA trial and did only describe their individual study experiences. The choice of ePRO and reasons for it were not discussed. Recall bias cannot be excluded, e.g., due to time difference between app use and the interview. In addition, results may be biased toward the benefits as participants agreed to participate in the study in the first hand. A major strength of this study is the open and explorative study design and the diversity of the included patients .This study shows that digitally supported SDM using ePRO is perceived as beneficial and feasible by RA patients, assistant and specialist physicians, who participated in qualitative interviews. Study participants of both user groups reported that the approach trialed could improve current rheumatology care.Supplementary file1 (DOCX 20 KB)Supplementary file2 (DOCX 21 KB)Supplementary file3 (DOCX 14 KB)Supplementary file4 (PDF 481 KB)Below is the link to the electronic supplementary material.


Model selection is often implicit: when performing an ANOVA, one assumes that the normal distribution is a good model of the data; fitting a tuning curve implies that an additive and a multiplicative scaler describes the behavior of the neuron; even calculating an average implicitly assumes that the data were sampled from a distribution that has a finite first statistical moment: the mean. Model selection may be explicit, when the aim is to test whether one model provides a better description of the data than a competing one. As a special case, clustering algorithms identify groups with similar properties within the data. They are widely used from spike sorting to cell type identification to gene expression analysis. We discuss model selection and clustering techniques from a statistician’s point of view, revealing the assumptions behind, and the logic that governs the various approaches. We also showcase important neuroscience applications and provide suggestions how neuroscientists could put model selection algorithms to best use as well as what mistakes should be avoided. As neuroscience is becoming increasingly quantitative with “big data” approaches gaining a firm foothold in neurophysiology, neurogenetics and animal behavior, proper statistics for neuroscience is critically important. Nevertheless, probability theory and statistics is a dynamically evolving branch of mathematics; therefore, frequent cross-fertilization is required between probability theory and neuroscience. Statistical model selection, either implicit or explicit, is an integral part of data analysis in many neuroscience studies. Here, we review available and upcoming methods for statistical model selection, with an enhanced focus on cluster analysis as a special case and provide neuroscience examples for their application.“All models are wrong, but some are useful .”Broadly speaking, model selection encompasses all knowledge and assumptions about the underlying distributions for an observed sample. These assumptions have strong relevance to subsequent statistical decisions, like how to estimate central tendencies , which statistical test to employ, how to describe and visualize the data. Arguably, the model selection trap most commonly walked into is performing it without noticing it. For instance, pitfalls of machine learning-inspired dimensionality reduction approaches have been demonstrated recently , suggestIn simple cases, beaten paths are of help. For instance, limit theorems explain the laws that determine asymptotic distributions of the sums of independent random variables; as a result, normal distribution may be assumed for many types of data on the basis of the central limit theorem, or the Poisson process is a good model for “random” spiking of neurons, because of the Poisson limit theorem. Nevertheless, we suggest that conscious evaluation of such assumptions is not a futile exercise even in seemingly simple situations.n–1 (or higher) will provide a perfect fit. However, this is a poor argument to propose it as the “true” or even the “best” model. More likely, the data may be generated from a distribution with some level of stochasticity, better captured by a lower degree model.In less straightforward scenarios, one might have to resort to a statistical model selection approach, that is, to choose from possible models underlying a set of observations based on statistical principles . HoweverProblems of this sort are frequently encountered in neuroscience. Generally, the GOF is discounted by measures of complexity; however, there is no single recipe for this equation, and as often happens, the multitude of proposed solutions demonstrates that none of them is perfect . While cIs it one bump or two bumps in my plot? More formally, does the mixture of two Gaussians provide a better model than a single one? Is the dependence between the measurements linear, logarithmic, exponential or best described by a quadratic equation? At this point, we hit a roadblock: how to arbitrate between models of disparate complexity?P from the distribution (model) Q is defined byThe most often-used statistical tools for model selection in neuroscience are so-called information criteria, stemming from the maximum likelihood concept . GeneralP, denoted by H(P) and cross-entropy of P and Q, denoted by H:Important to the derivation of AIC, the KL divergence can be decomposed into entropy (information content) of P). It is easy to see that this optimization problem only depends on the cross-entropy term, as the entropy of P is cancelled in the difference. The cross-entropy is mathematically tractable and relatively easy to estimate  is accepted , 1978. Testimate ; howeverestimate . This rerameters .We demonstrate a use case of MAICE in Although MAICE is a strong tool, some caution should be raised. First, when the true model is not among the tested ones, comparing poorly fitting models with AIC may lead to a false confidence in a model that is marginally better than some others. In Second, as noted above, the heart of the derivation of AIC is a correction that eliminates the bias introduced by the specific mode of MLE employed for the formula. However, this bias estimation only works asymptotically; it is therefore not recommended to use AIC on small sample sizes. A “corrected” AIC (AICc) was proposed for these cases; it is worth noting, however, that AICc relies on more specific assumptions on the distribution of the underlying data .Third, AIC is based on MLE, which is widely used and has strongly established, favorable statistical properties. Nevertheless, it may yield unstable parameter estimates for complex models, and therefore, a range of “regularized”  MLE methods are available . Since AThe Bayesian approach to model selection is rooted in calculating the posterior probability of a candidate model, that is, how probable a given model is, provided the observed data . Then, c1M and 2M:In practical applications, the Bayes factor is often used, comparing the relative strength of evidence for two models Of note, the Bayes factor becomes the ratio of posterior probabilities of the models in case their prior probabilities are the same (the case of “uniform priors”), used for instance in hypothesis testing, where an equally plausible null and alternate hypothesis are compared . AlthougIn 1978, Schwarz introduced the Bayesian information criterion , whereas BIC has a term ln(n)k (where n is the sample size), thus BIC implies a stronger penalty and hence tends to select simpler models  is an extension of AIC, penalizing similarly for model parameters, but applying a different GOF measure, defined as the likelihood of the data averaged over the entire posterior distribution . By depaIt is still debated among statisticians which information criterion is better and when . Since cWhen complex systems, like those that determine the exact firing activity of neurons, are considered, it is unlikely that our models will capture all aspects of the true generating model. However, model selection approaches will always announce a winner, which raises a set of issues . First, As a take-home message, information criteria are strong tools to contrast competing models, but researchers should always ask “Is my data sufficient and appropriate to discriminate these models?” , and if the answer is yes, cautiously conclude that “this particular information criterion provides a statistical argument for model A describing the data better than model B.”Resampling techniques are strong tools of modern statistics with firm mathematical foundations while having minimal assumptions on the statistics of underlying data. On the flip side, they require substantial, sometimes prohibitively large, CPU-power.n–1 data points, and tested on the remaining one, repeated for all data points as the “test set”  wins. A practical application of parametric bootstrap is arbitrating the number of modes in an empirical distribution, using a mixture of unimodal distributions described by few parameters . For instance, hippocampal neurons are often characterized by the systematic relationship between their action potentials and the dominant ongoing local population activity, the theta rhythm , one can generate a bootstrap set of simulated data from these models, and use a GOF measure to describe the relationship of the model and the simulated data. A bootstrap distribution of such GOFs can then be used to estimate the probability of the original data violating the tested model . Thus, ua rhythm . Neuronsa rhythm , but is a rhythm e,f.Bootstrap techniques also differ from information criteria in their capabilities to evaluate standalone models, while AIC, BIC, and related methods can only perform comparison of competing models. As a middle ground, one may ask the question whether a given model is better than a minimalistic model that still captures selected features of the data. Bayesian decoding can be used to extract relevant features of the data in a model-free paradigm . In neurClustering problems form a special class of model selection that deserves attention because of its broad usefulness in neuroscience. Neuroscientists often aim to identify groups with similar properties: action potentials that likely belong to the same neuron based on similar spike shape  in There are two fundamental approaches to create the clusters . In hierIn contrast to hierarchical algorithms, a number of arbitrary cluster centers are assigned in K-means clustering, updated repeatedly based on proximity of data points to these centroids . With a More robust results may be achieved by a relatively new technique called “spectral clustering” that combines K-means clustering with dimensionality reduction, however, at the price of losing the appealing simplicity of K-means clustering lauded above . AdditioAn interesting algorithm based on physical properties of an inhomogeneous ferromagnetic model, called superparamagnetic clustering gained considerable popularity in neuroscience owing to its unsupervised nature that does not pose assumptions on the underlying data . It has In graph-like data structures where data points (“nodes”) are connected with links (“edges”), graph theory-based methods can be applied to detect clusters (“communities”) of the network. In such methods, a “modularity” measure is optimized that compares the link density inside versus outside the communities . Lee andThere is an important model selection problem often at the heart of clustering: how many clusters are there (mine #9)? A number of tools have been developed to aid this decision. The ratio of the between-cluster variance to the total variance monotonically increases as a function of the number of clusters, but typically flattens significantly at a point, called the “elbow” e. The loWe showcased widely used model selection and clustering approaches especially relevant to neuroscience problems, also pointing to promising “up-and-coming” methods. Nevertheless, an exhaustive overview would stretch beyond the limits of this review. Most importantly, we would like to stress that model selection is a scientific field on its own right and urge neuroscientists to take conscious decisions about selecting the appropriate techniques and parameters, very much the same way as deciding on experimental design. The bad news is there is no free lunch or rules of thumbs that solve it all; however, the overwhelming good news is that a whole, exciting, and dynamically evolving world waits out there to be discovered and used to the full benefit of neuroscience.No original data were generated.https://github.com/kiralyb/model_selection_mines.We generated code in MATLAB 2016b (MathWorks) for the simulations presented in the figures, available at