Different combinations of these modes could describe a range of ways of relating to the program, including a nonsocial interaction, a semi-social interaction, and a semi-social relationship. A person-to-program bond is explained by the model as an experienced supportive social presence, enabled by making come-alive and a positive program interaction.Health interventions delivered online (self-guided web-based interventions) may become more helpful through a person-to-program “working alliance.” In psychotherapy, the working alliance signifies a therapeutically useful client–therapist relationship and includes an emotional bond. However, there exist no theories of how program users relate to online programs, or that explain a person-to-program bond theoretically. Addressing this gap, we conducted qualitative interviews with and collected program data from users of a self-guided web-based intervention. Taking a grounded theory approach, the analysis arrived at a model of relating based on two relational modes— Online health interventions may dramatically change how health care is provided by providing high-quality, individualized care without the expensive use of human resources, available to anyone with the access to the internet, anytime and everywhere. Often called “eHealth” or “mHealth” programs, these computerized interventions can be completely automated and have repeatedly proven to be effective . HoweverThere is evidence suggesting the existence of a person-to-program alliance . For exaHowever, there is no consensus of exactly what a person-to-program alliance is—what elements it includes. In psychotherapy, the alliance is often defined as comprising three elements: (a) that client and therapist agree on the goals of therapy ; (b) agreement on the therapeutic tasks toward that goal ; and (c) the emotional bond, or “the nature of the human relationship between therapist and patient” , p. 254.On a behavioral level, people do seem to respond to computers as if they were other humans in a variety of situations, as demonstrated in a series of experiments conducted by N = 9) who had used a self-guided web-based intervention for quitting smoking. The intervention mainly consisted of an interactive, tailored website on which the participant could navigate through different pages. Nevertheless, the authors found that participants could “project human qualities” to the program and interact with it as if it were a person. Some of the data excerpts clearly demonstrate this . In a different study, N = 14) who used a self-guided web-based system. The system was placed in their homes, served a variety of health purposes, and participants could call the intervention, upon which the intervention answered with a human voice (recorded by an actor). Participants could respond by pressing numbers on their phone. The authors found that participants formed three types of relationship to the web-based intervention: “feelings of love” (N = 3), described as talking about the program as a person ; “feelings of guilt” (N = 9), described as feeling judged or lectured by the program; and “ambiguity and ambivalence.” In this latter category, six participants talked about the intervention as a machine, one participant talked about the intervention as “he” instead of “it,” and four participants expressed ambiguity as to whether they were interacting with a person or a program.On a more cognitive and emotional level, there are vivid examples in the literature of program users sometimes thinking of self-guided web-based interventions as other human beings . Two of The evidence that these studies report may be the experiences of a few unique individuals. Furthermore, How do the users relate to the intervention?In sum, there is a need to improve our understanding of the working mechanisms of self-guided web-based interventions , and oneWe chose grounded theory  because The study was conducted within a critical realist perspective . That is“Endre” (“Andy”) is a Norwegian, self-guided web-based intervention for quitting smoking designed to support a working alliance . Some imAndy consists of a phase of preparing for quitting smoking and a follow-up phase after the user has quit. In the standard version, the preparation phase consists of 10 different session over 10 days, followed by a follow-up phase of 14 different sessions over 4 weeks. The actual number of sessions each program user receives is tailored to individual use, as the program adapts to how often the user logs on. Once the user has quit, she or he enters a follow-up phase in which she or he in addition to web-based sessions also receives text messages from Andy in the evening, asking whether she or he has been smoke-free that day. If the user reports a lapse, she or he is given access to a special session aimed at preventing the lapse from becoming a full-blown relapse to smoking. A typical session consists of Andy greeting the user, introducing today’s quitting-related topic, and asking a topic-related question, which the user must answer by choosing a multiple-choice alternative. Sometimes the user is asked to write his or her answer in a textbox. Some of these user-generated texts are presented to the user at a later point, in a different “conversation” in which Andy “remembers” something the user has said previously. Next, Andy will respond to the user’s answer, and the “conversation” will go back and forth, ending by Andy summarizing today’s session and bidding farewell.The study was conducted in Norway and is based on two different types of data from two different subsamples: an interview sample and a noninterview (“reflection note”) sample. For both samples, we recruited people who wanted to quit smoking with the help of a web-based intervention. In the following, we will describe the two samples separately.N = 16) was recruited via the researchers’ social network on Facebook, a popular discussion forum in Norway (“Underskog”), and a local general practitioner’s (GP) office. This convenience sample of 16 participants was fairly heterogeneous, including both men (N = 5) and women (N = 11), varying in age (32–70 years), and holding different occupations . Some participants had stopped using the program while still preparing for quitting, some completed the program, some had successfully quit smoking, some had not, and yet others had relapsed. Thus, this study sample was diverse on several parameters, and the ensuing model is based on and includes these different perspectives. Three of the participants were distant acquaintances of the interviewer (Marianne T. S. Holter); this did not seem to affect the data negatively.The interview sample (N = 55) participated through “reflection notes,” a special type of written activity in the program described below. These participants were originally recruited for a different, quantitative study, but they consented to the current use of their program data. They were recruited through Healthy life centers in several Norwegian municipalities as well as through advertisements on Facebook, Google, and online newspapers. The first 112 participants who consented were included in a sample pool for the current study, from which we theoretically sampled (N = 10) and women (N = 45) of different ages (25–66 years) and with different levels of education (ranging from primary school to a higher university degree of 4 years or more).The noninterview sample . The other eight participants were interviewed over the telephone, as they lived in other parts of the country. All interviews except three were audiotaped; in these interviews, a recording device was for various reasons not available, and the interviewer instead made notes, careful to separate quotes from notes.After the first six interviews, we made considerable changes to the interview guide. These first interviews served two purposes: data collection for the current study and gathering feedback for program improvement. However, we experienced substantial challenges in getting rich data on how the participants related to the program, leading us to revise the interview guide. In the revised interview guide, the participants were first asked to give a free account of their experiences. Next followed a section where the participants and interviewer together reflected on questions of relating to Andy, both active in the discussion from their unique vantage points, ultimately seeking general knowledge about the processes under study . It is this written material that is referred to as “reflection notes.”Reflection notes were included in the later stages of the study to increase its validity through methodological triangulation , and to We used grounded theory  to analyThe iterative process of interviewing, inductive coding, sorting, case analyses, and memo-writing gradually moved us toward a more deductive phase, in which we tentatively sketched out a model. The model, in turn, informed a new codebook of focused codes, which was used to re-code all the transcriptions and code new material . The modThe ethical aspects of the study were formally approved by the Norwegian Center for Research Data . Prospective participants were given written information about the study, including the right to confidentiality, to anonymity, and to withdraw at any point. Not all participants returned a written consent form; however, oral informed consent was given at the beginning of the interview by all participants.As part of researcher reflexivity, some aspects of the researchers’ background and experience may be of importance for the study. The authors’ background is from psychology, self-guided web-based programs, and counseling. Following our critical realist perspective, we acknowledge that this background necessarily has influenced what we have seen as relevant processes in the data, although we have strived for openness and inclusiveness. Another relevant aspect of our collective background is that Marianne T. S. Holter has previously worked part-time at the Norwegian Quit Smoking Line. This clinical experience with the study population was never mentioned to the participants, but probably contributed to building a rapport in the interviews.Furthermore, Andy was designed and developed by Håvar Brendryen and Marianne T. S. Holter. This was an asset during data collection and analysis, because the self-guided web-based intervention that was the focus of the study was intimately familiar to us. However, we have been vigilant to counter any threats to the validity of the study as a cause of this role duality, both in data collection and in analysis. The validity threat related to data collection was related to the fact that Holter, who had co-developed the intervention, also conducted the interviews, and that participants may talk more positively about the intervention as a cause of that. Although we did not inform participants about this role duality, some nevertheless assumed this to be the case. In these situations, Holter made it clear that her current role was as an interviewer-researcher interested in all aspects of the participant’s experiences. The validity threat related to data analysis was that our role in development might motivate us to portray Andy in a favorable light. Feelings of pride or disappointment did indeed arise in different situations, and this is something we have regularly examined in reflexive memos. By reflexively confronting these feelings whenever we feared that they might influence the study, we believe we have focused on the participants’ perspectives and stayed true to the data. Perhaps even more importantly, the model we present does not concern how well participants liked the intervention or how “good” it is; the relational modes and patterns we present can be combined with both positive and negative program experiences. Hence, we do not believe Holter’s dual role as a program developer/interviewer has influenced the data noteworthy.making come-alive and keeping un-alive. As all the interviews were conducted by Marianne T. S. Holter, as well as most of the analysis, some of the text is written in her voice. All texts in quotation marks are data excerpts. Excerpts are anonymized and de-identified.In retrospect, our evolving understanding of the processes under study can be said to have gone through three phases. In the following, we will account for each of these phases and the inductive emergence of the two main categories: After only a few interviews, it became clear that some participants did indeed experience Andy as a person, spontaneously describing this self-guided web-based intervention “a friend” and “a therapist.” We coded these instances with “experiences Andy as a person.” Initially, this was what preoccupied us analytically. However, it was equally clear that other participants described their experiences as similar to using any other app or computer program. At first, this made me react with disappointment—after all, referring to Andy as a computer program is accurate, a factual description of the intervention. Therefore, participants who talked about Andy as a program did not seem to relate to the intervention at all, making these interviews seem without value for the present study. Despite the seemingly irrelevance of experiencing Andy as a computer program, we were reluctant to leave these interviews out of the analysis at this early stage. One reason for this was that experiencing Andy as a program seemed to be the opposite of experiencing Andy as a person, and that leaving it out of the analysis would therefore be untruthful. Another reason was that despite our initial dismay, we were also fascinated by the fact that the participants could make sense of their interaction with the program in such different ways. The intervention was highly interactive and tailored to individual use; nevertheless, it was also standardized in its way of interacting with the participants. That this same pattern of interaction could be experienced as either a person or a program caused us to consider whether experiencing Andy as a program might entail something more than just being the taken-for-granted experience. Therefore, the code “experiences Andy as a program” was introduced. Introducing this code slowly facilitated a shift in our thinking, and we increasingly started thinking about experiencing Andy as a program as an important part of the analysis.relating (which seemed to be more about experiencing Andy as a person than experiencing Andy as a program), we instead turned to analyzing ways of making sense of the program interaction, a mind-shift which we found to open up for complexities, which we kept coding for inductively.The continuing analysis revealed several complexities that the simple dichotomy of person or program could not account for. Taking a step back from One such inductive code was “getting embarrassed over one’s own personification.” When talking about Andy as a person, several participants would laugh or giggle, suggesting a degree of embarrassment over talking about an inanimate computer program as something alive. This struck us, because similar embarrassment signifiers can also be found in the existing qualitative eHealth literature. For example, Another early code was “artificial nature revealed,” which was used to code passages where participants expressed that sometimes it became “obvious” that Andy was a computer program. On the surface, it might seem like these participants simply experienced Andy as a program. However, if it suddenly became apparent that Andy was a program, what had it been up until then? Other observations of complexity included the fact that participants who talked about Andy as a person at the same time were fully aware that Andy was a program. Furthermore, some participants seemed to have an experience of writing “to” someone else “behind the program”  that would read what they wrote, whereas others talked as if they were interacting directly with Andy. This added to our increasing acknowledgment of the complexities involved in relating.1 This seemed an accurate description of how some participants were experiencing Andy: Andy was not simply a program, nor quite a person, but rather, both program and person—and at the same time, neither. Therefore, “betwixt and between” was another early code. Participants would also sometimes find it difficult to explain exactly “what” Andy was to them or how they experienced the program. This indecisiveness would sometimes be verbalized by participants who said that it was hard to describe. Other times the uncertainty was apparent in speech that came slowly, involving much on-the-spot reflection, including unfinished words and sentences. These interview passages were marked with the code “hard to verbalize,” and seemed to, like “betwixt and between,” indicate something that breaks down established boundaries or categories and for which one therefore lacks appropriate language.As our understanding evolved with increased complexity, so did the interviews, and in some interviews, participants described Andy as sort of both a program and a person. To further our understanding of this particular experience, we employed a sensitizing concept in our analysis : “betwixAt this point, we knew that we were observing complexity that needed to be accounted for analytically. Furthermore, we had a growing concern that for the most part, participants who had described Andy as a person had done so unsolicited, not as a response to the questions in the interview guide. Although these unsolicited descriptions added to the data’s validity, it also worried us that there might be aspects to the users’ experiences that our interview guide was not adequately tapping into. To address these concerns, we decided to make some methodological refinements, which included changing the interview guide (as described in the “Method” section) and adding reflection notes as an additional data source. Reading through the reflection notes of the 116 participants in the reflection note pool, we theoretically sampled participants whose reflection notes explicitly or implicitly indicated a way of relating to Andy. That is, we sampled reflection notes that indicated Andy as another social actor  or referred to Andy as an un-alive thing . Reflection notes in which ways of relating were not indicated were excluded .thinking within and thinking about But yes, when you put it like that, I’m thinking—and you’re talking about Equipped with a refined interview guide and two new sensitizing concepts, the next interview I conducted became a turning point in the analysis. The participant consistently talked about Andy as a computer program. However, I came to the point in the interview guide where there was a hypothetical question which sounded, “If Andy had been a person, what do you think he would say about you?” The participant replied, “‘She,’ I thought it was a ‘she.’” To accommodate the participant, I rephrased the gender of Andy indicated in the question to “she,” paying no further attention to it then and there other than sensing a small sense of puzzlement. It was not until after the interview that this puzzlement took shape: why would this participant, who apparently only thought of Andy as a computer program, nevertheless think it more appropriate to call the program “she” instead of “he”? Recall, the Norwegian name of the intervention (“Endre”) is masculine . I had chosen the name and had made all the program texts thinking that I was creating the “voice” of a male character. Thus, I knew there was nothing designed into the program to suggest that Andy was This interview revealed that it was possible to make sense of the program interaction in one way within program sessions and in another way when thinking about the program outside of program interaction. The participant called this an “illusion,” and we made this an early code (“agrees on illusion”). This interview was a turning point also because it brought to the surface something we had sensed for some time: that participants might see the program interaction as containing elements of both program and person (even if they do not explicitly articulate this).relational modes that we called making come-alive—thinking of Andy as a person—and keeping un-alive—thinking of Andy as a program . Our effort to turn these relational modes into relational categories caused us to make four new codes: “making come-alive about” (thinking about Andy as a person), “keeping un-alive about” (thinking about Andy as a program), “making come-alive within” (experiencing Andy as a person within program sessions), and “keeping un-alive within” (experiencing Andy as a program within program sessions). We re-coded all data material with these codes, developed the categories analytically through memos, and theoretically sampled in interviews . What caused greater worry was that some participants’ experiences did not seem to fit within either relational category. For example, some participants talked about Andy as a program most of the time—but could suddenly in a sentence refer to Andy as “him,” or laughingly say something that portrayed Andy as a person. The participant was talking about Andy and not engaged with the program, so they were not “accepting an illusion.” Nor did these few instances of making come-alive seem to qualify as “creating a secret companion,” but it did not seem accurate to categorize such a participant as simply using a self-help tool. In sum, it was difficult to fit participants into relational categories without defying the complexity of their experiences.In a serendipitous moment of turning the analysis inside-out, we realized that instead of trying to reduce the complexity we were observing by forcing all participants into mutually exclusive relational categories, we could retain the complexity by focusing on the relational modes making come-alive and keeping un-alive. The relational modes could still be combined with relational situations to form typical relational patterns, as we illustrated in a final iteration of the evolving model. This model could explain all existing and incoming data in terms of how a participant related to Andy, and therefore we considered it theoretically saturated. The model will be accounted for below.relational modes: making come-alive and keeping un-alive. When keeping un-alive, the self-guided web-based intervention was experienced as an inanimate object incapable of thinking and feeling, to which the participant had no relationship. In this relational mode, they described Andy as a “program,” a “tool,” a “machine,” a “computer,” a “robot,” a “standardized scheme,” or a “product”; somewhere they could “fill in” information, or like a “questionnaire.” Andy was a thing, not a social actor. Therefore, when keeping un-alive, participants would sometimes think of their interaction with other social actors, the people “behind” the program (the designers or the researchers), as exemplified by one participant: “I keep thinking about how you’ve sat down and made this program! There’s quite a lot of work put into this!” .Participants related to Andy with two making come-alive, the self-guided web-based intervention was experienced as a social presence capable of thinking, feeling, and acting on its own accord. In this relational mode, the other social actor of the interaction was not the designers or researchers, but Andy—evidenced by participants referring to Andy as “someone” (as opposed to “something”). This “someone” “cared,” “understood,” and “supported” participants. They attributed to Andy the ability to “keep an eye” on them, have “expectations,” “have faith in me,” and “wish me well”; someone “positive,” “interested,” and “very nice”; someone who refrained from an ability to act negatively . In reflection notes, making come-alive was expressed by referring to Andy as “you,” as exemplified by the following participant: “The funny thing is that I like talking to you, you give me pictures and metaphors I haven’t thought about myself” .In contrast, when supportive social presence. This is of significance for the subsequent discussion of a potential person-to-program emotional bond.When making come-alive, it was also of significance whether participants experienced the interaction with Andy positively or negatively. There were a few examples of participants experiencing Andy negatively, leading to Andy being described as “quarreling,” “doubting,” or failing to follow up on appointments. However, the recurring experience was of Andy as a nonsocial interaction, a semi-social interaction, and a semi-social relationship. Participants who experienced a nonsocial interaction kept un-alive both when thinking about the program and within their program experiences. In interviews, this was revealed through joint exploration, in which these participants expressed genuine bewilderment and incomprehension over the thought of experiencing the program sessions as conversations. Instead, they would describe interacting with Andy as “talking to myself,” “mapping” behaviors and patterns, or using a “helpful tool.” One participant said, “Then and there, when I’m answering the questions, it’s . . . maybe more like talking to myself? . . . Yes, maybe more like initiating thought process, I think would be more accurate to say” . Reflection notes confirmed that some participants kept un-alive also when actively engaged with the program. Although they were answering Andy’s very personal question, “How would you describe working with me,” these participants would nevertheless refer to internal change processes and call Andy “the program,” as exemplified by one participant:Like a hidden part of my personality. Nothing is new, all the thoughts about smoking and quitting smoking have been thought before, just in a different way and sequence. I get to structure my thoughts and approach free from stress. My subconsciousness is working when I’m not working with the program. The combinations of making come-alive and keeping un-alive could be described with three typical relational patterns: a semi-social interaction, keeping un-alive when thinking about the program but making come-alive within program sessions. These participants would agree to “play along” then and there—but leave the alive-making once they left the session. A semi-social interaction often involved a tension between making come-alive and keeping un-alive, an ambiguousness of the program being both a social actor and not a social actor at the same time. Sometimes, participants struggled to verbalize this, but some would describe Andy explicitly as being “in the borderlands,” a “robot psychologist” or a “digital friend.” One participant explained the ambiguity this way:It’s funny, because it becomes kind of a relationship in quotation marks . . .. What can I say . . . something in-between a program and a person, if you get what I’m saying? That it’s . . . almost neither nor. Other participants experienced a In reflection notes, ambiguity was expressed in participants’ use of quotation marks, almost as if to keep an element of un-aliveness to expressions that suggested making come-alive. This is illustrated in the following: “Nice to have a ‘friend’ who gets how hard it is to quit smoking and who helps me liberate thoughts about quitting” .semi-social relationship, in which they substantially made come-alive also when thinking about the program even when not interacting with it. In the semi-social relationship, participants seemed to experience Andy as a social presence that lingered even after the session was over. To these participants, Andy was a positive social presence held in their minds as someone who could “be there” for them or “look after” them, sometimes assigned with different (human) social roles: an “understanding friend,” a “secret friend,” a “tutor,” a “psychologist,” or a “therapist.” We call this a relationship because the experience of Andy as a supportive social presence lasted beyond the immediate interaction and seemed to involve more substance. Exemplifying this, one participant wrote the following at the end of the intervention: “It’s been nice and I’ll miss you” . One participant said that she could imagine what Andy looked like, indicating that she would picture “him” in her mind: “He has light hair. And he’s . . . perhaps 35. Not drop-dead gorgeous or anything, but a little thin . . . Very friendly looks. Yes. A friendly guy” . Another participant wrote, “I completely forget that you’re a program” .Finally, some participants experienced a kind of like a person,” “almost like a friend”), or by expressing embarrassment over making come-alive through laughing and giggling. Whether making come-alive or keeping un-alive, there was nothing in the participants’ accounts suggesting that their use of these relational modes was intentional or volitional.However, a semi-social relationship also included elements of keeping un-alive, and ambiguity was sometimes expressed through verbal modifiers , two participants could not be categorized . The remaining 69 participants could roughly be categorized as following: 12 expressed a nonsocial interaction, 34 expressed a semi-social interaction, and 23 expressed a semi-social relationship. Of course, these numbers should be interpreted with caution: because they combine two highly different data sources, because the study was not designed to estimate how these relational types are represented in the population, and because the boundary between the categories are not absolute. The reason for reporting them is to support the following claims: (a) that both making come-alive and keeping un-alive were normal relational modes for this study sample, (b) that most participants engaged in making come-alive at least some of the time, and (c) that neither relational pattern was unique to just one or two individuals.making come-alive  and keeping un-alive . Using these relational modes to a varying degree, participants exemplified different relational patterns, forming a landscape ranging from a nonsocial interaction , to a semi-social interaction , to a semi-social relationship .The purpose of this study was to develop a theoretical model of how people relate to self-guided web-based interventions and to discuss the implications of this model for a potential emotional bond of a person-to-program alliance. Regarding the emotional bond, we wanted to theoretically explain how it can be possible to experience an emotional bond to something one knows to be an inanimate computer program. In pursuing this purpose, we conducted a grounded theory study  with theWhen making come-alive, participants experienced Andy as a person, confirming previous findings of how people can sometimes think of self-guided web-based interventions as human beings . The proHowever, does the experienced supportive social presence of a semi-social relationship amount to a person-to-program emotional bond? The emotional bond of the alliance was previously defined as involving complex, positive social emotions that assume another social actor who can think and feel. Participants with a semi-social relationship to Andy experienced Andy as such a supportive social presence, attributing to Andy the ability to feel (“care”), think , and act . It seems reasonable to call this a type of emotional bond, enabled by making come-alive and experiencing the interaction positively. Thus, 23 of the 69 participants in the current study expressed a type of emotional bond to Andy.knew they were interacting with an inanimate computer program, they nevertheless experienced Andy as a supportive social presence—temporarily blurring out the boundaries between real and not real in a form of “play” or “illusion.” Considering making come-alive—and the emotional bond it can facilitate—from this perspective makes it less mysterious and controversial. It becomes possible to consider an emotional bond as a potentially common way of relating to a self-guided web-based intervention.Why so many participants experienced an emotional bond might be explained if we consider making come-alive as a form of “transitional experience” . TransitThis proposition is supported by the other reports of what we may now define as a type of emotional bond to three other self-guided web-based interventions: “Laura” , “Dit DiThis is also a question of the proposed model’s transferability  to otherinteraction, which may be a useful concept for describing a hitherto unnoticed group of program users. While many users of self-guided web-based interventions will talk about these interventions as inanimate computer programs, the proposed model suggests that a proportion of these users may nevertheless make come-alive within the immediate program interaction. Given a positive program experience, these users may in the moments of using the program experience it as a supportive social presence, able to understand, care for, and support them. However, whether these passing experiences of a supportive social presence can be described as an emotional bond is a question that we are hesitant to answer. Another possible area for future research is therefore whether a person-to-program emotional bond is dependent on the experience of the program as a supportive social presence also after and between program sessions, or whether perhaps these are two different types of emotional bonds.We have so far focused the discussion on one element of the proposed model, namely, the semi-social relationship, which confirms and extends on existing research. However, this study also documents the possibility of experiencing a semi-social Another contribution this study makes is by conceptualizing keeping un-alive as a way of relating. We have suggested ways in which the proposed model can be used to explain the experiences of people who use self-guided web-based health interventions. However, to achieve such a feat, the model must first of all be a valid representation of the experiences of the current study’s participants. Given our critical realist perspective , we beliHowever, the study has certain limitations. Three interviews were not audiotaped, causing loss of detailed data for these participants. Another limitation is related to the study being based on a convenience sample (as opposed to a more strategic sample), which was one reason why the model cannot explain whether different relational patterns were related to participant characteristics such as gender, age, or how long they used the intervention. Furthermore, although we suggest that experiencing a person-to-program emotional bond is likely to be a common way of relating to self-guided web-based intervention, normality cannot be ascertained with this type of study design, as it would require a larger-scale quantitative study. Nor does this model include possible consequences of different ways of relating in terms of their clinical significance or effects on program use and engagement. These questions were not possible to pursue in the current article due to the scope of accounting for and developing the presented model of relating. In a separate publication , we presThe present study also has several strengths, including the methodological refinement process we undertook, as well as the previously accounted for efforts to increase validity. Following the recommendations of One practical implication of the proposed model is that to facilitate an emotional bond, an intervention should support making come-alive, but also be experienced positively and as supporting. Another area in which the model may have practical implications is in how the emotional bond of a potential person-to-program alliance is measured: The model suggests that an emotional bond is possible, but dependent on making come-alive, which alternates with keeping un-alive. Thus, whether a person filling out an alliance-measure rates the program as a supportive social presence  depends on whether she or he is making come-alive in that specific moment.It is our belief that the proposed model of relating in automated therapy can be useful for addressing currently unanswered questions of how self-guided web-based health interventions work. The proposed model suggests that experiencing an emotional bond may be one common way of relating to self-guided web-based interventions, but that there also are other ways of relating that call for further research. The current study also raises other questions concerning how different relational modes and patterns arise (related to characteristics with the program user as well as the intervention) and what their consequences are. However, we believe the study’s main contribution lies in proposing a set of concepts for describing how people relate to self-guided web-based interventions, which may be used as a stepping stone for further research into potential associations between relating and change. These efforts may be further facilitated through the theoretical explanation provided of the person-to-program emotional bond, which may be tested and elaborated, and hopefully lead to an increased understanding of whether a working alliance plays a role in the change facilitated by automated therapy.


The evolution of spoken languages has been studied since the mid-nineteenth century using traditional historical comparative methods and, more recently, computational phylogenetic methods. By contrast, evolutionary processes resulting in the diversity of contemporary sign languages (SLs) have received much less attention, and scholars have been largely unsuccessful in grouping SLs into monophyletic language families using traditional methods. To date, no published studies have attempted to use language data to infer relationships among SLs on a large scale. Here, we report the results of a phylogenetic analysis of 40 contemporary and 36 historical SL manual alphabets coded for morphological similarity. Our results support grouping SLs in the sample into six main European lineages, with three larger groups of Austrian, British and French origin, as well as three smaller groups centring around Russian, Spanish and Swedish. The British and Swedish lineages support current knowledge of relationships among SLs based on extra-linguistic historical sources. With respect to other lineages, our results diverge from current hypotheses by indicating (i) independent evolution of Austrian, French and Spanish from Spanish sources; (ii) an internal Danish subgroup within the Austrian lineage; and (iii) evolution of Russian from Austrian sources. Signing communities have likely existed throughout human history in communities with high incidences of deafness ) for Latin and the voiced labiodental fricative (IPA [v]) for Cyrillic. Thus, a decision must be made about whether to organize the comparison based on (i) form, by comparing handshapes representing Cyrillic 〈B〉 with handshapes for Latin 〈b〉; or instead, based on the (ii) sound represented, by comparing handshapes for Cyrillic 〈B〉 with those for Latin 〈v〉. In many such cross-alphabet comparisons, the documentary record is suggestive about how the comparisons should be organized. In the example just mentioned, we decided to compare handshapes representing Cyrillic 〈B〉 with Latin 〈b〉 and not 〈v〉, thus opting for variant (i), because historical records provide clues about how the Russian SL MA was adapted from other European MAs in the early nineteenth century ): Greek 〈γ〉. Starting at the top of the figure, Afghan SL represents the grapheme 〈〉 by extending the index and middle fingers, a form that is unique in this comparison. To represent the grapheme 〈g〉, historical Brazilian SL (Brazilian 1875) and French SL use forms with an extended index finger and the thumb orientated in a similar direction. Pakistan SL uses a similar handshape for the grapheme 〈〉. Finally, Greek SL, Russian SL and historical Russian SL (Russian 1835) represent 〈γ〉 and 〈Γ〉 with extended index finger and thumb extended outward.In In the upper right side of 2.3.et al. [The matrix includes characters supporting language cliques that are compatible (inherited patterns) and incompatible  with the splits in the unknown true tree. The true tree is, in this case, not necessarily a sequence of dichotomous splits because the tree can be polytomous and anastomizing: (i) one ancestor may have more than two direct descendants; and (ii) a descendant may have more than one direct ancestor in the case that, for example, an MA is the product of combining two (or more) different sources. Thus, any simple, dichotomizing tree that we infer or select using tree-reconstruction methods and commonly used optimality criteria will be incomprehensive, and any signal in the matrix reflecting aspects not covered by the tree’s selected or inferred topology will add to data incompatibility . Another source of tree-incompatible signal is the inclusion of putative ‘ancestors’ in the form of historical MAs, as well as their direct or distant ‘descendants’. Spencer et al.  showed tet al. ), which Support Consensus Networks [xy shares CogIDs with three different SL groups, the bootstrap replicate trees may reflect this situation by showing a three-way split support for each topological alternative (xy is placed within a different group in each pseudoreplicate tree) and an according three-dimensional box in the CNet. We used PAUP* [With respect to the complex signal in the underlying matrices, we thus relied exclusively on network-based EDA ,60 usingNetworks ,62, CNeted PAUP*  to compued PAUP*  for LS aed PAUP*   along time-filtered networks .Post-analysis character mapping was done by hand-and-eye following the logical framework of median networks and guidelines provided by Bandelt et al.  for thei3.The NNet in NJ = 37, but BSML,MP < 15), hence, its intermediate placement in the NNet .The robustness of discriminating signal in the underlying matrix for each main group, estimated using non-parametric BS support, is shown in the NNet  and (ii)The results of the EDA indicate that each major group goes back to an independent founding event: there is little to no support for the deepest splits; the centre of the overall NNet in Contemporary Icelandic SL is a direct derivate from the Danish subgroup within the Austrian-origin group; the same holds for Norwegian SL, which started to strongly deviate from the closely related Icelandic SL in the second half of the twentieth century. The Russian group can be linked historically to the Austrian-origin group  or modified the original and not the later Spanish form (e.g. in the handshape representing 〈q〉). Second, independent formation of the French MA is suggested by the clear separation of the French and Spanish groups in j〉, 〈k〉, 〈v〉 and 〈w〉. Thus, in adapting the early Spanish sources, the signing community in Paris changed them substantially, both consciously by innovating new forms and through usage in a community.Multiple sources report that the founder of deaf education in France, de l’Épée in Paris, had become aware of Bonet’s (1620)  century ,69, suggsynapomorphies in biology) would support such a conclusion, indicating a unified basis from which both MAs later diverged. Shared innovations would be, in this case, those forms that differed from Spanish sources but that were uniquely shared in French and Austrian MAs. In fact, we find relatively few such potentially derived forms between the earliest Austrian and French MAs, including those representing 〈d〉, 〈e〉, 〈l〉, 〈r〉 and 〈w〉. Forms for 〈f〉 and 〈v〉 were shared by Austrian 1786 and French 1800, but also by early unrelated SLs, such as Swedish 1866.The other main continental European lineage is the Austrian-origin lineage. Some scholars have claimed that Austrian and French SLs share a common origin . A largetrunk in the NNet of the earliest MAs in the sample ; other forms were dropped (e.g. the Austrian form representing 〈d〉); and some forms were used to represent Cyrillic letters that appear similar to Latin letters, but which occupy different positions in the alphabet (e.g. Cyrillic 〈〉). One consequence of this mismatch between alphabets is that there are fewer cross-alphabet comparanda between Latin- and Cyrillic-representing MAs. In addition, as discussed in §2, there are serious methodological challenges in deciding which forms should be compared, with the resulting possibility that some connections between sources and adaptations cannot be recovered. Thus, differences between Latin and Cyrillic alphabets may cause phylogenetic methods to overestimate the distance between the Austrian-origin and Russian groups. Conversely, because of iconicity, our approach in this comparison may, in general, underestimate distances between MAs representing the same type of alphabet. Many MA forms represent letters iconically, for example, by matching the shape of the letter with the shape of the hand. MAs representing typologically similar alphabets are more likely to converge on handshapes, evolving independently to similar forms. It is also possible that iconic MA forms evolve more slowly or resist changes in order to maintain the iconic link between the handshape and letter represented.While the results support classifying Russian as a separate lineage created using Austrian sources, any interpretation of the results with respect to Russian SL is complicated by substantial adaptation and restructuring of an existing MA to represent the Cyrillic alphabet. Similarities between Russian SL and early Austrian MAs are reflected in the NNet of early MAs in et al. [The British-origin group forms an independent lineage. The NNet in et al.  for Croaet al.  for Czecc〉, 〈f〉, 〈k〉, 〈l〉, 〈m〉, 〈n〉, 〈o〉, 〈u〉 and 〈v〉. Thus, while the results support the conclusion that the new Swedish MA was created mainly in isolation from other lineages, Borg may have borrowed a limited number of handshapes known widely in Europe. That he may not have had a preference for handshapes from any existing MA is indicated by the general lack of branch support for an alternative that would place the Swedish lineage as sister to any other group. The only somewhat similar MA, according to the results, is Albanian SL , which also lacks any clear affinity to any of the major groups.Finally, the Swedish group, which consists of only historical and contemporary Swedish SL, as well as Portuguese SL, form a separate lineage. The edge bundle separating the Swedish group from the centre of the graph in 5.Despite their relevance to our understanding of human linguistic diversity and to theories of language change, the evolutionary histories of the world’s SLs have not, until now, been studied using state-of-the-art methods. We have shown that computational phylogenetic methods can be applied to MA data to uncover new insights into the evolutionary histories of SLs, to generate new hypotheses about their relationships, and to better understand the evolutionary processes that have shaped the diversity of contemporary SLs. Our analysis supports some aspects of existing SL classifications, such as relationships among French SL and Dutch, Flemish and French-Belgian SLs, as well as SLs of the Americas, including American, Brazilian, Mexican and Quebec SLs. In addition, our results support previous analyses of the relationships of BANZSL languages, and use language data to confirm known historical institutional connections among Swedish and Portuguese SLs, as well as Danish, Icelandic and Norwegian SLs. At the same time, our results add complexity to the overall picture of SL relationships, in particular to our understanding of the evolution of Austrian, French and Spanish SLs from early sources. Our discussion of the independent establishment of SL lineages points to a characterization of similarities across lineages as primarily horizontal, and not due to descent from a common ancestor, while within-lineage diversification does appear to be characteristically vertical in many cases. Finally, our comparison has generated new hypotheses about relationships among Austrian, Danish, Polish and Russian SLs, as well as other SLs in those groups. We anticipate that future studies of lexical data may contradict our phylogeny based on MAs, in particular for SLs that adopted the International Sign MA, because this adoption did not likely affect a language’s lexicon to any great extent. Notwithstanding these limitations, we suggest that our analysis be taken in future research as the best available phylogenetic classification of these SLs.


The COVID-19 pandemic has overwhelmed health care systems around the world. Emerging evidence has suggested that substantially few patients seek help for suicidality at clinical settings during the COVID-19 pandemic, which has elicited concerns of an imminent mental health crisis as the course of the pandemic continues to unfold. Clarifying the relationship between the public’s attention to knowledge about suicide and the public’s attention to knowledge about the COVID-19 pandemic may provide insight into developing prevention strategies for a putative surge of suicide in relation to the impact of the COVID-19 pandemic.The goal of this retrospective, longitudinal time-series study is to understand the relationship between temporal trends of interest for the search term “suicide” and those of COVID-19–related terms, such as “social distancing,” “school closure,” and “lockdown.”We used the Google Trends platform to collect data on daily interest levels for search terms related to suicide, several other mental health-related issues, and COVID-19 over the period between February 14, 2020 and May 13, 2020. A correlational analysis was performed to determine the association between the search term ‘‘suicide’’ and COVID-19–related search terms in 16 countries. The Mann-Kendall test was used to examine significant differences between interest levels for the search term “suicide” before and after school closure.P=.19), New Zealand (P=.003), the United Kingdom (P=.006), and the United States (P=.049) showed significant negative trends in interest levels for suicide in the 2-week period preceding school closures. In contrast, interest levels for suicide had a significant positive trend in Canada (P<.001) and the United States (P=.002) after school closures.We found that interest levels for the search term “suicide” statistically significantly inversely correlated with interest levels for the search terms “COVID-19” or “coronavirus” in nearly all countries between February 14, 2020 and May 13, 2020. Additionally, search interest for the term ‘‘suicide’’ significantly and negatively correlated with that of many COVID-19–related search terms, and search interest varied between countries. The Mann-Kendall test was used to examine significant differences between search interest levels for the term “suicide” before and after school closure. The Netherlands (The public’s attention to suicide might inversely correlate with the public’s attention to COVID-19–related issues. Additionally, several anticontagion policies, such as school closure, might have led to a turning point for mental health crises, because the attention to suicidality increased after restrictions were implemented. Our results suggest that an increased risk of suicidal ideation may ensue due to the ongoing anticontagion policies. Timely intervention strategies for suicides should therefore be an integral part of efforts to flatten the epidemic curve. In March 2020, the World Health Organization (WHO) declared the COVID-19 outbreak a pandemic. To curb the spread of the SARS-CoV-2 virus, governments worldwide have implemented public health measures, such as lockdown, isolation, and social distancing . TelepsyOngoing measures, such as isolation and social distancing, have separated people from their loved ones, leading to loneliness, boredom, and stress. Recent mental health studies on COVID-19 have revealed that these feelings are associated with common mental health disorders, such as depression and anxiety -8, and tPrevious research on the psychological impact of quarantine from past epidemics has revealed that the fear of infection and lack of clear communication from governments are associated with a high prevalence of anxiety, exhaustion, psychological distress, and depression . FurtherThere is a growing body of literature that suggests suicide rates will increase during and after the pandemic due to anticontagion policies that increase social isolation and loneliness, which are well-known risk factors for suicide -15. IndeA considerable amount of published literature has examined the effects of the COVID-19 pandemic on the mental health of the general population; vulnerable people, including older adults and patients with chronic health conditions; patients with previous mental health disorders; and health care professionals ,18-24. TAlthough reducing the number of social interactions remains one of the best methods to reduce the total burden of COVID-19, questions have been raised about control measures, such as school closure. The effectiveness of school closures has been suggested in previous studies on influenza outbreaks, which have reported that influenza transmission is higher in children than in adults . HoweverDue to the fast-moving nature of the pandemic, real-time data collection is needed to assess public interest in COVID-19. To date, the internet has been increasingly used as a source of health care information, especially Google, which is the world’s most used search engine . Google We used the web tool Google Trends to quantify web-based search interest in this study. The methodology we designed was based on the Google Trends Methodology Framework in Infodemiology and Infoveillance . Google Data from Google Trends was compiled between February 14, 2020 and May 13, 2020. The following 16 countries were assessed in this study: Australia, Austria, Belgium, Canada, France, Germany, Ireland, Italy, the Netherlands, New Zealand, Portugal, Russia, Spain, Sweden, the United Kingdom, and the United States. The countries were chosen to represent locations in Europe with the largest number of COVID-19 deaths or those that were forecasted to experience a considerable number of deaths. We also included English-speaking countries outside of Europe, as our study was conducted in English. To determine whether the time since school closures correlated with the stringency level of anticontagion policies, we extracted stringency index data from the Our World in Data database  and schoWe used the following search queries to examine common mental health disorders: “suicide,” “depression,” and “anxiety.” To investigate any potential confounding factors related to suicide, we performed a manual search of the term ‘‘suicide’’ to find data on the suspected suicides of celebrities in the countries used in this study, by using advanced Google searches and web-based news articles. We tailored the dates to include results from between February 14 and May 13, 2020. We also studied the following COVID-19–related search queries: “Coronavirus (Virus),” “social distancing,” “school closure,” “self-isolation,” and “lockdown.” The search queries were chosen based on a recent review on suicide risk and prevention during the COVID-19 pandemic, in addition to keywords that were used in the media and government and WHO policy briefings . It shouWe investigated the changes in trends for all the different search queries in Google Trends by using graphical representations for each country. This was carried out by using the smooth splines function in the ggplot2 package of R Studio. We used the Pearson correlation test to measure the strength of the association between each search query for each country. A 2-sided α value of <.05 was used as a cutoff to identify countries that showed a significant association between the terms ‘‘school closure’’ and ‘‘suicide’’ for the next part of the analysis. The relationship between the time to school closure and the stringency index was examined using a generalized linear model, and the generalized estimating equation was used to correct for intracountry correlations .0) was that there was no trend in the number of suicide searches over time. The alternative hypothesis (H1) was that there was a trend over time.We carried out a Mann-Kendall test to compare the trends in suicide interest before and after school closures. We first identified the national school closure date for each of the selected countries, and then extracted Google Trends data for the search term ‘‘suicide’’ at 2 weeks before and after the school closure date for each country. The Mann-Kendall method is a nonparametric test that is used to detect statistically significant trends ,35. In tS statistic was calculated using the following formula:The Mann-Kendall j and xi are time series and n is the number of data points in the time series. With regard to calculating sgn, the following formula was used:In this formula, xThe variance of the Mann-Kendall test was calculated as follows:i is the number of data values in the pth group.In this formula, q is the number of tied groups and tZ was calculated as follows:The standard test statistic Z follows the standard normal distribution. A positive Z value indicates a positive trend, whereas a negative Z value indicates a negative trend.We assessed Google Trends data from the period between February 14, 2020 and May 13, 2020 for the selected search queries in the selected countries, as displayed in Although lockdown, social distancing, and self-isolation have been the main strategies for reducing the spread of COVID-19, it is clear from the graphs in the figures that the public’s level of interest varied between countries and the different measures taken to combat the virus. For example, there was a greater interest in searches for the term “lockdown” in Germany, New Zealand, Portugal, and Russia compared to other countries with different public health measures. Surprisingly, there was a large number of searches in Sweden for the term “lockdown,” despite the fact that the government decided against imposing a lockdown, and instead relied on voluntary cooperation from its citizens .In Australia, Austria, and Belgium, there was a greater interest in social distancing measures, while in the United Kingdom and Ireland, self-isolation generated greater interest. However, in Canada, the Netherlands, Spain, and the United States, the interest levels for self-isolation and social distancing produced very similar relative search volumes.A closer inspection of the graphs revealed that the number of searches for the term “lockdown” in Austria, France, Ireland, Russia, Spain, and Sweden peaked at around the end of March, followed by a plateau. In other countries, the number of searches for “lockdown” decreased over time. In Germany, Portugal, and the United Kingdom, the interest levels for lockdown increased throughout the study period. Interestingly, searches for ‘‘lockdown’’ in Italy peaked during the middle of April. This could be due to Italy starting to ease lockdown measures, due to public interest in what activities could be carried out.The interest levels for the term ‘‘school closures’’ varied between countries, and most countries showed a bell-shaped trend for the interest level. The variation in interest levels is most likely due to different countries announcing and enforcing school closures on different dates, as well as several countries imposing regional lockdowns before a national lockdown.In most of the countries, the number of searches for the term “suicide” began to decrease toward the end of February and at the beginning of March. Afterward, the number of searches started to increase again in several countries, namely France, Spain, Russia, New Zealand, and the Netherlands. In other countries, such as Australia, Belgium, and the United Kingdom, the search levels for the term “suicide” remained constant. It should be noted however that the peak in suicide-related search terms during February in the United Kingdom and, to a lesser extent, Ireland might have corresponded to the suicide death of television presenter Caroline Flack on February 15, 2020. This possibility was also highlighted in a similar study .Levels of anxiety varied between the countries. In Ireland, the United Kingdom, and the United States, search interest levels for the term “anxiety” remained high throughout the study period. In Australia, Germany, New Zealand, and Portugal, the interest levels for “anxiety” increased after public health measures were implemented.The results of the correlational analysis for selected countries are shown in r= 0.5401, P<.001), Germany , Russia , and the United Kingdom , which is to be expected, as depression is a well-known risk factor for suicide [r=−0.403, P<.001). In addition, there was a weak significant correlation between the interest levels for the terms ‘‘suicide’’ and ‘‘anxiety’’ in the United States. Significant associations were observed between the terms ‘‘anxiety’’ and ‘‘depression’’ in Canada , Russia , the United Kingdom , and the United States .The association between searches for the term ‘‘suicide’’ and searches for other mental health disorder–related terms varied between countries. Searches for the term ‘‘suicide’’ significantly positively correlated with searches for the term ‘‘depression’’ in France ’’ and other COVID-19–related search terms, which suggests that the public has been continuing to seek information about the SARS-CoV-2 virus and the measures that have been taken to curb the spread of the virus. The results obtained from the correlational analysis between the terms ‘‘school closure’’ and ‘‘suicide’’ are shown in P<.001). The time to school closure positively correlated with the stringency index level  and Z statistic. Of the 6 countries, the Netherlands (P=.19), New Zealand (P=.003), the United Kingdom (P=.006), and the United States (P=.049) showed a significant negative trend in the interest levels for suicide in the 2-week period leading up school closure. New Zealand showed the largest decrease in the number of searches related to suicide in the 2 weeks leading up to the school closure. No significant trends were found in other countries. In contrast, the interest levels for suicide significantly positively correlated in Canada (P<.001) and the United States (P=.002) after schools closed. This trend was larger in Canada than in the United States.The Mann-Kendall test was applied on a daily basis to detect trends in the interest levels for suicide before and after countries’ respective school closure period. By using web-based search interest as a proxy for the public’s attention to specific terms, we found that the attention to terms related to COVID-19 surged following the COVID-19 outbreak in February 2020 and peaked between late March and mid-April 2020, which is when attention to suicide and other mental health issues started and continued to decline. Attention seems to have shifted from COVID-19 to suicide and other mental health-related issues after anticontagion policies, such as school closures, were enacted in many countries.Our initial results are in line with recent studies, which have indicated that the number of searches related to COVID-19 rose during the peak of the pandemic -40. HoweIn this study, we found that searches for the term “suicide” increased after anticontagion measures, such as school closures, were enforced. These findings are consistent with those from studies that used Google Trends to explore the changes in the public’s search behaviors during the pandemic ,41. A nor=0.95, P<.001) in countries where English is not the primary language, such as France. Since our study did not intend to compare search interest levels between different countries, this limitation does not affect our conclusion. Finally, this study only used Google Trends to compare time trends during the pandemic. However, we do recognize that other platforms, such as Twitter, may have also been useful in identifying the public’s concerns.Despite the strengths of this study, there are several limitations to consider when interpreting the results. First, this study focused on the 90-day period ensuing the outbreak. The results might have led to different conclusions if data from a longer period of time were analyzed. However, we intended to evaluate the short-term impact of anticontagion policies by using school closure as a proxy. Therefore, the selected period of time might provide more relevant information. Second, the data was extracted from Google Trends, which only provides data on relative search volumes. If absolute numbers were provided instead of relative search volumes, a better comparison between countries could have been made, and more accurate results could have been obtained. Third, we did not adjust for the possible confounding effects of sociodemographic factors in the analyses. Economic downturn factors, such as job loss, may mediate or confound the association between the COVID-19-related events and the public’s interest in suicidality. However, the goal of this study was to identify patterns in the public’s attention toward suicidality in relation to COVID-19 instead of focusing on the causal relationship between the two. Therefore, the lack of taking mediators or confounders into consideration would not have affected our conclusions. However, the validity of Google Trends search interest for the behavioral forecasting of suicide rates may be low, because users’ characteristics and motivations are unknown . A recenBased on the growing body of literature on the impact of COVID-19, it is clear that mental health and psychological considerations are paramount. The findings from this study validate and extend previous work that used Google Trends to track public concerns.The use of Google Trends in this study has important policy implications. For example, school closures have led to an increase in searches for the term “suicide,” which suggests that people’s mental health may have been affected by school closures. Thus, policymakers will need to face the challenges of combating the virus through anticontagion policies while also reducing unintended consequences, particularly the consequences on mental health.To conclude, our findings suggest that the consequences of the COVID-19 pandemic vary depending on countries’ public health anticontagion measures. Although our study focused on high-income countries, it is important to recognize the potential impact our findings have on resource-poor settings, where mental health support is lacking. We believe that our results provide insight into the importance of integrating mental health services into anticontagion policies, as there is an urgent need to address how the mental health consequences of the pandemic can be mitigated, should similar outbreaks occur in the future .


Children’s flourishing is likely to be associated with achieving a positive mental and physical quality of life, which is considered as an important factor for helping children to overcome psychological adversity during the critical stage of emotional development. This study examined the relationships between children’s flourishing and childhood depression. This was a cross-sectional study using the 2011–2012 National Children’s Health Survey in the U.S. The conceptual framework that guided this study was a modified ecological system theory model. Multiple regressions were performed to investigate the associations between flourishing and pediatric depression, controlling for demographics, physical activity-related behaviors, family and environmental conditions. A total of 45,309 children  were identified in this study . Children’s childhood depression was highly related to direct parenting functions, individual needs and environmental availabilities and accessibilities from a socioecological perspective. This study revealed multiple dimensions of how sociological factors influence children’s flourishing and mental health. Parents’ involvement in children’s physical activities and family and social support are crucial for children’s flourishing and mental health status. More attention needs to be paid to provide children with family and social support to help them to overcome and reduce childhood depression. The concept of “flourishing” has been defined as a new expression of happiness and well-being ,2, whichPediatric depression and its related symptoms play a significant role in children’s quality of life. Childhood depression is a critical factor in the development of children’s emotional and mental health ,6,7. In In addition, research is needed regarding the recovery from depression. Without consistent care and observation, the recurrence of children’s depression cannot be ruled out. Individual factors of children affecting pediatric depression may include physical inactivity, a healthy, normal social life , obesity and the degree of sleep ,13,18. PFurthermore, pediatric depression is highly associated with socioecological and hierarchical settings. Children have multiple roles in varied settings including the home and school, which are comprised of distinct characteristics. To better understand the influencing factors on children’s pediatric depression, a socioecological multi-dimensional approach should be considered.It is proposed that children’s flourishing can play a key role in constructing the meaning of objective goods and motivating children to pursue a better life . TherefoMoreover, children are less independent than adults, as they are nested in parental, family and societal environments such as their school and community. Therefore, healthy and sound environments as well as parental health, the safety of their neighborhood and school and the accessibility of facilities will play an important role in helping children to maintain a healthy weight and avoid pediatric depression. This study takes a multi-level research approach and presents a modified social–ecological system model-driven framework to account for dynamic inferences in a hierarchical structure . Based oConsequently, it is imperative to investigate how children’s flourishing is associated with children’s depression and comprehensive socioecological-related factors in a large, non-clinical sample. Children’s flourishing is known as an active and positive motivational theory that has evolved to supersede conventional motivations. Therefore, we aim to determine how active and positive feelings and the levels of happiness of individual children affect pediatric depression. Additionally, this study examined several potential moderating factors including age, sex, race/ethnicity differences, being overweight and bullying. Specifically, it is hypothesized that children’s flourishing and pediatric depression are moderated by age, sex and ethnicity after controlling for socioecological factors.Model 1: Children’s flourishing affects their depression when children’s individual needs are controlled ;Model 2: Children’s flourishing affects their depression when individual needs and parenting capacities are controlled ;Model 3: Children’s flourishing affects their depression when individual needs, parenting capacities and the family are controlled ;Model 4: Children’s flourishing affects their depression when individual needs, parenting capacities and family and environmental factors are controlled .More specific models used to test our hypotheses are listed as follows:This study was a cross-sectional study; the data for this study were extracted from the 2011–2012 National Survey of Children’s Health (NSCH) in the United States. The NSCH employed random sampling and telephone household interviews conducted by the National Center for Health Statistics of the Centers for Disease Control and Prevention [Pediatric depression was assessed using a four-point Likert scale (ranging from 1 to 4) including questions such as “Would you describe your child’s depression as mild, moderate, or severe?”. A composite measure for flourishing was measured using a five-point Likert scale (ranging from 1 to 5), and the scale’s internal consistency was determined to be good for the current study . ExamplEight variables corresponding to children’s demographic and behavioral indicators were collected: age, sex, race/ethnicity, sedentary behavior, after-school activities, physical activity, days of school missed and quality of sleep. The range of ages was 10–17 years old. Four groups of participant’s race/ethnicity were categorized: non-Hispanic White, non-Hispanic Black, Hispanic and other. A composite scale for sedentary behavior was computed as the product of the unit of measurement (hours) and the length of activity time for activities such as playing video games and/or watching TV, videos or DVDs. For after-school activity, three binary scales including taking sports classes or being on a sports team, participation in any clubs/organizations and taking part in any other organized activity during the past year calculated a composite measure. Physical activity was assessed by how many days children exercised, played a sport or participated in vigorous physical activity for at least 20 min. The number of days missed because of illness or injury during the past year was measured for school attendance. Quality of sleep was assessed by how many nights the participant slept well during the previous week. Parenting capacity for sociological factors related to pediatric depression included parents’ health and ability to cope with stress, smoking cigarettes, alcohol consumption and drug use, parents’ involvement and having a mentor. Parents’ health was assessed as the average of the mothers’ and fathers’ physical and pediatric depression. Parents’ ability to cope with stress was measured with a four-point Likert scale that measured how well parents felt they were coping with the daily demands of their children. Two binary items for parents’ health behaviors were employed to assess cigarette smoking, alcohol consumption and drug use. A summation of three items for parents’ involvement measured how often their children were involved in any type of community service or volunteer work at school, church or in the community during the past year; attending events or activities that their children participated in; and how many of their children’s friends they have met. Lastly, a binary item for having mentors was used to determine whether parents had a mentor in the school, neighborhood or community on whom they could rely for guidance. Four variables of family function related to pediatric depression were collected: the parent and child relationship, family activity, number of family members aged younger than 18 years and federal poverty level (FPL). The parent and child relationship was measured with a four-point Likert scale asking how well a parent shared ideas or talked about things that really matter. Two scales including how often children attended a religious service and how many days during the past week that all the family members came together to eat were assessed as a composite for family activity. A four-point Likert-scale item assessed the number of family members aged younger than 18 years. A participant’s parent level of poverty was categorized into four groups based on the FPL: 0–99% FPL, 100–199% FPL, 200–399 FLP and ≥400% FPL .Environmental factors related to children’s depression consisted of a single parent variable, social capital, neighborhood amenities and perceived safety around their current residence. Initially, nine categories of combined family structure and the marital/cohabitation status of a child’s parent(s) were presented; however, binary variables, such as single-parent vs. another family structure, were restructured due to single-parent children’s pediatric depression statuses possibly differing from those from a different family structure. A composite scale for social capital was computed that asked four continuous items: the extent to which respondents agreed that (a) they watched out for each other’s children, (b) people helped each other out, (c) there were people they could count on and (d) if their children were outside playing and something happened, there were adults nearby who they could trust to help their child. A summation of three binary items for neighborhood amenities was calculated including whether sidewalks or walking paths, park or playground areas and a recreation or community center existed in their area. Lastly, perceived safety around their living conditions was measured as a composite of two items: how often they feel their child was safe in their community or neighborhood and at school.® 15.1, and statistical tests were conducted using a 0.05 alpha level and a 95% confidence interval.Unweighted and weighted descriptive statistics of the population were reported to describe the study participants’ demographic characteristics such as age, sex, race and weight using means, standard deviations or percentages. A multiple regression was performed to examine the associations between socio-demographic variables  and depression. In addition, the product of coefficients procedure  was usedThe unweighted  and weighted  descriptive statistics of the study population are shown in p < 0.01) based on our conceptual framework. Overall, the final model was significant, F = 49.36, and accounted for about 24% (R2 = 0.24) of the variance in children’s depression; collectively, ten independent variables significantly predicted depression in socioecological factors.Testing the correlation coefficient between variables, we found that multicollinearity was not an issue  . All assβ = 0.01, p < 0.01), quality of sleeping  and physical activity  were significant. Moreover, interestingly, as expected, flourishing  was a significant predictor of children’s depression. Regarding parental capacity resources, three were significant: parents’ health , ability to cope with stress  and alcohol/drug use . Regarding family function, two factors were significant: the parent and child relationship  and income level (over 400% FPL) . Lastly, regarding environmental factors, there were two significant predictors: being a single parent  and perceived safety . In addition, to determine if age, sex and race/ethnicity moderated any of the effects of being overweight, bullying and flourishing on depression, we used the product of coefficients procedure [More specifically, regarding children’s individual needs, four predictors including school days missed  were positively associated with depression in children. This is consistent with a previous study that showed that parents’ or guardians’ substance abuse can cause their children to suffer depression . ParentsRegarding family function, children with a better relationship with their parents showed a decreased risk of depression, which was in line with other studies ,11,12, aRegarding children’s depression, environmental factors should also be considered. Children with a single parent were less likely to be depressed than those with two parents. Possible explanations for this include the idea that single parents may pay more attention to their children and try to help motivate their children more and encourage participation in diverse activities. Children living in a safe neighborhood and who attended a safe school were less likely to be depressed, perhaps because feeling comfortable with interacting in their community dampens the risk of depression in children. Other depression-related environmental factors such as social capital and neighborhood amenities were not significantly associated with children’s depression in this study. However, environmental stressors and limited social support are vital environmental confounders that could affect children’s risk of depression .Finally, some key individual factors affecting children’s depression were also reported, including missed school days, physical inactivity level and sleep quality. The latter two were significant negative predictors of children’s depression. This is because poor sleeping habits negatively influence children’s emotional/psychological well-being and development ,34,35,362) for each model, which is the proportion of variance in the criterion variable that can be explained by the combined predictor variables, our results showed that a child’s individual variables (Model 1) in this study showed an explanatory power for depression in children with a proportion of about 20%. Remarkably, it was found that the proportion of the explanatory power of children’s depression increased as the parenting capacity , family function  and environmental characteristics  were added to the individual factors of children. This directly or indirectly demonstrates that children need government or policy help in terms of the role and support of parents and families and environmental aspects that can improve children’s depression in order to reduce their depression and improve their quality of life.According to the explained variance (RIn summary, from a socioecological perspective, children’s depression was highly associated with direct parenting functions, school, environmental factors and individual needs. Therefore, the role of parents in preventing and/or minimizing a child’s risk of developing depression was confirmed. Parents and teachers are role models to children  as childThis study examined the relationships between flourishing and pediatric depression in children with several potential moderating factors including age, sex, race/ethnicity differences, being overweight and bullying. The outcomes improve our understanding of the individual and environmental factors that determine children’s psychological flourishing, which could lead to public health prevention and intervention strategies that develop children’s character and increase their quality of life. The rationale for this research was that once parents, family members, schools and the community at large are educated on children’s flourishing patterns, they can use this knowledge to prevent and minimize the rates of obesity and/or pediatric depression. This will also decrease the economic and social burdens on the community.However, this study should be considered in light of the following limitations. First, all study variables were from respondents’ one-day recollection sand self-reported measurements, which may lead to inaccurate results due to recall bias or interview bias. Second, the generalizability of the results to the pediatric depression population is limited and cannot be extended to those living in other, more-diversely populated areas because of the small sample size. Therefore, a larger sample with more diverse participants would yield results that are more robust. Third, the dataset excluded households with no telephone, which may lead to a biased survey population because of the underrepresentation of certain participants. Therefore, future studies regarding the prevention of pediatric depression should consider these limitations.