Regardless of geography or income, effective help for depression and anxiety only reaches a small proportion of those who might benefit from it. The scale of the problem suggests a role for effective, safe, anonymized public health–driven Web-based services such as Big White Wall (BWW), which offer immediate peer support at low cost.Using Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) methodology, the aim of this study was to determine the population reach, effectiveness, cost-effectiveness, and barriers and drivers to implementation of BWW compared with Web-based information compiled by UK’s National Health Service  in people with probable mild to moderate depression and anxiety disorder.A pragmatic, parallel-group, single-blind randomized controlled trial (RCT) is being conducted using a fully automated trial website in which eligible participants are randomized to receive either 6 months access to BWW or signposted to the NHS Moodzone site. The recruitment of 2200 people to the study will be facilitated by a public health engagement campaign involving general marketing and social media, primary care clinical champions, health care staff, large employers, and third sector groups. People will refer themselves to the study and will be eligible if they are older than 16 years, have probable mild to moderate depression or anxiety disorders, and have access to the Internet.The primary outcome will be the Warwick-Edinburgh Mental Well-Being Scale at 6 weeks. We will also explore the reach, maintenance, cost-effectiveness, and barriers and drivers to implementation and possible mechanisms of actions using a range of qualitative and quantitative methods.This will be the first fully digital trial of a direct to public online peer support program for common mental disorders. The potential advantages of adding this to current NHS mental health services and the challenges of designing a public health campaign and RCT of two digital interventions using a fully automated digital enrollment and data collection process are considered for people with depression and anxiety.International Standard Randomized Controlled Trial Number (ISRCTN): 12673428; http://www.controlled-trials.com/ISRCTN12673428/12673428 (Archived by WebCite at http://www.webcitation.org/6uw6ZJk5a) Unipolar depression and anxiety are the second and seventh leading causes of years lived with disability in the world among all health problems, respectively, according to the World Health Organization . The 12-Big White Wall (BWW) is a well-established digital service (website and apps) being accessed by approximately 13,000 people in the United Kingdom in the past year. Currently, BWW has been purchased by the armed forces, some universities, and 25% of clinical commissioning groups throughout England that provide free access to 98% of the users. Only 2% of the users are individuals who pay for the service through a £25 monthly subscription. It is also available or being piloted in the United States, Canada, Australia, and New Zealand.BWW offers the following to people over the age of 16 years : (1) Webwall guides facilitate interactions, ensuring that a culture of respect toward others, tolerance, mutual learning, and safety are maintained at all times. Mental well-being is seen as a systemic interplay of factors that contribute to an individual’s engagement with themselves, their social networks and communities, and the society in which they live. Mental well-being is also seen as being intimately connected with physical health and general well-being.On the basis of public health principles, BWW emphasizes a recovery model to improve well-being and is theoretically based on a social model of depression emphasizing autonomy, hopefulness, and support   in England and Wales . HoweverMoreover, BWW is a complex intervention that operates differently to a psychological intervention in that people choose exactly when and how they utilize it rather than commit themselves to a course of treatment of a defined duration and frequency, which may be restricted by external issues such as clinician availability.Peer support Internet interventions take less effort for service users than Internet-guided CBT . The invThus, a person with depression and anxiety has access through BWW to appropriate psychosocial support when they require it. They are given guidance and are enabled to choose to intervene in their own mental well-being (empowerment). This early intervention approach may preempt the need for later intervention if the person becomes worse. Although guided, people are encouraged to make their own decisions on how to use BWW so they retain their autonomy because perceived control is thought to improve outcome in depression and anxiety ,16.Alternatively, the National Health Service (NHS) has developed a free website providing information on mental health conditions and locally available resources called NHS Moodzone. It does not provide anonymized moderated peer support.The RE-AIM (model ), which The overall aim was to utilize a strategy that is broadly similar to how BWW is usually implemented in a geographical area, with clinical champions from primary care and service users alongside publicity through local public health campaigns and commissioners.The objectives for this study (known as “the REBOOT study”) are bound within the RE-AIM framework:Reach: To determine the number and representativeness of participants invited and eligible to receive BWW or the NHS Choices Moodzone website compared with the expected number of participants (based on estimates from census data in the study area).Effectiveness: To determine the short-term clinical effectiveness of randomization to BWW versus the Moodzone website on well-being (primary outcome), depression symptoms, anxiety symptoms, social function, and QOL in one locality over 3 months.Adoption (by services): To determine the number, percent, and representativeness of NHS primary care practices and organizations, secondary care mental health, community and acute trust, third sector, and social care organizations that referred people to either BWW or the Moodzone website.Implementation: To explore the implementation of the BWW program, including barriers and drivers to reach, effectiveness and adoption, and an economic evaluation of its costs and cost-effectiveness from personal, social, and health care perspectives.Maintenance: To determine (1) The maintenance of treatment effects on well-being, depressive symptoms, anxiety symptoms, QOL, and social function over 6 months and (2) The take-up by organizations and implementation  after the trial has been completed.We will also explore how the interventions may work by quantitatively examining moderators and mediators of outcome and conducting a qualitative analysis, and in the case of the BWW intervention, discourse analysis.The discourse analysis aims to answer the following research questions:How social support is provided within peer support exchanges within BWW?What topics are discussed by trial participants when using BWW?A single-blind, pragmatic RCT will be conducted in the county of Nottinghamshire, United Kingdom using a fully automated bespoke study website. Eligible participants, recruited through self-referral methods such as social media, general practitioner (GP) advertisements, and general marketing will be allocated at random and without stratification to receive either 6 months free access to BWW or be signposted to the NHS Moodzone website. The primary outcome will be clinical effectiveness  of BWW versus Moodzone as measured by the 14-item Warwick-Edinburgh Well-being Scale ,24 from This RCT forms part of the East Midlands Collaboration for Leadership in Applied Health Research and Care (CLAHRC-EM), an applied health care research center funded by the National Institute for Health Research (NIHR), the Universities of Nottingham and Leicester, and over 50 partners from health care, social care, and industry across the East Midlands. Funded across the United Kingdom, there are 13 CLAHRCs that focus on the clinical and cost-effectiveness of translational research that has been identified as priority areas by local services. Service delivery studies of this kind, therefore, require more than just the clinical outcomes that traditional RCTs might produce and present the need to explore cost-effectiveness, risk, adoption by services, reach, and the barriers and drivers to implementation.Ethical approval has been granted by the Local Research Ethics Committee (REC 16/EM/0204), and final approval was received from the Health Research Authority.To optimize recruitment to the trial as well as the regional uptake of BWW, a public health engagement campaign will run alongside participant recruitment via local services to maximize the opportunities for people without, as well as with current access to mental health services, to take part in the trial. Potential participants will all self-refer to the study website after accessing information about the study via two main routes: (1) general media and digital social media advertising and (2) recommendation from health care professionals and other support workers in NHS primary care, NHS secondary care, social care, and third sector and community services. Recruitment and adoption throughout the study will be closely monitored and recorded in order that networks and reach can be determined and analyzed.The public engagement strategy and recruitment to the study will run in parallel with each other. The engagement strategy will, for 1 month, publicize the study to NHS, local authority, and third sector organizations in Nottinghamshire a month before recruitment to the trial begins. The study will be integrated into the early intervention stream of the Clinical Commissioning Groups and Health and Well-being Board Mental Health Strategy for Nottinghamshire. A range of methods will be used to engage with potential study participants, including:Identification of primary care practices and other groups of health professionals, for example, health visitors and community pharmacists that are known to have an interest in mental health and a willingness to adopt new interventions early after their introduction in one part of the county before moving to other partsIntroducing a local general media advertising campaign , followed by a Facebook and social media campaignPresenting the study to interested community groups, targeting both the young  and old , vulnerable groups , third sector mental health groups , and local organizations promoting self-help Producing online and offline presentations and written materialsOther novel approaches as they might arise in public health and primary care, for example, public health message with receipts in high footfall supermarkets or shopping centersPatient and public involvement is central to the design and delivery of this study. A user consultant has been appointed to develop novel approaches and monitor, iterate, and evaluate the effectiveness of the different engagement methods, with an action-research type approach. The user consultant will develop a lived experience advisory panel to help shape the study process throughout. They will work with at least one GP knowledge broker  in other areas where BWW has been implemented successfully; this role has been important linking with GP and other community health professionals and the early intervention stream. Regular checks will show which methods are best for broadening the reach of the study and BWW to a wider range of people and groups and especially those isolated.On the basis of 6- to 12-month engagement strategies using a part-time engagement officer carried out by BWW in the West Midlands , 1950 participants (1.6% of the population) were recruited. We anticipate a similar rate of recruitment in Nottinghamshire, with approximately 2200 participants recruited to our study website over a 12-month recruitment period, and we will have the additional help of the user consultant and the public health campaign publicizing the study.The learning achieved from the recruitment period will inform the best approaches to engage with the public in later stages of the study. A feedback conference will engage with study participants and other stakeholders groups  to enable understanding of the wider validity and relevance of emerging findings. An effective public dissemination strategy, informed by our earlier learning, will communicate the findings of the research to a wide audience to help mobilize the new knowledge as part of the implementation strategy (see later section).Potential participants from the county of Nottinghamshire will be able to self-refer, and their eligibility will be assessed by an automated digital program on the study website. The study website requests GP practice contact details in case the participant is ineligible for the study.The inclusion criteria are as follows:Aged 16 years or overResident in the County of NottinghamshireScores between 10 and 20 on the 9-item Personal Health Questionnaire (PHQ-9)  and/or 1Access to the Internet through a computer, tablet, or smartphone Able and willing to give informed consent (through electronic consent)The exclusion criteria are as follows:Scores 21 or more on the 9-item Personal Health Questionnaire Scores 2 or 3 on PHQ-9 item “thoughts that you would be better off dead or of hurting yourself in some way.”Scores below 10 on PHQ-9 and 7-item Generalized Anxiety Disorder (GAD-7) questionnaireBWW and Moodzone are only available in English. Therefore, the website will recommend to participants that if they do not feel that they are sufficiently proficient in the use of the English language, they need not take part. There will be no test of proficiency.Participants who are ineligible for the trial because they score in the severe range on the PHQ-9 or signs of suicidality will be provided with an opportunity to request that the study team inform their GP, mental health care team, or carer of their current mood state. If they choose not to take this offer, the study team will follow this up on one occasion with an email to ask them again if they would like the team to inform their GP or care team. If they do not reply, the team will consider this confirmation that they do not wish us to act on their behalf.Participants that are under age will be given details of local children’s mental health services. They will be informed that they may return to the study website if they turn 16 years within the recruitment period, should they still wish to participate.Information for participants and the associated consent forms are provided electronically within the study website. Participants who wished to discuss the study could email and telephone the study team if they had any further questions before consenting to the study. An email confirming consent is sent to a participant once they have fully enrolled in the study.Participation in the study will be for 6 months  and interact with other users within the boundaries of the site’s house rules. . AnonymiParticipants will be directed to the Moodzone area of the NHS Choices website, a free to access website providing information on mental health conditions and locally available resources. Participants will be able to access all of the available material on mental health, including depression and anxiety. The site contains reading material and suggestions about maintaining mental health and provides measures of depression and anxiety for visitors to use.All participants will be asked about whether they have received or used any additional mental health resources during their time in the study. NHS Moodzone access is used as the control digital resource so that all the participants are offered some help for their problems with depression or anxiety, but the control group do not access moderated, anonymized peer social support.Once consented, participants will be asked to complete self-rated questionnaires to measure well-being, depression, anxiety, work and social adjustment, QOL , receipt of services , social support, and personality dysfunction see . These wChange in self-rated well-being at from baseline to 3 and 6 weeks after baseline using the 14-item Warwick-Edinburgh Mental Well-Being Scale (WEMWBS)Maintenance of well-being 12 and 26 weeks using the WEMWBSGAD-7 scale—completed as part of eligibility at baseline and then at 3, 6, 12, and 26 weeks as a brief measure of the severity of 7 symptoms of anxietyDiagnostic Statistical Manual, 4th edition criteria [PHQ-9—completed as part of eligibility at baseline and then at 3, 6, 12, and 26 weeks as a measure of depression severity according to the criteria 12-item Medical Outcomes Study Short Form health survey version 2.0 (SF-12v2) —a short Social support is measured using the 8-item Medical Outcomes Study Social Support Survey (MOS-SS) , and it Social function on the 8-item Work and Social Adjustment Scale (WSAS) —a simpleMediators of the effectiveness of BWW compared with Moodzone on mental well-being might be the emotional and informational support subscales of the MOS-SS . Data coModerators of outcome at baseline might be the presence of life events in the previous 6 months, measured by the 12-item Brugha Inventory of Life Events , and theTo explore the reach, adoption, and implementation arms of the RE-AIM framework, a number of process measures will be recorded as the study progresses:Live log of services, individuals, groups, and charities that are approached to engage with the study. The networks that surround these and how this may spread will be recorded and used to determine reach and adoption. A network of practice comprising all health care stakeholders will be established to explore implementation pathways.Reach will be further explored using Internet analytic software or to derive anonymous data around hit and bounce rates, pages visited, length of visit, type of geography, and simple demographics to the study website can be recorded, downloaded, and analyzedImplementation will be explored when highlighting the barriers and drivers to reach, effectiveness, and adoption assessed through qualitative work with the services, groups, and individuals engaged through recruitment, as well as an economic evaluation of its costs and cost-effectiveness from personal, social, and health care perspectives. In particular, we will explore how participants utilize BWW in relation to the literature on similar websites offering peer support and information for physical illness and literature on social and organizational aspects of depression and anxiety care.Working with the local Academic Health Science Network to record the take-up by organizations and implementation  of BWW across the East Midlands after the trial has been completed should it prove to be clinically and cost effective.In terms of socioeconomic inequalities, we will record the following provided such detailed recording of information does not deter participation: postcode, age, gender, ethnicity marital status, highest level of education, and employment.Given that the focus of BWW is on improving mental well-being rather than specifically depression or anxiety, a clinically important difference on the WEWBS was selected as the primary outcome. Data from BWW online support groups show clinically important differences in depression by 6 weeks, so the primary outcome will be a change in the WEWBS from baseline to 3 and 6 weeks. The minimal clinically important difference (MCID) for adults on the 14-item WEWBS with mild to moderate depression and anxiety is 3 points and WEWBS scores are normally distributed . On the The analysis will use multilevel modeling because of repeated measures in the same individuals. People directly participate in the study so there is no other form of clustering operating in this study. Our power calculation performed has already taken into consideration clustering because of repeated measures. Assuming a 50% loss to follow-up, a total of 1352 participants are required for the RCT ,34 but tThe treatment to which a participant is assigned will be determined by a computer-generated pseudorandom code using random permuted blocks of varying size by a randomization system embedded within the website. No stratification or minimization is required. The outcome will be single-blind with the clinical trials manager and research associates responsible for the collection, cleaning, and analysis of the data remaining blind to arm allocation until data collection has been completed. Cases of unblinding will be recorded electronically but will not be excluded. Unblinding will only be necessary upon completion of data collection.Overall, trial monitoring and oversight will be carried out by the CLAHRC-EM Scientific Committee who will act as the trial steering committee and data monitoring committee. The CLAHRC-EM scientific committee is composed of independent experts in statistics, epidemiology, medicine, and patient and public involvement. They will be sent quarterly reports on the status of the study and have the power to recommend or implement changes to the protocol if necessary. They are also able to stop the study if it is deemed unsafe or is failing to recruit after all avenues to recruitment have been exhausted.There is no planned assessment of safety within the study design, and unless participants report any intercurrent illness or adverse events directly to the study team, it is unlikely that these can be recorded systematically.Analysis of the primary and secondary outcome measure data will be carried out by the trial statistician, who will remain blind to arm allocation, using STATA 14.In a RE-AIM study, all outcomes are considered to address important facets of a public health intervention. Therefore, we will examine reach (the percentage and representativeness) of participants entering the trial. Exploratory analysis will summarize outcome variables and participant background variables by treatment arms across follow-up time with mean (SD) for normally distributed data, median (interquartile range) for skewed variables, and frequency (percentage) for observed level of categorical variables. The analysis to examine the treatment effect will be performed on an intention-to-treat basis. As the primary outcome WEWBS score will be repeatedly measured at baseline, 3 weeks, and 6 weeks, multilevel modeling will be performed to quantify the treatment effect with participant as a level 2 unit and baseline measurement as a covariate using Stata 14. Missing values will be imputed using a multiple imputation approach under a missing at random assumption. Path analysis will be used to explore changes in perceived emotional social support and empowerment at 3 weeks as potential mediators for treatment effects on mental well-being at 6 weeks, alongside life events, loneliness, having a partner, gender, age, anxiety without depression, and personality dysfunction at baseline as possible moderators for treatment effect. Such information will provide important information to refine both the targeting and content of BWW to optimize its reach and effectiveness.A detailed statistical analysis plan setting out full details of the proposed analyses will be finalized before the trial database is locked for final analysis. Stata 14 will be used to perform exploratory data analysis and multilevel modeling, Mplus software  will be used to perform path analysis .Adoption will be examined according to the prevalence of uptake and promotion of the study by primary care, secondary care, social care, and third sector organizations. Implementation will be examined in four ways: (1) a quantitative analysis of patterns of acute use of BWW from baseline to 6 weeks according to clinical and sociodemographic factors; (2) an economic evaluation; (3) long-term effects will also be explored using qualitative interviews and patterns of use of data from the websites. An analysis of barriers and drivers of participants, public and patient groups, and health professionals to reach, adoption, effectiveness, and maintenance of BWW using digitally recorded and thematically analyzed individual qualitative interviews; and (4) textual analysis of written comments made by participants and qualitative interviews to explore the process of self-management, peer support, and organization of care on BWW.The study will be conducted from an NHS and societal perspective, which will include cost to the individual but will in addition to clinical outcomes measure the participant's own health status using the SF12-v2 during participant interviews . Thus, tQualitative interviewing and analysis will be used to determine the barriers and motivations for participant engagement in the study to inform and further develop the study’s engagement strategy. We also intend to explore participants’ experiences of taking part in the study to gain and understand the motivations for use of online peer support. For those in the BWW arm of the study, this will include patterns and levels of engagement , negative experiences and beliefs about efficacy, and role in personal empowerment. For those in the Moodzone arm, we will interview participants about negative and positive experiences of receiving just information in relation to managing depression and anxiety. Qualitative interviews will be sought with a maximum variance sample based on sociodemographic factors, scores on baseline clinical measures, and whether or not they are using other health services. Participants will be contacted after they have been in the study for 6 weeks. We will also interview those who did not wish to participate or dropped out but indicated they wished to be contacted to leave feedback.All conversation threads over 6 months in which at least one message has been posted by a trial participant will be retrieved by BWW and sent securely and anonymously to the research team. These will include messages posted by nonstudy participants. However, these messages will not be analyzed; rather they are provided to understand the context of the messages posted by the study participants.The data generated for this phase will be analyzed using content analysis . This apHow social support is provided within peer support exchanges within BWW?We propose to employ the Social Support Behavior Code  as our gWhat topics are discussed by trial participants when using BWW?We propose to use thematic analysis to address this research question using previous guidelines . This apOnly 33% of people with depression or anxiety receive any help from health services in England  and otheThe design of such a large automated study in one geographical area poses a number of methodological challenges, and our study design has a number of strengths as well as inevitable weaknesses.The pragmatic design of the trial means that our estimates of effectiveness and cost-effectiveness are likely to be generalizable to other areas of the United Kingdom and other high income populations as this is a direct to the public study that does not rely on health service infrastructure that varies from place to place.The public health (RE-AIM) approach of the REBOOT study will help to raise awareness of the possibility of digital intervention with a large group of people with depression and anxiety who are not currently engaged with primary care or mental health services. It will also explore whether it provides additional help to those who are already engaged with these services in relation to immediate moderated anonymized support and digital approaches to socialization and recovery that might be more convenient and approachable than comparable face-to-face approaches. It will provide a great deal of information surrounding the reach and adoption of such a resource and the role it plays in their well-being. We will know the clinical, sociodemographic, and health care service use of participants and visitors to the site who complete baseline information but decide not to be randomized as well as those who utilize the interventions, and completers and dropouts from the study. We will also be able to explore through process evaluation moderators and mediators of mental well-being through both treatment arms and in more detail in the BWW treatment arm by detailed quantitative and qualitative analysis of social messaging through BWW. Therefore, there is the opportunity through such process evaluation to improve the reach or effectiveness of digital interventions such as BWW and also to predict more clearly what the impact of BWW might be in an area outside Nottinghamshire if such services were commissioned . The proTherefore, we have carefully coproduced the REBOOT study website with e-mental website developers such as M-Habitat  and the A further complication in the digital study is the issue of contamination. People with depression or anxiety might access BWW or Moodzone independently, although in the county of Nottinghamshire the opportunity to enroll in BWW is limited to access through the Armed Forces as new personal subscription has been suspended for the duration of recruitment and follow-up in the REBOOT study. Only one randomization is possible from each web browser within a 30 day period. Participants may be able to deceive the randomization process through access from a different computer or web browser or by clearing the cookie installed to prevent multiple attempts. Internet protocol (IP) address was not used as a marker to prevent multiple randomizations in case several people might be sharing the same computer. There may also be potential leakage with people who live outside Nottinghamshire using work postcodes that are within the county. Furthermore, there are potential problems with contamination through digital users utilizing alternative sources of digital support and information rather than the study treatment allocated to them. For instance people might join Facebook and offer support to each other even though it is not anonymized, and there is no profession moderation or support. If any of these occurred to a significant extent, it may be difficult to show real improvements in clinical and cost-effectiveness. To understand these potential sources of contamination, we are asking all participants about use of other sites.The design of studies to evaluate the reach, adoption, clinical-effectiveness, and cost-effectiveness of direct to the public digital health interventions for mental health problems are important, given the reach, popularity, and low cost of such approaches in a world that is increasingly digitally connected even in the poorest countries of the world. However, we are in the early stages of understanding how to optimally design such studies.


We observe that some traits have effects on the stability of the social signatures as well as network turnover and rank dynamics. As an example, individuals who score highly in the Openness to Experience trait tend to have higher levels of network turnover and larger alter rank variations. On broader terms, our study shows that personality traits clearly affect the ways in which individuals maintain their personal networks.Strong and supportive social relationships are fundamental to our well-being. However, there are costs to their maintenance, resulting in a trade-off between quality and quantity, a typical strategy being to put a lot of effort on a few high-intensity relationships while maintaining larger numbers of less close relationships. It has also been shown that there are persistent individual differences in this pattern; some individuals allocate their efforts more uniformly across their networks, while others strongly focus on their closest relationships. Furthermore, some individuals maintain more stable networks than others. Here, we focus on how personality traits of individuals affect this picture, using mobile phone calls records and survey data from the Mobile Territorial Lab (MTL) study. In particular, we look at the relationship between  We interact with a wide network of people on a daily basis, and these social relationships play an important functional role in our lives. A large number of studies has shown that having strong and supportive relationships is essential for health and subjective well-being , 2. As aego) has with family members and friends  may be subject to general constraints associated with time available for interactions [However, there are also costs to maintain close and diverse relationships and it has been shown that the interactions and relationships a subject , and play an important role in influencing people behaviour , 29. Howet al. [et al. [et al. [et al. [For example, Staiano et al.  consider [et al.  found a  [et al.  showing  [et al.  studied persistence of social signatures, namely the similarity of the social signature shape of an individual measured in different time intervals; (ii) the turnover in egocentric networks, that is, differences in the set of alters present at two consecutive temporal intervals; and (iii) the rank dynamics defined as the variation of alter rankings in egocentric networks in consecutive intervals.In this work, we focus on understanding whether and how personality traits affect the (i) Specifically, we combine detailed mobile phone call records with personality traits scores collected from survey data. The mobile phone calls records were collected during the Mobile Territorial Lab (MTL) study , and traOn broader terms, our study shows that personality traits clearly affect the ways in which individuals maintain their personal networks. Specifically our results show that extroverts tend to show slightly lower temporal persistence of their social signatures, as compared to introverts. Moreover, people with high scores in the Openness to Experience personality trait exhibit a higher network turnover with respect to their counterpart and interestingly agreeable individuals have a lower turnover inside their network of alters than disagreeable ones. In addition we found that changes in the intensity of relationships result in increased or decreased communication with alters, which is reflected in the alter rank dynamics inside ego-networks. We found a larger variation in the alters’ ranks of egos who show higher scores in the Openness to Experience personality trait, and the opposite for egos who show lower scores in the same trait. This is also true for the Agreeableness personality trait.In the current study, we leverage the sensing technologies available in smartphones and track the daily communication networks of 93 individuals in Trento, Italy, for a period of 15 months. The study was conducted within the Mobile Territorial Lab (MTL), a joint living lab created by Telecom Italia, Fondazione Bruno Kessler, MIT Media Lab and Telefónica. Following Italian regulations, all participants were asked to sign an informed consent form and the study was conducted in accordance to it. The general study and the form were also approved by a joint Ethical Committee of University of Trento and Province of Trento.The MTL living lab consists of a group of more than 100 volunteers who carry an instrumented smartphone in exchange for a monthly credit bonus of voice, SMS and data access. The sensing system installed on the smartphones is based on the FunF framework  and keepAll volunteers were recruited within the target group of young families with children, using the snowball sampling approach, where existing study participants recruit future participants from among their acquaintances . Upon joSelf-assessment questionnaires have been used to measure the personality of each individual in terms of the Big Five model . This moThe Italian version of the Big Five Marker Scale (BFMS)  was usedet al. [I1, I2 and I3 of 5 months each. We chose a 5 month interval because the entire period of 15 months was the period that allowed us to have the higher number of participants for a longer period of time. Moreover, choosing too short intervals, we could face the problem of mostly measuring fluctuations. Too long intervals would not work either, since social signatures would contain too many alters who have already left the network. We retain all the participants that made at least 150 calls and contacted at least 20 people in each of the three intervals. The result of this process leaves us with a set of 93 out of 142 participants, 56 females and 37 males. The participants’ ages range from 28 to 48 years, with an average of 39 years.All analyses presented in the following sections are based on 15 months of data collected between October, 2013 and December, 2014. Social signatures are generated by following the method of Saramäki et al. , by counFirst, following the assumption that individuals in the extreme of the scale for a given trait would exhibit largest differences in communication patterns, we identify for each of the Big Five personality traits people falling in the 25th percentile  and the 75th percentile . Thus, for example, for the Extraversion trait we find the most extroverted individuals and the most introverted individuals. These groups of top and bottom scoring individuals will be used throughout the study for comparisons. The sizes of the groups are presented in Pi = {pi(r)} is a social signature and pi(r) represents the fraction of calls made by an ego to the alter of rank r in signature i. H represents the Shannon entropy defined asp(r) is defined as above and k represents the total number of alters called by a particular ego. The lower bound of the JSD is zero and intuitively the lower the value of the JSD the more similar two signatures are.In order to evaluate the shape similarity of two different social signatures, we used the Jensen-Shannon divergence (JSD):dself for each ego, which quantifies the similarity of the ego’s signatures in two consecutive intervals . We also computed reference distances dref which quantify, for each interval, the similarity between the signature of a particular ego i and the signatures of all other egos j. Following  and usinA(Ii) and A(Ij) represent the set of alters called by a particular ego in time intervals Ii and Ij, respectively. J〉 = 0.257).The turnover inside each ego network, namely the differences between the sets of alters present in two consecutive intervals, is measured with the Jaccard similarity coefficient asIn this section we present the results of our analysis on personality traits and ego-network dynamics. Typically, when looking at different aspects of the social signatures of the 25th and 75th percentile subgroups for a given trait, we find that their distributions do not follow a normal distribution. Therefore, in order to assess if there are significant differences between the distributions of the two opposite subgroups we apply two statistical tests: (1) the non-parametric Kruskal-Wallis test to verify whether the population medians of the two subgroups are equal, and (2) the non-parametric Kolmogorov-Smirnov test to verify whether the cumulative distribution functions of the two subsets are identical.p < 0.05; for these personality traits, network sizes are significantly different for the 25th and 75th percentiles, that is the trait does have an effect on network size.We first evaluate whether personality traits have some effect on the ego-network size. For each subgroup, we find that the distribution of network sizes is right skewed (positive skewed). We use the network size of the subgroups in each of the three intervals. In The median values of the network size distribution of subgroups of people with high and low scores in the Agreeableness personality trait, show statistically significant differences, with median network sizes of 80.0 and 66.0, respectively. The subgroups of people with high and low scores in the Openness to Experience trait have a median network size of 75.5 and 86.0, respectively, but they show a significant statistical difference only with the Kruskal-Wallis test. Non significant differences are found between the subgroups of the other three personality traits .Here, we try to understand the relationship between the persistence of a social signature and the Big Five personality traits. We investigate whether the self-distances of subgroups of opposite personality traits (e.g. extroverts and introverts) exhibit differences, which would indicate that the signatures are more persistent for one group than for the other. We thus try to understand whether a particular personality disposition influences the stability of an individual signature over time.We find a significant difference only in the distributions of the self-distances of the subgroups of extroverts and introverts, namely people with high and low scores in the Extraversion personality trait: the signatures of extroverts are less persistent than the signatures of introverts see . HoweverWe also investigated the association between personality traits and the turnover in ego-networks in two consecutive intervals. Again, we use the Kruskal-Wallis and the Kolmogorov-Smirnov tests see . As showNetwork turnover seems to be characterized by the Agreeableness personality trait as well. Finally, we do not find any significant differences for Extraversion, Conscientiousness and Emotional Stability.A as follows: if there is a transition of an alter from rank i in interval It to rank j in interval It+1, then Aij = 1. We limit the maximum rank to 20, because this guarantees that the population of 93 individuals has an alter at each rank in each 5-month interval.In the previous section, we have seen that the Openness to Experience and the Agreeableness traits associate with network turnover. Here, we take a detailed look at what happens inside the network of a focal ego by focusing at the alters rank dynamics and subsequently we analyze the effect of personality traits on such dynamics. To this end, for two consecutive temporal intervals for each ego, we build a transition matrix i (21st row) to represent the probability for alters inside an ego network to enter ranks 1-20 from beyond the maximum considered rank of 20 in the next time interval. The row labelled in (22nd row) is then introduced to represent the probability for a new alter to join the ego network in the next time interval. The o (21st) and on (22nd) columns represent the probability of moving beyond the 20th rank or completely dropping out of the network, respectively.We also introduce a row labelled In this way, the transition matrix of each ego keeps track of rank dynamics of alters and also the dynamics of alters exiting or entering the network.B.We then used the transition matrices of egos to represent the alter rank variations of entire subgroups. To this end, we simply sum the matrices of all egos in the subgroup and normalize them by the number of egos in that particular subgroup, in order to have probabilities on both rows and columns. The resulting matrix now contains the alters rank dynamics represented as probabilities of moving up and down rank positions. We call this resulting matrix B of the entire population in both  and .o and on), while it is easier to enter the network to lower-rank positions (columns i and in).For the top ranks, the probability mass is clearly concentrated on the diagonal, meaning that the top ranks are more stable. This is expected, since people in the top positions of the network are the people that a particular ego contacts more frequently, such as for example family members, and these relationships are expected to be more close and stable. Also notice that approximately beyond the 10th rank, alters have a higher probability to drop out of the network with respect to higher-ranked alters  exhibit a higher network turnover with respect to their counterpart. Interestingly, agreeable individuals have a lower turnover inside their ego-networks than disagreeable ones. In social psychology, Agreeableness and Extraversion are the traits having the most direct implications for social interactions . HoweverAs said, Openness to Experience and Agreeableness have an impact on the turnover inside the ego network of an individual, and partially explain why new alters are added to the network and why old alters are replaced. In addition to this turnover, changes in the intensity of relationships may result in increased or decreased communication with alters, which is reflected in the ranks dynamics inside ego-networks. We found a larger variation in the alter ranks of egos who display higher scores in the Openness to Experience personality trait, and the opposite in the subgroup of people with low scores. The Agreeableness trait also affects turnover: more agreeable people have a lower network turnover and thus longer relationships as compared to their counterpart. However, those more agreeable exhibit ego networks with larger alter rank variations. A possible explanation is given by the fact that agreeable people are more likable and easy going and thus they do not display preferences in adapting their behaviour to alters with respect to their counterpart. Therefore, it seems that these two personality traits play a relevant role in the rank dynamics of the ego networks.Turning to the limitations of the present study, we list the following ones: the relatively small size of the sample; the fact that it comes from a group of young families with children living in the same geographical area ; the non-availability of data from different communication channels such as Whatsapp, FaceTime, etc.et al., [Despite these limitations, our results overall provide one possible explanation for the uniqueness and stability of the individuals’ social signatures. As pointed out by Saramäki et al.,  social set al., .e.g. joint effects of multiple personality traits together with other drivers). Determining these will require more detailed studies and access to different kinds of data.However, additional constraints, such as available time , 46 and S1 Datasets(ZIP)Click here for additional data file.


Despite an extensive body of knowledge exists on network outcomes and on how hospital network structures may contribute to the creation of outcomes at different levels of analysis, less attention has been paid to understanding how and why hospital organizational networks evolve and change. The aim of this paper is to study the dynamics of networking behaviors of hospital organizations.Stochastic actor-based model for network dynamics was used to quantitatively examine data covering six-years of patient transfer relations among 35 hospital organizations. Specifically, the study investigated about determinants of patient transfer evolution modeling partner selection choice as a combination of multiple organizational attributes and endogenous network-based processes.The results indicate that having overlapping specialties and treating patients with the same case-mix decrease the likelihood of observing network ties between hospitals. Also, results revealed as geographical proximity and membership of the same LHA have a positive impact on the networking behavior of hospitals organizations, there is a propensity in the network to choose larger hospitals as partners, and to transfer patients between hospitals facing similar levels of operational uncertainty.Organizational attributes , institutional factors (LHA), and geographical proximity matter in the formation and shaping of hospital networks over time. Managers can benefit from the use of these findings by clearly identifying the role and strategic positioning of their hospital with respect to the entire network. Social network analysis can yield novel information and also aid policy makers in the formation of interventions, encouraging alliances among providers as well as planning health system restructuring. Interest in understanding how and why hospital organizations choose collaborative partners overtime is a relatively recent issue and is related to a new strand of research that investigates these phenomena using concepts and methods from organizational sociology and network theory –5. NetwoPrevious literature assumes that existing and past relations among organizations may act endogenously to induce networking , 12, andA quantitative exploration based on social network analysis (SNA) and specifically on stochastic actor-based model for network dynamics –17 was eRecently, a number of studies have addressed the issues of the determinants of patient transfer between hospital organizations. In order to reduce staff uncertainty and coordinate their efforts, hospitals tend to routinize destination selection such that staff immediately contacted a “usual” transfer destination . TransfeDespite this abundance of studies, most of them have in common the limit of being studies with a cross-sectional data setting or that have not been pushed to longitudinally analyze the evolution of patient transfer dynamics in a wide span of time. There are two researches that, however, are an exception to this limitation. The first, conducted by Lomi et al. , observeThe dynamics of patient transfer relations within the entire network of hospitals providing services to patients in Abruzzo, a region in central Italy with a population of approximately 1,300,000 residents, have been analyzed. The Italian National Health Service (I-NHS) is a publicly funded health system that provides universal coverage. The government, at the central level, allocates resources to 20 Italian regions and is responsible for defining the core benefit packages and ensuring that basic coverage is provided to the entire population. Regional governments have wide autonomy in planning, allocating resources, and organizing regional level services, and are responsible for delivering health care services to their resident populations.The Abruzzo regional health system is entrusted to six LHAs, and health care services are provided by 35 hospital organizations (22 public and 13 private). Of the 22 public hospitals, two are teaching hospitals. Public hospitals provide highly specialized hospital care and are characterized by technical, economic, and financial autonomy. Teaching hospitals are hospitals linked to universities, and provide education, research, and tertiary care. Private hospitals are partially financed by the regional healthcare service and are investor-owned organizations that provide ambulatory assistance, hospital care, and diagnostic services.The study setting seems to be particularly appropriate for the purpose of this research. The first reason is that earlier research in this context , 24, 25 The second reason is that, given the great strategic and organizational autonomy of our empirical setting, there are no significant external factors that influence the networking process for which to control. In the period considered in this study, there were no significant policy interventions that substantially altered the institutional framework, the number of providers, or the structure of the local inter-organizational network. Exception is the progressive reduction in the number of beds set by regional authorities, but this has affected proportionally all hospitals.The third reason why this is an ideal case to study network dynamics is that Abruzzo health care system suffers from a lack of systemic planning and strategy coordination among its hospitals . Unlike The analysis draws on a range of rich data. Data on patterns of collaborative interdependencies during the period 01/01/2003–31/12/2008 among all hospitals in the region were extracted from the hospital information system database managed by the Abruzzo Region. Data on hospital activities, and information on demographics and performance, were taken from the Abruzzo Health Agency archives and yearly reports. These data are collected regularly and archived digitally by the Region for administrative purposes, and by the Health Agency for its operational and reporting activities. Archival sources are generally more precise and detailed than surveys and provided complete information on the network of hospitals: there were no missing data.Since this study is interested in understanding the dynamics of networking behavior of hospital organizations, the dependent variable is inter-hospital collaboration measured as transfers of patients , 2, 22. This research tested for several organizational-level variables that might influence networking behavior of hospital organizations. Specifically:Measure of the level of complexity of the cases which are treated in a given hospital. It measures if hospitals facing with highly complex cases  have different networking behavior compared to hospitals treating patients with a low degree of disease severity .It counts the number of overlapping specialties, and indicates to what extent two hospitals are alike because they do the same thing or not. It serves to investigate whether the transfer of patients occurs between hospitals that overlap in knowledge stocks. Using available data on specialties  present in each hospital in the region, was built a “35 × 35” matrix. The rows and columns of the matrix respectively report the hospitals in the Region, while the cells of intersections report the number of overlapping specialties between each pair of hospitals. The matrix was computed for the year 2003 and was regarded as a constant in the statistical model as there have not been major changes in the number and types of specialties present at each hospital during the six years.A proxy of dimension, measured as the number of staffed beds , 20.It considers the affiliation of hospitals to the distinct LHAs in which the region is divided . In a “3Measured as productivity, has been computed as the total number of admissions adjusted for case mix, divided by total number of staffed beds .A “35 × 35” matrix, the rows and columns of the matrix respectively report the hospitals in the Region, while the cells of intersections report the distance between each pair of hospitals expressed in km .It represents unplanned emergency admissions as a percentage on the total admitted patients, as in previous studies . It is cEach variable was computed yearly, for the six-year period 2003–2008. Table Geographical distance between hospitals, LHA membership and the number of specialties are constant over time, showing the absence of structural policies for the re-designing of the hospitals regional system. The mean of staffed beds reduces over time (such as the percentage of emergency admissions) while the case mix complexity and the productivity indicators slightly increase in the six years analyzed. Table In the estimation, the model controlled also for some structural endogenous effects named respectively outdegree, reciprocity, transitive ties, three cycles, balance, indegree-popularity, and outdegree–activity. Table The R-Siena Software Package  allowed Table To explore networking behavior dynamics more in-depth, have been also considered the collaborative patterns at dyadic level over time  is negatively and significantly correlated with the dependent variable, meaning that network ties are more likely to be observed among organizations with different values in this index. Geographical distance is negative and significant. This implies that there is a geographical proximity effect , namely In line with previous studies , 27, theWith reference to the proxy of size, staffed beds  is positive and significant, showing a tendency in the network to choose larger hospitals as partners to which transfer patients .The proxy used to measure whether similarities or differentials in performance levels (ie productivity) induce the networking behavior of hospitals organizations is not significant. It follows that if on the one hand it is widely recognized in literature that higher levels of collaboration produce a positive impact on organizational performance , on the Finally, the variable LHA is positive and significant, meaning that collaborative ties are more likely to develop between hospital organizations belonging to the same LHA.Understanding the factors that stimulate or hinder networking behavior of organizations is a matter of significant theoretical interest and has remained high on the list of priorities of researchers interested in network relations . Using sThe networking behavior of hospital organizations was observed through the study of patient flows, since the previous literature has amply demonstrated to us how this represents a valid proxy reflecting collaboration and the existence of underlying relationships between the hospitals involved in patient transfers, because of the high levels of coordination and communication that patient transfer requires , 22, 25.This research found that high levels of overlapping specialties reduce the propensity to exchange collaborative ties. This seems to suggest that perceiving another hospital organization as similar , in this context seems to increase the competition between similar organizations and consequently inhibits the formation of network ties. Indeed, when two hospitals have many overlapping specialties, it implies that they are vey similar, they take care of the same diseases and treat the same type of patients, and thus may perceive each other as potential competitors . Also thThe results of the empirical analysis show that, over time, collaborative ties are more likely to develop between hospital organizations belonging to the same LHA. This can be interpreted in line with what was found by Veinot et al. , i.e. paThe last explanatory variable is operational uncertainty, that manifests when internal organizational activities are difficult to plan, or planned activities are difficult to execute, and it comes from unpredictable variation in internal operating conditions, which require change in original plans and routines, and the revision of resource allocation decisions. Although it is well known in the literature that health care organizations respond to uncertainty by creating ties , 27, thiFinally, results found that the formation of network ties between organizations is explained by peculiar forms of structural  configurations, composed of subsets of two or three network actors and the possible ties among them . These dTo reduce the risk of over-interpreting the results, it is useful to reflect on the main limitations of this research, which provide opportunities for future research. First, one specific relation, i.e. patient transfer among hospitals, was analyzed. Although inter-hospital collaboration is widely used in the literature , it is pThis study provides new insights by addressing the application of longitudinal models for social network analysis, which so far have received scant attention in health care. Delivery of hospital services is highly influenced by the formation of collaborative networks between providers. Hospital managers and policymakers are invited to use network analytic techniques that allow them to be informed about the current collaborative network. Also, through the use of these tools they can obtain novel information and understand better the effects of these networks, supporting the formation of structured agreements between hospitals, and allowing to draw proper patient flow at the regional level.Health care networks are strongly self-organizing and emergent in nature, independent from (or even negatively influenced by) management and policymakers’ interventions (absence of interventions). It is therefore recommended to the latters of carefully define organizational characteristics , institutional factors (LHAs) and geographical proximity as they count in determining the formation and shaping over time of hospital networks.


Although the revolution in bacterial sequencing has left us with an unprecedented abundance of data regarding the diversity and abundance of bacteria in caries and periodontal diseases, detailed information on the organization of dental biofilms in man in micrometre scale is lacking. By using spectral imaging fluorescence h et al.  describeh et al.  for addih et al. . CLASI-FThe 10 bacterial probes applied in the current study targeted 96–98% of the cells in healthy supragingival plaque as suggested by rRNA tag sequencing data from the Human Microbiome Project (HMP) . Each ofAdmittedly, previous literature has described bacterial coadhesion or coaggregation in great detail as well as ecological succession in dental biofilm; however, we have so far lacked the micrometre-scale resolution necessary to study the spatial organization of individual bacterial cell consortia . CLASI-F2, and H2O2 and tended to localize close to each other. Even if these were the most characteristic and reproducible features of the plaque structure, there was a spatial, temporal, and individual variation in the abundance of hedgehogs and other types of consortia in the supragingival plaque. Mark Welch et al.  reportedCorynebacterium, which was abundant and the genus most characteristic of supragingival plaque, bound to the already existing dental biofilm substrate composed of Streptococcus and Actinomyces. Corynebacterium, projected in three planes, was found to structure the environment and create habitats for other bacteria to grow in the biofilm. Accordingly, Corynebacterium could be considered a bridging genus in supragingival plaque and probably more important as such than Fusobacterium. Corncob structures were produced at the distal tips of the Corynebacterium filaments (Streptococcus and Porphyromonas, which were in direct contact with the Corynebacterium filament. Further, Haemophilus/Aggregatibacter established contact with Streptococcus, and clusters of the family Neisseriaceae also appeared in the periphery of the hedgehog. In the radial arrangement, intermingling taxa probably displayed a complex set of physical and metabolic interactions. Due to the Streptococcus cells, a micro-environment rich in CO2, lactate, acetate, H2O2, and low O2 was established. In this low O2/high CO2 environment, elongated filaments of Fusobacteria and Leptotrichia were proliferating in a so-called annulus proximal to the peripheral corncob shell. In addition, the CO2-loving Capnocytophaga was abundant in and around the filament-rich annulus. Accordingly, the hedgehog consortia were seeded into a pre-existing biofilm located on the dental surface by Corynebacterium filaments. Once the Corynebacterium filament had attached to the biofilm substrate, it seemed to play a central role organizing the consortium since it provided attachment through its surface to other bacteria in its periphery. This also implied that a complex set of physical and metabolic interactions established a framework for spatial organization of the consortium.The authors’ hypothesis suggested that ilaments . These sPorphyromonas could survive at the periphery of the hedgehog unless it was represented by relatively aerotelerant species. Certainly more refined taxonomic methods will have to be developed to determine which species of the described genera/families are present in the hedgehog structure. It will also be important to make similar studies with subgingival plaque and related diseased tissue. Studies on the microbiota of diseased tissue could increase our knowledge on what is going in microbial dysbiosis. Whether the social interactions between taxa rely on a mutualistic, commensal, or parasitic foundation will have to be elucidated.The relative importance of physical and metabolic influences on this arrangement remains to be established. The number of probes used was too small to obtain a full picture of the supragingival biofilm with its hundreds of species. It has been pointed out that the variability in binding efficiency and accessibility to different 16S rRNA target sites can cause false-negative binding [C. matruchottii is a key bacterium in the biofilm architecture of supra- and subgingival plaque, it opens up the possibility to prevent formation of mature plaque by applying antibacterial substances [If bstances . FurtherNoteworthy, the principles revealed on the micron-scale biogeography of supragingival plaque have relevance to the organization of microbial consortia elsewhere in the body, e.g. in the small and large intestine and in the caecum. FISH and sequencing have also been used to demonstrate that bacterial biofilms are associated with colorectal cancers and that their structural organization of the associated microbial community has a potential to contribute to progression of this disease .


Many effective innovations and interventions are never effectively scaled up. Implementation research (IR) has the promise of supporting scale-up through enabling rapid learning about the intervention and its fit with the context in which it is implemented. We integrate conceptual frameworks addressing different dimensions of scaling up  and questions commonly addressed by IR  to explore how IR can support scale-up.We draw upon three IR studies conducted by Future Health Systems (FHS) in Afghanistan, Bangladesh and Uganda. We reviewed project documents from the period 2011–2016 to identify information related to the dimensions of scaling up. Further, for each country, we developed rich descriptions of how the research teams approached scaling up, and how IR contributed to scale-up. The rich descriptions were checked by FHS research teams. We identified common patterns and differences across the three cases.The three cases planned quite different innovations/interventions and had very different types of scale-up strategies. In all three cases, the research teams had extensive prior experience within the study communities, and little explicit attention was paid to contextual factors. All three cases involved complex interactions between the research teams and other stakeholders, among stakeholders, and between stakeholders and the intervention. The IR planned by the research teams focussed primarily on feasibility and effectiveness, but in practice, the research teams also had critical insights into other factors such as sustainability, acceptability, cost-effectiveness and appropriateness. Stakeholder analyses and other project management tools further complemented IR.IR can provide significant insights into how best to scale-up a particular intervention. To take advantage of insights from IR, scale-up strategies require flexibility and IR must also be sufficiently flexible to respond to new emerging questions. While commonly used conceptual frameworks for scale-up clearly delineate actors, such as implementers, target communities and the support team, in our experience, IR blurred the links between these groups.The online version of this article (doi:10.1186/s12961-017-0270-0) contains supplementary material, which is available to authorized users. While the global health community frequently knows which interventions are effective, it is typically much harder to design policies and programmes that support the implementation of such interventions at scale . BarrierThere has been a long-standing interest, both within and outside of the health sector, in how innovations get adopted and spread , 5. WithThe framework employed herein to consider critical elements of scaling up draws primarily upon two similar frameworks developed by Yamey  and SimmSimmons et al.  suggesteWhile the topic of scale-up is of significant interest in its own right, there is also a strong association between approaches to scaling up and how sustainable an intervention may be over the long term. For example, ‘standalone’ strategies for scaling up, such as vertically organised programmes, can be effective in reaching large numbers of people quickly, but often fail to build the necessary health system capacities to sustain that service over time .adaptive, dynamic, multiscale phenomenon” then implementation research has a natural role to play in examining these processes. Indeed, going back as far as 1980, Korten [but an organization with the capacity for embracing error, learning with the people and building new knowledge and institutional capacity through action.” (p. 480).Several papers have called for greater investment in implementation research as a means to learn about effective strategies for scaling up health interventions and innovations in low- and middle-income countries , 12, 13., Korten  made a sthe scientific inquiry into questions concerning implementation—the act of carrying an intention into effect, which in health research can be policies, programs, or individual practices ” [Implementation research has been defined as “ntions)” , and canntions)” .In this paper, we present case studies of how three Future Health Systems (FHS) research teams, in Afghanistan, Bangladesh and Uganda, used implementation research to address questions regarding the scalability and sustainability of the innovations that they were supporting. Specifically, we (1) analyse how implementation research contributed to understanding of the attributes of the service or innovation being scaled up, the scaling up strategy, the actors involved and the context, and thus informed the overall process of scaling-up and (2) reflect on the characteristics of implementation research strategies that make them best suited to support scale-up and sustainability, and how this may vary for different types of scaling up strategies.At the time of writing, only one out of the three interventions described (that in Afghanistan) had been formally adopted as government policy and was in the process of being rolled out across the country. Our analysis therefore, focuses on how the implementation research informed intervention design and scale-up strategies, rather than the ultimate issue of whether scale-up was finally successful.FHS engaged in implementation research across five countries – Afghanistan, Bangladesh, China, India  and Uganda – with the overarching goal of promoting more accessible, high-quality health services for the poor. This paper draws upon FHS experiences in Afghanistan, Bangladesh and Uganda. The second phase of FHS, which informs this analysis, took place in Bangladesh and Uganda in 2011–2016, and in Afghanistan in 2011–2014. This paper does not include the experiences of the FHS teams in India or China as in neither case was the team actively supporting the scale-up of an intervention. Rather, the FHS India team aimed to provide information about health conditions and service in the Sundarbans so as to stimulate local stakeholders to develop interventions and, in China, the FHS team was investigating how government health reforms were being implemented.Our analysis sought to analyse the FHS-supported interventions across the four different dimensions of scale-up discussed above Table . For eacFor the three study contexts, we collected and collated information on the attributes of the innovation, the attributes of the target community and other actors involved (including the research team supporting the intervention), the context and the scaling-up strategy. Further, we sought to document how these factors were assessed or designed at the start of the project and how they evolved over the life of the project. Initially, we drew primarily upon project documentation to accomplish this. The lead author (SB) reviewed country team research proposals, country theories of change, completed both at the start of the project and then 3 years later (2014), country presentations at annual meetings, routine monitoring reports initially submitted monthly and then for the last 18 months of the project on a quarterly basis, annual reports submitted by the teams to the consortium management, and country publications, including articles and policy briefs, as the sources of project information.Based upon these documentary sources we developed rich descriptions of the intervention and scale-up plans in each country addressing the different dimensions of the framework presented in Table Tables While all three of the country initiatives sought to improve the quality of health services available to the rural poor, the intervention strategies employed across the three settings were quite different. The community scorecard in Afghanistan was thought by the research team and local stakeholders to be an appropriate intervention given the high levels of existing mistrust within communities, and particularly between communities and authorities . The community scorecard was perceived as a means to engage multiple stakeholders, including provincial and district directorates, non-governmental organisations (NGOs), healthcare providers, councils and communities. At the same time, it was intended to strengthen social accountability and audit of healthcare services, enhance community governance and engagement toward state building, and build trust in a post-conflict environment. Further, it built upon an established system of balanced scorecards that had been employed nationwide to track the performance of health facilities and hospitals. The intervention in Afghanistan took place over a shorter period than those in Bangladesh or Uganda, and there was relatively limited evolution of the intervention during the FHS project so the intervention, as originally conceived, closely matched the final form of the intervention implemented by FHS; however, the intervention evolved after FHS had formally closed out its work. Specifically, an NGO was asked to test the same intervention in a different ethnic community, and after the successful conclusion of this, in 2015, the government passed a national policy mandating the community scorecard as a key strategy for all NGOs delivering the Basic Package of Health Services, and by 2017 the intervention was being implemented in five different provinces.In Bangladesh, the project team started with a more fluid formulation of the intervention noting that they sought to support informal healthcare providers (VDs) in the intervention area through the provision of technology resources to formally trained healthcare providers or of decision guides. Initially, the project team planned to adopt two interventions, one, known as the ‘Health Box’, was intended as a self-diagnosis and treatment guide for communities, which could also be used by VDs, and the other was the telephone link to a medical call centre run by a private for-profit partner. The Health Box technology was dropped during the first year as it was found to be designed to assist in diagnosis and treatment of specific diseases such as HIV/AIDS and was not easily adaptable for a wider array of conditions.The Bangladesh project team focussed their efforts on technologies to support VDs, and the call centre was launched in mid-2011. The call centre services were provided for a fixed rate fee set by the private for-profit firm. Unfortunately, by the end of 2011 it was clear that this system was not working. The number of clients using the call centre was low due to low levels of awareness and trust, resulting in insufficient financial gain for the firm to continue its services. Hence, the private for-profit firm lost interest in running the call centre and became unresponsive. Further, communication between patients and the call centre doctors was difficult due to the different dialects spoken. Subsequent to the withdrawal of services by the for-profit firm, ICDDR,B decided to launch its own call centre, building on what it had learnt from the earlier collaboration. At the time of writing, the ICDDR,B call centre is still functional but has not significantly scaled up its operations.The FHS Uganda team consciously adopted a flexible intervention design, initially tentatively identifying a package of related interventions to improve the use of maternal health services but committing to work with stakeholders during the early years of the project through a participatory action research approach in order to refine the intervention. While, throughout this process, the three main pillars of the intervention remained the same , the actual intervention details changed substantively through the process. In particular, the use of both transport and healthcare vouchers were dropped early in the process as being both financially unsustainable and too logistically complicated to implement. Instead, the team sought to work with local savings groups to help women save funds to cover the costs of their own maternity care, and also instituted a system of recognition awards as a low-cost way to motivate health workers. While the interventions as implemented by the FHS team have not been fully replicated elsewhere, the interventions have been sustained by local actors and aspects of them continue to influence broader government policy.The three different country teams each adopted very different strategies to scaling up their interventions; however, none of the teams articulated clear scale-up plans in their initial proposals. The Afghanistan team recognised relatively early in the implementation process that it sought to stimulate a change in government policy towards the use of the scorecard, and this insight was important in terms of the implementing team established and in particular the strong links built by the research team with the Department of Community-based Health Care in the Ministry of Health. However, the Afghan Government was not initially convinced by the results of the community scorecard intervention, observing that the initiative had not included Pashtun communities  and that before seeking to scale-up, it would be important to implement the community scorecards in contexts that included Pashtuns. It was only after this further piloting, which was implemented with another NGO in Nangarhar province after the formal end of the FHS project in Afghanistan demonstrating similar evidence of improvements in health facility services, that in September 2015, the Ministry of Public Health gave official support to the community scorecard strategy incorporating it as part of the new Community-based Health Strategy.In Bangladesh, the FHS implementing team quickly recognised (by the end of 2011) that there was an explosion of mHealth technologies with many different organisations experimenting with different mHealth applications. The FHS team sought to better understand the array of different mHealth models being implemented, through conducting a scoping study . Of the While the FHS Uganda team did not articulate a clear scale-up strategy at the start of its work, the broad approach adopted did not hinge upon an organised replication of the scheme. Instead, the FHS Uganda team sought firstly to design an intervention that could be managed and sustained by local communities and stakeholders themselves, and secondly to build relationships with a variety of stakeholders, including the Ministry of Health, parliamentary representatives, NGOs and development partners, so as to encourage them to adopt and roll out successful elements of the FHS approach.All three research teams conducted a stakeholder analysis at the start of their work, although these were conducted with varying degrees of thoroughness. In Uganda, the stakeholder analysis suggested that the community was very supportive of the intervention, though concerned about feasibility considering local resource constraints . The queAll three of the research teams identified a complex array of stakeholders that they needed to engage Table  to suppoReviewing the project documentation across the three sites, there is surprisingly little explicit recognition or discussion of contextual factors that may affect scale-up. While teams invested significant effort in actor management, and nurtured relationships with local and national level actors whose support they perceived to be critical to the success and ultimately scale-up of the intervention, there is much less analysis of the context.There were some partial exceptions to this. First, in Afghanistan, the unfolding security situation meant that the team provided regular assessments and reports on the security risks, but other dimensions of context were not fully studied. In Uganda, community engagement through the participatory action research identified factors important for continuity of interventions, including local political ownership of the intervention, building of local capacity and committed leadership from both the political and technical arms of the districts in which the intervention was implemented.One explanation for the limited systematic analysis of context may be the fact that, in all three countries, the research teams were local and/or had long histories of engagement in the regions where the interventions were occurring. For example, Makerere’s work in the Eastern region of Uganda dates back to the beginning of FHS1 (around 2006), Johns Hopkins School of Public Health involvement in Afghanistan dates back to 2002, and ICDDR,B has been working in Chakaria promoting self-help for health since 1994 . It is pIn both Afghanistan and Uganda, the planned research focused primarily on the questions of feasibility and effectiveness, namely the extent to which the intervention could be carried out in that particular setting and whether or not it was effective. The Bangladesh team sought to examine both effectiveness and acceptability of the intervention.Many of the findings from the implementation research studies related to the intervention itself, and in particular its acceptability, appropriateness, feasibility and implementation costs. These findings bore significant implications for the design and implementation of the intervention. For example, in Afghanistan, the studies underlined the importance of investing sufficiently in skilled facilitators who could inform communities about entitlements and mitigate unrealistic expectations. In Uganda, the implementation and documentation process pointed to the need for supportive supervision for CHWs and district implementation teams. In Bangladesh, the whole intervention design was brought into question as it became apparent that the intervention, as initially conceived, struggled to achieve acceptability with community members, and did not offer sufficient financial incentives to either the village doctors, or the private for-profit call centre.In addition to lessons about the design of the intervention, the research also yielded insights into how stakeholders perceived the intervention and how best to frame and implement the intervention to promote its acceptability and perceived appropriateness. For example, in Afghanistan, in conservative communities, it was important to figure out modalities that allowed women’s views to be fed into the community consultative processes, even though, in some settings, the women themselves were unable to participate alongside the men. Further, the research team realised that local stakeholders found the intervention more acceptable when framed as part of a national system of cascading scorecards, rather than as a separate and stand-alone intervention. While all three of the country teams pursued qualitative research, such as focus group discussions, which could help cast light on acceptability and appropriateness of the intervention, many important findings emerged spontaneously from the research team’s close and regular engagement with stakeholders on the ground.Frequently, the original objectives of the implementation research undertaken by FHS appeared unnecessarily narrow. While research protocols focused on feasibility, acceptability and effectiveness, the close involvement of the research teams in these implementation processes also cast light on factors such as cost, fidelity, sustainability and the perceived appropriateness of the intervention. These insights offered important and actionable lessons for implementing teams.While there are obvious connections between the conceptual frameworks on scaling up , 9 and fAlthough the projects presented here are only now reaching completion, and it is therefore too early to fully assess their influence upon scale-up and sustainability, the research conducted offered substantial insights into the barriers and facilitators of scaling up, including the design of interventions, and relevant attributes of stakeholders. Frequently, these insights could be translated into concrete changes in the design or framing of an intervention that might make it more feasible or acceptable. The research process in all three cases involved substantial stakeholder engagement, which, while burdensome on the research team, was critical to both developing lessons and encouraging the adoption of these lessons.In both Bangladesh and Uganda, the interventions were consciously framed in a relatively flexible way that facilitated adaptation to new learnings about stakeholders, or the intervention design. This flexibility was of critical importance, and all three FHS projects went through substantial adaptations to the initial design, including dropping some elements of the original package of interventions (Bangladesh and Uganda), adding new elements to the package of interventions (Uganda), reframing or re-packaging the intervention (Afghanistan), changes in implementation partners (Bangladesh), and extending the time frame and the geographical spread of the intervention (Afghanistan). Other authors have pointed out that scaling up interventions or innovations involves intervening in complex systems , 27, sucThere were considerable differences in the implementation research designs adopted across the different country settings. While all three country research teams sought to assess effectiveness, they had differing foci in terms of other implementation questions . Further, there were differing degrees of flexibility in the research design, with Uganda perhaps being the most flexible and adopting a participatory action research approach, and Afghanistan, for primarily logistical reasons (namely the shorter period of study), being the least flexible. However, in addition to the formal protocols, all three teams were required by the FHS research consortium and the funders to also develop theories of change  and condIn Uganda, the Makerere research team also engaged in regular review and reflection meetings. To give one example, during the first year, participatory self-assessments were conducted where both the research team and the district health management teams involved in the intervention reflected on what they had learnt, the nature of their relationships, where there were gaps and how these gaps could be addressed. This process helped to identify where collaboration between the research team and the district health management teams could be improved, and where responsibilities needed to be handed over from the research team to district health management teams to strengthen effectiveness and sustainability. Such formal processes for reflection likely helped to reap the full benefits of implementation research.The three cases reviewed here had varied approaches to scaling up, including a more vertically oriented approach (Afghanistan), a more horizontal approach (Uganda) and a replication approach (Bangladesh). In Afghanistan, the research focussed primarily on the feasibility, acceptability and appropriateness of the intervention. Presumably in this context, where government already used balanced scorecards at higher levels in the system, determination of feasibility and desirability may be sufficient to support scale-up. In contrast, the research in Bangladesh, where it was anticipated that private market-based actors may be key in scaling-up the intervention, focussed more on acceptability and adoption, i.e. attributes of the target community. In a sense, the Bangladesh study provided ‘market research’ for private sector entities interested in entering the market. However, these conclusions are tentative, and much more needs to be done to understand how best to tailor implementation research to different types of scale-up strategy.As previously noted, this paper is written at the time of the close out of the FHS projects, and thus it is too early to fully appreciate lessons concerning scale-up and sustainability. In addition, all members of the writing team are internal to the FHS project, and indeed the lead author is also the project leader of FHS. This has certain advantages in that, as participants in the processes described here, the authors brought extensive knowledge of the contexts, interventions and implementation research. However, it may also lead to certain biases, such as an inclination to make the interventions supported appear successful. We sought to guard against this bias as far as possible by triangulating between different data sources, including the researchers' own recollections, but also historical project documentation to describe the initial ambitions of the projects and how they changed over time.Many implications emerge from this paper regarding how best to design and undertake implementation research that is designed to support scale-up.With regard to implementation research, a number of apparent good practices have emerged, many of which reinforce what has already been described in the published literature. For example, the need for engaging stakeholders from the start of the process and providing ongoing nurturing of these relationships is highlighted across multiple implementation research texts , 30, andWhile it is important that teams involved in implementation research are not overly ambitious in their objectives, teams should think more broadly about the kind of questions that their research can address, as well as being receptive to unexpected learnings.Finally, while scaling up frameworks typically consider the characteristics of the target community and those of the implementing team separately, FHS research suggests that this is an oversimplification. There were multiple stakeholders involved in each of the country cases, who had differing degrees of responsibility for and engagement with the intervention. In no case was the community a passive recipient of the intervention. We also found substantial blurring between the research team and the ‘implementing team’. In all three of the cases considered here, the research team worked closely with stakeholders responsible for implementation, and had a direct implementation role themselves. In Uganda, where participatory action research was employed, community members were in effect part of the implementing team.There is a growing interest in the notion of ‘embedded research’ , 33, wheImplementation research can provide significant insights into how best to scale-up a particular intervention, including offering learnings about the design of the intervention, the characteristics of stakeholders engaged in the intervention and the scaling up strategy. To take advantage of the promise of implementation research, scale-up strategies require flexibility, as does the design of implementation research intended to examine it. Project management tools, such as theories of change, and stakeholder analyses can be critical adjuncts to the research, ensuring that adaptations made to initial plans continue to serve overarching project objectives and take account of complex stakeholder landscapes.Peer review reports for this article are available in Additional file Additional file 1:Rich description of cases. (DOCX 155 kb)Additional file 2:Open peer review reports. (DOCX 38 kb)