Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) now known as COVID-19 changed the world and the outdoor adventure and experiential education (OAEE) fields were not immune. These changes significantly impacted various OAEE programs in multiple ways and at different levels of intensity. Purpose: The purpose of this study was to ascertain the impacts of the COVID-19 pandemic on the OAEE fields and identify how OAEE organizations have responded to those impacts. Methodology/Approach: Using a three-phase study and a multi-method approach to data collection and analyses, respondents from 115 OAEE organizations (N=115) were asked to indicate how and in what ways their organizations have been impacted by the pandemic and in what ways their organizations have responded to those challenges. Findings/Conclusions: Many organizations responded not being ready for the impacts from the COVID-19 pandemic. Significant impacts were noted from most of the OAEE organizations responding and included closings, staff reductions, and downsizing as well as operational changes. Many organizations reported ways they are attempting to mitigate the pandemic effects. Implications: Important questions were raised in this paper as to how well prepared the OAEE fields might be for the next crisis. In numerous ways, the coronavirus-2019 (COVID-19) pandemic represents a perfect storm for the outdoor adventure and experiential education (OAEE) fields on three operational dimensions: timing, economic impacts, and enrollment in programs. In January of 2020, the virus was detected in humans, first in China, then elsewhere . The appBy February of 2020, public awareness was in a steep upward trajectory relative to both the presence and threats posed by the virus . MitigatBy March, the accumulating effects of COVID-19 were apparent in a variety of dimensions including the number of cases and world-wide travel and economic impacts. Close-downs and stay at home orders were becoming the norm and began to exert dramatic effects on the OAEE fields, both in terms of reduced numbers of enrollees and canceled or withdrawn registrations. Serious questions began to be asked as to whether OAEE programs could fulfill anticipated program enrollments, with an increasing number of OAEE organizations reported having to close due to stay at home orders and campus closures, not knowing when or if they would reopen .April and May of 2020 saw cumulative cases in the United States increasing from 256,503 to 1,863,787 and cumulative deaths increasing from 6,440 to 107,907 . This inJuly 2020 saw a rapid increase in positive test results with a second COVID-19 peak occurring in the United States on July 24. COVID-19 cases appeared to level out through mid-September, increasing hope that there would be a decrease in cases. Early October, however, saw cases increasing, peaking again in early January 2021, with the United States leading the world in COVID-19 cases and deaths . On DeceIn numerous ways, the COVID-19 pandemic represents a perfect storm for the outdoor adventure and experiential education (OAEE) fields on three operational dimensions: timing, economic impacts, and enrollment in programs. In January of 2020, the virus was detected in humans, first in China, then elsewhere . The appBy February of 2020, public awareness was in a steep upward trajectory relative to both the presence and threats posed by the virus . MitigatBy March, communities around the globe began to see the accumulating effects of COVID-19 in a variety of dimensions, including with the number of cases and impacts on world-wide travel and the global economy. Close-downs and stay at home orders were becoming the norm and began to exert dramatic effects on the OAEE fields, both in terms of reduced numbers of enrollees and canceled or withdrawn registrations. OAEE programs began to question whether they could fulfill anticipated program enrollments, with an increasing number of OAEE organizations reported having to close due to stay at home orders and campus closures .In April and May of 2020 COVID-19 cases in the U.S. increased from 256,503 to 1,863,787 with cumulative deaths increasing from 6,440 to 107,907 . This inBy July 2020, the national tally of positive test results increased, peaking for a second time in the United States on July 24. After leveling off in September 2020, by early October cases were on the rise again, peaking in January 2021 with the United States leading the world in COVID-19 cases and deaths . The WorA growing body of literature suggests the COVID-19 pandemic has had significant consequences for many programs including those of OAEE fields . AlthougFor example, the Lawrence Hall of Science at the University of California, Berkeley, conducted a study in the United States in April 2020 on the impact of COVID-19 on environmental and outdoor science education programs . Their rA survey conducted in May 2020 by the Outdoor Recreation Roundtable (ORR), in partnership with Oregon State University Outdoor Recreation Economy Initiative, suggested that the impacts of the pandemic significantly worsened since May 2020. ORR represents over 100,000 recreational vehicles, camping, boating, fishing, horseback, hunting, skiing, hiking, and biking businesses nationwide. The May 2020 ORR survey indicated that the outdoor industry is one of the most significantly impacted sectors in the United States with 95% of businesses reporting a significant decrease in revenue and 79% reporting significant financial impacts due to COVID-19. . Census In another study conducted by Leave No Trace (LNT) and Pennsylvania State University, Due to the unpredictable and changing nature of the pandemic, maintaining operations while rethinking strategies for recovery is difficult . As a reThe purpose of this study was to ascertain the impacts of the COVID-19 pandemic on the OAEE fields and identify how OAEE organizations have responded to those impacts. A multi-phased study design was used to examine how the OAEE fields in the United States and elsewhere have been impacted by the COVID-19 pandemic during 2020. Using a multi-method design, employing both a survey instrument and interview process to collect quantitative and qualitative data, allowed for a complementary approach during data analysis as advocated by RQ1: What are some of the ways in which OAEE programs have been impacted, for example, required to close, loss of staff, or loss of clients, by the emergence of COVID-19?RQ2: What specific variables, for example, organization size, age of the organization, or age of clients, have been influential in OAEE operations?RQ3: What are the OAEE management concerns related to COVID-19 as a function of program type?RQ4: What factors related to COVID-19 did OAEE program leaders perceive as having an impact on program viability during the pandemic?We asked four research questions to examine specific impacts from the COVID-19 pandemic on responding to OAEE programs. These impacts included variables that have influenced program viability, program manager concerns, and those factors related to COVID-19 that program leaders felt most impacted their program viability. The following research questions guided this project:When the study was initiated, we intended that the representative responding to the study be an individual within the upper levels of the organization's leadership who could adequately assess and address the impact of the pandemic on their organization, including its future direction. This study utilized a multiphase framework and collected data using telephone interviews, a survey instrument, and document examination. The multiphase approach allowed researchers to gather data over time and to make adjustments to the instrument as needed between phases, and within each phase, each person in our sample had the opportunity to respond.Data collection was divided into four sequential phases, with Phases 1–3 designed to capture information from the beginning of the pandemic through 2020. Phase 4 is designed to continue data collection into 2021 and is not included in this analysis. The research was approved by the Prescott College Institutional Review Board. All survey instruments are available upon request to the lead author.Phase 1, completed by May 6, 2020, provided a grounding for understanding how organizations were dealing with the pandemic and to inform the design of Phases 2–3 of the survey instrument. Phone interviews were conducted with leadership staff from 12 OAEE organizations to gather general information about the state of their programs during the initial months of the coronavirus pandemic. The 12 organizations in the Phase 1 sample were selected using a convenience sample . From thPhase 2 data collection began in June 2020 and ended in July 2020 . SurveysPhase 3 data collection began in July 2020 and continued through September 2020  and resuPhase 2 data and Phase 3 data were analyzed both in aggregate and as separate sets . Quantitative analysis included descriptive statistics and frequencies with qualitative data being codified using an exploratory method of holistic coding during the first cycle Dey, , p. 142.An eclectic method of evaluation and emotion coding was developed for second cycle coding Goleman, . EvaluatMember checking as a form of verification, though widely used , has weaThe effects of COVID-19 caused many OAEE programs to close. As depicted in Data on closures represent a single point in time and do not indicate the length of closure—programs that reported closures may have been closed for a short time or a long time.A total of 33% of SCU programs that reported being closed reported likely reopen dates while 18% were unsure of when they would reopen. Up to 37% of OAE programs that reported being closed reported likely reopen dates while 7% were unsure of when they would reopen.All OBH programs (n = 16) reported that they were operational at the time they completed our survey. This may be influenced by the decision made by the CDC, state, and local governments to include mental health providers as essential services. This was the only program type surveyed that did not report closures. There was no indication that OBH programs were better prepared for a crisis or were more resilient organizations when compared to OAE and SCU programs. In some states, these programs were designated essential with OBH program managers indicating they responded to high market demand during 2020.SCU program managers reported a higher percent of closures (53%) than OBH (0%) or OAE (44%) programs. Responses from program managers indicate this may be due to their often not having full administrative control over SCU programs as they are subject to state policies and guidelines as well as administrative decisions made by SCU leadership. A total of 33% of SCU program managers who indicated they were closed at the time they completed our survey also indicated they would attempt to reopen during the Fall 2020 semester, often planning to offer new and creative options such as on-campus outdoor activities and self-guided solo adventures into local parks and other public lands. Of these closed programs, 18% were unsure of when they would reopen.Another indicator that COVID-19 has impacted OAEE programs is change in staffing levels. As illustrated in Respondents also indicated that their outdoor activities staff often require specialized training, and in some cases specialized skill certification. OAE and SCU programs, often with broad activity portfolios equating to needing a greater amount of outdoor activity skillsets from their staff, may experience challenges rehiring staff at a future date.RQ2 examined the influence of three variables that impacted OAEE operations: population served, year the organization was established, and number of people served. Data related to longevity , depicted in Program managers responded with many concerns related to the COVID-19 pandemic with program viability, financial viability, and risk management at the top of their list across all three program types. The data suggest that the impacts to OAEE programs due to COVID-19 were variable dependent on the program type, for example, OAE, OBH, or SCU.The impacts of COVID-19 are immense, and program managers’ growing frustration is clear. Apart from OBH programs (n = 16), which were deemed essential and reported no closures, 53% of SCU (n = 45) and 44% of OAE (n = 54) respondents reported being closed at some point between June and September 2020. One OAE program manager responded, “Well, it was a complete shutdown on a very short notice. As with so many places, we initially thought we would close the office for two weeks. Currently [July 2020] we have 1–4 people present on any given day and others working remotely. Work is a fraction of what we projected for 2020 so the financial implications are huge.”Due to large institution leadership structures that often pass down mandates from top administration leadership, SCU program managers reported having little autonomy when deciding how to respond to the pandemic as well as little control over operations and finances beyond their immediate responsibilities. One university program manager stated, “Financially it has been a total headache and costly to untangle all the finances of canceling programs.” Another SCU program manager said, “I don’t think there was any way to greatly prepare for this pandemic programmatically in terms of how we usually operate. We did not have the resources or authority to continue providing programs.” SCU program managers reported a higher degree of concern for overall program viability. They were also concerned with the effects reducing operations or closing their program may have on students (their clients) and the overall health of the OAEE fields. At some point between May 2020 and September 2020, 53% of SCU programs were closed, and we suspect that the number of closed programs fluctuated (after we completed data collection for Phase 3 in September 2020) as universities determined if and/or how to reopen during the Fall semester of 2020.Primarily at the early stages of the pandemic, there was a significant management concern regarding financial viability. However, OBH programs continued to operate throughout all three phases of data collection, and many increased their number of clients. Technology use grew to include telehealth and other virtual activities and virtual nature-based experiences, especially during the often-required 2-week quarantine phase when a new client entered the program. OBH program leaders reported being well connected with other OBH organizations and were consistently looking for ways to continue to support their clients through innovation. Program manager concerns were primarily focused on client care, staff retention, and their ability to meet program outcomes while mitigating the risk of transmission.The most common management concerns in OAE programs between June and September 2020 were how to respond to the pandemic and remain open. In the sample, 44% of programs reported closing operations at some point between May 2020 and September 2020. OAE programs reported the need for risk management solutions, program or outdoor activity options, transportation accommodations, and access to resources that would help them remain viable until, as one respondent stated, “things get back to normal.” OAE program managers often reported the use of message boards from professional membership organizations and associations such as the American Camping Association, the AEE, and the Association for Outdoor Recreation and Education, to help them solve emergent problems.When asked to share what they were doing to reopen, one OAE program manager simply responded “What can I pivot to? More day programming?” Managers indicated that preventing disease transmission was the primary concern, which necessitated radical changes in human resources, outdoor activities, lodging, food management, and transportation. Financial viability was also a primary concern except in cases where organizations benefited from endowments or access to other stable sources of funding. Toward the end of our Phase 3 data collection in September 2020, managers became increasingly concerned with staff retention, the ability to rehire furloughed staff, and maintaining staff credentials and/or training.Although differences were noted between program types, as described above, many of the themes that emerged from the data were consistent across types. The following findings supporting Research Question 4 are more general and emerged across two or more of the program types. Program types are mentioned when it is needed to situate data in the correct context.Program managers were asked about a variety of factors that may have impacted the program's ability to remain viable during and after the pandemic and their responses were organized into three themes: (a) risk management; (b) readiness; and (c) the changing nature of OAEE programming.Risk management during the pandemic was a critical concern for leadership. Respondents indicated that the immediate need to reduce or eliminate potential virus transmission during the start of the pandemic created initial chaos. Because existing risk management plans were not designed with COVID-19 in mind, program managers initially relied on organizational leaders and state and federal government guidance and direction. Their early concerns included staff and client safety, community safety, how to respond to a client or staff member developing symptoms, and how to safely mitigate COVID-19 transmission. Operational impacts from risk management decisions included alterations to group transportation, sleeping, and eating procedures and a need to adapt outdoor activities to COVID-19 standards. In some cases, organizations with larger outdoor activity portfolios reported having greater success in adapting to COVID-19 standards because they had multiple activities that could be more easily modified.Out of 77 responses to the question “Discuss ways in which you (your program) were prepared for this pandemic?” A total of 48% suggested that they or their organization were not prepared or could not identify a way in which they were prepared, 3% of responses were neutral, and 49% were able to provide some way in which they felt that they or their organization were prepared. There was no significant difference between Phase 2 and Phase 3 responses.Readiness in our study is an examination of an OAEE program's level of preparedness, an indicator of an organization actively building and maintaining resilience. Data from this study identified three elements of organizational resilience we used to determine levels of readiness: (a) financial reserves; (b) organizational leadership; and (c) standards and policies .Programs that indicated some degree of readiness suggested they had (a) financial reserves that allowed for continued operations (even if at a reduced capacity), (b) operational leadership that was able to navigate the complexity of the pandemic, and (c) existing policies that either mitigated the impact of COVID-19 or were easy to adapt. For example, several programs already had work-at-home policies and respondents suggested that those policies made transitions easier. One organization stated that the procedures they developed during a prior pandemic enabled them to cope with the impact of the COVID-19 pandemic.Programs that showed some degree of readiness also made decisions as needed including reducing staff, reducing capacity, redesigning and/or eliminating certain activities , and immediately incorporating COVID-19 safety and risk management protocols as they were developed internally and adopted from other programs.After concerns for the safety of participants and their communities, a common concern among program managers was losing the essence of outdoor adventure and experiential education. Many respondents felt that effective OAEE programs require face-to-face interactions that are relational and are not easy to translate to the virtual environment. One respondent said, “We are losing our most powerful tool of in-person experiential activities if we opt for virtual [programming] as the new norm.”While the most common form of planned adaptation involved using different forms of virtual integration, several other themes emerged from the data about how organizations planned to adapt. Several of the respondents suggested that they would move their programs from distant destinations to local parks, nature areas, or urban areas. One organization suggested that they would focus on “sense of place, creating more opportunities to engage in local history.” Another respondent suggested that they would keep their program more local and would increase their focus on indigenous history of the area. One respondent suggested that the focus of their programming would be meeting students in their local places. This organization said that they plan on “shifting completely to short local trips offered throughout the country near our student's hometowns. We are no longer thinking about bringing students to us but utilizing resources in their own communities to organize programming for them.”There can be little doubt that the COVID-19 pandemic has impacted the OAEE fields. Program closures, a significant decrease in enrollment, and staff reductions all followed the emergence of the pandemic. OAEE organizations confronting the pandemic found themselves faced with the realities of choosing whether to fire/furlough/retain employees and planning for reopening or permanently closing amid uncertainty.Clearly OBH programs, identified as essential, were able to remain open while OAE programs responded to governmental orders and SCU programs responded to governmental orders and their higher administration. While there is data to support organizational age (younger organizations more likely to remain open) and size  affected open and closure rates, of interest is how organizations responded to risk and what effect organizational readiness has had.A total of 49% of organizations indicated some readiness for the pandemic, but none responded that they were fully prepared. Many organizations aggressively sought out information on how they should respond regarding risk management, looking to both professional organizations and other leaders in the field for solutions. This response may be due to OAEE program managers’ experience while understanding their simultaneous responsibilities to their staff, their clients, and to the health of their business.OBH programs remained operational during 2020 and worked to meet their client's needs while coping with the organizational stress caused by the pandemic. While the OBH program managers who responded to our study indicated they had the same general concerns around program viability as the OAE and SCU program managers, they also responded with more general optimism for the future as they found ways to continue to provide services to their clients.These challenges are magnified by what The findings from this study suggest organizations within the OAEE fields lacked a certain type of readiness to address and respond to a global pandemic strategically, financially, and organizationally. The impact of the readiness and response varied depending on the scope of the program.Implications suggest that future research focus on organizational resilience, resistance to stress  and organizational recovery during the post-vaccine rollout period. Resilience in this study is the result of pre-pandemic preparedness resulting in an organizations’ ability resist the stress , whereasThis study also highlighted the vulnerability of personnel who design and deliver OAEE programs. These individuals  are foundational for the OAEE fields; without them, programs cease to exist. Many OAEE organizations had to furlough or lay off staff, which challenges these individuals’ resiliency and ability to recover personally and professionally. Future research should look at what type of emotional, mental, spiritual, and financial support organizations provided to their staff during the pandemic, as well as what changes were implemented to hiring and retraining as programs began to offer services once again.As OAEE organizations recover from the pandemic, the research from McKinsey & Company  suggesteLastly, the study asserts that many OAEE organizations faced the reality of how a pandemic can devastate an operation when it relies primarily on providing in-person experiences. Examining research comparing how private companies and educational programs fared compared to local, state, or federal government supported OAEE entities,  could provide guidance to how to prepare for future disruptions. This research could also aid OAEE organizations that suffered significant setbacks with relevant data which can inform new accepted practices, helping them transition from a recovery mindset to a growth mindset.Questions remain as to what OAEE programs will look like in the future. Will programs remain essentially unchanged from before the pandemic? Or will they enact changes such as in program delivery, business models, and staff training protocols in response to COVID-19? Will organizations that recover from the impacts of COVID-19 pandemic operate at greater or lesser capacity? Will program outcomes change as a result of reaction to the pandemic . MoreoveThere are a substantial number of OAEE organizations in the United States and tens of thousands more globally. Given the relatively small sample size, the data may not comprehensively reflect the broader population of OAEE organizations and practitioners.This study utilized data collected between June 2020 and September 2020 . The panIn addition, the survey process did not include questions that could provide a comparison over time. That is, did organizations improve, stay the same, or diminish in their ability to deal with the pandemic. To account for the timing limitation, future research should involve tools such as a retrospective pre-test instrument  measurinIt should also be noted that the respondents in all three phases were predominately from the United States (10 of 115 respondents were from outside of the United States). As such, these data may not be fully representative of a more international perspective and experience.As noted in the research responses, preparedness and the ability to respond to a pandemic was not typically part of an organization's policy or procedures. The lack of precedent on how to adapt and manage an organization's strategic mission with new operational objectives during a pandemic left organizations without a model providing guidance on accepted practices and standards in response to risk mitigation, health, and safety issues, or how to manage fiscally during a long-term crisis. The lack of precedent coupled with an evolving long-term crisis, with critical national health and safety guidelines constantly changing, creates an environment where organizational responses and procedures can be obsolete within a few days.Moreover, the research relied on an organization's representative to respond appropriately to the research questions. The data generated in the study were strongly based on anecdotal information and due to the accelerated nature of timing of the study, it is challenging to corroborate all the responses from the research participants to fully address the impact, implications, and corroboration of qualitative responses.Due to the nature and the duration of COVID-19, especially as it is on the cusp of disrupting a second year of business priorities, future studies should include organizational resiliency, decision-making modalities, stringent qualitative analysis to include verification and affirmation of sources through peer review, and larger sample sizes of organizations within the outdoor industry.Participant responses present a compelling picture that the COVID-19 pandemic had a significant impact on many OAEE program's ability to operate as well as potentially on future operations. COVID-19 has exerted a broad spectrum of negative and often debilitating impacts across the globe. Organizations in the OAEE fields have been particularly vulnerable to a number of these impacts because they usually have a heavy reliance on face-to-face activities with program participants and are only partially amenable to online or other electronic meeting venues. Moreover, they can be influenced by several externalities beyond their control such as public health decrees and governmental-imposed lockdowns.What is also clear is that many OAEE organizations in this study were unprepared for this crisis. It remains to be seen if the COVID-19 crisis recovery will substantially differ from other crises that the OAEE fields has experienced such as the Great Recession beginning in 2008 when enrollments significantly dropped. Or is this a VUCA environment  or a blaSeveral important issues have emerged from this current crisis and that have ramifications to the OAEE fields. First is the structural inequity  of the OAEE fields. Second, is whether and what the OAEE fields will learn from the COVID-19 experience. Once past, will we go back to business as usual or will programs and organizations change to be better prepared and able to respond in a nimbler manner? Learning these and other findings represents the difference between acting nimble out of necessity and acting nimble out of design. COVID-19 has caused some OAEE organizations to close, and it is hoped that the outdoor adventure and experiential educations fields will be better positioned to react to the next set of challenges that remain in the future.This study demonstrates that OAEE programs can successfully operate in a COVID-19 environment and supports


Generation Health (GH) is a 10-week family-based lifestyle program designed to promote a healthy lifestyle for families with children who are off the healthy weight trajectory in British Columbia, Canada. GH uses a blended delivery format that involves 10 weekly in-person sessions, and self-guided lessons and activities on a web portal. The blended program was adapted to be delivered virtually due to the COVID-19 pandemic. Currently, the effectiveness of the virtual GH program compared with that of the blended GH program remains unclear.We aimed to (1) compare the effectiveness of the virtual GH program delivered during the COVID-19 pandemic with that of the blended GH program delivered prior to the pandemic for changing child physical activity, sedentary and dietary behaviors, screen time, and parental support–related behaviors for child physical activity and healthy eating, and (2) explore virtual GH program engagement and satisfaction.This study used a single-arm pre-post design. The blended GH program (n=102) was delivered from January 2019 to February 2020, and the virtual GH program (n=90) was delivered during the COVID-19 pandemic from April 2020 to March 2021. Families with children aged 8-12 years and considered overweight or obese (BMI ≥85th percentile according to age and sex) were recruited. Participants completed preintervention and postintervention questionnaires to assess the children’s physical activity, dietary and sedentary behaviors, and screen time, and the parent’s support behaviors. Intervention feedback was obtained by interviews. Repeated measures ANOVA was used to evaluate the difference between the virtual and blended GH programs over time. Qualitative interviews were analyzed using thematic analyses.F1,380=18.37; P<.001; ηp2=0.07) and reduced screen time . However, vegetable intake was significantly greater in the virtual GH group than in the blended GH group at the 10-week follow-up . Parents in both groups showed significant improvements in support behaviors for children’s physical activity  and healthy eating , as well as self-regulation of parental support for children’s physical activity  and healthy eating . Families in both groups were satisfied with program delivery. There were no significant differences in attendance for the weekly in-person or group video chat sessions; however, portal usage was significantly greater in the virtual GH group  than in the blended GH group .Both the virtual and blended GH programs improved children’s moderate-to-vigorous physical activity (The study findings suggested that the virtual GH program was as effective as the blended program for improving child lifestyle behaviors and parental support–related behaviors. The virtual program has the potential to improve the flexibility and scalability of family-based childhood obesity management interventions. Children who are off the healthy weight trajectory have increased risks of chronic diseases, psychological distress, and lower quality of life . The prePhysical inactivity, increased screen time, and unhealthy food choices have all contributed to overweight or obesity among children ,5. The lFamily-based lifestyle interventions have been shown to be effective for managing childhood obesity -11. FamiThis study used a single-arm pre-post comparison design. Eligible families participated in study assessments at baseline and following the 10-week intervention. Families were invited for an exit interview at the end of the study to collect qualitative program feedback data. The blended GH program was delivered and evaluated from January 2019 to February 2020. The virtual GH program was delivered and evaluated during the COVID-19 pandemic from April 2020 to March 2021. All participants enrolled in the blended and virtual GH programs were included in this analysis. Families were recruited using social media; email mailouts to provincial networks; and posters displayed in recreation centers, medical offices, and schools.This study was approved by the research ethics board at the University of Victoria (H20-00564).Families with at least one child between the ages of 8 and 12 years and considered overweight or obese (BMI ≥85th percentile according to age and sex) were included. At least one parent/caregiver was required to participate in the program. Children with one or more comorbidities were excluded and referred to the Shapedown British Columbia clinical program.The blended GH program was delivered at the following local community centers in British Columbia, Canada: Prince George (YMCA of Northern British Columbia), Kelowna (YMCA of Okanagan), Surrey (Tong Louie Family YMCA), Surrey (City of Surrey), Burnaby (City of Burnaby), and Greater Victoria (West Shore Parks and Recreation Society). The program was theoretically informed by the multi-process action control (M-PAC) framework, which emphasizes social cognitive approaches to facilitate intention formation, adoption of action control through self-regulation, and an action control maintenance phase where behavior becomes habitual and self-identified . The in-The virtual GH program contained the same curriculum and used the same theoretical framework (M-PAC) as the blended GH program; however, the content of the program was adapted to be delivered online over a group video call . Family activities were modified to accommodate this new delivery format. Program modifications included (1) reformatting the layout of each session ; (2) modifying activities and games for at-home delivery; and (3) replacing the additional community-based activities with virtual expert sessions . The self-guided component of the online portal was enhanced to include additional interactive videos and content to help families achieve a healthy lifestyle during the COVID-19 pandemic lockdown . See Study data were collected from the parents and children using an online questionnaire at baseline and at follow-up. Demographic data, including the child’s age and ethnicity, parents’ education, annual household income, number of people in the household, and family structure status , were collected at baseline. Child BMI was collected by a research assistant at the delivery sites for the blended program. However, child BMI was self-reported by parents for the virtual program owing to physical distancing measures.The Physical Activity Questionnaire for older children (PAQ-C) was used to evaluate the number of days in the past week that children engaged in 60 minutes of MVPA . SpecifiThe Physician-based Assessment & Counseling for Exercise (PACE) adolescent psychosocial instrument was used to measure screen time and sedentary behavior . The valChild dietary behaviors  were assessed using questions drawn from the Centre for Disease Control and Prevention Behavioral Risk Factor Surveillance System (BRFSS) 7-day recall (intraclass correlation=0.50) . The BRFA subscale drawn from the Family Life, Activity, Sun, Health, and Eating (FLASHE)-EAT survey (α=.77)  and the The Parent Support of Child Physical Activity Questionnaire was adapted from previous research -27 for mWeekly GH program attendance for the in-person and virtual group video sessions was recorded by facilitators using a tracking form. Web analytics captured the total minutes spent interacting with the web portal content. The average minutes per week a family spent logged into the portal was calculated by dividing the total time by 10 (the length in weeks of the GH program).Program feedback questionnaires for participants were administered at the end of the interventions. The surveys prompted participants to (1) rate the weekly sessions , (2) rate the level of satisfaction with intervention components , and (3) rate the information given in weekly sessions . Parents were also invited for a phone interview to provide further program feedback.t tests and chi-square tests were conducted to compare continuous and categorical demographic variables between groups, respectively. A repeated measures ANOVA was conducted to examine the main effects of time (baseline and follow-up), as well as the group  by time (baseline and follow-up) interaction for all outcome variables. Independent t tests were used to evaluate program satisfaction and engagement for the blended and virtual GH programs. All quantitative statistical techniques used in this study to generate the results had established a significance set at P<.05. Qualitative data from postprogram interviews on program feedback were transcribed using Transcriptive software  and analyzed using NVivo 12 . General categories and themes were identified using a framework analysis approach [All statistical analyses were conducted using R statistical software version 4.1.0 . We determined that the data were missing at random and performed mean imputation for missing outcome variables . Indepenapproach . Themes 26 [N=192]=18.03; P=.01).Overall, 192 participants were enrolled in the GH program and completed baseline surveys. Participants’ demographic data are shown in F1,380=18.37; P<.001; ηp2=0.07) and significantly lower screen time . We also observed a significant interaction between group and time for vegetable intake among children. Specifically, participants in the virtual GH group reported significantly greater vegetable intake than those in the blended GH group at the 10-week follow-up . No significant main effect of time or a group-by-time interaction was observed for fruit intake, sugary drink intake, or sedentary time (P>.05).There was a main effect of time for days of MVPA and screen time , suggestF1,380=3.91; P<.001; ηp2=0.01), self-regulation of support for healthy eating , parental support for physical activity , and self-regulation of support for physical activity . After the intervention, parents reported higher scores on all these variables compared to the findings at baseline  and parental support for physical activity . In both cases, parents in the blended GH group scored significantly higher than parents in the virtual GH group at follow-up.We detected a main effect of time for parental support for healthy eating . Web portal usage was significantly greater for the virtual GH program than the blended GH program. Families who completed the blended GH program spent an average of 17 (SD 15.3) minutes per week on the family portal, while families in the virtual GH program spent 50 (55.82) minutes (P<.001).Blended GH attendance at the weekly in-person sessions was 77% for those who completed the program. Similarly, virtual GH attendance at the weekly group sessions was 76% for those who completed the program. There was no significant difference between blended and virtual GH completion rates  and virtual (3.8/5) GH programs. Postprogram interviews with parents identified areas of improvement for the virtual GH program. These included (1) a reminder from delivery staff about upcoming sessions on the day of the program; (2) support for implementing lifestyle changes for families who do not have a nuclear family structure; (3) a more in-depth explanation of how to navigate the family portal; (4) additional cooking class sessions; and (5) additional resources to support goal setting after the program ends.This study compared the effectiveness of a virtual GH program delivered during the COVID-19 pandemic with that of a blended GH program delivered prior to the pandemic. We observed that the virtual GH program was as effective as the blended GH program in improving child MVPA and reducing screen time. The virtual GH program appeared more effective than the blended GH program in improving vegetable intake among children. Additionally, parents in both the virtual and blended GH programs showed significant improvements in support behaviors for child physical activity and healthy eating, as well as self-regulation of support for child physical activity and healthy eating. Families in both the virtual and blended GH programs were satisfied with the program delivery. Overall, the findings from this study suggested that the virtual GH program was a feasible and effective option that has the added potential to improve the flexibility and scalability of delivering family-based childhood obesity management interventions.Our results showed a large increase in child MVPA and a reduction in screen time following GH. Similar to the blended GH program, the virtual GH program added almost 1 day per week of at least 60 minutes of MVPA and reduced about 45 minutes of screen time per day. The multiple physical activity opportunities  during each session for children, the parent portal resources about limiting screen time and support for child physical activity, and the weekly family-based challenges may have contributed to intervention success. Our findings are consistent with the findings of previous studies. For example, a previous 12-week family-based childhood obesity management intervention (children aged 8-12 years) showed that MVPA increased by 53 minutes per week and screen time decreased by 34 minutes per day . SimilarFurthermore, the findings about the levels of program engagement and satisfaction between the virtual and blended GH programs were noteworthy, as they suggested that families were willing to engage with the virtual delivery format. However, our results suggested that being a single parent may influence program completion, which has been previously reported ,33. FutuChild vegetable intake following the intervention was significantly higher in the virtual GH group than in the blended GH group. This may have been due to the lockdown, as parents may have more opportunities to influence children’s vegetable intake while they are at home every day . HoweverThe findings of this study have several implications for family-based interventions aimed at promoting a healthy lifestyle for children who are overweight or obese. First, this is one of the first studies to demonstrate the effectiveness of adapting a blended family-based program to be delivered virtually during the COVID-19 pandemic for Canadians living in British Columbia. Second, this study showed that virtual family-based interventions could be as effective and engaging as a blended program to promote a healthy lifestyle among children. This suggests that a virtual approach is another GH program delivery option for families even after the pandemic. The virtual delivery format has the potential to improve the flexibility and scalability of family-based lifestyle programs designed for children who are overweight or obese. The results from this study add to the existing body of literature showing the effectiveness of virtual and online health interventions ,34-36. TWe recognize that this study is not without limitations. First, the program evaluation was only up to 10 weeks. Thus, the long-term effects of virtual and blended GH programs remain unclear, and future research is warranted. Second, this study lacked a control group, which may introduce potential bias. Third, we did not control for potential secular effects , which may influence lifestyle behaviors. Future studies are warranted to explore the effects of these potential variables on intervention effectiveness. Fourth, even though all the child and parental measures have been validated, the self-report measures may introduce potential bias. Furthermore, some questions used to assess parental support for physical activity were not pertinent during the COVID-19 pandemic. For example, parents were asked to respond to the statement, “I go out of my way to enroll my child in sports and other activities to get him/her to be physically active.” During the initial months of the COVID-19 pandemic, schools were closed and extracurricular activities for children were cancelled. Therefore, we cannot be sure that parent responses to these survey items accurately reflected their opinions and attitudes or the contextual factors. Finally, the children’s BMI was self-reported by caregivers during virtual GH delivery, and this may introduce bias. Future studies could consider collecting parental BMI, as it can influence a child’s weight and lifestyle behaviors . FinallyOverall, a 10-week family-based intervention (the GH program) was effective in improving days of MVPA among children and reducing screen time, regardless of the delivery method . Similarly, satisfaction was high across delivery methods. Our findings suggest that virtually delivered early intervention programs are not inferior to in-person programs and offer an alternative delivery approach that enhances program flexibility and potential scalability.


This study examines individual and community factors related to older adults’ perceived losses in places to socialize with people of similar and different ages in their neighborhoods during COVID-19. In the 11-month wave of the COVID-19 Coping Study from March-April 2021, responses to perceived availability were “Less,” “About the Same,” or “More.” Most respondents reported less availability in places to socialize with those of similar (68.0%) or different (68.4%) ages. Ordinal logistic regressions showed respondents who lived alone perceived less availability in places to socialize with those of similar or different ages than those living with others . Those living in metropolitan compared to non-metropolitan areas also perceived less availability in places to socialize with those of similar ages . These findings enhance our understanding of COVID-19-related losses in community resources that facilitate healthy aging in place.


N = 92 subjects with overweight and obesity  of this randomized controlled trial, which were assigned to an interactive (ONLINE: intervention group) or non-interactive (CON: control group) web-based weight loss program, were included in the data analysis. COVIDAge and cardiometabolic risk factors, including anthropometric outcomes, blood pressure, flow-mediated dilatation, and blood parameters, were assessed before and after the 12-week intervention phase. There was a significant group difference in the change of COVIDAge . The ONLINE group also showed significantly greater reductions in anthropometric outcomes and systolic blood pressure than the CON group (p < 0.05). To the authors' knowledge, this was the first study investigating the effects of regular attendance in a web-based health program on lifestyle-related risk factors for COVID-19. The results demonstrated that adults with overweight and obesity can improve their COVIDAge and specific cardiometabolic risk factors by using this interactive web-based weight loss program regularly. However, this needs to be confirmed by future studies. This study is registered at the German Clinical Trials Register .The coronavirus disease 2019 (COVID-19) pandemic has induced unhealthy lifestyles, particularly an increase in overweight and obesity, which have been shown to be associated with an increased risk of unfavorable COVID-19 outcomes. Web-based health programs could be a helpful measure, especially in times of severe restrictions. Therefore, the present study aimed to investigate the effects of regular attendance in a 12-week web-based weight loss program on COVIDAge, a new construct for risk assessment of COVID-19, and lifestyle-related cardiometabolic risk factors.  In late 2019, the first coronavirus disease 2019 (COVID-19) cases caused by severe acute respiratory syndrome coronavirus type 2 were discovered in China , 4.Consequently, dietary and physical activity behavior could change in health-compromising ways during phases of quarantine, isolation, or lockdown . Recent To counteract weight gain and increased cardiometabolic risk, besides the well-known advice to maintain a healthy lifestyle, people should consider participating in web-based weight loss programs . EspeciaTo investigate the influence of lifestyle-related risk factors for COVID-19, a construct accounting for the heterogeneity of risk factors is needed. The COVIDAge Risk Calculator™  represenhttps://www.drks.de). Detailed study information can be found in the published study protocol   ranging from 27.5 to 34.9 kg/ml media) .All recruitment efforts focused on directing interested subjects to a landing page to inform and register for the study, including a written informed consent form. Subsequently, participants were screened by telephone regarding the inclusion and exclusion criteria. After successful screening, an appointment was fixed for the first medical examination. Seven days after this examination, participants were given access to a 12-week web-based weight loss program. The second medical examination was realized after program completion .The program delivered to the intervention group (ONLINE) was one of the coaching modules (“TK-WeightLossCoaching”) of a multimodal health program  offered by a national statutory health insurance fund (Techniker Krankenkasse). With an interactive, self-directed, and flexible usability, this program enables personalized health coaching according to the users' health profiles . The priFor program setup, users first completed a multi-step anamnesis and set their personal weight loss goal (3 or 5 kg). They were then taken to an online platform made up of three areas. The first area  displayed a personal dashboard as a weekly and an overall 12-week coaching plan  that users compiled and modified according to their individual needs. Thereby they could choose from a wide range of mainly 12 weight loss activities, such as “I achieve a green energy density” or “I eat fruit two times and vegetables three times a day,” to be supported in implementing the energy density concept and the principles of a balanced, healthy diet. One preset daily activity was the nutritional protocol, as it was the main activity of the weight loss program. It provided a helpful management tool on energy density, calorie balance, and fluid intake and included an analysis of macronutrients and lots of healthy recipes. Another preset weekly activity was to report body weight and waist circumference. All completed activities had to be logged daily or weekly. Moreover, the dashboard featured current health goal achievements (weight and waist circumference loss), daily tips, notifications, and support opportunities. The second area (“Knowledge”) supplied scientific background information on different health topics  through interactive knowledge courses with texts, videos, tools, and tests. One course (“Weight loss with energy density concept”) was an integral part of the coaching plan to teach users the energy density concept. Finally, in the third area (“My data”), users could view an analysis profile of logged activity data as well as their personal and health profile data, study information including written informed consent, and the option to withdraw study participation. To monitor physical activity behavior, users received an activity tracker (Fitbit Charge 3™) and could synchronize it with the program. At the end of the program, users obtained a coaching summary of their achieved successes .The program was provided as a web-based version that could be used on various electronic devices, such as computers, laptops, tablets, or smartphones. This made the program easily accessible to users anywhere and at any time. Furthermore, the users could adapt the program flexibly to their individual needs, which kept the program interesting and varied. In addition, the program tried to accompany the users in everyday life by providing daily activities, tips, and notifications. All these factors together should increase the acceptance and success of the program. On the other hand, there were some potential barriers to program acceptance and success. Because the program was designed on a scientific, evidence-based foundation, the program usability could be perceived as quite complex. Consequently, program use could be considered time-consuming. This could be very challenging, especially in times of the COVID-19 pandemic in a stressful private and professional situation.The control group (CON) program was similarly designed as an online platform to assist users in losing and maintaining weight over a 12-week intervention phase. However, this program merely offered a health-related knowledge transfer. Therefore, the first area  presented only evidence-based information on various health topics, particularly on the energy density concept and a healthy diet. These contents were taught in small lessons and without any interactive components. In the second area (“My data”), users got personal data, study information, including written informed consent, and the option of withdrawing study participation. For monitoring physical activity behavior, participants of the control group also received an activity tracker (Fitbit Charge 3™) but could not link it to the program. After study completion, access to the interactive web-based weight loss program was provided .https://calculator.covid-age.com) (The primary outcome (COVIDAge) comprised a construct newly developed during the COVID-19 pandemic by Everist Health to estimate an individual's risk for complications of COVID-19 infection . It coveage.com) , conside1c) and blood lipid  levels were taken from blood samples. Furthermore, diseases, medication, and daily cigarette consumption were asked via anamnesis. In order to perform these examinations in a standardized manner, participants appeared fastened , and all examinations were preferably performed at the same time of the day , bioelectrical impedance analysis scale (seca mBCA 515), and measuring tape (seca 201) . Flow-me4, bioele the day .*Power software (Version 3.1) (RRID: SCR_013726)  using the G_013726) . Assumin_013726) , 30 as wol group , 21.With successful online registration, participants were immediately assigned to the intervention and control group by permuted block randomization with variable block sizes of 4, 6, and 8 and an allocation ratio of 1:1. The random allocation sequence was generated by the Section of Health Care Research and Rehabilitation Research of the Medical Center of the University of Freiburg using the RITA software (Version 1.50) . The seqThe study participants could not be blinded due to study information, i.e., the allocation to an interactive and non-interactive web-based weight loss program. In contrast, the clinical trial's healthcare providers and outcomes assessors could be blinded as they would know about group allocation only after data collection was completed. Nevertheless, it could not be ruled out that study participants might report allocation at the medical examinations. The data analysts also could not be blinded because group allocation might have been suspected on the data , 21.t-tests were calculated to compare both groups. For secondary outcomes analyses, repeated measures analyses of variance were computed.For all statistical analyses, the IBM SPSS Statistics software (Version 27.0.0.0) (RRID: SCR_019096)  was usedThe level of significance was set at 0.05 for all comparisons. Participants of the intervention group with a program adherence of ≤ 50%  were excluded from the analysis. This was done to examine program effectiveness based on regular attendance in the weight loss program. Thus, our aim was not to investigate the dose-response relationship within the weight loss program but to examine the effects of a regularly used weight loss program compared to a control intervention. The following data are presented as mean ± SD if not indicated otherwise.N = 53 of the intervention and control group, respectively, had already completed the second medical examination at the time of data analysis. In total, n = 7 dropouts were reported in each study group. None of the dropouts was associated with any side effects of the study. There were also no adverse events reported in either study group. For the following analyses, participants of the intervention group with an adherence rate of > 50% were included (n = 39). Details of the study participant flow are illustrated in p > 0.05).Study participants were recruited from January to July 2020. After randomization and successful screening, they attended the first medical examination and the second after completing the 12-week weight loss program. p = 0.037, d = 0.446; From pre to post intervention, the ONLINE group decreased COVIDAge by −4.2% from 23,152 ± 6,073 days (~ 63.4 years) to 22,178 ± 5,761 days (~ 60.7 years). In contrast, a decline of −1.3% in COVIDage was observed in the CON group . There was a significant group difference  were observed in any other lifestyle-related cardiometabolic risk factor  in adults with overweight and obesity.The construct of COVIDAge has been designed to indicate COVID-19 severity and risk of associated complications. The scale incorporates all major determinants and relies on recent scientific data, including the 10 year Framingham study . This siDuring the COVID-19 pandemic, the importance of home-based health programs significantly increased. Implementing non-pharmacological countermeasures, such as lockdowns or orders to stay and work at home, has essentially contributed to reducing the spreading and thus mortality of COVID-19 , 36. Fron = 57 people participated in a 12-week weight loss program. The modules included individualized sessions addressing dietary and exercise modifications. Additionally, two groups received a web-based program to monitor eating habits and a wearable device monitoring energy expenditure. For both online groups, these features were provided either continuously or intermittently  during the intervention . The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication The article processing charge was funded by the Baden-Wrttemberg Ministry of Science, Research and Art and the University of Freiburg in the funding program Open Access Publishing.JB and DK report funding from Techniker Krankenkasse for the study design, implementation, and evaluation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Self-management can increase self-efficacy and quality of life and improve disease outcomes. Effective self-management may also help reduce the pressure on health care systems. However, patients need support in dealing with their disease and in developing skills to manage the consequences and changes associated with their condition. Web-based self-management support programs have helped patients with cardiovascular disease (CVD) and rheumatoid arthritis (RA), but program use has been low.This study aimed to identify the patient, disease, and program characteristics that determine whether patients use web-based self-management support programs or not.A realistic evaluation methodology was used to provide a comprehensive overview of context (patient and disease characteristics), mechanism (program characteristics), and outcome (program use). Secondary data of adult patients with CVD (n=101) and those with RA (n=77) were included in the study. The relationship between context  and outcome (program use) was analyzed using logistic regression analyses. The relationship between mechanism  and outcome was analyzed through a qualitative interview study.This study included 68 nonusers and 111 users of web-based self-management support programs, of which 56.4% (101/179) were diagnosed with CVD and 43.6% (78/179) with RA. Younger age and a lower level of education were associated with program use. An interaction effect was found between program use and diagnosis and 4 quality of life subscales . Patients with CVD with higher self-management and quality of life scores were less likely to use the program, whereas patients with RA with higher self-management and quality of life scores were more likely to use the program. Interviews with 10 nonusers, 10 low users, and 18 high users were analyzed to provide insight into the relationship between mechanisms and outcome. Program use was encouraged by an easy-to-use, clear, and transparent design and by recommendations from professionals and email reminders. A total of 5 BCTs were identified as potential mechanisms to promote program use: tailored information, self-reporting behavior, delayed feedback, providing information on peer behavior, and modeling.This realistic evaluation showed that certain patient, disease, and program characteristics  are associated with the use of web-based self-management support programs. These results represent the first step in improving the tailoring of web-based self-management support programs. Future research on the interaction between patient and program characteristics should be conducted to improve the tailoring of participants to program components. Chronic diseases are a major burden for patients, and the growing number of people with  chronic conditions puts a strain on our health care systems. The pressure on health care services may be decreased and the quality of life of people with chronic conditions may be improved if these individuals can self-manage their condition and adapt to their situation -3. Self-Self-management support interventions have already been developed for a broad range of long-term medical conditions and have shown improvements in self-management and other health outcomes ,8. Howevthe law of attrition and is a major challenge when developing and evaluating eHealth interventions [We recently developed 2 comprehensive, multicomponent, and theory-based web-based self-management interventions using the intervention mapping framework : one forventions .There are many reasons why participants use or do not use a web-based self-management program. Patient characteristics, such as older age, lower education levels, and lower income, have been associated with lower eHealth use -20. SelfIn this study, we identified the patient, disease, and program characteristics that determine whether patients with CVD and patients with RA use the Vascular View and Coping with RA web-based self-management support programs. The findings can be used to tailor web-based self-management support programs to individual patients and thereby increase their use.The realist evaluation methodology was used to gain a comprehensive understanding of why patients use or do not use web-based self-management support interventions . We struThis study included data of 2 patient groups with a chronic disease. Patients in the CVD group had experienced a myocardial infarction, cerebrovascular disease, or peripheral artery disease or a combination of these within 2 months to 1 year of the study starting. Patients in the RA group were diagnosed with RA, a chronic autoimmune disease that predominantly affects the joints, before the start of the study. The baseline data were collected at the start of each study. Inclusion criteria were as follows: (1) aged ≥18 years; (2) ability to read and understand Dutch; (3) access to a computer, internet, and email account; and (4) not receiving psychiatric or psychological treatment.The included patient and disease characteristics are expected to be associated with self-management and might, therefore, be related to program use. The following patient characteristics were studied to determine whether they were associated with program use: sex , age (years), education , work participation (yes or no), living situation , self-management, quality of life, and communication efficacy. Self-management was measured using the Patient Activation Measure (PAM-13), which includes statements about an individual’s knowledge, confidence, and skills for self-management of their behavior in response to their chronic illness and about their level of activation. The PAM-13 scores 13 items on a 5-point scale, with a higher score indicating a higher level of patient activation ,27. QualA total of 2 comprehensive, multicomponent, web-based self-management programs were studied for this realistic evaluation: Vascular View and Coping with RA. Vascular View was developed for patients with CVD  and contCoping with RA was developed for patients with RA . The proProgram use was a dichotomous outcome and was divided into nonusers (0 or 1 visit) and users (≥2 visits). The cut-off point between users and nonusers was arbitrarily set at 2 visits because this was seen as a reflection of whether a patient would have had the opportunity to benefit from the program.t tests and chi-square tests. A 2-sided P value of <.05 was considered statistically significant in all analyses.All quantitative data were analyzed using SPSS Statistics . Descriptive analyses were used to describe the patient and disease characteristics of nonusers and users. Differences between the characteristics were tested using 2-tailed P value of <.20 were tested in the final model. The model adequacy in the bivariate logistic regression was confirmed with a backward likelihood ratio test. As this is an explorative analysis, the Bonferroni correction was not applied to counteract the problem of multiple variables.Logistic regression analyses were used to determine which characteristics were associated with program use. Program use (nonuser or user) was the dependent factor. Patient and disease characteristics  were tested as possible factors. The strength of the relations was interpreted using odds ratios with 95% CIs. Factors with a Sensitivity analysis was performed to compare the characteristics of users and nonusers in the CVD and RA groups. Logistic regression analyses were performed for all characteristics  and for diagnosis, characteristic, and diagnosis×characteristic. These analyses determined whether there was an interaction between the diagnosis and characteristic. The strengths of the relationships were interpreted using odds ratios with 95% CI.As a sequence of efficacy studies of Vascular View and Coping with RA, interviews were conducted to provide insight into (1) why patients used or did not use the web-based program and (2) the experiences with the web-based program among users. The results of the qualitative study on the Coping with RA program have been described elsewhere . In thisA random selection of Vascular View and Coping with RA users and nonusers were invited for an interview after data on the explorative randomized controlled trials were collected. Purposive sampling was used to select patients regarding the degree to which they used the program. The participants were divided into 3 groups: nonusers, low users, and high users. After providing written consent, each patient was interviewed once via telephone. Semistructured interviews, lasting no longer than 30 minutes, were audio-recorded and anonymized. Interviews were transcribed verbatim and transferred to Excel (Microsoft). A total of 3 themes were determined beforehand: Program design, implementation strategies, and BCTs. The first researcher (ME) thematically analyzed the interviews to identify the potential program characteristics that influence use. First, the verbatim text was read and the relevant parts were marked. Next, the researcher determined barriers and facilitating factors for program use, which were divided into the 3 themes.We investigated the relations between context, mechanism, and outcome to determine which factors are associated with the use of a web-based self-management support program. To analyze the relation between patient and disease characteristics (context) and program use (outcome), 68 patients were defined as nonusers and 111 were defined as users. More users were diagnosed with CVD  than with RA . Patient and disease characteristics of the nonuser and user groups are presented in Univariate analyses showed that age, education, and communication efficacy with health care professionals (5-item perceived efficacy in patient-physician interactions) were associated with the use of web-based self-management interventions . Table 2Sensitivity analysis showed a significant interaction between diagnosis and the RAND-36 subscales social functioning, physical role limitations, vitality, and bodily pain . The desA random sample of study participants was interviewed to gain insight into why they did or did not use the web-based self-management program. In the CVD group, 6 nonusers, 6 low users, and 6 high users were interviewed. In the RA group, 4 nonusers, 4 low users, and 13 high users were interviewed. The results were divided into 3 themes: program design, implementation strategies, and BCTs. Most interviewees were pleased with the program design. However, some experienced difficulties in using the program, and so they did not use it as often. A search function would make it easier to find relevant information. Several users and nonusers stated that they had overlooked parts of the program; for example, 1 participant only used the diaries because he did not know that training modules were available. Another major reason for not using the program were problems with logging in. These observations indicate that ease of use was an important factor for program use among our respondents.Explanations given by the respondents as to why they did or did not use the program also revealed factors affecting program use. Several respondents stated that they did not participate for their own benefit but rather to facilitate scientific research. Others used the program following advice from their health care professional or because they were curious and wanted to better understand their disease. The biweekly reminders to fill out the diaries in the Coping with RA program helped many respondents to use the diaries.Comments related to program content were assigned to the relevant BCTs, and some of these BCTs were identified as potential mechanisms affecting program use. The first BCT  was often mentioned in the interviews. For example, respondents with a long disease history stated that the information was too general. Furthermore, some respondents saw on the overview page that none of the modules contained new or interesting information, and so they did not use the program further. Respondents reported that reliable information was a reason for using the program. The Vascular View program includes a physical activity and nutrition diary (for the self-monitoring of behavior BCT), which was rarely used. One respondent said they had missed a feedback function in the diaries and had already used other, more advanced, mobile apps instead. The pain and fatigue diaries in the Coping with RA program were used more often by respondents (for the self-report of behavior BCT). Patients appreciated the possibility of keeping track of their pain and fatigue and of receiving a graphical overview of their input (the delayed feedback of behavior BCT). Program users also liked the stories and videos of peers (which provided information about peer behavior BCT and modeling BCT). One respondent said that these made her feel recognized and supported and showed her that she was on the right track.In this realistic evaluation of 2 web-based self-management interventions, we searched for patient, disease, and program characteristics that determine whether patients will use the programs. Regarding the relationship between context and outcome, patient and disease characteristics, younger age, and lower level of education were associated with program use. In addition, 4 quality of life subscales  interacted significantly with the diagnosis group to affect program use. Regarding the relationship between Potential Mechanisms (program characteristics) and outcome, participants indicated that an easy-to-use, clear, and transparent design would motivate them to use the program. Email reminders and recommendations from health care professionals were found to be potential implementation mechanisms for promoting program use. The top five BCT techniques that encouraged interviewees to use the program were (1) tailored information, (2) self-report of behavior, (3) delayed feedback, (4) information about peer behavior, and (5) modeling.digital divide, which is the gap between patients who are able to use eHealth and those who are not [Our findings show that patient and disease characteristics can be used to tailor web-based self-management interventions and, therefore, increase their use. Younger age increased program use in our study, which is in agreement with the results of previous studies. However, in contrast to our finding that a lower level of education increased program use, earlier studies showed that a higher level of education increased program use -20,32,33 are not . Our stu are not , and eHeDisease burden can be both mental and physical and is another possible factor related to the use of web-based self-management support programs. Patients with RA have a lower physical quality of life and experience more pain than those with CVD. Individuals with episodic or deteriorating diseases such as RA have different self-management support needs than those with stable chronic diseases ,37. PatiThe study participants provided some recommendations for an effective web-based self-management support program. These recommendations included being easy to use, providing appropriate reminders, tailoring information to the user, allowing patients to self-report their behavior and receive delayed feedback, and providing information about peer behavior and modeling. These results are in line with those of a Delphi study that identified new information and the possibility of monitoring personal progress as important factors promoting the use of an eHealth self-management intervention . In addiOur results emphasize that one program will not be suitable for every patient. Self-management programs should be tailored to patients’ individual needs. It should also be noted that not all patients can use and benefit from web-based interventions. The validated Self-Management Screening (SeMaS) questionnaire can help identify potential barriers to self-management and can help health care professionals determine their patients’ support needs . The facGiven the complexity of web-based self-management interventions, realistic evaluations can reveal what makes an intervention work, which a simple cause-and-effect relationship between an intervention and its outcome may not be able to do. This is especially important for eHealth interventions because dropout and nonuse rates are high . The aimpotential mechanisms and should be tested in future research. Another limitation was that physical comorbidity and time since diagnosis were measured using a questionnaire. Although this provides insight into patients’ experiences, the self-reporting of clinical variables is not always reliable. The last limitation of this study was that we only included participants who had access to the internet and an email address. This may have biased our results by excluding people with a very low level of eHealth literacy. However, the internet is easily accessible in the Netherlands , so mosThis realistic evaluation identified contexts and potential mechanisms, in the form of patient, disease, and program characteristics, that are associated with the use of web-based self-management support programs. Our results emphasized the importance of (1) tailoring interventions to patients’ needs  to increase program use and (2) considering whether all patients can use eHealth interventions  and providing alternative self-management support when needed. These results are a first step toward improving the tailoring and use of web-based self-management support programs. Future research into the interaction between patient and program characteristics and how this affects program use should be conducted to improve the tailoring of participants to program components.