This paper is in the following e-collection/theme issue:

Aging with Chronic Disease (98) Chronic Conditions (249) Equity and Digital Divide (206) Aging in Place (264) Prevention and Health Promotion (945) Knowledge Translation and Implementation Science (386) Outbreak and Pandemic Preparedness and Management (1709)

Published on in Vol 7 (2024)

This is a member publication of University of Washington

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52069, first published .
Remote Evidence-Based Programs for Health Promotion to Support Older Adults During the COVID-19 Pandemic and Beyond: Mixed Methods Outcome Evaluation

Remote Evidence-Based Programs for Health Promotion to Support Older Adults During the COVID-19 Pandemic and Beyond: Mixed Methods Outcome Evaluation

Remote Evidence-Based Programs for Health Promotion to Support Older Adults During the COVID-19 Pandemic and Beyond: Mixed Methods Outcome Evaluation

Authors of this article:

Lesley Steinman1 Author Orcid Image ;   Kelly Chadwick1 Author Orcid Image ;   Erica Chavez Santos1 Author Orcid Image ;   Sruthi Sravanam1 Author Orcid Image ;   Selisha Snowy Johnson2 Author Orcid Image ;   Elspeth Rensema3 Author Orcid Image ;   Caitlin Mayotte1 Author Orcid Image ;   Paige Denison4 Author Orcid Image ;   Kate Lorig5 Author Orcid Image
Article Authors Cited by Tweetations (4) Metrics

    Original Paper

    1Health Promotion Research Center, Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, WA, United States

    2Office of Community Outreach and Engagement, Fred Hutch Cancer Center, Seattle, WA, United States

    3Department of Anesthesiology and Pain Medicine, University of Washington School of Medicine, Seattle, WA, United States

    4Sound Generations, Seattle, WA, United States

    5Self Management Resource Center, Aptos, CA, United States

    Corresponding Author:

    Lesley Steinman, MPH, MSW, PhD

    Health Promotion Research Center, Department of Health Systems and Population Health, University of Washington School of Public Health

    UW Box 351621

    Seattle, WA, 98195

    United States

    Phone: 1 2065439837

    Email: lesles@uw.edu


    Background: Evidence-based programs (EBPs) for health promotion were developed to reach older adults where they live, work, pray, and play. When the COVID-19 pandemic placed a disproportionate burden on older adults living with chronic conditions and the community-based organizations that support them, these in-person programs shifted to remote delivery. While EBPs have demonstrated effectiveness when delivered in person, less is known about outcomes when delivered remotely.

    Objective: This study evaluated changes in remote EBP participants’ health and well-being in a national mixed methods outcome evaluation in January 1, 2021, to March 31, 2022.

    Methods: We used the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) for equity framework to guide the evaluation. We purposively sampled for diverse remote EBP delivery modes and delivery organizations, staff, and traditionally underserved older adults, including people of color and rural dwellers. We included 5 EBPs for self-management, falls prevention, and physical activity: videoconferencing (Chronic Disease Self-Management Program, Diabetes Self-Management Program, and EnhanceFitness), telephone plus mailed materials (Chronic Pain Self-Management Program), and enhanced self-directed mailed materials (Walk With Ease). Participant and provider data included validated surveys, in-depth interviews, and open-ended survey questions. We used descriptive statistics to characterize the sample and the magnitude of change and paired t tests (2-tailed) and the Fisher exact test to test for change in outcomes between enrollment and 6-month follow-up. Thematic analysis was used to identify similarities and differences in outcomes within and across programs. Joint display tables facilitated the integration of quantitative and qualitative findings.

    Results: A total of 586 older adults, 198 providers, and 37 organizations providing EBPs participated in the evaluation. Of the 586 older adults, 289 (49.3%) provided follow-up outcome data. The mean age of the EBP participants was 65.4 (SD 12.0) years. Of the 289 EBP participants, 241 (83.4%) were female, 108 (37.3%) were people of color, 113 (39.1%) lived alone, and 99 (34.3%) were experiencing financial hardship. In addition, the participants reported a mean of 2.5 (SD 1.7) chronic conditions. Overall, the remote EBP participants showed statistically significant improvements in health, energy, sleep quality, loneliness, depressive symptoms, and technology anxiety. Qualitatively, participants shared improvements in knowledge, attitudes, and skills for healthier living; reduced their social isolation and loneliness; and gained better access to programs. Three-fourths of the providers (149/198, 75.2%) felt that effectiveness was maintained when switching from in-person to remote delivery.

    Conclusions: The findings suggest that participating in remote EBPs can improve health, social, and technological outcomes of interest for older adults and providers, with benefits extending to policy makers. Future policy and practice can better support remote EBP delivery as one model for health promotion, improving access for all older adults.

    JMIR Aging 2024;7:e52069

    doi:10.2196/52069

    Keywords

    older adults; health equity; rural; chronic disease; outcome evaluation; behavior change; technology; community based; evidence based; health promotion; mobile phone 


    Background

    One in 6 adults living in the United States is an older adult (aged ≥60 y). This number is expected to double in the next 40 years [1]. While older adults possess the wisdom of experience and are often actively involved in taking care of themselves, their family members, and their communities, they also face a wide range of unique health challenges that come with aging [2]. A disproportionate number of older adults live with chronic health conditions: 85% have 1 chronic health condition, and 60% report managing at least two [3]. One in 4 older adults reports at least 1 fall every year, and falls remain a leading cause of death and injury among this age group [4]. These health issues—combined with social determinants of health such as older adults’ built environment, social context, and access to medical care—put older adults at risk for premature death and poorer quality of life [5]. While only accounting for 17% of the population, older adults make up 35% of health care costs, according to 2019 Medical Expenditure Panel data [6]. Furthermore, 86 cents of every dollar of health care spending goes toward chronic conditions [7,8], and the burden of chronic disease is unevenly borne by women; older adults; people of color; and people living in poverty who experience disparities in access to, and quality of, care [9].

    To address these challenges, many health promotion programs have been created based on the Chronic Care Model [10], to improve access and quality of care through community-based programs that teach knowledge, skills, and self-efficacy to enhance older adults’ health and well-being in their daily lives. Among these are evidence-based programs (EBPs) that have been researched and recognized by national and federal agencies, such as the Administration for Community Living (ACL) and the Centers for Disease Control and Prevention (CDC) Arthritis Management and Well-Being Program, as being effective in promoting health outcomes through standardized interventions [11,12].

    Investigating the Effectiveness of Remote EBPs

    Before the COVID-19 pandemic, many of these EBPs were primarily offered in person. This was not possible during the pandemic due to safety guidelines regarding physical distancing. At the same time, there was an increased need for programs because older adults became more isolated and less physically active, and they had less access to an overtaxed medical system [13,14]. Seeing this need, EBPs quickly pivoted to remote delivery by mail, telephone, videoconferencing, or a combination of these modes. While researchers suggest that EBPs will work as intended using different forms as long as core functions are not modified [15,16], it is unknown whether program effectiveness is maintained when switching from in-person to remote delivery. To date, there have been limited studies investigating the effectiveness of remote EBPs that were originally designed to be offered in person [17-21]. As such, we conducted a longitudinal national outcome evaluation of several EBPs to assess changes in older participants’ health.


    Framework and Design

    We used the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) for equity framework [22] to evaluate the potential impact of remote EBPs on older adults’ health and well-being. The equity lens means that in addition to evaluating impact, we looked at outcomes across programs, sampled organizations that reach older adults who are underserved, reported EBP reach, and assessed whether there were any unintended consequences. As such, we used mixed methods to “give voice to participants as well as report statistical trends” [23]. We conducted a multisite single-group pre-post evaluation. Multiple sites were selected to facilitate the generalizability of the findings, and a single-group design was chosen to make it feasible to conduct the evaluation during the COVID-19 pandemic and due to the descriptive nature of this study.

    Ethical Considerations

    This study was considered exempt from University of Washington Institutional Review Board review because the activities fell under category 2 with regard to quality improvement and program evaluation (STUDY00011549). Participants who completed the preprogram survey were given a US $10 gift card, and participants who completed the follow-up survey were given a US $20 gift card (we provided electronic gift cards unless participants requested a physical card). Providers who completed the survey received a US $10 electronic gift card.

    Selected EBPs

    EBPs are health promotion programs that have been evaluated and proven to be effective. We included 5 EBPs in our evaluation: Chronic Disease Self-Management Program (CDSMP), Diabetes Self-Management Program (DSMP), EnhanceFitness (EF), Chronic Pain Self-Management (CPSMP), and Walk With Ease (WWE). These EBPs are all currently recognized by the national social services agency for older adults (ACL) and public health agencies (CDC) [11,12]. Before the COVID-19 pandemic, these programs were offered in person in group formats; they were adapted as follows for remote delivery in response to the COVID-19 pandemic: videoconferencing (CDSMP, DSMP, and EF), telephone plus mailed materials (CPSMP), and self-directed mailed materials plus enhanced support (WWE).

    These 5 EBPs were selected because they had sufficient program reach and represented different remote modes (telephone, videoconferencing, mail, or a combination of these modes) and health topics (chronic disease self-management, falls prevention, and physical activity). The adaptation process largely involved planned changes to modify delivery context (eg, smaller class size; adding a cofacilitator to support engagement with, and the usability of, technology; and providing telephone options for people without access to reliable internet or videoconferencing technology) as well as some unplanned changes that occurred organically during field experience in consultation with the program leads (rather than changes to program content) [24]. It should be noted that WWE had created an enhanced self-directed program before the pandemic; we included this program in the evaluation to assess different modes of remote EBP delivery, given partners’, policy makers’, and older adults’ interest in diverse ways to engage in remote health promotion. The enhanced self-directed WWE is delivered via a cohort, with a trained leader providing web-based motivation and support to individual participants during the program period. More information about the included programs is provided below (Figure 1) and on each program’s website, including guidance for remote delivery.

    ‎
    Figure 1. Participating remote evidence-based programs.

    Recruitment

    In early 2021, we recruited organizations delivering remote EBPs with a brief web-based interest survey and webinars through several networks: EBP training listserves, the CDC Arthritis Program and ACL grantees, the Evidence-Based Leadership Collaborative, and regional EBP networks. The organizations included social services, public health, and health care agencies that were largely community based, although some were located in clinical settings. We used maximum variation purposive sampling [25] to identify organizations of diverse sizes and types, varied provider characteristics, and from different geographic areas to aid the generalizability of the evaluation findings. This sampling prioritized organizations engaging older populations with low-income status with multiple chronic conditions who are vulnerable to both COVID-19 infection and poor health outcomes and care (eg, people of color, those with disabilities, and those living in rural areas). The eligibility criteria for organizations was the delivery of at least 1 of the 5 remote EBPs from January 2021 through March 2022.

    We then used convenience sampling to invite all remote EBP participants to take part in the evaluation. Participant surveys were primarily conducted on the web using REDCap (Research Electronic Data Capture; Vanderbilt University) [26], with options to complete surveys by telephone or by mail. Participants were surveyed when they enrolled in the program and again 6 months after program enrollment (regardless of when they finished the program). At both time points, a link to the survey was emailed to participants, and they received up to 3 additional reminder emails and 3 reminder calls during the month of survey eligibility. In addition, community-based organizations encouraged baseline survey completion as part of program intake and orientation.

    Furthermore, all EBP providers at participating organizations were invited to take part in a 1-time survey eliciting their perspectives regarding the impact of remote EBPs on participants as well as providers. The EBP providers included leaders (people who delivered remote programs directly) and managers (people who coordinated and oversaw program delivery).

    Data Collection

    Quantitative

    The participant outcome survey (Multimedia Appendix 1) combined several brief self-rated health measures that have been validated with older adults: single-item self-rated health [27,28], pain [29], fatigue or energy [30], loneliness (University of California Los Angeles 3-item Loneliness Scale [31]), social isolation (4-item Social Network Index [32]), depression (Patient Health Questionnaire [PHQ]-8 [33]), anxiety (Generalized Anxiety Disorder-2 [34]), physical activity (Exercise Vital Sign [35]), and social needs [36]. These cross-cutting outcomes were selected in partnership with organizations, developers or administrators, and policy makers to identify key outcomes of interest across the health promotion programs.

    We also collected several program-specific outcomes [37] related to their health focus: self-efficacy for CDSMP, hypoglycemia for DSMP, and pain and the use of opioid medications for CPSMP. In addition, the preprogram survey included questions about remote EBP participant demographics: age, gender, race, ethnicity, living alone, caregiving, and chronic conditions. Rurality was defined using the participant’s zip code and several federal criteria for rural funding [38]. The 6-month follow-up survey included 3 additional quantitative items: two examining the usability of and anxiety about technology using the Senior Technology Acceptance Measure [39] and the other calculating a single-item net promoter score [40,41] to assess acceptability [42].

    Provider survey data included multiple-choice questions about demographics, experience delivering EBPs, and the impact of remote EBPs on both participants and providers. Response choices were created from open-ended responses to an earlier Evidence-Based Leadership Collaborative remote EBP web-based survey in 2020 (L, Steinman, personal communication, December 2020).

    Qualitative

    The 6-month follow-up participant survey included open-ended items about program acceptability and unintended consequences or impacts. Participants who completed the follow-up survey over the telephone were also asked 2 additional qualitative questions about participating in the evaluation. Providers were also asked an open-ended question about any additional benefits for both remote EBP participants and providers.

    Data Analysis

    Quantitative

    Data were managed in REDCap [26] and analyzed using R software (R Foundation for Statistical Computing) [43]. Our primary aim was to evaluate changes in remote EBP participants’ health and well-being (self-rated health, fatigue or energy, exercise, pain, sleep, depression, anxiety, loneliness, social isolation, and program-specific outcomes) between program enrollment and 6-month follow-up. Only participants who included both baseline and 6-month follow-up data were included in these analyses. We assessed the magnitude of the changes using descriptive statistics, percentage change, and Cohen d effect sizes and tested for statistical significance of the changes using paired t tests (2-tailed). Percentage change was reported for both people who improved—or maintained—outcomes between remote EBP enrollment and 6-month follow-up, given the importance of maintaining health in an aging population [44]. Effect sizes were calculated as mean (SD) [45]. We also used descriptive statistics to characterize the sociodemographic characteristics of remote EBP participants and providers. We chose not to use the Bonferroni correction to adjust the P values because we carried out tests on multiple outcomes of interest across programs without a priori hypotheses (rather than 1 primary outcome of interest) [46].

    Qualitative

    Audio-recorded data were transcribed into Microsoft Word documents. These text data from transcripts and open-ended survey questions were analyzed using Dedoose software (SocioCultural Research Consultants, LLC) [47]. For qualitative data, we used thematic analysis [48,49] to understand similarities and differences across and within remote EBP acceptability and benefits or unintended consequences. Two researchers (KC and LS) created a codebook to organize important text for comparison, using deductive codes from the interview guide and inductive codes from an initial read of the transcripts [50]. We conducted 2 rounds of reliability coding, adjusted the codebook codes and definitions as needed, then completed coding using 1 coder. Coded exports were then organized in interpretation memos to summarize possible explanations for what was happening, including a summary of findings, key distinctions and counterexamples, and further points for consideration [51].

    Integration

    We used joint display tables [52] and compared quantitative and qualitative results to see where the findings converged, diverged, or expanded [23].


    Participants

    A total of 586 older adults participated in the evaluation over the study period, of whom 289 (49.3%) completed the 6-month follow-up survey (n=25, 8.7% completed the survey over the telephone). The primary reason for noncompletion was our inability to contact the older adults by telephone or email; only 10 (1.7%) of the 586 older adults declined to participate in the follow-up survey after being contacted.

    Table 1 shows remote EBP evaluation participant demographics for those who completed both baseline and 6-month follow-up surveys. The mean age of the participants was 65.4 (SD 12.0) years. The majority were female (241/289, 83.4%) and White (184/289, 63.7%). A little more than one-third (108/289, 37.3%) identified as people of color: American Indian or Alaska Native (2/289, 0.7%), Asian (12/289, 4.2%), Black or African American (77/289, 26.6%), and Latinx (17/289, 5.9%). One in 7 (41/289, 14.2%) lived in a rural area, and 1 in 3 (99/289, 34.3%) found it “somewhat hard” or “very hard” to pay for basics such as food and housing. Of the 289 participants, 113 (39.1%) lived alone, and 58 (20.1%) were providing caregiving. The participants reported a mean of 2.5 (SD 1.7) chronic conditions, with the most common being hypertension (145/289, 50.2%), arthritis (131/289, 45.3%), diabetes (130/289, 45%), and mental health conditions (80/289, 27.7%). The prevalence of all chronic conditions is provided in Table 1, including more rare but still impactful conditions such as Crohn disease and ulcerative colitis.

    Table 1. Demographics of remote evidence-based program evaluation participants.
    DemographicsTotal (n=289)CDSMPa (n=69)CPSMPb (n=47)DSMPc (n=118)EFd (n=12)WWEe (n=40)
    Age (y), mean (SD)65.4 (12.0)60.2 (13.5)67.5 (12.4)64.9 (11.1)72.2 (6.13)71.2 (8.5)
    Gender, n (%)

    		Female241 (83.4)58 (84.1)39 (83)93 (78.8)12 (100)36 (90)

    		Male48 (16.6)11 (15.9)8 (17)25 (21.2)0 (0)4 (10)

    		Nonbinary0 (0)0 (0)0 (0)0 (0)0 (0)0 (0)
    Race, n (%)

    		American Indian or Alaska Native2 (0.7)1 (1.4)0 (0)1 (0.8)0 (0)0 (0)

    		Asian12 (4.2)2 (2.9)2 (4.3)6 (5.1)2 (16.7)0 (0)

    		Black or African American77 (26.6)15 (21.7)23 (48.9)25 (21.2)1 (8.3)13 (32.5)

    		Native Hawaiian or Pacific Islander0 (0)0 (0)0 (0)0 (0)0 (0)0 (0)

    		White184 (63.7)47 (68.1)19 (40.4)81 (68.6)8 (66.7)26 (65)
    Ethnicity (Latinx), n (%)17 (5.9)9 (13)2 (4.3)5 (4.2)1 (8.3)0 (0)
    Living in a rural area, n (%)41 (14.2)12 (17.4)7 (14.9)12 (10.2)3 (25)6 (15)
    Somewhat hard or very hard to pay for basics, n (%)99 (34.3)28 (40.6)12 (25.6)45 (38.1)2 (16.7)12 (30)
    Living alone, n (%)113 (39.1)25 (36.2)24 (51.1)40 (33.9)5 (41.7)18 (45)
    Caregiver, n (%)58 (20.1)14 (20.3)8 (17)26 (22)2 (16.7)7 (17.5)
    Chronic conditions, mean (SD)2.5 (1.7)2.5 (1.5)2.6 (2.0)2.7 (1.7)1.7 (2.0)1.8 (1.3)
    Chronic conditionsf, n (%)

    		Asthma, emphysema, chronic obstructive pulmonary disease, or chronic bronchitis46 (15.9)12 (17.4)11 (23.4)18 (15.3)2 (16.7)3 (7.5)

    		Arthritis (rheumatoid arthritis)18 (6.2)3 (4.3)2 (4.3)8 (6.8)1 (8.3)4 (10)

    		Arthritis (osteoarthritis)72 (24.9)18 (26.1)6 (12.8)28 (23.7)3 (25)17 (42.5)

    		Arthritis (other diagnosis)41 (14.2)11 (15.9)16 (34)8 (6.8)0 (0)6 (15)

    		Cancer15 (5.2)1 (1.4)3 (6.4)9 (7.6)2 (16.7)0 (0)

    		Diabetes130 (45)24 (34.8)13 (27.7)84 (71.2)3 (25)6 (15)

    		Heart trouble (eg, angina, congestive heart failure, and coronary artery disease)49 (17)12 (17.4)7 (14.9)25 (21.2)2 (16.7)3 (7.5)

    		Hypertension or high blood pressure145 (50.2)29 (42)27 (57.4)65 (55.1)5 (41.7)19 (47.5)

    		Irritable bowel syndrome18 (6.2)7 (10.1)2 (5.9)7 (5.9)0 (0)2 (5)

    		Kidney problems22 (7.6)6 (8.7)5 (10.6)9 (7.6)1 (8.3)1 (2.5)

    		Liver problems (eg, cirrhosis)3 (1)1 (1.4)0 (0)2 (1.7)0 (0)0 (0)

    		Mental health conditions (eg, depression, anxiety, posttraumatic stress disorder, and bipolar disorder)80 (27.7)28 (40.6)15 (31.9)31 (26.3)0 (0)6 (15)

    		Other digestive problems (besides irritable bowel syndrome, ulcerative colitis, and Crohn disease)34 (11.8)10 (14.5)3 (6.4)15 (12.7)1 (8.3)5 (12.5)

    		Stroke and other cerebrovascular disease13 (4.5)13 (4.3)4 (8.5)6 (5.1)0 (0)0 (0)

    aCDSMP: Chronic Disease Self-Management Program.

    bCPSMP: Chronic Pain Self-Management Program.

    cDSMP: Diabetes Self-Management Program.

    dEF: EnhanceFitness.

    eWWE: Walk With Ease.

    fLess than 1% of the participants reported these chronic conditions: HIV or AIDS, Crohn disease, and ulcerative colitis.

    Participants who completed the 6-month follow-up survey had similar demographic characteristics and baseline health status as those who completed only the baseline survey, with a few exceptions. A larger proportion of the CDSMP and CPSMP survey completers identified as Black compared to non–survey completers (15/69, 22% vs 13/98, 13%, and 23/47, 49% vs 31/75, 41%, respectively). WWE survey completers were less likely to be living alone (18/40, 45% vs 24/41, 59%). EF survey completers were more likely to be caregivers than not (2/12, 16% vs 0/10, 0%). Finally, across all programs except EF, survey completers were less likely to be living in a rural area than non–survey completers (37/274, 13.5% vs 109/284, 38.4%).

    A total of 198 remote EBP providers (n=123, 62.1% leaders; n=75, 37.9% managers) from 107 EBP organizations in 33 states participated in the evaluation. The majority of the leaders identified as female (113/120, 94.2%). Furthermore, 4.3% (5/117) identified as Asian, 12.8% (15/117) as Black or African American, 11.7% (14/120) as Latinx, and 0.9% (1/117) as biracial. One-quarter of the leaders (31/113, 27.4%) lived in rural settings, one-third (36/120, 30%) were caregivers, and one-third (37/123, 30.1%) had ≥2 chronic conditions. One-fourth (30/121, 24.7%) identified as certified health professionals and 43.3% (52/120) as community health workers, promotoras, or other lay health providers. The leaders had a range of experience in EBP delivery: a little more than half (65/123, 52.8%) had delivered both in-person and remote programs before the survey, while 36.6% (45/123) were conducting remote EBPs for the first time. In addition to completing the survey, 26 EBP administrators, managers, and leaders took part in qualitative interviews. Most of the interview participants (22/26, 85%) identified as female and worked at community or government organizations.

    Outcomes (Quantitative)

    Overview

    Outcomes are reported by specific program and across the 5 programs included in our evaluation (Tables 2 and 3; Figure 2; Multimedia Appendix 2).

    Table 2. Participant health outcomes at enrollment and 6-month follow-up by remote evidence-based program.

    		Total (n=289), mean (SD)CDSMPa (n=69), mean (SD)CPSMPb (n=47), mean (SD)DSMPc (n=118), mean (SD)EFd (n=12), mean (SD)WWEe (n=40), mean (SD)
    OutcomePrefFollow-upPreFollow-upPreFollow-upPreFollow-upPreFollow-upPreFollow-up
    Health (range 1-5) ↓g3.23 (0.9)2.80h (0.9)3.36 (0.9)2.91h (1)3.38 (0.8)3.21 (1.0)3.19 (0.8)2.71h (0.8)2.86 (1.0)2.27 (1.0)2.98 (0.8)2.59h (0.8)
    Fatigue (range 1-10) ↓5.49 (2.3)4.98h (2.3)6.05 (2.1)5.61h (2.4)5.92 (2.3)4.88h (2.1)5.28 (2.4)4.91 (2.5)4.05 (1.7)3.91 (2.2)4.86 (2.1)4.42 (2.1)
    Pain (range 1-10) ↓4.86 (2.6)4.44 (2.7)5.02 (2.4)4.78 (2.6)6.41 (2.3)5.67h (2.3)4.14 (2.6)4.16 (2.9)4.14 (2.4)3.64 (2.7)4.13 (2.3)3.68 (2.3)
    Sleep quality (range 1-10) ↓4.95 (2.5)4.49h (2.6)5.32 (2.4)5.07h (2.8)5.64 (2.6)5.19 (2.5)4.73h (2.5)4.15 (2.5)3.91 (2.4)3.91 (3.1)4.14 (2.5)3.79 (2.3)
    Loneliness (range 3-9) ↓4.67 (1.8)4.45h (1.8)5.04 (1.9)4.74 (2.0)4.57 (1.7)4.67 (2.0)4.54 (1.8)4.33 (1.8)3.65 (1.0)3.91 (1.0)4.64 (1.7)4.11 (1.3)
    Social isolation (range 5-25) ↑i15.7 (3.7)16.2 (3.8)15.2 (3.4)16.3 (3.5)15.9 (4.1)16.7 (4.5)15.6 (3.7)15.6 (3.8)18.1 (2.8)18.6 (1.8)16.5 (3.6)16.8 (4.0)
    Physical activity (day; range 0-7) ↑2.3 (2.3)2.6 (2.3)2.0 (2.1)2.2 (2.2)2.5 (2.7)2.6 (2.5)2.2 (2.3)2.4 (2.4)3.3 (1.9)3.8 (1.7)2.7 (2.4)3.3g (2.1)
    Physical activity (min; range 0-679) ↑82.5 (113)98.5h (124)54.7 (74)74.7h (106)86.4 (117)99.4 (149)90.1 (128)94.7 (126)140.8 (95)179.5h (94)107.5 (118)130.1h (118)
    Depression (range 0-24) ↓6.20 (5.3)5.05h (5.2)7.47 (5.8)6.07 (5.2)7.63 (5.6)6.50 (5.7)5.43 (4.6)4.83 (5.2)4.17 (5.6)2.10 (2.7)3.77 (3.6)2.77 (4.0)
    Anxiety (range 0-6) ↓1.48 (1.6)1.30 (1.6)1.95 (1.7)1.65 (1.6)1.67 (1.7)1.54 (1.6)1.27 (1.5)1.26 (1.7)0.74 (0.9)0.60 (0.8)1.11 (1.3)0.67 (1.2)
    Technology anxiety (range 1-10) ↓2.74 (2.4)2.53h (2.4)3.24 (2.7)2.81 (2.6)3.30 (3.1)2.87 (2.5)2.28 (2.1)2.52 (2.4)2.38 (2.7)2.13 (2.1)2.30 (1.8)1.97 (1.6)
    Technology usability (range 1-10) ↑7.94 (2.6)8.22 (2.6)7.66 (2.5)8.48 (2.4)7.15 (3.3)7.82 (2.9)8.54 (2.3)8.17 (2.6)7.56 (3.0)8.61 (2.0)8.16 (2.4)8.22 (2.7)
    Self-efficacy (range 1-10) ↑N/AjN/A6.16 (2.3)6.89h (2.2)N/AN/AN/AN/AN/AN/AN/AN/A
    Pain interference (range 6-30) ↓N/AN/AN/AN/A16.6 (7.3)16.7 (7.5)N/AN/AN/AN/AN/AN/A
    Diabetes (hypo-glycemia; range 1-7) ↓N/AN/AN/AN/AN/AN/A1.92 (2.0)1.84 (2.2)N/AN/AN/AN/A

    aCDSMP: Chronic Disease Self-Management Program.

    bCPSMP: Chronic Pain Self-Management Program.

    cDSMP: Diabetes Self-Management Program.

    dEF: EnhanceFitness.

    eWWE: Walk With Ease.

    fPre: health outcomes at program enrollment; follow-up: health outcomes at 6-month follow-up from program enrollment.

    gLower scores indicate better health.

    hP<.05 (paired 2-tailed t tests, except for EF, which used the Fisher exact test).

    iHigher scores indicate better health.

    jN/A: not applicable.

    Table 3. Six-month effect sizes for remote evidence-based program participant health outcomes.
    OutcomeRange directionEffect size


    		Total (n=289)CDSMPa (n=69)CPSMPb (n=47)DSMPc (n=118)EFd (n=12)WWEe (n=40)
    Health1-5↓f−0.37g−0.37g−0.23h−0.44g−0.58i−0.35g
    Fatigue1-10↓−0.23h−0.20h−0.42g−0.13h−0.25h−0.33g
    Pain1-10↓−0.13h−0.11h−0.49g0.05j−0.17h−0.21h
    Sleep quality1-10↓−0.18h−0.19h−0.08j−0.21h−0.66i−0.22h
    Loneliness3-9↓−0.13h−0.24h0.06j−0.13h0.58j−0.30g
    Social isolation5-25↑k0.08j0.03j0.07j0.00j0.23h0.23h
    Physical activity (days)0-7↑0.13h0.17h0.07j0.10j0.35g0.18h
    Physical activity (min)3-679↑0.15h0.22h0.18h0.08j0.36g0.18h
    Depression0-24↓−0.23h−0.24h−0.27h−0.23h−0.34g0.06j
    Anxiety0-6↓−0.08j−0.12h−0.36g0.03j0.00j−0.06j
    Technology anxiety1-10↓−0.13h−0.17h−0.23h−0.08j−0.03j−0.24h
    Technology usability1-10↑−0.05j−0.06j0.23h−0.13h0.59i−0.07j
    Self-efficacy1-10↑N/Al0.38gN/AN/AN/AN/A
    Pain interference6-30↓N/AN/A−0.37gN/AN/AN/A
    Diabetes (hypoglycemia)1-7↓N/AN/AN/A−0.06jN/AN/A

    aCDSMP: Chronic Disease Self-Management Program.

    bCPSMP: Chronic Pain Self-Management Program.

    cDSMP: Diabetes Self-Management Program.

    dEF: EnhanceFitness.

    eWWE: Walk With Ease.

    fLower scores indicate better health.

    gCohen d effect sizes 0.3 to 0.5=moderate.

    hCohen d effect sizes 0.1 to 0.3=small.

    iCohen d effect sizes >0.5=large.

    jCohen d effect sizes <0.1=trivial.

    kHigher scores indicate better health.

    lN/A: not applicable.

    ‎
    Figure 2. Change in the number of physical activity (PA) days between enrollment and 6-month follow-up for remote evidence-based program outcome evaluation participants. The participants were asked about the number of days on which they were physically active per week at both program enrollment and 6-month follow-up. The completion rates for this question, by program and overall, are as follows: Walk With Ease (WWE)=90% (36/40), EnhanceFitness (EF)=100% (12/12), Diabetes Self-Management Program (DSMP)=87.3% (103/118), Chronic Pain Self-Management Program (CPSMP)=45% (21/47), Chronic Disease Self-Management Program (CDSMP)=94% (65/69), and overall=82% (237/289).
    CDSMP Participants

    When testing for whether these changes over time were not due to chance, CDSMP participants showed statistically significant improvements in health (mean change 0.32, 95% CI 0.117-0.530; P=.003; t67=3.12), energy (fatigue; mean change 0.73, 95% CI 0.061-1.408; P=.03; t63=2.18), sleep quality (mean change 0.89, 95% CI 0.216-1.561; P=.01; t62=2.64), and self-efficacy (mean change −0.53, 95% CI −0.970 to −0.091; P=.02; t43=-2.49). Participants with depression at baseline (19/69, 28%; PHQ-8 score <10) also significantly reduced their depression symptom severity from mean 14.7 (SD 4.2) to mean 11.5 (SD 5.1) on the PHQ-8 (P=.03). Overall, 58% (40/69) of the participants improved their self-efficacy over time, with a moderate effect size of 0.38. From program enrollment to 6-month follow-up, 40% (26/65) of the participants showed improvement, and 34% (22/65) of the participants maintained the number of days on which they were physically active.

    CPSMP Participants

    CPSMP participants significantly improved their energy (fatigue; mean change 1.11, 95% CI 0.248-1.968; P=.01; t36=2.61) and pain (mean change 0.97, 95% CI 0.186-1.763; P=.02; t38=2.50). Moderate effect sizes were seen for fatigue (−0.42), pain (−0.49), anxiety (−0.36), and pain interference (−0.38). The participants with pain interference at baseline (30/289, 10.4%; PROMIS [Patient-Reported Outcomes Measurement Information System] score >6) also reduced their pain interference from mean 16.9 (SD 6.6) to mean 15.5 (SD 7.3). Only 1 (2.1%) of the 47 participants was taking opioid medications at enrollment. From program enrollment to 6-month follow-up, 29% (6/21) of the participants showed improvement, and 43% (9/21) of the participants maintained the number of days on which they were physically active.

    DSMP Participants

    DSMP participants demonstrated better health (mean change 0.41, 95% CI 0.259-0.552; P<.001; t110=5.48). People with at least 1 hypoglycemia symptom at baseline (70/289, 24.2%) reduced their symptoms from mean 2.4 (SD 1.3) to mean 2.1 (SD 1.7); the effect size was low (−0.06). DSMP participants had a moderate effect size for overall improvement in health (−0.44). Participants with depression at baseline (19/118, 16.1%) also significantly reduced their depression symptom severity from mean 13.9 (SD 3.0) to mean 11.1 (SD 6.5) on the PHQ-8. From program enrollment to 6-month follow-up, 39.8% (41/103) of the participants showed improvement, and 33% (34/103) of the participants maintained the number of days on which they were physically active.

    EF Participants

    EF participants demonstrated better sleep quality (Fisher exact test: P=.001), with a large effect size of −0.66. Participants also showed strong improvements in health (effect size: −0.58) and technology usability (effect size: 0.60). From program enrollment to 6-month follow-up, 42% (5/12) of the participants showed improvement, and 33% (4/12) of the participants maintained the number of days on which they were physically active.

    WWE Participants

    WWE participants demonstrated better health (mean change 0.25, 95% CI 0.046-0.454; P=.02; t35=2.49), with a moderate effect size of −0.35. Participants also had moderate effect sizes for improvements in fatigue (−0.33) and loneliness (−0.30). From program enrollment to 6-month follow-up, 36% (13/36) of the participants showed improvement, and 33% (12/36) of the participants maintained the number of days on which they were physically active.

    Across Remote EBPs

    Across programs, remote EBP participants showed statistically significant improvements in their health (mean change 0.33, 95% CI 0.235-0.422; P<.001; t267=6.92), energy (mean change 0.56, 95% CI 0.264-0.853; P<.001; t264=3.73), sleep quality (mean change 0.53, 95% CI 0.245-0.812; P<.001; t262=3.67) loneliness (mean change 0.25, 95% CI 0.057-0.437; P=.01; t242=2.55), depressive symptoms (mean change 0.60, 95% CI 0.111-1.091; P=.02; t212=2.42), and technology anxiety (mean change 0.34, 95% CI 0.010-0.665; P=.04; t233=2.03). For people living with clinically significant depressive symptoms (PHQ-8 score ≥10; 24/114, 21.1% of the sample), the overall mean change in the PHQ-8 score from enrollment to 6-month follow-up was 3.025 (95% CI 1.379-4.671; P<.001; t39=3.717). People with clinically significant depression at baseline (52/289, 18%) also significantly reduced their depression symptom severity from mean 14.7 (SD 4.2) to mean 11.5 (SD 5.1).

    The percentage change is reported in Multimedia Appendix 2. Across programs, 26.7% (66/247) to 49.8% (136/273) of the participants improved health outcomes over time, and 17.4% (41/235) to 53.8% (133/247) of the participants maintained their health. Most effect sizes (Table 3) were small (<0.3), except for change in health over time, which had an effect size of −0.37.

    Outcomes (Qualitative)

    Participants

    Overall, participants shared that they liked and enjoyed participating in the remote EBPs and identified several ways in which the program impacted their lives. First, they reported changes in their knowledge, attitudes, and practices in promoting their health and well-being. Participating in remote EBPs helped older adults manage their chronic conditions, become more active, and feel more confident and better equipped to take care of themselves:

    I feel as if this program literally changed the trajectory of my life. Prior to it, I was diagnosed as prediabetic and was put on medication, which made me very sick. My next option was a very expensive diabetes drug. But, through following this program, I learned about insulin resistance and what I could do to combat my descent into diabetes. I was encouraged and coached in inspiring ways. I am now barely considered even prediabetic.
    [Female DSMP participant #1454; aged 66 years; living with ulcerative colitis]
    The program helped me understand how exercise can improve my mobility, and it encouraged me to remain active.
    [Male WWE participant #1346; aged 63 years; living with arthritis and asthma]

    Likewise, the remote EBP participants stated that they learned a lot from the program, including from the materials, leaders, and other participants. This was true across programs: WWE participants learned about new walking spots in their communities from other walkers in their cohort, while self-management program participants learned new ways and tips for managing their condition (expanded their sense of their own options) and broadened their understanding of what life was like at different severity levels of their condition. The group-based formats of the remote EBPs helped provide accountability and motivation and also provided a variety of perspectives and ideas. For some participants, the remote program helped them accept the reality and seriousness of their condition and the changes needed to manage it. In addition, participants believed that others could benefit from the program as well.

    The participants also reported social benefits from being part of the programs. Many participants felt less alone, gained a sense of comfort from talking with others struggling with the same conditions, and made friends over the course of the program. A sense of camaraderie was reported often:

    I liked the interaction with other people, it’s helpful to find out how others are going through. To know that there’s other people out there with a lot of pain and they’re struggling with it, made me feel not alone. Some are worse and some are better. It feels isolating a lot with pain, so that was really nice to experience, seeing others.
    [Female CPSMP participant #1344; aged 74 years; living alone and managing multiple chronic conditions]

    Furthermore, remote EBP participants from various programs who were grappling with new or existing chronic conditions, changes in mobility and function due to aging, and the challenges of physical distancing during the pandemic, emphasized the value of learning together and feeling less alone. Even participants in the remote WWE program (self-directed tool kit enhanced with a leader virtually supporting a cohort of participants) found social benefits:

    I did appreciate the opportunity to meet with the group assigned to me and get the encouragement to get out and walking.
    [Female WWE participant #1249; aged 65 years; living with arthritis and a mental health condition]

    In addition, older adults shared how remote EBPs improved their access to the programs. The remote format made accessing these programs during the pandemic both safe and very convenient, in particular for people living with chronic pain or disabilities. Some participants would not have been able to participate if the programs had only been offered in person. That said, a few participants did not like the remote format and found it more difficult to access. For these participants, the downsides of not being able to meet and connect in person or the challenges with participating via telephone, mail, or videoconferencing outweighed the benefits of participating in class. Examples include issues with the technology itself (eg, poor internet connections that made the videoconferencing software freeze up) and discomfort with using technology (eg, unfamiliarity with navigating Zoom functions or unease using a mobile phone where they cannot see other participants). It should be noted that technology encompasses using a telephone (landline, smartphone, or other mobile phone) in addition to laptop computers, tablet devices, and PCs; dial-up or broadband internet; and videoconferencing platforms such as Zoom and Webex.

    Finally, many participants shared that they wanted to take part in the remote EBP again, and some participants shared that they desired more follow-ups after the program ended. For these participants, there was a sense of having missed or forgotten some of what had been taught and wanting to refresh their knowledge. Others felt that they needed the motivation of continued check-ins to keep using what they had learned. In addition, some were not sure how to get their questions answered after the program ended, how to sign up for other programs offered by the organization, or whether they were allowed to take part in the program again. This points to opportunities for future supports and services after remote EBP engagement, such as monthly check-ins via telephone, videoconferencing, or social media to “keep the feelings of motivation and community after the program ends...[to] meet or discuss what folks are doing and what works and encourage each other to keep going” (female DSMP participant #1190; aged 61 years; living with multiple chronic conditions [arthritis, hypertension, diabetes, heart troubles, and a mental health condition]). Post-EBP supports and services could also provide a way to reinforce and deepen knowledge and skills that are learned and practiced during a relatively brief program and offer ways for family, friends, and caregivers to support the maintenance of program gains as well as widen program benefits to other people in the participants’ communities.

    Providers

    Table 4 summarizes provider’s perspectives on the impact of remote EBPs. Some of these impacts were expected; for example, 3 in 4 providers (149/198, 75.2%) reported improved health outcomes for older remote EBP participants, and reducing social isolation and loneliness emerged as the most common benefit for both participants and EBP providers (leaders and managers). In addition, half of the providers (102/198, 51.5%) noted that connections to other supports and services were a participant benefit; while this typically occurs during in-person EBP delivery, remote EBP delivery allowed for sharing timely and ever-changing information about testing for COVID-19 infection and recommended safety protocols, as well as referrals to services that may have paused or been shifted due to pandemic-related closures or physical distancing requirements.

    Table 4. Perceived benefits of remote evidence-based program (EBP) delivery for participants (from providers’ perspectives) and for providers (n=198).
    BenefitsSurvey data, n (%)Interview data

    		Leaders (n=123)Managers (n=75)
    Benefits for participants (from providers’ perspectives)

    		Improved health outcomes93 (75.6)56 (74.7)
    • Kept participants safe while also allowing them to access the benefits of these programs.
    • The program was still effective, and the benefit it had on participants could be seen.

    		Reduced social isolation and loneliness116 (94.3)66 (88)
    • Enhanced socialization and helped with isolation, which is really needed right now. The bonds formed within the groups are really important.
    • Clients appreciated getting checked on; many were feeling isolated during the COVID-19 pandemic.

    		Improved access to technology52 (42.3)30 (40)
    • By participating in the remote EBP, participants gained access to new or loaner technology.

    		Improved comfort with using technology91 (74)52 (69.3)
    • Increased technology literacy and comfort of participants, which encourages them to explore other web-based resources.
    • Clients felt accomplished to have completed a class that required new technology, such as video-conferencing platforms, without help.

    		Enhanced access to other supports and services67 (54.5)35 (46.6)
    • Able to educate older adults in their program about COVID-19 vaccines.
    • Remote program improved cross-referrals, which is good for holistically addressing health.

    		Improved access to EBPsN/AaN/A
    • Participants can repeat the program because it is easier to access.
    • No concerns about driving in bad weather .
    • Some participants liked the virtual class and want remote options in the future.
    Benefits for providers

    		Improved health outcomes58 (47.2)27 (36)N/A

    		Reduced social isolation and loneliness61 (49.6)39 (52)
    • Able to keep working and connecting with colleagues and participants.

    		Improved access to technology34 (27.6)22 (29.3)
    • Gained access to technology via work or family, friends, and neighbors.
    • For leaders without access, some stopped delivering the program.

    aN/A: not applicable (either the benefit was not one of the multiple-choice answer choices in the provider survey or did not emerge during the interview data analysis).

    Some of the impacts were positive but unintended; for example, providers reported improved comfort using technology as a benefit for remote EBP participants. Increasing the usability of technology and reducing anxiety about technology are not original outcomes that in-person EBPs strived to impact, but in remote EBP delivery, opportunities arose for some participants to become more comfortable using technology (telephone, videoconferencing, or tool kits) for engaging in, and receiving, other supports and services. Comfort using technology was a more prevalent impact than access to technology, which aligns with other findings that access was a challenge even when organizations provided software or hardware [53]. Other unexpected positive impacts from the providers’ perspectives include that delivering EBPs remotely allowed them to continue providing the program to older adults throughout the COVID-19 pandemic, reach participants they had not reached before, connect participants to each other, learn how to use technology, and be able to continue working or volunteering during the pandemic.

    We also gathered data via surveys and interviews on the unintended negative consequences of delivering EBPs remotely, an important aspect when evaluating the public health impact of these programs with an equity lens. For some leaders, teaching remotely was too difficult or disconnecting due to not being able to see people’s nonverbal cues and having to work harder to teach technology, engage people, or address emotional issues such that they felt that “something was lost.” Strategies for mitigating this reduced impact included reducing class sizes or duration (for both telephone and videoconferencing sessions) and requiring people attending videoconferencing sessions to have their camera on and having 2 leaders taking part so that one could focus on engagement while one managed technology.


    Principal Findings

    Our evaluation found that remote EBP participants showed improvements on various outcomes from program enrollment to 6-month follow-up, including their self-rated health, energy, sleep quality, loneliness, depressive symptoms, and technology anxiety, within and across programs. Some program participants also reduced their anxiety, pain, pain interference, physical activity and self-efficacy, and enhanced their technology usability. The quantitative findings yielded mainly small effect sizes. This may be due to the heterogeneity of the participants; in particular, participants who did not enter the program as lonely or inactive would have no room to improve over time. The qualitative findings suggest that remote EBP participants improved knowledge, attitudes, and skills on how to live healthier lives; reduced social isolation and loneliness; and gained better access to programs. In addition, providers shared that they too benefited from delivering programs remotely by staying connected, having access to technology, and improving their own health and well-being.

    In some cases, the findings support previous research on the effectiveness of the remote EBPs that were part of our evaluation; for example, 1 study of 213 videoconferencing CDSMP participants in rural and remote Ontario, Canada, found similar improvements in self-rated health, energy, and psychological well-being (a measure related to depressive symptoms) 4 months after their last class [54]. Another study of 97 telephone plus tool kit CPSMP participants in Cleveland, Ohio, United States, also reported better pain outcomes immediately after program completion [17]. Furthermore, a study of self-directed WWE participants (n=270) in rural and urban North Carolina, United States, who were living with arthritis found that participants also reduced fatigue at follow-up 1 year after program enrollment [21]. Our findings are also comparable to those of similar health promotion programs, such as increased physical activity and reduced depression for a remote DSMP [55].

    In other cases, our findings contrast with those of previous research; for instance, 1 study of remote-delivered EF [20] found that the participants (n=15) decreased their knee pain as measured by the Knee Injury and Osteoarthritis Outcome Score; however, all study participants had symptomatic knee osteoarthritis, and the postprogram outcome was measured directly after active intervention at 12 weeks. It may be that our evaluation participants did not significantly improve pain outcomes due to their less severe baseline pain. In addition, the study of CPSMP participants mentioned previously [17] found statistically significant improvements in sleep and depression; our evaluation participants too improved their sleep quality and reduced their depressive symptom severity, but this change was not statistically significant. Our differing findings may be due to our smaller sample size (47 vs 97), which lowered our power to detect significant change, or a longer follow-up period (6 months vs 6 weeks), during which improvements may have been attenuated.

    Furthermore, it should be noted that our outcome evaluation was not designed to compare in-person EBP delivery with remote EBP delivery. We used a common set of outcome measures of interest to older adults, EBP delivery organizations, and policy makers; as such, some of these measures look at similar constructs as the in-person EBP effectiveness studies but use different instruments, and some of these measures that are newly being evaluated (eg, loneliness and depression) were not evaluated in the in-person EBP research studies. In addition, we used a different time period compared to studies that evaluated the effectiveness of in-person EBP delivery: we looked at changes between program enrollment and 6-month follow-up, regardless of program duration, whereas prior effectiveness research looked at pre-post change over time based on program duration [21,56-59]. Our sample sizes were also smaller than those of previous in-person EBP research studies; therefore, the lack of statistical significance may be due to a lack of power to detect change. That said, it may be helpful to contextualize our remote EBP outcome evaluation findings with those from in-person EBP outcome evaluations. Remote CDSMP evaluation participants reported improvements in self-efficacy, health, energy (fatigue), sleep quality, physical activity, and depression, all outcomes that were also reported in previous studies of in-person CDSMP [37,60-62]. Furthermore, remote CDSMP evaluation participants did not show the significant improvements in pain outcomes that were seen in research with in-person program participants. Remote CPSMP evaluation participants reported outcome improvements similar to those of in-person CPSMP participants regarding pain and pain interference [56,63], while our evaluation added evidence for remote CPSMP participant improvements in energy (fatigue), anxiety, and physical activity; these 3 outcomes were not assessed in in-person CPSMP studies. However, remote CPSMP participants did not report significant improvements in self-efficacy, as was reported in previous in-person CPSMP studies. Looking at the DSMP, both remote and in-person delivery participants showed reduced hypoglycemia symptoms, health, and depression [57]. Earlier research with in-person program participants found improved self-efficacy as well, and remote program participants showed improvement in terms of their physical activity days. For EF, both remote and in-person program participants reported improvements in self-rated health as well as physical activity [64,65]. This evaluation adds novel findings on improved sleep quality and the usability of technology for remote program participants. In addition, in-person EF participants have reported reduced depression in other studies [64]. Looking at WWE, both in-person and remote program participants have demonstrated improvements in health, fatigue, and physical activity [21]. Remote WWE participants reduced their loneliness, which was not assessed in previous studies of in-person WWE [21]. Previous research of in-person WWE also found participant improvement in pain and self-efficacy [21].

    Although EBPs shifting to remote delivery was necessitated by the pandemic-related lockdown and other restrictions [24], the findings suggest that remote delivery can improve access to quality health promotion programs outside the COVID-19 pandemic context; for example, a caregiver for a person living with dementia can join a physical activity program from home to support their own health while not having to find respite care for their loved one, an older adult living with chronic pain can join a class even if they are experiencing elevated pain levels that would make it hard to leave even their bedroom, and a program leader can deliver the program across a region in Spanish to participants who may not have access to language-specific health promotion. This is similar to other studies of remote EBPs (eg, the value for people living with cancer to be able to participate from a distance despite living with weakened immune systems [66]).

    Adapting the format and channels through which an EBP is delivered [67] is considered an appropriate modifiable intervention characteristic to better align with the needs and preferences of participants, as well as organizational and geographic contexts in which a remote option improves program feasibility and sustainability [68]. Implementation science increasingly recognizes the need for adaptations [69] to improve the intervention-context fit (eg, increasing both practical and value fit [70]; increasing EBP feasibility to a given context [71]; and making sure that the EBPs can be delivered for different systems, organizations, providers, and participants than was originally studied [72]), support people living with multiple chronic conditions because comorbidity is the rule rather than the exception [73], and ensure EBP effectiveness by evaluating the intervention in different settings with varying provider and participant attributes [74]. Our complementary process evaluation found that delivering in-person EBPs remotely did not require modifications to core program components [53], suggesting that fidelity to the active ingredients of the program models was maintained. That said, some of the observed lack of improvement in health outcomes by remote EBP participants may be due to failures in implementation (eg, inadequate intervention dose due to technology issues) that negatively impacted program effectiveness [42].

    Strengths and Limitations

    Our evaluation comes with several strengths. First, we conducted a pragmatic evaluation across the country in partnership with policy makers and delivery organizations so that the findings would have direct implications for improving older adults’ health equity. Specifically, we built upon trusting relationships and took time to foster new relationships to engage people most impacted by implementing remote EBPs; we also gathered qualitative data to put quantitative outcomes in context for future quality improvement [75]. Second, we included multiple brief, validated health outcomes across various EBPs and diverse participants and providers, centering outcomes that are important and meaningful to participants, providers, organizations, and policy makers that were not measured in previous studies [76]. These include social factors such as social isolation and loneliness and mental health factors such as depression and anxiety, which lead to premature mortality for older adults [77,78]. While measuring multiple outcomes across heterogeneous populations and programs made it harder to see large effect sizes, it better reflects how organizations work (delivering multiple programs) and how participants view health more holistically rather than as just 1 primary outcome. It may also be that small effect sizes suggest that these programs provided primary or secondary prevention to delay the onset of more impairing symptoms and conditions that was not picked up from our measures or relatively study time frame [79]. Third, collecting qualitative as well as quantitative data from the perspectives of both participants and providers provides consistent measures to compare across studies as well as stories and unanticipated outcomes to explore more broadly in future research.

    Looking at limitations, first, using a single-group design with the lack of a comparison group or randomization means that we cannot attribute change in health outcomes over time to participating in the remote EBP. Second, we recognize that the lack of statistical significance when assessing for whether change was due to chance or program participation may be due to small sample sizes that lack power. This is partly why we collected qualitative data from both providers and participants: we wanted to understand the magnitude of change from their perspectives. Third, our response rate was lower than is typical in controlled research studies, with only half of the remote EBP participants (289/586, 49.3%) providing follow-up data. This was due to pandemic-related logistical and methodological challenges faced by our evaluation team, program providers, and older adult participants; conducting an evaluation during the COVID-19 pandemic has been deemed more challenging than conducting evaluations in conflict areas [80]. This suggests limitations to both internal validity through biases such as selection bias (eg, persons who liked the program or had access to a mobile phone or the internet to complete the evaluation were overrepresented) and external validity, with the COVID-19 pandemic being a unique context, because of which our findings may not be generalizable (eg, were improvements in social connection due to the high level of disconnectedness faced by everyone during the pandemic-related lockdown?). Finally, our convenience sample only included group-based programs and as such may not be applicable to one-on-one EBPs. We also did not have access to data on the entire remote EBP delivery population; therefore, we cannot comment on the representativeness of our outcome evaluation sample. Our process evaluation [24] suggests that persons who have been historically underserved by EBPs (eg, people of color, those with disabilities, and those living in rural areas) can be reached through remote delivery; however, care must be taken to ensure that remote delivery does not widen the health inequities caused by the digital divide.

    Conclusions

    In closing, the findings from this outcome evaluation suggest that participating in remote EBPs can improve health, social, and technology outcomes of interest for older adults, providers, and policy makers (Textbox 1). Future policy and practice can better support remote EBPs, improving access for all older adults (regardless of income, geography, and ability) and strengthening delivery organizations.

    Textbox 1. Lessons learned for evidence-based program (EBP) administrators, developers, and implementers.

    Key takeaways

    • Delivering EBPs remotely (by telephone, videoconferencing, mail, or a combination of these modes) offers a new format for engaging older adults in quality health promotion programming.
    • Outcome evaluation findings suggest that remote EBPs are effective at improving health, social, and technology outcomes for older adult participants and providers who deliver the programs, although impacts are not experienced universally across programs or outcomes.
    • Interviews and surveys with remote EBP participants and providers suggest that these outcomes can be achieved because remote EBPs are acceptable; increase knowledge, skills, motivation, support, and accountability; connect people to peers and leaders; and support tech access and comfort.
    • Include brief, validated pre- and postprogram surveys in your routine program delivery to understand changes in outcomes that matter to your community, organization, and funders. (The surveys may include traditional health outcomes you assessed with in-person EBPs as well as new outcomes, such as social isolation, loneliness, technology anxiety, and technology usability. Our survey measures are cited in this paper and available on request.)
    • Gather data on remote EBP participant demographics to understand who is being reached and who is not being reached by remote programs to strategize engagement via other outreach strategies or in-person EBP modes.
    • Remote EBP delivery may improve access to health promotion programming for people facing inequitable access to in-person programming; however, caution is needed to ensure that remote delivery does not widen the digital divide; for example, while remote delivery can support rural-dwelling older adults from areas without nearby programs or dependable transportation, the lack of reliable internet in some rural areas may necessitate a telephone or mail remote EBP delivery mode.
    • Supports for remote EBPs include orienting participants to using program technology before and during the program and providing ways for program participants to connect after the program ends.

    Acknowledgments

    The authors thank the remote evidence-based program participants, leaders, and staff who came together during this difficult pandemic to continue health promotion from a distance. Study data were collected and managed using REDCap (Research Electronic Data Capture) tools hosted at the Institute of Translational Health Sciences and supported by the National Center for Advancing Translational Sciences of the National Institutes of Health (UL1 TR002319). This evaluation would not have been possible without generous support from the funders. This project was supported by a cooperative agreement (NU38OT000315) funded to the Society for Public Health Education by the National Center for Chronic Disease Prevention and Health Promotion (Division of Population Health, Arthritis Management and Well-Being Program) of the Centers for Disease Control and Prevention; by the Administration for Community Living (RTI contract 37-312-0214780 to Sound Generations); and by the RRF Foundation for Aging (grant 2020183 to Sound Generations). The contents are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention, Administration for Community Living, RRF Foundation for Aging, or the Department of Health and Human Services.

    Conflicts of Interest

    PD and KL work for organizations that administer remote evidence-based programs. All other authors declare no other conflicts of interest.

    Multimedia Appendix 1

    Participant outcomes survey.

    PDF File (Adobe PDF File), 142 KB
    Multimedia Appendix 2

    The percentages of improvement and maintenance in participant health outcomes at enrollment and 6-month follow-up in remote evidence-based programs for health promotion.

    DOCX File , 45 KB
    1. 2020 profile of older Americans. Administration for Community Living, U.S. Department of Health and Human Services. May 2021. URL: https:/​/acl.​gov/​sites/​default/​files/​aging%20and%20Disability%20In%20America/​2020Profileolderamericans.​final_.​pdf [accessed 2022-12-18]
    2. The Longevity Economy® Outlook: how people age 50 and older are fueling economic growth, stimulating jobs, and creating opportunities for all. AARP. 2019. URL: https:/​/www.​aarp.org/​content/​dam/​aarp/​research/​surveys_statistics/​econ/​2019/​longevity-economy-outlook.​doi.​10.​26419-2Fint.​00042.​001.​pdf [accessed 2022-12-18]
    3. Fong JH. Disability incidence and functional decline among older adults with major chronic diseases. BMC Geriatr. Nov 21, 2019;19(1):323. [CrossRef] [Medline]
    4. Bergen G, Stevens MR, Burns ER. Falls and fall injuries among adults aged ≥65 years - United States, 2014. MMWR Morb Mortal Wkly Rep. Sep 23, 2016;65(37):993-998. [CrossRef] [Medline]
    5. Healthy Aging Through The Social Determinants of Health. Washington, DC. American Public Health Association; 2021.
    6. McGough M, Claxton G, Amin K, Cox C. How do health expenditures vary across the population? Peterson-KFF Health System Tracker. Jan 4, 2024. URL: https:/​/www.​healthsystemtracker.org/​chart-collection/​health-expenditures-vary-across-population/​#Proportion%20of%20individuals%20by%20health%20status,%202019 [accessed 2023-08-12]
    7. Gerteis J, Izrael D, Deitz D, LeRoy L, Ricciardi R, Miller T, et al. Multiple chronic conditions chartbook. Agency for Healthcare Research and Quality. 2014. URL: https:/​/www.​ahrq.gov/​sites/​default/​files/​wysiwyg/​professionals/​prevention-chronic-care/​decision/​mcc/​mccchartbook.​pdf [accessed 2023-08-12]
    8. Health, United States, 2015: with special feature on racial and ethnic health disparities. National Center for Health Statistics (US). 2016. URL: https://pubmed.ncbi.nlm.nih.gov/27308685/ [accessed 2024-04-20]
    9. CDC health disparities and inequalities report — United States, 2011. Centers for Disease Control and Prevention. 2011. URL: https://www.cdc.gov/mmwr/preview/ind2011_su.html#HealthDisparities2011 [accessed 2024-04-20]
    10. Wagner EH. Chronic disease management: what will it take to improve care for chronic illness? Eff Clin Pract. 1998;1(1):2-4. [Medline]
    11. Health promotion. Administration for Community Living. URL: https://acl.gov/programs/health-wellness/disease-prevention [accessed 2024-04-20]
    12. Arthritis-Appropriate, Evidence-Based Interventions (AAEBI). Osteoarthritis Action Alliance. URL: https:/​/oaaction.​unc.edu/​aaebi/​#:~:text=Arthritis%2DAppropriate%2C%20Evidence%2DBased%20Interventions%20(AAEBIs)%20are,pain%20or%20limitations%20in%20function [accessed 2024-04-20]
    13. Tyrrell CJ, Williams KN. The paradox of social distancing: implications for older adults in the context of COVID-19. Psychol Trauma. Aug 2020;12(S1):S214-S216. [CrossRef] [Medline]
    14. Smith ML, Steinman LE, Casey EA. Combatting social isolation among older adults in a time of physical distancing: the COVID-19 social connectivity paradox. Front Public Health. Jul 21, 2020;8:403. [CrossRef] [Medline]
    15. Aarons GA, Sklar M, Mustanski B, Benbow N, Brown CH. "Scaling-out" evidence-based interventions to new populations or new health care delivery systems. Implement Sci. Sep 06, 2017;12(1):111. [CrossRef] [Medline]
    16. Perez Jolles M, Lengnick-Hall R, Mittman BS. Core functions and forms of complex health interventions: a patient-centered medical home illustration. J Gen Intern Med. Jun 2019;34(6):1032-1038. [CrossRef] [Medline]
    17. Sheth K, Ritter PL, Lorig K, Steinman L, FallCreek S. Remote delivery of the chronic pain self-management program using self-directed materials and small-group telephone support: a pilot study. J Appl Gerontol. May 2022;41(5):1329-1335. [CrossRef] [Medline]
    18. Jernigan VB, Lorig K. The internet diabetes self-management workshop for American Indians and Alaska Natives. Health Promot Pract. Mar 2011;12(2):261-270. [CrossRef] [Medline]
    19. Lorig K, Ritter PL, Plant K, Laurent DD, Kelly P, Rowe S. The South Australia health chronic disease self-management internet trial. Health Educ Behav. Feb 2013;40(1):67-77. [CrossRef] [Medline]
    20. Patel KV, Hoffman EV, Phelan EA, Gell NM. Remotely delivered exercise to rural older adults with knee osteoarthritis: a pilot study. ACR Open Rheumatol. Aug 2022;4(8):735-744. [CrossRef] [Medline]
    21. Callahan LF, Shreffler JH, Altpeter M, Schoster B, Hootman J, Houenou LO, et al. Evaluation of group and self-directed formats of the Arthritis Foundation's Walk With Ease Program. Arthritis Care Res (Hoboken). Aug 01, 2011;63(8):1098-1107. [CrossRef] [Medline]
    22. Shelton RC, Chambers DA, Glasgow RE. An extension of RE-AIM to enhance sustainability: addressing dynamic context and promoting health equity over time. Front Public Health. 2020;8:134. [CrossRef] [Medline]
    23. Creswell JW, Plano Clark VL. Designing and Conducting Mixed Methods Research Third Edition. Thousand Oaks, CA. SAGE Publications; 2017.
    24. Onyeaka H, Anumudu CK, Al-Sharify ZT, Egele-Godswill E, Mbaegbu P. COVID-19 pandemic: a review of the global lockdown and its far-reaching effects. Sci Prog. Jun 01, 2021;104(2):368504211019854. [CrossRef] [Medline]
    25. Palinkas LA, Horwitz SM, Green CA, Wisdom JP, Duan N, Hoagwood K. Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Adm Policy Ment Health. Sep 6, 2015;42(5):533-544. [CrossRef] [Medline]
    26. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. Apr 2009;42(2):377-381. [CrossRef] [Medline]
    27. Schoenfeld DE, Malmrose LC, Blazer DG, Gold DT, Seeman TE. Self-rated health and mortality in the high-functioning elderly--a closer look at healthy individuals: MacArthur field study of successful aging. J Gerontol. May 01, 1994;49(3):M109-M115. [CrossRef] [Medline]
    28. Ware JE, Sherbourne CD. The MOS 36-ltem Short-Form Health Survey (SF-36). Med Care. 1992;30(6):473-483. [CrossRef]
    29. Ritter PL, González VM, Laurent DD, Lorig KR. Measurement of pain using the visual numeric scale. J Rheumatol. Mar 2006;33(3):574-580. [Medline]
    30. Lorig KR, Ritter PL, Jacquez A. Outcomes of border health Spanish/English chronic disease self-management programs. Diabetes Educ. Dec 07, 2005;31(3):401-409. [CrossRef] [Medline]
    31. Hughes ME, Waite LJ, Hawkley LC, Cacioppo JT. A short scale for measuring loneliness in large surveys: results from two population-based studies. Res Aging. Aug 19, 2004;26(6):655-672. [CrossRef] [Medline]
    32. Berkman LF, Syme SL. Social networks, host resistance, and mortality: a nine-year follow-up study of Alameda County residents. Am J Epidemiol. Feb 1979;109(2):186-204. [CrossRef] [Medline]
    33. Kroenke K, Strine TW, Spitzer RL, Williams JB, Berry JT, Mokdad AH. The PHQ-8 as a measure of current depression in the general population. J Affect Disord. Apr 2009;114(1-3):163-173. [CrossRef] [Medline]
    34. Kroenke K, Spitzer RL, Williams JB, Monahan PO, Löwe B. Anxiety disorders in primary care: prevalence, impairment, comorbidity, and detection. Ann Intern Med. Mar 06, 2007;146(5):317-325. [CrossRef] [Medline]
    35. Coleman KJ, Ngor E, Reynolds K, Quinn VP, Koebnick C, Young DR, et al. Initial validation of an exercise "vital sign" in electronic medical records. Med Sci Sports Exerc. Nov 2012;44(11):2071-2076. [CrossRef] [Medline]
    36. Protocol for responding to and assessing patient assets, risks, and experiences (PRAPARE). Association of Asian Pacific Community Health Organizations (AAPCHO). URL: https://aapcho.org/focusareas/prapare/ [accessed 2024-04-20]
    37. Lorig KR, Sobel DS, Ritter PL, Laurent D, Hobbs M. Effect of a self-management program on patients with chronic disease. Eff Clin Pract. 2001;4(6):256-262. [Medline]
    38. Am I rural? – tool. Rural Health Information Hub. URL: https://www.ruralhealthinfo.org/am-i-rural [accessed 2023-08-13]
    39. Chen K, Lou VW. Measuring senior technology acceptance: development of a brief, 14-item scale. Innov Aging. Jun 27, 2020;4(3):igaa016. [CrossRef] [Medline]
    40. Reichheld FF. The one number you need to grow. Harvard Business Review. Dec 2003. URL: https://hbr.org/2003/12/the-one-number-you-need-to-grow [accessed 2024-04-21]
    41. Hamilton DF, Lane JV, Gaston P, Patton JT, MacDonald DJ, Simpson AH, et al. Assessing treatment outcomes using a single question. Bone Joint J. May 01, 2014;96-B(5):622-628. [CrossRef]
    42. Proctor E, Silmere H, Raghavan R, Hovmand P, Aarons G, Bunger A, et al. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health. Mar 2011;38(2):65-76. [CrossRef] [Medline]
    43. R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing. 2019. URL: https://www.R-project.org/ [accessed 2024-04-21]
    44. Ageing and health. World Health Organization. Oct 01, 2022. URL: https://www.who.int/news-room/fact-sheets/detail/ageing-and-health [accessed 2023-08-13]
    45. Dankel SJ, Loenneke JP. Effect sizes for paired data should use the change score variability rather than the pre-test variability. J Strength Cond Res. Jun 01, 2021;35(6):1773-1778. [CrossRef] [Medline]
    46. Armstrong RA. When to use the Bonferroni correction. Ophthalmic Physiol Opt. Sep 2014;34(5):502-508. [CrossRef] [Medline]
    47. Dedoose version: web application for managing, analyzing, and presenting qualitative and mixed method research data. SocioCultural Research Consultants. 2019. URL: https://www.dedoose.com/ [accessed 2024-04-21]
    48. Nowell LS, Norris JM, White DE, Moules NJ. Thematic analysis: striving to meet the trustworthiness criteria. Int J Qual Methods. 2017;16(1):1-13. [CrossRef]
    49. Gale RC, Wu J, Erhardt T, Bounthavong M, Reardon CM, Damschroder LJ, et al. Comparison of rapid vs in-depth qualitative analytic methods from a process evaluation of academic detailing in the Veterans Health Administration. Implement Sci. Feb 01, 2019;14(1):11. [CrossRef] [Medline]
    50. Saldaña J. The Coding Manual for Qualitative Researchers. Thousand Oaks, CA. SAGE Publications; 2015.
    51. Gale NK, Heath G, Cameron E, Rashid S, Redwood S. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol. Sep 18, 2013;13:117. [CrossRef] [Medline]
    52. Guetterman TC, Fetters MD, Creswell JW. Integrating Quantitative and Qualitative Results in Health Science Mixed Methods Research Through Joint Displays. Ann Fam Med. Nov 2015;13(6):554-561. [FREE Full text] [CrossRef] [Medline]
    53. Steinman L, Chavez Santos E, Chadwick K, Mayotte C, Johnson SS, Kohn M, et al. Remote evidence-based health promotion programs during COVID: a national evaluation of reach and implementation for older adult health equity. Health Promot Pract. Jun 06, 2023:15248399231175843. [CrossRef] [Medline]
    54. Jaglal SB, Haroun VA, Salbach NM, Hawker G, Voth J, Lou W, et al. Increasing access to chronic disease self-management programs in rural and remote communities using telehealth. Telemed J E Health. Jun 2013;19(6):467-473. [CrossRef] [Medline]
    55. Rosal MC, Heyden R, Mejilla R, Capelson R, Chalmers KA, Rizzo DePaoli M, et al. A virtual world versus face-to-face intervention format to promote diabetes self-management among African American women: a pilot randomized clinical trial. JMIR Res Protoc. Oct 24, 2014;3(4):e54. [CrossRef] [Medline]
    56. LeFort SM, Gray-Donald K, Rowat KM, Jeans ME. Randomized controlled trial of a community-based psychoeducation program for the self-management of chronic pain. Pain. Feb 1998;74(2-3):297-306. [CrossRef] [Medline]
    57. Lorig K, Ritter PL, Villa FJ, Armas J. Community-based peer-led diabetes self-management: a randomized trial. Diabetes Educ. Apr 30, 2009;35(4):641-651. [CrossRef] [Medline]
    58. Leveille SG, Wagner EH, Davis C, Grothaus L, Wallace J, LoGerfo M, et al. Preventing disability and managing chronic illness in frail older adults: a randomized trial of a community-based partnership with primary care. J Am Geriatr Soc. Oct 1998;46(10):1191-1198. [CrossRef] [Medline]
    59. Lorig KR, Sobel DS, Stewart AL, Brown BW, Bandura A, Ritter P, et al. Evidence suggesting that a chronic disease self-management program can improve health status while reducing hospitalization: a randomized trial. Med Care. Jan 1999;37(1):5-14. [CrossRef] [Medline]
    60. Ritter PL, Ory MG, Laurent DD, Lorig K. Effects of chronic disease self-management programs for participants with higher depression scores: secondary analyses of an on-line and a small-group program. Transl Behav Med. Dec 2014;4(4):398-406. [CrossRef] [Medline]
    61. Ory MG, Ahn S, Jiang L, Smith ML, Ritter PL, Whitelaw N, et al. Successes of a national study of the chronic disease self-management program: meeting the triple aim of health care reform. Med Care. Nov 2013;51(11):992-998. [FREE Full text] [CrossRef] [Medline]
    62. Ahn S, Jiang L, Smith ML, Ory MG. Improvements in sleep problems among the chronic disease self-management program participants. Fam Community Health. 2014;37(4):327-335. [FREE Full text] [CrossRef] [Medline]
    63. Pullyblank K, Brunner W, Scribani M, Krupa N, Wyckoff L, Strogatz D. Evaluation of a peer led chronic pain self-management program in a rural population. J Prim Care Community Health. Sep 12, 2022;13:21501319221121464. [CrossRef] [Medline]
    64. Wallace JI, Buchner DM, Grothaus L, Leveille S, Tyll L, LaCroix AZ, et al. Implementation and effectiveness of a community-based health promotion program for older adults. J Gerontol A Biol Sci Med Sci. Jul 01, 1998;53(4):M301-M306. [CrossRef] [Medline]
    65. Belza B, Shumway-Cook A, Phelan EA, Williams B, Snyder SJ, LoGerfo JP. The effects of a community-based exercise program on function and health in older adults: the EnhanceFitness program. J Appl Gerontol. Jun 29, 2016;25(4):291-306. [CrossRef]
    66. Gell NM, Dittus K, Caefer J, Martin A, Bae M, Patel KV. Remotely delivered exercise to older rural cancer survivors: a randomized controlled pilot trial. J Cancer Surviv. Apr 14, 2024;18(2):596-605. [CrossRef] [Medline]
    67. Stirman SW, Miller CJ, Toder K, Calloway A. Development of a framework and coding system for modifications and adaptations of evidence-based interventions. Implement Sci. Jun 10, 2013;8(1):65. [CrossRef] [Medline]
    68. Aarons GA, Ehrhart MG, Farahnak LR, Sklar M. Aligning leadership across systems and organizations to develop a strategic climate for evidence-based practice implementation. Annu Rev Public Health. 2014;35:255-274. [CrossRef] [Medline]
    69. von Thiele Schwarz U, Aarons GA, Hasson H. The value equation: three complementary propositions for reconciling fidelity and adaptation in evidence-based practice implementation. BMC Health Serv Res. Nov 21, 2019;19(1):868. [CrossRef] [Medline]
    70. Moore JE, Bumbarger BK, Cooper BR. Examining adaptations of evidence-based programs in natural contexts. J Prim Prev. Jun 19, 2013;34(3):147-161. [CrossRef] [Medline]
    71. Rabin BA, McCreight M, Battaglia C, Ayele R, Burke RE, Hess PL, et al. Systematic, multimethod assessment of adaptations across four diverse health systems interventions. Front Public Health. 2018;6:102. [CrossRef] [Medline]
    72. Green LW, Glasgow RE. Evaluating the relevance, generalization, and applicability of research: issues in external validation and translation methodology. Eval Health Prof. Mar 23, 2006;29(1):126-153. [CrossRef] [Medline]
    73. Tinetti ME, Fried TR, Boyd CM. Designing health care for the most common chronic condition--multimorbidity. JAMA. Jun 20, 2012;307(23):2493-2494. [CrossRef] [Medline]
    74. Wiltsey Stirman S, Gamarra JM, Bartlett BA, Calloway A, Gutner CA. Empirical examinations of modifications and adaptations to evidence‐based psychotherapies: methodologies, impact, and future directions. Clin Psychol Sci Pract. Dec 2017;24(4):396-420. [CrossRef]
    75. Metz A, Woo B, Loper A. Equitable implementation at work. Stanford Social Innovation Review. 2021. URL: https://ssir.org/articles/entry/equitable_implementation_at_work# [accessed 2024-04-20]
    76. Kerkhoff AD, Farrand E, Marquez C, Cattamanchi A, Handley MA. Addressing health disparities through implementation science-a need to integrate an equity lens from the outset. Implement Sci. Jan 31, 2022;17(1):13. [CrossRef] [Medline]
    77. Holt-Lunstad J. The potential public health relevance of social isolation and loneliness: prevalence, epidemiology, and risk factors. Public Policy Aging Report. 2017;27(4):127-130. [CrossRef]
    78. Hawkley LC, Cacioppo JT. Aging and loneliness: downhill quickly? Curr Dir Psychol Sci. Jun 24, 2016;16(4):187-191. [CrossRef]
    79. Picture of America. Centers for Disease Control and Prevention. URL: https://stacks.cdc.gov/view/cdc/142637 [accessed 2023-08-13]
    80. Srivastava A. Challenges for evaluation practices and innovative approaches: lessons during COVID-19 pandemic. Eval Program Plann. Jun 2022;92:102095. [CrossRef] [Medline]

    ‎
    ACL: Administration for Community Living
    CDC: Centers for Disease Control and Prevention
    CDSMP: Chronic Disease Self-Management Program
    CPSMP: Chronic Pain Self-Management Program
    DSMP: Diabetes Self-Management Program
    EBP: evidence-based program
    EF: EnhanceFitness
    PHQ: Patient Health Questionnaire
    PROMIS: Patient-Reported Outcomes Measurement Information System
    RE-AIM: Reach, Effectiveness, Adoption, Implementation, and Maintenance
    REDCap: Research Electronic Data Capture
    WWE: Walk With Ease

    Edited by G Seçkin; submitted 22.08.23; peer-reviewed by D Paone, H Murphy, L Erck; comments to author 01.02.24; revised version received 28.03.24; accepted 09.04.24; published 13.06.24.

    Copyright

    ©Lesley Steinman, Kelly Chadwick, Erica Chavez Santos, Sruthi Sravanam, Selisha Snowy Johnson, Elspeth Rensema, Caitlin Mayotte, Paige Denison, Kate Lorig. Originally published in JMIR Aging (https://aging.jmir.org), 13.06.2024.

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