Factors Affecting Online Health Promotion Program Adherence Among People with Disabilities

Abstract

As online health and wellness programs become more ubiquitous post-pandemic, there is a need to better understand how people with physical disabilities respond to different types of program offerings. Online health promotion programs have become popular in the disability community, and programs offer a range of activities across various wellness domains (e.g., exercise, nutrition). This study examined factors predicting adherence to three different types of online health promotion programs tailored for people with physical disabilities. A survey was developed to examine factors associated with high, moderate, and low adherence to three different types of health promotion programs. Participants who completed an online wellness program were sent a survey that asked questions related to adherence to a range of wellness activities. The three programs included the MENTOR (Mindfulness, Exercise, and Nutrition to Optimize Resilience), GROWTH (Growing Resilience Out of Wellness and Thoughtful Habits), and SOSE (State of Slim Everybody) programs, all of which focus on different self-care strategies. MENTOR focused on educating participants about mindfulness, exercise, and nutrition; GROWTH aimed to deliver mental and emotional health techniques, while SOSE’s purpose was to teach participants how to implement healthy weight loss, weight management, and daily exercise practices. Results indicated that participant perceptions of program delivery, specifically programs being disability friendly, virtual environment enjoyment, having positive instructor relationships, adaptable content, the instructor’s knowledge about disability, the instructor’s use of appropriate language, and program satisfaction, affected the likelihood of high adherence among people with physical disabilities enrolled in the health and wellness programs.

1. Introduction

Engaging in health promotion and secondary condition prevention (HP/SCP) is crucial for people with physical disabilities due to their increased risk of chronic diseases and health disparities. People with physical disabilities face significantly higher rates of obesity, type 2 diabetes, cardiovascular comorbidities, and chronic pain compared to the general population [1,2,3,4]. Regular participation in health-enhancing activities can help reduce these risks and improve overall health outcomes.

Participating in health promotion activities is essential to a higher quality of life, as it contributes to a plethora of lifestyle-related advantages. As reported by the Centers for Disease Control and Prevention (CDC), physical activity, one primary component of health promotion, is associated with immediate and long-term health benefits [5]. Immediate benefits include better sleep quality and quantity, less anxiety, and blood pressure reduction, while long-term benefits include improved brain health, weight loss or maintenance, balance and coordination skills, immune function, among others [5].

Physical activity and other components of health promotion (e.g., nutrition, mental health) are critical for overcoming barriers experienced by people with physical disabilities. This leads to more engagement in these programs, providing people with physical disabilities the opportunity to achieve better physical fitness, mental health, and quality of life while also reducing the prevalence of preventable chronic conditions [6,7]. However, recent research from Rimmer et al. (2022), and Rimmer et al. (2024), noted that there must be a great focus on participant engagement, which includes adherence to the program (i.e., attendance) and retention (i.e., completion of the program) [8,9]. Understanding the role of program characteristics with respect to adherence and retention in health promotion programs is an understudied area of research. Tailored programs not only address specific barriers and needs but also ensure that people with physical disabilities are included in the planning and delivery of such programs. This is fundamental to ensuring that the necessary support and resources are available.

One such way of implementing facilitative, inclusive, and accessible health promotion opportunities for people with physical disabilities is through online programs. Since the COVID-19 pandemic, online wellness programs have become more popular with delivery through virtual portals (i.e., YouTube), communication platforms (i.e., Zoom), and various methods of synchronicity options, and they can be accessed from the comfort of the participant’s home or preferred setting, such as a senior center or an independent living center. Online programs are also unique from the perspective that they can include multiple health promotion strategies (e.g., exercise, diet, mindfulness, stress management) at a time of day that is more convenient for the participant [6,10]. Moreover, these programs alleviate commonly reported barriers noted in community-based participation, such as transportation, a community’s lack of knowledge about disability, and participants feeling fatigued having to travel to and from a certain facility [11,12]. Facilitators that alleviate these barriers, integrated into the online delivery of community programs, include no travel time necessary, disability-trained staff, and having access to personnel to ask detailed questions about the program [11,13,14].

Another imbedded facilitator is the virtual delivery of program content. Many online programs offer participation at three different levels—synchronous (in real time), asynchronous (previously delivered), or a combination of both [15,16]. This allows participants to stay engaged and progress toward achieving their health-related goals in a timely manner with direct access to trained personnel who can tailor the participant’s experience. However, for online programs to be impactful, they must demonstrate that participants enjoy the program and are adhering to the protocol. Program adherence is an important aspect of successful delivery as it is directly tied to post-program lifestyle implementation [17].

Three ongoing health promotion programs designed specifically for adults with physical disabilities are offered in the National Center on Health, Physical Activity and Disability (NCHPAD). NCHPAD serves as a national resource center and health promotion hub, enrolling participants in tailored, virtually delivered, evidence-based online programs [7]. All programs are offered to adults over 18 years of age with an existing disability or recently acquired chronic condition leading to a mobility disability. The purpose of this retrospective study was to analyze program adherence among people with physical disabilities who participated in three types of health promotion programs, all offered online and focused on achieving increased physical and mental well-being. Special emphasis was placed on physical activity, as it is well-established that when comparing physical activity routines, resources, facilitators and barriers, and tailored programs among people with physical disabilities to people without physical disabilities, retention and participation rates are disproportionate [2,3,11,13]. To explore this, we examined participant descriptives, including adherence, physical activity, and qualitative feedback regarding each program, and correlational relationships, including common associations found between programs. Inherently, these questions informed the major objective of this study. Our overall research aim was to investigate ongoing programs with a quality improvement perspective. Therefore, our research question was, what program factors predict high adherence among people with physical disabilities?

2. Materials and Methods

2.1. Online Health Promotion Programs

NCHPAD currently offers three online health programs. Each of these programs was part of this retrospective evaluation study. This study was approved by the University Institutional Review Board for Human Use.

2.1.1. MENTOR

Mindfulness, Exercise, and Nutrition to Optimize Resilience (MENTOR) is a free, online 8-week holistic wellness program focused on physical, mental, and emotional health for people with physical disabilities. Participants meet virtually, via Zoom, with disability-trained mindfulness, exercise, and nutrition instructors (exercise—2 h/wk, nutrition 1 h/wk, mindfulness 1 h/wk, health coaching 1 h/wk). Participants are sent accessible exercise equipment to use during and after the program. They are also given access to an online portal where the entire program is housed and can access their class schedule, insert and track personal health goals, access a coach via a chat feature, and schedule individual meetings with a coach. More information may be found in the literature by Rimmer et al. [8], online (https://www.nchpad.org/nchpad-connect/mentor/, accessed on 9 June 2024) [18] or in Figure S1 in the Supplementary Materials.

2.1.2. GROWTH

Growing Resilience Out of Wellness and Thoughtful Habits (GROWTH) is a free, online 6-week program designed to equip people with physical disabilities with education and awareness surrounding mental health and well-being. Participants meet twice each week via Zoom for two hours and thirty minutes with disability-trained mental health coaches to discuss topics such as coping strategies, gratitude, and other various whole-body self-care techniques. More information may be found in the literature [19], online (https://www.nchpad.org/nchpad-connect/growth/, accessed on 9 June 2024) [18] or in Figure S2.

2.1.3. SOSE

State of Slim Everybody (SOSE) is a free, online 16-week weight loss transformation program targeting people with physical disabilities, aiming to reduce pain, minimize chronic disease risks, and improve overall quality of life. SOSE participants meet once a week for one hour over the 16-week period via Zoom to learn about diet, nutrition, and physical activity healthy habits. Participants are mailed a wellness box of resources to support their time within and after the program. More information may be found in the literature [20], online (https://www.nchpad.org/nchpad-connect/state-of-slim-everybody/, accessed on 9 June 2024) [18] or in Figure S3.

2.2. Survey

An online survey was developed using results from a previous study that targeted participants who were highly adherent to an online health promotion program [21]. In this qualitative study, eight participants were interviewed to determine program components of importance. In this current quantitative study, an online survey was developed asking participants reasons for high adherence, program expectations, motivation to stay connected, perceived facilitators and barriers, and overall experiences.

The survey went through two rounds of iterations where it was reviewed and revised by disability content experts. These revision rounds included ensuring (1) online accessibility, (2) readability and length appropriateness, (3) question-grouping (i.e., ensuring quality order of questions from the participant’s point of view), and (4) excluding questions not pertinent to study focus prior to posting on the UAB Qualtrics survey management software (Qualtrics, September 2024, Provo, UT, USA). The survey also included participant demographics, including date of birth, state of residency, race, gender, disability, use of assistive devices, level of education, marital status, and employment status. The participants were also asked to self-report (1) physical activity using the Godin Leisure-Time Exercise Questionnaire (GLTEQ) and (2) online program class attendance. The GLTEQ inquires about the frequency of mild, moderate, and strenuous physical activity in bouts of 15 min or more per day during the previous week. The total score was calculated by multiplying the frequency of strenuous, moderate, and mild physical activity by nine, five, and three metabolic equivalents, respectively, and summing the weighted scores. The GLTEQ Health Contribution Score (HCS) provides a measure of health-promoting physical activity based on the frequency of strenuous and moderate physical activity, multiplied by nine and five metabolic equivalents, respectively, and summing the weighted scores. The GLTEQ has been previously validated in disability populations [22].

In addition to demographic and self-report data, participants were asked about their online wellness program-specific preferences. The complete survey can be found in File S1 in the Supplementary Materials. Qualtrics is Health Information Trust Alliance (HITRUST) certified, and participants received a University IRB-approved information sheet with a required participant signature to enter the online survey, representing obtained written consent [23]. The study was approved by the Institutional Review Board (IRB).

2.3. Participants

Participants included individuals who completed one of the online health promotion programs offered through the National Center on Health, Physical Activity and Disability, MENTOR, GROWTH, and/or SOSE. Participants who participated in the study had agreed to be contacted for research opportunities. These individuals reside across the continental United States. Participants were recruited using an email and a chat to their personal online portal, where they were given access to the survey link via email. A follow-up message was then sent directly to a chat feature in their password-protected participant portal via Healthie software (Healthie, September 2024) [24]. Participants were informed about the contents of the survey questions and were also informed they could start and/or stop, complete, or not complete the survey once received with no penalty.

2.4. Procedures

2.4.1. Data Collection

Survey Data. The online survey was securely delivered to participants using their provided email address and the password-protected online participant portal chat feature via Healthie software. A NCHPAD staff member delivered the survey to eliminate researcher bias. As UAB Qualtrics is HITRUST certified and password protected, once the survey was initiated, any and all data were stored within UAB Qualtrics and were only accessible to the primary researcher. Additionally, all participant survey data were housed via Qualtrics until completion of data collection (August 2024). Participants were instructed to only complete the survey if they had completed the program(s) and to not complete the survey for a program(s) they were currently enrolled in or had not begun. The online survey included both qualitative and quantitative items, including (1) participant adherence and experiences in MENTOR, GROWTH, and/or SOSE; (2) an open-ended response option for each question to explain why a certain question was answered extremely low or extremely high; and (3) participation in physical activity after the program ended measured by the GLTEQ.

Logic existed in the survey to deliver questions only applicable to the program that participants completed. For example, if a participant selected that they only completed SOSE, they would only receive questions asking about their participation in SOSE, not in MENTOR or GROWTH. All questions were structured using a Likert-scale format where 1 was ‘strongly disagree’ and 5 was ‘strongly agree’ and were the same wording but substituted the name of the program depending on what program the participant completed. For example, if a participant selected that they completed MENTOR and GROWTH, the following sample questions would populate:

• I enjoyed the virtual teaching components of MENTOR.
• I enjoyed the virtual teaching components of GROWTH.

Participant Adherence. We defined adherence according to the current literature as class attendance rates [25]. Program adherence was determined by calculating the number of classes each participant attended by the total number of sessions offered in each program. Program adherence is a proxy measurement for class attendance and signifies participants’ overall involvement within the program, including, but not limited to, engagement with program content, fellow participants, program staff, and lifestyle implementation following program completion. Participant adherence was collected from adherence records kept by program staff from individual programs. The total number of MENTOR, GROWTH, and SOSE classes available to attend were 32, 12, and 16 classes, respectively. Adherence percentages were based on a priori criteria for three categories: low, moderate, and high adherence. By providing three categories to classify adherence, a clearer overall view of participants’ attendance is created. Adherence was classified proportionately as each level of adherence covered roughly one third of program totality.

For MENTOR, low adherence was categorized as attending between 1 and 17 classes (2.5% to 42.5%), moderate adherence was categorized as attending between 18 and 29 classes (45% to 72.5%), and high adherence was categorized as attending 30 or more classes (75% or more). MENTOR is considered a high-touch program, meaning participants’ designated schedules have them placed in classes where they are in contact with program personnel for 5 h each week. Outside of class times, program personnel are in contact with participants, sending resources, program assignments, class material, etc.

For GROWTH, low adherence was categorized as attending between 1 and 4 classes (8.3% to 33%), moderate adherence was categorized as attending between 5 and 8 classes (42% to 67%), and high adherence was categorized as attending 9 or more classes (75% or more). GROWTH is considered a moderate-touch program, meaning participants’ designated schedules have them placed in classes where they are in contact with program personnel for 2 h and 30 min each week. Outside of class times, program personnel are in minimal contact with participants.

For SOSE, low adherence was categorized as attending between 1 and 6 classes (6.3% to 38%), moderate adherence was categorized as attending between 7 and 11 classes (44% to 69%), and high adherence was categorized as attending 12 or more classes (75% or more). SOSE is considered a low-touch program, meaning participants’ designated schedules have them placed in classes where they are in contact with program personnel for 1 h each week. Outside of class times, participants are responsible for communicating with their fellow participants and turning in class assignments to coaches via email before the next class.

2.4.2. Data Analysis

Data were housed in Qualtrics, exported to Excel for organization and coding purposes, and imported into SPSS (Version 29.0 2.0, Armonk, NY, USA) for analysis [23,26]. All categorical survey variables were assessed for statistical significance using the chi-square test (or Fisher’s exact test if the assumptions for the chi-square test were not tenable). Chi-square tests were run on each program (MENTOR, GROWTH, SOSE) against each dependent variable and independent variable to examine an association between variables. Spearman correlation analysis was then performed on all variables where chi-square was found to be significant to illustrate the strength between associated variables. Additionally, logistic regression analyses were performed to determine potential predictors of successful program adherence. Logistic regression was employed as the dependent variable (adherence) was categorical and had three levels: low, moderate, and high. Variables entered into the logistic regression model included the dependent variable (adherence; low, moderate, or high) and the independent variable (programs) or the dependent variable (adherence; low, moderate, or high) and the independent variable (demographics). Logistic regression models were created based on survey response data and programs being investigated within our survey (MENTOR, GROWTH, and SOSE). Supplemental Appendix B demonstrates the structure of each logistic regression entry and how survey variables (dependent variables) were measured against each program and each demographic (independent variables). For example, motivation to stay connected was measured against the MENTOR program and age.

Qualitative data were also collected at various points within the survey, specifically where participants answered extremely low (1) or extremely high (5) to certain Likert-scale questions. Participants were given an opportunity to open-endedly express why they indicated being extremely unsatisfied (1) or extremely satisfied (5), reflecting their experiences.

3. Results

All programs were assessed for participant adherence, statistical relational and predictive significance, and post-program implementation as measured by the GLTEQ. Research questions that drove this evaluation were, What were the adherence rates of each program? and What were the post-program GLTEQ scores and categories of participants’ physical activity levels?

Table 1. Participant demographics.

Demographics (N = 42)	N (%)
Race
White	28 (66.7%)
Black	11 (26.2%)
Other	3 (7.2%)
Region
Southeast	19 (45.2%)
Southwest	4 (9.5%)
West	3 (7.1%)
Midwest	3 (7.1%)
Northeast	13 (31%)
Disability @
Mobility	28 (66.7%)
Bone or Joint	12 (28.6%)
Other	2 (4.8%)
Assistive Devices +
Manual wheelchair	16 (38.1%)
Power wheelchair	11 (26.2%)
Cane	14 (33.3%)
Caregiver/personal assistance	13 (31%)
Walker	11 (26.2%)
Ankle–foot orthosis	9 (21.4%)
Rollator	6 (14.3%)
Scooter	2 (4.8%)
Crutches	1 (2.4%)
Other	1 (2.4%)
None	1 (2.4%)
Gender
Female	32 (76.2%)
Male	10 (23.8%)
Age
30–39	3 (7.1%)
40–49	7 (16.7%)
50–59	15 (35.7%)
60–69	9 (21.4%)
70–79	8 (19%)
Level of Education
Did not complete high school	2 (4.8%)
High school degree or equivalent	1 (2.4%)
Some college or associate’s degree	13 (31%)
Bachelor’s degree	13 (31%)
Master’s degree	10 (23.8%)
Doctoral degree	3 (7.1%)
Marital Status
Married	17 (40.5%)
Widowed	2 (4.8%)
Divorced	6 (14.3%)
Separated	2 (4.8%)
Never married	11 (26.2%)
Domestic partnership	2 (4.8%)
Prefer not to answer	2 (4.8%)
Employment Status
Currently employed (no)	31 (73.8%)
Currently employed (yes)	11 (26.2%)
GLTEQ Scores (N = 37) #
Sedentary	3 (7.1%)
Moderately Active	10 (23.8%)
Active	24 (57.1%)

⁺ Assistive devices used: participants could select more than one assistive device. ^# GODIN scores: five participants’ data were omitted due to incorrect entry. ^@ Mobility Disability: spinal cord injury, stroke, multiple sclerosis, traumatic brain injury, cerebral palsy; bone or joint disability: arthritis, osteoporosis, peripheral neuropathy; Others: visual and hearing impairments.

illustrates participant demographics. The survey was delivered to 507 participants, with a total of 42 participants (8% response rate) completing the survey. The majority of the sample were white (66.7%) and female (76.2%). Participants who did respond completed the respective programs within one month to twenty-four months (MENTOR), two months to eleven months (GROWTH), and four months to sixteen months (SOSE).

Table 2. Participant adherence of MENTOR, GROWTH, and SOSE.

MENTOR Program Adherence (N = 41)
n/a	1 (2.4%)
Low	12 (29.3%)
Moderate	16 (39.0%)
High	12 (29.3%)
GROWTH Program Adherence (N = 12)
Low	3 (25%)
Moderate	5 (41.6%)
High	4 (33.3%)
SOSE Program Adherence (N = 6)
High	6 (100%)

illustrates the results of adherence between all three programs. The majority of MENTOR and GROWTH participants were found to be moderately adherent, meaning they attended between 45% and 72.5% of the total program for MENTOR and between 42% and 67% of the total program for GROWTH. All SOSE participants were found to be highly adherent to the program.

Twelve participants who completed GROWTH also completed MENTOR, and six participants who completed SOSE also completed GROWTH and MENTOR.

Table 3. Results of chi-square analyses and Spearman correlation analyses.

Virtual Health Program	X2 (df, N, p-Value)	rs (Correlation Coefficient, p-Value)
MENTOR●Disability
I enjoyed the virtual teaching component.	(8, N = 41, p = 0.007)	(−0.085, p = 0.599)
The virtual environment was accessible.	(4, N = 41, p ≤ 0.001)	(−0.295, p = 0.061)
My mindfulness instructor interacted with my class other than to give out class instruction or feedback.	(10, N = 41, p = 0.005)	(−0.304, p = 0.054)
The class content was modified or adapted to my disability or comfortability.	(8, N = 41, p = 0.006)	(−0.040, p = 0.805)
My instructors were knowledgeable about disability.	(10, N = 41, p = 0.001)	(−0.259, p = 0.102)
I had a positive relationship with my virtual instructors.	(6, N = 41, p = 0.037)	(−0.305, p = 0.053)
My instructors used appropriate vocabulary and language when giving class instruction.	(4, N = 41, p ≤ 0.001)	(−0.298, p = 0.059)
I was frustrated my instructors played videos in class instead of providing live instruction.	(6, N = 41, p = 0.002)	(−0.075, p = 0.646)
I appreciated the tailored curriculum.	(4, N = 41, p = 0.012)	(−0.235, p = 0.139)
I feel I benefitted mentally from the program.	(4, N = 41, p = 0.012)	(−0.235, p = 0.139)
I was satisfied with the virtual health program.	(6, N = 41, p = 0.002)	(−0.168, p = 0.295)
MENTOR●Age
I feel the lecture portion of nutrition classes lasted an appropriate amount of time.	(16, N = 41, p = 0.004)	(0.004, p = 0.981)
I would recommend this program to others.	(12, N = 41, p = 0.001)	(0.177, p = 0.268)
MENTOR●Race
I enjoyed the virtual teaching component.	(12, N = 41, p = 0.003)	(−0.271, p = 0.086)
I enjoyed the virtual participant interaction.	(12, N = 41, p ≤ 0.001)	(−0.060, p = 0.708)
My online group interacted well amongst ourselves.	(12, N = 41, p = 0.002)	(−0.069, p = 0.666)
The virtual environment was accessible.	(6, N = 41, p = 0.003)	(−0.112, p = 0.485)
My mindfulness instructor interacted with my class other than to give out class instruction or feedback.	(15, N = 41, p = 0.043)	(0.106, p = 0.511)
The class content was modified or adapted to my disability or comfortability.	(12, N = 41, p ≤ 0.001)	(−0.257, p = 0.104)
I enjoyed the class content.	(12, N = 41, p ≤ 0.001)	(−0.215, p = 0.177)
My instructors were knowledgeable about disability.	(15, N = 41, p ≤ 0.001)	(−0.248, p = 0.119)
My instructors used appropriate vocabulary and language when giving class instruction.	(6, N = 41, p ≤ 0.001)	(−0.080, p = 0.619)
I was frustrated when my instructors played videos in class instead of providing live instruction.	(9, N = 40, p = 0.004)	(0.160, p = 0.323)
I feel I benefitted physically from the program.	(6, N = 41, p = 0.048)	(−0.252, p = 0.112)
I was satisfied with the virtual health program.	(9, N = 41, p = 0.006)	(−0.225, p = 0.157)
I would recommend this program to others.	(9, N = 41, p = 0.004)	(0.034, p = 0.834)
MENTOR●Marital Status
I feel the lecture portion of nutrition classes lasted an appropriate amount of time.	(24, N = 41, p = 0.032)	(−0.151, p = 0.346)
My online group interacted well amongst ourselves.	(24, N = 41, p = 0.046)	(−0.014, p = 0.933)
The virtual environment was accessible.	(12, N = 41, p = 0.008)	(−0.054, p = 0.737)
My mindfulness instructor interacted with my class other than to give out class instruction or feedback.	(30, N = 41, p = 0.006)	(−0.204, p = 0.201)
My instructors were knowledgeable about disability.	(30, N = 41, p = 0.044)	(−0.188, p = 0.238)
My instructors used appropriate vocabulary and language when giving class instruction.	(12, N = 41, p = 0.011)	(−0.185, p = 0.247)
I often felt fatigued by the effort of participating in the Zoom meetings.	(24, N = 41, p = 0.023)	(0.102, p = 0.527)
I was satisfied with the virtual health program.	(18, N = 41, p = 0.003)	(−0.070, p = 0.663)
I would recommend this program to others.	(18, N = 41, p = 0.025)	(−0.293, p = 0.063)
GROWTH●Disability
The program was disability friendly.	(2, N = 12, p = 0.027)	(−0.771, p = 0.003) *
I enjoyed the virtual teaching component.	(6, N = 13), p ≤ 0.001)	(−0.839, p ≤ 0.001) *
I enjoyed the virtual participant interaction.	(6, N = 12, p = 0.008)	(−0.664, p = 0.019) *
The virtual environment was accessible.	(4, N = 12, p = 0.016)	(−0.393, p = 0.207)
Participating in the virtual health program made me feel optimistic about my health.	(6, N = 12, p = 0.029)	(−0.548, p = 0.065)
My instructor interacted with my class other than to give verbal instruction.	(6, N = 12, p = 0.008)	(−0.695, p = 0.012) *
The class content was modified or adapted to my disability or comfortability.	(6, N = 12, p = 0.008)	(−0.741, p = 0.006) *
My instructors were knowledgeable about disability.	(6, N = 12, p = 0.008)	(−0.741, p = 0.006) *
I had a positive relationship with my virtual instructors.	(2, N = 12, p = 0.002)	(−0.996, p ≤ 0.001) *
My instructors used appropriate vocabulary and language when giving class instruction.	(4, N = 12, p ≤ 0.001)	(−1.000, p = 0.001) *
I was frustrated my instructors played videos in class instead of providing live instruction.	(6, N = 12, p = 0.029)	(0.471, p = 0.122)
I appreciated my virtual health instructors.	(2, N = 12, p = 0.027)	(−0.771, p = 0.003) *
I was frustrated by other participants in my class.	(2, N = 12, p = 0.027)	(−0.771, p = 0.003) *
I was satisfied with the virtual health program.	(6, N = 12, p ≤ 0.001)	(−0.776, p = 0.003) *
GROWTH●Age
I had a positive relationship with my virtual instructors.	(2, N = 12, p = 0.027)	(−0.632, p = 0.027) *
I would like for my support network to be involved in the program with me.	(6, N = 12, p ≤ 0.001)	(−0.776, p = 0.003) *
GROWTH●Education #
The virtual environment was accessible.	(6, N = 12, p = 0.008)	(−0.658, p = 0.020) *
My instructors were knowledgeable about disability.	(9, N = 12, p = 0.040)	(−0.351, p = 0.263)
My online group interacted well amongst ourselves.	(2, N = 12, p = 0.050)	(−0.612, p = 0.035) *
I would like for my support network to be involved in the program with me.	(2, N = 6, p = 0.050)	(−0.816, p = 0.001) *
GROWTH●Race #
The program was disability friendly.	(3, N = 12, p = 0.007)	(−0.812, p = 0.001) *
I enjoyed the virtual participant interaction.	(9, N = 12, p = 0.035)	(−0.843, p ≤ 0.001) *
The class content was modified or adapted to my disability or comfortability.	(9, N = 12, p = 0.009)	(−0.830, p ≤ 0.001) *
I enjoyed the class content.	(9, N = 12, p = 0.007)	(−0.899, p ≤ 0.001) *
I had a positive relationship with my virtual instructors.	(3, N = 12, p = 0.007)	(−0.698, p = 0.012) *
I appreciated my virtual health instructors.	(3, N = 12, p = 0.038)	(−0.691, p = 0.013) *
I was frustrated by other participants in my class.	(3, N = 12, p = 0.038)	(−0.691, p = 0.013) *
I was satisfied with the virtual health program.	(9, N = 12, p = 0.025)	(−0.854, p ≤ 0.001) *
I would recommend this program to others.	(6, N = 12, p = 0.020)	(−0.812, p = 0.001) *
GROWTH●Marital Status
My instructors interacted with my class other than to give out class instruction or feedback.	(12, N = 12, p = 0.018)	(−0.533, p = 0.074)
The class content was modified or adapted to my disability or comfortability.	(12, N = 12, p = 0.016)	(−0.364, p = 0.245)
I did not feel like I needed a one-on-one appointment with my instructor.	(8, N = 12, p = 0.034)	(−0.280, p = 0.377)
I would recommend this program to others.	(8, N = 12, p = 0.022)	(−0.245, p = 0.442)
SOSE●Age
I made a commitment to my fellow participants.	(2, N = 6, p = 0.050)	(−0.775, p = 0.070)
My instructors were knowledgeable about disability.	(2, N = 6, p = 0.050)	(0.775, p = 0.070)
I appreciated my virtual health instructors.	(2, N = 6, p = 0.050)	(0.775, p = 0.070)
I appreciated the virtual health delivery.	(2, N = 6, p = 0.050))	(0.775, p = 0.070)
I was frustrated by my disability during the program.	(2, N = 6, p = 0.050)	(0.775, p = 0.070)
I would like for my support network to be involved in the program with me.	(2, N = 6, p = 0.050)	(0.000, p = 1.000)
SOSE●Education #
I would like for my support network to be involved in the program with me.	(2, N = 6, p = 0.050)	(−0.866, p = 0.026) *
SOSE●Race
The program was disability friendly.	(2, N = 6, p = 0.050)	(−0.707, p = 0.116)
I enjoyed the virtual teaching component.	(2, N = 6, p = 0.050)	(−0.707, p = 0.116)
I enjoyed the virtual participant interaction.	(2, N = 6, p = 0.050)	(−0.707, p = 0.116)

● represents the paired factorial variables being analyzed together. * represents statistical significance of the Spearman correlation coefficient, r_s. ^# represents statistically significant survey variables that are non-fluctuating, such as education level at the time of survey completion and race; program staff were notified of relevant data for quality improvement.

and the results below illustrate the statistically significant relationships that were found when the chi-square test was executed and the strength of those relationships using the Spearman correlation analyses.

Table 4. Post-program completion physical activity status of MENTOR, GROWTH, and SOSE participants measured using GLTEQ.

Program	Post-Program GLTEQ Completion	GLTEQ Scores
MENTOR	1–6 months (n = 22, 59%)	Active: n = 13, 59% Moderately active: n = 7, 32% Sedentary: n = 2, 9%
	7–12 months (n = 4, 11%)	Active: n = 4, 100%
	13–24 months (n = 11, 30%)	Active: n = 8, 73% Moderately active: n = 2, 18% Sedentary: n = 1, 9%
GROWTH	2–6 months (n = 5, 14%)	Active: n = 4, 80% Moderately active: n = 1, 20%
	7–11 months (n = 3, 8%)	Active: n = 2, 67% Sedentary: n = 1, 33%
SOSE	4–10 months (n = 1, 2.7%)	Active: n = 1, 100%
	11–16 months (n = 4, 31%)	Active: n = 4, 100%

illustrates the physical activity status of MENTOR, GROWTH, and SOSE participants measured using GLTEQ following respective program completion.

3.1. Chi-Square Tests and Spearman Correlation Analyses

Our results for correlational relationships were informed by the research question, What factors are associated with adherence in each program? All variables from the Supplemental Appendices B factorial design were tested for statistical significance. Statistical significance was determined with an a priori alpha value of p = 0.05. Chi-square tests resulted in statistically significant relationships between the GROWTH program and disability, age, education, and race categories, and between the SOSE program for education, as seen in Table 3. The MENTOR program did not produce any statistically significant relationships and therefore could not be further explored with the Spearman correlation coefficient. Where a statistically significant relationship existed among dependent variables, a Spearman correlation analysis was performed to examine the strength of the relationship.

Spearman correlations were employed as all survey variables were categorical. All statistically significant Spearman’s rho correlation coefficients (r_s) were between moderate to high (0.5 and 0.7) and high to very high (0.8 and 1.0), as seen in Table 3. Of interest, every r_s was negative, meaning that as the independent variable changed, the dependent variable would inversely relate.

3.2. Logistic Regression Analysis

No statistically significant predictor variables were found from logistic regression analyses, presumably due to the small, homogenous sample; however, the negative Spearman correlation coefficients indicate how participants perceive their involvement and experience within each program.

3.3. Results of Spearman Correlation Analyses for GROWTH and SOSE

GROWTH*Disability: as the participant’s disability status increases, participants are:

• Highly likely to perceive the program as not disability friendly (−0.771, p = 0.003);
• Highly likely to not enjoy the virtual teaching component of the program (−0.839, p ≤ 0.001);
• Highly likely to not enjoy the virtual participant interaction of the program (−0.664, p = 0.019);
• Highly likely to perceive the instructor did not interact with the class other than to give verbal instruction (−0.695, p = 0.012);
• Highly likely to perceive class content as not modified or adapted to their disability or comfortability (−0.741, p = 0.006);
• Highly likely to perceive their instructors are not knowledgeable about disability (−0.741, p = 0.006);
• Very highly likely to have a negative relationship with their instructors (−0.996, p ≤ 0.001);
• Very highly likely to not perceive instructors using appropriate language and/or vocabulary during class (−1.000, p = 0.001);
• Highly likely to not appreciate their instructors (−0.771, p = 0.003);
• Highly likely to be disengaged from other participants (−0.771, p = 0.003);
• Highly likely to not be satisfied with the virtual program (−0.776, p = 0.003).

GROWTH*Age: as the participant’s age increases, participants are:

• Highly likely to have a negative relationship with their instructors (−0.632, p = 0.027);
• Highly likely to not want their support network involved in the program (−0.776, p = 0.003).

GROWTH*Education: as the status of the level of education does not fluctuate within the sample, participants are:

• Highly likely to perceive the virtual environment as inaccessible (−0.658, p = 0.020);
• Highly likely to perceive their online group as interacting poorly amongst themselves (−0.612, p = 0.035);
• Very highly likely to not want their support network involved in the program (−0.816, p = 0.001).

GROWTH*Race: as the status of race does not fluctuate within the sample, participants are:

• Very highly likely to perceive the program as not disability friendly (−0.812, p = 0.001);
• Very highly likely to not enjoy the virtual teaching component of the program (−0.843, p ≤ 0.001);
• Very highly likely to perceive class content as not modified or adapted to their disability or comfortability (−0.830, p ≤ 0.001);
• Very highly likely to perceive the class content as not enjoyable (−0.899, p ≤ 0.001);
• Highly likely to have a negative relationship with their instructors (−0.698, p = 0.012);
• Highly likely to not appreciate their instructors (−0.691, p = 0.013);
• Highly likely to be disengaged from other participants (−0.691, p = 0.013);
• Very highly likely to not be satisfied with the virtual program (−0.854, p ≤ 0.001);
• Very highly likely to not recommend this program to others (−0.812, p = 0.001).

SOSE*Education: as the status of the level of education does not fluctuate within the sample, participants are:

• Very highly likely to not want their support network involved in the program (−0.866, p = 0.026).

3.4. Participant Experiences

Additionally, we collected qualitative participant experiences to answer the question, What were the participants’ overall experiences of each program? When given a survey question where the Likert scale was 1 ‘strongly disagree’ and 5 was ‘strongly agree’ and participants answered either a 1 or a 5, they were provided an open-ended text box where a qualitative answer could be given as a rationale for their ‘extreme’ selection. Questions where qualitative answers were collected with selected anecdotes from participants can be found in

Table 5. Exemplary quotes from participants.

Question:	Program:	Quote:
1. What would you change about the lecture portion?	MENTOR	“I felt that the lecture portion was too long”.—MM23 “Exercise was extremely long”.—MM39
2. What was inaccessible about the virtual environment?	MENTOR	“For the blind or visually impaired it was difficult”.—MM3
3. Explain why the class content was not modifiable or adaptable to your disability or comfortability.	MENTOR	“Teachers were not disabled so I did not feel they could understand the reality of a disability”.—MM3
	GROWTH	“Better messaging on activities; i.e., go shopping, take a pic in a 24 h period. I assume many others depend on transportation based on some else’s schedule. It would be helpful to have some time to absorb new content each week prior to action. The overall program is fantastic, a little adjustment would make it super fantastic”.—MM17
4. Why would you like for your support network to be involved in virtual health classes with you?	MENTOR	“For their benefit and to share/encourage one another in the journey…especially after the program ends. We could speak the same language and have the same base of understanding and help each other with our goals”.—MM37
	GROWTH	“Makes it more fun”.—MM13
	SOSE	“To see what I do”.—MM34
5. How did you physically benefit from this virtual health program?	MENTOR	“Now I have a routine that is solid”.—MM17
	GROWTH	“Knowing my limitations”.—MM16
	SOSE	“I exercise regularly. I take less medication. I have improved core strength. I use the manual wheelchair less. I have maintained a weight loss of 34.7 pounds”.—MM6
6. How did you mentally benefit from this virtual health program?	MENTOR	“Taught me great coping techniques”.—MM33
	GROWTH	“More aware”.—MM5
	SOSE	“Helped me stop overthinking”.—MM7
7. How did you spiritually benefit from this virtual health program?	MENTOR	“I was able to express my spiritual views and learn from others”.—MM6
	GROWTH	“Understanding myself”.—MM16
	SOSE	“It helped me deal with my grief losing my mother…”—MM7

. These open-ended questions and qualitative answers give insight as to what participants perceive while enrolled in the online programs, their program opinion in hindsight, what they enjoyed and did not enjoy, and where the programs met and did not meet their needs.

4. Discussion

The purpose of the present study was to analyze program factors among people with physical disabilities who participated in three online health promotion programs (MENTOR, GROWTH, SOSE) to determine which factors predicted high adherence. While no statistically significant predictor variables were found, statistically significant relationships and the strength of those relationships were found. This shows robust, meaningful interpretations about people with physical disabilities adhering to virtual health programs. Additionally, we can gather significant strategies for determining the likelihood of high adherence among people with physical disabilities for programs delivered in the future.

In a recent systematic review analyzing predictors of adherence to home-based rehabilitation, statistically significant factors associated with adherence to home-based rehabilitation included (1) perceptions of health status, (2) perceived ability to complete therapy, (3) motivation and intention to adhere, (4) previous adherence behavior, (5) current physical activity level, (6) stress and coping, (7) negative conditions or emotional experiences, and (8) social support [27]. These factors relate to people with physical disabilities adhering to the programs observed in the current study. Unlike traditional interventions, community-based programming allows participants to enter programs without any inclusion or exclusion qualifying criteria, such as current physical activity level. However, because community-based programs rely on the adherence of participants to disseminate health behavior strategies and teach successful lifestyle implementation strategies, one downfall of community-based programs is the behavioral component. Participants must volitionally express a readiness to change to fully experience the potential benefits of self-managing their health by implementing physical activity and other areas of wellness as a lifestyle routine [28].

Another layer to making health promotion programs inclusive to people with physical disabilities is the call to action for tailored content and experiences for participants. Recent evidence has shown effectiveness in tailoring interventions accompanied by behavior change techniques [29]. Specifically, the most effective behavior change techniques found among populations with chronic disease and disability were action planning, problem solving, and providing feedback on behavior, which all depend on a tailored approach [29]. MENTOR, GROWTH, and SOSE prioritize their delivery based on tailoring curriculum for participants to understand how they may all attend the same class yet finish the program with individual, meaningful, and impactful lessons learned. When delivered successfully and participants are taught how to implement strategies of self-care (i.e., physical activity, diet and nutrition, mental and emotional health), literature suggests effective lifestyle changes can be observed up to 3 years post-participation [30].

Participant adherence data were also collected. Participants were asked in the survey to subjectively report how many classes they attended out of all offered classes. These subjective scores were compared to objectively observed attendance scores using a paired-samples t-test. Results demonstrated a statistical significance between objective and subjective adherence reports from the MENTOR and GROWTH programs (t = −4.86, df = 40, p = 0.024, 95% CI: −10.81 to −4.46, and t = −3.44, df = 11, p = 0.005, 95% CI: −4.65 to −1.02, respectively). MENTOR participants correctly reported their adherence 49% of the time, GROWTH participants correctly reported their adherence 58% of the time, and SOSE participants correctly reported their adherence 100% of the time. Speculatively, SOSE did not have a statistical significance value of paired samples because the small sample and 100% attendance accurately predicted their own adherence measured against objectively observed adherence.

Major differences exist between these three programs. MENTOR is high-touch, with little to no instructor-enforced accountability outside of the parameters of class times. Participants in MENTOR are in synchronous classes five times each week, totaling five hours per week. The majority of MENTOR’S participants were moderately adherent (39%). GROWTH is a moderate-touch program where participants are in synchronous classes twice each week, totaling two hours and thirty minutes. The majority of GROWTH’s participants were also moderately adherent (41.6%). SOSE is a low-touch, highly instructor-enforced accountability program outside of the parameters of class times. In SOSE, participants are in class once each week, totaling one hour, but are required to communicate with coaches and fellow participants throughout the week. Contrasted with MENTOR and GROWTH, 100% of SOSE’s participants were high-adherers.

Participants were also asked to self-report their level of physical activity after they completed the program. Thirty-seven participants completed these questions, while five participants had their data omitted for either giving written, non-computable answers (i.e., “I walk everyday”) or leaving this section blank. As these online wellness programs focus on increasing strategies of self-care such as physical activity, mental health, and weight loss, it is promising to have the majority of participants self-report being active following completion of a program(s). Notably, as our GLTEQ data indicated, future research should focus on educating participants on ways to increase their strategies of self-care during their time within the program so that once they complete the program, they are routinely implementing self-care, such as physical activity, mental health techniques, and weight loss practices, leading to highly active, productive, and educated individuals.

We specifically asked participants to self-report their level of physical activity to quantify their perceived level of physical activity following their time in the online wellness programs that expressly teach how to maintain strategies of self-care. When compared to other variables collected, such as self-reported versus objectively observed program adherence, participant experiences, and program preferences, GLTEQ scores can theoretically confirm or deny that a participant’s perceived level of physical activity matches the likelihood of maintaining strategies taught during the program (i.e., physical activity guideline recommendations, mental health practices, weight loss strategies). If participants self-reported low GLTEQ scores indicating a sedentary lifestyle, we would expect to find poor survey reports of experiences and preferences regarding their time within the program(s) they participated in and low adherence for class attendance. Instead, 34 participants (80.9%) self-reported as maintaining moderately active-to-active levels of physical activity following the program, which parallels program adherence as MENTOR, GROWTH, and SOSE demonstrated moderate-to-high levels of program adherence at 39% (n = 16), 74.9% (n = 9), and 100% (n = 6), respectively.

Finally, the term ‘adherence’ is continually operationally defined throughout literature due to the subjective nature of assigning values to levels of completion in a program or intervention. Many researchers use the term interchangeably for retention, attendance, and task completion, among others [27,31]. This study specifically defined adherence as class attendance. By working towards a common definition and process for collecting and reporting on adherence, researchers will be able to categorically create normative pools of data for successful guidelines of program and intervention adherence. This is specifically important because basing a participant’s predictive success of lifestyle implementation on program or intervention adherence allows researchers, program instructors, and health professionals to insert behavior change techniques where applicable and to further tailor content to meet the needs of participants before they complete the program or study.

4.1. Limitations

This study had several limitations. First, the sample size of the current study, although robust, was not large enough to find definitive statistical evidence of predictive variables. Moreover, due to the exploratory nature of this study and a limited number of participants available for recruitment, a correction for multiple comparisons was not employed, and further, more sophisticated statistical analyses were not performed. Predictive variables are important and highly desired when collecting post-participation data to advise on future developments. As such, we were not able to theorize or discuss what specific program factors would be successful in future program delivery strategies. Despite the survey being kept open for over one month, this was not enough time to collect enough data to adequately search for potential predictive variables.

Secondly, the population that answered the survey questions had more of the same opinions regarding the programs than differing opinions. This could have been due to how the questions were asked or simply because the view of collective participants truly was that similar following program participation. It is important to take into consideration that low response rates and nonresponse bias could skew our study findings. Low response rates and nonresponses introduce a potential distortion of the overall view of collected and analyzed data. There is also a potential to misinterpret conclusive behavioral evidence as the parameters of interest were limited to a small homogenous sample. Future survey-based research should consider administering a perception-structured survey to a focus panel of different levels of adherers to gauge how questions are perceived by different levels of adherers and demographics.

Third, the survey response rate was low; speculatively, a one-time email was sent to the participant’s preferred email address, and then a one-time chat was sent to their online participant portal with no additional follow-up communication (i.e., phone call, additional emails, or additional chats). Also, because a third party delivered the survey to participants, the primary researcher of this study had no personal contact with participants. Hypothetically, if an additional layer of personal interaction with participants was included, such as a phone call, it is plausible that more participants would feel drawn to completing a survey for a program they participated in. It is important to note, also, that participants did not receive an incentive for completing the survey.

Finally, the design and timeline of the study required a quick turnaround from data collection to data analysis. As mentioned previously, MENTOR, GROWTH, and SOSE are all programs delivered by NCHPAD. As such, the natural order of participation for enrolling in these programs includes MENTOR first, GROWTH second, and then SOSE. This is simply because of the way programs were initially developed and not due to a pre-defined path for participants. However, because MENTOR is the first program often completed, it has the largest sample size, followed by GROWTH’s sample size, then SOSE’s sample size. Hypothetically, if this survey were to have been a rolling completion for an extended data-collection timeline, a more heterogenous subset of data would have been collected.

4.2. Future Directions

Future research should focus on participant behavior as it relates to engagement and commitment to program adherence and the use of people with lived experiences in stakeholder positions. According to current literature, some lifestyle interventions require participants to sign a contract, committing to staying engaged and actively participatory within the intervention [32,33]. Furthermore, factors affecting exercise adherence have been found to be statistically significantly linked with exercise education and theoretical constructs such as exercise attitude, perceived behavioral control/self-efficacy, perceived social support, and perceived benefits/barriers to continued activity [34]. These factors are vitally important in the delivery, uptake, and implementation of programs designed for and delivered to people with disabilities and should ultimately be informed by people with lived experiences, as the value of user-centered design and stakeholder input is maximally attributable to program delivery, quality improvement, success, and sustainment [35,36]. Finally, future research should also consider merging physical health with mental health, as mental health is a large portion of participants’ choices to adhere to health promotion programs and behavior change strategies.

Future research should also investigate the best practices for delivering perception-based surveys to an adequate sample size representing diversity. In doing this, predictor variables might be found. While our survey was created specifically to collect data applicable to specific health-promotion programs, additional steps need to be taken to consider this instrument valid and reliable. This survey must be employed among a larger sample size of people with physical disabilities who have completed the MENTOR, GROWTH, and SOSE programs to determine if responses from a larger sample mimic responses found in a small sample. This survey should also be used among unaffiliated health-promotion programs in a similar population. The current study utilized a small percentage of what is a much larger database of participants who have completed the observed studies but was limited by time, limiting availability to achieve sampling diversity. This ultimately led to finding no predictor variables of factors that affect adherence among this population. Concurrently, it would be interesting to track longitudinal data of participants who complete community-based programming and implementation of learned strategies into lifestyle behaviors to predict long-term adherence.

Finally, future research should prioritize creating and validating an adaptive version of the GLTEQ to represent inclusivity for people with physical disabilities. While the GLTEQ has been shown to be valid and reliable among people with physical disabilities, participants within this study were given contact information for reference if any questions, concerns, complaints, or discomfort were to arise while completing the GLTEQ. Though no participants expressed feelings of dissatisfaction, it is important to identify where structural ableism imposes hinderances in the generalizability and transferability of data, as well as take participants’ perspectives of their own reality into consideration.

5. Conclusions

In conclusion, statistically significant relational factors affecting the likelihood of high adherence among people with physical disabilities within health and wellness programs was found. These include participant demographics, specifically age, disability status, level of education, and race. Demographics were determined to have a statistically significant relationship with participant perceptions of program delivery, specifically programs being disability friendly, virtual environment enjoyment (teaching and participant interaction), positive relationships with instructors, adaptable content, the instructor’s knowledge about disability, the instructor’s use of appropriate language and vocabulary when delivering class content, and program satisfaction. Understanding factors that relate to participants’ perception of maintaining high adherence within community-based health and wellness programs is critical to predicting successful lifestyle implementation.