Rural Family Satisfaction With Telehealth Delivery of an Intervention for Pediatric Obesity and Associated Family Characteristics

Abstract

Objective:

To describe satisfaction with the telehealth aspect of a pediatric obesity intervention among families from multiple rural communities and assess differences in satisfaction based on sociodemographic factors.

Methods:

This is a secondary analysis of data from a pilot randomized controlled trial of a 6-month intensive lifestyle intervention (iAmHealthy) delivered through telehealth to children 6–11 years old with BMI ≥85th%ile and their parents from rural communities. Parents completed a sociodemographic survey and a validated survey to assess satisfaction with the telehealth intervention across four domains (technical functioning, comfort of patient and provider with technology and perceived privacy, timely and geographic access to care, and global satisfaction) on a 5-point Likert scale. Kruskal–Wallis nonparametric rank test were used to compare mean satisfaction scores based on parent sociodemographics.

Results:

Forty-two out of 52 parents (67% White, 29% Black, 5% multiracial, and 50% with household income <$40,000) completed the survey. Mean satisfaction scores ranged from 4.16 to 4.54 (standard deviation 0.44–0.61). Parents without a college degree reported higher satisfaction across all domains compared with parents with a college degree, including global satisfaction (mean 4.64 vs. 4.31, p = 0.03). Parents reporting a household income <$40,000 (mean 4.70) reported higher scores in the comfort with technology and perceived privacy domain compared with parents with higher incomes (mean 4.30–4.45, p = 0.04).

Discussion:

Parents from rural communities, especially those from lower socioeconomic backgrounds, were highly satisfied with the iAmHealthy telehealth intervention. These findings can be used to inform future telehealth interventions among larger more diverse populations. ClinicalTrials.gov Identifier: NCT04142034

Introduction

Pediatric overweight and obesity is a serious public health concern in the United States, with one in three children affected¹ and associations with increased risk of medical and psychological sequelae, such as cardiometabolic disease,² depression, and decreased quality of life.³ Youth from rural communities are disproportionately impacted, with an up to 30% higher odds of having overweight or obesity compared with youth living in urban communities.⁴ Therefore, it is critical to understand what types of interventions would be effective for children living in rural communities.

Multidisciplinary behavioral interventions are the cornerstone of treatment for pediatric obesity.⁵ However, multiple factors, including transportation, scheduling challenges, and a family's dissatisfaction with a program, have been noted to be significant barriers to treatment.^6,7 Family sociodemographic characteristics, such as racial and ethnic background and socioeconomic status, have also been associated with treatment success.^6–9 These factors may be compounded in rural communities, which are challenged by more negative social determinants of health (e.g., lower health literacy, less commercial insurance, and greater travel time) and a lack of local health care infrastructure.^10–12

Telehealth has been proposed as a solution for delivering obesity treatment to rural communities with the potential to reduce many of these barriers, including costs to families in terms of travel, lodging, and time away from school and work to attend visits.^13–15 Studies show that family-based behavioral interventions for pediatric obesity delivered through telehealth to rural families are both feasible and widely accepted, with families expressing high satisfaction with both the intervention and the technology.^16–19 However, study findings have been limited to pilot trials at single sites or evaluation of individual telehealth visits. In addition, there are even more limited data on family characteristics that may be associated with satisfaction with telehealth to inform what populations may benefit most from telehealth interventions.

Telehealth has grown exponentially since the coronavirus disease-19 (COVID-19) pandemic and will continue to be utilized in health care. Understanding how families experience telehealth and identifying which populations are most satisfied with telehealth can inform how best to optimize the use of telehealth in future pediatric obesity care. Therefore, the primary aim of this article is to describe family satisfaction with the telehealth aspect of a 6-month intensive behavioral obesity intervention among a diverse cohort of families of children with overweight and obesity from multiple rural sites across the country. We also sought to explore the association between family satisfaction with the telehealth intervention and family sociodemographic characteristics.

Methods

Context

This is a secondary analysis of data collected as part of a larger feasibility study of the iAmHealthy intervention as part of the Environmental Influences on Child Health Outcomes (ECHO) IDeA State Pediatric Clinical Trials Network (ISPCTN). iAmHealthy is a behavioral intervention for the treatment of pediatric obesity that has been tailored to rural children and their families.²⁰ The intervention was first implemented in partnership with rural elementary schools,²¹ but for the current trial the intervention was carried out in partnership with rural medical clinics.²² The intervention is composed of 15 hours of didactic family group sessions and 11 hours of health coaching for the family, delivered over a 6-month period. The treatment manual focused on behavioral, nutrition, and activity topics for the whole family with a positive health behavior change approach.

Families were seen in cohorts of 8–14 child/caregiver dyads per clinic. Individual and group sessions were conducted remotely over interactive televideo (Zoom™) using study-provided tablets (iPads) with provided data connections and lasted ∼1 hour per meeting. Make-up sessions were provided using the same technology. Access to educational content for individual sessions, such as handouts, was available on the tablets for families during the intervention. Families were also provided with online resources by their behavioral intervention team during individual sessions. Families were allowed to share these resources and other resources available in their communities during the group sessions. Contact information for their health coach was also provided.

Participants

Recruitment for the study was conducted in four clinics serving children from rural communities, one each in Delaware, Nebraska, South Carolina, and West Virginia. Clinics were eligible to participate if they had a previous relationship with their state's ECHO ISPCTN site, if at least 40% of their visits were covered by Medicaid, and if they provided care to at least 100 children meeting eligibility criteria. Each ISPCTN site was asked to identify one eligible clinic from their state and all identified clinics agreed to participate. Children were eligible if they were 6–11 years of age, had overweight or obesity (BMI ≥85% for age and sex), lived in a rural neighborhood (Rural Urban Commuting Area code ≥4),²³ and had a parent who was proficient in English.

Children were excluded if they already had a sibling enrolled in the trial, if they were already receiving another weight management intervention, or if they had a known physical or developmental condition that would affect ability to participate in the trial activities. Participants were children and one primary caregiver/parent (hereafter referred to as “parent”), who provided written informed consent and assent when appropriate. For the overall project, 104 parent–child dyads were enrolled across all four clinics, with 52 randomized to receive the iAmHealthy intervention plus a general health newsletter and 52 randomized to receive a general health newsletter alone. Of those who were randomized to receive the iAmHealthy intervention, 42 (80.8%) completed the postintervention survey about family satisfaction with telehealth and were included in this analysis.

Measures

Parent sociodemographic survey

All consented parents completed a 19-item parent sociodemographic survey at their screening/baseline visit. The survey was administered through REDCap²⁴ and was completed by parents on an iPad provided by a blinded research coordinator, on their own devices through a secure link e-mailed to the parents by a blinded research coordinator, or by phone call and transcribed into REDCap by a blinded research coordinator. This survey included information about parent age, sex, ethnicity, race (categorized for the purpose of analysis into Black, White, or multiracial), educational status (categorized for the purpose of analysis into college degree or no college degree), and household income (categorized for the purpose of analysis into ≤$39,999, $40,000–$79,999, and ≥$80,000 based on the distribution of income among respondents).

The survey also included items about parent preference for and access to technology, including whether the parent owned and routinely used a tablet computer, the type of internet access the parent had at home [categorized for the purpose of analysis into served (wireless), underserved (dialup, DSL, or satellite), and unserved (no internet or cellular)], and parent preference for form completion (internet, paper and pencil, or no preference). In addition to the sociodemographic survey, blinded research coordinators collected information from the parents about their zip code, which was recorded in REDCap and classified into small and isolated small rural town (rural-urban commuting area [RUCA] ≥7) or large rural town (RUCA 4–6) using the 4-tiered RUCA system.

Parent satisfaction with telehealth survey

A 12-item survey measuring parent satisfaction with the telehealth intervention was completed by parents who were randomized and completed the iAmHealthy intervention. The survey responses were collected in OpenClinica by an unblinded coordinator through a phone call 1–3 weeks after the intervention was completed. The survey was adapted from a survey developed by Myers et al.²⁵ to assess parent satisfaction with telepsychiatry for their child and reflects domains reported to be highly correlated with global satisfaction for pediatric telemedicine patients.²⁶ Adaptations to the survey only involved changes with wording to reflect the terms used in the trial, including replacing “specialist” on the original survey with “health coach” on this survey and replacing “television or telemedicine” on the original survey with “iPad or iPad meetings” on this survey.

The survey consists of four domains: (1) four items related to technical functioning; (2) three items related to comfort of patient and provider with the technology and perceived privacy; (3) three items related to timely and geographic access to care; and (4) two items related to global satisfaction. Each item was rated by the parent on a 5-point Likert scale, with 1 representing very low satisfaction and 5 representing very high satisfaction. In the study by Myers et al., the survey demonstrated strong internal consistency (inter-correlations for all 12 items of r = 0.262–0.712) and strong correlations (>0.50) of each item with global satisfaction.

Data Analysis

Parent sociodemographic survey results are described with count and proportions and parent satisfaction survey results are described with mean scores for each domain and overall. Kruskal–Wallis nonparametric rank test were used to compare mean satisfaction scores based on parent sociodemographics. For these comparisons, two-sided p-values <0.05 were considered significant. Pearson's correlation tests were used to describe the internal consistency of the items on the parent satisfaction survey. All the analyses were carried out with SAS software version 9.4.

Results

Parent Sociodemographics

Table 1 shows the sociodemographic characteristics of the 42 parents who completed the telehealth satisfaction survey. All parents who completed the survey were female, and of diverse racial backgrounds (67% White, 29% Black, 5% multiracial, with 12% of Hispanic ethnicity). Half of the participants did not have a college degree and/or had a household income <$40,000. Around 60% of participants used a tablet computer on a routine basis, whereas only 31% had wireless access to the internet. Most participants were from large rural cities/towns and preferred to complete data collection forms on the internet.

Table 1.

Sociodemographic Characteristics of Parent Completing Survey

	Total
	N	%
Age (years), median (range)	37.0 (26.0–69.0)
Sex (female)	42	100.0
Ethnicity
Hispanic	5	11.9
Not Hispanic	36	85.7
Unknown/prefer not to answer	1	2.4
Race
Black or African American	12	28.6
White	28	66.7
Multiracial	2	4.8
Education (college degree vs. non-college degree)
College degree	21	50.0
No college degree	21	50.0
Household income
≤$39,999	21	50.0
$40,000–$79,999	10	23.8
≥$80,000	11	26.2
Routine electronic device usage
Tablet computer	25	59.5
Internet access
Served (wireless)	13	31.0
Underserved (dialup, DSL, satellite)	24	57.1
Unserved (no internet, cellular)	5	11.9
Preference for form completion
Forms on the internet	27	64.3
Paper and pencil forms	6	14.3
No preference	9	21.4
Degree of rurality
Large rural city/town (RUCA 4–6)	37	88.1
Small and isolated small rural town (RUCA ≥7)	5	11.9
Total	42	100.0

RUCA, rural-urban commuting area.

Parent Satisfaction With Telehealth Intervention

Table 2 shows the overall mean for each satisfaction survey domain and its associated questions. Participants were highly satisfied in all domains, with comfort of patient and provider with technology and perceived privacy domain having the highest satisfaction (mean = 4.54), followed by overall global satisfaction (mean = 4.48). The timely and geographic access to care domain had the lowest, but still positive satisfaction (mean = 4.16).

Table 2.

Parent Satisfaction With Telehealth Intervention Survey Scores

Survey domain and item	Overall, mean (SD)^a
Technical functioning	4.46 (0.44)
I could see the health coach very well.	4.60 (0.50)
I could hear the health coach very well.	4.57 (0.50)
I could understand the health recommendations.	4.52 (0.51)
The iPad meetings were as good as an in-person visit.	4.14 (0.72)
Comfort of patient and provider with technology and perceived privacy	4.54 (0.45)
I could talk comfortably with the health coach.	4.67 (0.48)
I felt confident that my child's information was not being overheard by others in the room.	4.45 (0.55)
I felt the health coach was comfortable with seeing my child over the iPad.	4.50 (0.55)
Timely and geographic access to care	4.16 (0.61)
The iPad allowed my child to see a health coach sooner.	4.26 (0.66)
My child would not have received services of a health coach without using the iPad meetings.	4.07 (0.87)
My child received the help he/she needed because of our iPad meetings with the health coach.	4.14 (0.72)
Global satisfaction	4.48 (0.49)
I would be willing to have my child see a health coach or medical provider using iPad meetings again in the future.	4.43 (0.55)
Overall, I am satisfied with the quality of services provided by the iPad meetings.	4.52 (0.55)

Based on Likert scale from 1 (Strongly disagree) to 5 (Strongly agree).

SD, standard deviation.

Comparisons of Parent Satisfaction Based on Sociodemographic Factors

Parent's satisfaction with all aspects of the telehealth intervention differed based on their level of education, with parents without a college degree reporting greater satisfaction across all domains than those with college degrees (Tables 3 and 4). Parent's comfort and trust in the privacy of the procedures significantly differed by household income (p = 0.04), with parents reporting the lowest income (<$40,000) having the most comfort and trust (mean = 4.70) followed by the highest income families (>$80,000; mean = 4.45), and then middle-income families (mean = 4.30).

Table 3.

Comparison of Mean Parent Satisfaction Survey Scores by Sociodemographic Characteristics

Parent sociodemographic	Technical functioning	Comfort of patient and provider with technology and perceived privacy	Timely and geographic access to care	Global satisfaction
Ethnicity	0.949	0.521	0.118	0.685
Race	0.741	0.636	0.970	0.674
Education	0.007	0.005	0.044	0.029
Household income	0.421	0.039	0.311	0.512
Routine tablet device usage	0.598	0.566	0.078	0.503
Internet access	0.602	0.377	0.271	0.952
Preference for form completion	0.989	0.825	0.694	0.899
Degree of rurality	0.021	0.013	0.074	0.066

Kruskal–Wallis nonparametric rank test (p values reported).

Table 4.

Comparison of Family Satisfaction Survey Domain Scores by Education, Household Income, and Degree of Rurality

Survey domain	Overall, mean (SD)	Education			Household income				Degree of rurality
Survey domain	Overall, mean (SD)	College degree	No college degree	p^*	<$40,000	$40,000–$79,999	≥$80,000	p^*	Large rural city/town	Small and isolated small rural town	p^*
Technical functioning	4.46 (0.44)	4.27 (0.41)	4.64 (0.41)	0.007	4.56 (0.40)	4.40 (0.44)	4.32 (0.51)	0.421	4.52 (0.44)	4.00 (0.00)	0.021
Comfort of patient and provider with technology and perceived privacy	4.54 (0.45)	4.35 (0.40)	4.73 (0.42)	0.005	4.70 (0.42)	4.30 (0.37)	4.45 (0.48)	0.039	4.60 (0.44)	4.07 (0.15)	0.013
Timely and geographic access to care	4.16 (0.61)	3.95 (0.62)	4.37 (0.55)	0.044	4.30 (0.55)	4.00 (0.47)	4.03 (0.81)	0.311	4.21 (0.63)	3.80 (0.30)	0.074
Global satisfaction	4.48 (0.49)	4.31 (0.46)	4.64 (0.48)	0.029	4.57 (0.48)	4.40 (0.46)	4.36 (0.55)	0.512	4.53 (0.50)	4.10 (0.22)	0.066

Kruskal–Wallis nonparametric rank test.

Although there were significant differences in parent satisfaction with the telehealth intervention across multiple domains based on degree of neighborhood rurality, the findings are difficult to interpret as only five participants met the criteria for living in a small rural town. No significant differences were noted based on race, ethnicity, or indicators of technology preference or access.

Internal Consistency

Supplementary Table S1 shows the correlation among the four domains on the parent satisfaction survey. There was strong internal consistency, as indicated by significant inter-correlations for the four domains (r = 0.645–0.830, Spearman's rho).

Discussion

This study demonstrates family satisfaction with the telehealth aspects of an intensive group-based lifestyle behavior intervention targeting families of children with obesity from multiple rural sites as part of a randomized controlled trial. More than 80% of families randomized to receive the telehealth intervention completed a survey and expressed high satisfaction with the telehealth intervention. This included satisfaction across multiple domains, including comfort and perceived privacy, technical function, and timely and geographic access to care. These findings are consistent with other studies, which describe telehealth as a highly accepted mode of delivery for obesity treatment in both rural and nonrural communities,^{16–19,27–31} with families reporting that telehealth visits are easy to use, convenient, and helpful in enhancing their ability to reach their lifestyle goals.^30–32

Although not assessed in this study, previous studies have reported conflicting findings as to whether telehealth is preferred over traditional in-person visits. In some studies, families have reported that telehealth visits for pediatric obesity management are comparable¹⁸ or even more preferable to traditional face-to-face visits,²⁷ but other studies found that when given the choice between the two, patients chose in-person visits more frequently.^33,34 Although most of these studies described satisfaction with individual telehealth visits at single sites, ours is one of the first studies to describe family satisfaction with an intensive behavioral intervention delivered over 6 months at multiple sites across the country within the context of a rigorous network-based study.

Interestingly, parents with lower socioeconomic status, including those with lower household income and those without a college degree, reported higher satisfaction with the telehealth intervention compared with parents with higher household income and with a college degree. Studies have shown that time, scheduling, and transportation challenges are parent-reported reasons for attrition in obesity care,⁶ with one study citing that most patients reported they would not seek obesity care if telehealth visits were not available due to distance concerns.²⁷ Although particular reasons for satisfaction were not assessed in this study, it is possible that higher satisfaction was found among parents with lower income and those without a college degree, because telehealth can help mitigate these access challenges.

It is also possible that families with higher income levels or a college degree had more concerns related to cybersecurity and data privacy due to increased familiarity with these risks, which may be reflected in lower satisfaction scores in this domain. To date, studies have been inconsistent whether telehealth has been successful among socioeconomically disadvantaged communities.^35–39 Our findings, therefore, demonstrate the potential for telehealth to have an equalizing impact on health care delivery and access, especially for previously underserved populations such as rural communities and socioeconomically disadvantaged communities. However, systemic barriers to telehealth use that disproportionally affect marginalized groups, including lower digital literacy and unequal access to internet-enabled devices and broadband internet, must continue to be addressed to ensure equitable access, uptake, adherence, and effectiveness.^40–42

There were no significant differences in satisfaction with telehealth based on race and ethnicity, which parallels findings from other studies also conducted during the COVID-19 pandemic.^43–45 There were also no significant differences in satisfaction with telehealth based on access to high-speed internet. Single area studies have shown conflicting results on whether access to high-speed internet impacts satisfaction with telehealth,^44,45 but there are limited studies involving multiple geographies such as this study. Importantly, there were high levels of satisfaction among all families, no matter the degree of rurality of their neighborhood, but there were too few families from very small rural towns to evaluate whether they differed in satisfaction from families from larger rural towns.

Strengths and Limitations

This article describes parent satisfaction with the telehealth components of an evidence-based telehealth intervention delivered over 6 months to families from four geographically diverse rural areas as part of a multisite randomized controlled trial. The secondary analysis includes comprehensive data collection of important family-reported measures, such as perceived comfort and access to care, and objective measures, such as rurality. The design of this interventional study helps address the historical lack of access to clinical care for rural populations.^10–12

Despite these strengths, there are limitations to this study. The sample size for this feasibility study was small, which limits our ability to make definitive conclusions and conduct multivariate analyses. For example, the sample could not be further stratified to assess differences in satisfaction based on the child's weight class. However, the nearly universal high levels of satisfaction with telehealth among our cohort indicates that it is a method to continue to explore in future research. There may be a selection bias in that highly under-resourced families, including those with poor access to care, may have not been reached or did not choose to participate in the study, although participants were recruited from a large sample of patients who had been seen within the past year in rural clinics.

Moreover, participants in this study were provided a tablet computer with cellular connection, which may have impacted their satisfaction with the telehealth intervention. Next, the survey was not completed by every participant randomized to the iAmHealthy intervention. There is a possible survivorship bias in that participants who did not complete the intervention also did not complete the survey, which may possibly reflect and remove low satisfaction; however, 81% of participants did complete both the intervention and the survey. Furthermore, we specifically targeted rural populations with this study, as iAmHealthy is an intervention tailored to rural communities who have limited access to health care. Because of the feasibility nature of this study, participants were also limited to families that were proficient in English. Therefore, our results cannot be generalizable to nonrural populations who are not proficient in English. Finally, we did not comprehensively assess reasons for satisfaction with telehealth in this study, satisfaction over the course of the study, if degree of participation in the intervention affected satisfaction scores, or compare satisfaction of this telehealth intervention with a similar in-person intervention. Future research focused on telehealth satisfaction should include a larger and more diverse population with under-represented patients, including those who are non-English speaking, those who live in communities with varying degrees of rurality, and should further assess the reasons contributing to satisfaction with telehealth, including degree of participation and qualitative data to inform interpretation of satisfaction scores and satisfaction with the intervention over time.

Conclusion

With the ever-growing expansion of telehealth, it is important to understand what types of interventions can be delivered by telehealth and who might benefit most from telehealth interventions. The findings from our feasibility study contribute to our understanding of family satisfaction with the use of telehealth for pediatric obesity interventions. Importantly, our study is one of the first to describe high levels of satisfaction with the telehealth aspects of a 6-month intensive behavioral intervention for obesity, not just individual telehealth visits. It is also one of the first studies to evaluate satisfaction with an intervention specifically tailored for families from rural communities across the country. Finally, it is also one of the first studies to describe high levels of satisfaction among socioeconomically disadvantaged populations. These findings point to the need to continue to develop and test telehealth interventions for underserved rural children with obesity.

Footnotes

Authors' Contributions

Conceptualization, methodology, and writing—review and editing by L.N., T.-L.P., L.F., D.C., L.C., E.D., C.W.H., T.V.W., P.M.D., and A.M.D. Formal analysis by D.C. Supervision by P.M.D. and A.M.D. Visualization by T.-L.P. and D.C. Writing original draft by L.N., T.-L.P., L.F., D.C., L.C., E.D., C.W.H., T.V.W., and A.M.D.

Funding Information

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article. This study was supported by the Environmental influences on Child Health Outcomes Program, the National Institutes of Health, Office of the Director [Grant Nos. UG1 OD04943, UG1 OD024945, UG1 OD024950, UG1 OD024953, UG1 OD024956, UG1 OD024958, UG1 OD030016, UG1 OD030019, and U24 OD024957].

Author Disclosure Statement

No competing financial interests exist.

Supplementary Material

References

Fryar

, Carroll

, Afful

Prevalence of overweight, obesity, and severe obesity among children and adolescents aged 2–19 years: United States, 1963–1965 through 2017–2018. Report, National Center for Health Statistics, Hyattsville, MD, USA, 2021.

Friedemann

, Heneghan

, Mahtani

, et al. Cardiovascular disease risk in healthy children and its association with body mass index: Systematic review and meta-analysis. BMJ, 2012; 345:e4759.

Phillips

, Gaudette

, McCracken

, et al. Psychosocial functioning in children and adolescents with extreme obesity. J Clin Psychol Med Settings, 2012; 19:277–284.

Liu

, Jones

, Sun

, et al. Diet, physical activity, and sedentary behaviors as risk factors for childhood obesity: An urban and rural comparison. Child Obes, 2012; 8:440–448.

Grossman

, Bibbins-Domingo

, Curry

, et al. Screening for obesity in children and adolescents: US Preventive Services Task Force recommendation statement. JAMA, 2017; 317:2417–2426.

Hampl

, Demeule

, Eneli

, et al. Parent perspectives on attrition from tertiary care pediatric weight management programs. Clin Pediatr, 2013; 52:513–519.

Skelton

, Goff

Jr , Ip

, et al. Attrition in a multidisciplinary pediatric weight management clinic. Child Obes, 2011; 7:185–193.

De Niet

, Timman

, Jongejan

, et al. Predictors of participant dropout at various stages of a pediatric lifestyle program. Pediatrics, 2011; 127:e164–e170.

Zeller

, Kirk

, Claytor

, et al. Predictors of attrition from a pediatric weight management program. J Pediatr, 2004; 144:466–470.

10.

Randolph

, Pathman

. Trends in the rural-urban distribution of general pediatricians. Pediatrics, 2001; 107:e18.

11.

Bolin

, Bellamy

, Ferdinand

, et al. Rural healthy people 2020: New decade, same challenges. J Rural Health, 2015; 31:326–333.

12.

Herendeen

, Schaefer

. Practical applications of telemedicine for pediatricians. Pediatr Ann, 2009; 38:567–569.

13.

Chu

, Cram

, Pang

, et al. Rural telemedicine use before and during the COVID-19 pandemic: Repeated cross-sectional study. J Med Internet Res, 2021; 23:e26960.

14.

Marcin

, Shaikh

, Steinhorn

. Addressing health disparities in rural communities using telehealth. Pediatr Res, 2016; 79:169–176.

15.

Wood

, White

, Peebles

, et al. Outcomes of a rapid adolescent telehealth scale-up during the COVID-19 pandemic. J Adolesc Health, 2020; 67:172–178.

16.

Irby

, Boles

, Jordan

, et al. TeleFIT: Adapting a multidisciplinary, tertiary-care pediatric obesity clinic to rural populations. Telemed J E Health, 2012; 18:247–249.

17.

Davis

, James

, Boles

, et al. The use of TeleMedicine in the treatment of paediatric obesity: Feasibility and acceptability. Matern Child Nutr, 2011; 7:71–79.

18.

Mulgrew

, Shaikh

, Nettiksimmons

. Comparison of parent satisfaction with care for childhood obesity delivered face-to-face and by telemedicine. Telemed J E Health, 2011; 17:383–387.

19.

Davis

, Sampilo

, Gallagher

, et al. Treating rural paediatric obesity through telemedicine vs. telephone: Outcomes from a cluster randomized controlled trial. J Telemed Telecare, 2016; 22:86–95.

20.

Davis

, James

, Curtis

, et al. Pediatric obesity attitudes, services, and information among rural parents: A qualitative study. Obesity, 2008; 16:2133–2140.

21.

Davis

, Beaver

, Dreyer Gillette

, et al. iAmHealthy: Rationale, design and application of a family-based mHealth pediatric obesity intervention for rural children. Contemp Clin Trials, 2019; 78:20–26.

22.

Davis

, Darden

, Snowden

, et al. Rationale and protocol for a cluster randomized, cross-over trial of recruitment methods of rural children in primary care clinics: A feasibility study of a pediatric weight control trial in the IDeA States Pediatric Clinical Trials Network. Contemp Clin Trials, 2021; 107:106476.

23.

United States Department of Agriculture. Rural-Urban Commuting Area Codes. 2020. https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes (Last accessed May 26, 2022).

24.

Harris

, Taylor

, Thielke

, et al. Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform, 2009; 42:377–381.

25.

Myers

, Valentine

, Melzer

. Child and adolescent telepsychiatry: Utilization and satisfaction. Telemed J E Health, 2008; 14:131–137.

26.

Dick

, Filler

, Paven

. Participant satisfaction and comfort with multidisciplinary pediatric telemedicine consultations. J Pediatr Surg, 1999; 37:137–142.

27.

Fleischman

, Hourigan

, Lyon

, et al. Creating an integrated care model for childhood obesity: A randomized pilot study utilizing telehealth in a community primary care setting. Clin Obes, 2016; 6:380–388.

28.

Schiel

, Beltschikow

, Radón

, et al. Long-term treatment of obese children and adolescents using a telemedicine support programme. J Telemed Telecare, 2008; 14:13–16.

29.

Bala

, Price

, Horan

, et al. Use of telehealth to enhance care in a family-centered childhood obesity intervention. Clin Pediatr, 2019; 58:789–797.

30.

Chai

, Collins

, May

, et al. Fidelity and acceptability of a family-focused technology-based telehealth nutrition intervention for child weight management. J Telemed Telecare, 2021; 27:98–109.

31.

Bohlin

, Hagman

, Klaesson

, et al. Childhood obesity treatment: Telephone coaching is as good as usual care in maintaining weight loss—A randomized controlled trial. Clin Obes, 2017; 7:199–205.

32.

Tripicchio

, Ammerman

, Neshteruk

, et al. Technology components as adjuncts to family-based pediatric obesity treatment in low-income minority youth. Child Obes, 2017; 13:433–442.

33.

Siegel

, Stackpole

, Kirk

, Kharofa

. Families chose in-person visits over telehealth for pediatric weight management during the COVID-19 pandemic. Childhood Obes, 2022; 18:572–575.

34.

Predmore

, Roth

, Breslau

, et al. Assessment of patient preferences for telehealth in post–COVID-19 pandemic health care. JAMA Netw Open, 2021; 4:e2136405.

35.

Du Toit

, Van Der Linde

, Swanepoel

. mHealth developmental screening for preschool children in low-income communities. J Child Health Care, 2021; 25:573–586.

36.

Adeli

, Bloom

. Implementing telemedicine visits in an underserved ophthalmology clinic in the COVID-19 era. J Prim Care Community Health, 2021; 12:1–6.

37.

Raffenaud

, Gurupur

, Fernandes

, et al. Utilizing telemedicine in oncology settings: Patient favourability rates and perceptions of use analysis using Chi-Square and neural networks. Technol Health Care, 2019; 27:115–127.

38.

Pritchett

, Haddad

. Avoiding the “Faux Equalizer.”. Mayo Clin Proc, 2021; 96:2963–2965.

39.

Shah

, Badawy

. Telemedicine in pediatrics: Systematic review of randomized controlled trials. JMIR Pediatr Parent, 2021; 4:e22696.

40.

Veinot

, Mitchell

, Ancker

. Good intentions are not enough: How informatics interventions can worsen inequality. J Am Med Inform Assoc, 2018; 25:1080–1088.

41.

Badawy

, Radovic

. Digital approaches to remote pediatric health care delivery during the COVID-19 pandemic: Existing evidence and a call for further research. JMIR Pediatr Parent, 2020; 3:e20049.

42.

Rodriguez

, Shachar

, Bates

. Digital inclusion as health care—Supporting health care equity with digital-infrastructure initiatives. N Engl J Med, 2022; 386:1101–1103.

43.

Waqar-Cowles

, Chuo

, Weiss

, et al. Evaluation of pediatric rheumatology telehealth satisfaction during the COVID-19 pandemic. Pediatr Rheumatol, 2021; 19:170.

44.

Kyle

, Blendon

, Findling

, et al. Telehealth use and satisfaction among U.S. households: Results of a national survey. J Patient Exp, 2021; 8:1–7.

45.

Thomson

, Mariani

, Williams

, at al. Factors associated with use of and satisfaction with telehealth by adults in rural Virginia during the COVID-19 pandemic. JAMA Netw Open, 2021; 4:e2119530.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB