A mixed-methods examination of early care and education providers' routine and pandemic use of digital technology for nutrition training: Insights for digital nutrition training policies and practices

Abstract

Objectives

This study examined early care and education (ECE) providers’ routine and pandemic-related use of digital technology for nutrition training and explored their attitudes and perceptions toward digital technology using the Technology Acceptance Model as a guiding framework to inform future training delivery.

Methods

We employed a convergent mixed-methods design consisting of a national survey and semi-structured qualitative interviews with licensed ECE providers. Inclusion criteria were licensed/registered ECE providers, English-speaking, age ≥18, and currently caring for children for pay. Exclusion criteria included unlicensed providers and inactive programs. Survey data was analyzed using chi-square tests to examine differences by variable type and logistic regressions to examine predictors of digital technology use. Qualitative analysis underwent thematic analysis using a blended deductive/inductive approach guided by the Braun and Clarke multi-step method. Integrated analyses were conducted and shown through a joint display to contextualize the findings.

Results

Most ECE providers (97.9%) had access to high-speed internet, and 95.1% used electronic devices for work-related purposes. Approximately 83.2% had received nutrition training since the COVID-19 pandemic began, with 53.2% accessing training via virtual platforms. ECE providers enrolled in the Child and Adult Care Food Program were more likely to receive nutrition training digitally than those not enrolled (OR = 24.0, p < .001). As ECE providers’ age increases, the odds of receiving training digitally decreased (OR = 0.52, p = .043). Providers reported that digital training improved accessibility and flexibility. However, the study also identified challenges such as reduced social connectedness, technical difficulties, and limited knowledge or awareness of digital nutrition training programming.

Conclusion

Findings suggest that digital technology enhances access to nutrition training for ECE providers, yet barriers remain. Future efforts should focus on expanding digital training programming, improving technological support, and balancing digital and in-person training to optimize hands-on learning and social connectedness.

Keywords

Early care and education digital technology nutrition training mixed-methods research

Introduction

Early care and education (ECE) providers are critical for our society, meeting the childcare needs of nearly 12 million families with young children under the age of 5.¹ ECE providers are tasked with supporting young children's physical, social, cognitive, and emotional development. A significant part of ECE providers’ care includes meeting nutrition needs and cultivating a healthy mealtime environment, thereby playing an important role in shaping positive and healthy practices among young children.^2,3 To support ECE providers, the Child and Adult Care Food Program (CACFP), a federal nutrition assistance program run by the USDA's Food and Nutrition Service, reimburses CACFP participating ECE providers for purchasing nutritious foods, especially for foods served to children from families who are low income.⁴ Studies have shown the benefits to CACFP participation, including higher quality meals being served and consumed,^5–7 reduction in food insecurity for low-income families,^8,9 and links to healthier BMIs.¹⁰

Along with these reimbursements, ECE providers can receive in-person or virtual nutrition training often administered by CACFP state or sponsoring agencies in addition to routine state licensing training requirements.¹¹ Although the frequency and modality for nutrition training vary by state, nutrition training ensures providers have the latest nutrition information to support children's health. Topics include meal pattern compliance, menu planning, food safety, and specialized training for infant feeding. State or sponsoring agencies can host workshops and webinars; training delivery methods vary depending on state and available resources. ECE providers can also access asynchronous digital nutrition training content from the National CACFP Sponsors Association's website. However, not all ECE providers access or utilize this digital content, and the reasons behind this lack of access remain unclear.¹¹

Research gap

Training is a cornerstone of childcare licensing systems, serving as a foundational element to ensure quality practices and compliance with health and safety standards. Many states require pre-service qualifications and ongoing annual training for childcare providers, emphasizing topics such as nutrition, health, and safety.^12,13 Research has shown that participation in tailored digital nutrition training significantly enhanced providers’ adherence to nutrition best practices and improved provider nutrition communication skills, fostering healthier environments for early childhood obesity prevention.^14,15

The COVID-19 pandemic disrupted traditional in-person nutrition training for ECE providers, with many states implementing stay-at-home orders. At the same time, COVID-19-related nutrition policies for the ECE setting emerged, such as meal pattern flexibilities, making nutrition training crucial during the pandemic.^12,13 Digital technology served as a way to connect providers to online training and reduce participation barriers during the pandemic. However, little is known about how ECE providers used digital technology for nutrition training, their perceptions of its benefits and challenges within the context of the pandemic, and their routine use to support nutrition training pre-pandemic.

Beyond the pandemic, informed guidance is needed on how to best leverage digital technology in the ECE setting for work-related purposes, including nutrition training. Understanding the factors that influence ECE providers’ adoption and sustained use of digital nutrition training is essential for developing effective, accessible professional development programs that can improve nutrition practices in early care settings.

Theoretical framework

The technology acceptance model (TAM) is a helpful theoretical framework for understanding digital technology use among ECE providers. The model posits that perceived usefulness and ease of use of digital technology directly predict attitudes and intent to use. Attitudes can also predict intent to use, and intent predicts the actual use of digital technology.¹⁶ The TAM has been used to guide studies examining adoption of health care and education technologies, as well as providing guidance on how to improve on the delivery of digital health services.^17–21

Research objectives

This study examines ECE providers’ routine and pandemic use of digital technology and explores attitudes and perceptions of using digital technology for nutrition training. For this study, digital technologies are devices, electronics, and platforms, including social media. Examples of devices and electronics include smartphones, computers, and the iPad. Examples of platforms include mobile apps, websites, and social media platforms like YouTube, Facebook, and Instagram.

Specific research objectives include:

Assessing ECE providers’ access to and routine use of digital technology for work-related purposes, including nutrition training.

Examining changes in digital technology use for nutrition training during the COVID-19 pandemic.

Exploring ECE providers’ perceptions of ease of use, usefulness, and attitudes toward digital nutrition training through the lens of the TAM.

Identifying barriers and facilitators to digital technology adoption for nutrition training among ECE providers.

This study fills a critical gap by providing empirical evidence on digital training adoption patterns and user experiences among a national sample of ECE providers. By applying the TAM, we identify specific factors influencing technology adoption, offering actionable insights for policymakers, training developers, and CACFP agencies to enhance nutrition training accessibility and effectiveness.

Methods

Study design

We used a convergent mixed-method design, in which surveys were conducted, and semi-structured qualitative interviews were used to contextualize the survey data. This approach allowed us to better understand the survey results and ECE providers’ experiences with digital technology for nutrition training before and during the COVID-19 pandemic. The study followed the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines for reporting qualitative research (see supplement 1).

Data collection

Instrument development

The TAM served as the guiding theoretical framework for developing both the survey and the semi-structured interview guide (see supplemental information). While we did not directly adopt or adapt measures from a single validated TAM instrument, we reviewed multiple validated TAM-based instruments from prior research to inform the development of survey items and interview questions.^20–24 Specifically, we examined how TAM constructs (perceived usefulness, perceived ease of use, attitudes, and behavioral intentions) had been operationalized in previous studies across healthcare and educational settings. Using this knowledge, we created original survey items and interview questions tailored to the specific context of digital technology use for nutrition training among ECE providers. This approach allowed us to maintain theoretical alignment with TAM while ensuring questions were contextually appropriate for our study population and research objectives.

We developed a 60-item self-administered online survey that assesses digital technology use for (1) work-related purposes, (2) promotion of healthy nutrition and active play, (3) nutrition and active play training, (4) family engagement, and (5) nutrition training during COVID-19, and included a demographic questionnaire. Each survey had a unique identifier number (see supplement 2). We also developed a semi-structured interview guide to explore ECE providers’ perceptions of ease of use and usefulness, attitudes, and perceptions of support and barriers to digital technology use for nutrition training. The interview guide was explicitly designed to capture the core TAM constructs through open-ended questions that allowed for in-depth exploration of providers’ experiences with digital nutrition training (see supplement 3).

Establishing face validity

To establish face validity, we employed a multi-step process. First, content experts within the study team, including researchers with expertise in ECE, nutrition, digital health, and survey methodology, reviewed both TAM-based instruments from prior research and ECE-based surveys to inform instrument development. The team examined the survey and interview guide and provided iterative feedback on item clarity, relevance, and comprehensiveness. Second, we pilot tested both the survey and interview guide with an ECE provider who met our eligibility criteria. The pilot participant completed the survey and participated in a practice interview, after which we solicited feedback on question comprehension, response options, survey length, technical functionality, and overall experience. Based on this pilot testing, we incorporated recommended changes to improve clarity and user experience before launching the full study.

Recruitment and data collection

We recruited a national convenience sample of ECE providers via recruitment flyers that were sent to ECE providers through the National CACFP Sponsors Association newsletter, social media, partnership websites, state agency listservs, and direct emails to ECE providers that had collaborative relationships with members of the Nutrition Obesity Policy Research and Evaluation Network (NOPREN). Each flyer contained a link and QR code to the TAM-based self-administered online survey delivered via REDCAP. REDCap is a secure, web-based software platform that supports data capture for research studies.²⁵ The National CACFP Sponsors Association sent biweekly reminders to potential participants to take the survey.

The inclusion criteria for eligibility included 1) Licensed or registered ECE providers within their state 2) at least 18 years of age 3) Able to read and speak English 4) currently providing childcare for pay at the time of study. Exclusion criteria included 1) unlicensed or unregistered with the state, 2) unable to speak and read English, and 3) inactive childcare operations (temporarily or permanently closed). At the end of the survey, we selected a convenience sample of participants who indicated interest in completing a 45-min follow-up semi-structured interview. The semi-structured interviews were conducted by the Principal Investigator, PhD-prepared, first author, a female Assistant Professor with significant training in community engaged and ECE research and no prior relationship with the participants before the study launched. The interviews aimed to understand barriers, facilitators, attitudes, perceived ease of use, usefulness, and intention to use digital technology to receive nutrition training. We sent emails with a proposed date and time for an interview based on the participants’ survey responses regarding the best day and time for an interview. All interviews were audio recorded and transcribed using the Zoom platform. Each transcription was de-identified and reviewed to identify discrepancies between the audio recordings and the transcripts. The study was conducted between June and December 2022.

For surveys alone, all participants were placed in a virtual raffle, and 30 unique Identification numbers were randomly selected to receive a $100 electronic gift card to Amazon. Google's random number generator was used to determine the numbers chosen for the prizes.²⁶ All participants who participated in an interview received a $25 gift card to Amazon.

Data analyses

We summarized measures of central tendency (mean, median, mode, percentages) stratified by each variable (i.e. Digital Technology Use, Type of Digital Technology Use, Training, type of ECE setting (Family Child Care Home vs. Center-based Child care). Descriptive statistics were used to describe participants’ demographic characteristics. Chi-square tests were used to examine differences between organization type in reasons for using digital technology. Logistic regression examined organization and provider characteristics as predictors of who receives training on nutrition. STATA 16 was used to analyze the quantitative data.²⁷

The phenomenological constructivist perspective was our theoretical framing for the qualitative analysis and interpretation approach. This approach allows us to dive deeply into the contextual dimensions of technology use, amplify providers’ voices, and develop person-centered, context-sensitive recommendations for digital nutrition training in the ECE setting.²⁸ The Braun and Clarke multi-step method guided our analysis, incorporating a blended deductive and inductive analytic approach.²⁹ In the first step, two experienced coders (LF and WC) read the transcript content multiple times to familiarize themselves with the data. Then, the coders used a multi-cycle qualitative coding approach for thematic development and analysis.³⁰ In the first cycle, the coders developed an a priori coding framework guided by TAM to capture the main concepts in the interview guide: Ease of Use/Usefulness, Attitudes, Intent, Barriers, and Facilitators. Then, the two coders independently used a combination of description, in vivo, and emotion coding. Second-cycle coding was done using a consultative process with the two coders. Coding sessions were held weekly to review codes for each participant, and detailed notes were taken to track the coding decisions made, including resolving minor discrepancies and identifying new codes. Content analysis under each code was conducted to formulate themes. Data saturation was achieved as determined by the non-emergence of new themes. This determination was made collaboratively by the coding team during our iterative weekly debriefing sessions, following established guidelines for assessing saturation in qualitative research. Themes were merged with the survey data through a joint display of the key findings of the quantitative survey results, qualitative semi-structured interviews, and integrated analyses. We coded the qualitative data in Atlas.ti version 23.³¹ Coding reports were discussed with the study team via biweekly and monthly conference calls to refine and finalize.

Ethics

This study was approved by the Johns Hopkins Medicine institutional review board, IRB00312473, through expedited review. For the online survey, a waiver of written consent was granted by the IRB given the minimal risk nature of the study and the online survey format. Participants provided implied consent by completing the survey after reviewing an information sheet that described study procedures, confidentiality measures, voluntary participation, potential risks and benefits, and compensation. The information sheet was presented before any data collection, and participants could exit the survey at any time without penalty. For participants indicating interest in being contacted for interviews, oral consent was obtained before conducting the semi-structured interviews. At the beginning of each Zoom interview session, the interviewer reviewed the consent information verbally, addressed any questions, and obtained recorded verbal consent confirming the participant's agreement to participate, understanding of procedures, and permission for audio recording. This verbal consent process was audio-recorded and documented in study records. Consent covered study procedures, confidentiality measures, voluntary participation, right to withdraw, and compensation.

Results

Quantitative survey results

A total of 254 ECE providers completed the eligibility questionnaire, of which 237 (93.3%) were screened as eligible for participation. Of those eligible, 195 (82.3%) consented to the study, and 143 (60.3%) completed the online survey. Of these, most were from center-based programs (i.e. Head Start, Early Head Start) (n = 91), followed by Family Child Care Homes (FCCHs, n = 52) representing 33 states across all regions of the US. More than half of participants (50.4%) were ECE administrators, 61.5% non-Hispanic White, 52.5% between the ages 40–59; 62.9% held a college degree, and 81.1% were enrolled in the CACFP (Table 1).

Table 1.

Organizational and provider-level demographic data for surveys and semi-structured interviews.

	Survey	Interview
	Total n (%)n = 143	Total n (%)n = 20
Early care and education setting
Child care center (i.e. Early Head Start, Head Start)	91 (63.6%)	13 (65%)
Family child care homes	52 (36.4%)	7 (35%)
State funding support (select all that apply)
Enrolled in the child and adult care food program	116 (81.1%)	15 (75%)
State or locally supported preschool program for low-income children	60 (42%)	5 (25%)
Accepts child care subsidies	99 (69.2%)	16 (80%)
State quality rating and improvement system (QRIS) or other accreditation bodies (i.e. NAFCC, NAEYC, Youngstars)	62 (40.6%)	10 (50%)
US states represented (Top 5)
Connecticut	14 (9.8%)	3 (15%) [Wisconsin]2 (10%) [New York]2 (10%) [Connecticut]1 (5%) [Alabama, Arkansas, California, Florida, Idaho, Illinois, Louisiana, Nebraska, Ohio, Oregon, Pennsylvania, Rhode Island, Virginia]
Kansas	11 (7.7%)
New York	11 (7.7%
Florida	9 (6.3%)
Alabama	9 (6.3%)
US states represented by region
New England (CT, RI, VT)	18 (12.6%)	3 (15%)
Middle Atlantic (NY, PA, *DC)	20 (14%)	3 (15%)
Midwest (IL, IN, IA, KS, MI, MN, NE, OH, WI)	42 (29.4%)	6 (30%)
South (AL, AR, FL, GA, LA, MS, MO, NC, VA, WV)	37 (25.9%)	5 (25%)
Southwest (AZ, OK, TX)	10 (7%)	0 (0%)
West (AK, CA, CO, ID, OR)	16 (11.1%)	3 (15%)
Ages of children enrolled
Infants (0–11 months)	98 (68.5%)	12 (60%)
Toddlers (12–35 months)	118 (82.5%)	15 (75%)
Preschool (3–5 years)	134 (93.7%)	20 (100%)
School-Aged (older than 5 years)	68 (47.6%)	8 (40%)
Current role of participant (select all that apply)
Owner/Franchisee	6 (37.1%)	8 (40%)
Administrator (i.e. Director, Assistant Director)	72 (50.4%)	15 (75%)
Food Service (i.e. Cook, CACFP coordinator)	42 (29.4%)	10 (50%)
Health and/or Nutrition Professional (i.e. Dietitian, Nutritionist)	13 (9.1%)	4 (20%)
Lead or assistant teacher	31 (21.7%)	8 (40%)
Race/Ethnicity
Non-Hispanic Black	27 (18.9%)	6 (30%)
Non-Hispanic White	88 (61.5%)	14 (70%)
Non-Hispanic Asian	3 (2.1%)	0
American Indian/Alaskan	4 (2.8%)	0
Multiracial	2 (1.4%)	0
Hispanic	19 (13.3%)	0
Age of participants
18–29	11 (7.7%)	1 (5%)
30–39	39 (27.3%)	6 (30%)
40–59	75 (52.5%)	11 (55%)
60+	18 (12.5%)	2 (10%)
Years of experience
0–5 years	27 (18.9%)	4 (20%)
6–10 years	17 (11.9%)	3 (15%)
11–15 years	22 (15.4%)	1 (5%)
16–20 years	15 (10.5%)	5 (25%)
21 years+	62 (43.3%)	7 (35%)
Educational status
High school or less	18 (12.6%)	2 (10%)
College degree (Associate's or Bachelors)	90 (62.9%)	12 (60%)
Graduate degree	35 (24.5%)	6 (30%)

*Federal District.

CACFP: Child and Adult Care Food Program; NAFCC: National Association for Family Child Care; NAEYC: National Association for the Education of Young Children.

Routine Digital Technology Access and Usage. Almost all providers reported having access to the internet for work-related purposes (97.9%), having access to high-speed internet (94.4%), and using electronic devices (95.1%), primarily tablets (71.3%), laptop computers (70.3%), and personal cell phones (52.5%). Most (83.9%) of ECE providers had used virtual conferencing (e.g. WebEX, Zoom) for work-related purposes. The top four main reasons for using digital technology in ECE settings were parent engagement (70.0%), idea development for enrichment (68.5%), business management (62.2%), and training (60.8%). Most (81.1%) of ECE providers reported being confident about using digital technology to receive nutrition training (Table 2). Center-based ECE providers were more likely to routinely use digital technology for parent engagement (74.7% vs. 61.5%, p = .008) and training (65.9% vs. 51.9%, p = .099) than Family Child Care Home providers. The other reasons for routine use of digital technology did not differ by type of ECE setting.

Table 2.

Routine digital technology access and usage for work-related purposes.

Questions	Total n (%)
Do you have access to the internet in your childcare setting for work-related purposes?	140 (97.9%)
Type of internet access (select all)
High speed (often fast and reliable, i.e. Fiber, cable, DSL, satellite, wireless/Wi-Fi)	135 (94.4%)
Dial-up (often slow, computer dials a phone number)	2 (1.4%)
Access through smartphone or cellphone	34 (23.8%)
Inconsistent access	3 (2.1%)
I don't know	1 (0.7%)
Use ^a electronic devices for ^b work-related purposes
Yes	136 (95.1%)
No, have access but wish NOT to use	4 (2.8%)
No, have no access and wish NOT to use	1 (0.7%)
No, have no access but wish to use	2 (1.4%)
Types of electronics or devices used
Desktop computer	73 (51.1%)
Laptop computer	101 (70.3%)
Tablet/iPad	103 (71.3%)
Work-specific cellphone	15 (10.5%)
Personal cellphone	75 (52.5%)
Projector screens	17 (11.9%)
Televisions	36 (25.2%)
Other devices [CD player, Smart board (3), Bluetooth radio (2), walkie talkies]	7 (4.9%)
Platforms used
Mobile apps	60 (42%)
Virtual conferencing (i.e. WebEx, Zoom)	120 (83.9%)
Social media (i.e. Facebook, Instagram, Twitter)	66 (46.2%)
Websites (i.e. training material, games)	75 (52.5%)
Mobile apps/Software used Brightwheel, ChildPlus, Class Dojo, Gusto, Paylocity, Quickbooks, Paycom, Hi Mamma, Khan Academy, Crew app, KidKare, Learning Genie, Remini, Procare, Remind, See Saw, Smartcare, USDA, Child Care Aware, Kindertales, SproutAbout, TS Gold, Bubbles Learning, ABC mouse, Tumble Library, My Big World, The Mailbox, Novel Effect, Adopt a Cow, ABC mouse, PbsKids, Youtube, Facebook, Pinterest, Zoom, Teams, Instagram, Messenger, Pandora
Main reasons for use
Children engage with the digital technology (i.e. YouTube) for enrichment purposes	71 (49.7%)
Access to nutrition games for children [Noodles, Sesame Street]	10 (7%)
Get ideas for enrichment	98 (68.5%)
Business management	89 (62.2%)
Parent engagement	100 (70.0%)
Training	87 (60.8%)
Level of confidence in using digital technology to receive nutrition/Active play training or guidance
Not confident or somewhat unconfident	11 (7.7%)
Neither	16 (11.2%)
Somewhat or very confident	116 (81.1%)

^a = Electronics includes devices that connect to the internet to provide information. ^b= Work-related purposes include accessing information devices but wish to use for continuing education and conducting business-related tasks (e.g. payroll), using with children for learning, and communicating with parents. Using a device for personal use would NOT count.

Digital Technology for Nutrition Training Since the Beginning of the COVID-19 Pandemic. Most ECE providers (83.2%) had received nutrition training since the COVID-19 pandemic started in March 2020, with most providers reporting that it was CACFP-related (49.0%). Most of the sample (74.8%) utilized digital technology to receive nutrition training, with 53.2% participating through virtual live platforms (e.g. Zoom, WebEx) and 24.5% participating through self-paced web training. However, only about 19% covered COVID-19-related changes in policies regarding food presentation and 30.1% on food preparation in the ECE setting. Other topics covered included food and beverage recommendations (57.3%), serving sizes for children (58.7%), importance of variety in a child's diet (53.2%), creating healthy mealtime environments (51.1%), using positive feeding practices (37.8%), communicating with families about nutrition (35%), developing policies on child nutrition (24.5%), CACFP guidelines (53.2%), and garden/farm-to-table practices (26.6%). Lack of knowledge or awareness of reputable nutrition training programs and technology equipment were two top challenges in receiving nutrition training digitally (Table 3). Providers enrolled in the CACFP were also more likely to receive nutrition training digitally than those not enrolled (OR = 24.0, p < .001). As providers’ age increases, the odds of receiving training digitally decreases (OR = 0.52, p = .043).

Table 3.

Nutrition training since the beginning of the COVID-19 pandemic (March 2020–time of survey).

	Total n (%)
Have you or your childcare workers received nutrition training anytime since the beginning of the Covid-19 pandemic (March 2020-today)?	119 (83.2%)
Source of nutrition training (select all)
CACFP	70 (49%)
SNAP-Ed	8 (5.6%)
Child care resource & referral agency	29 (20.3%)
State agency (i.e. Department of Health or Education)	21 (14.7%)
College/University	4 (Michigan University, University of Nebraska Lincoln, University of RI SNAP-Ed)
Other (20) [NAPSACC, Dr Yum Preschool Food Adventure, ChildCare Education Institute Online Training, Smart Beginnings, Institute of Child Nutrition, Pro-Solutions, Texas A&M AgriLife extension, WOW nutritional specialist, prosolutionstraining.com]
Mode of training (select all)
In-person	36 (25.2%)
Virtual (Zoom, WebEx, other meeting live platforms)	76 (53.2%)
Self-paced web training	35 (24.5%)
Mobile app or desktop application	2 (1.4%)
Topics covered in nutrition training (Quality)
Food and beverage recommendations	82 (57.3%)
Serving sizes for children	84 (58.7%)
Importance of variety in a child's diet	76 (53.2%)
Creating healthy mealtime environments	73 (51.1%)
Using positive feeding practices	54 (37.8%)
Communicating with families about nutrition	50 (35%)
Developing policies on child nutrition	35 (24.5%)
Food prep during COVID-19	43 (30.1%)
Food presentation during COVID-19	27 (18.9%)
CACFP guidelines	76 (53.2%)
Garden/farm-to-table	38 (26.6%)
Number of topics covered in nutrition training
1–3 topics	31 (21.7%)
4–6 topics	47 (32.9%)
7–9 topics	32 (22.4%)
10 + topics	8 (5.6%)
Number of sessions
1 session	43 (37.4%)
2–3 sessions	39 (33.9%)
4 or more sessions	30 (26.1%)
Other	0
I don't remember	3 (2.6%)
Length of training sessions
Under 1 h	18 (15.7%)
1–2 h	92 (80%)
3 + hours	4 (3.5%)
I don’t remember	1 (0.87%)
Challenges in using digital technologies for training (select all)
Lack of knowledge of nutrition training programming	40 (28%)
Lack of technology equipment (e.g. computers, iPads/tablets)	38 (26.6%)
Unreliable internet	14 (9.8%)
Low internet speed	12 (8.4%)
Not comfortable with technology	8 (5.6%)
Other: [i.e. Lack of knowledge regarding reputable nutrition training programming, can't attend during work hours, costs of training, time scheduling]	37 (25.9%)
What would you like more training on with respect to nutrition?
Recipes (especially snack ideas)/menu planning (12), food allergies/special diet (5), serving sizes/portion control (5), science behind poor nutrition and health (3), how to share nutrition resources to families (3), sensory learning during mealtime (2), garden-to-table (2), cultivating enthusiasm among teachers during mealtimes (2), use of the center monitoring report form (1), training requirements for food service staff vs teachers (1)

CACFP: Child and Adult Care Food Program; SNAP-ED: Supplemental Nutrition Assistance Program-Education; NAPSACC: Nutrition and Physical Activity Self-Assessment for Child Care.

Qualitative interview results

Of the 143 completed surveys, 64 agreed to participate in interviews, and 20 participants were finally interviewed. Of the 20 participants, 13 (65%) were from child care centers, 15 (75%) participated in CACFP, and 70% were non-Hispanic White. Complete demographic information for interview participants can be found in Table 1.

Seven themes emerged from the thematic analysis: (1) convenience and accessibility of virtual training, (2) digital technology improves access to nutrition training, (3) lack of social connectedness due to virtual training, (4) mixed perceptions about satisfaction and support, (5) technical difficulties, (6) lack of attention among staff during virtual training, and (7) not enough digital nutrition training content. Table 4 provides the joint display of the key findings of the quantitative survey results, qualitative semi-structured interviews, and integrated analyses.

Table 4.

Joint display of the key findings of the quantitative survey results, qualitative semi-structured interviews, and the integrated analyses.

TAM constructs related to using digital technology for nutrition training (n represents unique interview participants)	Survey results survey results (N = 143)	Thematic analysis (N = 20)	Integrated analysis
Ease of use/usefulness (n = 16)	−The vast majority of providers (97.9%) have internet access for work, with 94.4% reporting high-speed connections and 95.1% using electronic devices, while 50–70% rely on personal devices like cellphones or laptops. Over 80% use virtual platforms like Zoom, and 81.1% feel confident using digital tools for training, with 83.2% receiving nutrition training during COVID-19—nearly half via CACFP (49.0%) and over half through Zoom (53.2%).	−Providers found online training, particularly via Zoom, useful and easy to use, citing benefits like eliminating commutes, cost savings, flexible learning options, and a comfortable environment for engagement.	Digital technology improved access to nutrition training by removing travel barriers and offering flexibility. Providers generally preferred virtual training for convenience, but some missed in-person learning and interactions.
Attitudes & beliefs (n = 12)	−77.6% reported that with COVID-19, use of digital technology increased.	−ECE providers found digital nutrition training more accessible and convenient during the pandemic, eliminating travel and work disruptions, though some preferred in-person learning for social connection and interpersonal engagement. While virtual platforms like Zoom improved access, a subset highlighted the impersonal nature of online interactions as a drawback.	While digital training was valued for accessibility, some providers preferred in-person training for networking and hands-on engagement. Hybrid models could improve accessibility while ensuring social connections.
Perceived level of support (n = 5)	−71.3% report 1–3 sessions within nutrition training80% report nutrition training sessions are 1–2 h.	− perceived to be supported while also perceiving that in person training is important.	Perceptions of support were mixed. Some felt adequately supported, while others believed more digital training programming was needed. Improving providers’ awareness of digital nutrition training programming is necessary.
Barriers/challenges/concerns (n = 8)	28.0% reported lack of knowledge on reputable nutrition training offerings26.6% Lack of technology equipment (e.g. computers, iPads/tablets)9.8% Unreliable internet8.4% Low Internet Speed5.6% Not comfortable with technology25.9% Other:[i.e. Can't attend during work hours, Boredom among staff, costs of training, time scheduling].	−perceived technical difficulties regarding environment, quality of digital content, and Wi-Fi lags−multi-tasking while in training−perceived insufficient nutrition programming for training.	Despite nearly 94.4% of participants reporting access to high-speed internet, technical difficulties, staff issues, and lack of nutrition training programming were perceived. Addressing internet technical issues and offering hybrid learning models could improve attention and participation among providers.

ECE: early care and education; TAM: Technology Acceptance Model; CACFP: Child and Adult Care Food Program.

Ease of use/usefulness (n = 16)

Convenience and accessibility of virtual training

ECE providers perceived receiving online training was useful, with Zoom being the most cited as easy to use. Several factors contributed to its ease of use and usefulness, namely increased access to training without a long commute, free cost of virtual training, the ability to re-watch and pace learning, and the ability to share more in the comfort of their own surroundings.

‘So, it switched it to Zoom, and it's much easier to get the things that we need and to be able to talk face to face, and yet not necessarily really face to face with people’ (Child Care Center, Midwest, 60 years or above)

‘… I mean during the pandemic I really like virtual. It's better for me, because now you know, I don’t have to get on the plane. It goes away and leave all my work, you know, because when you come back that work is still there, so I’m able to. Uh, I guess better manage. I prefer virtual.’ (Early Head Start or Head Start, South, 40–59 years old)

Attitudes & beliefs (n = 12)

Digital technology improves access to nutrition training

ECE providers expressed positive attitudes towards virtual nutrition training. During the pandemic, providers perceived that nutrition training became more accessible due to the increased offerings of virtual nutrition training and the fact that they did not have to travel long distances, thereby not disrupting day-to-day work activities. Providers also noted that receiving nutrition guidance through Zoom and mobile apps would be most beneficial.

‘During the pandemic I really like virtual. It's better for me, because now I don’t have to get on the plane and leave all my work, you know…I prefer virtual’ (Early Head Start or Head Start program, South, 40–59 years old)

Lack of social connectedness

Although there were largely positive attitudes towards virtual learning, some providers preferred in-person learning due to the need for social connectedness.

‘So, we used to do in-person trainings prior to the pandemic. And then everything went Zoom, which I feel is a little more impersonal, but we still get the workshops in. Okay, I prefer in person. That's just me.’ (Family Child Care Home provider, New England region, 40–59 years old)

‘Nothing at all to be honest the it's not pertaining to the nutrition training. All people need social and emotional connection, so connection with people is necessary. But the information can be given in person or the internet’ (Child Care Center, Midwest, 30–39 years old)

Perceived level of support (n = 5)

Perceived satisfaction and support mixed

Although ECE providers perceived to be supported while using digital technology for their training, they also perceived that they miss out on the hands-on experiences that in-person training provides.

‘I mean. I feel like there could be more trainings out there, but at the same time I don’t know. I feel like I have a decent amount of support, like my CCRR [Child Care Resource and Referral] has those trainings, I can go on the gateway database and take trainings. And then I stay connected by, you know, getting updated emails on, like, I said, the different resources… so I feel like I have a decent amount of support. I just don’t know what direction I can say. It could go or to better, you know, be better’ (Family Child Care Home provider, Midwest, 30–39 years old)

‘Well, I know that if I have questions. I think we just got a new person. But you know, like I had phone number or the email of the people there. So, if I have a question I can, you know, zip out to them, and I usually get a response pretty easily.’ (Child Care Center, Midwest, 60 years or above)

Barriers/challenges/concerns (n = 8)

Technical difficulties

Participants perceived technical difficulties to be a barrier. Concerns regarding the quality of sound level were noted due to noise in the environment, small print on slides, and Wi-Fi lags.

‘Sometimes with getting on to a zoom, you know, has its challenges … I’ve certainly been on others where there's like technology lag or you know Wi-Fi lags and things like that’ [tablets, iPads] (Child Care Center, MidAtlantic, 40–59 years old)

Lack of attention among staff

Participants perceived that virtual learning allowed them to work on something else/multitask instead of providing total focus to virtual training.

‘Um, I would say it doesn’t really pertain to me. But I can see, like some other people not really paying attention, and you know, just doing the training because they have to.’ (Child Care Center, Mid-Atlantic, 30–39 years old)

Not enough digital nutrition training content

Participants perceived insufficient nutrition programming for training, and if it exists, they are unaware of it.

‘I think it would make it easier if there was more available like I just feel like there's not that much out there for nutritional training’ (Child Care Center, Mid-Atlantic, 30–39 years old)

Discussion

This mixed-methods study explored how ECE providers utilized digital technology for nutrition training before and during the COVID-19 pandemic. Approximately 95% of ECE providers in this study reported having access to high-speed internet, and 95% reported using electronic devices for work-related purposes and felt confident doing so. According to the Pew Research Center, 98% of Americans own a cell phone, and 79% have broadband internet at home.³² Although there is high use, digital access and use disparities persists, especially for those who are older and live in rural settings. In our study, over half of our participants were 40–59 years old, with 43.1% (total) having over 21 years of experience as an ECE provider. The ECE providers were from all childcare settings, including Family Child Care Homes and center-based child care, such as Early Head Start and Head Start programs. Although we did not classify by rural or urban setting, states across the US were represented, with the Midwest having the highest regional representation. Center-based childcare settings were more likely to routinely use digital technology for training and family engagement than Family Child Care Homes. This finding is consistent with research showing that center-based ECE programs tend to receive support from institutional funding to purchase tablets, computers, and electronic software.³³ We also found that the odds of receiving training digitally decreases as providers age. This finding resonates with broader research on the digital divide where privacy concerns, costs, complexity, and technical challenges have been cited in the literature as perceived barriers to technology adoption among older adults.³⁴ Unlike previous studies that focused primarily on personal technology use, our research demonstrates that age-related barriers persist even when technology is used for mandatory professional development, suggesting the need for age-appropriate training strategies for ECE providers.

Along with high digital use, over half of the ECE providers received nutrition training digitally since the beginning of the pandemic, with the CACFP being the primary source of nutrition training. Studies have found that participation in tailored digital nutrition training significantly enhanced providers’ adherence to nutrition best practices and improved provider nutrition communication skills, fostering healthier environments for early childhood obesity.^14,15

Training is also a cornerstone of childcare licensing systems, serving as a foundational element to ensure quality practices and compliance with health and safety standards. Many states require pre-service qualifications and ongoing annual training for childcare providers, emphasizing topics such as nutrition, health, and safety. Digital platforms offer an opportunity to streamline these training requirements while ensuring accessibility for providers across diverse settings. Our study provides empirical evidence that digital platforms can effectively support these licensing requirements, even during disruptions to traditional in-person training.

Licensing systems increasingly view training as integral to quality improvement efforts, not merely a compliance measure. High-quality training equips providers with the skills to foster environments supporting children's health and learning outcomes. For example, incorporating digital nutrition training into licensing requirements can enhance provider adherence to best practices while promoting healthier mealtime environments. The CDC's Spectrum of Opportunities framework underscores the importance of integrating comprehensive obesity prevention content into childcare training programs.³⁵ This approach aligns with licensing standards by providing technical assistance on topics such as meal planning, portion control, physical activity, and screen time reduction. Expanding digital training modules to include these topics can further strengthen childcare licensing systems while addressing critical public health goals.

Early care and education provider perceptions and attitudes

ECE providers generally had positive perceptions and attitudes about using technology for nutrition training. Due to convenience, ECE providers perceived that digital technology improved their access to nutrition training. This finding aligns with TAM research in other professional contexts,^17–21 but our qualitative data revealed additional nuances specific to ECE settings. ECE providers valued the elimination of travel time and the flexibility to complete training around their work schedules. However, ECE providers felt that they were unaware of digital nutrition training programs. This finding highlights the importance of marketing and outreach in technology adoption. With 28% of providers unaware of credible digital resources, proactive promotion of available programs is critical. Policymakers and organizations like CACFP should prioritize enhancing digital training outreach to advance nutrition training. Simultaneously, investments in user-friendly design can broaden accessibility. Prior research on educational technology has emphasized that usability, particularly ease of use, navigability, and interface design, is central to learner engagement and technology adoption. Our findings confirm that these principles can apply to professional development platforms for ECE providers.

ECE providers also perceived that virtual learning reduced their opportunities to connect in person and engage in hands-on training. Similarly, in a study focusing on a digital parenting program for families who are low-income, parents reported valuing the program for its convenience. Still, the absence of social connectivity was noted as a significant limitation.³⁶ The authors propose adding social networks to foster peer support and accountability. While digital platforms like Zoom demonstrated potential in enhancing accessibility and flexibility in this study, diminished social connectedness was also identified as a concern.

Hybrid learning may be the right approach, combining digital and in-person training methods with strong technical support to bolster social connections and improve its impact. Training topics should also expand and include topics related to diverse recipes and menu planning, food allergies, serving sizes and portion control, the science behind poor nutrition and health, best practices for nutrition-related family engagement, sensory learning during mealtimes, gardening, and cultivating positive mealtime time environments.

Theoretical and practical implications

Theoretical implications

This study contributes to technology acceptance literature by demonstrating the applicability of TAM in the ECE professional development context while revealing limitations that suggest the need for adaptation. Our findings suggest that additional factors-including organizational setting (center-based vs. Family Child Care Home), awareness of available resources, and social connectedness-may play a crucial role in technology adoption for professional training. Future research might benefit from integrating TAM with other frameworks, such as the Unified Theory of Acceptance and Use of Technology to capture these additional dimensions.³⁷

Additionally, our mixed-methods approach demonstrated the value of combining TAM-guided quantitative assessment with qualitative exploration of lived experiences, providing a more complete picture of technology adoption than either method alone.

Practical implications

This study informs evidence-based recommendations for enhancing digital nutrition training programs for ECE providers. These recommendations are to:

1. Expand digital training programming: Develop and promote a comprehensive library of digital nutrition training modules that address the full range of topics providers identified as important, including recipe adaptation, food allergies, portion control, and family engagement strategies.

2. Improve awareness and outreach: Implement targeted marketing campaigns to ensure providers know about available digital training resources. This should include multiple communication channels including email, social media, and through sponsoring agencies, with particular attention to reaching non-CACFP providers who may be less connected to nutrition training resources.

3. Enhance technical support: Establish dedicated technical assistance resources to help providers troubleshoot connectivity issues, navigate training platforms, and optimize their learning experience. This is particularly important for older providers who may face greater technology adoption barriers.

4. Develop hybrid training models: Design training programs that combine digital content delivery with opportunities for synchronous interaction, peer networking, and hands-on practice. This could include virtual live sessions with breakout discussions, online communities of practice, or periodic in-person meetups that complement digital learning.

5. Address age-related barriers: Create age-friendly digital training interfaces and provide supplementary support for older providers, such as tutorial videos, simplified navigation, and peer mentoring programs.

Strengths and limitations of the study

The topic of digital use for nutrition training among ECE providers is understudied and not well understood. Our workgroup noted the importance of integrating and leveraging digital health for health promotion and risk reduction in the ECE setting.³⁸ To do this, we need a better understanding of current digital use among ECE providers. Although we utilized convenience sampling, we surveyed a national sample of ECE providers, which yielded a diverse representation of ECE providers and ECE type across all the US regions. Our study was theoretically grounded and used a robust mixed-methods approach to understand digital use for nutrition training in the ECE setting. Nonetheless, the study had some limitations. Due to self-reporting, the study is subject to reporter and social desirability bias. Also, since the survey and interviews had to be conducted virtually, there can be selection bias because providers with access to the internet and electronic devices are most likely to fill out the survey and interview. We relied on the NOPREN network and the National CACFP sponsoring agency to circulate our recruiting flyer, which produced more CACFP ECE providers in our sample. Finally, we set the ability to read and speak English as an eligibility criterion for surveys and interviews. Although about 13% of survey participants identified as Hispanic, none were part of the interviews. This language limitation means we did not capture the experiences of providers who primarily speak languages other than English, representing an important gap given the linguistic diversity of the ECE workforce. Finally, another limitation of this study relates to our instrument development approach. While TAM served as the guiding theoretical framework, we did not directly adopt or adapt a single validated TAM instrument. Instead, we reviewed multiple validated TAM-based measures from prior research to inform the development of original survey items and interview questions tailored to our specific context. This approach ensured contextual appropriateness for ECE providers and nutrition training, but it means our instrument lacks the psychometric validation of established TAM scales. We attempted to address this limitation through several strategies. First, we grounded our instrument development in a comprehensive review of validated TAM instruments across multiple contexts, extracting common approaches to measuring TAM constructs while adapting language and examples for the ECE nutrition training context. Second, we established face validity through expert review by our multidisciplinary research team and pilot testing with an ECE provider who provided feedback on comprehension and relevance. Third, our mixed-methods design allowed us to triangulate findings across quantitative survey responses and qualitative interviews, providing convergent validity for our key findings. Future research would benefit from conducting exploratory and confirmatory factor analyses to establish the survey's reliability and validity.

Despite these limitations, this study's insights show how ECE providers can maximize the benefits of digital technology for nutrition training. Findings can provide the basis for state policy considerations for ECE training agencies, including the CACFP, regarding developing and disseminating high-quality nutrition training while prioritizing hybrid training models to ensure social connectivity, hands-on learning, and technical support. Policymakers should consider leveraging hybrid approaches to address concerns about social connectivity and align training with frameworks like the CDC's Spectrum of Opportunities. Tailored outreach and training efforts to older adult providers could help improve digital access and use.³⁹ By optimizing digital nutrition training, we can better equip ECE providers to create healthy mealtime environments, ultimately contributing to improved child health outcomes.

Supplemental Material

sj-pdf-1-dhj-10.1177_20552076261431595 - Supplemental material for A mixed-methods examination of early care and education providers' routine and pandemic use of digital technology for nutrition training: Insights for digital nutrition training policies and practices

Supplemental material, sj-pdf-1-dhj-10.1177_20552076261431595 for A mixed-methods examination of early care and education providers' routine and pandemic use of digital technology for nutrition training: Insights for digital nutrition training policies and practices by Lucine Francis, Chelsea L. Kracht, Wenyi Chen, Nancy A. Perrin, Kim M. Gans, Tayla von Ash, Rebecca E. Lee and Alison Tovar in DIGITAL HEALTH

Supplemental Material

sj-pdf-2-dhj-10.1177_20552076261431595 - Supplemental material for A mixed-methods examination of early care and education providers' routine and pandemic use of digital technology for nutrition training: Insights for digital nutrition training policies and practices

Supplemental material, sj-pdf-2-dhj-10.1177_20552076261431595 for A mixed-methods examination of early care and education providers' routine and pandemic use of digital technology for nutrition training: Insights for digital nutrition training policies and practices by Lucine Francis, Chelsea L. Kracht, Wenyi Chen, Nancy A. Perrin, Kim M. Gans, Tayla von Ash, Rebecca E. Lee and Alison Tovar in DIGITAL HEALTH

Supplemental Material

sj-docx-3-dhj-10.1177_20552076261431595 - Supplemental material for A mixed-methods examination of early care and education providers' routine and pandemic use of digital technology for nutrition training: Insights for digital nutrition training policies and practices

Supplemental material, sj-docx-3-dhj-10.1177_20552076261431595 for A mixed-methods examination of early care and education providers' routine and pandemic use of digital technology for nutrition training: Insights for digital nutrition training policies and practices by Lucine Francis, Chelsea L. Kracht, Wenyi Chen, Nancy A. Perrin, Kim M. Gans, Tayla von Ash, Rebecca E. Lee and Alison Tovar in DIGITAL HEALTH

Footnotes

Acknowledgments

This study was funded and conducted under a collaborative agreement between a multi-institutional research sub workgroup called Digital Solutions in the ECE setting within the Nutrition and Obesity Policy Research and Evaluation Network (NOPREN). NOPREN is a national research network that informs policies and practices designed to improve nutrition and prevent obesity with an equally focused lens. The workgroup leads efforts in assessing, developing, and directing strategic initiatives related to digital health solutions in the ECE setting to address childhood obesity and social determinants of health.

ORCID iDs

Lucine Francis

Chelsea L. Kracht

Wenyi Chen

Nancy A. Perrin

Kim M. Gans

Tayla von Ash

Rebecca E. Lee

Alison Tovar

Author contribution

LF contributed to funding acquisition, conceptualization, data collection, data analysis, data interpretation, manuscript preparation, and submission. WC and NP contributed to conceptualization, data collection, data analysis, data interpretation, manuscript preparation, and submission. CK, KG, TvA, REL, and AT contributed to conceptualization, data interpretation, manuscript preparation, and submission.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Nutrition & Obesity Policy Research Evaluation Network (NOPREN) COVID-19 Response Fund, CDC Division of Nutrition, Physical Activity, and Obesity and Prevention Research Centers Program (U48DP006374). Johns Hopkins Alliance for a Healthier World.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental material

Supplemental material for this article is available online.

References

De Brey

Snyder

Zhang

, et al. Digest of Education Statistics 2019 (NCES 2021-009). 2021. https://nces.ed.gov/pubs2021/2021009.pdf.

Kaphingst

Story

. Child care as an untapped setting for obesity prevention: state child care licensing regulations related to nutrition, physical activity, and media use for preschool-aged children in the United States. Prev Chronic Dis 2009; 6: A11–A11.

Story

Kaphingst

French

. The role of child care settings in obesity prevention. Future Children 2006; 16: 143–168.

United States Department of Agriculture. Child and Adult Care Food Program. https://www.fns.usda.gov/cacfp (accessed 28 March 2025).

Francis

Perrin

Black

, et al. Mealtime environment and feeding practices in urban family child care homes in the United States. Child Obes 2021; 18: 102–111.

Andreyeva

Henderson

. Center-reported adherence to nutrition standards of the child and adult care food program. Child Obes 2018; 14: 421–428.

Williams

Sisson

Padasas

, et al. Food program participation influences nutrition practices in early care and education settings. J Nutr Educ Behav 2021; 53: 299–308.

Ettinger de Cuba

Bovell-Ammon

Ahmad

, et al. Child care feeding programs associated with food security and health for young children from families with low incomes. J Acad Nutr Diet 2023; 123: 1429–1439.

Heflin

Arteaga

Gable

. The child and adult care food program and food insecurity. Social Service Review 2015; 89: 77–98.

10.

Korenman

Abner

Kaestner

, et al. The child and adult care food program and the nutrition of preschoolers. Early Child Res Q 2013; 28: 325–336.

11.

National CACFP Association. CACFP USDA & State Agencies. https://www.cacfp.org/usda-state-agencies/ (accessed 28 March 2025).

12.

Erinosho

Jana

Loefstedt

, et al. Experiences pertaining to child nutrition and care provision among early care and education stakeholders, sponsors, and center directors during the COVID-19 pandemic: a multi-method study. Front Public Health 2022; 10: 999272.

13.

Schermbeck

Lin

Leider

, et al. Change in nutrition and physical activity practices in early childcare and education settings in Illinois during the COVID-19 pandemic. J Public Health Manag Pract 2024; 30: 540–549.

14.

Schachtner-Appel

Moses

Jackey

, et al. Evaluation of an online training course for childcare providers participating in the CACFP infant feeding program. Matern Child Health J 2024; 28: 391–399.

15.

Ramsay

Armstrong Shultz

Johnson

, et al. Child care mealtime and active play partnerships (child care MAPP): evaluation of a training resource web site for enhancing nutrition education communication. J Nutr Educ Behav 2013; 45: 803–805.

16.

Davis

. A technology acceptance model for empirically testing new end-user information systems: theory and results. Manage Sci 1986; 32: 982–1003.

17.

Lee

JWY

Tan

Bello

. Technology acceptance model in medical education: systematic review. JMIR Med Educ 2025; 11: e67873.

18.

Shin

Yoon

, et al. Comprehensive senior technology acceptance model of daily living assistive technology for older adults with frailty: cross-sectional study. J Med Internet Res 2023; 25: e41935.

19.

Luo

Lee

, et al. Exploring usability and patient attitude towards a smart hospital service with the technology acceptance model. Int J Environ Res Public Health 2022; 19(10): 6059.

20.

Cajita

Hodgson

Lam

, et al. Facilitators of and barriers to mHealth adoption in older adults with heart failure. CIN Comput Inf Nurs 2018; 36: 376–382.

21.

Cajita

Hodgson

Budhathoki

, et al. Intention to use mHealth in older adults with heart failure. J Cardiovasc Nursing 2017; 32: E1–E7.

22.

Abu

. A questionnaire approach based on the technology acceptance model for Mobile tracking on patient progress applications. J Comput Sci 2013; 9: 763–770.

23.

Gagnon

Orruno

Asua

, et al. Using a modified technology acceptance model to evaluate healthcare professionals’ adoption of a new telemonitoring system. Telemed J E Health 2012; 18: 54–59.

24.

Wang

. What drives mobile commerce? Inf Manag 2005; 42: 719–729.

25.

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.

26.

Google. Random Number Generator. 2022. https://share.google/bxDCyfMSKk3Reoo57.

27.

StataCorp. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC, 2019.

28.

Howell

. An Introduction to the Philosophy of Methodology. London, UK: Sage Publications Ltd, 2013, https://methods.sagepub.com/book/mono/an-introduction-to-the-philosophy-of-methodology/toc (accessed 2 September 2025).

29.

Clarke

Braun

. Teaching thematic analysis: overcoming challenges and developing strategies for effective learning. Psychologist 2013; 26: 120–123.

30.

Saldana

. The Coding Manual for Qualitative Researchers. 3rd ed. London Sage Publications, 2016.

31.

ATLAS.ti Scientific Software Development GmbH. 2023. https://atlasti.com/.

32.

Watnick-Gelles

. Americans’ Use of Mobile Technology and Home Broadband. 2024. https://www.pewresearch.org/internet/2024/01/31/americans-use-of-mobile-technology-and-home-broadband/.

33.

Blackwell

Lauricella

Wartella

, et al. Adoption and use of technology in early education. Comput Educ 2013; 69: 310–319.

34.

Harris

Blocker

Rogers

. Older adults and smart technology: facilitators and barriers to use. Front Comput Sci 2022; 4: 835927.

35.

Centers for Disease Control & Prevention (CDC). Spectrum of Opportunities Framework. https://www.cdc.gov/early-care-education/php/getting-started/spectrum-of-opportunities.html (accessed 11 April 2025).

36.

Brager

Breitenstein

Miller

, et al. Low-income parents’ perceptions of and engagement with a digital behavioral parent training program: a mixed-methods study. J Am Psychiatr Nurses Assoc 2021; 27: 33–43.

37.

Venkatesh

Morris

Davis

, et al. User acceptance of information technology: toward a unified View1. Manage Inf Syst Q 2003; 27: 425–478.

38.

Kracht

Tovar

Gans

, et al. How to integrate and leverage digital health modalities for health promotion in early childhood education: Opportunities to improve intervention access and engagement. Transl Behav Med 2025; 15(1): ibaf006.

39.

Gleason

Suen

. Going beyond affordability for digital equity: closing the “Digital Divide” through outreach and training programs for older adults. J Am Geriatr Soc 2022; 70: 75–77.

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