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Abstract

Objectives

This study aimed to develop and evaluate theory-guided eHealth literacy support for tele-pulmonary rehabilitation (PR) in older adults with chronic obstructive pulmonary disease (COPD).

Methods

Sixty-five COPD patients in the intervention group received an eHealth literacy intervention guided by self-regulated learning model and self-efficacy theory (SRL-SEe PR group), while 65 control patients received an eHealth literacy intervention guided by the self-efficacy theory (SEe PR group). Both groups (n = 130) received interventions for 8 weeks, followed up for 4 weeks, and received tele-PR simultaneously. The primary outcome was eHealth literacy, and the secondary outcomes were technophobia, aging attitudes, online learning self-efficacy, exercise self-efficacy, online SRL ability, symptom burden, and knowledge.

Results

There were statistically significant differences in the eHealth literacy level (MD = 4.03; P < 0.001), technophobia level (MD = −8.08; P < 0.001), attitude toward aging (MD = 14.17; P < 0.001), online learning self-efficacy (MD = 10.80; P < 0.001), exercise self-efficacy (MD = 13.79; P < 0.001), online SRL ability (MD = 8.60; P < 0.001), and COPD knowledge (MD = 2.19; P < 0.001). No statistically significant differences in symptom burden were noticed between the two groups (P > 0.05).

Conclusion

The SRL-SEe PR intervention is feasible and effective. This suggests that eHealth literacy interventions should not only focus on building individuals’ learning confidence but also on teaching learning methods and health knowledge that patients are concerned about. These strategies can enhance self-efficacy, leading to better learning outcomes and higher eHealth literacy levels. Future tele-chronic disease management programs should start with disease-specific knowledge and skills, and carry out eHealth literacy training.

Keywords

eHealth literacy older patients with chronic obstructive pulmonary disease technophobia aging attitudes self-efficacy

Introduction

Chronic obstructive pulmonary disease (COPD) is a widespread chronic respiratory disease globally.¹ There are around 300 million COPD patients worldwide,² with a prevalence rate of around 12.2%.³ COPD is expected to shortly become the third leading cause of death and the fifth largest economic burden worldwide.^4,5 Chronic disease management is recognized as an important means to reduce the disease burden.⁶ Pulmonary rehabilitation (PR) is a cost-effective, guideline-recommended treatment for COPD.⁷ However, due to the uneven distribution of existing health resources and the limitations of input costs, traditional PR cannot fully meet patient needs.^8,9 With the development of information technology, tele-PR has emerged as a promising solution, offering resource efficiency, overcoming time and space constraints, and being economical and convenient.^10,11 Recent studies have further demonstrated the clinical benefits of tele-PR in improving dyspnea, 6-min walk distance, and psychological well-being in patients with COPD.¹² The systematic review conducted by Adhikari et al. further supports remote self-management and mental health interventions for individuals with COPD, particularly in low- and middle-income countries, underscoring the increasing significance of digital health solutions in chronic disease management.¹³ Multicenter trials have compared supervised versus personalized home-based tele-PR models and demonstrated their feasibility in different health care systems.¹⁴ In addition, Qiu et al. reported that tele-interventions effectively alleviate anxiety and depression symptoms in COPD patients.¹⁵ Despite these advances, few studies have explored how digital learning strategies and eHealth literacy training can optimize patient engagement and treatment outcomes in tele-PR. However, tele-PR success depends on patients’ eHealth literacy—the ability to search, access, understand, and evaluate health information from electronic resources and apply the acquired knowledge to resolve health-related issues.^16–19 Older adults often have lower levels of eHealth literacy, and there are phenomena such as technophobia, negative aging attitudes, and low self-efficacy.^17,20 It makes the spread of tele-PR difficult.^16,17

Current eHealth literacy interventions for older adults primarily emphasize training in mobile device operation skills, often designed around self-efficacy theory (SEe) to boost confidence in using the internet and mobile terminals.^18,21,22 However, the integration of teaching methods is not universal, with few studies adhering to adult education principles.^23,24 Only Xie focused on the aging attitude of patients,²⁵ and the measurement of individual technophobia remained in the computer anxiety measurement.^26,27 In addition, solving health issues involves reflective and creative thinking, where diverse thinking styles and effective learning methods enable co-stakeholders to advance digital health.^28–30 A self-regulated learning (SRL) model consists of three phases: the planning phase, the volitional or behavioral control phase, and the self-reflection phase.^31,32 As a learning strategy, this model enhances individual learning abilities and informs intervention design in educational programs.^31,32 This model boosts learning efficiency and is essential for online learning. Bandura's SEe, which increases individuals’ learning confidence through four primary sources of self-efficacy,^33–35 is currently the most widely applied theoretical framework in eHealth literacy interventions.²¹

The combination of the two theories mentioned above is more suitable for the learning patterns of eHealth literacy courses. However, no studies have reported on eHealth literacy interventions guided by the self-regulated learning model and self-efficacy theory (SRL-SEe). In the context of tele-PR, the effects of such interventions on technophobia, aging attitudes, and self-efficacy among older COPD patients remain unclear. This study aims to develop an effective, accessible program to enhance eHealth literacy skills for older patients with chronic diseases and to evaluate its impact and mechanisms.

Methods

Study design

This 12-week parallel randomized controlled trial study assessed participants at baseline, at the end of week 4, at the end of the intervention at week 8, and at the end of the follow-up at week 12. The assessments were conducted either in person or via telephone calls, and participants were guaranteed the right to withdraw from the study. This study follows the CONSORT guidelines for reporting results from clinical trials and has been registered.³⁶

Participants

From February 2023 to January 2025, patients were continuously recruited through leaﬂets and posters from the respiratory departments of three general hospitals in Wuxi City by a convenience sampling method. The recruitment sites consisted of two tertiary general hospitals and one district hospital. All hospitals have separate respiratory medicine departments that provide outpatient and inpatient care for patients with chronic respiratory diseases. Patient recruitment was carried out continuously on a rolling basis. Inclusion criteria included: (1) age ≥65 years; (2) diagnosed with COPD according to guidelines for the diagnosis and management of COPD: forced expiratory volume in 1 second (FEV₁)/forced vital capacity (FVC) < 0.70, (FEV₁) < 80% of predicted, at a stable stage of COPD (no acute exacerbation within the past 4 weeks)^11,37; (3) patients were treated according to the GOLD 2023 guideline³⁸; and (4) patients or someone living with them have a smartphone. Exclusion criteria were: (1) patients with severe visual, hearing, or communication impairment that prevents participation in online learning or tele-PR; (2) patients with severe mental disorders or cognitive impairment that hinder the implementation of tele-PR; (3) patients with severe heart, liver, or kidney diseases, limiting the dose of rehabilitation exercise; (4) patients with severe frailty or limb dysfunction, unable to participate in muscle strength testing or exercise training; and (5) participants who participated in another rehabilitation or intervention trial during the study period.

Sample and randomization

The primary outcome of this study was eHealth literacy, as calculated by the eHealth Literacy Scale (eHEALS) for the sample size of this study. Assuming a confidence interval (CI) (α) of 95% and 90% power, a 1:1 sample ratio between the two groups was used, with an effect size of 0.7 based on the eHEALS scale.^39–42 Considering the 15% dropout rate in the previous study,^11,43 at least 51 patients were required in each group. Therefore, the included sample size was at least 102. Recruitment was closed at each site. A total of 350 people were contacted through flyers and posters, and eventually 280 verbally agreed to participate in the study. However, among these 280 participants, 30 did not meet the inclusion criteria, 114 declined to participate when recontacted, and 6 were lost to contact. Finally, 130 participants were enrolled in the randomized trial. Of the 130 participants, 43 (33%) were recruited from a tertiary general hospital, 35 subjects (27%) were from another tertiary general hospital, and 52 subjects (40%) came from a district hospital. All hospitals followed the same recruitment criteria, intervention protocol, and follow-up schedule. To ensure consistency across the hospitals, researchers received uniform training and utilized the same electronic data collection form.

The 130 participants were then randomly assigned to either the intervention group or the control group using a WeChat mini-program and the random number table method at a 1:1 ratio, ensuring the randomness of the groups.

Intervention

Participants in both groups received the 8-week eHealth literacy course learning program and the tele-PR program, followed by a 4-week follow-up.

SRL-SEe PR group (intervention group)

This intervention was guided by the SRL-SEe. Before starting the SRL-SEe PR intervention, a WeChat group was established for patients. Participants received electronic eHealth literacy course leaflet (with QR code, one-page), the course manual obtained by scanning the QR code, and the electronic reflection journal; and simultaneously received the paper version of the material containing the above content (Supplemental Notes S1 and S2). The SRL-SEe PR course duration was of 8 weeks, with 2 weeks for each stage. Patients completed the four-stage goals of “fashionistas”; “half-doctor”; “half-expert”; and “leading the pack” by self-learning according to the information provided in the course brochure. The “Fashionistas” course content covers internet connectivity, input methods, enlarging fonts, and taking screenshots. The “half-doctor” course focuses on searching, evaluating, and differentiating online health information, while the “half-expert” course emphasizes creating, sharing, and exchanging such information. Finally, the “leading the pack” course consolidates prior content. Patients can access specific steps via QR code or printed manual based on their preferences. Meanwhile, team members will remind patients that they can watch the Q&A videos provided through the “PeR” WeChat public account. After completing each stage, the patient will write a reflective journal. The research team monitors video engagement and can contact patients via WeChat to discuss eHealth literacy progress. Recognition is given to the top 20% of participants, while the bottom 20% receive verbal encouragement.

SEe PR group (control group)

The control group was consistent with the intervention group based on the SEe (Supplemental Note S3). Additionally, patients in both groups simultaneously completed the tele-PR program.

PR (both intervention and control groups received the same tele-PR)

Participants received a comprehensive PR program developed by a multidisciplinary team, building on previous research conducted by the same group. The protocol included exercise training (e.g. strength training for both upper and lower limbs, and balance training). The exercise intensity was set at 55% to 69% of the participant's maximum heart rate and lasted for 20 to 30 min, at least three times per week.¹¹ Additionally, dietary guidance was provided to help patients assess their body weight and activity levels, enabling them to create personalized diet plans. Medication guidance covered the types, usage, and precautions of common inhalers. Clinicians and rehabilitation therapists assessed each patient's physical function and developed an individualized PR plan.¹¹ Nurses assisted patients in registering for and using the “PeR” platform, where they could access PR-related videos, track their training progress, and maintain rehabilitation diaries.¹¹ Nurses regularly pushed relevant videos, monitored patients’ engagement through the “PeR” platform's background, answered patients’ questions, and collaborated with team members to adjust training intensity as necessary.¹¹

Outcome measures

The primary outcome was eHealth literacy, assessed using the eHEALS (Cronbach's α = 0.913),^44,45 with higher scores indicating greater literacy. Secondary outcomes included technophobia (measured by Technophobia Scale, TS, Cronbach's α = 0.911, with a higher score indicating a higher level of technophobia),^46,47 aging attitudes (assessed by Attitudes to Aging Questionnaire, AAQ, Cronbach's α = 0.830, with higher scores reflecting more positive attitudes),^48,49 self-efficacy (evaluated using Online Learning Self-Efficacy Scale, OLSS, and Exercise Self-Regulatory Efficacy Scale, Ex-SRES, Cronbach's α = 0.913 and 0.917, with higher scores indicating better self-efficacy),^35,50,51 online SRL ability (measured by Online Self-Regulated Learning Questionnaire, OSLQ, Cronbach's α = 0.938, with higher scores indicating greater self-regulation learning),⁵² COPD knowledge (measured by Chronic Obstructive Pulmonary Disease Knowledge Questionnaire, COPD-Q, Cronbach's α = 0.720, with higher scores indicating greater knowledge),^53,54 and symptom burden (assessed with COPD Assessment Test, CAT, Cronbach's α = 0.840, where higher scores denote worse health).⁵⁵

Statistical analysis

To ensure data quality, all personnel involved in data collection received standardized training on how to complete questionnaires, enter data, and follow confidentiality procedures before the study began. Data were collected through face-to-face interviews and online surveys conducted via the “PeR” platform. The collected data were entered twice by two independent researchers and were cross-checked to identify discrepancies. Additionally, the corresponding author also performed a randomized review to verify data accuracy and completeness. The data was input and analyzed using SPSS version 26. We followed the intent-to-treat principle and used the Last Observation Carried Forward method to fill in any missing data. For analyzing baseline data, we used independent sample t-tests or rank sum tests for normally distributed variables. Categorical variables were evaluated using the chi-square test or Fisher's exact test. Continuous variables following a normal distribution are expressed as mean ± standard deviation, while categorical variables are presented as frequency and percentage. To assess overall intervention effects, we conducted repeated measures analysis of variance (ANOVA). The interaction effect of time and group was analyzed by two-way repeated measures ANOVA (time × group). Depending on whether the assumption of sphericity was met, we applied either the uncorrected method or the Greenhouse–Geisser correction method. Within-group and between-group differences were compared using the Bonferroni method, with a statistical significance level set at α = 0.05.

Ethics

This study was approved by the Medical Ethics Committee of Jiangnan University (JNU20220310IRB17). And the trial was registered at Chinese Clinical Trial Registry (ChiCTR1900028563). All participants signed an informed consent form after the investigator explained the study.

Results

Basic demographic characteristics and measurements

The study followed the CONSORT flowchart shown in Figure 1, illustrating the patient recruitment process, structured intervention programs, and loss to follow-up. A total of 130 patients (SRL-SEe PR group 65, SEe PR group 65) who met the inclusion criteria participated voluntarily. During the follow-up period, the control group (SEe PR) experienced a loss of five participants. Out of these, two individuals died due to COVID-19, while three others dropped out because they experienced a sense of meaninglessness. Similarly, the intervention group (SRL-SEe PR) also lost five participants; four died due to COVID-19, and one left the study due to feelings of meaninglessness. To address the missing data, a data imputation method was employed.

Figure 1.

Consolidated standards of reporting trials flowchart.

The demographic data is in Table 1. The independent sample t-test was used for age, eHEALS, TS, AAQ, OLSS, Ex-SRES, OSLQ, CAT, and COPD-Q. Chi-square test was used for gender, body mass index (BMI), marital status, education level, type of mobile terminals, duration of internet usage, smoking status, year(s) with COPD, and global initiative for chronic obstructive lung disease (GOLD) stage. The Fisher's exact test was used for hospitalizations for COPD in the last 1 year. The mean age of participants in the SRL-SEe PR group was 73.88 (SD = 5.17) years, and that in the SEe PR group was 73.95 (SD = 5.15) years. Males accounted for 81.50% and 84.60% of the participants in the two groups. The proportion of participants with a normal BMI was 44.60% in both groups. The proportion of married individuals in the two groups was 81.50% and 84.60%. The education level of most patients in both groups was junior high school or below. Most of the patients in both groups used mobile phones. In total, 76.90% of the intervention group and 83.10% of the control group had internet usage for more than 3 years. In both groups, more than half of the patients had already quit smoking. The majority had been diagnosed with COPD for less than 5 years, and only a small proportion had been hospitalized more than twice for COPD in the past year. Most patients were classified as GOLD stage II or III. There were no statistically significant differences in baseline sociodemographic, clinical characteristics, or measurements either across hospitals or between the SRL-SEe PR and SEe PR groups (P > 0.05).

Table 1.

Comparison of baseline information between the two groups (n = 130).

	x ± s/ M (P₂₅, P₇₅) / n (%)			χ2/Z/t	P
Characteristic	All cases (n = 130)	Control group (n = 65)	Intervention group (n = 65)	χ2/Z/t	P
Age (years)	73.92 (5.14)	73.95 (5.15)	73.88 (5.17)	0.09	0.93^a
Gender				0.22	0.64^b
Male	108 (83.10)	55 (84.60)	53 (81.50)
Female	22 (16.90)	10 (15.40)	12 (18.50)
BMI				0.10	0.99^b
Low	30 (23.10)	15 (23.10)	15 (23.10)
Normal	58 (44.60)	29 (44.60)	29 (44.60)
Overweight	25 (19.20)	13 (20.00)	12 (18.50)
Obese	17 (13.10)	8 (12.30)	9 (13.80)
Marital status				0.22	0.64^b
Single (divorced or separated or unmarried)	22 (16.90)	10 (15.40)	12 (18.50)
Married (spouse in tow)	108 (83.10)	55 (84.60)	53 (81.50)
Education level				0.24	0.63^b
Junior high school and below	110 (84.60)	54 (83.10)	56 (86.20)
Above junior high school	20 (15.40)	11 (16.90)	9 (13.80)
Types of mobile terminals				0.29	0.59^b
Mobile phones predominate	114 (87.70)	58 (89.20)	56 (86.20)
Dominant on other devices (such as computers, tablets, etc.)	16 (12.30)	7 (10.80)	9 (13.80)
Duration of internet usage				0.77	0.38^b
0–2 year(s)	26 (20.00)	11 (16.90)	15 (23.10)
≥3 years	104 (80.00)	54 (83.10)	50 (76.90)
Smoking status				1.02	0.60^b
Current smoker	41 (31.50)	23 (35.40)	18 (27.70)
Ex-smoker	75 (57.70)	36 (55.40)	39 (60.00)
Never smoked	14 (10.80)	6 (9.20)	8 (12.30)
Year(s) with COPD				0.58	0.45^b
≤5 year(s)	90 (69.20)	47 (72.30)	43 (66.20)
＞6 years	40 (30.80)	18 (27.70)	22 (33.80)
Hospitalizations for COPD in the last 1 year				2.06	0.41^c
0	57 (43.80)	29 (44.60)	28 (43.10)
1	65 (50.00)	34 (52.30)	31 (47.70)
≥2	8 (6.20)	2 (3.10)	6 (9.20)
GOLD stage				0.15	0.93^b
Stage II: moderate	48 (36.90)	25 (38.50)	23 (35.40)
Stage III: severe	58 (44.60)	28 (43.10)	30 (46.20)
Stage IV: very severe	24 (18.50)	12 (18.50)	12 (18.50)
eHEALS	24.08 (5.63)	24.68 (5.73)	23.49 (5.51)	−1.20	0.23^a
TS	35.55 (8.00)	35.17 (8.20)	35.92 (7.84)	0.54	0.59^a
AAQ	77.24 (14.80)	77.92 (15.01)	76.55 (14.66)	−0.53	0.60^a
OLSS	68.98 (13.22)	68.58 (14.78)	69.37 (11.55)	0.34	0.74^a
Ex-SRES	71.65 (19.47)	71.69 (18.93)	71.62 (20.15)	−0.02	0.98^a
OSLQ	60.79 (9.46)	61.46 (9.60)	60.12 (9.34)	−0.81	0.42^a
CAT	22.65 (4.83)	22.57 (5.05)	22.72 (4.63)	0.18	0.86^a
COPD-Q	6.74 (1.90)	6.57 (1.84)	6.91 (1.96)	1.02	0.31^a

GOLD: Global Initiative for Chronic Obstructive Lung Disease; eHEALS: eHealth Literacy Scale; TS: Technophobia Scale; AAQ: Attitudes to Aging Questionnaire; OLSS: Online Learning Self-Efficacy Scale; Ex-SRES: Exercise Self-Regulatory Efficacy Scale; OSLQ: Online Self-Regulated Learning Questionnaire; CAT: COPD Assessment Test; COPD-Q: Chronic Obstructive Pulmonary Disease Knowledge Questionnaire.

Unpaired t test.

Chi-square test.

Fisher's exact test.

Primary outcome

Repeated measures ANOVA was used to find the eHEALS score was increased in the 12th week compared with the baseline in both groups (P < 0.001, P < 0.001) (Table 2), and the amount of change was higher in the SRL-SEe PR group than in the SEe PR group (mean difference (MD) = 4.03; 95% CI [2.36, 5.71]; P < 0.001) (Table 2). The changing trend of the EHEALS score over time is shown in Figure 2. There were statistically significant group × time interaction effects for the primary outcome (P < 0.001). When the assumption of sphericity was violated, the Greenhouse–Geisser correction was applied, and Bonferroni posthoc comparisons confirmed consistent results.

Figure 2.

Evaluation indicators and their change over time.

Table 2.

Within-group differences and between-group differences of outcomes (n = 130).

Characteristic	Group	N	Baseline	4 weeks	8 weeks	12 weeks	Within-group difference						Between-group difference
			Baseline	4 weeks	8 weeks	12 weeks	At 4 weeks	P	At 8 weeks	P	At 12 weeks	P	At 4 weeks	P	At 8 weeks	P	At 12 weeks	P
			Mean (SD)	Mean (SD)	Mean (SD)	Mean (SD)	Mean (95% CI)	P	Mean (95% CI)	P	Mean (95% CI)	P	Mean (95% CI)	P	Mean (95% CI)	P	Mean (95% CI)	P
eHEALS	Intervention	65	23.49 (5.51)	23.83 (5.20)	32.97 (4.44)	33.78 (4.27)	0.34 (0.10, 0.58)	0.001	9.48 (8.92, 10.03)	＜0.001	10.29 (9.64, 10.95)	＜0.001	−0.94 (−2.85, 0.97)	0.33	3.49 (1.82, 5.16)	＜0.001	4.03 (2.36, 5.71)	＜0.001
eHEALS	Control	65	24.68 (5.73)	24.77 (5.80)	29.48 (5.14)	29.75 (5.32)	0.09 (−0.15, 0.33)	1.00	4.80 (4.24, 5.36)	＜0.001	5.08 (4.42, 5.73)	＜0.001	−0.94 (−2.85, 0.97)	0.33	3.49 (1.82, 5.16)	＜0.001	4.03 (2.36, 5.71)	＜0.001
TS	Intervention	65	35.92 (7.84)	35.46 (7.12)	24.89 (6.77)	22.88 (6.01)	−0.46 (−0.95, 0.02)	0.07	−11.03 (−11.77, −10.30)	＜0.001	−13.05 (−13.96, −12.13)	＜0.001	0.45 (−2.17, 3.06)	0.74	−6.69 (−9.29, −4.10)	＜0.001	−8.08 (−10.47, −5.69)	＜0.001
TS	Control	65	35.17 (8.20)	35.02 (7.94)	31.58 (8.12)	30.95 (7.65)	−0.15 (−0.64, 0.33)	1.00	−3.59 (−4.32, −2.85)	＜0.001	−4.22 (−5.13, −3.30)	＜0.001	0.45 (−2.17, 3.06)	0.74	−6.69 (−9.29, −4.10)	＜0.001	−8.08 (−10.47, −5.69)	＜0.001
AAQ	Intervention	65	76.55 (14.66)	77.54 (12.80)	98.26 (9.76)	100.29 (9.46)	0.99 (−0.07, 2.04)	0.08	21.71 (19.87, 23.55)	＜0.001	23.74 (21.72, 25.75)	＜0.001	−0.85 (−5.51, 3.82)	0.72	12.97 (8.57, 17.37)	＜0.001	14.17 (9.84, 18.50)	＜0.001
AAQ	Control	65	77.92 (15.01)	78.38 (14.05)	85.29 (15.05)	86.12 (14.88)	0.46 (−0.59, 1.52)	1.00	7.37 (5.53, 9.21)	＜0.001	8.20 (6.19, 10.22)	＜0.001	−0.85 (−5.51, 3.82)	0.72	12.97 (8.57, 17.37)	＜0.001	14.17 (9.84, 18.50)	＜0.001
OLSS	Intervention	65	69.37 (11.55)	79.23 (11.28)	85.49 (10.06)	87.49 (9.94)	9.86 (9.16, 10.56)	＜0.001	16.12 (14.17, 18.07)	＜0.001	18.12 (16.16, 20.09)	＜0.001	7.75 (3.15, 12.36)	0.001	9.66 (6.06, 13.27)	＜0.001	10.80 (7.26, 14.34)	＜0.001
OLSS	Control	65	68.58 (14.78)	71.48 (14.98)	75.83 (10.71)	76.69 (10.47)	2.89 (2.19, 3.59)	＜0.001	7.25 (5.30, 9.20)	＜0.001	8.11 (6.14, 10.07)	＜0.001	7.75 (3.15, 12.36)	0.001	9.66 (6.06, 13.27)	＜0.001	10.80 (7.26, 14.34)	＜0.001
Ex-SRES	Intervention	65	71.62 (20.15)	85.66 (20.40)	97.12 (19.60)	94.54 (19.59)	14.05 (12.38, 15.71)	＜0.001	25.51 (23.53, 27.48)	＜0.001	22.92 (20.42, 25.43)	＜0.001	7.71 (0.92, 14.50)	0.03	12.63 (5.99, 19.27)	＜0.001	13.79 (6.85, 20.72)	＜0.001
Ex-SRES	Control	65	71.69 (18.93)	77.95 (18.69)	84.49 (18.67)	80.75 (20.37)	6.26 (4.60, 7.93)	＜0.001	12.80 (10.83, 14.78)	＜0.001	9.06 (6.55, 11.57)	＜0.001	7.71 (0.92, 14.50)	0.03	12.63 (5.99, 19.27)	＜0.001	13.79 (6.85, 20.72)	＜0.001
OSLQ	Intervention	65	60.12 (9.34)	60.58 (8.54)	79.17 (5.86)	80.60 (5.99)	0.46 (−0.21, 1.13)	0.41	19.05 (17.92, 20.17)	＜0.001	20.48 (19.30, 21.65)	＜0.001	−1.34(−4.34, 1.66)	0.38	7.85 (5.11, 10.58)	＜0.001	8.60 (5.87 , 11.34)	＜0.001
OSLQ	Control	65	61.46 (9.60)	61.92 (8.75)	71.32 (9.48)	72.00 (9.40)	0.46 (−0.21, 1.13)	0.41	9.86 (8.74, 10.98)	＜0.001	10.54 (9.36, 11.72)	＜0.001	−1.34(−4.34, 1.66)	0.38	7.85 (5.11, 10.58)	＜0.001	8.60 (5.87 , 11.34)	＜0.001
CAT	Intervention	65	22.72 (4.63)	22.43 (4.15)	22.46 (4.24)	21.92 (3.89)	−0.29 (−0.63, 0.04)	0.12	−0.26 (−0.56, 0.04)	0.13	−0.80 (−1.24, −0.36)	＜0.001	0.11 (−1.42, 1.64)	0.89	0.19 (−1.36 , 1.73)	0.81	−0.14 (−1.59, 1.31)	0.85
CAT	Control	65	22.57 (5.05)	22.32 (4.64)	22.28 (4.65)	22.06 (4.45)	−0.25 (−0.58, 0.09)	0.30	−0.29 (−0.59, 0.01)	0.06	−0.51 (−0.95, −0.07)	0.02	0.11 (−1.42, 1.64)	0.89	0.19 (−1.36 , 1.73)	0.81	−0.14 (−1.59, 1.31)	0.85
COPD-Q	Intervention	65	6.91 (1.96)	10.43 (1.37)	11.02 (1.35)	11.35 (1.01)	3.52 (3.04, 4.01)	＜0.001	4.11 (3.55, 4.67)	＜0.001	4.45 (3.82, 5.07)	＜0.001	2.43 (1.96, 2.90)	＜0.001	1.00 (0.59, 1.41)	＜0.001	2.19 (1.69, 2.68)	＜0.001
COPD-Q	Control	65	6.57(1.84)	8.00(1.32)	10.02(0.96)	9.17(1.76)	1.43(0.95∼1.91)	＜0.001	3.45(2.89∼4.00)	＜0.001	2.60(1.97∼3.23)	＜0.001	2.43 (1.96, 2.90)	＜0.001	1.00 (0.59, 1.41)	＜0.001	2.19 (1.69, 2.68)	＜0.001

eHEALS: eHealth Literacy Scale; TS: Technophobia Scale; AAQ: Attitudes to Aging Questionnaire; OLSS: Online Learning Self-Efficacy Scale; Ex-SRES: Exercise Self-Regulatory Efficacy Scale; OSLQ: Online Self-Regulated Learning Questionnaire; CAT: COPD Assessment Test; COPD-Q: Chronic Obstructive Pulmonary Disease Knowledge Questionnaire; CI: confidence interval.

Secondary outcome

Psychology and attitude-related outcomes (technophobia, aging attitudes, exercise self-efficacy): repeated measures ANOVA was used for data analysis

Compared with the baseline, in the 12th week, the TS scores in both the SRL-SEe PR group and the SEe PR group were lower (P < 0.001, P < 0.001) (Table 2), and the amount of change in the SRL-SEe PR group was higher than those in the SEe PR group (MD = –8.08; 95% CI [−10.47, −5.69]; P < 0.001) (Table 2). The changing trend of the TS scores over time is shown in Figure 2. There was an interactive effect between time and group (P < 0.001). The Greenhouse–Geisser correction was applied when the assumption of sphericity was violated, and the Bonferroni posthoc comparisons yielded consistent results.

Repeated measures ANOVA showed that compared with baseline, AAQ scores and Ex-SRES scores increased in both groups in the 12th week (P < 0.001, P < 0.001) (Table 2). The changing trends of AAQ scores and Ex-SRES scores over time are shown in Figure 2. The interaction between AAQ scores and Ex-SRES scores and groups was statistically significant (P < 0.001, P < 0.001). The mean change in AAQ score from baseline to the 12th week was MD = 14.17 (95% CI [9.84, 18.50], P < 0.001), Ex-SRES score was MD = 13.79 (95% CI [6.85, 20.72], P < 0.001) (Table 2). The Greenhouse–Geisser correction was applied when the assumption of sphericity was violated, and the Bonferroni posthoc comparisons yielded consistent results.

Learning-related outcomes (online learning self-efficacy, online SRL ability, COPD knowledge): repeated measures ANOVA was used for data analysis

Compared with baseline, OLSS scores, OSLQ scores, and COPD knowledge scores increased in both groups in the 12th week (P < 0.001, P < 0.001, P < 0.001) (Table 2). The changing trends of OLSS scores, OSLQ scores, and COPD knowledge scores over time are shown in Figure 2. The interaction between OLSS scores, OSLQ scores, COPD knowledge scores, and groups was statistically significant (P < 0.001, P < 0.001, P < 0.001). The mean change in OLSS score was MD = 10.80 (95% CI [7.26, 14.34]; P < 0.001), and OSLQ score was MD = 8.60 (95% CI [5.87, 11.34]; P < 0.001), COPD knowledge score was MD = 2.19 (95% CI [1.69, 2.68]; P < 0.001) (Table 2). The Greenhouse–Geisser correction was applied when the assumption of sphericity was violated, and the Bonferroni posthoc comparisons yielded consistent results.

Physical health-related outcomes (symptom burden): repeated measures ANOVA was used for data analysis

Repeated measures ANOVA showed that in the 12th week, CAT scores were lower in the SRL-SEe PR group compared to the baseline (MD = −0.80; 95% CI [−1.24, −0.36]; P < 0.001), CAT scores decreased in the SEe PR group (MD = −0.51; 95% CI [−0.95, −0.07]; P = 0.02) (Table 2), and there was no statistical difference in CAT scores between the two groups (P > 0.05) (Table 2). The change in CAT score with intervention time is shown in Figure 2, and there was no interaction effect between time and group (P = 0.23).

As shown in Figure 2, both groups showed improvement over time in all measures except for symptom burden. However, the SRL-SEe PR group showed a more rapid and sustained increase in all measures except for symptom burden, especially after 8 weeks. In contrast, there was a slower upward trend in the SEe PR group.

Discussion

The results of this study demonstrate that the SRL-SEe PR for older COPD patients is feasible and effective. It outperforms the SEe PR group in improving eHealth literacy, aging attitudes, self-efficacy, online SRL abilities, COPD knowledge, and reducing technophobia. Additionally, the intervention was equally effective as the control group in alleviating symptom burden.

This study is the first to use a standardized 13-item TS to measure technophobia levels in older patients during an eHealth literacy intervention. It also confirmed that improving eHealth literacy can alleviate technophobia in this population. Our previous research identified a significant negative correlation between technophobia and eHealth literacy.¹⁷ Technophobia, as opposed to the narrower concept of computer anxiety, is a broader term referring to individuals’ negative behavioral, emotional, and attitudinal responses to modern or complex technological devices.^46,47 The existence of such negative emotions makes it difficult for patients to accept new technology. In this study, an eHealth literacy brochure was used to guide patients in participating and learning. The single-page format outlined a structured four-phase course and included mnemonic learning tips, making the eHealth literacy curriculum feel accessible and easy to use for patients. Reflective journals were also employed to help older adults consolidate their learning. These journals encouraged reflection on the course content, learning techniques, self-reaction, self-evaluation, and personal satisfaction across the four phases. This approach boosted learning confidence before, during, and after the course and helped patients adopt a more positive attitude toward eHealth technologies, ultimately reducing technophobia. Thus, combining learning strategies with the use of electronic devices can help older patients more effectively grasp and utilize technology. This study offers insights for alleviating technophobia in older adults. Ultimately reducing their digital divide and ensuring they benefit from digital health innovations, thereby contributing to successful aging.^56,57 Additionally, this approach can partially address the challenges in offline medical treatment for older adults with mobility issues and the scarcity of healthcare resources in remote areas, improving the healthcare system overall.

As eHealth literacy skills improve among older adults, their self-efficacy increases, and their aging attitudes improve accordingly. This study also confirmed this phenomenon, suggesting that self-efficacy may be a potential predictor of changes in aging attitudes. Most current eHealth literacy interventions focus on improving operational skills and learning confidence, often neglecting the teaching of learning methods.^18,21–24 The results of this study indicate that combining learning methods with confidence-building is more effective in improving aging attitudes. Aging attitudes encompass psychosocial loss, physical changes, and psychological gain.⁴⁸ This study demonstrated several advantages in improving aging attitudes. First, the eHealth literacy course taught older individuals SRL strategies, enabling them to independently acquire, understand, apply, and evaluate electronic health information. This empowerment helps to counteract negative aging stereotypes and mitigate the psychosocial losses associated with aging.^16,17,58 Second, integrating learning methods with confidence-building enhanced the effectiveness of the eHealth literacy course, allowing older individuals to better engage with tele-PR and maintain optimal physical health, leading to better health outcomes.^11,59,60 Third, during the eHealth literacy intervention, older patients reflected on their experience of creating, sharing, and exchanging online health information, which they perceived as a form of social participation.^17,56 This recognition reinforced their sense of personal social value and contributed to positive psychological growth and mental health during aging.

The minimal clinically important difference (MCID) for the CAT in previous studies was about 2 points.⁶¹ There was no significant difference in CAT and the proportion of patients achieving MCID between the SRL-SEe PR and SEe PR groups (26.20% vs. 15.40%, χ² = 2.29, P = 0.13), which may be related to the insufficient observation time. The 8-week intervention and 4-week follow-up period may not have been sufficient to produce a change in symptom burden. However, the patterns of change differed between the two groups, and longer-term observation may reveal differences. Moreover, exercise self-efficacy improved in the intervention group, and this indicator is the best predictor of behavior.

This study demonstrates several advantages in its intervention approach. The three phases of the SRL model inherently promote improvement in self-efficacy.^31–35^,50,51 When combined with intervention strategies based on SEe, this approach is particularly effective in enhancing self-efficacy. For example, in the planning phase, setting phased goals and achieving them step-by-step fosters a sense of accomplishment. In the stage of behavioral performance, individuals need to complete the task of self-observation, which is conducive to individuals finding their progress. The above process is in line with the direct experience strategy in SEe. In the self-reflection phase, individuals perform a self-attribution analysis of their learning outcomes, another key source of self-efficacy. Moreover, throughout the intervention, encouraging language and sharing exemplary stories reinforce verbal persuasion and vicarious experience, both crucial for improving self-efficacy.^34,35,50 The SRL model's phases include the planning phase, the volitional or behavioral control phase, and the self-reflection phase which enable patients to master SRL methods and are beneficial for older adults learning eHealth literacy courses at home. This study illustrates the integrated application of the SRL-SEe throughout the intervention process.

With the advancement of healthcare digitalization, eHealth literacy has become crucial for helping patients navigate the digital healthcare environment. Improving patients’ eHealth literacy helps them better utilize electronic health records and monitoring tools, optimizing clinical workflows and reducing the burden on healthcare professionals.^16,62 Moreover, when patients possess a certain level of eHealth literacy, they can access personalized support online, reducing misunderstandings and anxiety caused by information asymmetry between healthcare providers and patients.^11,16,63,64 This fosters a more trusting and harmonious relationship between both parties and improves the quality and efficiency of care services. Furthermore, enhancing patients’ eHealth literacy encourages their active participation in managing their health and improving care quality and safety.^11,19,65,66 Moving forward, healthcare professionals should prioritize patient education and training to enhance digital skills and information literacy, creating a more efficient and safe healthcare system.

This study has several strengths. First, this study pays special attention to the training of eHealth literacy for older patients when implementing tele-PR. The knowledge and skills of PR were integrated into eHealth literacy training. Such knowledge is closely related to the health of older people. Thus, the interest of older adults in learning to search, discriminate, and apply knowledge is improved. In addition, as a way of COPD self-management, tele-PR itself includes online education. Integrating the knowledge related to tele-PR and eHealth literacy intervention can make tele-PR enhance and help to improve the performance of chronic disease management. Third, the intervention design was guided by both the SRL-SEe, which provided a solid theoretical foundation. The multicenter randomized controlled design and standardized data quality assurance procedures further improved the reliability and generalizability of the study results. From a public health perspective, improving eHealth literacy among older adults with COPD can help ensure their equitable access to tele-rehabilitation services and improve the accessibility and availability of chronic disease management programs. Empowering older adults with digital literacy not only facilitates their participation in remote mental health rehabilitation programs but also enhances their autonomy in health decision-making. This aligns with global health strategies such as the World Health Organization's Digital Health Framework and the Healthy China 2030 initiative.⁶⁷ Future studies should extend these findings through long-term follow-up investigations to assess whether improvements in eHealth literacy and self-efficacy can translate into sustained behavioral and clinical outcomes. Multiregional studies with larger and more diverse samples are also needed to further validate the effectiveness of theory-based eHealth literacy interventions. In addition, integrating such training into community health and primary care systems could further improve its scalability and accessibility to reach more older people with chronic conditions.

Limitations

However, the study also has limitations. First, the sample size was small and primarily from a single city, necessitating larger multicenter trials for greater reliability and generalizability. Second, the 4-week follow-up was short. Future research should extend this period to evaluate long-term effects. Third, due to limitations in sample size and time, the study included only two randomized controlled groups. Future research should incorporate a control group without theoretical support for comparison across different conceptual models. Finally, outcome measures relied on patient self-reports, which may introduce recall bias, warranting cautious interpretation of the results to ensure data accuracy and reliability.

Conclusions and implications

These findings suggest that the SRL-SEe PR intervention is feasible and effective. eHealth literacy interventions should not only focus on building individuals’ learning confidence but also on teaching learning methods. These strategies can enhance self-efficacy, leading to better learning outcomes and higher eHealth literacy levels. Future remote chronic disease management programs should start with disease-specific knowledge and skills, and incorporate eHealth literacy training.

Supplemental Material

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Footnotes

Acknowledgments

The authors gratefully thank all the patients who participated in this study.

ORCID iDs

Jing Gao

Shiya Cui

Yuyu Jiang

Yi Hou

Xueying Huang

Xueqiong Zou

Xinkang Shi

Chengwang Zuo

Ethical approval and informed consent statement

This study was approved by the Medical Ethics Committee of Jiangnan University (JNU20220310IRB17). Written informed consent from patients has been obtained and their anonymous information will be published in this article.

Author contributions

All authors (Jing Gao, Shiya Cui, Yuyu Jiang, Yi Hou, Xueying Huang, Xueqiong Zou, Xinkang Shi) meet criteria for authorship as stated in the Uniform Requirements for Manuscripts Submitted to Biomedical Journals. Their specific areas of contributions are listed as follows:

Study concept and design: Jing Gao, Shiya Cui, and Yuyu Jiang.

Acquisition of data: Xueqiong Zou and Xinkang Shi.

Analysis and interpretation of data: Jing Gao, Shiya Cui, Yuyu Jiang, Yi Hou, and Xueying Huang.

Drafting of the manuscript: Jing Gao, Shiya Cui, and Yuyu Jiang.

Critical revision of the manuscript for important intellectual content: Jing Gao, Shiya Cui, Yuyu Jiang, Yi Hou, and Xueying Huang.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the Subjects of Nursing Psychology Professional Committee of China Mental Health Association (grant no. 22-23-74) and National Natural Science Foundation of China (grant no. 72274080).

Declaration of conflicting interests

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

Data availability statement

Data are available on request from the authors.

Trial registration

Chinese Clinical Trial Registry (ChiCTR): ChiCTR1900028563; .

Supplemental material

Supplemental material for this article is available online.

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