Growth Mindset Intervention's Impact on Positive Response to eHealth for Older Adults With Chronic Disease: Randomized Controlled Trial
Bibliographic record
Abstract
Background: Although eHealth has shown promise in managing chronic diseases, there remains a substantial digital divide among older adults. The concept of a growth mindset, based on psychological theory, offers a new direction and potential breakthrough for addressing this dilemma. Objective: This study aims to develop and explore the feasibility and efficacy of a growth mindset intervention for older adults with chronic diseases and their positive response to eHealth. Methods: A randomized controlled trial was conducted at the internal medicine departments of a hospital in Hangzhou, Zhejiang Province, China, from September 2021 to October 2022. A total of 77 older patients with chronic disease initially participated in the study. The mean age of the participants was 67.16 (SD 7.04) years, with 42.86% (33/77) being women and 57.14% (44/77) being men. The experimental group received an eHealth program intervention plus a growth mindset intervention over 12 weeks, with weekly sessions for the first 6 weeks and biweekly follow-up phone calls for the next 6 weeks. Each session lasted at least 25-45 minutes. Data were collected using a personal information form, the Implicit Theories of Intelligence Scale-6 (ITIS-6), and a questionnaire on knowledge, willingness, confidence, and practice of smart medicine (KWCP-SM). Measurements were taken at the beginning of the study (T0), immediately after the 6 weeks of training provided to the experimental group (T1), and after the 12 weeks of training for the intervention (T2). Data were analyzed using repeated-measures analysis of variance and analysis of covariance. Results: The final sample comprised 74 participants, of which 36 were in the experimental group and 38 in the control group. After 12 weeks of intervention, the level of growth mindset was significantly higher in the intervention group (P<.05) and significant group × time interaction was observed (Wald=11.57; P<.05) between the two groups. KWCP-SM scores increased in both groups (P<.05), with more significant changes in the intervention group. Conclusions: This study demonstrated the effectiveness of the intervention program in improving the growth mindset level of older adults with chronic diseases and bridging the "digital divide" among them. Future studies should refine this intervention, considering the characteristics and needs of this population, to create fault-tolerant and lifelong growth environments that enhance growth mindset in older adults.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".