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Record W3215730694 · doi:10.2196/33118

Development of a Community-Based e-Health Program for Older Adults With Chronic Diseases: Pilot Pre-Post Study

2021· article· en· W3215730694 on OpenAlexvenueaboutno aff
Xi Vivien Wu, Yanhong Dong, Poh Choo Tan, Peiying Gan, Di Zhang, Yuchen Chi, Felicia Fang Ting Chao, Jinhua Lu, Boon Heng Dennis Teo, Yue Tan

Bibliographic record

VenueJMIR Aging · 2021
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsnot available
FundersChangi General HospitalNational University of Singapore
KeywordsHealth literacyMedicineGerontologyEmpowermentGlycated hemoglobinQuality of life (healthcare)Health careeHealthPhysical therapyPsychologyNursingDiabetes mellitus

Abstract

fetched live from OpenAlex

Background Chronic diseases may impact older adults’ health outcomes, health care costs, and quality of life. Self-management is expected to encourage individuals to make autonomous decisions, adhere to treatment plans, deal with emotional and social consequences, and provide choices for healthy lifestyle. New eHealth solutions significantly increase the health literacy and empower patients in self-management of chronic conditions. Objective This study aims to develop a Community-Based e-Health Program (CeHP) for older adults with chronic diseases and conduct a pilot evaluation. Methods A pilot study with a 2-group pre- and posttest repeated measures design was adopted. Community-dwelling older adults with chronic diseases were recruited from senior activity centers in Singapore. A systematic 3-step process of developing CeHP was coupled with a smart-device application. The development of the CeHP intervention consists of theoretical framework, client-centric participatory action research process, content validity assessment, and pilot testing. Self-reported survey questionnaires and health outcomes were measured before and after the CeHP. The instruments used were the Self-care of Chronic Illness Inventory (SCCII), Healthy Aging Instrument (HAI), Short-Form Health Literacy Scale, 12 Items (HLS-SF 12), Patient Empowerment Scale (PES), and Social Support Questionnaire, 6 items. The following health outcomes were measured: Montreal Cognitive Assessment, Symbol Digit Modalities Test, total cholesterol (TC), high-density lipoproteins, low-density lipoproteins/very-low-density lipoproteins (LDL/VLDL), fasting glucose, glycated hemoglobin (HbA1c), and BMI. Results The CeHP consists of health education, monitoring, and an advisory system for older adults to manage their chronic conditions. It is an 8-week intensive program, including face-to-face and eHealth (Care4Senior App) sessions. Care4Senior App covers health education topics focusing on the management of hypertension, hyperlipidemia, and diabetes, brain health, healthy diet, lifestyle modification, medication adherence, exercise, and mindfulness practice. Content validity assessment indicated that the content of the CeHP is valid, with a content validity index (CVI) ranging 0.86-1 and a scale-CVI of 1. Eight participants in the CeHP group and 4 in the control group completed both baseline and post intervention assessments. Participants in the CeHP group showed improvements in fasting glucose, HbA1c, TC, LDL/VLDL, BMI, SCCII indices (Maintenance, Monitoring, and Management), HAI, and PES scores post intervention, although these changes were not significant. For the participants in the control group, the scores for SCCII (management and confidence) and HLS-SF 12 decreased post intervention. Conclusions The CeHP is feasible, and it engages and empowers community-dwelling older adults to manage their chronic conditions. The rigorous process of program development and pilot evaluation provided valid evidence to expand the CeHP to a larger-scale implementation to encourage self-management, reduce debilitating complications of poorly controlled chronic diseases, promote healthy longevity and social support, and reduce health care costs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.347
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2021
Admission routes2
Has abstractyes

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