FRAILSURVEY—an mHealth App for Self-Assessment of Frailty Based on the Portuguese Version of the Groningen Frailty Indicator: Validation and Reliability Study
Bibliographic record
Abstract
BACKGROUND: Portugal is facing the challenge of population ageing, with a notable increase in the proportion of older individuals. This has positioned the country among those in Europe with a high prevalence of frailty. Frailty, a geriatric syndrome characterized by diminished physiological reserve and heightened vulnerability to stressors, imposes a substantial burden on public health. OBJECTIVE: This study seeks to address two primary objectives: (1) translation and psychometric evaluation of the European Portuguese version of the Groningen Frailty Indicator (GFI); and (2) development and evaluation of the FRAILSURVEY app, a novel assessment tool for frailty based on the GFI. By achieving these objectives, the study aims to enhance the accuracy and reliability of frailty assessment in the Portuguese context, ultimately contributing to improved health care outcomes for older individuals in the region. METHODS: To accomplish the objectives of the study, a comprehensive research methodology was used. The study comprised 2 major phases: the initial translation and validation of the GFI into European Portuguese and the development of the FRAILSURVEY app. Following this, an extensive examination of the app's validity and reliability was conducted compared with the conventional paper version of the GFI. A randomized repeated crossover design was used to ensure rigorous evaluation of both assessment methods, using both the paper form of the GFI and the smartphone-based app FRAILSURVEY. RESULTS: The findings of the study revealed promising outcomes in line with the research objectives. The meticulous translation process yielded a final version of the GFI with robust psychometric properties, ensuring clarity and comprehensibility for participants. The study included 522 participants, predominantly women (367/522, 70.3%), with a mean age of 73.7 (SD 6.7) years. Psychometric evaluation of the European Portuguese GFI in paper form demonstrates good reliability (internal consistency: Cronbach a value of 0.759; temporal stability: intraclass correlation coefficient=0.974) and construct validity (revealing a 4D structure explaining 56% of variance). Evaluation of the app-based European Portuguese GFI indicates good reliability (interinstrument reliability: Cohen k=0.790; temporal stability: intraclass correlation coefficient=0.800) and concurrent validity (r=0.694; P<.001). CONCLUSIONS: Both the smartphone-based app and the paper version of the GFI were feasible and acceptable for use. The findings supported that FRAILSURVEY exhibited comparable validity and reliability to its paper counterpart. FRAILSURVEY uses a standardized and validated assessment tool, offering objective and consistent measurements while eliminating subjective biases, enhancing accuracy, and ensuring reliability. This app holds promising potential for aiding health care professionals in identifying frailty in older individuals, enabling early intervention, and improving the management of adverse health outcomes associated with this syndrome. Its integration with electronic health records and other data may lead to personalized interventions, improving frailty management and health outcomes for at-risk individuals.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".