Observational studyThe British journal of general practice : the journal of the Royal College of General Practitioners2020
Detection of frailty in older patients using a mobile app: cross-sectional observational study in primary care.
Observational study in The British journal of general practice : the journal of the Royal College of General Practitioners, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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17 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Frailty detection by healthcare professionals: a systematic review of the available English and French tools and their validation.European geriatric medicine · 2023Pooled it
- Implementing mHealth-Enabled Integrated Care for Complex Chronic Patients With Osteoarthritis Undergoing Primary Hip or Knee Arthroplasty: Prospective, Two-Arm, Parallel Trial.Journal of medical Internet research · 2021Trial
- Why Frailty Should Be Assessed in Older Adults Affected by Leprosy and How the Use of Smartphones Could Enhance This Health Monitoring.JMIR aging · 2026Article
- The association of frailty with health care costs using the FRAIL scale.Aging clinical and experimental research · 2026Article
- Medication Use by Older Adults with Frailty: A Scoping Review.Pharmacy (Basel, Switzerland) · 2025Review
- Comparison of the long-term prognostic value of different frailty instruments in older inpatients: a 5-year prospective cohort study.European journal of medical research · 2025Article
- Frailty in older adults patients: a prospective observational cohort study on subtype identification.European journal of medical research · 2025Observational
- Article
- Development and validation of a generalizable electronic frailty index: a prospective study in China.BMC public health · 2025Article
- Digital health tools applications in frail older adults-a review article.Frontiers in digital health · 2025Review
- Enhancing the convenience of frailty index assessment for elderly Chinese people with machine learning methods.Scientific reports · 2024Article
- Development and Validation of a Nomogram for Predicting Nutritional Risk Based on Frailty Scores in Older Stroke Patients.Aging clinical and experimental research · 2024Article
- Associations Between Frailty and the Increased Risk of Adverse Outcomes Among 38,950 UK Biobank Participants With Prediabetes: Prospective Cohort Study.JMIR public health and surveillance · 2023Article
- Information and Communication Technologies for Managing Frailty: A Systematic Literature Review.Aging and disease · 2021Review
- Implementing Mobile Health-Enabled Integrated Care for Complex Chronic Patients: Intervention Effectiveness and Cost-Effectiveness Study.JMIR mHealth and uHealth · 2021Article
- The relevance of mobile applications helping in doctor-patient relationships.The British journal of general practice : the journal of the Royal College of General Practitioners · 2020Article
- Rapid Geriatric Assessment Using Mobile App in Primary Care: Prevalence of Geriatric Syndromes and Review of Its Feasibility.Frontiers in medicine · 2020Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe main instruments used to assess frailty are the Fried frailty phenotype and the Fatigue, Resistance, Ambulation, Illnesses, and Loss of Weight (FRAIL) scale. Both instruments contain items that must be obtained in a personal interview and cannot be used with an electronic medical record only.
aimTo develop and internally validate a prediction model, based on a points system and integrated in an application (app) for Android, to predict frailty using only variables taken from a patient's clinical history. DESIGN AND
settingA cross-sectional observational study undertaken across the Valencian Community, Spain.
methodA sample of 621 older patients was analysed from January 2017 to May 2018. The main variable was frailty measured using the FRAIL scale. Candidate predictors were: sex, age, comorbidities, or clinical situations that could affect daily life, polypharmacy, and hospital admission in the last year. A total of 3472 logistic regression models were estimated. The model with the largest area under the receiver operating characteristic curve (AUC) was selected and adapted to the points system. This system was validated by bootstrapping, determining discrimination (AUC), and calibration (smooth calibration).
resultsA total of 126 (20.3%) older people were identified as being frail. The points system had an AUC of 0.78 and included as predictors: sex, age, polypharmacy, hospital admission in the last year, and diabetes. Calibration was satisfactory.
conclusionA points system was developed to predict frailty in older people using parameters that are easy to obtain and recorded in the clinical history. Future research should be carried out to externally validate the constructed model.
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