Using Smartphone Applications to Manage Chronic Conditions in Older Adults – A Review on Level of Evidence
Bibliographic record
Abstract
Background: From health monitoring to health education and from behaviour change to falls sensing and health alerts to the simple pleasure of communication and connectedness, the mobile technologies (smartphone applications) are changing the lives of older adults.Objective: To examine current evidence of use of smartphones by older adults for health purposes (including communication, education, and health monitoring), and understand gaps and challenges in order to inform the design of future systems given the ubiquity of mobile phone technology.Methods: MEDLINE, CINAHL and Google scholar databases were searched from October 2016 to January 2017. Keywords used include ‘smartphone apps’, ‘mobile phone’, ‘chronic disease’, ‘chronic condition’, ‘older adults’ and ‘elderly’. A total of 12 articles were selected for quality assessment and grading of evidence.Results: Twelve different articles were found and categorized into nine different clinical domains with specific health related interventions. Articles were focused on diabetes care (2 articles), followed by COPD (2 articles), heart disease (1 article), Alzheimer’s/dementia Care (2 articles), osteoarthritis and pain management (1 article), fall prevention (1 article), colon cancer (1 article), palliative care (1 article), chronic kidney disease (1 article). Areas of interest studied included feasibility, acceptability, functionality and thereby determining their effectiveness. There were many different clinical domains; however, most of the studies were pilot studies. Current work in using mobile phones for older adult use are spread across a variety of clinical domains. Findings from different studies indicate that the use of mobile phone interventions has the potential to support successful management of chronic conditions and health behaviour change in older adults.Conclusion: Perceived benefits and willingness to use the smartphone apps are high; however, technical training and cost are main concerns. A common problem with elderly users was their reluctance to press buttons due to the fear of breaking something which has been resolved by touch screen technology of the smartphones. However, the advanced user clicked around the screen until he found what he was looking for, while the others spent a lot of time observing the screen and trying to determine the correct step. Promotion of user-friendly apps are expected especially for older adults having a diminished physical and cognitive abilities.International Journal of Human and Health Sciences Vol. 05 No. 04 October’21 Page: 381-387
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".