MétaCan
Menu
Back to cohort
Record W4385565060 · doi:10.2196/46738

Co-Creating a Digital Life-Integrated Self-Assessment for Older Adults: User Experience Study

2023· article· en· W4385565060 on OpenAlexvenueno aff
Melissa J Böttinger, Elena Litz, Katharina Gordt, Carl-Philipp Jansen, Nicole Memmer, Christian Mychajliw, Leon Radeck, Jürgen M. Bauer, Clemens Becker

Bibliographic record

VenueJMIR Aging · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
FundersUniversität Heidelberg
KeywordsThematic analysisPsychologyIntervention (counseling)Digital healthApplied psychologyMedicineQualitative researchMedical educationGerontologyNursingHealth care

Abstract

fetched live from OpenAlex

BACKGROUND: Older adults are at increased risk of developing health disorders and functional decline. However, owing to time constraints and considerable effort, physicians rarely conduct comprehensive assessments to detect early signs of negative trajectories. If designed properly, digital technologies could identify health risks already at a preclinical stage, thereby facilitating preventive efforts and targeted intervention. For this purpose, a Life-integrated Self-Assessment (LiSA) tablet system will be developed through a structured co-creation process. OBJECTIVE: This study aims to investigate older adults' perceptions of different self-assessment domains, components affecting user experience, risks and benefits associated with LiSA, characteristics of potential LiSA users, and the LiSA concept in general. METHODS: A total of 10 community-dwelling older adults aged ≥70 years were recruited. In total, 6 co-creation workshops were held and started with expert input followed by semistructured discussion rounds. Participants performed hands-on activities with a tablet, including testing of preinstalled self-assessment apps. All workshops were audio recorded and additionally documented by the researchers using flipcharts, notes, and photos. Qualitative content analysis was used to analyze the data following a deductive-inductive approach guided by the Optimized Honeycomb Model for user experience. RESULTS: The group (mean age 77.8, SD 5.1 years) was heterogeneous in terms of previous technology experience and health status. The mean workshop duration was 2 hours (122.5, SD 4.43 min), and an average of 8 (SD 1.15) participants attended each workshop. A total of 11 thematic categories were identified, covering results on all research questions. Participants emphasized a strong interest in conducting a digital self-assessment of physical activity and function and sensory and cognitive functions and requested additional features such as recommendations for actions or reminders. LiSA was perceived as empowering and a motivator to engage in active health care planning as well as enabling shared and informed decision-making. Concerns and barriers included the lack of technical competence, feelings of frustration, and fear of being left alone, with negative assessment results. In essence, participants expressed a positive attitude toward using LiSA repeatedly and identified it as an option to increase the chances of maintaining independence when growing older. CONCLUSIONS: The co-creation participants supported the LiSA approach and were interested in performing regular self-assessments on a long-term basis. In their opinion, LiSA should include relevant assessments capturing physical activity and function and sensory and cognitive functions as well as recommendations for actions. It should be customizable to individual needs. These results will form the basis for a prototype. Iterative development and validation will aim to make LiSA accessible in the public domain as a reliable tablet-based system for self-assessment.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.015
GPT teacher head0.358
Teacher spread0.343 · 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 designQualitative
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

Citations13
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJMIR AgingSame topicTechnology Use by Older AdultsFrench-language works237,207