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Record W2285968018 · doi:10.2196/humanfactors.4853

Using the NIATx Model to Implement User-Centered Design of Technology for Older Adults

2016· article· en· W2285968018 on OpenAlexvenueno aff
David H. Gustafson, Adam Maus, Julianne Judkins, Susan Dinauer, Andrew Isham, Roberta Ann Johnson, Gina Landucci, Amy K. Atwood

Bibliographic record

VenueJMIR Human Factors · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
FundersAgency for Healthcare Research and Quality
KeywordsContext (archaeology)Process (computing)Computer scienceKey (lock)eHealthField (mathematics)Process managementUser-centered designmHealthKnowledge managementEngineering managementManagement scienceEngineeringHuman–computer interactionPsychologyHealth careComputer securityPsychological interventionPolitical science

Abstract

fetched live from OpenAlex

What models can effectively guide the creation of eHealth and mHealth technologies? This paper describes the use of the NIATx model as a framework for the user-centered design of a new technology for older adults. The NIATx model is a simple framework of process improvement based on the following principles derived from an analysis of decades of research from various industries about why some projects fail and others succeed: (1) Understand and involve the customer; (2) fix key problems; (3) pick an influential change leader; (4) get ideas from outside the field; (5) use rapid-cycle testing. This paper describes the use of these principles in technology development, the strengths and challenges of using this approach in this context, and lessons learned from the process. Overall, the NIATx model enabled us to produce a user-focused technology that the anecdotal evidence available so far suggests is engaging and useful to older adults. The first and fourth principles were especially important in developing the technology; the fourth proved the most challenging to use.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.429

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.099
GPT teacher head0.382
Teacher spread0.284 · 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 designBench or experimental
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

Citations23
Published2016
Admission routes1
Has abstractyes

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