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Record W3108372330 · doi:10.2196/22488

Habit Formation in Wearable Activity Tracker Use Among Older Adults: Qualitative Study

2020· article· en· W3108372330 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJMIR mhealth and uhealth · 2020
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsnot available
FundersCollege of Engineering, Michigan State UniversityMichigan State University
KeywordsActivity trackerWearable computerThematic analysisBitTorrent trackerWearable technologyPsychologyHabitApplied psychologyHuman–computer interactionComputer scienceQualitative researchSocial psychologyEye trackingArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: Wearable activity trackers are popular devices used to motivate behavior change. Wearable activity trackers are especially beneficial for encouraging light physical activity such as walking, which is an ideal behavior for older adults or individuals who cannot be physically active at moderate and vigorous levels. A common problem is that people do not continue to use these wearable devices, with initial behavioral change gains eroding as people disengage. Limited research is available regarding the continued use of wearable activity trackers. The habit formation literature may provide insights into the long-term use of wearables and other health informatics devices. OBJECTIVE: This study aims to uncover the mechanism underlying the long-term continued use of wearable devices among older adults through the theoretical lens of habit formation. METHODS: In-depth interviews were conducted with 20 participants who were aged 65 years or older and had used wearable activity trackers for more than 6 months to understand their experiences and the strategies they employed to support continued use. RESULTS: Thematic analysis of data revealed 8 themes related to habit formation, including aspects in initiation and goal setting, use of contextual cues, action planning, and coping planning. Long-term users tended to have meaningful initiation of wearable activity trackers. They usually started with a small behavioral change goal and gradually increased it. They used consistent time and locational cues to make the use of wearable activity trackers routine. Long-term users also used creative contextual cues and reminders to facilitate action planning, engaged in coping planning to deal with anticipated problems, and had a positive mindset and inventive strategies for managing unfulfillment and lapses. CONCLUSIONS: The results of this qualitative study of long-term users of wearable activity trackers suggest specific ways to enhance long-term habit formation among older adults. These best practices by long-term users can inform the future design of technology-based behavior interventions.

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.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.890
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.001
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 head0.080
GPT teacher head0.400
Teacher spread0.320 · 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