Embracing digital self-tracking for fitness and health: The rise of smartwatches in Ghana's fitness communities
Bibliographic record
Abstract
Considering the growing adoption of smartwatches in Ghanaian fitness communities, this study examines the socio-cultural factors contributing to the rise in digital self-tracking practices. This study is based on four ethnographic revisits to Ghana, where we draw on 20 semi-structured interviews and participant observations with fitness enthusiasts. We apply algorithmic subjectivity as the theoretical lens to analyse the rise and adoption of smartwatches in the construction of fitness and social identities. Using reflexive thematic analysis, we identified four primary features of the adoption of smartwatches in Ghana: minimising the fear of developing non-communicable diseases, counteracting a sitting culture, habit hacking and symbolic power. The study argues that as users strive to achieve their fitness goals, smartwatches in Ghana can be understood as a new form of socio-technical infrastructure that promotes fitness and health-oriented behaviours. We, however, caution that smartwatch use contributes to new ‘algorithmic subjectivities’ which emphasise the body and its health as tied to quantitative outputs.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".