How Mobile Health Technologies Can Transform Social Relationships in a Population-Level Fitness Promotion Campaign
Bibliographic record
Abstract
Interest in mobile health (mHealth) technologies has grown steadily over the past decade, with individuals as well as public and private organizations turning to fitness tracking devices as a technological means of monitoring their health. Research on mHealth technologies has focused mainly on the use of fitness tracking devices on an individual-basis or as part of wellness plans that operate on the organizational level. There appears to be a paucity of research that attends to the use of mHealth technologies in national, population-level health and fitness campaigns. Singapore’s National Steps Challenge (NSC) is one of the first national, population-level campaigns that uses wearable fitness trackers and an accompanying digital app (Healthy 365) to monitor selected health metrics and encourage residents to participate in physical activity. Participants in the NSC are rewarded with health points based on their level of physical activity. These points can be used to redeem goods and services. While there is some research on the health outcomes of the NSC, the social impact of this Challenge has yet to be fully understood. The NSC leverages on the technological affordances and social affordances of wearable fitness trackers and the Healthy 365 app to encourage participants to engage in physical activity through fitness-oriented challenges like the “steps challenge”. Technological affordances extend human affordances or action possibilities by way of technology. Social affordances refer to the action possibilities for socialization that can be actualized when people interact with others and establish social connections. This paper seeks to understand how the use of mHealth technologies among NSC participants can transform their social relationships with others, including their family and friends. We propose that when digital technologies intervene in the relationships between different users, the technological affordances of the technology (consisting of a wearable fitness tracker and the Healthy 365 app) employed in the NSC presents users with action possibilities for socialization, which constitute the social affordances of the technology. How the users engage with the social affordances while actively participating in the NSC would vary depending on changes in their social context, which is contingent on the specific group of people with whom the users choose to socialize and the types of activities that they perform together. In this paper, we offer a small-scale exploratory pilot study that seeks to gain a preliminary understanding of the social impact of the NSC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".