ArticleDigital health
Seeking connectivity to everyday health and wellness experiences: Specificities and consequences of connective gaps in self-tracking data.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.
- Health Care Professionals' Views on the Use of Passive Sensing, AI, and Machine Learning in Mental Health Care: Systematic Review With Meta-Synthesis.JMIR mental health · 2024Pooled it
- How Self-tracking and the Quantified Self Promote Health and Well-being: Systematic Review.Journal of medical Internet research · 2021Pooled it
- The double-edged sword of self-tracking: investigating factors of technostress in performance-oriented cycling and triathlon.Frontiers in sports and active living · 2024Article
- Interrelationships Between Patients' Data Tracking Practices, Data Sharing Practices, and Health Literacy: Onsite Survey Study.Journal of medical Internet research · 2020Article
- User types, psycho-social effects and societal trends related to the use of consumer health technologies.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveSelf-tracking technologies have created high hopes, even hype, for aiding people to govern their own health risks and promote optimal wellness. High expectations do not, however, necessarily materialize due to connective gaps between personal experiences and self-tracking data. This study examines situations when self-trackers face difficulties in engaging with, and reflecting on, their data with the aim of identifying the specificities and consequences of such connective gaps in self-tracking contexts.
methodsThe study is based on empirical analyses of interviews of inexperienced, experienced and extreme self-trackers (in total 27), who participated in a pilot study aiming at promoting health and wellness.
resultsThe study shows that people using self-tracking devices actively search for constant connectivity to their everyday experiences and particularly health and wellness through personal data but often become disappointed. The results suggest that in connective gaps the personal data remains invisible or inaccurate, generating feelings of confusion and doubt in the users of the self-tracking devices. These are alarming symptoms that may lead to indifference when disconnectivity becomes solidified and data ends up becoming dead, providing nothing useful for the users of self-tracking technologies.
conclusionsHigh expectations which are put on wearables to advance health and wellness may remain unmaterialised due to connective gaps. This is problematic if individuals are increasingly expected to be active in personal data collection and interpretation regarding their own health and wellness.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.