Evidence map›Paper›PMID 39360241›Full record

ArticleDigital health

Distal and proximal factors of wearable users' quantified-self dependence: A cognitive-behavioral model.

Jiaqi Liu, Shuang Chen

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jiaqi LiuSchool of Journalism and Communication, Tsinghua University, Beijing, China.ORCID https://orcid.org/0000-0002-6445-8083
Shuang ChenSchool of Journalism and Communication, Tsinghua University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: While using self-tracking devices for physical health has become ubiquitous, the potential for quantified-self (QS) dependence as a detrimental outcome for mental health is under-explored. This study examined the mechanism of wearable users' QS dependence by investigating both the distal and proximal factors based on a cognitive-behavioral model. Methods: A total of 535 wearable users aged 18-35 years were surveyed in this study. The surveys included control variable questions related to age, gender, monthly income, BMI, and wearable use experience. Key variable measures included distal factor (habitual use of wearables), proximal factors (perceived external regulation, recognition, and perceived irreplaceability), and perceived QS dependence. Structural equation modeling (SEM) was used to test research hypotheses. Results: The results revealed that habitual use of wearables as a distal factor alone was negatively associated with perceived QS dependence. However, it positively influenced perceived external regulation, recognition, and perceived irreplaceability, which in turn significantly contributed to perceived QS dependence, suggesting the suppression effect of the proximal factors. Conclusions: The relationships between habitual use of wearables and QS dependence are complex. Although habitual use may seem apparently harmless, it can indirectly foster maladaptive cognitions, thereby promoting dependence. These findings underscore the potential threats of maladaptive cognitions that may arise from leveraging technology to promote physical health, thus offering guidance to technology designers for interventions.

Indexed as

habitual useperceived external regulationperceived irreplaceabilityquantified-self dependencerecognitionSelf-tracking

Identifiers

PMID39360241
PMCPMC11445773

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.