Evidence map›Paper›PMID 38313251›Full record

ArticleResearch square2024

Decoding Heterogeneity in Data-Driven Self-Monitoring Adherence Trajectories in Digital Lifestyle Interventions for Weight Loss: A Qualitative Study.

Shiyu Li, Yan Du, Christiane Meireles, Dan Song, Kumar Sharma, Zenong Yin, Bradley Brimhall, Jing Wang

Registry-linked trialOpen access · greenAbstract readPreprint
In one paragraph

Article in Research square, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05071287 (Precision Metabolic Mechanisms of Low Carbohydrate and Ketogenic Diet), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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.

NCT05071287 nacompletednot on this map

Precision Metabolic Mechanisms of Low Carbohydrate and Ketogenic Diet

TypeinterventionalSponsorThe University of Texas Health Science Center at San AntonioRan2021 to 2022Enrolled60ConditionsDiabetic Kidney Disease, Cognitive Function, Weight Loss, Diabetic ControlArmsmHealth + low-fat diet, mHealth + low-carb/ketogenic diet
3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

8 authors at 2 institutions in 1 country.

Shiyu LiDepartment of Kinesiology, Pennsylvania State University.
Yan DuSchool of Nursing, UT Health San Antonio.
Christiane MeirelesSchool of Nursing, UT Health San Antonio.
Dan SongCollege of Nursing, Florida State University.
Kumar SharmaLong School of Medicine, UT Health San Antonio.
Zenong YinDepartment of Public Health, The University of Texas at San Antonio.
Bradley BrimhallLong School of Medicine, UT Health San Antonio.
Jing WangCollege of Nursing, Florida State University.
Florida State University · USThe University of Texas Health Science Center at San Antonio · US

Funding

Using social networks to map and evaluate team science across CTSA hubsUL1TR001427 · NCATS · UNIVERSITY OF FLORIDA · PI MITCHELL, DUANE A. · 2015 to 2024
$37.2M
San Antonio OAIC - Research Education Component (REC)P30AG044271 · NIA · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI Adam Salmon, Elena Volpi · 2015 to 2026
$14.1M
NCATS NIH HHS UL1 TR001427
6 · The paper itself

Abstract

Background: Data-driven trajectory modeling is a promising approach for identifying meaningful participant subgroups with various self-monitoring (SM) responses in digital lifestyle interventions. However, there is limited research investigating factors that underlie different subgroups. This qualitative study aimed to investigate factors contributing to participant subgroups with distinct SM trajectory in a digital lifestyle intervention over 6 months. Methods: Data were collected from a subset of participants (n = 20) in a 6-month digital lifestyle intervention. Participants were classified into Lower SM Group (n = 10) or a Higher SM (n = 10) subgroup based on their SM adherence trajectories over 6 months. Qualitative data were obtained from semi-structured interviews conducted at 3 months. Data were thematically analyzed using a constant comparative approach. Results: Participants were middle-aged (52.9 ± 10.2 years), mostly female (65%), and of Hispanic ethnicity (55%). Four major themes with emerged from the thematic analysis: Acceptance towards SM Technologies, Perceived SM Benefits, Perceived SM Barriers, and Responses When Facing SM Barriers. Participants across both subgroups perceived SM as positive feedback, aiding in diet and physical activity behavior changes. Both groups cited individual and technical barriers to SM, including forgetfulness, the burdensome SM process, and inaccuracy. The Higher SM Group displayed positive problem-solving skills that helped them overcome the SM barriers. In contrast, some in the Lower SM Group felt discouraged from SM. Both subgroups found diet SM particularly challenging, especially due to technical issues such as the inaccurate food database, the time-consuming food entry process in the Fitbit app. Conclusions: This study complements findings from our previous quantitative research, which used data-drive trajectory modeling approach to identify distinct participant subgroups in a digital lifestyle based on individuals' 6-month SM adherence trajectories. Our results highlight the potential of enhancing action planning problem solving skills to improve SM adherence in the Lower SM Group. Our findings also emphasize the necessity of addressing the technical issues associated with current diet SM approaches. Overall, findings from our study may inform the development of practical SM improvement strategies in future digital lifestyle interventions. Trial registration: The study was pre-registered at ClinicalTrials.gov (NCT05071287) on April 30, 2022.

Indexed as

adherencedata sciencelifestyle interventionsobesityqualitative researchself-monitoring

Identifiers

PMID38313251
PMCPMC10836100
OpenAlexW4391015310

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.