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Record W4395049458 · doi:10.2196/53307

Associations Between Stress, Health Behaviors, and Quality of Life in Young Couples During the Transition to Survivorship: Protocol for a Measurement Burst Study

2024· article· en· W4395049458 on OpenAlexvenueno aff
Dalnim Cho, Michael Roth, Susan K. Peterson, Kristofer Jennings, Seokhun Kim, Shiao‐Pei Weathers, Sairah Ahmed, J. Andrew Livingston, Carlos H. Bárcenas, Y. Nancy You, Kathrin Milbury

Bibliographic record

VenueJMIR Research Protocols · 2024
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsnot available
FundersU.S. Department of Defense
KeywordsSurvivorship curveYoung adultQuality of life (healthcare)GerontologyMedicinePsychologyCancer survivorCancerPerceived Stress ScaleClinical psychologyStress (linguistics)

Abstract

fetched live from OpenAlex

BACKGROUND: Cancer is a life-threatening, stressful event, particularly for young adults due to delays and disruptions in their developmental transitions. Cancer treatment can also cause adverse long-term effects, chronic conditions, psychological issues, and decreased quality of life (QoL) among young adults. Despite numerous health benefits of health behaviors (eg, physical activity, healthy eating, no smoking, no alcohol use, and quality sleep), young adult cancer survivors report poor health behavior profiles. Determining the associations of stress (either cancer-specific or day-to-day stress), health behaviors, and QoL as young adult survivors transition to survivorship is key to understanding and enhancing these survivors' health. It is also crucial to note that the effects of stress on health behaviors and QoL may manifest on a shorter time scale (eg, daily within-person level). Moreover, given that stress spills over into romantic relationships, it is important to identify the role of spouses or partners (hereafter partners) in these survivors' health behaviors and QoL. OBJECTIVE: This study aims to investigate associations between stress, health behaviors, and QoL at both within- and between-person levels during the transition to survivorship in young adult cancer survivors and their partners, to identify the extent to which young adult survivors' and their partners' stress facilitates or hinders their own and each other's health behaviors and QoL. METHODS: We aim to enroll 150 young adults (aged 25-39 years at the time of cancer diagnosis) who have recently completed cancer treatment, along with their partners. We will conduct a prospective longitudinal study using a measurement burst design. Participants (ie, survivors and their partners) will complete a daily web-based survey for 7 consecutive days (a "burst") 9 times over 2 years, with the bursts spaced 3 months apart. Participants will self-report their stress, health behaviors, and QoL. Additionally, participants will be asked to wear an accelerometer to assess their physical activity and sleep during the burst period. Finally, dietary intake (24-hour diet recalls) will be assessed during each burst via telephone by research staff. RESULTS: Participant enrollment began in January 2022. Recruitment and data collection are expected to conclude by December 2024 and December 2026, respectively. CONCLUSIONS: To the best of our knowledge, this will be the first study that determines the interdependence of health behaviors and QoL of young adult cancer survivors and their partners at both within- and between-person levels. This study is unique in its focus on the transition to cancer survivorship and its use of a measurement burst design. Results will guide the creation of a developmentally appropriate dyadic psychosocial or behavioral intervention that improves both young adult survivors' and their partners' health behaviors and QoL and potentially their physical health. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/53307.

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 imitation

Not 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.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.027
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.020
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0030.003
Science and technology studies0.0050.002
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0270.006

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.

Opus teacher head0.459
GPT teacher head0.589
Teacher spread0.130 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreProtocol

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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