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Record W3202177903 · doi:10.2196/26226

Features That Middle-aged and Older Cancer Survivors Want in Web-Based Healthy Lifestyle Interventions: Qualitative Descriptive Study

2021· article· en· W3202177903 on OpenAlexvenueno aff
Nataliya V. Ivankova, Laura Q. Rogers, Ivan Herbey, Michelle Y. Martin, Maria Pisu, Dori Pekmezi, Lieu Thompson, Yu‐Mei Schoenberger, Robert A. Oster, Kevin R. Fontaine, Jami L. Anderson, Kelly Kenzik, David Farrell, Wendy Demark‐Wahnefried

Bibliographic record

VenueJMIR Cancer · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsPsychological interventionThematic analysisMedicineFocus groupGerontologyQualitative researchQuality of life (healthcare)ResidenceCancer preventionDescriptive statisticsIntervention (counseling)Family medicineCancerNursingDemography

Abstract

fetched live from OpenAlex

BACKGROUND: With the increasing number of older cancer survivors, it is imperative to optimize the reach of interventions that promote healthy lifestyles. Web-based delivery holds promise for increasing the reach of such interventions with the rapid increase in internet use among older adults. However, few studies have explored the views of middle-aged and older cancer survivors on this approach and potential variations in these views by gender or rural and urban residence. OBJECTIVE: The aim of this study was to explore the views of middle-aged and older cancer survivors regarding the features of web-based healthy lifestyle programs to inform the development of a web-based diet and exercise intervention. METHODS: Using a qualitative descriptive approach, we conducted 10 focus groups with 57 cancer survivors recruited from hospital cancer registries in 1 southeastern US state. Data were analyzed using inductive thematic and content analyses with NVivo (version 12.5, QSR International). RESULTS: A total of 29 male and 28 female urban and rural dwelling Black and White survivors, with a mean age of 65 (SD 8.27) years, shared their views about a web-based healthy lifestyle program for cancer survivors. Five themes emerged related to program content, design, delivery, participation, technology training, and receiving feedback. Cancer survivors felt that web-based healthy lifestyle programs for cancer survivors must deliver credible, high-quality, and individually tailored information, as recommended by health care professionals or content experts. Urban survivors were more concerned about information reliability, whereas women were more likely to trust physicians' recommendations. Male and rural survivors wanted information to be tailored to the cancer type and age group. Privacy, usability, interaction frequency, and session length were important factors for engaging cancer survivors with a web-based program. Female and rural participants liked the interactive nature and visual appeal of the e-learning sessions. Learning from experts, an attractive design, flexible schedule, and opportunity to interact with other cancer survivors in Facebook closed groups emerged as factors promoting program participation. Low computer literacy, lack of experience with web program features, and concerns about Facebook group privacy were important concerns influencing cancer survivors' potential participation. Participants noted the importance of technology training, preferring individualized help to standardized computer classes. More rural cancer survivors acknowledged the need to learn how to use computers. The receipt of regular feedback about progress was noted as encouragement toward goal achievement, whereas women were particularly interested in receiving immediate feedback to stay motivated. CONCLUSIONS: Important considerations for designing web-based healthy lifestyle interventions for middle-aged and older cancer survivors include program quality, participants' privacy, ease of use, attractive design, and the prominent role of health care providers and content experts. Cancer survivors' preferences based on gender and residence should be considered to promote program participation.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.095
GPT teacher head0.412
Teacher spread0.317 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations16
Published2021
Admission routes1
Has abstractyes

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