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Record W4285066986 · doi:10.2196/34471

An Intervention Offering Self-management Support Through mHealth and Health Coaching to Patients With Prostate Cancer: Interpretive Description of Patients’ Experiences and Perspectives

2022· article· en· W4285066986 on OpenAlexvenueno aff
Louise Faurholt Øbro, Palle Jørn Sloth Osther, Jette Ammentorp, Gitte Thybo Pihl, Kasper Heiselberg, Peter Gall Krogh, Charlotte Handberg

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsmHealthCoachingIntervention (counseling)Prostate cancerSelf-managementMedicinePsychologyCancerMedical educationPsychological interventionNursingPsychotherapistComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: Observational management strategies such as active surveillance and watchful waiting are considered to be acceptable approaches in patients with low-risk localized prostate cancer and a safe alternative to aggressive treatment. During observational management, treatment is postponed until the disease progresses, which often never occurs. However, approximately 90% of patients with a low-risk disease choose aggressive treatment owing to anxiety. Strategies to address anxiety are needed for optimal management of this population and to improve the quality of life of patients with low-risk localized prostate cancer. A review highlighted that mobile health (mHealth) in tandem with health coaching can support patients' self-management of health behaviors and improve well-being. OBJECTIVE: This study aims to explore patients' experiences with and perspectives on an intervention offering self-management support through the use of mHealth devices and health coaching to identify supportive features that enable patients to perform sustainable changes that improve well-being. METHODS: We used an interpretive description approach, combining semistructured interviews with 13 purposively selected patients with prostate cancer and participant observations of patient-coach interactions in coaching sessions. The interviews were transcribed and analyzed. The self-determination theory was used as a theoretical lens. Field notes and coaching notes from each session were used to orient data generation and confirm or challenge the analysis. RESULTS: Our analysis suggested that patients' self-awareness and psychological identity influenced their experiences with and perspectives on the self-management support offered by mHealth and health coaching in clinical practice. The patients' individual experiences and perspectives indicated that they placed themselves in a dynamic continuum of sustaining or repressing their identity, self-awareness, and individual qualities. Our analysis revealed 4 interacting themes, all related to the psychological identity of the patients. CONCLUSIONS: For the group of patients with prostate cancer to experience well-being, we found it important for them to sustain their self-image when offered a self-management intervention. Motivation and autonomy were important aspects for the individual patients to sustain their self-image throughout the intervention. In contrast, demotivation and a sense of paternalism could result in fostering an experience of having to repress self-awareness.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.048
GPT teacher head0.406
Teacher spread0.358 · 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 designQualitative
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

Citations8
Published2022
Admission routes1
Has abstractyes

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