The Extent of Engagement With Telehealth Approaches by Patients With Advanced Cancer: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Telehealth approaches are increasingly being used to support patients with advanced diseases, including cancer. Evidence suggests that telehealth is acceptable to most patients; however, the extent of and factors influencing patient engagement remain unclear. OBJECTIVE: The aim of this review is to characterize the extent of engagement with telehealth interventions in patients with advanced, incurable cancer reported in the international literature. METHODS: This systematic review was registered with PROSPERO (International Prospective Register of Systematic Reviews) and is reported in line with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. A comprehensive search of databases was undertaken for telehealth interventions (communication between a patient with advanced cancer and their health professional via telehealth technologies), including MEDLINE, Embase, CINAHL, PsycINFO, Cochrane Library, Sociological Abstracts, and Web of Science, from the inception of each electronic database up until December 31, 2020. A narrative synthesis was conducted to outline the design, population, and context of the studies. A conceptual framework of digital engagement comprising quantitative behavioral measures (frequency, amount, duration, and depth of use) framed the analysis of engagement with telehealth approaches. Frequency data were transformed to a percentage (actual patient engagement as a proportion of intended engagement), and the interventions were characterized by intensity (high, medium, and low intended engagement) and mode of delivery for standardized comparisons across studies. RESULTS: Of the 19,676 identified papers, 40 (0.2%) papers covering 39 different studies were eligible for inclusion, dominated by US studies (22/39, 56%), with most being research studies (26/39, 67%). The most commonly reported measure of engagement was frequency (36/39, 92%), with substantial heterogeneity in the way in which it was measured. A standardized percentage of actual patient engagement was derived from 17 studies (17/39, 44%; n=1255), ranging from 51% to 100% with a weighted average of 75.4% (SD 15.8%). A directly proportional relationship was found between intervention intensity and actual patient engagement. Higher engagement occurred when a tablet, computer, or smartphone app was the mode of delivery. CONCLUSIONS: Understanding engagement for people with advanced cancer can guide the development of telehealth approaches from their design to monitoring as part of routine care. With increasing telehealth use, the development of meaningful and context- and condition-appropriate measures of telehealth engagement is needed to address the current heterogeneity in reporting while improving the understanding of optimal implementation of telehealth for oncology and palliative care. TRIAL REGISTRATION: PROSPERO (International Prospective Register of Systematic Reviews) CRD42018117232; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42018117232.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".