Evidence map›Paper›PMID 27492880›Full record

ArticleJournal of medical Internet research2016

Depression Screening Using Daily Mental-Health Ratings from a Smartphone Application for Breast Cancer Patients.

Junetae Kim, Sanghee Lim, Yul Ha Min, Yong-Wook Shin, Byungtae Lee, Guiyun Sohn, Kyung Hae Jung, Jae-Ho Lee, Byung Ho Son, Sei Hyun Ahn and 2 more

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04172350 (Developing and Examining Preliminary Effects of An Innovative Care-improvement Smartphone-based Perioperative Solution for Women Undergoing Breast Cancer Surgery), which is not on this map. Cited by 47 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 6 pooled it
9.3field-weighted citation impact, top 2% of its field
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.

NCT04172350 naunknown statusnot on this mapstarted 2019, after this paper: background citation

Developing and Examining Preliminary Effects of An Innovative Care-improvement Smartphone-based Perioperative Solution for Women Undergoing Breast Cancer Surgery (iCareBreast): A Pilot Randomized Controlled Trial

TypeinterventionalSponsorHE Hong-GuRan2019 to 2021Enrolled112ConditionsBreast Cancer Female, SurgeryArmsiCareBreast, Routine care
3 · Its place in the literature

Who cites it

47 citing papers in PubMed, 6 syntheses or guidelines pooled it, 97 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Trial
  8. Trial
  9. Article
  10. Review
  11. Article
  12. Article
  13. Exploration of a Machine Learning Model Using Self-rating Questionnaires for Detecting Depression in Patients with Breast Cancer.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2024
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
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

12 authors at 4 institutions in 2 countries.

Ulsan College · KRKorea Advanced Institute of Science and Technology · KRGachon University · KRJohns Hopkins University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMobile mental-health trackers are mobile phone apps that gather self-reported mental-health ratings from users. They have received great attention from clinicians as tools to screen for depression in individual patients. While several apps that ask simple questions using face emoticons have been developed, there has been no study examining the validity of their screening performance.

objectiveIn this study, we (1) evaluate the potential of a mobile mental-health tracker that uses three daily mental-health ratings (sleep satisfaction, mood, and anxiety) as indicators for depression, (2) discuss three approaches to data processing (ratio, average, and frequency) for generating indicator variables, and (3) examine the impact of adherence on reporting using a mobile mental-health tracker and accuracy in depression screening.

methodsWe analyzed 5792 sets of daily mental-health ratings collected from 78 breast cancer patients over a 48-week period. Using the Patient Health Questionnaire-9 (PHQ-9) as the measure of true depression status, we conducted a random-effect logistic panel regression and receiver operating characteristic (ROC) analysis to evaluate the screening performance of the mobile mental-health tracker. In addition, we classified patients into two subgroups based on their adherence level (higher adherence and lower adherence) using a k-means clustering algorithm and compared the screening accuracy between the two groups.

resultsWith the ratio approach, the area under the ROC curve (AUC) is 0.8012, indicating that the performance of depression screening using daily mental-health ratings gathered via mobile mental-health trackers is comparable to the results of PHQ-9 tests. Also, the AUC is significantly higher (P=.002) for the higher adherence group (AUC=0.8524) than for the lower adherence group (AUC=0.7234). This result shows that adherence to self-reporting is associated with a higher accuracy of depression screening.

conclusionsOur results support the potential of a mobile mental-health tracker as a tool for screening for depression in practice. Also, this study provides clinicians with a guideline for generating indicator variables from daily mental-health ratings. Furthermore, our results provide empirical evidence for the critical role of adherence to self-reporting, which represents crucial information for both doctors and patients.

Indexed as

Mobile ApplicationsSmartphoneBreast NeoplasmsDepressionFemaleHumansMass ScreeningMiddle AgedROC CurveTelemedicinebreast cancer (neoplasms)depressionmental healthsmartphone applications

Identifiers

PMID27492880
PMCPMC4990716
OpenAlexW2498898584

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.