Clinical trial · Interventional
Development of Distress Management Algorithms Using Mobile Device Based Health Logs in Breast Cancer Survivors
NCT03072966CI-TRIAL-00036446completedN/AClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
Distress monitoring is an important issue in cancer survivors. However, conventional distress screening is very difficult to perform. This study investigates the efficacy of wearable device as a tool of distress monitoring in breast cancer survivors.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Breast Cancer | Malignant Breast Neoplasm | CURATED_EXACT | 0.92 |
| Quality of Life | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Wearable device (Fitbit Charge HR® or Fitbit Alta ®) | Device | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- type
- EXPERIMENTAL
- label
- Distress screening group
- description
- Physical activities will be monitored by wearable device. Patient-reported outcomes including distress, depression, physical activities and quality of life are going to be collected by questionnaires based on smartphone application and paper. The algorithm of distress screening will be developed with the analysis of patterns of physical activities.
- interventionNames
- Device: Wearable device (Fitbit Charge HR® or Fitbit Alta ®)
Primary outcomes (1)
- measure
- Efficacy of distress screening of wearable device
- timeFrame
- 6months
- description
- The results of distress screening questionnaire and patterns of physical activities are going to be compared. The association between those two data will be analyzed. Using data of physical activities, the prediction model of distress will be tested and validated.
Eligibility
Eligibility (as posted)
- Sex
- Female
- Minimum age
- 20 Years
- Maximum age
- 60 Years
Show eligibility criteria text
Inclusion Criteria: * Stage 0-III breast cancer Exclusion Criteria: * Stage IV breast cancer * Breast cancer recurrence or metastasis * Severe medical illness
References
Publications (3)
- DERIVEDJung M, Lee S, Kim J, Kim H, Ko B, Son BH, Ahn SH, Park YR, Cho D, Chung H, Park HJ, Lee M, Lee JW, Chung S, Chung IY. A Mobile Technology for Collecting Patient-Reported Physical Activity and Distress Outcomes: Cross-Sectional Cohort Study. JMIR Mhealth Uhealth. 2020 May 4;8(5):e17320. doi: 10.2196/17320. PMID 32364508
- DERIVEDChung IY, Jung M, Park YR, Cho D, Chung H, Min YH, Park HJ, Lee M, Lee SB, Chung S, Son BH, Ahn SH, Lee JW. Exercise Promotion and Distress Reduction Using a Mobile App-Based Community in Breast Cancer Survivors. Front Oncol. 2020 Jan 10;9:1505. doi: 10.3389/fonc.2019.01505. eCollection 2019. PMID 31998651
- DERIVEDChung IY, Jung M, Lee SB, Lee JW, Park YR, Cho D, Chung H, Youn S, Min YH, Park HJ, Lee M, Chung S, Son BH, Ahn SH. An Assessment of Physical Activity Data Collected via a Smartphone App and a Smart Band in Breast Cancer Survivors: Observational Study. J Med Internet Res. 2019 Sep 6;21(9):13463. doi: 10.2196/13463. PMID 31493319