Understanding the Relationship between Mood Symptoms and Mobile App Engagement Among Breast Cancer Patients: A Machine Learning Process (Preprint)

2021 ◽  
Author(s):  
Anna N Baglione ◽  
Lihua Cai ◽  
Aram Bahrini ◽  
Isabella Posey ◽  
Mehdi Boukhechba ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mental Health ◽  
Random Forest ◽  
Cancer Patients ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Self Report ◽  
Breast Cancer Patients ◽  
The Relationship

BACKGROUND Health interventions delivered via smart devices are increasingly being used to address mental health challenges associated with cancer treatment. Engagement with mobile interventions has been associated with treatment success, yet the relationship between mood and engagement among cancer patients remains poorly understood. One reason is the lack of a data-driven process for analyzing mood and app engagement data for cancer patients. OBJECTIVE The purpose of this study is to provide a step-by-step process for using app engagement metrics to predict continuously assessed mood outcomes in breast cancer patients. We describe the steps of data preprocessing, feature extraction, and data modeling and prediction. We then apply this process as a case study to data collected from breast cancer patients who engaged with a mobile mental health app intervention (IntelliCare) over 7-weeks. We compare engagement patterns over time (e.g., frequency, days of use) between high- and low-anxious and high- and low-depressed participants. We then use a Linear Mixed Model to identify significant effects and evaluate the performance of Random Forest and XGBoost classifiers in predicting weekly state mood from baseline affect and engagement features. METHODS We describe the steps of data preprocessing, feature extraction, and data modeling and prediction. We then apply this process as a case study to data collected from breast cancer patients who engaged with a mobile mental health app intervention (IntelliCare) over 7-weeks. We compare engagement patterns over time (e.g., frequency, days of use) between high- and low-anxious and high- and low-depressed participants. We then use a Linear Mixed Model to identify significant effects and evaluate the performance of Random Forest and XGBoost classifiers in predicting weekly state mood from baseline affect and engagement features. RESULTS We observed differences in engagement patterns between high- and low-anxious and depressed participants. Linear Mixed Model results varied by the featureset; these results revealed weak effects for several features of engagement, including duration-based metrics and frequency. Accuracy of predicting state mood varied according to classifier and featureset. The XGBoost classifier achieved the highest accuracy for state anxiety prediction when self-report scores and engagement features were used for only the most highly-used apps. The Random Forest classifier achieved the highest accuracy for state depression prediction when self-report scores and engagement features were used from all apps. CONCLUSIONS The results from the case study support the feasibility and potential of our analytic process for understanding the relationship between app engagement and mood outcomes in breast cancer patients. The ability to leverage both self-report and engagement features to predict state mood during an intervention could be used to enhance decision-making for researchers and clinicians, as well as assist in developing more personalized interventions for breast cancer patients.

A dyadic study of employment outcomes and quality of life in patients with breast cancer and their support persons.

2019 ◽  
Vol 37 (27_suppl) ◽  
pp. 176-176
Author(s):  
Christine M Veenstra ◽  
Thomas Braun ◽  
Chandler McLeod ◽  
Daniela Wittmann ◽  
Sarah T. Hawley
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Cancer Patients ◽  
Job Loss ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Employment Outcomes ◽  
Breast Cancer Patients ◽  
Employment Impacts

176 Background: Many women with breast cancer face job loss related to their diagnosis, but little is known about employment outcomes among their partners and other supporters. Moreover, virtually nothing is known about associations between patients’ quality of life and supporters’ employment outcomes. Methods: Breast cancer patients reported to Georgia and LA SEER registries in 2014-15 (N = 2,502, 68% RR) and their key decision support person (DSP) were surveyed separately. 1234 DSPs responded (71% RR). Patients and DSPs were asked about employment impacts of the patient’s breast cancer. Patients’ quality of life (QOL) was measured with the PROMIS scale for global health. Descriptive analyses of employment outcomes (job loss, missed days due to cancer) were generated for patients and DSPs. Associations between patients’ QOL and employment outcomes of patients and their DSPs were assessed using linear mixed model regression analyses stratified by dyad type (partner vs. non-partner DSP). Results: Among DSPs, 43% were partners. 57% were non-partners (daughters, other family, friends). 67% were employed at time of patient’s diagnosis. Among these, 11% were no longer employed at survey completion. 39% missed >30 days work. Non-partner DSPs were as likely as partners to lose their job or miss work because of the patient’s cancer. 65% patients were employed at diagnosis. Compared to patients whose DSP was a partner, patients with non-partner DSP were more likely to lose their job (39% vs. 24%; p<0.01) or miss >30 days work (55% vs. 45%; p<0.01). For patients with partner and non-partner DSPs, having an employed DSP at diagnosis and having an employed DSP who stays employed were associated with improved patient QOL after adjustment for DSP sociodemographic and patient clinical variables. Conclusions: Both non-partner and partner DSPs faced negative employment impacts related to patients’ breast cancer. Job loss and >30 days of missed work were more likely among patients with non-partner DSPs. Having an employed DSP and having an employed DSP who stays employed positively contributed to patients’ QOL.

scholarly journals Use of Mental Health Apps by Breast Cancer Patients and Their Caregivers in the United States: Protocol for a Pilot Pre-Post Study (Preprint)

2018 ◽  
Author(s):  
Philip I Chow ◽  
Shayna L Showalter ◽  
Matthew S Gerber ◽  
Erin Kennedy ◽  
David R Brenin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mental Health ◽  
Mobile Phone ◽  
Cancer Patients ◽  
The United States ◽  
Mobile App ◽  
Self Report ◽  
Phase Iii ◽  
Breast Cancer Patients ◽  
Health Apps

BACKGROUND Over one-third of cancer patients experience clinically significant mental distress, and distress in caregivers can exceed that of the cancer patients for whom they care. There is an urgent need to identify scalable and cost-efficient ways of delivering mental health interventions to cancer patients and their loved ones. OBJECTIVE The aim of this study is to describe the protocol to pilot a mobile app–based mental health intervention in breast cancer patients and caregivers. METHODS The IntelliCare mental health apps are grounded in evidence-based research in psychology. They have not been examined in cancer populations. This pilot study will adopt a within-subject, pre-post study design to inform a potential phase III randomized controlled trial. A target sample of 50 individuals (with roughly equal numbers of patients and caregivers) at least 18 years of age and fluent in English will be recruited at a US National Cancer Institute designated clinical cancer center. Consent will be obtained in writing and a mobile phone will be provided if needed. Self-report surveys assessing mental health outcomes will be administered at a baseline session and after a 7-week intervention. Before using the apps, participants will receive a 30-min coaching call to explain their purpose and function. A 10-min coaching call 3 weeks later will check on user progress and address questions or barriers to use. Self-report and semistructured interviews with participants at the end of the study period will focus on user experience and suggestions for improving the apps and coaching in future studies. RESULTS This study is ongoing, and recruitment will be completed by the end of 2018. CONCLUSIONS Results from this study will inform how scalable mobile phone-delivered programs can be used to support breast cancer patients and their loved ones. CLINICALTRIAL ClinicalTrials.gov NCT03488745; https://clinicaltrials.gov/ct2/show/NCT03488745 INTERNATIONAL REGISTERED REPOR DERR1-10.2196/11452

scholarly journals An Application of the Multivariate Linear Mixed Model to the Analysis of Shoulder Complexity in Breast Cancer Patients

2016 ◽  
Vol 13 (3) ◽  
pp. 274 ◽  
Author(s):  
Gholamreza Oskrochi ◽  
Emmanuel Lesaffre ◽  
Youssof Oskrochi ◽  
Delva Shamley
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Breast Cancer Patients

The relationship of self-acceptance with decision making duration in cancer patient to perform mastectomy

2021 ◽  
pp. 1-5
Author(s):  
Ayu Ratuati Setiawan ◽  
Feny Tunjungsari ◽  
Mochamad Aleq Sander
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Cancer Patients ◽  
Breast Cancer Patients ◽  
Cross Sectional ◽  
Abnormal Growth ◽  
Self Acceptance ◽  
Observation Method ◽  
Relationship Of ◽  
The Relationship

BACKGROUND: Cancer is a disease caused by abnormal growth of body cells that turn malignant and continue to grow uncontrollably. One of the treatments for breast cancer is mastectomy. The quickness of decision-making determines the survival rate of prognosis patients. OBJECTIVE: This study aimed to determine the relationship of self-acceptance with decision-making duration in cancer patients to perform a mastectomy. METHODS: An analytic observation method with cross-sectional design. The samples were taken by purposive sampling method with 50 samples of breast cancer patients. Data collected include age, last level of education, marital status, profession, stage of cancer during mastectomy, self-acceptance score, and decision-making duration to perform a mastectomy. RESULTS: The data analyzed with the Kruskal–Wallis test. The test showed the relationship of self-acceptance (p = 0.027) with decision-making duration in breast cancer patients to perform a mastectomy. CONCLUSION: In Conclusion, there is a relationship of self-acceptance with decision-making duration in breast cancer patients to perform a mastectomy.

The relationship of plasma 7α-hydroxy dehydroepiandrosterone to disease stage and adrenal androgens in breast cancer patients

1980 ◽  
Vol 16 (2) ◽  
pp. 223-228 ◽  
Author(s):  
S.J.M. Skinner ◽  
R.A.F. Couch ◽  
S. Thambyah ◽  
R.J. Dobbs ◽  
S.M. Jordan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Disease Stage ◽  
Breast Cancer Patients ◽  
Adrenal Androgens ◽  
Relationship Of ◽  
The Relationship

Adopting a knowledge translation approach in healthcare co-production. A case study

2020 ◽  
Vol 58 (9) ◽  
pp. 1841-1862 ◽  
Author(s):  
Francesca Dal Mas ◽  
Helena Biancuzzi ◽  
Maurizio Massaro ◽  
Luca Miceli
Keyword(s):  
Breast Cancer ◽  
Knowledge Translation ◽  
Cancer Patients ◽  
Healthcare Sector ◽  
Emotional Engagement ◽  
Breast Cancer Patients ◽  
Case Study Methodology ◽  
Content Type ◽  
Production Program

PurposeThe paper aims to contribute to the debate concerning the use of knowledge translation for implementing co-production processes in the healthcare sector. The study investigates a case study, in which design was used to trigger knowledge translation and foster co-production.Design/methodology/approachThe paper employs a case study methodology by analysing the experience of “Oncology in Motion”, a co-production program devoted to the recovery of breast cancer patients carried on by the IRCCS C.R.O. of Aviano, Italy.FindingsResults show how design could help to translate knowledge from various stakeholders with different skills (e.g. scientists, physicians, nurses) and emotional engagement (e.g. patients and patients' associations) during all the phases of a co-production project to support breast cancer patients in a recovery path. Stewardship theory is used to show that oncology represents a specific research context.Practical implicationsThe paper highlights the vast practical contribution that design can have in empowering knowledge translation at different levels and in a variety of co-production phases, among different stakeholders, facilitating their engagement and the achievement of the desired outcomes.Originality/valueThe paper contributes to the literature on knowledge translation in co-production projects in the healthcare sector showing how design can be effectively implemented.

scholarly journals Daily Collection of Self-Reporting Sleep Disturbance Data via a Smartphone App in Breast Cancer Patients Receiving Chemotherapy: A Feasibility Study (Preprint)

2014 ◽  
Author(s):  
Yul Ha Min ◽  
Jong Won Lee ◽  
Yong-Wook Shin ◽  
Min-Woo Jo ◽  
Guiyun Sohn ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Sleep Disturbance ◽  
Cancer Patients ◽  
Compliance Rate ◽  
Self Report ◽  
Smartphone App ◽  
Breast Cancer Patients ◽  
Individual Patient Level ◽  
Patient Level ◽  
Compliance Curve

BACKGROUND Improvements in mobile telecommunication technologies have enabled clinicians to collect patient-reported outcome (PRO) data more frequently, but there is as yet limited evidence regarding the frequency with which PRO data can be collected via smartphone applications (apps) in breast cancer patients receiving chemotherapy. OBJECTIVE The primary objective of this study was to determine the feasibility of an app for sleep disturbance-related data collection from breast cancer patients receiving chemotherapy. A secondary objective was to identify the variables associated with better compliance in order to identify the optimal subgroups to include in future studies of smartphone-based interventions. METHODS Between March 2013 and July 2013, patients who planned to receive neoadjuvant chemotherapy for breast cancer at Asan Medical Center who had access to a smartphone app were enrolled just before the start of their chemotherapy and asked to self-report their sleep patterns, anxiety severity, and mood status via a smartphone app on a daily basis during the 90-day study period. Push notifications were sent to participants daily at 9 am and 7 pm. Data regarding the patients&#8217; demographics, interval from enrollment to first self-report, baseline Beck&#8217;s Depression Inventory (BDI) score, and health-related quality of life score (as assessed using the EuroQol Five Dimensional [EQ5D-3L] questionnaire) were collected to ascertain the factors associated with compliance with the self-reporting process. RESULTS A total of 30 participants (mean age 45 years, SD 6; range 35-65 years) were analyzed in this study. In total, 2700 daily push notifications were sent to these 30 participants over the 90-day study period via their smartphones, resulting in the collection of 1215 self-reporting sleep-disturbance data items (overall compliance rate=45.0%, 1215/2700). The median value of individual patient-level reporting rates was 41.1% (range 6.7-95.6%). The longitudinal day-level compliance curve fell to 50.0% at day 34 and reached a nadir of 13.3% at day 90. The cumulative longitudinal compliance curve exhibited a steady decrease by about 50% at day 70 and continued to fall to 45% on day 90. Women without any form of employment exhibited the higher compliance rate. There was no association between any of the other patient characteristics (ie, demographics, and BDI and EQ5D-3L scores) and compliance. The mean individual patient-level reporting rate was higher for the subgroup with a 1-day lag time, defined as starting to self-report on the day immediately after enrollment, than for those with a lag of 2 or more days (51.6%, SD 24.0 and 29.6%, SD 25.3, respectively; <i>P</i>=.03). CONCLUSIONS The 90-day longitudinal collection of daily self-reporting sleep-disturbance data via a smartphone app was found to be feasible. Further research should focus on how to sustain compliance with this self-reporting for a longer time and select subpopulations with higher rates of compliance for mobile health care.

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