Dataset Information

Comparing Approaches to Mobile Depression Assessment for Measurement-Based Care: Prospective Study.

ABSTRACT: BACKGROUND:To inform measurement-based care, practice guidelines suggest routine symptom monitoring, often on a weekly or monthly basis. Increasingly, patient-provider contacts occur remotely (eg, by telephone and Web-based portals), and mobile health tools can now monitor depressed mood daily or more frequently. However, the reliability and utility of daily ratings are unclear. OBJECTIVE:This study aimed to examine the association between a daily depressive symptom measure and the Patient Health Questionnaire-9 (PHQ-9), the most widely adopted depression self-report measure, and compare how well these 2 assessment methods predict patient outcomes. METHODS:A total of 547 individuals completed smartphone-based measures, including the Patient Health Questionnaire-2 (PHQ-2) modified for daily administration, the PHQ-9, and the Sheehan Disability Scale. Multilevel factor analyses evaluated the reliability of latent depression based on the PHQ-2 (for repeated measures) between weeks 2 and 4 and its correlation with the PHQ-9 at week 4. Regression models predicted week 8 depressive symptoms and disability ratings with daily PHQ-2 and PHQ-9. RESULTS:The daily PHQ-2 and PHQ-9 are highly reliable (range: 0.80-0.88) and highly correlated (r=.80). Findings were robust across demographic groups (age, gender, and ethnic minority status). Daily PHQ-2 and PHQ-9 were comparable in predicting week 8 disability and were independent predictors of week 8 depressive symptoms and disability, though the unique contribution of the PHQ-2 was small in magnitude. CONCLUSIONS:Daily completion of the PHQ-2 is a reasonable proxy for the PHQ-9 and is comparable to the PHQ-9 in predicting future outcomes. Mobile assessment methods offer researchers and clinicians reliable and valid new methods for depression assessment that may be leveraged for measurement-based depression care.

SUBMITTER: Bauer AM

PROVIDER: S-EPMC6030575 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Comparing Approaches to Mobile Depression Assessment for Measurement-Based Care: Prospective Study.

Bauer Amy M AM Baldwin Scott A SA Anguera Joaquin A JA Areán Patricia A PA Atkins David C DC

Journal of medical Internet research 20180619 6

<h4>Background</h4>To inform measurement-based care, practice guidelines suggest routine symptom monitoring, often on a weekly or monthly basis. Increasingly, patient-provider contacts occur remotely (eg, by telephone and Web-based portals), and mobile health tools can now monitor depressed mood daily or more frequently. However, the reliability and utility of daily ratings are unclear.<h4>Objective</h4>This study aimed to examine the association between a daily depressive symptom measure and th ...[more]

PMID: 29921564

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Project description:BackgroundConsumer electronics and Web-enabled mobile devices are playing an increasing role in patient care, and their use in the oncologic sector opens up promising possibilities in the fields of supportive cancer care and systematic patient follow-up.ObjectiveThe objective of our study was to assess the acceptance and possible benefits of a mobile app-based concept for supportive care of cancer patients undergoing radiotherapy.MethodsIn total, 975 patients presenting for radiotherapy due to breast or prostate cancer were screened; of them, 200 owned a smartphone and consented to participate in the survey. Patients were requested to complete a questionnaire at 2 time points: prior to the initiation (T0) and after the completion (T1) of radiotherapy. The questionnaire included questions about the habits of smartphone usage, technical knowledge and abilities of the participants, readiness to use a mobile app within the context of radiotherapy, possible features of the mobile app, and general attitude toward the different aspects of oncologic treatments. For quantitative analysis, sum scores were calculated for all areas of interest, and results were correlated with patient characteristics. Additionally, answers were quantitatively compared between time points T0 and T1.ResultsMedian patient age was 57 (range 27-78) years. Of the 200 participants, 131 (66.2%) reported having the ability to use their smartphones with minimal to no help and 75.8% (150/200) had not used their smartphones in a medical context before. However, 73.3% (146/200) and 83.4% (166/200) of patients showed a strong interest in using a mobile app for supportive care during radiotherapy and as part of the clinical follow-up, respectively. Patients most commonly requested functionalities regarding appointment scheduling in the clinic (176/200, 88.0%) and the collection of patient-reported outcome data regarding their illness, therapy, and general well-being (130/200, 65.0%). Age was identified as the most influential factor regarding patient attitude, with patients aged <55 years being significantly more inclined toward and versed in smartphone use (P<.001). The acceptance of mobile apps was significantly higher in patients exhibiting a Karnofsky performance index <80% (P=.01). Support in the context of therapy-related side effects was judged most important by patients with poor clinical performance (P=.006). The overall acceptance of mobile apps in the context of radiotherapy surveillance was high at a median item sum score of 71.4/100 and was not significantly influenced by tumor stage, age, gender, treatment setting, or previous radiotherapies.ConclusionsThe acceptance of mobile apps for the surveillance and follow-up of cancer patients undergoing radiotherapy is high; this high acceptance level will serve as a basis for future clinical trials investigating the clinical benefits of mobile app-based treatment support. Introduction of mobile apps into the clinical routine should be considered as an opportunity to improve and intensify supportive treatment for cancer patients.

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Project description:IntroductionComprehensive geriatric assessment (CGA) is a multidimensional, interdisciplinary diagnostic process used to determine the medical, psychological and functional capabilities of frail older people. The primary aim of our current study is to confirm whether CGA-based outpatient care is superior than usual care in terms of health-related outcomes, resource use and costs.Methods and analysisThe Geriatric Mobile Team trial is designed as a single-centre randomised, controlled, assessor-blinded (at baseline) trial. All participants will be identified via local healthcare registries with the following inclusion criteria: age ≥75 years, ≥3 different diagnoses and ≥3 visits to the emergency care unit (with or without admittance to hospital) during the past 18 months. Nursing home residency will be an exclusion criterion. Baseline assessments will be done before the 1:1 randomisation. Participants in the intervention group will, after an initial CGA, have access to care given by a geriatric team in addition to usual care. The control group receives usual care only. The primary outcome is the total number of inpatient days during the follow-up period. Assessments of the outcomes: mortality, quality of life, health care use, physical functional level, frailty, dependence and cognition will be performed 12 and 24 months after inclusion. Both descriptive and analytical statistics will be used, in order to compare groups and for analyses of outcomes over time including changes therein. The primary outcome will be analysed using analysis of variance, including in-transformed values if needed to achieve normal distribution of the residuals.Ethics and disseminationEthical approval has been obtained and the results will be disseminated in national and international journals and to health care leaders and stakeholders. Protocol amendments will be published in ClinicalTrials.gov as amendments to the initial registration NCT02923843. In case of success, the study will promote the implementation of CGA in outpatient care settings and thereby contribute to an improved care of older people with multimorbidity through dissemination of the results through scientific articles, information to politicians and to the public.Trial registration numberNCT02923843; Pre-results.

Project description:BackgroundApproximately 1 in 5 American adults experience mental illness every year. Thus, mobile phone-based mental health prediction apps that use phone data and artificial intelligence techniques for mental health assessment have become increasingly important and are being rapidly developed. At the same time, multiple artificial intelligence-related technologies (eg, face recognition and search results) have recently been reported to be biased regarding age, gender, and race. This study moves this discussion to a new domain: phone-based mental health assessment algorithms. It is important to ensure that such algorithms do not contribute to gender disparities through biased predictions across gender groups.ObjectiveThis research aimed to analyze the susceptibility of multiple commonly used machine learning approaches for gender bias in mobile mental health assessment and explore the use of an algorithmic disparate impact remover (DIR) approach to reduce bias levels while maintaining high accuracy.MethodsFirst, we performed preprocessing and model training using the data set (N=55) obtained from a previous study. Accuracy levels and differences in accuracy across genders were computed using 5 different machine learning models. We selected the random forest model, which yielded the highest accuracy, for a more detailed audit and computed multiple metrics that are commonly used for fairness in the machine learning literature. Finally, we applied the DIR approach to reduce bias in the mental health assessment algorithm.ResultsThe highest observed accuracy for the mental health assessment was 78.57%. Although this accuracy level raises optimism, the audit based on gender revealed that the performance of the algorithm was statistically significantly different between the male and female groups (eg, difference in accuracy across genders was 15.85%; P<.001). Similar trends were obtained for other fairness metrics. This disparity in performance was found to reduce significantly after the application of the DIR approach by adapting the data used for modeling (eg, the difference in accuracy across genders was 1.66%, and the reduction is statistically significant with P<.001).ConclusionsThis study grounds the need for algorithmic auditing in phone-based mental health assessment algorithms and the use of gender as a protected attribute to study fairness in such settings. Such audits and remedial steps are the building blocks for the widespread adoption of fair and accurate mental health assessment algorithms in the future.

Dataset Information

Comparing Approaches to Mobile Depression Assessment for Measurement-Based Care: Prospective Study.

Publications

Comparing Approaches to Mobile Depression Assessment for Measurement-Based Care: Prospective Study.

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