scholarly journals Feature Extraction in Motor Activity Signal: Towards a Depression Episodes Detection in Unipolar and Bipolar Patients

Diagnostics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 8 ◽  
Author(s):  
Laura Zanella-Calzada ◽  
Carlos Galván-Tejada ◽  
Nubia Chávez-Lamas ◽  
M. Gracia-Cortés ◽  
Rafael Magallanes-Quintanar ◽  
...  
Keyword(s):  
Motor Activity ◽  
Wearable Device ◽  
Portable Devices ◽  
Motion Sensors ◽  
A Value ◽  
Bipolar Patients ◽  
Ecological Momentary ◽  
Momentary Assessment ◽  
Automated Tool

Depression is a mental disorder characterized by recurrent sadness and loss of interest in the enjoyment of the positive aspects of life, in addition to fatigue, causing inability to perform daily activities, which leads to a loss of quality of life. To monitor depression (unipolar and bipolar patients), traditional methods rely on reports from patients; nevertheless, bias is commonly present in them. To overcome this problem, Ecological Momentary Assessment (EMA) reports have been widely used, which include data of the behavior, feelings and other types of activities recorded almost in real time through the use of portable devices and smartphones containing motion sensors. In this work a methodology was proposed to detect depressive subjects from control subjects based in the data of their motor activity, recorded by a wearable device, obtained from the “Depresjon” database. From the motor activity signals, the extraction of statistical features was carried out to subsequently feed a random forest classifier. Results show a sensitivity value of 0.867, referring that those subjects with presence of depression have a degree of 86.7% of being correctly classified, while the specificity shows a value of 0.919, referring that those subjects with absence of depression have a degree of 91.9% of being classified with a correct response, using the motor activity signal provided from the wearable device. Based on these results, it is concluded that the motor activity allows distinguishing between the two classes, providing a preliminary and automated tool to specialists for the diagnosis of depression.

scholarly journals Ecological momentary assessment versus retrospective assessment for measuring change in health-related quality of life following cardiac intervention

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Tom H. Oreel ◽  
Philippe Delespaul ◽  
Iris D. Hartog ◽  
José P. S. Henriques ◽  
Justine E. Netjes ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Ecological Momentary Assessment ◽  
Health Related Quality ◽  
Cardiac Intervention ◽  
Measuring Change ◽  
Related Quality ◽  
Health Related ◽  
Ecological Momentary ◽  
Momentary Assessment

Abstract Background Measuring change in health-related quality-of-life (HRQoL) is important to assess the impact of disease and/or treatment. Ecological momentary assessment (EMA) comprises the repeated assessment of momentary HRQoL in the natural environment and is particularly suited to capture daily experiences. Our objective was to study whether change in momentary measures or retrospective measures of HRQoL are more strongly associated with criterion measures of change in HRQoL. Twenty-six coronary artery disease patients completed momentary and retrospective HRQoL questionnaires before and after coronary revascularization. Momentary HRQoL was assessed with 14 items which were repeatedly presented 9 times a day for 7 consecutive days. Each momentary assessment period was followed by a retrospective HRQoL questionnaire that used the same items, albeit phrased in the past tense and employing a one-week time frame. Criterion measures of change comprised the New York Heart Association functioning classification system and the Subjective Significance Change Questionnaire. Regression analysis was used to determine the association of momentary and retrospective HRQoL change with the criterion measures of change. Results Change according to momentary HRQoL items was more strongly associated with criterion measures of change than change according to retrospective HRQoL items. Five of 14 momentary items were significantly associated with the criterion measures. One association was found for the retrospective items, however, in the unexpected direction. Conclusion Momentary HRQoL measures better captured change in HRQoL after cardiac intervention than retrospective HRQoL measures. EMA is a valuable expansion of the armamentarium of psychometrically sound HRQoL measures.

scholarly journals Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study (Preprint)

2019 ◽  
Author(s):  
Rüdiger Pryss ◽  
Winfried Schlee ◽  
Burkhard Hoppenstedt ◽  
Manfred Reichert ◽  
Myra Spiliopoulou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mobile Health ◽  
Daily Life ◽  
Research Question ◽  
Support Vector ◽  
Life Data ◽  
Machine Learning Methods ◽  
Ecological Momentary ◽  
Momentary Assessment

BACKGROUND Tinnitus is often described as the phantom perception of a sound and is experienced by 5.1% to 42.7% of the population worldwide, at least once during their lifetime. The symptoms often reduce the patient’s quality of life. The TrackYourTinnitus (TYT) mobile health (mHealth) crowdsensing platform was developed for two operating systems (OS)—Android and iOS—to help patients demystify the daily moment-to-moment variations of their tinnitus symptoms. In all platforms developed for more than one OS, it is important to investigate whether the crowdsensed data predicts the OS that was used in order to understand the degree to which the OS is a confounder that is necessary to consider. OBJECTIVE In this study, we explored whether the mobile OS—Android and iOS—used during user assessments can be predicted by the dynamic daily-life TYT data. METHODS TYT mainly applies the paradigms ecological momentary assessment (EMA) and mobile crowdsensing to collect dynamic EMA (EMA-D) daily-life data. The dynamic daily-life TYT data that were analyzed included eight questions as part of the EMA-D questionnaire. In this study, 518 TYT users were analyzed, who each completed at least 11 EMA-D questionnaires. Out of these, 221 were iOS users and 297 were Android users. The iOS users completed, in total, 14,708 EMA-D questionnaires; the number of EMA-D questionnaires completed by the Android users was randomly reduced to the same number to properly address the research question of the study. Machine learning methods—a feedforward neural network, a decision tree, a random forest classifier, and a support vector machine—were applied to address the research question. RESULTS Machine learning was able to predict the mobile OS used with an accuracy up to 78.94% based on the provided EMA-D questionnaires on the assessment level. In this context, the daily measurements regarding how users concentrate on the actual activity were particularly suitable for the prediction of the mobile OS used. CONCLUSIONS In the work at hand, two particular aspects have been revealed. First, machine learning can contribute to EMA-D data in the medical context. Second, based on the EMA-D data of TYT, we found that the accuracy in predicting the mobile OS used has several implications. Particularly, in clinical studies using mobile devices, the OS should be assessed as a covariate, as it might be a confounder.

scholarly journals Motion Sensors in Automatic Steering of Hearing Aids

2021 ◽  
Vol 42 (03) ◽  
pp. 237-247
Author(s):  
Eric Branda ◽  
Tobias Wurzbacher
Keyword(s):  
Hearing Aids ◽  
Hearing Aid ◽  
Decision Making Process ◽  
Motion Sensors ◽  
Acoustic Sensors ◽  
Sound Sources ◽  
Acoustic Characteristics ◽  
Automatic Steering ◽  
Ecological Momentary ◽  
Momentary Assessment

AbstractA requirement for modern hearing aids is to evaluate a listening environment for the user and automatically apply appropriate gain and feature settings for optimal hearing in that listening environment. This has been predominantly achieved by the hearing aids' acoustic sensors, which measure acoustic characteristics such as the amplitude and modulation of the incoming sound sources. However, acoustic information alone is not always sufficient for providing a clear indication of the soundscape and user's listening needs. User activity such as being stationary or being in motion can drastically change these listening needs. Recently, hearing aids have begun utilizing integrated motion sensors to provide further information to the hearing aid's decision-making process when determining the listening environment. Specifically, accelerometer technology has proven to be an appropriate solution for motion sensor integration in hearing aids. Recent investigations have shown benefits with integrated motion sensors for both laboratory and real-world ecological momentary assessment measurements. The combination of acoustic and motion sensors provides the hearing aids with data to better optimize the hearing aid features in anticipation of the hearing aid user's listening needs.

scholarly journals Health outcomes among adults initiating medical marijuana for chronic pain: A 3-month prospective study incorporating ecological momentary assessment (EMA)

Cannabis ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 69-83
Author(s):  
Yan Wang ◽  
Jennifer Jacques ◽  
Zhigang Li ◽  
Kimberly Sibille ◽  
Robert Cook
Keyword(s):  
Quality Of Life ◽  
Chronic Pain ◽  
Sleep Quality ◽  
Prospective Study ◽  
Ecological Momentary Assessment ◽  
Medical Cannabis ◽  
Ecological Momentary ◽  
Anxiety Depression ◽  
Momentary Assessment

In response to the need of more rigorous data on medical cannabis and chronic pain, we conducted a 3-month prospective study incorporating ecological momentary assessment (EMA) to examine the effects of medical cannabis on pain, anxiety/depression, sleep, and quality of life. Data were collected from 46 adults (Mean age=55.7±11.9, 52.2% male) newly initiating medical cannabis treatment for chronic pain. Participants completed a baseline survey, EMA for approximately 1 week pre- and up to 3 weeks post- medical cannabis treatment, and a 3-month follow-up survey. The self-reported EMA data (2535 random and 705 daily assessments) indicated significant reductions in momentary pain intensity (b = -16.5, p < .001, 16.5 points reduction on 0-100 visual analog) and anxiety (b = -0.89, p < .05), and significant increase in daily sleep duration (b = 0.34, p < .01) and sleep quality (b = 0.32, p <.001) after participants initiated medical cannabis for a few weeks. At 3 months, self-reported survey data showed significantly lower levels of worst pain (t = -2.38, p < .05), pain interference (t = -3.82, p < .05), and depression (t = -3.43, p < .01), as well as increased sleep duration (t = 3.95, p < .001), sleep quality (t = -3.04, p < .01), and quality of life (t = 4.48, p < .001) compared to baseline. In our sample of primarily middle-aged and older adults with chronic pain, medical cannabis was associated with reduced pain intensity/inference, lower anxiety/depression, and improved sleep and quality of life.

scholarly journals Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study

10.2196/15547 ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. e15547 ◽  
Author(s):  
Rüdiger Pryss ◽  
Winfried Schlee ◽  
Burkhard Hoppenstedt ◽  
Manfred Reichert ◽  
Myra Spiliopoulou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mobile Health ◽  
Daily Life ◽  
Research Question ◽  
Support Vector ◽  
Life Data ◽  
Machine Learning Methods ◽  
Ecological Momentary ◽  
Momentary Assessment

Background Tinnitus is often described as the phantom perception of a sound and is experienced by 5.1% to 42.7% of the population worldwide, at least once during their lifetime. The symptoms often reduce the patient’s quality of life. The TrackYourTinnitus (TYT) mobile health (mHealth) crowdsensing platform was developed for two operating systems (OS)—Android and iOS—to help patients demystify the daily moment-to-moment variations of their tinnitus symptoms. In all platforms developed for more than one OS, it is important to investigate whether the crowdsensed data predicts the OS that was used in order to understand the degree to which the OS is a confounder that is necessary to consider. Objective In this study, we explored whether the mobile OS—Android and iOS—used during user assessments can be predicted by the dynamic daily-life TYT data. Methods TYT mainly applies the paradigms ecological momentary assessment (EMA) and mobile crowdsensing to collect dynamic EMA (EMA-D) daily-life data. The dynamic daily-life TYT data that were analyzed included eight questions as part of the EMA-D questionnaire. In this study, 518 TYT users were analyzed, who each completed at least 11 EMA-D questionnaires. Out of these, 221 were iOS users and 297 were Android users. The iOS users completed, in total, 14,708 EMA-D questionnaires; the number of EMA-D questionnaires completed by the Android users was randomly reduced to the same number to properly address the research question of the study. Machine learning methods—a feedforward neural network, a decision tree, a random forest classifier, and a support vector machine—were applied to address the research question. Results Machine learning was able to predict the mobile OS used with an accuracy up to 78.94% based on the provided EMA-D questionnaires on the assessment level. In this context, the daily measurements regarding how users concentrate on the actual activity were particularly suitable for the prediction of the mobile OS used. Conclusions In the work at hand, two particular aspects have been revealed. First, machine learning can contribute to EMA-D data in the medical context. Second, based on the EMA-D data of TYT, we found that the accuracy in predicting the mobile OS used has several implications. Particularly, in clinical studies using mobile devices, the OS should be assessed as a covariate, as it might be a confounder.

scholarly journals Use of Ecological Momentary Assessment to Determine Which Structural Factors Impact Perceived Teaching Quality of Attending Rounds

2012 ◽  
Vol 4 (3) ◽  
pp. 322-328 ◽  
Author(s):  
Lisa Willett ◽  
Thomas K. Houston ◽  
Gustavo R. Heudebert ◽  
Carlos Estrada
Keyword(s):  
Ecological Momentary Assessment ◽  
Teaching Methods ◽  
Teaching Style ◽  
Teaching Quality ◽  
Structural Factors ◽  
Evaluation Tool ◽  
Attending Rounds ◽  
Ecological Momentary ◽  
Momentary Assessment

Abstract Introduction Providing high-quality teaching to residents during attending rounds is challenging. Reasons include structural factors that affect rounds, which are beyond the attending's teaching style and control. Objective To develop a new evaluation tool to identify the structural components of ward rounds that most affect teaching quality in an internal medicine (IM) residency program. Methods The authors developed a 10-item Ecological Momentary Assessment (EMA) tool and collected daily evaluations for 18 months from IM residents rotating on inpatient services. Residents ranked the quality of teaching on rounds that day, and questions related to their service (general medicine, medical intensive care unit, and subspecialty services), patient census, absenteeism of team members, call status, and number of teaching methods used by the attending. Results Residents completed 488 evaluation cards over 18 months. This found no association between perceived teaching quality and training level, team absenteeism, and call status. We observed differences by service (P &lt; .001) and patient census (P  =  .009). After adjusting for type of service, census was no longer significant. Use of a larger variety of teaching methods was associated with higher perceived teaching quality, regardless of service or census (P for trend &lt; .001). Conclusions The EMA tool successfully identified that higher patient census was associated with lower perceived teaching quality, but the results were also influenced by the type of teaching service. We found that, regardless of census or teaching service, attendings can improve their teaching by diversifying the number of methods used in daily rounds.

Emotionsregulation bei Depression

2013 ◽  
Vol 64 (4) ◽  
pp. 235-243 ◽  
Author(s):  
Sven Barnow ◽  
Maren Aldinger ◽  
Ines Ulrich ◽  
Malte Stopsack
Keyword(s):  
Ecological Momentary Assessment ◽  
Ecological Momentary ◽  
Momentary Assessment

Die Anzahl der Studien, die sich mit dem Zusammenhang zwischen Emotionsregulation (ER) und depressiven Störungen befassen, steigt. In diesem Review werden Studien zusammengefasst und metaanalytisch ausgewertet, die den Zusammenhang zwischen ER und Depression mittels Fragebögen bzw. Ecological Momentary Assessment (EMA) erfassen. Dabei zeigt sich ein ER-Profil welches durch die vermehrte Nutzung von Rumination, Suppression und Vermeidung bei gleichzeitig seltenerem Einsatz von Neubewertung und Problemlösen gekennzeichnet ist. Mit mittleren bis großen Effekten, ist der Zusammenhang zwischen Depression und maladaptiven Strategien besser belegt als bei den adaptiven Formen, wo die Effekte eher moderat ausfielen. EMA-Messungen bestätigen dieses Profil. Da EMA-Studien neben der Häufigkeit des Strategieeinsatzes auch die Erfassung anderer ER-Parameter wie Effektivität und Flexibilität ermöglichen, sollten solche Designs in der ER-Forschung zukünftig vermehrt Einsatz finden.

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