title: Harnessing Context Sensing to Develop a Mobile Intervention for Depression Utilization of the PICO framework to improve searching PubMed for clinical questions Longitudinal monitoring of heartbeat dynamics predicts mood changes in bipolar patients: A pilot study. Using Smartphones to Monitor Bipolar Disorder Symptoms: A Pilot Study Detecting Bipolar Depression From Geographic Location Data Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults - a prospective cohort study Effects of a Sedentary Behavior-Inducing Randomized Controlled Intervention on Depression and Mood Profile in Active Young Adults. Voice analysis as an objective state marker in bipolar disorder
abstract: Background Mobile phone sensors can be used to develop context -aware systems that automatically detect when patients require assistance . Mobile phones can also provide ecological momentary interventions that deliver tailored assistance during problematic situations . However , such approaches have not yet been used to treat major depressive disorder . Objective The purpose of this study was to investigate the technical feasibility , functional reliability , and patient satisfaction with Mobilyze ! , a mobile phone- and Internet-based intervention including ecological momentary intervention and context sensing . Methods We developed a mobile phone application and supporting architecture , in which machine learning models ( ie , learners ) predicted patients ’ mood , emotions , cognitive/motivational states , activities , environmental context , and social context based on at least 38 concurrent phone sensor values ( eg , global positioning system , ambient light , recent calls ) . The website included feedback graphs illustrating correlations between patients ’ self-reported states , as well as didactics and tools teaching patients behavioral activation concepts . Brief telephone calls and emails with a clinician were used to promote adherence . We enrolled 8 adults with major depressive disorder in a single-arm pilot study to receive Mobilyze ! and complete clinical assessment s for 8 weeks . Results Promising accuracy rates ( 60 % to 91 % ) were achieved by learners predicting categorical context ual states ( eg , location ) . For states rated on scales ( eg , mood ) , predictive capability was poor . Participants were satisfied with the phone application and improved significantly on self-reported depressive symptoms ( betaweek = –.82 , P < .001 , per- protocol Cohen d = 3.43 ) and interview measures of depressive symptoms ( betaweek = –.81 , P < .001 , per- protocol Cohen d = 3.55 ) . Participants also became less likely to meet criteria for major depressive disorder diagnosis ( bweek = –.65 , P = .03 , per- protocol remission rate = 85.71 % ) . Comorbid anxiety symptoms also decreased ( betaweek = –.71 , P < .001 , per- protocol Cohen d = 2.58 ) . Conclusions Mobilyze ! is a scalable , feasible intervention with preliminary evidence of efficacy . To our knowledge , it is the first ecological momentary intervention for unipolar depression , as well as one of the first attempts to use context sensing to identify mental health-related states . Several lessons learned regarding technical functionality , data mining , and software development process are discussed . Trial Registration Clinical trials.gov NCT01107041 ; http:// clinical trials.gov/ct2/show/NCT01107041 ( Archived by WebCite at http://www.webcitation.org/60CVjPH0n Background Supporting 21st century health care and the practice of evidence -based medicine ( EBM ) requires ubiquitous access to clinical information and to knowledge-based re sources to answer clinical questions . Many questions go unanswered , however , due to lack of skills in formulating questions , crafting effective search strategies , and accessing data bases to identify best levels of evidence . Methods This r and omized trial was design ed as a pilot study to measure the relevancy of search results using three different interfaces for the PubMed search system . Two of the search interfaces utilized a specific framework called PICO , which was design ed to focus clinical questions and to prompt for publication type or type of question asked . The third interface was the st and ard PubMed interface readily available on the Web . Study subjects were recruited from interns and residents on an inpatient general medicine rotation at an academic medical center in the US . Thirty-one subjects were r and omized to one of the three interfaces , given 3 clinical questions , and asked to search PubMed for a set of relevant articles that would provide an answer for each question . The success of the search results was determined by a precision score , which compared the number of relevant or gold st and ard articles retrieved in a result set to the total number of articles retrieved in that set . Results Participants using the PICO templates ( Protocol A or Protocol B ) had higher precision scores for each question than the participants who used Protocol C , the st and ard PubMed Web interface . ( Question 1 : A = 35 % , B = 28 % , C = 20 % ; Question 2 : A = 5 % , B = 6 % , C = 4 % ; Question 3 : A = 1 % , B = 0 % , C = 0 % ) 95 % confidence intervals were calculated for the precision for each question using a lower boundary of zero . However , the 95 % confidence limits were overlapping , suggesting no statistical difference between the groups . Conclusion Due to the small number of search es for each arm , this pilot study could not demonstrate a statistically significant difference between the search protocol s. However there was a trend towards higher precision that needs to be investigated in a larger study to determine if PICO can improve the relevancy of search results OBJECTIVES Recent research indicates that Heart Rate Variability ( HRV ) is affected in Bipolar Disorders ( BD ) patients . To determine whether such alterations are a mere expression of the current mood state or rather contain longitudinal information on BD course , we examined the potential influence of states adjacent in time upon HRV features measured in a target mood state . METHODS Longitudinal evaluation of HRV was obtained in eight BD patients by using a wearable monitoring system developed within the PSYCHE project . We extracted time-domain , frequency-domain and non-linear HRV-features and trained a Support Vector Machine ( SVM ) to classify HRV-features according to mood state . To evaluate the influence of adjacent mood states , we trained SVM with different HRV-feature sets : 1 ) belonging to each mood state considered alone ; 2 ) belonging to each mood state and normalized using information from the preceding mood state ; 3 ) belonging to each mood state and normalized using information from the preceding and subsequent mood states ; 4 ) belonging to each mood state and normalized using information from two r and omly chosen states . RESULTS SVM classification accuracy within a target state was significantly greater when HRV-features from the previous and subsequent mood states were considered . CONCLUSIONS Although preliminary and in need of replications our results suggest for the first time that psychophysiological states in BD contain information related to the subsequent ones . Such characteristic may be used to improve clinical management and to develop algorithms to predict clinical course and mood switches in individual patients Background Relapse prevention in bipolar disorder can be improved by monitoring symptoms in patients ' daily life .  Smartphone apps are easy-to-use , low-cost tools that can be used to assess this information . To date , few studies have examined the usefulness of smartphone data for monitoring symptoms in bipolar disorder . Objective We present results from a pilot test of a smartphone-based monitoring system , Social Information Monitoring for Patients with Bipolar Affective Disorder ( SIMBA ) , that tracked daily mood , physical activity , and social communication in 13 patients . The objective of this study was to investigate whether smartphone measurements predicted clinical symptoms levels and clinical symptom change . The hypotheses that smartphone measurements are ( 1 ) negatively related to clinical depressive symptoms and ( 2 ) positively related to clinical manic symptoms were tested . Methods Clinical rating scales were administered to assess clinical depressive and manic symptoms . Patients used a smartphone with the monitoring app for up to 12 months . R and om-coefficient multilevel models were computed to analyze the relationship between smartphone data and externally rated manic and depressive symptoms . Overall clinical symptom levels and clinical symptom changes were predicted by separating between-patient and within-patient effects . Using established clinical thresholds from the literature , marginal effect plots displayed clinical relevance of smartphone data . Results Overall symptom levels and change in clinical symptoms were related to smartphone measures . Higher overall levels of clinical depressive symptoms were predicted by lower self-reported mood measured by the smartphone ( beta=-.56 , P<.001 ) . An increase in clinical depressive symptoms was predicted by a decline in social communication ( ie , outgoing text messages : beta=-.28 , P<.001 ) and a decline in physical activity as measured by the smartphone ( ie , cell tower movements : beta=-.11 , P=.03 ) . Higher overall levels of clinical manic symptoms were predicted by lower physical activity on the smartphone ( ie , distance travelled : beta=-.37 , P<.001 ) , and higher social communication ( beta=.48 , P=.03 ) . An increase in clinical manic symptoms was predicted by a decrease in physical activity on the smartphone ( beta=-.17 , P<.001 ) . Conclusions Clinical symptoms were related to some objective and subjective smartphone measurements , but not all smartphone measures predicted the occurrence of bipolar symptoms above clinical thresholds . Thus , smartphones have the potential to monitor bipolar disorder symptoms in patients ’ daily life . Further validation of monitoring tools in a larger sample is needed . Conclusions are limited by the low prevalence of manic and depressive symptoms in the study sample . Trial Registration International St and ard R and omized Controlled Trial Number ( IS RCT N ) : 05663421 ; http://www.controlled-trials.com/IS RCT N05663421 ( Archived by WebCite at http://www.webcitation.org/6d9wsibJB < italic > Objective : < /italic > This paper aims to identify periods of depression using geolocation movements recorded from mobile phones in a prospect i ve community study of individuals with bipolar disorder ( BD ) . < italic > M</italic > ethods : Anonymized geographic location recordings from 22 BD participants and 14 healthy controls ( HC ) were collected over 3 months . Participants reported their depressive symptomatology using a weekly question naire ( QIDS-SR < sub>16</sub > ) . Recorded location data were preprocessed by detecting and removing imprecise data points and features were extracted to assess the level and regularity of geographic movements of the participant . A subset of features were selected using a wrapper feature selection method and presented to 1 ) a linear regression model and a quadratic generalized linear model with a logistic link function for question naire score estimation ; and 2 ) a quadratic discriminant analysis classifier for depression detection in BD participants based on their question naire responses . < italic > R < /italic > esults : HC participants did not report depressive symptoms and their features showed similar distributions to nondepressed BD participants . Question naire score estimation using geolocation-derived features from BD participants demonstrated an optimal mean absolute error rate of 3.73 , while depression detection demonstrated an optimal ( median < inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula > IQR ) < inline-formula > < tex-math notation="LaTeX">$\text{F}_{1}$</tex-math></inline-formula > score of 0.857 < inline-formula > < tex-math notation="LaTeX">$\pm$</tex-math></inline-formula > 0.022 using five features ( classification accuracy : 0.849 < inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula > 0.016 ; sensitivity : 0.839 < inline-formula > < tex-math notation="LaTeX">$\pm$</tex-math></inline-formula > 0.014 ; specificity : 0.872 < inline-formula > < tex-math notation="LaTeX">$\pm$</tex-math></inline-formula > 0.047 ) . < italic > C</italic > onclusion : These results demonstrate a strong link between geographic movements and depression in bipolar disorder . < italic > S</italic > ignificance : To our knowledge , this is the first community study of passively recorded objective markers of depression in bipolar disorder of this scale . The techniques could help individuals monitor their depression and enable healthcare providers to detect those in need of care or treatment Background Because of the quick development and widespread use of mobile phones , and their vast effect on communication and interactions , it is important to study possible negative health effects of mobile phone exposure . The overall aim of this study was to investigate whether there are associations between psychosocial aspects of mobile phone use and mental health symptoms in a prospect i ve cohort of young adults . Methods The study group consisted of young adults 20 - 24 years old ( n = 4156 ) , who responded to a question naire at baseline and 1-year follow-up .  Mobile phone exposure variables included frequency of use , but also more qualitative variables : dem and s on availability , perceived stressfulness of accessibility , being awakened at night by the mobile phone , and personal overuse of the mobile phone . Mental health outcomes included current stress , sleep disorders , and symptoms of depression . Prevalence ratios ( PRs ) were calculated for cross-sectional and prospect i ve associations between exposure variables and mental health outcomes for men and women separately . Results There were cross-sectional associations between high compared to low mobile phone use and stress , sleep disturbances , and symptoms of depression for the men and women . When excluding respondents reporting mental health symptoms at baseline , high mobile phone use was associated with sleep disturbances and symptoms of depression for the men and symptoms of depression for the women at 1-year follow-up . All qualitative variables had cross-sectional associations with mental health outcomes . In prospect i ve analysis , overuse was associated with stress and sleep disturbances for women , and high accessibility stress was associated with stress , sleep disturbances , and symptoms of depression for both men and women . Conclusions High frequency of mobile phone use at baseline was a risk factor for mental health outcomes at 1-year follow-up among the young adults . The risk for reporting mental health symptoms at follow-up was greatest among those who had perceived accessibility via mobile phones to be stressful . Public health prevention strategies focusing on attitudes could include information and advice , helping young adults to set limits for their own and others ' accessibility OBJECTIVE To examine the effects of a free-living , sedentary behavior-inducing r and omized controlled intervention on depression and mood profile . PATIENTS AND METHODS Participants who were confirmed to be active via self-report and accelerometry were r and omly assigned to either a sedentary behavior intervention group ( n=26 ) or a control group  ( n=13 ) by using a 2:1 sample size ratio for intervention and control groups . The intervention group was asked to eliminate all exercise and minimize steps to 5000 or less steps/d for 1 week , whereas the control group was asked to continue normal physical activity levels for 1 week . Both groups completed a depression ( Patient Health Question naire-9 ) and mood ( Profile of Moods States ) survey preintervention and immediately postintervention . The intervention group was asked to resume normal physical activity levels for 1 week postintervention and then completed the assessment s for a third time . All data collection occurred between September 1 , 2015 , and December 1 , 2015 . RESULTS Patient Health Question naire-9 group × time interaction analysis revealed that depression scores significantly increased from visit 1 to visit 2 ( F=11.85 ; P=.001 ) . Paired t tests comparing depression scores from visit 2 to visit 3 exhibited a significant decrease from visit 2 to visit 3 ( P<.001 ) . Profile of Moods States group × time interaction analysis paralleled depression results ; mood scores significantly increased from visit 1 to visit 2 ( F=10.03 ; P=.003 ) and significantly decreased from visit 2 to visit 3 ( P<.001 ) . CONCLUSION A 1-week sedentary behavior-inducing intervention has deleterious effects on depression and mood . To prevent mental health decline in active individuals , consistent regular physical activity may be necessary Changes in speech have been suggested as sensitive and valid measures of depression and mania in bipolar disorder . The present study aim ed at investigating ( 1 ) voice features collected during phone calls as objective markers of affective states in bipolar disorder and ( 2 ) if combining voice features with automatically generated objective smartphone data on behavioral activities ( for example , number of text messages and phone calls per day ) and  electronic self-monitored data ( mood ) on illness activity would increase the accuracy as a marker of affective states . Using smartphones , voice features , automatically generated objective smartphone data on behavioral activities  and  electronic self-monitored data  were collected from 28 out patients with bipolar disorder in naturalistic setting s on a daily basis during a period of 12 weeks . Depressive and manic symptoms were assessed using the Hamilton Depression Rating Scale 17-item and the Young Mania Rating Scale , respectively , by a research er blinded to smartphone data . Data were analyzed using r and om forest algorithms . Affective states were classified using voice features extracted during everyday life phone calls . Voice features were found to be more accurate , sensitive and specific in the classification of manic or mixed states with an area under the curve (AUC)=0.89 compared with an AUC=0.78 for the classification of depressive states . Combining voice features with automatically generated objective smartphone data on behavioral activities  and electronic self-monitored data  increased the accuracy , sensitivity and specificity of classification of affective states slightly . Voice features collected in naturalistic setting s using smartphones may be used as objective state markers in patients with bipolar disorder
target: Hence , continuous and everyday monitoring of behavioral aspects in affective disorders could be a promising supplementary objective measure for estimating depressive mood symptoms .
background: BACKGROUND Several studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms in patients with affective disorders ( unipolar and bipolar disorders ) .
  However , individual studies have reported on different and sometimes contradicting results , and no quantitative systematic review of the correlation between objective behavioral features and depressive mood symptoms has been published .
  OBJECTIVE The objectives of this systematic review were to ( 1 ) provide an overview of the correlations between objective behavioral features and depressive mood symptoms reported in the literature and ( 2 ) investigate the strength and statistical significance of these correlations across studies .
  The answers to these questions could potentially help identify which objective features have shown most promising results across studies .