Voice analysis as an objective state marker in bipolar disorder A Pilot Study of Mood Ratings Captured by Mobile Phone Versus Paper-and-Pencil Mood Charts in Bipolar Disorder Reproductive cycle-associated mood symptoms in women with major depression and bipolar disorder. Baseline mood-state measures as predictors of antidepressant response to scopolamine Clinical significance of lifetime panic spectrum symptoms in the treatment of patients with bipolar I disorder. Predictors of recurrence in bipolar disorder: primary outcomes from the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). Breaking Open the Black Box: Isolating the Most Potent Features of a Web and Mobile Phone-Based Intervention for Depression, Anxiety, and Stress Lithium treatment of acute mania in adolescents: a large open trial. The Brown Assessment of Beliefs Scale: reliability and validity. Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild Self-help interventions for depressive disorders and depressive symptoms: a systematic review Rayyan—a web and mobile app for systematic reviews Harnessing Context Sensing to Develop a Mobile Intervention for Depression Clinical features of depressed outpatients with and without co-occurring general medical conditions in STAR*D. Co-Variation of Depressive Mood and Locomotor Dynamics Evaluated by Ecological Momentary Assessment in Healthy Humans Does psychomotor agitation in major depressive episodes indicate bipolarity? Association between bipolar spectrum features and treatment outcomes in outpatients with major depressive disorder. Is winter depression a bipolar disorder? Longitudinal monitoring of heartbeat dynamics predicts mood changes in bipolar patients: A pilot study. Detecting Bipolar Depression From Geographic Location Data Clinical correlates and symptom patterns of anxious depression among patients with major depressive disorder in STAR*D. Physician Personality Characteristics and Inquiry About Mood Symptoms in Primary Care Effects of Light Treatment on Sleep, Cognition, Mood, and Behavior in Alzheimer’s Disease: A Systematic Review
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 Objective : Patient-reported mood charts are frequently used in management of bipolar disorder . Although mood charts have recently been programmed in electronic devices such as mobile phones , little is known about the impact of the method of data capture on the psychometric properties and validity of these data . Methods : In an ongoing pilot study , a sample of out patients with bipolar disorder were r and omized to complete mood charts either on a mobile phone or with st and ard paper and pencil as part of a 12-week intervention ( primary outcomes for the trial await study completion ) . We compared these conditions across a single item rating of mood state , and we hypothesized that mobile phone-based data capture would produce greater compliance to mood ratings , variability between and within participants , and concurrent validity with blinded clinician-rated affective symptom severity . Results : A total of 56 participants were r and omized and 40 participants were included in the analyses . There were no significant differences between conditions on demographic or clinical variables . The rate of compliance was significantly higher in paper- and -pencil versus mobile phone ratings . Ratings demonstrated significantly more variability within individuals in the mobile phone condition . Mobile phone mood ratings were significantly correlated with clinician-rated depressive symptom severity across the study and with manic symptom severity at the week-6 assessment , whereas paper- and -pencil ratings were not significantly associated with clinician-rated depression or mania . Conclusions : Although preliminary , our results suggest a lower rate of compliance with mobile phones compared to paper- and -pencil daily mood rating in bipolar disorder , yet a greater ability to capture variability and concurrent validity in quantifying affective symptoms . This clinical trial is registered at www . clinical trials.gov as NCT01670123 BACKGROUND We sought to determine the prevalence of , and association between , reproductive cycle-associated mood symptoms in women with affective disorders . We hypothesized that symptoms would correlate with each other across a woman 's reproductive life span in both major depression ( MDD ) and bipolar I disorder ( BP ) . METHODS 2412 women with , MDD or BP were asked st and ardized questions about mood symptoms prior to menstruation , within a month of childbirth and during perimenopause .  Lifetime rates for each of these symptom types were determined and an odds ratio was calculated correlating each of the types with the others . RESULTS Of 2524 women with mood disorders , 67.7 % reported premenstrual symptoms . Of those at risk , 20.9 % reported postpartum symptoms and 26.4 % reported perimenopausal symptoms . The rates did not differ between women with MDD and BP but were significantly different from women who were never ill . The symptoms were significantly correlated in women with MDD with odds ratios from 1.66 to 1.82 , but were not in women with BP . LIMITATIONS This is a secondary analysis of a sample that was collected for other purpose s and is based upon retrospective reporting . CONCLUSIONS Reproductive cycle-associated mood symptoms were commonly reported in women with mood disorders and did not differ based on diagnosis . In MDD , but not BP , the occurrence of these symptoms was trait-like as the presence of one predicted the occurrence of the others . Further prospect i ve study is required to clarify the determinants of this trait Identifying predictors of antidepressant response will facilitate the successful treatment of patients suffering from depression .  Scopolamine produces robust antidepressant responses in unipolar and bipolar depression . Here we evaluate the potential for baseline self-ratings to predict treatment response to scopolamine .  Fifty-one unipolar and bipolar patients participated in a double-blind , placebo-controlled crossover trial . Following a single-blind placebo session , participants r and omly received P/S or S/P ( P=3 placebo ; S=3 scopolamine ( 4μg/kg ) sessions ) . Mood-state self-ratings ( Profile of Mood State ( POMS ) and Visual Analog Scales ( VAS ) ) and depression severity ( Montgomery-Åsberg Depression Rating Scale ( MADRS ) ) were obtained before each infusion . Day 1 ( baseline/placebo ) self-ratings were used in a discriminant function analysis to identify linear combinations of individual items that predict response . The discriminant analysis significantly separated responders from non-responders in both the unipolar and bipolar diagnostic subgroups . The discriminant functions accurately classified over 85 % of patients as responders/non-responders . The POMS depression subscale significantly correlated with clinical response , as did the VAS restlessness , sad , and irritated scales . These results indicate that self-report mood-ratings obtained before treatment can predict response outcome to scopolamine , and suggest that a constellation of mood-state features may be related to clinical response BACKGROUND Given the observed association between panic disorder and bipolar disorder and the potential negative influence of panic symptoms on the course of bipolar illness , we were interested in the effects of what we have defined as " panic spectrum " conditions on the clinical course and treatment outcome in patients with bipolar I ( BPI ) disorder . We hypothesized that lifetime panic spectrum features would be associated with higher levels of suicidal ideation and a poorer response to acute treatment of the index mood episode in this patient population . METHODS A sample of 66 patients with BPI disorder completed a self-report measure of lifetime panic-agoraphobic spectrum symptoms .  Patients falling above and below a predefined clinical threshold for panic spectrum were compared for clinical characteristics , the presence of suicidal ideation during acute treatment , and acute treatment response . RESULTS Half of this outpatient sample reported panic spectrum features above the predefined threshold . These lifetime features were associated with more prior depressive episodes , higher levels of depressive symptoms , and greater suicidal ideation during the acute-treatment phase . Patients with BPI disorder who reported high lifetime panic-agoraphobic spectrum symptom scores took 27 weeks longer than those who reported low scores to remit with acute treatment ( 44 vs 17 weeks , respectively ) . CONCLUSIONS The presence of lifetime panic spectrum symptoms in this sample of patients with BPI disorder was associated with greater levels of depression , more suicidal ideation , and a marked ( 6-month ) delay in time to remission with acute treatment . Alternate treatment strategies are needed for patients with BPI disorder who endorse lifetime panic spectrum features OBJECTIVE Little is known about clinical features associated with the risk of recurrence in patients with bipolar disorder receiving treatment according to contemporary practice guidelines . The authors looked for the features associated with risk of recurrence . METHOD The authors examined prospect i ve data from a cohort of patients with bipolar disorder participating in the multicenter Systematic Treatment Enhancement Program for Bipolar Disorder ( STEP-BD ) study for up to 24 months . For those who were symptomatic at study entry but subsequently achieved recovery , time to recurrence of mania , hypomania , mixed state , or a depressive episode was examined with Cox regression . RESULTS Of 1,469 participants symptomatic at study entry , 858 ( 58.4 % ) subsequently achieved recovery . During up to 2 years of follow-up , 416 ( 48.5 % ) of these individuals experienced recurrences , with more than twice as many developing depressive episodes ( 298 , 34.7 % ) as those who developed manic , hypomanic , or mixed episodes ( 118 , 13.8 % ) . The time until 25 % of the individuals experienced a depressive episode was 21.4 weeks and until 25 % experienced a manic/hypomanic/mixed episode was 85.0 weeks . Residual depressive or manic symptoms at recovery and proportion of days depressed or anxious in the preceding year were significantly associated with shorter time to depressive recurrence . Residual manic symptoms at recovery and proportion of days of elevated mood in the preceding year were significantly associated with shorter time to manic , hypomanic , or mixed episode recurrence . CONCLUSIONS Recurrence was frequent and associated with the presence of residual mood symptoms at initial recovery . Targeting residual symptoms in maintenance treatment may represent an opportunity to reduce risk of recurrence Background Internet-delivered mental health ( eMental Health ) interventions produce treatment effects similar to those observed in face-to-face treatment . However , there is a large degree of variation in treatment effects observed from program to program , and eMental Health interventions remain somewhat of a black box in terms of the mechanisms by which they exert their therapeutic benefit . Trials of eMental Health interventions typically use large sample sizes and therefore provide an ideal context within which to systematic ally investigate the therapeutic benefit of specific program features . Furthermore , the growth and impact of mobile phone technology within eMental Health interventions provides an opportunity to examine associations between symptom improvement and the use of program features delivered across computer and mobile phone platforms . Objective The objective of this study was to identify the patterns of program usage associated with treatment outcome in a r and omized controlled trial ( RCT ) of a fully automated , mobile phone- and Web-based self-help program , “ myCompass ” , for individuals with mild-to-moderate symptoms of depression , anxiety , and /or stress . The core features of the program include interactive psychotherapy modules , a symptom tracking feature , short motivational messages , symptom tracking reminders , and a diary , with many of these features accessible via both computer and mobile phone . Methods Patterns of program usage were recorded for 231 participants with mild-to-moderate depression , anxiety , and /or stress , and who were r and omly allocated to receive access to myCompass for seven weeks during the RCT .  Depression , anxiety , stress , and functional impairment were examined at baseline and at eight weeks . Results Log data indicated that the most commonly used components were the short motivational messages ( used by 68.4 % , 158/231 of participants ) and the symptom tracking feature ( used by 61.5 % , 142/231 of participants ) . Further , after controlling for baseline symptom severity , increased use of these alert features was associated with significant improvements in anxiety and functional impairment . Associations between use of symptom tracking reminders and improved treatment outcome remained significant after controlling for frequency of symptom tracking . Although correlations were not statistically significant , reminders received via SMS ( ie , text message ) were more strongly associated with symptom reduction than were reminders received via email . Conclusions These findings indicate that alerts may be an especially potent component of eMental Health interventions , both via their association with enhanced program usage , as well as independently . Although there was evidence of a stronger association between symptom improvement and use of alerts via the mobile phone platform , the degree of overlap between use of email and SMS alerts may have precluded identification of alert delivery modalities that were most strongly associated with symptom reduction . Future research using r and om assignment to computer and mobile delivery is needed to fully determine the most ideal platform for delivery of this and other features of online interventions . Trial Registration Australian New Zeal and Clinical Trials Registry ( ACTRN ) : 12610000625077 ; http://www.anzctr.org.au/Trial Search .aspx ? ( Archived by WebCite http://www.webcitation.org/6WPqHK0mQ ) OBJECTIVE To examine initial response to treatment in a large sample of acutely manic bipolar I adolescents and to examine potential predictors of nonresponse , such as the presence of prominent depressive features , psychosis , or psychiatric comorbidity . METHOD Adolescents , 12 to 18 years of age , with an acute manic episode were treated with open lithium . Response was defined as a decline in Young Mania Rating Scale total score of > or=33 % and a rating of " much improved " or " very much improved " on the Clinical Global Impressions Improvement item at week 4 . Remission of mania was defined as a Young Mania Rating Scale score of < or=6 . Axis I diagnoses were assessed using the Lifetime Schedule for Affective Disorders and Schizophrenia for Adolescents . RESULTS Of 100 subjects , 63 met response criteria and 26 achieved remission of manic symptoms at the week 4 assessment . Prominent depressive features , age at first mood episode , severity of mania , and comorbidity with attention-deficit/hyperactivity disorder did not distinguish responders from nonresponders . When treated with adjunctive antipsychotic medication , subjects with psychotic features at baseline responded as well as subjects without psychosis . CONCLUSIONS In this largest systematic treatment trial of acutely manic adolescents to date , lithium appears effective for acute stabilization of symptoms . Controlled treatment studies in adolescents with acute mania are needed OBJECTIVE The authors developed and evaluated the reliability and validity of the Brown Assessment of Beliefs Scale , a clinician-administered seven-item scale design ed to assess delusions across a wide range of psychiatric disorders . METHOD The authors developed the scale after review ing the literature on the assessment of delusions . Four raters administered the scale to 20 patients with obsessive-compulsive disorder ( OCD ) , 20 patients with body dysmorphic disorder , and 10 patients with mood disorder with psychotic features .  Audiotaped interviews of scale administration conducted by one rater were independently scored by the other raters to evaluate interrater reliability . The scale was administered to 27 patients twice to determine test-retest reliability . Other insight instruments as well as scales that assess symptom severity were administered to assess convergent and discriminant validity . Sensitivity to change was assessed in a multicenter treatment study of sertraline for OCD . RESULTS Interrater and test-retest reliability for the total score and individual item scores was excellent , with a high degree of internal consistency . One factor was obtained that accounted for 56 % of the variance . Scores on the Brown Assessment of Beliefs Scale were not correlated with symptom severity but were correlated with other measures of insight . The scale was sensitive to change in insight in OCD but was not identical to improvement in severity . CONCLUSIONS The Brown Assessment of Beliefs Scale is a reliable and valid instrument for assessing delusionality in a number of psychiatric disorders . This scale may help clarify whether delusional and nondelusional variants of disorders constitute the same disorder as well as whether delusionality affects treatment outcome and prognosis Background Depression is a burdensome , recurring mental health disorder with high prevalence . Even in developed countries , patients have to wait for several months to receive treatment . In many parts of the world there is only one mental health professional for over 200 people . Smartphones are ubiquitous and have a large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms and providing context -sensitive intervention support . Objective The objective of this study is 2-fold , first to explore the detection of daily-life behavior based on sensor information to identify subjects with a clinical ly meaningful depression level , second to explore the potential of context sensitive intervention delivery to provide in-situ support for people with depressive symptoms . Methods A total of 126 adults ( age 20 - 57 ) were recruited to use the smartphone app Mobile Sensing and Support ( MOSS )  , collecting context -sensitive sensor information and providing just-in-time interventions derived from cognitive behavior therapy . Real-time learning-systems were deployed to adapt to each subject ’s preferences to optimize recommendations with respect to time , location , and personal preference . Biweekly , participants were asked to complete a  self-reported depression survey ( PHQ-9 ) to track symptom progression . Wilcoxon tests were conducted to compare scores before and after intervention . Correlation analysis was used to test the relationship between adherence and change in PHQ-9 . One hundred twenty features were constructed based on smartphone usage and sensors including  accelerometer , Wifi  , and  global positioning systems ( GPS  )  . Machine-learning models used these features to infer behavior and context for PHQ-9 level prediction and tailored intervention delivery . Results A total of 36 subjects used MOSS for ≥2 weeks . For subjects with clinical depression ( PHQ-9≥11 ) at baseline and adherence ≥8 weeks ( n=12 ) , a significant drop in PHQ-9 was observed ( P=.01 ) . This group showed a negative trend between adherence and change in PHQ-9 scores ( rho=−.498 , P=.099 ) . Binary classification performance for biweekly PHQ-9 sample s ( n=143 ) , with a cutoff of PHQ-9≥11 , based on R and om Forest and Support Vector Machine leave-one-out cross validation result ed in 60.1 % and 59.1 % accuracy , respectively . Conclusions Proxies for social and physical behavior derived from smartphone sensor data was successfully deployed to deliver context -sensitive and personalized interventions to people with depressive symptoms . Subjects who used the app for an extended period of time showed significant reduction in self-reported symptom severity . Nonlinear classification models trained on features extracted from smartphone sensor data including Wifi , accelerometer , GPS , and phone use , demonstrated a proof of concept for the detection of depression superior to r and om classification . While findings of effectiveness must be reproduced in a RCT to proof causation , they pave the way for a new generation of digital health interventions leveraging smartphone sensors to provide context sensitive information for in-situ support and unobtrusive monitoring of critical mental health states Background Research suggests that depressive disorders exist on a continuum , with subthreshold symptoms causing considerable population burden and increasing individual risk of developing major depressive disorder . An alternative strategy to professional treatment of subthreshold depression is population promotion of effective self-help interventions that can be easily applied by an individual without professional guidance . The evidence for self-help interventions for depressive symptoms is review ed in the present work , with the aim of identifying promising interventions that could inform future health promotion campaigns or stimulate further research . Methods A literature search for r and omised controlled trials investigating self-help interventions for depressive disorders or depressive symptoms was performed using PubMed , PsycINFO and the Cochrane Data base of Systematic Review s. Reference lists and citations of included studies were also checked . Studies were grouped into those involving participants with depressive disorders or a high level of depressive symptoms , or non- clinical ly depressed participants not selected for depression . A number of exclusion criteria were applied , including trials with small sample sizes and where the intervention was adjunctive to antidepressants or psychotherapy . Results The majority of interventions search ed had no relevant evidence to review . Of the 38 interventions review ed , the ones with the best evidence of efficacy in depressive disorders were S-adenosylmethionine , St John 's wort , bibliotherapy , computerised interventions , distraction , relaxation training , exercise , pleasant activities , sleep deprivation , and light therapy . A number of other interventions showed promise but had received less research attention . Research in non- clinical sample s indicated immediate beneficial effects on depressed mood for distraction , exercise , humour , music , negative air ionisation , and singing ; while potential for helpful longer-term effects was found for autogenic training , light therapy , omega 3 fatty acids , pets , and prayer . Many of the trials were poor quality and may not generalise to self-help without professional guidance . Conclusion A number of self-help interventions have promising evidence for reducing subthreshold depressive symptoms . Other forms of evidence such as expert consensus may be more appropriate for interventions that are not feasible to evaluate in r and omised controlled trials . There needs to be evaluation of whether promotion to the public of effective self-help strategies for subthreshold depressive symptoms could delay or prevent onset of depressive illness , reduce functional impairment , and prevent progression to other undesirable outcomes such as harmful use of substances Background Synthesis of multiple r and omized controlled trials ( RCTs ) in a systematic review can summarize the effects of individual outcomes and provide numerical answers about the effectiveness of interventions . Filtering of search es is time consuming , and no single method fulfills the principal requirements of speed with accuracy . Automation of systematic review s is driven by a necessity to expedite the availability of current best evidence for policy and clinical decision-making . We developed Rayyan ( http://rayyan.qcri.org ) , a free web and mobile app , that helps expedite the initial screening of abstract s and titles using a process of semi-automation while incorporating a high level of usability . For the beta testing phase , we used two published Cochrane review s in which included studies had been selected manually . Their search es , with 1030 records and 273 records , were uploaded to Rayyan . Different features of Rayyan were tested using these two review s. We also conducted a survey of Rayyan ’s users and collected feedback through a built-in feature . Results Pilot testing of Rayyan focused on usability , accuracy against manual methods , and the added value of the prediction feature . The “ taster ” review ( 273 records ) allowed a quick overview of Rayyan for early comments on usability . The second review ( 1030 records ) required several iterations to identify the previously identified 11 trials . The “ suggestions ” and “ hints , ” based on the “ prediction model , ” appeared as testing progressed beyond five included studies . Post rollout user experiences and a reflexive response by the developers enabled real-time modifications and improvements . The survey respondents reported 40 % average time savings when using Rayyan compared to others tools , with 34 % of the respondents reporting more than 50 % time savings . In addition , around 75 % of the respondents mentioned that screening and labeling studies as well as collaborating on review s to be the two most important features of Rayyan . As of November 2016 , Rayyan users exceed 2000 from over 60 countries conducting hundreds of review s totaling more than 1.6 M citations . Feedback from users , obtained mostly through the app web site and a recent survey , has highlighted the ease in exploration of search es , the time saved , and simplicity in sharing and comparing include-exclude decisions . The strongest features of the app , identified and reported in user feedback , were its ability to help in screening and collaboration as well as the time savings it affords to users . Conclusions Rayyan is responsive and intuitive in use with significant potential to lighten the load of review ers 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 A significant percentage of patients with major depressive disorder ( MDD ) suffer from concurrent general medical conditions ( GMCs ) . OBJECTIVE The objective of this preliminary report was to describe the rates of co-occurring significant GMCs and the clinical correlates and symptom features associated with the presence of GMCs . DESIGN Baseline cross-sectional case-control study of patients enrolling in a prospect i ve r and omized multistage treatment study of MDD . SETTING Fourteen regional U.S. centers representing 19 primary care and 22 psychiatric practice s. PATIENTS One thous and five hundred out patients with DSM-IV nonpsychotic MDD . MEASUREMENTS Sociodemographic status , medical illness ratings , psychiatric status , quality of life and DSM-IV depression symptom ratings . RESULTS The prevalence of significant medical comorbidity in this population was 52.8 % ( 95 % CI 50.3 - 55.3 % ) . Concurrent significant medical comorbidity was associated with older age , lower income , unemployment , limited education , longer duration of index depressive episode and absence of self-reported family history of depression . Somatic symptoms common in MDD were endorsed at a higher rate in those with GMCs . Those without a GMC had higher rates of endorsement of impaired mood reactivity , distinct mood quality and interpersonal sensitivity . CONCLUSIONS Concurrent GMCs are common among out patients with MDD in both primary care and specialty setting s. Concurrent GMCs appear to influence the severity and symptom patterns in MDD and describe a vulnerable population with sociodemographic challenges to effective assessment and treatment Computerized ecological momentary assessment ( EMA ) is widely accepted as a “ gold st and ard ” method for capturing momentary symptoms repeatedly experienced in daily life . Although many studies have addressed the within-individual temporal variations in momentary symptoms compared with simultaneously measured external criteria , their concurrent associations , specifically with continuous physiological measures , have not been rigorously examined . Therefore , in the present study , we first examined the variations in momentary symptoms by validating the associations among self-reported symptoms measured simultaneously ( depressive mood , anxious mood , and fatigue ) and then investigated covariant properties between the symptoms ( especially , depressive mood ) and local statistics of locomotor activity as the external objective criteria obtained continuously . Healthy subjects ( N = 85 ) from three different population s ( adolescents , undergraduates , and office workers ) wore a watch-type computer device equipped with EMA software for recording the momentary symptoms experienced by the subjects .  Locomotor activity data were also continuously obtained by using an actigraph built into the device . Multilevel modeling analysis confirmed convergent associations by showing positive correlations among momentary symptoms . The increased intermittency of locomotor activity , characterized by a combination of reduced activity with occasional bursts , appeared concurrently with the worsening of depressive mood . Further , this association remained statistically unchanged across groups regardless of group differences in age , lifestyle , and occupation . These results indicate that the temporal variations in the momentary symptoms are not r and om but reflect the underlying changes in psychophysiological variables in daily life . In addition , our findings on the concurrent changes in depressive mood and locomotor activity may contribute to the continuous estimation of changes in depressive mood and early detection of depressive disorders Abstract Background Kraepelin ’s partial interpretation of agitated depression  as a mixed state of “ manic-depressive insanity ” ( including the current concept of bipolar disorder ) has recently been the focus of much research . This paper tested whether , how , and to what extent both psychomotor symptoms , agitation and retardation in depression are related to bipolarity and anxiety . Method The prospect i ve Zurich Study assessed psychiatric and somatic syndromes in a community sample of young adults ( N = 591 ) ( aged 20 at first interview ) by six interviews over 20 years ( 1979–1999 ) .  Psychomotor symptoms of agitation and retardation were assessed by professional interviewers from age 22 to 40 ( five interviews ) on the basis of the observed and reported behaviour within the interview section on depression . Psychiatric diagnoses were strictly operationalised and , in the case of bipolar-II disorder , were broader than proposed by DSM-IV-TR and ICD-10 . As indicators of bipolarity , the association with bipolar disorder , a family history of mania/hypomania/cyclothymia , together with hypomanic and cyclothymic temperament as assessed by the general  behavior inventory  ( GBI ) [ 15 ] , and mood lability ( an element of cyclothymic temperament ) were used . Results Agitated and retarded depressive states were equally associated with the indicators of bipolarity and with anxiety . Longitudinally , agitation and retardation were significantly associated with each other ( OR = 1.8 , 95 % CI = 1.0–3.2 ) , and this combined group of major depressives showed stronger associations with bipolarity , with both hypomanic/cyclothymic and depressive temperamental traits , and with anxiety . Among agitated , non-retarded depressives , unipolar mood disorder was even twice as common as bipolar mood disorder . Conclusion Combined agitated and retarded major depressive states are more often bipolar than unipolar , but , in general , agitated depression ( with or without retardation ) is not more frequently bipolar than retarded depression ( with or without agitation ) , and pure agitated depression is even much less frequently bipolar than unipolar . The findings do not support the hypothesis that agitated depressive syndromes are mixed states . Limitations The results are limited to a population up to the age of 40 ; bipolar-I disorders could not be analysed ( small N ) CONTEXT It has been suggested that patients with major depressive disorder ( MDD ) who display pretreatment features suggestive of bipolar disorder or bipolar spectrum features might have poorer treatment outcomes . OBJECTIVE To assess the association between bipolar spectrum features and antidepressant treatment outcome in MDD . DESIGN Open treatment followed by sequential r and omized controlled trials . SETTING Primary and specialty psychiatric outpatient centers in the United States . PARTICIPANTS Male and female out patients aged 18 to 75 years with a DSM-IV diagnosis of nonpsychotic MDD who participated in the Sequenced Treatment Alternatives to Relieve Depression ( STAR*D ) study . INTERVENTIONS Open treatment with citalopram followed by up to 3 sequential next-step treatments . MAIN OUTCOME MEASURES Number of treatment levels required to reach protocol -defined remission , as well as failure to return for the postbaseline visit , loss to follow-up , and psychiatric adverse events . For this secondary analysis , putative bipolar spectrum features , including items on the mania and psychosis subscales of the Psychiatric Diagnosis Screening Question naire , were examined for association with treatment outcomes . RESULTS Of the 4041 subjects who entered the study , 1198 ( 30.0 % ) endorsed at least 1 item on the psychosis scale and 1524 ( 38.1 % ) described at least 1 recent maniclike/hypomaniclike symptom . Irritability and psychoticlike symptoms at entry were significantly associated with poorer outcomes across up to 4 treatment levels , as were shorter episodes and some neurovegetative symptoms of depression . However , other indicators of bipolar diathesis including recent maniclike symptoms and family history of bipolar disorder as well as summary measures of bipolar spectrum features were not associated with treatment resistance . CONCLUSION Self-reported psychoticlike symptoms were common in a community sample of out patients with MDD and strongly associated with poorer outcomes . Overall , the data do not support the hypothesis that unrecognized bipolar spectrum illness contributes substantially to antidepressant treatment resistance Sixty-one winter depressive patients were evaluated for evidence of bipolar illness .  Using the Schedule for Affective Disorders and Schizophrenia-Lifetime Version and the General Behavior Inventory , only nine ( 15 % ) could be considered bipolar . On prospect i ve evaluation of patients during the summer following winter depression , few showed signs of manic or hypomanic symptoms . Also , few patients had a family history of bipolar illness . When patients were asked to evaluate symptoms of winter depression , lack of energy was found to be the most prominent feature of the syndrome 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 < 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 Anxious depression , defined as Major Depressive Disorder ( MDD ) with high levels of anxiety symptoms , may represent a relatively common depressive subtype , with distinctive features . OBJECTIVE The objective of this study was to determine the prevalence of anxious depression and to define its clinical correlates and symptom patterns . METHOD Baseline clinical and sociodemographic data were collected on 1450 subjects participating in the STAR*D study . A baseline Hamilton Rating Scale for Depression ( HAM-D ) Anxiety/ Somatization factor score of > or = 7 was considered indicative of anxious depression . The types and degree of concurrent psychiatric symptoms were measured using the  Psychiatric Diagnostic Screening Question naire ( PDSQ ) , by recording the number of items endorsed by study participants for each diagnostic category . MDD symptoms were assessed by clinical telephone interview with the 30-item Inventory of Depressive Symptomatology ( IDS-C30 ) . RESULTS The prevalence of anxious depression in this population was 46 % . Patients with  anxious MDD were significantly more likely to be older , unemployed , less educated , more severely depressed , and to have suicidal ideation before and after adjustment for severity of depression . As far as concurrent psychiatric symptoms are concerned , patients with anxious depression were significantly more likely to endorse symptoms related to generalized anxiety , obsessive compulsive , panic , post-traumatic stress , agoraphobia , hypochondriasis , and somatoform disorders before and after adjustment for severity of depression . Anxious-depression individuals were also significantly less likely to endorse IDS-C30 items concerning atypical features , and were significantly more likely to endorse items concerning melancholic/endogenous depression features . CONCLUSION This study supports specific clinical and sociodemographic correlates of MDD associated with high levels of anxiety ( anxious depression ) ABSTRACT BACKGROUND Depression treatment is often initially sought from primary care physicians . OBJECTIVE To explore the influence of physician personality on depression assessment s. DESIGN Secondary analysis of data collected in a r and omized controlled trial . SETTING Offices of primary care physicians in Rochester , NY . PARTICIPANTS Forty-six physicians ; six female actors . InterventionEighty-six unannounced st and ardized patient ( SPs ) visits ;  physicians saw one SP with major depression and one with adjustment disorder . MEASUREMENTS SPs listened to audiotapes and completed a form on  doctoring behaviors and symptom inquiry  immediately following the visit . For the assessment of diagnostic documentation , SPs ’ medical records were review ed . Physician personality was assessed via items from the NEO-PI-R. RESULTS Physicians who are more dutiful and more vulnerable were more likely to document a diagnosis of depression ; those who are more dutiful also asked fewer questions concerning mood symptoms . LIMITATIONRoles portrayed by the SPs may not reflect the experience of a typical primary care patient . Most of the PCPs were white men . The sample of PCPs was limited to a single geographic location . Effect sizes were modest . CONCLUSIONS The clinical , educational , and translational , implication s of research showing that physician personality traits could affect practice behaviors warrant consideration . Current models of treatment for depression in primary care could be engineered to accommo date the variability in physician personality . Given that there is no single “ correct ” way to ask about mood disorders or suicide , clinicians are encouraged to adopt an approach that fits their personal style and preferences Background : Bright light treatment is a therapeutic intervention mainly used to treat sleep and circadian disturbances in Alzheimer ’s disease ( AD ) patients . Recently , a h and ful of studies also focused on the effect on cognition and behavior . Conflicting findings are reported in the literature , and no definite conclusions have been drawn about its specific therapeutic effect . Summary : The aim of this review is to provide a critical evaluation of available evidence in this field , highlighting the specific characteristics of effective bright light treatment . Eligible studies were required to assess at least one of the following outcome measures : sleep , cognition , mood , and /or behavior ( e.g. , depression , agitation ) . A total of 32 articles were included in this systematic review and identified as research intervention studies about light treatment in AD . The quality of the papers was evaluated based on the US Preventive Service Task Force guidelines . Key Messages : Overall , the current literature suggests that the effects of light treatment  in AD patients are mixed and may be influenced by several factors , but with a general trend toward a positive effect . Bright light seems to be a promising intervention treatment without significant adverse effects ; therefore , further well- design ed r and omized controlled trials are needed taking into account the highlighted recommendations
Hence , continuous and everyday monitoring of behavioral aspects in affective disorders could be a promising supplementary objective measure for estimating depressive mood symptoms .
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 .