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Alterations in energy and activity in bipolar disorder (BD) differ between affective states and compared with healthy control individuals (HC). Measurements of activity could discriminate between BD and HC and in the monitoring of affective states within BD. The aims were to investigate differences in 1) passively collected smartphone-based location data (location data) between BD and HC, and 2) location data in BD between affective states.Daily, patients with BD and HC completed smartphone-based self-assessments of mood for up to nine months. Location data reflecting mobility patterns, routine and location entropy was collected daily. A total of 46 patients with BD and 31 HC providing daily data was included.A total of 4,859 observations of smartphone-based self-assessments of mood and mobility patterns were available from patients with BD and 1,747 observations from HC. Patients with BD had lower location entropy compared with HC (B= -0.14, 95% CI= -0.24; -0.034, p=0.009). Patients with BD during a depressive state were less mobile compared with a euthymic state. Patients with BD during an affective state had lower location entropy compared with a euthymic state (p0.0001). The AUC of combined location data was rather high in classifying patients with BD compared with HC (AUC: 0.83).Individuals willing to use smartphones for daily self-monitoring may represent a more motivated group.Alterations in location data reflecting mobility patterns may be a promising measure of illness and illness activity in patients with BD and may be used to monitor the effects of treatments.

BackgroundIt is of crucial importance to be able to discriminate unipolar disorder (UD) from bipolar disorder (BD), as treatments, as well as course of illness, differ between the two disorders. Aims: to investigate whether voice features from naturalistic phone calls could discriminate between 1) UD, BD, and healthy control individuals (HC); 2) different states within UD.MethodsVoice features were collected daily during naturalistic phone calls for up to 972 days. A total of 48 patients with UD, 121 patients with BD, and 38 HC were included. A total of 115483 voice data entries were collected (UD (n= 16454), BD (n= 78733), and HC (n =20296)). Patients evaluated symptoms daily using a smartphone-based system, making it possible to define illness states within UD and BD. Data were analyzed using random forest algorithms.ResultsCompared to BD, UD was classified with a specificity of 0.84 (SD 0.07) /AUC of 0.58 (SD 0.07) and compared to HC with a sensitivity of 0.74 (SD 0.10)/ AUC=0.74 (SD 0.06). Compared to BD during euthymia, UD during euthymia was classified with a specificity of 0.79 (SD 0.05)/ AUC=0.43 (SD 0.16).Compared to BD during depression, UD during depression was classified with a specificity of 0.81 (SD 0.09)/ AUC=0.48 (SD 0.12). Within UD, compared to euthymia, depression was classified with a specificity of 0.70 (SD 0.31)/ AUC=0.65 (SD 0.11). In all models the user-dependent models outperformed the user-independent models.ConclusionsThe results from the present study are promising, but as reflected by the low AUCs, does not support that voice features collected during naturalistic phone calls at the current state of art can be implemented in clinical practice as a supplementary and assisting tool. Further studies are needed.

Digital health technology is promising for improving mental healthcare by enabling continuous monitoring of behaviour by smartphones and wearables, new paradigms for testing in virtual reality, and analysis of big data through machine learning for prediction models. This might advance prevention efforts, and contribute to diagnostics and treatment; however, high quality studies of clinical effects and applicability are needed before implementation. In this review, we summarize the current status of the field relevant for a Danish mental healthcare setting, and comment on challenges.

Objectives(1) To investigate daily smartphone-based self-reported and automatically generated sleep measurements, respectively, against validated rating scales; (2) to investigate if daily smartphone-based self-reported sleep measurements reflected automatically generated sleep measurements and (3) to investigate the differences in smartphone-based sleep measurements between patients with bipolar disorder (BD), unaffected first-degree relatives (UR) and healthy control individuals (HC).MethodsWe included 203 patients with BD, 54 UR and 109 HC in this study. To investigate whether smartphone-based sleep calculated from self-reported bedtime, wake-up time and screen on/off time reflected validated rating scales, we used the Pittsburgh Sleep Quality Index (PSQI) and sleep items on the Hamilton Depression Rating Scale 17-item (HAMD-17) and the Young Mania Rating Scale (YMRS).Findings(1) Self-reported smartphone-based sleep was associated with the PSQI and sleep items of the HAMD and the YMRS. (2) Automatically generated smartphone-based sleep measurements were associated with daily self-reports of hours slept between 12:00 midnight and 06:00. (3) According to smartphone-based sleep, patients with BD slept less between 12:00 midnight and 06:00, with more interruption and daily variability compared with HC. However, differences in automatically generated smartphone-based sleep were not statistically significant.ConclusionSmartphone-based data may represent measurements of sleep patterns that discriminate between patients with BD and HC and potentially between UR and HC.Clinical implicationDetecting sleep disturbances and daily variability in sleep duration using smartphones may be helpful for both patients and clinicians for monitoring illness activity.Trial registration numberclinicaltrials.gov (NCT02888262).

Diagnostic evaluations and early interventions of patients with bipolar disorder (BD) rely on clinical evaluations. Smartphones have been proposed to facilitate continuous and fine-grained self-monitoring of symptoms. The present study aimed to (1) validate daily smartphone-based self-monitored mood, activity, and sleep, against validated questionnaires and clinical ratings in young patients with newly diagnosed BD, unaffected relatives (UR), and healthy controls persons (HC); (2) investigate differences in daily smartphone-based self-monitored mood, activity, and sleep in young patients with newly diagnosed BD, UR, and HC; (3) investigate associations between self-monitored mood and self-monitored activity and sleep, respectively, in young patients with newly diagnosed BD. 105 young patients with newly diagnosed BD, 24 UR and 77 HC self-monitored 2 to 1077 days (median [IQR] = 65 [17.5–112.5]). There was a statistically significantly negative association between the mood item on Hamilton Depression Rating Scale (HAMD) and smartphone-based self-monitored mood (B = − 0.76, 95% CI − 0.91; − 0.63, p B = − 0.44, 95% CI − 0.63; − 0.25, p p p = 0.008) and was positively associated with smartphone-based self-reported activity (p p Trial registration clinicaltrials.gov NCT0288826

To investigate whether mood instability (MI) qualify as a trait marker for bipolar disorder (BD) we investigated: 1) differences in smartphone-based self-reported MI between three groups: patients with newly diagnosed BD, unaffected first-degree relatives (UR), and healthy control individuals (HC); 2) the correlation between MI and functioning, stress, and duration of illness, respectively; and 3) the validity of smartphone-based self-evaluated mood ratings as compared to observer-based ratings of depressed and manic mood.203 patients with newly diagnosed BD, 54 UR and 109 HC were included as part of the longitudinal Bipolar Illness Onset study. Participants completed daily smartphone-based mood ratings for a period of up to two years and were clinically assessed with ratings of depression, mania and functioning.Mood instability scores were statistically significantly higher in patients with BD compared with HC (mean=1.18, 95%CI: 1.12;1.24 vs 1.05, 95%CI: 0.98;1.13, p = 0.007) and did not differ between patients with BD and UR (mean=1.17, 95%CI: 1.07;1.28, p = 0.91). For patients, increased MI scores correlated positively with impaired functioning (p0.001), increased stress level (p0.001) and increasing number of prior mood episodes (p0.001). Smartphone-based mood ratings correlated with ratings of mood according to sub-item 1 on the Hamilton Depression Rating Scale 17-items and the Young Mania Rating Scale, respectively (p´s0.001).The study had a smaller number of UR than planned.Mood instability is increased in patients with newly diagnosed BD and unaffected relatives and associated with decreased functioning. The findings highlight MI as a potential trait marker for BD.

Objectives When reporting observational studies, authors should explicitly discuss the potential for confounding and other biases, but it is unclear to what extent this is carried out within the psychiatric field. Study Design and Setting We reviewed a random sample of 120 articles in the five psychiatric specialty journals with the highest 5-year impact factor in 2015–2018. We evaluated how confounding and bias was considered in the reporting of the discussion and abstract and assessed the relationship with yearly citations. Results The term “confounding” was explicitly mentioned in the abstract or discussion in 66 articles (55.0%; 95% confidence interval (CI): 46.1–63.6) and the term “bias” in 68 articles (56.7%; 95% CI: 47.7–65.2). The authors of 25 articles (20.8%; 95% CI: 14.5–28.9) acknowledged unadjusted confounders. With one exception (0.8%, 95% CI: 0.0–4.6), authors never expressed any caution, limitation, or uncertainty in relation to confounding or other bias in their conclusions or in the abstract. Articles acknowledging nonadjusted confounders were not less frequently cited than articles that did not (median 7.9 vs. 5.6 citations per year, P = 0.03). Conclusion Confounding is overall inadequately addressed in the reporting and bias is often ignored in the interpretation of high-impact observational research in psychiatry.

Introduction The DSM-5 has introduced elevated/irritable mood and increased activity/ energy as equal and necessary criterion A symptoms for a diagnosis of (hypo)mania. The impact of these changes is poorly elucidated. The aim of the study was to investigate differences in the prevalence of elevated/irritable mood with and without co-occurring increased activity, and the associations between these, in patients with an ICD-10 and DSM-IV diagnosis of BD, using real life daily smartphone-based patient-reported measures of mood, irritability and activity. Methods Data from two RCTs investigating the effect of smartphone-based treatment in patients with BD were combined. Patients with BD (N = 117) evaluated mood, irritability and activity level daily for six to nine months via a smartphone-based system. Analyses in this study are exploratory post hoc analyses based on previously published data. Results During the follow-up period, patients reported elevated mood 8.0% of the time, irritability 28.4% of the time and increased activity 20.6% of the time. Co-occurring elevated/irritable mood and activity were prevalent 0.12% of the time for four consecutive days (duration criteria for a hypomanic episode) compared to 24% of the time with elevated/irritable mood without co-occurring increased activity. In linear mixed effect models accommodating for inter-individual and intra-individual variation, there was a statistically significant positive association between mood and activity (B: 0.14, 95% CI: 0.046; 0.24, p = 0.004). There was no association between irritability and activity (p = 0.23). Conclusion Based on real life daily assessments, the prevalence of (hypo)manic episodes is substantial reduced as a result of the introduction of DSM-5 and with potentially clinical consequences.

Background More than half of patients with bipolar disorder (BD) experience anxiety, which is associated with impaired functioning. In patients with BD, the present study aimed (1) to validate daily patient-reported symptoms of anxiety measured using smartphones against clinically rated symptoms of anxiety, (2) to estimate the prevalence of anxiety symptoms, and (3) to investigate the associations between patient-reported anxiety symptoms and stress, quality of life and functioning. Methods A total of 84 patients with BD evaluated their anxiety symptoms daily for nine months using a smartphone-based system. Data on clinically evaluated symptoms of anxiety and functioning and patient-reported stress and quality of life were collected from each patient at five fixed time points during follow-up. Results The patients presented mild affective symptoms only. The reporting of anxiety symptoms was evaluated for validity according to clinically evaluated anxiety scores based on the two anxiety sub-items of the Hamilton Depression Rating Scale. The patients experienced symptoms of anxiety 19.3% of the time. There were statistically significant associations between anxiety and stress, quality of life and functioning (all p -values Conclusion In patients with BD in full or partial remission, the self-reporting of anxiety symptoms using smartphones was validated. Anxiety is associated with increased stress, decreased quality of life and functioning even during full or partial remission. Identifying anxiety symptoms thus has clinical impact, which suggests that smartphones may serve as a valid tool.

Objective The Cortisol Awakening Response (CAR) measured as the transient increase in cortisol levels following morning awakening appears to be a distinct feature of the HPA axis. Patients with bipolar disorder (BD) experience daily stress, mood instability (MI) and studies have shown disrupted HPA-axis dynamics. Aims: to evaluate (1) patient-evaluated stress against the CAR, (2) associations between the CAR and mood symptoms, and (3) the effect of smartphone-based treatment on the CAR. Methods Patients with BD (n = 67) were randomized to the use of daily smartphone-based monitoring (the intervention group) or to the control group for six months. Clinically rated symptoms according to the Hamilton Depression Rating Scale 17-items (HDRS), the Young Mania Rating Scale (YMRS), patient-evaluated perceived stress using Cohen’s Perceived Stress Scale (PSS) and salivary awakening cortisol samples used for measuring the CAR were collected at baseline, after three and six months. In the intervention group, smartphone-based data on stress and MI were rated daily during the entire study period. Results Smartphone-based patient-evaluated stress (B: 134.14, 95% CI: 1.35; 266.92, p = 0.048) and MI (B: 430.23, 95% CI: 52.41; 808.04, p = 0.026) mapped onto increased CAR. No statistically significant associations between the CAR and patient-evaluated PSS or the HDRS and the YMRS, respectively were found. There was no statistically significant effect of smartphone-based treatment on the CAR. Conclusion Our data, of preliminary character, found smartphone-based patient-evaluations of stress and mood instability as read outs that reflect CAR dynamics. Smartphone-supported clinical care did not in itself appear to disturb CAR dynamics.

As the COVID-19 pandemic has largely increased the utilization of telehealth, mobile mental health technologies – such as smartphone apps, vir­tual reality, chatbots, and social media – have also gained attention. These digital health technologies offer the potential of accessible and scalable interventions that can augment traditional care. In this paper, we provide a comprehensive update on the overall field of digital psychiatry, covering three areas. First, we outline the relevance of recent technological advances to mental health research and care, by detailing how smartphones, social media, artificial intelligence and virtual reality present new opportunities for “digital phenotyping” and remote intervention. Second, we review the current evidence for the use of these new technological approaches across different mental health contexts, covering their emerging efficacy in self-management of psychological well-being and early intervention, along with more nascent research supporting their use in clinical management of long-term psychiatric conditions – including major depression; anxiety, bipolar and psychotic disorders; and eating and substance use disorders – as well as in child and adolescent mental health care. Third, we discuss the most pressing challenges and opportunities towards real-world implementation, using the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework to explain how the innovations themselves, the recipients of these innovations, and the context surrounding innovations all must be considered to facilitate their adoption and use in mental health care systems. We conclude that the new technological capabilities of smartphones, artificial intelligence, social media and virtual reality are already changing mental health care in unforeseen and exciting ways, each accompanied by an early but promising evidence base. We point out that further efforts towards strengthening implementation are needed, and detail the key issues at the patient, provider and policy levels which must now be addressed for digital health technologies to truly improve mental health research and treatment in the future.

OBJECTIVE Mood instability in patients with bipolar disorder has been associated with impaired functioning and risk of relapse. The present study aimed to investigate whether increased mood instability is associated with increased perceived stress and impaired quality of life and functioning in patients with bipolar disorder. METHODS A total of 84 patients with bipolar disorder used a smartphone-based self-monitoring system on a daily basis for 9 months. Data on perceived stress, quality of life, and clinically rated functioning were collected at five fixed time points for each patient during follow-up. A group of 37 healthy individuals served as a control comparison of perceived stress, quality of life, and psychosocial functioning. RESULTS The majority of patients presented in full or partial remission. As hypothesized, mood instability was significantly associated with increased perceived stress (B: 10.52, 95% CI: 5.25; 15.77, P

OBJECTIVE To (i) validate patient-evaluated mixed symptoms and irritability measured using smartphones against clinical evaluations; (ii) investigate associations between mixed symptoms and irritability with stress, quality of life and functioning, respectively, in patients with bipolar disorder. METHODS A total of 84 patients with bipolar disorder used a smartphone-based system for daily evaluation of mixed symptoms and irritability for nine months. Clinically evaluated symptoms, stress, quality of life and clinically rated functioning were collected multiple times during follow-up. RESULTS Patients presented mild affective symptoms. Patient-reported mixed symptoms and irritability correlated with clinical evaluations. In analyses including confounding factors there was a statistically significant association between both mixed symptoms and irritability and stress (P

Background Mood instability in bipolar disorder is associated with a risk of relapse. This study investigated differences in mood instability between patients with bipolar disorder type I and type II, which previously has been sparingly investigated. Methods Patients with bipolar disorder type I (n = 53) and type II (n = 31) used a daily smartphone-based self-monitoring system for 9 months. Data in the present reflect 15.975 observations of daily collected smartphone-based data on patient-evaluated mood. Results In models adjusted for age, gender, illness duration and psychopharmacological treatment, patients with bipolar disorder type II experienced more mood instability during depression compared with patients with bipolar disorder type I (B: 0.27, 95% CI 0.007; 0.53, p = 0.044), but lower intensity of manic symptoms. Patients with bipolar disorder type II did not experience lower mean mood or higher intensity of depressive symptoms compared with patients with bipolar disorder type I. Conclusions Compared to bipolar disorder type I, patients with bipolar disorder type II had higher mood instability for depression. Clinically it is of importance to identify these inter-episodic symptoms. Future studies investigating the effect of treatment on mood instability measures are warranted. Trial registration NCT02221336

Background: The association between the International Physical Activity Questionnaire Short Form (IPAQ-SF) and objective measures of physical activity has never been evaluated in participants with newly diagnosed bipolar disorder (BD). Our aim was to compare IPAQ-SF to objective measures in participants with newly diagnosed BD, their unaffected first-degree relatives (UR), and healthy control individuals (HC) in groups combined and stratified by group. Materials and methods: Physical activity measurements were collected on 20 participants with newly diagnosed BD, 20 of their UR, and 20 HC using individually calibrated combined acceleration and heart rate sensing (Actiheart) for seven days. IPAQ-SF was self-completed at baseline. Correlation between measurements from the two methods was examined with Spearman rank correlation coefficient and agreement levels examined with modified Bland–Altman plots. Results: Physical activity energy expenditure (PAEE) from IPAQ-SF was weakly but significantly positively correlated with physical activity estimates measured using acceleration and heart rate in groups combined (Actiheart PAEE) (ρ= 0.301, p = 0.02). Correlations for each group were positive, but only in UR were it statistically significant (BD: p = 0.18, UR: p = 0.007, HC: p = 0.84). Self-reported PAEE and moderate-intensity were markedly underestimated [PAEE in all participants combined: 62.7 (Actiheart) vs. 24.3 kJ/day/kg (IPAQ-SF), p < 0.001], while vigorous-intensity was overestimated. Bland–Altman plots indicated proportional bias. Conclusion: These results suggest that the use of the IPAQ-SF to monitor levels of physical activity in participants with newly diagnosed BD, in a psychiatric clinical setting, should be used with caution and consideration.

IntroductionDespite current available treatment patients with bipolar disorder often experience relapses and decreased overall functioning. Furthermore, patients with bipolar disorder are often not treated medically or psychologically according to guidelines and recommendations. A Clinical Academic Group is a new treatment initiative bringing together clinical services, research, education and training to offer care and treatment that is based on reliable evidence backed up by research. The present Clinical Academic Group for bipolar disorder (the CAG Bipolar) randomised controlled trial (RCT) aims for the first time to investigate whether specialised outpatient treatment in CAG Bipolar versus generalised community-based treatment improves patient outcomes and clinician’s satisfaction with care in patients with bipolar disorder.Methods and analysisThe CAG Bipolar trial is a pragmatic randomised controlled parallel-group trial undertaken in the Capital Region of Denmark covering a catchment area of 1.85 million people. Patients with bipolar disorder are invited to participate as part of their outpatient treatment in the Mental Health Services. The included patients will be randomised to (1) specialised outpatient treatment in the CAG Bipolar (intervention group) or (2) generalised community-based outpatient treatment (control group). The trial started 13 January 2020 and has currently included more than 600 patients. The outcomes are (1) psychiatric hospitalisations and cumulated number and duration of psychiatric hospitalisations (primary), and (2) self-rated depressive symptoms, self-rated manic symptoms, quality of life, perceived stress, satisfaction with care, use of medication and the clinicians’ satisfaction with their care (secondary). A total of 1000 patients with bipolar disorder will be included.Ethics and disseminationThe CAG Bipolar RCT is funded by the Capital Region of Denmark and ethical approval has been obtained from the Regional Ethical Committee in The Capital Region of Denmark (H-19067248). Results will be published in peer-reviewed academic journals, presented at scientific meetings and disseminated to patient organisations and media outlets.Trial registration numberNCT04229875.

Objectives: The MONARCA I and II trials were negative but suggested that smartphone-based monitoring may increase quality of life and reduce perceived stress in bipolar disorder (BD). The present trial was the first to investigate the effect of smartphone-based monitoring on the rate and duration of readmissions in BD. Methods: This was a randomized controlled single-blind parallel-group trial. Patients with BD (ICD-10) discharged from hospitalization in the Mental Health Services, Capital Region of Denmark were randomized 1:1 to daily smartphone-based monitoring including a feedback loop (+ standard treatment) or to standard treatment for 6 months. Primary outcomes: the rate and duration of psychiatric readmissions. Results: We included 98 patients with BD. In ITT analyses, there was no statistically significant difference in rates (hazard rate: 1.05, 95% CI: 0.54; 1.91, p = 0.88) or duration of readmission between the two groups (B: 3.67, 95% CI: −4.77; 12.11, p = 0.39). There was no difference in scores on the Hamilton Depression Rating Scale (B = −0.11, 95% CI: −2.50; 2.29, p = 0.93). The intervention group had higher scores on the Young Mania Rating Scale (B: 1.89, 95% CI: 0.0078; 3.78, p = 0.050). The intervention group reported lower levels of perceived stress (B: -7.18, 95% CI: −13.50; −0.86, p = 0.026) and lower levels of rumination (B: −6.09, 95% CI: −11.19; −1.00, p = 0.019). Conclusions: Smartphone-based monitoring did not reduce rate and duration of readmissions. There was no difference in levels of depressive symptoms. The intervention group had higher levels of manic symptoms, but lower perceived stress and rumination compared with the control group.

Behavioral Activation (BA)therapy has shown to be effective in treating depression. Recommending healthy activities is a core principle in Behavioral Activation (BA), which is typically done by the therapist. However, most BA smartphone applications do not recommend specific activities. This article reports quantitative results from an 8-week feasibility study of a previously presented smartphone-based BA recommender system. The system supports the planning and enacting of pleasurable activities and promotes activation of diverse activity types. Enrollment included 43 clinically depressed patients who installed the system on their phone and initiated activity scheduling. Twenty-nine patients used the system daily for more than a week.These patients presented a significant reduction in depressive symptoms during the study period. They displayed a more personalized usage approach and created recurring health goals comprising of their own customized activities. Furthermore, they took inspiration within various types of activities, thereby displaying more activity diversity. This study suggests that enacting a diverse mixture of activities that promote good sleep, personal hygiene, exercise, social contact, and leisure time can be essential in managing depressive symptoms. A smartphone-based activity recommender system can help patients achieve this.

Background In DSM-5 activity is a core criterion for diagnosing hypomania and mania. However, there are no guidelines for quantifying changes in activity. The objectives of the study were (1) to investigate daily smartphone-based self-reported and automatically-generated activity, respectively, against validated measurements of activity; (2) to validate daily smartphone-based self-reported activity and automatically-generated activity against each other; (3) to investigate differences in daily self-reported and automatically-generated smartphone-based activity between patients with bipolar disorder (BD), unaffected relatives (UR) and healthy control individuals (HC). Methods A total of 203 patients with BD, 54 UR, and 109 HC were included. On a smartphone-based app, the participants daily reported their activity level on a scale from −3 to + 3. Additionally, participants owning an android smartphone provided automatically-generated data, including step counts, screen on/off logs, and call- and text-logs. Smartphone-based activity was validated against an activity questionnaire the International Physical Activity Questionnaire (IPAQ) and activity items on observer-based rating scales of depression using the Hamilton Depression Rating scale (HAMD), mania using Young Mania Rating scale (YMRS) and functioning using the Functional Assessment Short Test (FAST). In these analyses, we calculated averages of smartphone-based activity measurements reported in the period corresponding to the days assessed by the questionnaires and rating scales. Results (1) Smartphone-based self-reported activity was a valid measure according to scores on the IPAQ and activity items on the HAMD and YMRS, and was associated with FAST scores, whereas the majority of automatically-generated smartphone-based activity measurements were not. (2) Daily smartphone-based self-reported and automatically-generated activity correlated with each other with nearly all measurements. (3) Patients with BD had decreased daily self-reported activity compared with HC. Patients with BD had decreased physical (number of steps) and social activity (more missed calls) but a longer call duration compared with HC. UR also had decreased physical activity compared with HC but did not differ on daily self-reported activity or social activity. Conclusion Daily self-reported activity measured via smartphone represents overall activity and correlates with measurements of automatically generated smartphone-based activity. Detecting activity levels using smartphones may be clinically helpful in diagnosis and illness monitoring in patients with bipolar disorder. Trial registration clinicaltrials.gov NCT02888262

Background Bipolar disorder is a prevalent mental health condition that is imposing significant burden on society. Accurate forecasting of symptom scores can be used to improve disease monitoring, enable early intervention, and eventually help prevent costly hospitalizations. Although several studies have examined the use of smartphone data to detect mood, only few studies deal with forecasting mood for one or more days. Objective This study aimed to examine the feasibility of forecasting daily subjective mood scores based on daily self-assessments collected from patients with bipolar disorder via a smartphone-based system in a randomized clinical trial. Methods We applied hierarchical Bayesian regression models, a multi-task learning method, to account for individual differences and forecast mood for up to seven days based on 15,975 smartphone self-assessments from 84 patients with bipolar disorder participating in a randomized clinical trial. We reported the results of two time-series cross-validation 1-day forecast experiments corresponding to two different real-world scenarios and compared the outcomes with commonly used baseline methods. We then applied the best model to evaluate a 7-day forecast. Results The best performing model used a history of 4 days of self-assessment to predict future mood scores with historical mood being the most important predictor variable. The proposed hierarchical Bayesian regression model outperformed pooled and separate models in a 1-day forecast time-series cross-validation experiment and achieved the predicted metrics, R2=0.51 and root mean squared error of 0.32, for mood scores on a scale of −3 to 3. When increasing the forecast horizon, forecast errors also increased and the forecast regressed toward the mean of data distribution. Conclusions Our proposed method can forecast mood for several days with low error compared with common baseline methods. The applicability of a mood forecast in the clinical treatment of bipolar disorder has also been discussed.

BackgroundCognitive impairments in patients with bipolar disorder (BD) have been associated with reduced functioning. Aims: To investigate the association between (1) patient-evaluated cognitive function measured daily using smartphones and stress, quality of life and functioning, respectively, and (2) patient-evaluated cognitive function and objectively measured cognitive function with neuropsychological tests.MethodsData from two randomized controlled trials were combined. Patients with BD (N = 117) and healthy controls (HC) (N = 40) evaluated their cognitive function daily for six to nine months using a smartphone. Patients completed the objective cognition screening tool, the Screen for Cognitive Impairment in Psychiatry and were rated with the Functional Assessment Short Test. Raters were blinded to smartphone data. Participants completed the Perceived Stress Scale and the WHO Quality of Life questionnaires. Data was collected at multiple time points per participant. p-values below 0.0023 were considered statistically significant.ResultsPatient-evaluated cognitive function was statistically significant associated with perceived stress, quality of life and functioning, respectively (all p-values B:0.0009, 95% CI 0.0017; 0.016, p = 0.015). Patients exhibited cognitive impairments in subjectively evaluated cognitive function in comparison with HC despite being in full or partly remission (B:− 0.36, 95% CI− 0.039;− 0.032, p 0.0001).ConclusionThe present association between patient-evaluated cognitive function on smartphones and perceived stress, quality of life and functional capacity suggests that smartphones can provide a valid tool to assess disability in remitted BD. Smartphone-based ratings of cognition could not provide insights into objective cognitive function.

Background Heart rate variability (HRV) and psychomotor activity have been found reduced in bipolar disorder (BD) but has never been investigated in newly diagnosed BD and unaffected relatives. The present study aimed to compare HRV and psychomotor activity between newly diagnosed patients with BD, their unaffected first-degree relatives (UR), and healthy control individuals (HC). Methods 20 newly diagnosed patients with BD, 20 of their UR, and 20 age- and sex-matched HC were included. Measurements of HRV for five minutes and heart rate and acceleration for seven days were conducted. Activity energy expenditure (AEE) was derived from the latter. Linear mixed effect regression models were conducted to compare the three groups. Results HRV did not differ in any measure between the three groups of participants. Similarly, AEE (kJ/day/kg) did not differ between the three groups in neither daily means (BD: 63.6, UR: 64.1, HC: 62.1) nor when divided into quarter-daily intervals. Limitations The relatively small size of the study may affect the validity of the results. Conclusion Patients with newly diagnosed BD and UR do not present with decreased HRV or AEE. These results contrast prior findings from BD patients with more advanced stages of the disorder, suggesting that these outcomes progress with illness duration.

Over the past decade there has been an increasing awareness of the potential for data science to make important advances in brain and mental health research. This focus has coincided with the use of electronic health records in the clinic, the advent of electronic remote data capture through smart devices, publicly available de-identified large data sets, and the emergence of research consortia collaborating on the analysis of complex data sets. Interest in data science in mental health research has been further heightened by the widely acknowledged need for improved diagnostic precision and individualised risk prediction, long-term monitoring, and treatment. Using big data approaches to tackle complex mental health problems is now felt to be a key research priority moving forward. Therefore, Evidence Based Mental Health has …

There has been increasing interest in the use of smartphone applications (apps) and other consumer technology in mental health care for a number of years. However, the vision of data from apps seamlessly returned to, and integrated in, the electronic medical record (EMR) to assist both psychiatrists and patients has not been widely achieved, due in part to complex issues involved in the use of smartphone and other consumer technology in psychiatry. These issues include consumer technology usage, clinical utility, commercialization, and evolving consumer technology. Technological, legal and commercial issues, as well as medical issues, will determine the role of consumer technology in psychiatry. Recommendations for a more productive direction for the use of consumer technology in psychiatry are provided.

Background Psychiatric disorders often have an onset at an early age, and early identification and intervention help improve prognosis. A fine-grained, unobtrusive, and effective way to monitor symptoms and level of function could help distinguish severe psychiatric health problems from normal behavior and potentially lead to a more efficient use of clinical resources in the current health care system. The use of smartphones to monitor and treat children, adolescents, and young adults with psychiatric disorders has been widely investigated. However, no systematic review concerning smartphone-based monitoring and treatment in this population has been published. Objective This systematic review aims at describing the following 4 features of the eligible studies: (1) monitoring features such as self-assessment and automatically generated data, (2) treatment delivered by the app, (3) adherence to self-monitoring, and (4) results of the individual studies. Methods We conducted a systematic literature search of the PubMed, Embase, and PsycInfo databases. We searched for studies that (1) included a smartphone app to collect self-monitoring data, a smartphone app to collect automatically generated smartphone-based data, or a smartphone-based system for treatment; (2) had participants who were diagnosed with psychiatric disorders or received treatment for a psychiatric disorder, which was verified by an external clinician; (3) had participants who were younger than 25 years; and (4) were published in a peer-reviewed journal. This systematic review was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The risk of bias in each individual study was systematically assessed. Results A total of 2546 unique studies were identified through literature search; 15 of these fulfilled the criteria for inclusion. These studies covered 8 different diagnostic groups: psychosis, eating disorders, depression, autism, self-harm, anxiety, substance abuse, and suicidal behavior. Smartphone-based self-monitoring was used in all but 1 study, and 11 of them reported on the participants’ adherence to self-monitoring. Most studies were feasibility/pilot studies, and all studies on feasibility reported positive attitudes toward the use of smartphones for self-monitoring. In 2 studies, automatically generated data were collected. Three studies were randomized controlled trials investigating the effectiveness of smartphone-based monitoring and treatment, with 2 of these showing a positive treatment effect. In 2 randomized controlled trials, the researchers were blinded for randomization, but the participants were not blinded in any of the studies. All studies were determined to be at high risk of bias in several areas. Conclusions Smartphones hold great potential as a modern, widely available technology platform to help diagnose, monitor, and treat psychiatric disorders in children and adolescents. However, a higher level of homogeneity and rigor among studies regarding their methodology and reporting of adherence would facilitate future reviews and meta-analyses.

Background: Most people will also experience symptoms of stress at some point. Smartphone use has increased during the last decade and may be a new way of monitoring stress. Thus, it is of interest...

BACKGROUND Psychiatric disorders often have an onset at an early age, and early identification and intervention help improve prognosis. A fine-grained, unobtrusive, and effective way to monitor symptoms and level of function could help distinguish severe psychiatric health problems from normal behavior and potentially lead to a more efficient use of clinical resources in the current health care system. The use of smartphones to monitor and treat children, adolescents, and young adults with psychiatric disorders has been widely investigated. However, no systematic review concerning smartphone-based monitoring and treatment in this population has been published. OBJECTIVE This systematic review aims at describing the following 4 features of the eligible studies: (1) monitoring features such as self-assessment and automatically generated data, (2) treatment delivered by the app, (3) adherence to self-monitoring, and (4) results of the individual studies. METHODS We conducted a systematic literature search of the PubMed, Embase, and PsycInfo databases. We searched for studies that (1) included a smartphone app to collect self-monitoring data, a smartphone app to collect automatically generated smartphone-based data, or a smartphone-based system for treatment; (2) had participants who were diagnosed with psychiatric disorders or received treatment for a psychiatric disorder, which was verified by an external clinician; (3) had participants who were younger than 25 years; and (4) were published in a peer-reviewed journal. This systematic review was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The risk of bias in each individual study was systematically assessed. RESULTS A total of 2546 unique studies were identified through literature search; 15 of these fulfilled the criteria for inclusion. These studies covered 8 different diagnostic groups: psychosis, eating disorders, depression, autism, self-harm, anxiety, substance abuse, and suicidal behavior. Smartphone-based self-monitoring was used in all but 1 study, and 11 of them reported on the participants’ adherence to self-monitoring. Most studies were feasibility/pilot studies, and all studies on feasibility reported positive attitudes toward the use of smartphones for self-monitoring. In 2 studies, automatically generated data were collected. Three studies were randomized controlled trials investigating the effectiveness of smartphone-based monitoring and treatment, with 2 of these showing a positive treatment effect. In 2 randomized controlled trials, the researchers were blinded for randomization, but the participants were not blinded in any of the studies. All studies were determined to be at high risk of bias in several areas. CONCLUSIONS Smartphones hold great potential as a modern, widely available technology platform to help diagnose, monitor, and treat psychiatric disorders in children and adolescents. However, a higher level of homogeneity and rigor among studies regarding their methodology and reporting of adherence would facilitate future reviews and meta-analyses.

Currently, the golden standard for assessing the severity of depressive and manic symptoms in patients with bipolar disorder (BD) is clinical evaluations using validated rating scales such as the Hamilton Depression Rating Scale 17-items (HDRS) and the Young Mania Rating Scale (YMRS). Frequent automatic estimation of symptom severity could potentially help support monitoring of illness activity and allow for early treatment intervention between outpatient visits. The present study aimed (1) to assess the feasibility of producing daily estimates of clinical rating scores based on smartphone-based self-assessments of symptoms collected from a group of patients with BD; (2) to demonstrate how these estimates can be utilized to compute individual daily risk of relapse scores. Based on a total of 280 clinical ratings collected from 84 patients with BD along with daily smartphone-based self-assessments, we applied a hierarchical Bayesian modelling approach capable of providing individual estimates while learning characteristics of the patient population. The proposed method was compared to common baseline methods. The model concerning depression severity achieved a mean predicted R2 of 0.57 (SD = 0.10) and RMSE of 3.85 (SD = 0.47) on the HDRS, while the model concerning mania severity achieved a mean predicted R2 of 0.16 (SD = 0.25) and RMSE of 3.68 (SD = 0.54) on the YMRS. In both cases, smartphone-based self-reported mood was the most important predictor variable. The present study shows that daily smartphone-based self-assessments can be utilized to automatically estimate clinical ratings of severity of depression and mania in patients with BD and assist in identifying individuals with high risk of relapse.

The brain-derive neurotrophic factor (BDNF) may play an important role in the course of depression. We aimed to study the associations between peripheral whole blood BDNF levels in healthy individuals with and without a family history of depression. BDNF levels were significantly increased in healthy individuals with (n = 76), compared with healthy individuals without (n = 39) a family history of depression and persisted after adjustment for age and gender differences. Higher BDNF levels were associated with increasing age and seasonality. A family history of depression may contribute to an elevation of peripheral BDNF levels in healthy individuals.

Heart rate variability (HRV) is a validated measure of sympato-vagal balance in the autonomic nervous system. HRV appears decreased in patients with bipolar disorder (BD) compared with healthy individuals, but the extent of state-related alterations has been sparingly investigated. The present study examined differences in HRV between affective states in BD. A heart rate and movement sensor weighing 8 g collected average acceleration, heart rate and the two slowest and fastest heart beats (of the most recent 16 beats) every 30 s over a period of at least three consecutive weekdays and nights in a prospective longitudinal design from a total of 31 different affective states in 16 outpatients with BD. A proxy measure of HRV was calculated as the difference between the second-shortest and the second-longest inter-beat-interval collected during each of the epochs. Analyses were based on over 100.000 HRV data-points. In unadjusted analyses and in analyses adjusted for age, gender and heart rate, during a manic state HRV was increased by 18% compared with a depressed state (eB = 1.18, 95% CI: 1.16-1.20, p

Background Smartphones may offer a new and easy tool to assess stress, but the validity has never been investigated. Objective This study aimed to investigate (1) the validity of smartphone-based self-assessed stress compared with Cohen Perceived Stress Scale (PSS) and (2) whether smartphone-based self-assessed stress correlates with neuroticism (Eysenck Personality Questionnaire-Neuroticism, EPQ-N), psychosocial functioning (Functioning Assessment Short Test, FAST), and prior stressful life events (Kendler Questionnaire for Stressful Life Events, SLE). Methods A cohort of 40 healthy blood donors with no history of personal or first-generation family history of psychiatric illness and who used an Android smartphone were instructed to self-assess their stress level daily (on a scale from 0 to 2; beta values reflect this scale) for 4 months. At baseline, participants were assessed with the FAST rater-blinded and filled out the EPQ, the PSS, and the SLE. The PSS assessment was repeated after 4 months. Results In linear mixed-effect regression and linear regression models, there were statistically significant positive correlations between self-assessed stress and the PSS (beta=.0167; 95% CI 0.0070-0.0026; P=.001), the EPQ-N (beta=.0174; 95% CI 0.0023-0.0325; P=.02), and the FAST (beta=.0329; 95% CI 0.0036-0.0622; P=.03). No correlation was found between smartphone-based self-assessed stress and the SLE. Conclusions Daily smartphone-based self-assessed stress seems to be a valid measure of perceived stress. Our study contains a modest sample of 40 healthy participants and adds knowledge to a new but growing field of research. Smartphone-based self-assessed stress is a promising tool for measuring stress in real time in future studies of stress and stress-related behavior.

BackgroundNon-adherence to medication is associated with increased risk of relapse in patients with bipolar disorder (BD).ObjectivesTo (1) validate patient-evaluated adherence to medication measured via smartphones against validated adherence questionnaire; and (2) investigate characteristics for adherence to medication measured via smartphones.MethodsPatients with BD (n=117) evaluated adherence to medication daily for 6–9 months via smartphones. The Medication Adherence Rating Scale (MARS) and the Rogers’ Empowerment questionnaires were filled out. The 17-item Hamilton Depression Rating Scale, the Young Mania Rating Scale and the Functional Assessment Short Test were clinically rated. Data were collected multiple times per patient. The present study represents exploratory pooled reanalyses of data collected as part of two randomised controlled trials.FindingsDuring the study 90.50% of the days were evaluated as ‘medication taken’, 6.91% as ‘medication taken with changes’ and 2.59% as ‘medication not taken’. Adherence to medication measured via smartphones was valid compared with the MARS (B: −0.049, 95% CI −0.095 to −0.003, p=0.033). Younger age and longer illness duration were significant predictors for non-adherence to medication (model concerning age: B: 0.0039, 95% CI 0.00019 to 0.0076, p=0.040). Decreased affective symptoms measured with smartphone-based patient-reported mood and clinical ratings as well as decreased empowerment were associated with non-adherence.ConclusionsSmartphone-based monitoring of adherence to medication was valid compared with validated adherence questionnaire. Younger age and longer illness duration were predictors for non-adherence. Increased empowerment was associated with adherence.Clinical implicationsUsing smartphones for empowerment of adherence using patient-reported measures may be helpful in everyday clinical settings.Trial registration numberNCT01446406 and NCT02221336.

Prospective monitoring of mood was started by Kraepelin who made and recorded frequent observations of his patients. During the last decade, the number of research studies using remotely collected electronic mood data has increased markedly. However, standardized measures and methods to collect, analyze and report electronic mood data are lacking. To get better understanding of the nature, correlates and implications of mood and mood instability, and to standardize this process, we propose guidelines for reporting of electronic mood data (eMOOD). This paper provides an overview of remotely collected electronic mood data in mood disorders and discusses why standardized reporting is necessary to evaluate and inform mood research in Psychiatry. Adherence to these guidelines will improve interpretation, reproducibility and future meta-analyses of mood monitoring in mood disorder research.

BackgroundRecently, the MONARCA I randomized controlled trial (RCT) was the first to investigate the effect of smartphone-based monitoring in bipolar disorder (BD). Findings suggested that smartphone-based monitoring sustained depressive but reduced manic symptoms. The present RCT investigated the effect of a new smartphone-based system on the severity of depressive and manic symptoms in BD.MethodsRandomized controlled single-blind parallel-group trial. Patients with BD, previously treated at The Copenhagen Clinic for Affective Disorder, Denmark and currently treated at community psychiatric centres, private psychiatrists or GPs were randomized to the use of a smartphone-based system or to standard treatment for 9 months. Primary outcomes: differences in depressive and manic symptoms between the groups.ResultsA total of 129 patients with BD (ICD-10) were included. Intention-to-treat analyses showed no statistically significant effect of smartphone-based monitoring on depressive (B = 0.61, 95% CI −0.77 to 2.00, p = 0.38) and manic (B = −0.25, 95% CI −1.1 to 0.59, p = 0.56) symptoms. The intervention group reported higher quality of life and lower perceived stress compared with the control group. In sub-analyses, the intervention group had higher risk of depressive episodes, but lower risk of manic episodes compared with the control group.ConclusionsThere was no effect of smartphone-based monitoring. In patient-reported outcomes, patients in the intervention group reported improved quality of life and reduced perceived stress. Patients in the intervention group had higher risk of depressive episodes and reduced risk of manic episodes. Despite the widespread use and excitement of electronic monitoring, few studies have investigated possible effects. Further studies are needed.

BACKGROUND: The aims of the present multicenter pilot study were to examine the feasibility and usability of two different smartphone-based monitoring systems (the Pulso system and the Trilogis-Monsenso system) from two IT companies in patients with bipolar disorder, developed and selected to be testes as a part of a European Union funded Pre-Commercial Procurement (the NYMPHA-MD project).METHODS: Patients with bipolar disorder (ICD-10), > 18 years of age during a remitted, partial remitted or mild to moderate depressive state (HDRS-17 RESULTS: A total of 60 patients aged 18-69 years with bipolar disorder (ICD-10) recruited from Italy, Spain, Denmark were included-59 patients completed the study. In Denmark, the patients evaluated the Trilogis-Monsenso system with a statistically significant higher usability compared with the Pulso system. In Italy and Spain, the patients evaluated no statistically significant difference between the two systems in any of the categories, except for the usefulness category favoring the Trilogis-Monsenso system (z = 2.68, p CONCLUSIONS: Both monitoring systems showed acceptable usability and feasibility. There were differences in patient-based evaluations of the two monitoring systems related to the country of the study. Studies investigating the usability and feasibility during longer follow-up periods could perhaps reveal different findings. Future randomized controlled trials investigating the possible positive and negative effects of smartphone-based monitoring systems are needed.

Behavioral Activation (BA) psychotherapy for depression typically applies paper-based activity planning and registration. This is cumbersome, subject to recall bias, and provides limited support for personalized reflection on individual activity patterns. This paper presents MORIBUS; a smartphone tool for BA to be used in therapy. It provides a simple way of planning and registering activities, and their immediate emotional impact. Through visual analytic tools, the patient gains personalized insight into own behavior. We examined the feasibility of MORIBUS in a 4-week study including seven patients diagnosed with depression. The study revealed individual differences in BA patterns. We discuss the implications of these findings and argue for the necessity of automatic but still personalized technology.

Background Smartphone-based technology is developing at high speed, and many apps offer potential new ways of monitoring and treating a range of psychiatric disorders and symptoms. However, the effects of most available apps have not been scientifically investigated. Within medicine, randomized controlled trials (RCTs) are the standard method for providing the evidence of effects. However, their rigidity and long time frame may contrast with the field of information technology research. Therefore, a systematic review of methodological challenges in designing and conducting RCTs within mobile health is needed. Objective This systematic review aimed to (1) identify and describe RCTs investigating the effect of smartphone-based treatment in adult patients with a psychiatric diagnosis, (2) discuss methodological challenges in designing and conducting individual trials, and (3) suggest recommendations for future trials. Methods A systematic search in English was conducted in PubMed, PsycINFO, and EMBASE up to August 12, 2019. The search terms were (1) psychiatric disorders in broad term and for specific disorders AND (2) smartphone or app AND (3) RCT. The Consolidated Standards of Reporting Trials electronic health guidelines were used as a template for data extraction. The focus was on trial design, method, and reporting. Only trials having sufficient information on diagnosis and acceptable diagnostic procedures, having a smartphone as a central part of treatment, and using an RCT design were included. Results A total of 27 trials comprising 3312 patients within a range of psychiatric diagnoses were included. Among them, 2 trials were concerning drug or alcohol abuse, 3 psychosis, 10 affective disorders, 9 anxiety and posttraumatic stress disorder, 1 eating disorder, and 1 attention-deficit/hyperactivity disorder. In addition, 1 trial used a cross-diagnostic design, 7 trials included patients with a clinical diagnosis that was subsequently assessed and validated by the researchers, and 11 trials had a sample size above 100. Generally, large between-trial heterogeneity and multiple approaches to patient recruitment, diagnostic procedures, trial design, comparator, outcome measures, and analyses were identified. Only 5 trials published a trial protocol. Furthermore, 1 trial provided information regarding technological updates, and only 18 trials reported on the conflicts of interest. No trial addressed the ethical aspects of using smartphones in treatment. Conclusions This first systematic review of the methodological challenges in designing and conducting RCTs investigating smartphone-based treatment in psychiatric patients suggests an increasing number of trials but with a lower quality compared with classic medical RCTs. Heterogeneity and methodological issues in individual trials limit the evidence. Methodological recommendations are presented.

Objectives: Bipolar disorder is associated with a decreased life expectancy of 8–12 years. Cardiovascular disease is the leading cause of excess mortality. For the first time, we investigated the Framingham 30-year risk score of cardiovascular disease in patients with newly diagnosed/first-episode bipolar disorder, their unaffected first-degree relatives and healthy individuals. Methods: In a cross-sectional study, we compared the Framingham 30-year risk score of cardiovascular disease in 221 patients with newly diagnosed/first-episode bipolar disorder, 50 of their unaffected first-degree relatives and 119 healthy age- and sex-matched individuals with no personal or first-degree family history of affective disorder. Among patients with bipolar disorder, we further investigated medication- and illness-related variables associated with cardiovascular risk. Results: The 30-year risk of cardiovascular disease was 98.5% higher in patients with bipolar disorder ( p = 0.017) and 85.4% higher in unaffected first-degree relatives ( p = 0.042) compared with healthy individuals in models adjusted for age and sex. When categorizing participants in low cardiovascular risk without considering age and sex distribution among participants, 81% of patients were at low risk, versus 92% of unaffected relatives and 89% of healthy individuals. Of the patients 209 (94.6%) were diagnosed within the preceding 2 years. Smoking was more prevalent among patients with bipolar disorder (45.2%) and their unaffected first-degree relatives (20.4%) compared with healthy individuals (12.8%). Similarly, dyslipidemia was more common among patients with bipolar disorder compared with healthy individuals. Treatment with psychotropic medication with metabolic adverse effects was associated with higher 30-year cardiovascular disease risk score, whereas we did not find illness-related variables associated with cardiovascular risk among patients with bipolar disorder. Conclusion: We found an enhanced cardiovascular disease risk score in patients with newly diagnosed bipolar disorder and their unaffected first-degree relatives, which points to a need for specific primary preventive interventions against smoking and dyslipidemia in these populations.

Objective: Currently, the diagnosis in bipolar disorder relies on patient information and careful clinical evaluations and judgements with a lack of objective tests. Core clinical features of bipolar disorder include changes in behaviour. We aimed to investigate objective smartphone data reflecting behavioural activities to classify patients with bipolar disorder compared with healthy individuals. Methods: Objective smartphone data were automatically collected from 29 patients with bipolar disorder and 37 healthy individuals. Repeated measurements of objective smartphone data were performed during different affective states in patients with bipolar disorder over 12 weeks and compared with healthy individuals. Results: Overall, the sensitivity of objective smartphone data in patients with bipolar disorder versus healthy individuals was 0.92, specificity 0.39, positive predictive value 0.88 and negative predictive value 0.52. In euthymic patients versus healthy individuals, the sensitivity was 0.90, specificity 0.56, positive predictive value 0.85 and negative predictive value 0.67. In mixed models, automatically generated objective smartphone data (the number of text messages/day, the duration of phone calls/day) were increased in patients with bipolar disorder (during euthymia, depressive and manic or mixed states, and overall) compared with healthy individuals. The amount of time the smartphone screen was ‘on’ per day was decreased in patients with bipolar disorder (during euthymia, depressive state and overall) compared with healthy individuals. Conclusion: Objective smartphone data may represent a potential diagnostic behavioural marker in bipolar disorder and may be a candidate supplementary method to the diagnostic process in the future. Further studies including larger samples, first-degree relatives and patients with other psychiatric disorders are needed.

BACKGROUND: The association between the International Physical Activity Questionnaire Short Form (IPAQ-SF) and objective measures of physical activity has never been evaluated in participants with newly diagnosed bipolar disorder (BD). Our aim was to compare IPAQ-SF to objective measures in participants with newly diagnosed BD, their unaffected first-degree relatives (UR), and healthy control individuals (HC) in groups combined and stratified by group. MATERIALS AND METHODS: Physical activity measurements was collected on 20 participants with newly diagnosed BD, 20 of their UR, and 20 HC using individually calibrated combined acceleration and heart rate sensing (Actiheart) for seven days. IPAQ-SF was self-completed at baseline. Correlation between measurements from the two methods was examined with Spearman rank correlation coefficient and agreement levels examined with modified Bland-Altman plots. RESULTS: Physical activity energy expenditure (PAEE) from IPAQ-SF was weakly but significantly positively correlated with physical activity estimates measured using acceleration and heart rate in groups combined (Actiheart PAEE) (ρ= 0.301, p= 0.02). Correlations for each group were positive, but only in UR were it statistically significant (BD: p = 0.18, UR: p = 0.007, HC: p = 0.84). Self-reported PAEE and moderate-intensity were markedly underestimated (PAEE in all participants combined: 62.7 (Actiheart) vs. 24.3 kJ/day/kg (IPAQ-SF), p< 0.001), while vigorous-intensity was overestimated. Bland-Altman plots indicated proportional bias. CONCLUSION: These results suggest that the use of the IPAQ-SF to monitor levels of physical activity in participants with newly diagnosed BD, in a psychiatric clinical setting, should be used with caution and consideration.

Smartphones are useful in symptom-monitoring in bipolar disorder (BD). Objective smartphone data reflecting illness activity could facilitate early treatment and act as outcome in efficacy trials. A total of 29 patients with BD presenting with moderate to severe levels of depressive and manic symptoms used a smartphone-based self-monitoring system during 12 weeks. Objective smartphone data on behavioral activities were collected. Symptoms were clinically assessed every second week using the Hamilton Depression Rating Scale and the Young Mania Rating Scale. Objective smartphone data correlated with symptom severity. The more severe the depressive symptoms (1) the longer the smartphone's screen was "on"/day, (2) more received incoming calls/day, (3) fewer outgoing calls/day were made, (4) less answered incoming calls/day, (5) the patients moved less between cell towers IDs/day. Conversely, the more severe the manic symptoms (1) more outgoing text messages/day sent, (2) the phone calls/day were longer, (3) the fewer number of characters in incoming text messages/day, (4) the lower duration of outgoing calls/day, (5) the patients moved more between cell towers IDs/day. Further, objective smartphone data were able to discriminate between affective states. Objective smartphone data reflect illness severity, discriminates between affective states in BD and may facilitate the cooperation between patient and clinician. Copyright © 2016 John Wiley & Sons, Ltd.

Background A substantial proportion of patients with bipolar disorder remain symptomatic during inter-episode periods, and mood instability is associated with high risk of relapse and hospitalization. Few studies have investigated long-term daily illness activity and none has compared bipolar type I and II using daily data. The objectives were to investigate differences in daily illness activity between bipolar disorder type I and II. Methods A smartphone-based system for self-monitoring was developed. A total of 33 patients treated in a mood clinic used the system for daily self-monitoring during a median period of 310 days [IQR 189; 437]. Data presented summarize over 8500 observations. Results Patients with bipolar disorder type II ( n =20), compared to patients with bipolar disorder type I ( n =13), experienced a significant lower mean level of mood on a scale from −3; +3 (−0.54 (95% CI: −0.74; −0.35) versus −0.19 (95% CI: −0.35; −0.02), p =0.02), less time euthymic (51.0% (95% CI: 36.4; 65.7) versus 74.5% (95% CI: 62.4; 86.7), p =0.03) and a higher proportion of time with depressive symptoms (45.1% (95% CI: 30.6; 59.5) versus 18.8% (95% CI: 6.9; 30.7), p =0.01). The proportion of time spent with (hypo)manic symptoms did not differ (2.7% (95% CI: 0.1; 5.5) versus 5.5% (95% CI: 3.1; 7.8), p =0.17). Limitations Patients received different types, doses and combinations of psychopharmacological treatment. Conclusion Euthymia was obtained for a substantial proportion of time in patients with bipolar disorder type I, but despite on-going treatment only for half of the time for patients with bipolar disorder type II. This emphasizes the need for improving treatment strategies for bipolar disorder type II.

BackgroundSeveral 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. ObjectiveThe 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. MethodsWe conducted a systematic review of the scientific literature, reported according to the preferred reporting items for systematic reviews and meta-analyses guidelines. IEEE Xplore, ACM Digital Library, Web of Sciences, PsychINFO, PubMed, DBLP computer science bibliography, HTA, DARE, Scopus, and Science Direct were searched and supplemented by hand examination of reference lists. The search ended on April 27, 2017, and was limited to studies published between 2007 and 2017. ResultsA total of 46 studies were eligible for the review. These studies identified and investigated 85 unique objective behavioral features, covering 17 various sensor data inputs. These features were divided into 7 categories. Several features were found to have statistically significant and consistent correlation directionality with mood assessment (eg, the amount of home stay, sleep duration, and vigorous activity), while others showed directionality discrepancies across the studies (eg, amount of text messages [short message service] sent, time spent between locations, and frequency of mobile phone screen activity). ConclusionsSeveral studies showed consistent and statistically significant correlations between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms. Hence, continuous and everyday monitoring of behavioral aspects in affective disorders could be a promising supplementary objective measure for estimating depressive mood symptoms. However, the evidence is limited by methodological issues in individual studies and by a lack of standardization of (1) the collected objective features, (2) the mood assessment methodology, and (3) the statistical methods applied. Therefore, consistency in data collection and analysis in future studies is needed, making replication studies as well as meta-analyses possible.

Major reasons for the insufficient effects of current treatment options in bipolar disorder include delayed intervention for prodromal depressive and manic symptoms and decreased adherence to psychopharmacological treatment. The reliance on subjective information and clinical evaluations when diagnosing and assessing the severity of depressive and manic symptoms calls for less biased and more objective markers. By using electronic devices, fine-grained data on complex psychopathological aspects of bipolar disorder can be evaluated unobtrusively over the long term. Moreover, electronic data could possibly represent candidate markers of diagnosis and illness activity in bipolar disorder and allow for early and individualized intervention for prodromal symptoms outside clinical settings. The present dissertation concerns the use of electronic monitoring as a marker and treatment intervention in bipolar disorder and investigated the scientific literature and body of evidence within the area, which includes ten original study reports and two systematic reviews, one of which included a meta-analysis, conducted by the author of the dissertation. Taken together, the literature presented in this dissertation illustrates that 1) smartphone-based electronic self-monitoring of mood seems to reflect clinically assessed depressive and manic symptoms and enables the long-term characterization of mood instability in bipolar disorder; 2) preliminary results suggest that smartphone-based automatically generated data (e.g. the number of text messages sent/day; the number of incoming and outgoing calls/day; the number of changes in cell tower IDs/day; and voice features) seem to reflect clinically assessed depressive and manic symptoms in bipolar disorder; 3) smartphone-based electronic self-monitoring had no effects on the severity of depressive and manic symptoms in bipolar disorder, according to a randomized controlled trial; and 4) electronic monitoring of psychomotor activity and heart rate variability seems to reflect illness activity in bipolar disorder and differentiate between patients with bipolar disorder and healthy control individuals. These findings point toward the usefulness of electronic monitoring as a marker of illness in bipolar disorder. Using electronic monitoring as a treatment intervention could provide innovative and novel interventions on-demand with a potential global reach, filling the gap between availability and the need for treatment. However, future studies using rigorous methodology and more randomized controlled trials that carefully investigate the positive effects and possible harmful effects of electronic monitoring in bipolar disorder are needed. In addition, patient safety, privacy issues, data security and legal aspects are major concerns that must be considered and addressed when using electronic monitoring.

In 2001, the WHO stated that: “The use of mobile and wireless technologies to support the achievement of health objectives (mHealth) has the potential to transform the face of health service delivery across the globe”. Within mental health, interventions and monitoring systems for depression, anxiety, substance abuse, eating disorder, schizophrenia and bipolar disorder have been developed and used. The present paper presents the status and findings from studies using automatically generated objective smartphone data in the monitoring of bipolar disorder, and addresses considerations on the current literature and methodological as well as clinical aspects to consider in the future studies.

BACKGROUND Several studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable technologies and depressive mood symptoms in affective disorders (unipolar disorder and bipolar disorder). 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 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 in the identification on which objective features have shown most promising results across studies. METHODS A systematic review of the scientific literature reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines was conducted. IEEE Xplore, ACM Digital Library, Web of Sciences, PsychINFO, Pubmed, DBLP computer science bibliography, HTA, DARE, Scopus and Science Direct were searched and supplemented by hand examination of reference lists. The search ended 04/27-2017 and was limited to studies published 2007-2017. RESULTS A total of 46 studies were eligible for the review. These studies identified and investigated 85 unique objective behavioral features covering 17 various sensor data inputs. These features can be categorized into seven overall categories. Several features were found to have statistically significant and consistent correlation directionality with mood assessment (e.g., the amount of home stay, sleep duration, vigorous activity), while others showed directionality discrepancies across the studies (e.g., amount of SMS sent, time you spend between locations, frequency of smartphone screen activity). CONCLUSIONS Several studies showed consistent and statistically significant correlations between objective behavioral features collected by mobile and wearable technology and depressive mood symptoms. Hence, continuous and every-day monitoring of behavioral aspects in affective disorders could be a promising supplementary objective measure to estimate depressive mood symptoms. However, the evidence is limited by methodological issues in individual studies and by a lack of standardization of 1) the collected objective features, 2) the mood assessment methodology, and 3) the statistical methods applied. Therefore, consistency in data collection and analysis in future studies is needed making replication studies as well as meta-analyses possible.

BackgroundThe number of studies on electronic self-monitoring in affective disorder and other psychiatric disorders is increasing and indicates high patient acceptance and adherence. Nevertheless, the effect of electronic self-monitoring in patients with bipolar disorder has never been investigated in a randomized controlled trial (RCT). The objective of this trial was to investigate in a RCT whether the use of daily electronic self-monitoring using smartphones reduces depressive and manic symptoms in patients with bipolar disorder.MethodA total of 78 patients with bipolar disorder according to ICD-10 criteria, aged 18–60 years, and with 17-item Hamilton Depression Rating Scale (HAMD-17) and Young Mania Rating Scale (YMRS) scores ≤17 were randomized to the use of a smartphone for daily self-monitoring including a clinical feedback loop (the intervention group) or to the use of a smartphone for normal communicative purposes (the control group) for 6 months. The primary outcomes were differences in depressive and manic symptoms measured using HAMD-17 and YMRS, respectively, between the intervention and control groups.ResultsIntention-to-treat analyses using linear mixed models showed no significant effects of daily self-monitoring using smartphones on depressive as well as manic symptoms. There was a tendency towards more sustained depressive symptoms in the intervention group (B = 2.02, 95% confidence interval −0.13 to 4.17, p = 0.066). Sub-group analysis among patients without mixed symptoms and patients with presence of depressive and manic symptoms showed significantly more depressive symptoms and fewer manic symptoms during the trial period in the intervention group.ConclusionsThese results highlight that electronic self-monitoring, although intuitive and appealing, needs critical consideration and further clarification before it is implemented as a clinical tool.

BACKGROUND/OBJECTIVES: Gains in fat mass and lean mass during tuberculosis (TB) treatment may determine functional recovery and survival; yet, data are scarce. We aimed to assess predictors of fat and fat-free mass during 2 months of intensive TB treatment in a cohort in Mwanza, Tanzania.SUBJECTS/METHODS: Fat and fat-free mass were determined at the start of TB treatment and repeated after 2 months using the deuterium dilution technique. Gains in fat and fat-free mass were determined and predictors assessed using regression analysis.RESULTS: Data for 116 patients were available at baseline and during follow-up. Of these, 38.8% were females, mean age was 37.3 (s.d. 13.5) years, 69% (81) had sputum-positive TB, 45.7% (53) were HIV infected and 25% (29) were current smokers. The mean weight gain was 3.3 kg (95% confidence interval: 2.7; 3.8), and it did not differ by sex. However, compared with females, males had 1.0 (0.4; 1.6) kg/m(2) lower fat mass but 0.7 (0.2; 1.3) kg/m(2) higher fat-free mass gain. Current smoking was associated with higher fat mass (0.7 kg/m(2), 0.04; 1.4) but lower fat-free mass (-0.5 kg/m(2), -1.2; 0.07) gain. Among HIV-infected patients, antiretroviral therapy (ART) led to a lower fat gain (-1.2 kg/m(2), -2.2; -0.2) but to a higher fat-free mass among sputum-negative (2.9 kg/m(2), 0.8; 5.1) but not sputum-positive patients.CONCLUSIONS: During intensive phase of TB treatment, sex, smoking and ART were predictors of body composition. Larger studies are needed to further understand predictors of body composition during recovery, to help design interventions to improve treatment outcomes.