Smartphones and the Neuroscience of Mental Health.

Improvements in understanding the neurobiological basis of mental illness have unfortunately not translated into major advances in treatment. At this point, it is clear that psychiatric disorders are exceedingly complex and that, in order to account for and leverage this complexity, we need to collect longitudinal data sets from much larger and more diverse samples than is practical using traditional methods. We discuss how smartphone-based research methods have the potential to dramatically advance our understanding of the neuroscience of mental health. This, we expect, will take the form of complementing lab-based hard neuroscience research with dense sampling of cognitive tests, clinical questionnaires, passive data from smartphone sensors, and experience-sampling data as people go about their daily lives. Theory- and data-driven approaches can help make sense of these rich data sets, and the combination of computational tools and the big data that smartphones make possible has great potential value for researchers wishing to understand how aspects of brain function give rise to, or emerge from, states of mental health and illness.

Age-related behavioral resilience in smartphone touchscreen interaction dynamics.

We experience a life that is full of ups and downs. The ability to bounce back after adverse life events such as the loss of a loved one or serious illness declines with age, and such isolated events can even trigger accelerated aging. How humans respond to common day-to-day perturbations is less clear. Here, we infer the aging status from smartphone behavior by using a decision tree regression model trained to accurately estimate the chronological age based on the dynamics of touchscreen interactions. Individuals (N = 280, 21 to 87 y of age) expressed smartphone behavior that appeared younger on certain days and older on other days through the observation period that lasted up to ~4 y. We captured the essence of these fluctuations by leveraging the mathematical concept of critical transitions and tipping points in complex systems. In most individuals, we find one or more alternative stable aging states separated by tipping points. The older the individual, the lower the resilience to forces that push the behavior across the tipping point into an older state. Traditional accounts of aging based on sparse longitudinal data spanning decades suggest a gradual behavioral decline with age. Taken together with our current results, we propose that the gradual age-related changes are interleaved with more complex dynamics at shorter timescales where the same individual may navigate distinct behavioral aging states from one day to the next. Real-world behavioral data modeled as a complex system can transform how we view and study aging.

Deep learning to capture leaf shape in plant images: Validation by geometric morphometrics.

Plant leaves play a pivotal role in automated species identification using deep learning (DL). However, achieving reproducible capture of leaf variation remains challenging due to the inherent "black box" problem of DL models. To evaluate the effectiveness of DL in capturing leaf shape, we used geometric morphometrics (GM), an emerging component of eXplainable Artificial Intelligence (XAI) toolkits. We photographed Ranunculus auricomus leaves directly in situ and after herbarization. From these corresponding leaf images, we automatically extracted DL features using a neural network and digitized leaf shapes using GM. The association between the extracted DL features and GM shapes was then evaluated using dimension reduction and covariation models. DL features facilitated the clustering of leaf images by source populations in both in situ and herbarized leaf image datasets, and certain DL features were significantly associated with biological leaf shape variation as inferred by GM. DL features also enabled leaf classification into morpho-phylogenomic groups within the intricate R. auricomus species complex. We demonstrated that simple in situ leaf imaging and DL reproducibly captured leaf shape variation at the population level, while combining this approach with GM provided key insights into the shape information extracted from images by computer vision, a necessary prerequisite for reliable automated plant phenotyping.

Neural Network Enables High Accuracy for Hepatitis B Surface Antigen Detection with a Plasmonic Platform.

The detection of hepatitis B surface antigen (HBsAg) is critical in diagnosing hepatitis B virus (HBV) infection. However, existing clinical detection technologies inevitably cause certain inaccuracies, leading to delayed or unwarranted treatment. Here, we introduce a label-free plasmonic biosensing method based on the thickness-sensitive plasmonic coupling, combined with supervised deep learning (DL) using neural networks. The strategy of utilizing neural networks to process output data can reduce the limit of detection (LOD) of the sensor and significantly improve the accuracy (from 93.1%-97.4% to 99%-99.6%). Compared with widely used emerging clinical technologies, our platform achieves accurate decisions with higher sensitivity in a short assay time (∼30 min). The integration of DL models considerably simplifies the readout procedure, resulting in a substantial decrease in processing time. Our findings offer a promising avenue for developing high-precision molecular detection tools for point-of-care (POC) applications.

Noninvasive analysis of contractility during identical maturations revealed two phenotypes in ventricular but not in atrial iPSC-CM.

Patient-derived induced pluripotent stem cells (iPSCs) can be differentiated into atrial and ventricular cardiomyocytes to allow for personalized drug screening. A hallmark of differentiation is the manifestation of spontaneous beating in a two-dimensional (2-D) cell culture. However, an outstanding observation is the high variability in this maturation process. We valued that contractile parameters change during differentiation serving as an indicator of maturation. Consequently, we recorded noninvasively spontaneous motion activity during the differentiation of male iPSC toward iPSC cardiomyocytes (iPSC-CMs) to further analyze similar maturated iPSC-CMs. Surprisingly, our results show that identical differentiations into ventricular iPSC-CMs are variable with respect to contractile parameters resulting in two distinct subpopulations of ventricular-like cells. In contrast, differentiation into atrial iPSC-CMs resulted in only one phenotype. We propose that the noninvasive and cost-effective recording of contractile activity during maturation using a smartphone device may help to reduce the variability in results frequently reported in studies on ventricular iPSC-CMs.NEW & NOTEWORTHY Differentiation of induced pluripotent stem cells (iPSCs) into iPSC-derived cardiomyocytes (iPSC-CMs) exhibits a high variability in mature parameters. Here, we monitored noninvasively contractile parameters of iPSC-CM during full-time differentiation using a smartphone device. Our results show that parallel maturations of iPSCs into ventricular iPSC-CMs, but not into atrial iPSC-CMs, resulted in two distinct subpopulations of iPSC-CMs. These findings suggest that our cost-effective method may help to compare iPSC-CMs at the same maturation level.

Neuroscience meets behavior: A systematic literature review on magnetic resonance imaging of the brain combined with real-world digital phenotyping.

A primary goal of neuroscience is to understand the relationship between the brain and behavior. While magnetic resonance imaging (MRI) examines brain structure and function under controlled conditions, digital phenotyping via portable automatic devices (PAD) quantifies behavior in real-world settings. Combining these two technologies may bridge the gap between brain imaging, physiology, and real-time behavior, enhancing the generalizability of laboratory and clinical findings. However, the use of MRI and data from PADs outside the MRI scanner remains underexplored. Herein, we present a Preferred Reporting Items for Systematic Reviews and Meta-Analysis systematic literature review that identifies and analyzes the current state of research on the integration of brain MRI and PADs. PubMed and Scopus were automatically searched using keywords covering various MRI techniques and PADs. Abstracts were screened to only include articles that collected MRI brain data and PAD data outside the laboratory environment. Full-text screening was then conducted to ensure included articles combined quantitative data from MRI with data from PADs, yielding 94 selected papers for a total of N = 14,778 subjects. Results were reported as cross-frequency tables between brain imaging and behavior sampling methods and patterns were identified through network analysis. Furthermore, brain maps reported in the studies were synthesized according to the measurement modalities that were used. Results demonstrate the feasibility of integrating MRI and PADs across various study designs, patient and control populations, and age groups. The majority of published literature combines functional, T1-weighted, and diffusion weighted MRI with physical activity sensors, ecological momentary assessment via PADs, and sleep. The literature further highlights specific brain regions frequently correlated with distinct MRI-PAD combinations. These combinations enable in-depth studies on how physiology, brain function and behavior influence each other. Our review highlights the potential for constructing brain-behavior models that extend beyond the scanner and into real-world contexts.

Interpretable artificial intelligence-based app assists inexperienced radiologists in diagnosing biliary atresia from sonographic gallbladder images.

BACKGROUND: A previously trained deep learning-based smartphone app provides an artificial intelligence solution to help diagnose biliary atresia from sonographic gallbladder images, but it might be impractical to launch it in real clinical settings. This study aimed to redevelop a new model using original sonographic images and their derived smartphone photos and then test the new model's performance in assisting radiologists with different experiences to detect biliary atresia in real-world mimic settings. METHODS: A new model was first trained retrospectively using 3659 original sonographic gallbladder images and their derived 51,226 smartphone photos and tested on 11,410 external validation smartphone photos. Afterward, the new model was tested in 333 prospectively collected sonographic gallbladder videos from 207 infants by 14 inexperienced radiologists (9 juniors and 5 seniors) and 4 experienced pediatric radiologists in real-world mimic settings. Diagnostic performance was expressed as the area under the receiver operating characteristic curve (AUC). RESULTS: The new model outperformed the previously published model in diagnosing BA on the external validation set (AUC 0.924 vs 0.908, P = 0.004) with higher consistency (kappa value 0.708 vs 0.609). When tested in real-world mimic settings using 333 sonographic gallbladder videos, the new model performed comparable to experienced pediatric radiologists (average AUC 0.860 vs 0.876) and outperformed junior radiologists (average AUC 0.838 vs 0.773) and senior radiologists (average AUC 0.829 vs 0.749). Furthermore, the new model could aid both junior and senior radiologists to improve their diagnostic performances, with the average AUC increasing from 0.773 to 0.835 for junior radiologists and from 0.749 to 0.805 for senior radiologists. CONCLUSIONS: The interpretable app-based model showed robust and satisfactory performance in diagnosing biliary atresia, and it could aid radiologists with limited experiences to improve their diagnostic performances in real-world mimic settings.

A randomized controlled trial of an app-based intervention on physical activity and glycemic control in people with type 2 diabetes.

BACKGROUND: We investigated the effects of a physical activity encouragement intervention based on a smartphone personal health record (PHR) application (app) on step count increases, glycemic control, and body weight in patients with type 2 diabetes (T2D). METHODS: In this 12-week, single-center, randomized controlled, 12-week extension study, patients with T2D who were overweight or obese were randomized using ratio 1:2 to a group using a smartphone PHR app (control group) or group using the app and received individualized motivational text messages (intervention group) for 12 weeks. During the extension period, the sending of the encouraging text messages to the intervention group was discontinued. The primary outcome was a change in daily step count after 12 weeks and analyzed by independent t-test. The secondary outcomes included HbA1c, fasting glucose, and body weight analyzed by paired or independent t-test. RESULTS: Of 200 participants, 62 (93.9%) and 118 (88.1%) in the control and intervention group, respectively, completed the 12-week main study. The change in daily step count from baseline to week 12 was not significantly different between the two groups (P = 0.365). Among participants with baseline step counts < 7,500 steps per day, the change in the mean daily step count at week 12 in the intervention group (1,319 ± 3,020) was significantly larger than that in control group (-139 ± 2,309) (P = 0.009). At week 12, HbA1c in the intervention group (6.7 ± 0.5%) was significantly lower than that in control group (6.9 ± 0.6%, P = 0.041) and at week 24, changes in HbA1c from baseline were significant in both groups but, comparable between groups. Decrease in HbA1c from baseline to week 12 of intervention group was greater in participants with baseline HbA1c ≥ 7.5% (-0.81 ± 0.84%) compared with those with baseline HbA1c < 7.5% (-0.22 ± 0.39%) (P for interaction = 0.014). A significant reduction in body weight from baseline to week 24 was observed in both groups without significant between-group differences (P = 0.370). CONCLUSIONS: App-based individualized motivational intervention for physical activity did not increase daily step count from baseline to week 12, and the changes in HbA1c levels from baseline to week 12 were comparable. TRIAL REGISTRATION: ClinicalTrials.gov (NCT03407222).

Smartphone-Based Free-to-Total Prostate Specific Antigen Ratio Detection System Using a Colorimetric Reaction Integrated with Proximity-Induced Bio-Barcode and CRISPR/Cas12a Assay.

The free-to-total prostate-specific antigen (f/t-PSA) ratio is of great significance in the accurate diagnosis of prostate cancer. Herein, a smartphone-based detection system is reported using a colorimetric reaction integrated with proximity-induced bio-barcode and the clustered regularly interspaced short palindromic repeats (CRISPR)/Cas12a assay for f/t-PSA ratio detection. DNA/antibody recognition probes are designed to bind f-PSA or t-PSA and induce the release of the DNA bio-barcode. The CRISPR/Cas12a system is activated by the DNA bio-barcode to release Ag+ from the C-Ag+-C structure of the hairpin DNA. The released Ag+ is used to affect the tetramethylbenzidine (TMB)-H2O2-based colorimetric reaction catalyzed by Pt nanoparticles (NPs), as the peroxidase-like activity of the Pt NPs can be efficiently inhibited by Ag+. A smartphone with a self-developed app is used as an image reader and analyzer to analyze the colorimetric reaction and provide the results. A limit of detection of 0.06 and 0.04 ng mL-1 is achieved for t-PSA and f-PSA, respectively. The smartphone-based method showed a linear response between 0.1 and 100 ng mL-1 of t-PSA or f-PSA. In tests with clinical samples, the smartphone-based method successfully diagnosed prostate cancer patients from benign prostatic hyperplasia patients and healthy cases with high sensitivity and specificity.

Development of a measuring app for systemic sclerosis-related digital ulceration (SALVE: Scleroderma App for Lesion VErification).

OBJECTIVES: To test the hypothesis that photographs (in addition to self-reported data) can be collected daily by patients with systemic sclerosis (SSc) using a smartphone app designed specifically for digital lesions, and could provide an objective outcome measure for use in clinical trials. METHODS: An app was developed to collect images and patient reported outcome measures (PROMS) including Pain score and the Hand Disability in Systemic Sclerosis-Digital Ulcers (HDISS-DU) questionnaire. Participants photographed their lesion(s) each day for 30 days and uploaded images to a secure repository. Lesions were analysed both manually and automatically, using a machine learning approach. RESULTS: 25 patients with SSc-related digital lesions consented of whom 19 completed the 30-day study, with evaluable data from 27 lesions. Mean (standard deviation [SD]) baseline Pain score was 5.7 (2.4) and HDISS-DU 2.2 (0.9), indicating high lesion and disease-related morbidity. 506 images were used in the analysis (mean number of used images per lesion 18.7, SD 8.3). Mean (SD) manual and automated lesion areas at day 1 were 11.6 (16.0) and 13.9 (16.7) mm2 respectively. Manual area decreased by 0.08mm2 per day (2.4mm2 over 30 days) and automated area by 0.1mm2 (3.0mm2 over 30 days). Average gradients of manual and automated measurements over 30 days correlated strongly (r = 0.81). Manual measurements were on average 40% lower than automated, with wide limits of agreement. CONCLUSION: Even patients with significant hand disability were able to use the app. Automated measurement of finger lesions could be valuable as an outcome measure in clinical trials.

Smartphone-Based Sensing of Cortisol by Functionalized Rhodamine Probes.

During a stress condition, the human body synthesizes catecholamine neurotransmitters and specific hormones (called "stress hormones"), the most important of which is cortisol. The monitoring of cortisol levels should be extremely important to control the stress levels, and for this reason, it shows important medical applications. The common analytical methods (HPLC, GC-MS) cannot be used in real life, due to the bulky size of the instruments and the necessity of specialized personnel. Molecular probes solve these problems due to their fast and easy use. The synthesis of new fluorescent rhodamine probes, able to interact by non-covalent interactions with cortisol, the recognition properties in solution as well as in solid state by Strip Test, using a smartphone as detector, are here reported. DFT calculations and FT-IR measurements suggest the formation of supramolecular complexes through hydrogen bonds as main non-covalent interaction. The present study represents one of the first sensor, based on synthetical chemical receptors, able to detect cortisol in a linear range from 1 mM to 1 pM, based on non-covalent molecular recognition and paves the way to the realization of practical point-of-care device for the monitoring of cortisol in real live.

Lymphedema self-management mobile application with nurse support for post breast cancer surgery survivors: description of the design process and prototype evaluation.

BACKGROUND: Self-management is the key to control breast cancer related lymphedema (BCRL). This study aimed to develop a mobile application with nurse support for lymphedema self-management and evaluate its usability from the patients' points of view. METHODS: This applied developmental study was conducted on 87 women in a lymphedema clinic, Shiraz, Iran, May-November 2023. The study included three phases: development, distribution of the application and usability evaluation. In the development phase, the researchers developed application using the Java programming language. In distribution phase, the application was installed on the participants' phones. For usability evaluation, 87 patients completed the user satisfaction questionnaire after three months access to the application. Data was analyzed using descriptive and analytical statistics using SPSS software 22. P-value less than 0.05 was considered significant. RESULTS: We designed a self-management application specific to BCRL that included ten unique modules mainly related to patient education, interaction with peers and nurse, self-management support, and settings. The application mean usability score was 7.72 ± 1.08. The usability dimensions of "screen" (8.06 ± 1.02) and "terminology and systems information" (7.29 ± 1.62) received the highest and lowest mean scores, respectively. CONCLUSION: The application has new features to meet more patients' needs compared to what other existing lymphedema self-management applications already have addressed. The findings showed that the participants rated the application usability at the "good" level that is similar to some previous studies. Considering the unique nature of the application and its favorable usability, we recommend its use for BCRL self-management.

Investigating change in network structure of eating disorder symptoms after delivery of a smartphone app-based intervention.

BACKGROUND: Eating disorder (ED) research has embraced a network perspective of psychopathology, which proposes that psychiatric disorders can be conceptualized as a complex system of interacting symptoms. However, existing intervention studies using the network perspective have failed to find that symptom reductions coincide with reductions in strength of associations among these symptoms. We propose that this may reflect failure of alignment between network theory and study design and analysis. We offer hypotheses for specific symptom associations expected to be disrupted by an app-based intervention, and test sensitivity of a range of statistical metrics for identifying this intervention-induced disruption. METHODS: Data were analyzed from individuals with recurrent binge eating who participated in a randomized controlled trial of a cognitive-behavioral smartphone application. Participants were categorized into one of three groups: waitlist (n = 155), intervention responder (n = 49), and intervention non-responder (n = 77). Several statistical tests (bivariate associations, network-derived strength statistics, network invariance tests) were compared in ability to identify change in network structure. RESULTS: Hypothesized disruption to specific symptom associations was observed through change in bivariate correlations from baseline to post-intervention among the responder group but were not evident from symptom and whole-of-network based network analysis statistics. Effects were masked when the intervention group was assessed together, ignoring heterogeneity in treatment responsiveness. CONCLUSION: Findings are consistent with our contention that study design and analytic approach influence the ability to test network theory predictions with fidelity. We conclude by offering key recommendations for future network theory-driven interventional studies.

Can Smartphone Notifications Help With Gout Management? A Feasibility Study.

OBJECTIVE: This feasibility study aimed to assess the acceptability of using smartphone notifications to modify the medication beliefs of people with gout. We evaluated the feasibility and acceptability of a smartphone application using the Technology Acceptance Model. We explored adherence rate differences and outcomes between the intervention and control groups. METHODS: Fifty-two patients with gout who were prescribed allopurinol were randomly assigned to either active control (n = 24) or intervention group (n = 28). Over 3 months, both groups used the study app on their smartphones. The active control group received notifications about general health advice, whereas the intervention group received adherence-targeted notifications. The feasibility and acceptability of the smartphone app was measured through semistructured interviews. Adherence rate was assessed through serum urate levels and missed doses at 3 timepoints: baseline, 3 months (post intervention), and 6 months (follow-up). RESULTS: The smartphone app demonstrated high feasibility, with strong participant retention and compliance. The participants expressed high levels of satisfaction with the app's user-friendliness and content, highlighting its acceptability. Both groups showed a significant reduction in missed doses over time (P < 0.05), but no significant differences in serum urate levels were found between the groups. Patients who received adherence-targeted notifications reported finding it more convenient to take allopurinol and expressed higher overall treatment satisfaction throughout the study. CONCLUSION: Adherence-targeted notifications have the potential to be an effective and scalable approach to supporting medication adherence in patients with gout. Further research is needed with larger samples to refine the components of the intervention and explore its optimal implementation.

Progression independent of relapse activity can be predicted by passively acquired tapping speed through a smartphone for 1 month: A prospective study.

BACKGROUND: Tapping speed (TS) correlates with baseline disability scales in people with multiple sclerosis (pwMS). OBJECTIVE: The study aimed to address if progression independent of relapse activity (PIRA) could be predicted by first-month measurement of TS. METHODS: Prospective study including pwMS in one referral MS center. Consecutive patients were included and keys/second (Keys/s) were passively measured each day using an in-house smartphone application for 1 month. Median, mean, and maximum keys/s were obtained. Multivariate logistic regression models (including keys/s, age, sex, and baseline disability scores) were obtained for prediction of a PIRA event after 1 year. RESULTS: Overall, 59 patients were included in the final analysis (64.4% women, median age of 44.5 years). However, 10 patients presented a PIRA event, without differences regarding baseline characteristics between PIRA and no-PIRA groups. PIRA group presented lower median keys/s (2 vs 4 keys/s, p = 0.002) and mean keys/s (2.8 vs 4.6, p = 0.008), while maximum keys/s were similar (p = 0.32). A median ⩽ 3 keys/s was independently associated with PIRA (aOR = 16.8, p = 0.03), as did a mean ⩽ 3.7 keys/s (aOR = 17, p = 0.02). These differences were not detected regarding other variables analyzed. CONCLUSION: Low median or mean keys/s obtained during initial month of assessment were indicative of a PIRA event within the next year.

Konectom™ cognitive processing speed test enables reliable remote, unsupervised cognitive assessment in people with multiple sclerosis: Exploring the use of substitution time as a novel digital outcome measure.

BACKGROUND: The Konectom™ smartphone-based cognitive processing speed (CPS) test is designed to assess processing speed and account for impact of visuomotor function on performance. OBJECTIVE: Evaluate reliability and validity of Konectom CPS Test, performed in clinic and remotely. METHODS: Data were collected from people with multiple sclerosis (PwMS) aged 18-64 years and healthy control participants (HC) matched for age, sex, and education. Remote test-retest reliability (intraclass correlation coefficients, ICC); correlation with established clinical measures (Spearman correlation coefficients); group analyses between cognitively impaired/unimpaired PwMS; and influence of age, sex, education, and upper limb motor function on CPS Test measures were assessed. RESULTS: Eighty PwMS and 66 HC participated. CPS Test measures from remote tests had good test-retest reliability (ICC of 0.67-0.87) and correlated with symbol digit modalities test (highest |ρ| = 0.80, p < 0.0001). Remote measures were stable (change from baseline < 5%) and correlated with MS disability (highest |ρ| = 0.39, p = 0.0004) measured by Expanded Disability Status Scale. CPS Test measures displayed sensitivity to cognitive impairment (highest d = 1.47). Demographics and motor function had the lowest impact on CPS Test substitution time, a measure accounting for visuomotor function. CONCLUSION: Konectom CPS Test measures provide valid, reliable remote measurements of cognitive processing speed in PwMS.

Sound-based cough peak flow estimation in patients with neuromuscular disorders.

INTRODUCTION/AIMS: Cough impairment is common in individuals with neuromuscular disorders and is associated with respiratory infections and shorter survival. Cough strength is assessed by measuring cough peak flow (CPF) using a flow meter, but this method requires a complex device setup and trained staff. The aim of the study is to evaluate the reliability of a smartphone app to estimate CPF based on cough sounds in a cohort of individuals with neuromuscular disorders. METHODS: Individuals with neuromuscular disorders underwent CPF measurement with a flow meter and a smartphone app. A CPF <270 L/min was considered abnormal. RESULTS: Of the 50 patients studied, 26 had amyotrophic lateral sclerosis (52%), 15 had hereditary myopathies (30%), and 9 had myasthenia gravis (18%). The intraclass correlation coefficient (ICC) between the CPF measured with a flow meter and CPF estimated with cough sounds was 0.774 (p < .001) even if the patients had orofacial weakness (ICC = 0.806, p < .001). The smartphone app had 94.4% sensitivity and 100% specificity to detect patients with CPF of less than 270 L/min. DISCUSSION: Our findings suggest that sounds measured with a smartphone app provide a reliable estimate of CPF in patients with neuromuscular disorders, even in the presence of with orofacial weakness. This may be a convenient way to monitor respiratory involvement in patients with neuromuscular disorders, but larger studies of more diverse patient cohorts are needed.

KIOS: A smartphone app for self-monitoring for patients with bipolar disorder.

OBJECTIVES: This study examined the use of a self-monitoring/self-management smartphone application (app) for patients with bipolar disorder. The app was specifically designed with patient-centered computational software system based on concepts from nonlinear systems (chaos) theory. METHODS: This was a randomized, active comparator study of use of the KIOS app compared to an existing free app that has high utilization rates known as eMoods, over 52 weeks, and performed in three academic centers. Patients were evaluated monthly utilizing the Bipolar Inventory of Symptoms Schedule (BISS). The primary outcome measure was the persistence of using the app over the year of the study. RESULTS: Patients assigned to KIOS persisted in the study longer than those assigned to eMoods; 57 patients (87.70%) in the KIOS group versus 42 (73.69%) in the eMoods group completed the study (p = 0.03). By 52 weeks, significantly more of KIOS group (84.4%) versus eMoods group (54%) entered data into their programs (χ2 = 14.2, df = 1, p = 0.0002). Patient satisfaction for KIOS was greater (F = 5.21, df = 1, 108, p = 0.025) with a standardized effect size (Cohen's d) of 0.41. There was no difference in clinical outcome at the end of the study between the two groups. CONCLUSIONS: This is the first randomized comparison study comparing two apps for the self-monitoring/self-management of bipolar disorder. The study revealed greater patient satisfaction and greater adherence to a patient-centered software program (KIOS) than a monitoring program that does not provide feedback (eMoods).

The effect of digital health interventions on postpartum depression or anxiety: a systematic review and meta-analysis of randomized controlled trials.

OBJECTIVE: This study aimed to examine the effect of digital health interventions compared with treatment as usual on preventing and treating postpartum depression and postpartum anxiety. DATA SOURCES: Searches were conducted in Ovid MEDLINE, Embase, Scopus, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov. STUDY ELIGIBILITY REQUIREMENTS: The systematic review included full-text randomized controlled trials comparing digital health interventions with treatment as usual for preventing or treating postpartum depression and postpartum anxiety. STUDY APPRAISAL AND SYNTHESIS METHODS: Two authors independently screened all abstracts for eligibility and independently reviewed all potentially eligible full-text articles for inclusion. A third author screened abstracts and full-text articles as needed to determine eligibility in cases of discrepancy. The primary outcome was the score on the first ascertainment of postpartum depression or postpartum anxiety symptoms after the intervention. Secondary outcomes included screening positive for postpartum depression or postpartum anxiety --as defined in the primary study --and loss to follow-up, defined as the proportion of participants who completed the final study assessment compared with the number of initially randomized participants. For continuous outcomes, the Hedges method was used to obtain standardized mean differences when the studies used different psychometric scales, and weighted mean differences were calculated when studies used the same psychometric scales. For categorical outcomes, pooled relative risks were estimated. RESULTS: Of 921 studies originally identified, 31 randomized controlled trials-corresponding to 5532 participants randomized to digital health intervention and 5492 participants randomized to treatment as usual-were included. Compared with treatment as usual, digital health interventions significantly reduced mean scores ascertaining postpartum depression symptoms (29 studies: standardized mean difference, -0.64 [95% confidence interval, -0.88 to -0.40]; I2=94.4%) and postpartum anxiety symptoms (17 studies: standardized mean difference, -0.49 [95% confidence interval, -0.72 to -0.25]; I2=84.6%). In the few studies that assessed screen-positive rates for postpartum depression (n=4) or postpartum anxiety (n=1), there were no significant differences between those randomized to digital health intervention and treatment as usual. Overall, those randomized to digital health intervention had 38% increased risk of not completing the final study assessment compared with those randomized to treatment as usual (pooled relative risk, 1.38 [95% confidence interval, 1.18-1.62]), but those randomized to app-based digital health intervention had similar loss-to-follow-up rates as those randomized to treatment as usual (relative risk, 1.04 [95% confidence interval, 0.91-1.19]). CONCLUSION: Digital health interventions modestly, but significantly, reduced scores assessing postpartum depression and postpartum anxiety symptoms. More research is needed to identify digital health interventions that effectively prevent or treat postpartum depression and postpartum anxiety but encourage ongoing engagement throughout the study period.

Associations of screen use with cognitive development in early childhood: the ELFE birth cohort.

BACKGROUND: The associations of screen use with children's cognition are not well evidenced and recent, large, longitudinal studies are needed. We aimed to assess the associations between screen use and cognitive development in the French nationwide birth cohort. METHODS: Time and context of screen use were reported by parents at ages 2, 3.5 and 5.5. Vocabulary, non-verbal reasoning and general cognitive development were assessed with the MacArthur-Bates Communicative Development Inventory (MB) at age 2, the Picture Similarities subtest from the British Ability Scales (PS) at age 3.5 and the Child Development Inventory (CDI) at ages 3.5 and 5.5. Outcome variables were age-adjusted and standardized (mean = 100, SD = 15). Multiple imputations were performed among children (N = 13,763) with ≥1 screen use information and ≥1 cognitive measures. Cross-sectional and longitudinal associations between screen use and cognitive development were assessed by linear regression models adjusted for sociodemographic and birth factors related to the family and children, and children's lifestyle factors competing with screen use. Baseline cognitive scores were further considered in longitudinal analysis. RESULTS: TV-on during family meals at age 2, not screen time, was associated with lower MB scores at age 2 (ß [95% CI] = -1.67 [-2.21, -1.13]) and CDI scores at age 3.5 (-0.82 [-1.31, -0.33]). In cross-sectional analysis, screen time was negatively associated with CDI scores at ages 3.5 (-0.67 [-0.94, -0.40]) and 5.5 (-0.47 [-0.77, -0.16]), and, in contrast, was positively associated with PS scores (0.39 [0.07, 0.71]) at age 3.5. Screen time at age 3.5 years was not associated with CDI scores at age 5.5 years. CONCLUSIONS: Our study found weak associations of screen use with cognition after controlling for sociodemographic and children's birth factors and lifestyle confounders, and suggests that the context of screen use matters, not solely screen time, in children's cognitive development.