RESUMO
Drug development for mood disorders can greatly benefit from the development of robust, reliable, and objective biomarkers. The incorporation of smartphones and wearable devices in clinical trials provide a unique opportunity to monitor behavior in a non-invasive manner. The objective of this study is to identify the correlations between remotely monitored self-reported assessments and objectively measured activities with depression severity assessments often applied in clinical trials. 30 unipolar depressed patients and 29 age- and gender-matched healthy controls were enrolled in this study. Each participant's daily physiological, physical, and social activity were monitored using a smartphone-based application (CHDR MORE™) for 3 weeks continuously. Self-reported depression anxiety stress scale-21 (DASS-21) and positive and negative affect schedule (PANAS) were administered via smartphone weekly and daily respectively. The structured interview guide for the Hamilton depression scale and inventory of depressive symptomatology-clinical rated (SIGHD-IDSC) was administered in-clinic weekly. Nested cross-validated linear mixed-effects models were used to identify the correlation between the CHDR MORE™ features with the weekly in-clinic SIGHD-IDSC scores. The SIGHD-IDSC regression model demonstrated an explained variance (R2) of 0.80, and a Root Mean Square Error (RMSE) of ± 15 points. The SIGHD-IDSC total scores were positively correlated with the DASS and mean steps-per-minute, and negatively correlated with the travel duration. Unobtrusive, remotely monitored behavior and self-reported outcomes are correlated with depression severity. While these features cannot replace the SIGHD-IDSC for estimating depression severity, it can serve as a complementary approach for assessing depression and drug effects outside the clinic.
Assuntos
Transtorno Depressivo Maior , Aplicativos Móveis , Dispositivos Eletrônicos Vestíveis , Humanos , Smartphone , Autorrelato , Depressão/diagnósticoRESUMO
BACKGROUND: Central nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity. OBJECTIVE: This narrative literature review aims to provide an overview of the current landscape of biomarker development using mHealth technologies and ML. Additionally, it proposes recommendations to ensure the accuracy, reliability, and interpretability of these biomarkers. METHODS: This review extracted relevant publications from databases such as PubMed, IEEE, and CTTI. The ML methods employed across the selected publications were then extracted, aggregated, and reviewed. RESULTS: This review synthesized and presented the diverse approaches of 66 publications that address creating mHealth-based biomarkers using ML. The reviewed publications provide a foundation for effective biomarker development and offer recommendations for creating representative, reproducible, and interpretable biomarkers for future clinical trials. CONCLUSION: mHealth-based and ML-derived biomarkers have great potential for the remote monitoring of CNS disorders. However, further research and standardization of study designs are needed to advance this field. With continued innovation, mHealth-based biomarkers hold promise for improving the monitoring of CNS disorders.
Assuntos
Telemedicina , Dispositivos Eletrônicos Vestíveis , Humanos , Reprodutibilidade dos Testes , Sistema Nervoso Central , Aprendizado de Máquina , Biomarcadores , Telemedicina/métodosRESUMO
Although well-established therapies exist for post-traumatic stress disorder (PTSD), barriers to seek mental health care are high. Technology-based interventions may play a role in improving the reach of efforts to treat, especially when therapist availability is low. The goal of the current randomized controlled trial was to pilot the efficacy of a computer-based trauma intervention with elements of virtual reality (VR; 3MR system) and limited therapist involvement for the treatment of PTSD in a childhood sexual abuse (CSA) and war veteran sample and to compare this to "treatment as usual" (TAU). TAU consisted of evidence-based approaches such as imaginal exposure, EMDR, or narrative exposure therapy. A total of 44 patients with PTSD were included and randomly assigned to 12 sessions of 3MR intervention or TAU (completer n 3MR = 12, TAU = 18). Several measures (PCL-5, BDI-II, OQ-45-2, and the M.I.N.I. 5.0.0.) were administered to measure symptoms of PTSD and depression and scores of overall well-being at pre, post, and a three-month follow-up measurement. Analyses suggest that symptoms of PTSD and depression in the 3MR condition decreased, and overall well-being increased between pre and post measurements. Results did not indicate any clear differences between the treatment conditions over time which suggests that treatment gains of the 3MR intervention seem no less than those of TAU. Finally, both treatment conditions produced similar remission rates of PTSD and depression. Therefore, the 3MR intervention could possibly constitute an appropriate treatment alternative. The small sample size as well as evident drop-out rates in the 3MR condition (45%) do warrant further research. The procedures of this study were approved by the Medical Ethical Research Committee (MERC) of the Erasmus Medical Center in Rotterdam (MEC-NL46279.078.13) and pre-registered via ClinicalTrials.gov (Protocol Record CI1-12-S028-1).
RESUMO
BACKGROUND: Heart rate (HR) is an important and commonly measured physiological parameter in wearables. HR is often measured at the wrist with the photoplethysmography (PPG) technique, which determines HR based on blood volume changes, and is therefore influenced by blood pressure. In individuals with spinal cord injury (SCI), blood pressure control is often altered and could therefore influence HR accuracy measured by the PPG technique. OBJECTIVE: The objective of this study is to investigate the HR accuracy measured with the PPG technique with a Fitbit Charge 2 (Fitbit Inc) in wheelchair users with SCI, how the activity intensity affects the HR accuracy, and whether this HR accuracy is affected by lesion level. METHODS: The HR of participants with (38/48, 79%) and without (10/48, 21%) SCI was measured during 11 wheelchair activities and a 30-minute strength exercise block. In addition, a 5-minute seated rest period was measured in people with SCI. HR was measured with a Fitbit Charge 2, which was compared with the HR measured by a Polar H7 HR monitor used as a reference device. Participants were grouped into 4 groups-the no SCI group and based on lesion level into the