Computer Methods and Programs in Biomedicine, Journal Year: 2022, Volume and Issue: 226, P. 107132 - 107132
Published: Sept. 20, 2022
Language: Английский
BMC Psychiatry, Journal Year: 2022, Volume and Issue: 22(1)
Published: Feb. 21, 2022
Abstract Background Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response treatment identify early indicators relapse. Remote Measurement Technologies (RMT) provide an opportunity transform the measurement management MDD, via data collected from inbuilt smartphone sensors wearable devices alongside app-based questionnaires tasks. A key question for field extent which participants can adhere research protocols completeness collected. We aimed describe drop out in a naturalistic multimodal longitudinal RMT study, people with history recurrent MDD. further determine whether those experiencing depressive relapse at baseline contributed less complete data. Methods Assessment Disease Relapse – (RADAR-MDD) multi-centre, prospective observational cohort study conducted as part Central Nervous System (RADAR-CNS) program. People MDD were provided wrist-worn device, apps designed to: a) collect sensors; b) deliver questionnaires, speech tasks, cognitive assessments. Participants followed-up minimum 11 months maximum 24 months. Results Individuals ( n = 623) enrolled study,. report 80% completion rates primary outcome assessments across all follow-up timepoints. 79.8% participated amount time available 20.2% withdrew prematurely. found no evidence association between severity depression availability In total, 110 had > 50% types. Conclusions RADAR-MDD largest mental health. Here, we have shown that collecting clinical population feasible. comparable levels active passive forms collection, demonstrating both are feasible this patient group.
Language: Английский
Citations
83
IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 35219 - 35237
Published: Jan. 1, 2022
Wearable technology has played an essential role in the Mobile Health (mHealth) sector for diagnosis, treatment, and rehabilitation of numerous diseases disorders. One such neuro-degenerative disorder is Parkinson’s Disease (PD). It categorized by motor symptoms that affect a patient’s skills non-motor general health PD patient. The quality life patient with highly compromised. To date, there no cure disease, but early intervention assistive care can help to perform daily activities considerable ease. Many research works management discuss challenges healthcare professionals face detection this disease. Sensor devices have been promising overcome these certain degree because low cost accuracy measurement, yielding precise conclusive results detect, monitor, manage PD. This paper presents Systematic Literature Review (SLR) provides in-depth analysis symptoms, Motor Non-Motor Symptoms (NMS), current diagnosis techniques used their efficacy. also highlights work various researchers wearable sensors proposals improve diagnosing, monitoring, managing remotely via sensors. Another area focus commercially available wearables few progress. will be beneficial future identify existing gaps provide clinicians better insight into disease progression, avoid complications. analyzes around 50+ articles from 2016 2021 concludes still much room improvement during process. While attributed Symptom management, little on NMS Furthermore, management.
Language: Английский
Citations
78
npj Digital Medicine, Journal Year: 2023, Volume and Issue: 6(1)
Published: May 5, 2023
Abstract Given the limitations of traditional approaches, wearable artificial intelligence (AI) is one technologies that have been exploited to detect or predict depression. The current review aimed at examining performance AI in detecting and predicting search sources this systematic were 8 electronic databases. Study selection, data extraction, risk bias assessment carried out by two reviewers independently. extracted results synthesized narratively statistically. Of 1314 citations retrieved from databases, 54 studies included review. pooled mean highest accuracy, sensitivity, specificity, root square error (RMSE) was 0.89, 0.87, 0.93, 4.55, respectively. lowest RMSE 0.70, 0.61, 0.73, 3.76, Subgroup analyses revealed there a statistically significant difference specificity between algorithms, sensitivity devices. Wearable promising tool for depression detection prediction although it its infancy not ready use clinical practice. Until further research improve performance, should be used conjunction with other methods diagnosing Further are needed examine based on combination device neuroimaging distinguish patients those diseases.
Language: Английский
Citations
50
Cureus, Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 8, 2024
Wearable health devices are becoming vital in chronic disease management because they offer real-time monitoring and personalized care. This review explores their effectiveness challenges across medical fields, including cardiology, respiratory health, neurology, endocrinology, orthopedics, oncology, mental health. A thorough literature search identified studies focusing on wearable devices' impact patient outcomes. In wearables have proven effective for hypertension, detecting arrhythmias, aiding cardiac rehabilitation. these enhance asthma continuous of critical parameters. Neurological applications include seizure detection Parkinson's management, with showing promising results improving technology advances thyroid dysfunction monitoring, fertility tracking, diabetes management. Orthopedic improved postsurgical recovery rehabilitation, while help early complication oncology. Mental benefits anxiety detection, post-traumatic stress disorder reduction through biofeedback. conclusion, transformative potential managing illnesses by enhancing engagement. Despite significant improvements adherence outcomes, data accuracy privacy persist. However, ongoing innovation collaboration, we can all be part the solution to maximize technologies healthcare.
Language: Английский
Citations
25
Neuroscience & Biobehavioral Reviews, Journal Year: 2024, Volume and Issue: 158, P. 105541 - 105541
Published: Jan. 11, 2024
Smartphone-based digital phenotyping enables potentially clinically relevant information to be collected as individuals go about their day. This could improve monitoring and interventions for people with Major Depressive Disorder (MDD). The aim of this systematic review was investigate current features methods used in MDD. We searched PubMed, PsycINFO, Embase, Scopus Web Science (10/11/2023) articles including: (1) MDD population, (2) smartphone-based features, (3) validated ratings. Risk bias assessed using several sources. Studies were compared within analysis goals (correlating depression, predicting symptom severity, diagnosis, mood state/episode, other). Twenty-four studies (9801 participants) included. achieved moderate performance. Common themes included challenges from complex missing data (leading a risk bias), lack external validation. made progress towards relating phenotypes clinical variables, often focusing on time-averaged features. Methods investigating temporal dynamics more directly may beneficial patient monitoring. European Research Council consolidator grant: 101001118, Prospero: CRD42022346264, Open Framework: https://osf.io/s7ay4
Language: Английский
Citations
18
Frontiers in Psychiatry, Journal Year: 2021, Volume and Issue: 12
Published: June 17, 2021
In this study, a literature survey was conducted of research into the development and use wearable devices sensors in patients with depression. We collected 18 studies that had investigated for assessment, monitoring, or prediction report, we examine various types (e.g., actigraphy units, wristbands, fitness trackers, smartwatches) parameters measured through people addition, discuss future trends, referring to other areas employing devices, suggest challenges using field Real-time objective monitoring symptoms novel approaches diagnosis treatment will lead changes management During process, it is necessary overcome several issues, including limited data, reliability, user adherence, privacy concerns.
Language: Английский
Citations
84
Journal of Medical Internet Research, Journal Year: 2022, Volume and Issue: 25, P. e42672 - e42672
Published: Dec. 11, 2022
Anxiety and depression are the most common mental disorders worldwide. Owing to lack of psychiatrists around world, incorporation artificial intelligence (AI) into wearable devices (wearable AI) has been exploited provide health services.This review aimed explore features AI used for anxiety identify application areas open research issues.We searched 8 electronic databases (MEDLINE, PsycINFO, Embase, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, Google Scholar) included studies that met inclusion criteria. Then, we checked cited screened were by studies. The study selection data extraction carried out 2 reviewers independently. extracted aggregated summarized using narrative synthesis.Of 1203 identified, 69 (5.74%) in this review. Approximately, two-thirds depression, whereas remaining it anxiety. frequent was diagnosing depression; however, none treatment purposes. Most targeted individuals aged between 18 65 years. device Actiwatch AW4 (Cambridge Neurotechnology Ltd). Wrist-worn type commonly category model development physical activity data, followed sleep heart rate data. frequently set from sources Depresjon. algorithm random forest, support vector machine.Wearable can offer great promise providing services related depression. Wearable be prescreening assessment Further reviews needed statistically synthesize studies' results performance effectiveness AI. Given its potential, technology companies should invest more
Language: Английский
Citations
61
Pervasive and Mobile Computing, Journal Year: 2022, Volume and Issue: 83, P. 101621 - 101621
Published: May 24, 2022
Depression is a prevalent mental disorder. Current clinical and self-reported assessment methods of depression are laborious incur recall bias. Their sporadic nature often misses severity fluctuations. Previous research highlights the potential in-situ quantification human behaviour using mobile sensors to augment traditional management. In this paper, we study whether mood scores passive smartphone wearable sensor data could be used classify people as depressed or non-depressed. longitudinal study, our participants provided daily (valence arousal) collected their smartphones Oura Rings. We computed aggregations mood, sleep, physical activity, phone usage, GPS mobility from raw differences between non-depressed groups created population-level Machine Learning classification models depression. found statistically significant in mobility, activity groups. An XGBoost model with predictors classified an accuracy 81.43% Area Under Curve 82.31%. A Support Vector only sensor-based had 77.06% 74.25%. Our results suggest that digital biomarkers promising differentiating without symptoms. This contributes body evidence supporting role unobtrusive understanding its diagnosis monitoring.
Language: Английский
Citations
57
BMC Psychiatry, Journal Year: 2022, Volume and Issue: 22(1)
Published: June 22, 2022
This PRISMA systematic literature review examined the use of digital data collection methods (including ecological momentary assessment [EMA], experience sampling method [ESM], biomarkers, passive sensing, mobile ambulatory assessment, and time-series analysis), emphasizing on phenotyping (DP) to study depression. DP is defined as profile health information objectively.Four distinct yet interrelated goals underpin this study: (a) identify empirical research examining depression; (b) describe different technology employed; (c) integrate evidence regarding efficacy in examination, diagnosis, monitoring depression (d) clarify definitions mental records terminology.Overall, 118 studies were assessed eligible. Considering terms employed, "EMA", "ESM", "DP" most predominant. A variety sources reported, including voice, language, keyboard typing kinematics, phone calls texts, geocoded activity, actigraphy sensor-related recordings (i.e., steps, sleep, circadian rhythm), self-reported apps' information. Reviewed employed subjectively objectively recorded combination with interviews psychometric scales.Findings suggest links between a person's Future recommendations include deriving consensus definition expanding consider broader contextual developmental circumstances relation their data/records.
Language: Английский
Citations
53
Journal of Medical Internet Research, Journal Year: 2023, Volume and Issue: 25, P. e46778 - e46778
Published: July 31, 2023
The COVID-19 pandemic has increased the impact and spread of mental illness made health services difficult to access; therefore, there is a need for remote, pervasive forms monitoring. Digital phenotyping new approach that uses measures extracted from spontaneous interactions with smartphones (eg, screen touches or movements) other digital devices as markers status.
Language: Английский
Citations
42