Standing Balance Conditions and Digital Sway Measures for Clinical Trials of Friedreich's Ataxia.

BACKGROUND: Progressive loss of standing balance is a feature of Friedreich's ataxia (FRDA). OBJECTIVES: This study aimed to identify standing balance conditions and digital postural sway measures that best discriminate between FRDA and healthy controls (HC). We assessed test-retest reliability and correlations between sway measures and clinical scores. METHODS: Twenty-eight subjects with FRDA and 20 HC completed six standing conditions: feet apart, feet together, and feet tandem, both with eyes opened (EO) and eyes closed. Sway was measured using a wearable sensor on the lumbar spine for 30 seconds. Test completion rate, test-retest reliability with intraclass correlation coefficients, and areas under the receiver operating characteristic curves (AUCs) for each measure were compared to identify distinguishable FRDA sway characteristics from HC. Pearson correlations were used to evaluate the relationships between discriminative measures and clinical scores. RESULTS: Three of the six standing conditions had completion rates over 70%. Of these three conditions, natural stance and feet together with EO showed the greatest completion rates. All six of the sway measures' mean values were significantly different between FRDA and HC. Four of these six measures discriminated between groups with >0.9 AUC in all three conditions. The Friedreich Ataxia Rating Scale Upright Stability and Total scores correlated with sway measures with P-values <0.05 and r-values (0.63-0.86) and (0.65-0.81), respectively. CONCLUSION: Digital postural sway measures using wearable sensors are discriminative and reliable for assessing standing balance in individuals with FRDA. Natural stance and feet together stance with EO conditions suggest use in clinical trials for FRDA. © 2024 International Parkinson and Movement Disorder Society.

Sleep-wake variations of electrodermal activity in bipolar disorder.

BACKGROUND: Affective states influence the sympathetic nervous system, inducing variations in electrodermal activity (EDA), however, EDA association with bipolar disorder (BD) remains uncertain in real-world settings due to confounders like physical activity and temperature. We analysed EDA separately during sleep and wakefulness due to varying confounders and potential differences in mood state discrimination capacities. METHODS: We monitored EDA from 102 participants with BD including 35 manic, 29 depressive, 38 euthymic patients, and 38 healthy controls (HC), for 48 h. Fifteen EDA features were inferred by mixed-effect models for repeated measures considering sleep state, group and covariates. RESULTS: Thirteen EDA feature models were significantly influenced by sleep state, notably including phasic peaks (p < 0.001). During wakefulness, phasic peaks showed different values for mania (M [SD] = 6.49 [5.74, 7.23]), euthymia (5.89 [4.83, 6.94]), HC (3.04 [1.65, 4.42]), and depression (3.00 [2.07, 3.92]). Four phasic features during wakefulness better discriminated between HC and mania or euthymia, and between depression and euthymia or mania, compared to sleep. Mixed symptoms, average skin temperature, and anticholinergic medication affected the models, while sex and age did not. CONCLUSION: EDA measured from awake recordings better distinguished between BD states than sleep recordings, when controlled by confounders.

Development of a smartphone screening test for preclinical Alzheimer's disease and validation across the dementia continuum.

BACKGROUND: Dementia prevalence is predicted to triple to 152 million globally by 2050. Alzheimer's disease (AD) constitutes 70% of cases. There is an urgent need to identify individuals with preclinical AD, a 10-20-year period of progressive brain pathology without noticeable cognitive symptoms, for targeted risk reduction. Current tests of AD pathology are either too invasive, specialised or expensive for population-level assessments. Cognitive tests are normal in preclinical AD. Emerging evidence demonstrates that movement analysis is sensitive to AD across the disease continuum, including preclinical AD. Our new smartphone test, TapTalk, combines analysis of hand and speech-like movements to detect AD risk. This study aims to [1] determine which combinations of hand-speech movement data most accurately predict preclinical AD [2], determine usability, reliability, and validity of TapTalk in cognitively asymptomatic older adults and [3], prospectively validate TapTalk in older adults who have cognitive symptoms against cognitive tests and clinical diagnoses of Mild Cognitive Impairment and AD dementia. METHODS: Aim 1 will be addressed in a cross-sectional study of at least 500 cognitively asymptomatic older adults who will complete computerised tests comprising measures of hand motor control (finger tapping) and oro-motor control (syllabic diadochokinesis). So far, 1382 adults, mean (SD) age 66.20 (7.65) years, range 50-92 (72.07% female) have been recruited. Motor measures will be compared to a blood-based AD biomarker, phosphorylated tau 181 to develop an algorithm that classifies preclinical AD risk. Aim 2 comprises three sub-studies in cognitively asymptomatic adults: (i) a cross-sectional study of 30-40 adults to determine the validity of data collection from different types of smartphones, (ii) a prospective cohort study of 50-100 adults ≥ 50 years old to determine usability and test-retest reliability, and (iii) a prospective cohort study of ~1,000 adults ≥ 50 years old to validate against cognitive measures. Aim 3 will be addressed in a cross-sectional study of ~200 participants with cognitive symptoms to validate TapTalk against Montreal Cognitive Assessment and interdisciplinary consensus diagnosis. DISCUSSION: This study will establish the precision of TapTalk to identify preclinical AD and estimate risk of cognitive decline. If accurate, this innovative smartphone app will enable low-cost, accessible screening of individuals for AD risk. This will have wide applications in public health initiatives and clinical trials. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT06114914, 29 October 2023. Retrospectively registered.

Cognitive modeling of the Mnemonic Similarity Task as a digital biomarker for Alzheimer's disease.

BACKGROUND: The Mnemonic Similarity Task (MST) is a popular memory task designed to assess hippocampal integrity. We assessed whether analyzing MST performance using a multinomial processing tree (MPT) cognitive model could detect individuals with elevated Alzheimer's disease (AD) biomarker status prior to cognitive decline. METHOD: We analyzed MST data from >200 individuals (young, cognitively healthy older adults and individuals with mild cognitive impairment [MCI]), a subset of which also had existing cerebrospinal fluid (CSF) amyloid beta (Aß) and phosphorylated tau (pTau) data using both traditional and model-derived approaches. We assessed how well each could predict age group, memory ability, MCI status, Aß, and pTau status using receiver operating characteristic analyses. RESULTS: Both approaches predicted age group membership equally, but MPT-derived metrics exceeded traditional metrics in all other comparisons. DISCUSSION: A MPT model of the MST can detect individuals with AD prior to cognitive decline, making it a potentially useful tool for screening and monitoring older adults during the asymptomatic phase of AD. HIGHLIGHTS: The MST, along with cognitive modeling, identifies individuals with memory deficits and cognitive impairment. Cognitive modeling of the MST identifies individuals with increased AD biomarkers prior to changes in cognitive function. The MST is a digital biomarker that identifies individuals at high risk of AD.

REAL AD-Validation of a realistic screening approach for early Alzheimer's disease.

Early diagnosis is crucial to treatment success. This is especially relevant for Alzheimer's disease (AD), with its protracted preclinical phase. Most health care systems do not have the resources to conduct large-scale AD screenings in middle-aged individuals in need of novel AD treatment options and early, accurate diagnosis. Recent developments in blood-based biomarkers and remote cognitive testing offer novel, cost-effective, and scalable methods to detect cognitive and biomarker changes that may indicate early AD. In research cohorts, promising results have been reported, but these modalities have not been validated in population-based settings. The validation of a realistic screening approach for early Alzheimer's disease (REAL AD) study aims to validate the diagnostic and prognostic performance of the combined use of blood-based biomarkers and remote cognitive testing as a screening approach for early AD employing an existing health care infrastructure (the Swedish Västra Götaland Region Primary Healthcare). REAL AD aims to provide a concrete, individualized diagnostic framework, which could significantly improve AD prognosis. HIGHLIGHTS: In Sweden, most Alzheimer's disease (AD) diagnoses are made in primary care, where access to AD biomarkers is almost non-existent. Most health care systems have limited resources for the screening of middle-aged adults for early evidence of AD pathology. Blood-based biomarkers and remote cognitive testing offer novel, cost-effective, and scalable methods for detecting cognitive and biomarker changes that may indicate early AD. The REAL AD study aims to validate the diagnostic and prognostic performance of blood-based biomarkers and remote cognitive testing as a screening approach for early AD in an existing primary health care infrastructure in the Västra Götaland Region in Sweden. Studies such as REAL AD will play a vital role in helping to move the field toward concrete implementation of biomarkers in AD diagnostic workup at all care levels, eventually providing more comprehensive treatments options for the large and growing AD population, and for those at risk.

[Developments in the digitalization of public health since 2020 : Examples from the Leibniz ScienceCampus Digital Public Health Bremen]. / Entwicklungen in der Digitalisierung von Public Health seit 2020 : Beispiele aus dem Leibniz-WissenschaftsCampus Digital Public Health Bremen.

Digital public health has received a significant boost in recent years, especially due to the demands associated with the COVID-19 pandemic. In this report, we provide an overview of the developments in digitalization in the field of public health in Germany since 2020 and illustrate these with examples from the Leibniz ScienceCampus Digital Public Health Bremen (LSC DiPH).The following topics are central: How do digital survey methods as well as digital biomarkers and artificial intelligence methods shape modern epidemiology and prevention research? What is the status of digitalization in public health offices? Which approaches to health economics evaluation of digital public health interventions have been utilized so far? What is the status of training and further education in digital public health?The first years of the Leibniz ScienceCampus Digital Public Health Bremen (LSC DiPH) were also strongly influenced by the COVID-19 pandemic. Repeated population-based digital surveys of the LSC indicated an increase in use of health apps in the population, for example, in applications to support physical activity. The COVID-19-pandemic has also shown that the digitalization of public health enhances the risk of misinformation and disinformation.

Digital Phenotyping for Monitoring Mental Disorders: Systematic Review.

BACKGROUND: The COVID-19 pandemic has increased the impact and spread of mental illness and made health services difficult to access; therefore, there is a need for remote, pervasive forms of mental health monitoring. Digital phenotyping is a new approach that uses measures extracted from spontaneous interactions with smartphones (eg, screen touches or movements) or other digital devices as markers of mental status. OBJECTIVE: This review aimed to evaluate the feasibility of using digital phenotyping for predicting relapse or exacerbation of symptoms in patients with mental disorders through a systematic review of the scientific literature. METHODS: Our research was carried out using 2 bibliographic databases (PubMed and Scopus) by searching articles published up to January 2023. By following the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines, we started from an initial pool of 1150 scientific papers and screened and extracted a final sample of 29 papers, including studies concerning clinical populations in the field of mental health, which were aimed at predicting relapse or exacerbation of symptoms. The systematic review has been registered on the web registry Open Science Framework. RESULTS: We divided the results into 4 groups according to mental disorder: schizophrenia (9/29, 31%), mood disorders (15/29, 52%), anxiety disorders (4/29, 14%), and substance use disorder (1/29, 3%). The results for the first 3 groups showed that several features (ie, mobility, location, phone use, call log, heart rate, sleep, head movements, facial and vocal characteristics, sociability, social rhythms, conversations, number of steps, screen on or screen off status, SMS text message logs, peripheral skin temperature, electrodermal activity, light exposure, and physical activity), extracted from data collected via the smartphone and wearable wristbands, can be used to create digital phenotypes that could support gold-standard assessment and could be used to predict relapse or symptom exacerbations. CONCLUSIONS: Thus, as the data were consistent for almost all the mental disorders considered (mood disorders, anxiety disorders, and schizophrenia), the feasibility of this approach was confirmed. In the future, a new model of health care management using digital devices should be integrated with the digital phenotyping approach and tailored mobile interventions (managing crises during relapse or exacerbation).

Facial Expressions Track Depressive Symptoms in Old Age.

Facial expressions play a crucial role in the diagnosis of mental illnesses characterized by mood changes. The Facial Action Coding System (FACS) is a comprehensive framework that systematically categorizes and captures even subtle changes in facial appearance, enabling the examination of emotional expressions. In this study, we investigated the association between facial expressions and depressive symptoms in a sample of 59 older adults without cognitive impairment. Utilizing the FACS and the Korean version of the Beck Depression Inventory-II, we analyzed both "posed" and "spontaneous" facial expressions across six basic emotions: happiness, sadness, fear, anger, surprise, and disgust. Through principal component analysis, we summarized 17 action units across these emotion conditions. Subsequently, multiple regression analyses were performed to identify specific facial expression features that explain depressive symptoms. Our findings revealed several distinct features of posed and spontaneous facial expressions. Specifically, among older adults with higher depressive symptoms, a posed face exhibited a downward and inward pull at the corner of the mouth, indicative of sadness. In contrast, a spontaneous face displayed raised and narrowed inner brows, which was associated with more severe depressive symptoms in older adults. These findings suggest that facial expressions can provide valuable insights into assessing depressive symptoms in older adults.

Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood.

BACKGROUND: Visual and auditory signs of patient functioning have long been used for clinical diagnosis, treatment selection, and prognosis. Direct measurement and quantification of these signals can aim to improve the consistency, sensitivity, and scalability of clinical assessment. Currently, we investigate if machine learning-based computer vision (CV), semantic, and acoustic analysis can capture clinical features from free speech responses to a brief interview 1 month post-trauma that accurately classify major depressive disorder (MDD) and posttraumatic stress disorder (PTSD). METHODS: N = 81 patients admitted to an emergency department (ED) of a Level-1 Trauma Unit following a life-threatening traumatic event participated in an open-ended qualitative interview with a para-professional about their experience 1 month following admission. A deep neural network was utilized to extract facial features of emotion and their intensity, movement parameters, speech prosody, and natural language content. These features were utilized as inputs to classify PTSD and MDD cross-sectionally. RESULTS: Both video- and audio-based markers contributed to good discriminatory classification accuracy. The algorithm discriminates PTSD status at 1 month after ED admission with an AUC of 0.90 (weighted average precision = 0.83, recall = 0.84, and f1-score = 0.83) as well as depression status at 1 month after ED admission with an AUC of 0.86 (weighted average precision = 0.83, recall = 0.82, and f1-score = 0.82). CONCLUSIONS: Direct clinical observation during post-trauma free speech using deep learning identifies digital markers that can be utilized to classify MDD and PTSD status.

Reliability, construct and concurrent validity of a smartphone-based cognition test in multiple sclerosis.

BACKGROUND: Early detection and monitoring of cognitive dysfunction in multiple sclerosis (MS) may be enabled with smartphone-adapted tests that allow frequent measurements in the everyday environment. OBJECTIVES: The aim of this study was to determine the reliability, construct and concurrent validity of a smartphone-adapted Symbol Digit Modalities Test (sSDMT). METHODS: During a 28-day follow-up, 102 patients with MS and 24 healthy controls (HC) used the MS sherpa® app to perform the sSDMT every 3 days on their own smartphone. Patients performed the Brief International Cognitive Assessment for MS at baseline. Test-retest reliability (intraclass correlation coefficients, ICC), construct validity (group analyses between cognitively impaired (CI), cognitively preserved (CP) and HC for differences) and concurrent validity (correlation coefficients) were assessed. RESULTS: Patients with MS and HC completed an average of 23.2 (SD = 10.0) and 18.3 (SD = 10.2) sSDMT, respectively. sSDMT demonstrated high test-retest reliability (ICCs > 0.8) with a smallest detectable change of 7 points. sSDMT scores were different between CI patients, CP patients and HC (all ps < 0.05). sSDMT correlated modestly with the clinical SDMT (highest r = 0.690), verbal (highest r = 0.516) and visuospatial memory (highest r = 0.599). CONCLUSION: Self-administered smartphone-adapted SDMT scores were reliable and different between patients who were CI, CP and HC and demonstrated concurrent validity in assessing information processing speed.

Digital Biomarkers of Cognitive Frailty: The Value of Detailed Gait Assessment Beyond Gait Speed.

BACKGROUND: Cognitive frailty (CF), defined as the simultaneous presence of cognitive impairment and physical frailty, is a clinical symptom in early-stage dementia with promise in assessing the risk of dementia. The purpose of this study was to use wearables to determine the most sensitive digital gait biomarkers to identify CF. METHODS: Of 121 older adults (age = 78.9 ± 8.2 years, body mass index = 26.6 ± 5.5 kg/m2) who were evaluated with a comprehensive neurological exam and the Fried frailty criteria, 41 participants (34%) were identified with CF and 80 participants (66%) were identified without CF. Gait performance of participants was assessed under single task (walking without cognitive distraction) and dual task (walking while counting backward from a random number) using a validated wearable platform. Participants walked at habitual speed over a distance of 10 m. A validated algorithm was used to determine steady-state walking. Gait parameters of interest include steady-state gait speed, stride length, gait cycle time, double support, and gait unsteadiness. In addition, speed and stride length were normalized by height. RESULTS: Our results suggest that compared to the group without CF, the CF group had deteriorated gait performances in both single-task and dual-task walking (Cohen's effect size d = 0.42-0.97, p < 0.050). The largest effect size was observed in normalized dual-task gait speed (d = 0.97, p < 0.001). The use of dual-task gait speed improved the area under the curve (AUC) to distinguish CF cases to 0.76 from 0.73 observed for the single-task gait speed. Adding both single-task and dual-task gait speeds did not noticeably change AUC. However, when additional gait parameters such as gait unsteadiness, stride length, and double support were included in the model, AUC was improved to 0.87. CONCLUSIONS: This study suggests that gait performances measured by wearable sensors are potential digital biomarkers of CF among older adults. Dual-task gait and other detailed gait metrics provide value for identifying CF above gait speed alone. Future studies need to examine the potential benefits of gait performances for early diagnosis of CF and/or tracking its severity over time.

Digital Biomarker-Based Interventions: Systematic Review of Systematic Reviews.

BACKGROUND: The introduction of new medical technologies such as sensors has accelerated the process of collecting patient data for relevant clinical decisions, which has led to the introduction of a new technology known as digital biomarkers. OBJECTIVE: This study aims to assess the methodological quality and quality of evidence from meta-analyses of digital biomarker-based interventions. METHODS: This study follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline for reporting systematic reviews, including original English publications of systematic reviews reporting meta-analyses of clinical outcomes (efficacy and safety endpoints) of digital biomarker-based interventions compared with alternative interventions without digital biomarkers. Imaging or other technologies that do not measure objective physiological or behavioral data were excluded from this study. A literature search of PubMed and the Cochrane Library was conducted, limited to 2019-2020. The quality of the methodology and evidence synthesis of the meta-analyses were assessed using AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) and GRADE (Grading of Recommendations, Assessment, Development, and Evaluations), respectively. This study was funded by the National Research, Development and Innovation Fund of Hungary. RESULTS: A total of 25 studies with 91 reported outcomes were included in the final analysis; 1 (4%), 1 (4%), and 23 (92%) studies had high, low, and critically low methodologic quality, respectively. As many as 6 clinical outcomes (7%) had high-quality evidence and 80 outcomes (88%) had moderate-quality evidence; 5 outcomes (5%) were rated with a low level of certainty, mainly due to risk of bias (85/91, 93%), inconsistency (27/91, 30%), and imprecision (27/91, 30%). There is high-quality evidence of improvements in mortality, transplant risk, cardiac arrhythmia detection, and stroke incidence with cardiac devices, albeit with low reporting quality. High-quality reviews of pedometers reported moderate-quality evidence, including effects on physical activity and BMI. No reports with high-quality evidence and high methodological quality were found. CONCLUSIONS: Researchers in this field should consider the AMSTAR-2 criteria and GRADE to produce high-quality studies in the future. In addition, patients, clinicians, and policymakers are advised to consider the results of this study before making clinical decisions regarding digital biomarkers to be informed of the degree of certainty of the various interventions investigated in this study. The results of this study should be considered with its limitations, such as the narrow time frame. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/28204.

Objective Assessment of Upper-Extremity Motor Functions in Spinocerebellar Ataxia Using Wearable Sensors.

The study presents a novel approach to objectively assessing the upper-extremity motor symptoms in spinocerebellar ataxia (SCA) using data collected via a wearable sensor worn on the patient's wrist during upper-extremity tasks associated with the Assessment and Rating of Ataxia (SARA). First, we developed an algorithm for detecting/extracting the cycles of the finger-to-nose test (FNT). We extracted multiple features from the detected cycles and identified features and parameters correlated with the SARA scores. Additionally, we developed models to predict the severity of symptoms based on the FNT. The proposed technique was validated on a dataset comprising the seventeen (n = 17) participants' assessments. The cycle detection technique showed an accuracy of 97.6% in a Bland-Altman analysis and a 94% accuracy (F1-score of 0.93) in predicting the severity of the FNT. Furthermore, the dependency of the upper-extremity tests was investigated through statistical analysis, and the results confirm dependency and potential redundancies in the upper-extremity SARA assessments. Our findings pave the way to enhance the utility of objective measures of SCA assessments. The proposed wearable-based platform has the potential to eliminate subjectivity and inter-rater variabilities in assessing ataxia.

Gait Variability in Spinocerebellar Ataxia Assessed Using Wearable Inertial Sensors.

BACKGROUND: Quantitative assessment of severity of ataxia-specific gait impairments from wearable technology could provide sensitive performance outcome measures with high face validity to power clinical trials. OBJECTIVES: The aim of this study was to identify a set of gait measures from body-worn inertial sensors that best discriminate between people with prodromal or manifest spinocerebellar ataxia (SCA) and age-matched, healthy control subjects (HC) and determine how these measures relate to disease severity. METHODS: One hundred and sixty-three people with SCA (subtypes 1, 2, 3, and 6), 42 people with prodromal SCA, and 96 HC wore 6 inertial sensors while performing a natural pace, 2-minute walk. Areas under the receiver operating characteristic curves (AUC) were compared for 25 gait measures, including standard deviations as variability, to discriminate between ataxic and normal gait. Pearson's correlation coefficient assessed the relationships between the gait measures and severity of ataxia. RESULTS: Increased gait variability was the most discriminative gait feature of SCA; toe-out angle variability (AUC = 0.936; sensitivity = 0.871; specificity = 0.896) and double-support time variability (AUC = 0.932; sensitivity = 0.834; specificity = 0.865) were the most sensitive and specific measures. These variability measures were also significantly correlated with the scale for the assessment and rating of ataxia (SARA) and disease duration. The same gait measures discriminated gait of people with prodromal SCA from the gait of HC (AUC = 0.610, and 0.670, respectively). CONCLUSIONS: Wearable inertial sensors provide sensitive and specific measures of excessive gait variability in both manifest and prodromal SCAs that are reliable and related to the severity of the disease, suggesting they may be useful as clinical trial performance outcome measures. © 2021 International Parkinson and Movement Disorder Society.

Behavioural biometrics: Using smartphone keyboard activity as a proxy for rest-activity patterns.

Rest-activity patterns are important aspects of healthy sleep and may be disturbed in conditions like circadian rhythm disorders, insomnia, insufficient sleep syndrome, and neurological disorders. Long-term monitoring of rest-activity patterns is typically performed with diaries or actigraphy. Here, we propose an unobtrusive method to obtain rest-activity patterns using smartphone keyboard activity. The present study investigated whether this proposed method reliably estimates rest and activity timing compared to daily self-reports within healthy participants. First-year students (n = 51) used a custom smartphone keyboard to passively and objectively measure smartphone use behaviours and completed the Consensus Sleep Diary for 1 week. The time of the last keyboard activity before a nightly absence of keystrokes, and the time of the first keyboard activity following this period were used as markers. Results revealed high correlations between these markers and user-reported onset and offset of resting period (r ranged from 0.74 to 0.80). Linear mixed models could estimate onset and offset of resting periods with reasonable accuracy (R2 ranged from 0.60 to 0.66). This indicates that smartphone keyboard activity can be used to estimate rest-activity patterns. In addition, effects of chronotype and type of day were investigated. Implementing this method in longitudinal studies would allow for long-term monitoring of (disturbances to) rest-activity patterns, without user burden or additional costly devices. It could be particularly interesting to replicate these findings in studies amongst clinical populations with sleep-related problems, or in populations for whom disturbances in rest-activity patterns are secondary complaints, such as neurological disorders.

Toward Remote Assessment of Physical Frailty Using Sensor-based Sit-to-stand Test.

BACKGROUND: Traditional physical frailty (PF) screening tools are resource intensive and unsuitable for remote assessment. In this study, we used five times sit-to-stand test (5×STS) with wearable sensors to determine PF and three key frailty phenotypes (slowness, weakness, and exhaustion) objectively. MATERIALS AND METHODS: Older adults (n = 102, age: 76.54 ± 7.72 y, 72% women) performed 5×STS while wearing sensors attached to the trunk and bilateral thigh and shank. Duration of 5×STS was recorded using a stopwatch. Seventeen sensor-derived variables were analyzed to determine the ability of 5×STS to distinguish PF, slowness, weakness, and exhaustion. Binary logistic regression was used, and its area under curve was calculated. RESULTS: A strong correlation was observed between sensor-based and manually-recorded 5xSTS durations (r = 0.93, P < 0.0001). Sensor-derived variables indicators of slowness (5×STS duration, hip angular velocity range, and knee angular velocity range), weakness (hip power range and knee power range), and exhaustion (coefficient of variation (CV) of hip angular velocity range, CV of vertical velocity range, and CV of vertical power range) were different between the robust group and prefrail/frail group (P < 0.05) with medium to large effect sizes (Cohen's d = 0.50-1.09). The results suggested that sensor-derived variables enable identifying PF, slowness, weakness, and exhaustion with an area under curve of 0.861, 0.865, 0.720, and 0.723, respectively. CONCLUSIONS: Our study suggests that sensor-based 5×STS can provide digital biomarkers of PF, slowness, weakness, and exhaustion. The simplicity, ease of administration in front of a camera, and safety of 5xSTS may facilitate a remote assessment of PF, slowness, weakness, and exhaustion via telemedicine.

Mobility Performance in Community-Dwelling Older Adults: Potential Digital Biomarkers of Concern about Falling.

INTRODUCTION: Concern about falling is a prevalent worry among community-dwelling older adults and may contribute to a decline in physical and mental health. This study aimed to examine the association between mobility performance and concern about falling. METHODS: Older adults aged 65 years and older, with Mini-Mental State Examination score ≥24, and ambulatory (with or without the assistive device) were included. Concern about falling was evaluated with Falls Efficacy Scale-International (FES-I) scores. Participants with high concern about falling were identified using the cutoff of FES-I ≥23. Participants' motor capacity was assessed in standardized walking tests under single- and dual-task conditions. Participants' mobility performance was measured based on a 48-h trunk accelerometry signal from a wearable pendant sensor. RESULTS: No significant differences were observed at participant characteristics across groups with different levels of concern about falling (low: N = 64, age = 76.3 ± 7.2 years, female = 46%; high: N = 59, age = 79.3 ± 9.1 years, female = 47%), after propensity matching with BMI, age, depression, and cognition. With adjustment of motor capacity (stride velocity and stride length under single- and dual-task walking conditions), participants with high concern about falling had significantly poorer mobility performance than those with low concern about falling, including lower walking quantity (walking bouts, steps and time per day, and walking bout average, walking bout variability, and longest walking bout, p ≤ 0.013), and poorer daily-life gait (stride velocity and gait variability, p ≤ 0.023), and poorer walking quality (frontal gait symmetry, and trunk acceleration and velocity intensity, p ≤ 0.041). The selected mobility performance metrics (daily steps and frontal gait symmetry) could significantly contribute to identifying older adults with high concern about falling (p ≤ 0.042), having better model performance (p = 0.036) than only walking quantity (daily steps) with adjustment of confounding effects from the motor capacity (stride length under dual-task walking condition). CONCLUSION: There is an association between mobility performance and concern about falling in older adults. Mobility performance metrics can serve as predictors to identify older adults with high concern about falling, potentially providing digital biomarkers for clinicians to remotely track older adults' change of concern about falling via applications of remote patient monitoring.

Beyond non-inferior: how telepsychiatry technologies can lead to superior care.

Telepsychiatry refers to the use of technology to support the remote provision of psychiatric services. Discussions of this technology have often focussed on the use of video conferencing in place of in-person visits and how such care is found to be non-inferior to traditional care. New developments in the fields of remote-sensing and digital phenotyping have the potential to overcome the limitations inherent in remote visits as well as the limitations of current outpatient care models more generally. Such technologies may enable the collection of more relevant, objective clinical data which could lead to improved care quality and transformed care delivery models. The development and implementation of these new technologies raise important ethical questions.

Patient electronic communication data in clinical care: what is known and what is needed.

The novel coronavirus (COVID-19) and physical distancing guidelines around the world have resulted in unprecedented changes to normal routine and increased smartphone use to maintain social relationships and support. Reports of depressive and anxiety symptom are on the rise, contributing to suffering among people-especially adolescents and young adults-with pre-existing mental health conditions. Psychiatric care has shifted primarily to telehealth limiting the important patient nonverbal communication that has been part of in-person clinical sessions. Supplementing clinical care with patient electronic communication (EC) data may provide valuable information and influence treatment decision making. Research in the impact of patient EC data on managing psychiatric symptoms is in its infancy. This review aims to identify how patient EC has been used in clinical care and its benefits in psychiatry and research. We discuss smartphone applications used to gather different types of EC data, how data have been integrated into clinical care, and implications for clinical care and research.

Beacon-Based Remote Measurement of Social Behavior in ASD Clinical Trials: A Technical Feasibility Assessment.

In this work, we propose a Bluetooth low energy (BLE) beacon-based algorithm to enable remote measurement of the social behavior of the participants of an observational Autism Spectrum Disorder (ASD) clinical trial (NCT03611075). We have developed a mobile application for a smartphone and a smartwatch to collect beacon signals from BLE beacon sensors as well as to store information about the participants' household rooms. Our goal is to collect beacon information about the time the participants spent in different rooms of their household to infer sociability information. We applied the same technology and setup in an internal experiment with healthy volunteers to evaluate the accuracy of the proposed algorithm in 10 different home setups, and we observed an average accuracy of 97.2%. Moreover, we show that it is feasible for the clinical study participants/caregivers to set up the BLE beacon sensors in their homes without any technical help, with 96% of them setting up the technology on the first day of data collection. Next, we present results from one-week location data from study participants collected through the proposed technology. Finally, we provide a list of good practice guidelines for optimally applying beacon technology for indoor location monitoring. The proposed algorithm enables us to estimate time spent in different rooms of a household that can pave the development of objective sociability features and eventually support decisions regarding drug efficacy in ASD.