Smartphone data during the COVID-19 pandemic can quantify behavioral changes in people with ALS

Feasibility of Observing Cerebrovascular Disease Phenotypes with Smartphone Monitoring: Study Design Considerations for Real-World Studies

Accelerated by the adoption of remote monitoring during the COVID-19 pandemic, interest in using digitally captured behavioral data to predict patient outcomes has grown; however, it is unclear how feasible digital phenotyping studies may be in patients with recent ischemic stroke or transient ischemic attack. In this perspective, we present participant feedback and relevant smartphone data metrics suggesting that digital phenotyping of post-stroke depression is feasible. Additionally, we proffer thoughtful considerations for designing feasible real-world study protocols tracking cerebrovascular dysfunction with smartphone sensors.

Upper limb movements as digital biomarkers in people with ALS

BACKGROUND

Objective evaluation of people with amyotrophic lateral sclerosis (PALS) in free-living settings is challenging. The introduction of portable digital devices, such as wearables and smartphones, may improve quantifying disease progression and hasten therapeutic development. However, there is a need for tools to characterize upper limb movements in neurologic disease and disability.

METHODS

Twenty PALS wore a wearable accelerometer, ActiGraph Insight Watch, on their wrist for six months. They also used Beiwe, a smartphone application that collected self-entry ALS Functional Rating Scale-Revised (ALSFRS-RSE) survey responses every 1-4 weeks. We developed several measures that quantify count and duration of upper limb movements: flexion, extension, supination, and pronation. New measures were compared against ALSFRS-RSE total score (Q1-12), and individual responses to specific questions related to handwriting (Q4), cutting food (Q5), dressing and performing hygiene (Q6), and turning in bed and adjusting bed clothes (Q7). Additional analysis considered adjusting for total activity counts (TAC).

FINDINGS

At baseline, PALS with higher Q1-12 performed more upper limb movements, and these movements were faster compared to individuals with more advanced disease. Most upper limb movement metrics had statistically significant change over time, indicating declining function either by decreasing count metrics or by increasing duration metric. All count and duration metrics were significantly associated with Q1-12, flexion and extension counts were significantly associated with Q6 and Q7, supination and pronation counts were also associated with Q4. All duration metrics were associated with Q6 and Q7. All duration metrics retained their statistical significance after adjusting for TAC.

INTERPRETATION

Wearable accelerometer data can be used to generate digital biomarkers on upper limb movements and facilitate patient monitoring in free-living environments. The presented method offers interpretable monitoring of patients' functioning and versatile tracking of disease progression in the limb of interest.

FUNDING

Mitsubishi-Tanabe Pharma Holdings America, Inc.

Machine Learning and the Digital Measurement of Psychological Health

Since its inception, the discipline of psychology has utilized empirical epistemology and mathematical methodologies to infer psychological functioning from direct observation. As new challenges and technological opportunities emerge, scientists are once again challenged to define measurement paradigms for psychological health and illness that solve novel problems and capitalize on new technological opportunities. In this review, we discuss the theoretical foundations of and scientific advances in remote sensor technology and machine learning models as they are applied to quantify psychological functioning, draw clinical inferences, and chart new directions in treatment.

Wearable device and smartphone data quantify ALS progression and may provide novel outcome measures

Amyotrophic lateral sclerosis (ALS) therapeutic development has largely relied on staff-administered functional rating scales to determine treatment efficacy. We sought to determine if mobile applications (apps) and wearable devices can be used to quantify ALS disease progression through active (surveys) and passive (sensors) data collection. Forty ambulatory adults with ALS were followed for 6-months. The Beiwe app was used to administer the self-entry ALS functional rating scale-revised (ALSFRS-RSE) and the Rasch Overall ALS Disability Scale (ROADS) surveys every 2-4 weeks. Each participant used a wrist-worn activity monitor (ActiGraph Insight Watch) or an ankle-worn activity monitor (Modus StepWatch) continuously. Wearable device wear and app survey compliance were adequate. ALSFRS-R highly correlated with ALSFRS-RSE. Several wearable data daily physical activity measures demonstrated statistically significant change over time and associations with ALSFRS-RSE and ROADS. Active and passive digital data collection hold promise for novel ALS trial outcome measure development.

A systematic review of digital technology to evaluate motor function and disease progression in motor neuron disease

Amyotrophic lateral sclerosis (ALS) is the most common subtype of motor neuron disease (MND). The current gold-standard measure of progression is the ALS Functional Rating Scale-Revised (ALS-FRS(R)), a clinician-administered questionnaire providing a composite score on physical functioning. Technology offers a potential alternative for assessing motor progression in both a clinical and research capacity that is more sensitive to detecting smaller changes in function. We reviewed studies evaluating the utility and suitability of these devices to evaluate motor function and disease progression in people with MND (pwMND). We systematically searched Google Scholar, PubMed and EMBASE applying no language or date restrictions. We extracted information on devices used and additional assessments undertaken. Twenty studies, involving 1275 (median 28 and ranging 6-584) pwMND, were included. Sensor type included accelerometers (n = 9), activity monitors (n = 4), smartphone apps (n = 4), gait (n = 3), kinetic sensors (n = 3), electrical impedance myography (n = 1) and dynamometers (n = 2). Seventeen (85%) of studies used the ALS-FRS(R) to evaluate concurrent validity. Participant feedback on device utility was generally positive, where evaluated in 25% of studies. All studies showed initial feasibility, warranting larger longitudinal studies to compare device sensitivity and validity beyond ALS-FRS(R). Risk of bias in the included studies was high, with a large amount of information to determine study quality unclear. Measurement of motor pathology and progression using technology is an emerging, and promising, area of MND research. Further well-powered longitudinal validation studies are needed.

Travel Behaviour of Vulnerable Social Groups: Pre, during, and Post COVID-19 Pandemic

Since the emergence of COVID-19, travel restrictions due to the pandemic have influenced several activities, in particular the mobility patterns of individuals. Our main goal is to draw the attention of scholars and policy makers to a specific segment of the population, namely (1) older people, (2) persons with disabilities (PwDs), (3) females, and (4) low-income population that are more vulnerable for travel behaviour change due to crisis such as the COVID-19 pandemic. This article systematically reviews the studies that have explored the implications of COVID-19 for the mobility and activities of individuals pre-, during, and post-pandemic using the PRISMA method. It is found that there are a few studies regarding the travel and mobility needs and challenges of older people and PwDs, and there is no direct study concerning female and low-income individuals while such crisis exist. Questions such as "What are the adverse impacts of restrictions on their travel behaviour?", "How can they travel safely to work, shopping, and medical centres?", "Which transportation modes can be more effective for them?", and "What are the government and policy makers' role in providing accessible and affordable mobility services in the presence of such crisis?" are without relevant answers in the literature.

Considerations for Amyotrophic Lateral Sclerosis (ALS) Clinical Trial Design

Thoughtful clinical trial design is critical for efficient therapeutic development, particularly in the field of amyotrophic lateral sclerosis (ALS), where trials often aim to detect modest treatment effects among a population with heterogeneous disease progression. Appropriate outcome measure selection is necessary for trials to provide decisive and informative results. Investigators must consider the outcome measure's reliability, responsiveness to detect change when change has actually occurred, clinical relevance, and psychometric performance. ALS clinical trials can also be performed more efficiently by utilizing statistical enrichment techniques. Innovations in ALS prediction models allow for selection of participants with less heterogeneity in disease progression rates without requiring a lead-in period, or participants can be stratified according to predicted progression. Statistical enrichment can reduce the needed sample size and improve study power, but investigators must find a balance between optimizing statistical efficiency and retaining generalizability of study findings to the broader ALS population. Additional progress is still needed for biomarker development and validation to confirm target engagement in ALS treatment trials. Selection of an appropriate biofluid biomarker depends on the treatment mechanism of interest, and biomarker studies should be incorporated into early phase trials. Inclusion of patients with ALS as advisors and advocates can strengthen clinical trial design and study retention, but more engagement efforts are needed to improve diversity and equity in ALS research studies. Another challenge for ALS therapeutic development is identifying ways to respect patient autonomy and improve access to experimental treatment, something that is strongly desired by many patients with ALS and ALS advocacy organizations. Expanded access programs that run concurrently to well-designed and adequately powered randomized controlled trials may provide an opportunity to broaden access to promising therapeutics without compromising scientific integrity or rushing regulatory approval of therapies without adequate proof of efficacy.

Outcome Measures in Facioscapulohumeral Muscular Dystrophy Clinical Trials

Facioscapulohumeral muscular dystrophy (FSHD) is a debilitating muscular dystrophy with a variable age of onset, severity, and progression. While there is still no cure for this disease, progress towards FSHD therapies has accelerated since the underlying mechanism of epigenetic derepression of the double homeobox 4 (DUX4) gene leading to skeletal muscle toxicity was identified. This has facilitated the rapid development of novel therapies to target DUX4 expression and downstream dysregulation that cause muscle degeneration. These discoveries and pre-clinical translational studies have opened new avenues for therapies that await evaluation in clinical trials. As the field anticipates more FSHD trials, the need has grown for more reliable and quantifiable outcome measures of muscle function, both for early phase and phase II and III trials. Advanced tools that facilitate longitudinal clinical assessment will greatly improve the potential of trials to identify therapeutics that successfully ameliorate disease progression or permit muscle functional recovery. Here, we discuss current and emerging FSHD outcome measures and the challenges that investigators may experience in applying such measures to FSHD clinical trial design and implementation.

Passive Data Use for Ethical Digital Public Health Surveillance in a Postpandemic World

There is a fundamental need to establish the most ethical and effective way of tracking disease in the postpandemic era. The ubiquity of mobile phones is generating large amounts of passive data (collected without active user participation) that can be used as a tool for tracking disease. Although discussions of pragmatism or economic issues tend to guide public health decisions, ethical issues are the foremost public concern. Thus, officials must look to history and current moral frameworks to avoid past mistakes and ethical pitfalls. Past pandemics demonstrate that the aftermath is the most effective time to make health policy decisions. However, an ethical discussion of passive data use for digital public health surveillance has yet to be attempted, and little has been done to determine the best method to do so. Therefore, we aim to highlight four potential areas of ethical opportunity and challenge: (1) informed consent, (2) privacy, (3) equity, and (4) ownership.

Using Smartphones to Reduce Research Burden in a Neurodegenerative Population and Assessing Participant Adherence: A Randomized Clinical Trial and Two Observational Studies

Background

Smartphone studies provide an opportunity to collect frequent data at a low burden on participants. Therefore, smartphones may enable data collection from people with progressive neurodegenerative diseases such as amyotrophic lateral sclerosis at high frequencies for a long duration. However, the progressive decline in patients’ cognitive and functional abilities could also hamper the feasibility of collecting patient-reported outcomes, audio recordings, and location data in the long term.

Objective

The aim of this study is to investigate the completeness of survey data, audio recordings, and passively collected location data from 3 smartphone-based studies of people with amyotrophic lateral sclerosis.

Methods

We analyzed data completeness in three studies: 2 observational cohort studies (study 1: N=22; duration=12 weeks and study 2: N=49; duration=52 weeks) and 1 clinical trial (study 3: N=49; duration=20 weeks). In these studies, participants were asked to complete weekly surveys; weekly audio recordings; and in the background, the app collected sensor data, including location data. For each of the three studies and each of the three data streams, we estimated time-to-discontinuation using the Kaplan–Meier method. We identified predictors of app discontinuation using Cox proportional hazards regression analysis. We quantified data completeness for both early dropouts and participants who remained engaged for longer.

Results

Time-to-discontinuation was shortest in the year-long observational study and longest in the clinical trial. After 3 months in the study, most participants still completed surveys and audio recordings: 77% (17/22) in study 1, 59% (29/49) in study 2, and 96% (22/23) in study 3. After 3 months, passively collected location data were collected for 95% (21/22), 86% (42/49), and 100% (23/23) of the participants. The Cox regression did not provide evidence that demographic characteristics or disease severity at baseline were associated with attrition, although it was somewhat underpowered. The mean data completeness was the highest for passively collected location data. For most participants, data completeness declined over time; mean data completeness was typically lower in the month before participants dropped out. Moreover, data completeness was lower for people who dropped out in the first study month (very few data points) compared with participants who adhered long term (data completeness fluctuating around 75%).

Conclusions

These three studies successfully collected smartphone data longitudinally from a neurodegenerative population. Despite patients’ progressive physical and cognitive decline, time-to-discontinuation was higher than in typical smartphone studies. Our study provides an important benchmark for participant engagement in a neurodegenerative population. To increase data completeness, collecting passive data (such as location data) and identifying participants who are likely to adhere during the initial phase of a study can be useful.

Trial Registration

ClinicalTrials.gov NCT03168711; https://clinicaltrials.gov/ct2/show/NCT03168711

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

Infectious diseases and human behavior are intertwined. On one side, our movements and interactions are the engines of transmission. On the other, the unfolding of viruses might induce changes to our daily activities. While intuitive, our understanding of such feedback loop is still limited. Before COVID-19 the literature on the subject was mainly theoretical and largely missed validation. The main issue was the lack of empirical data capturing behavioral change induced by diseases. Things have dramatically changed in 2020. Non-pharmaceutical interventions (NPIs) have been the key weapon against the SARS-CoV-2 virus and affected virtually any societal process. Travel bans, events cancellation, social distancing, curfews, and lockdowns have become unfortunately very familiar. The scale of the emergency, the ease of survey as well as crowdsourcing deployment guaranteed by the latest technology, several Data for Good programs developed by tech giants, major mobile phone providers, and other companies have allowed unprecedented access to data describing behavioral changes induced by the pandemic. Here, I review some of the vast literature written on the subject of NPIs during the COVID-19 pandemic. In doing so, I analyze 348 articles written by more than 2518 authors in the first 12 months of the emergency. While the large majority of the sample was obtained by querying PubMed, it includes also a hand-curated list. Considering the focus, and methodology I have classified the sample into seven main categories: epidemic models, surveys, comments/perspectives, papers aiming to quantify the effects of NPIs, reviews, articles using data proxies to measure NPIs, and publicly available datasets describing NPIs. I summarize the methodology, data used, findings of the articles in each category and provide an outlook highlighting future challenges as well as opportunities.

Improving clinical trial outcomes in amyotrophic lateral sclerosis

Individuals who are diagnosed with amyotrophic lateral sclerosis (ALS) today face the same historically intransigent problem that has existed since the initial description of the disease in the 1860s - a lack of effective therapies. In part, the development of new treatments has been hampered by an imperfect understanding of the biological processes that trigger ALS and promote disease progression. Advances in our understanding of these biological processes, including the causative genetic mutations, and of the influence of environmental factors have deepened our appreciation of disease pathophysiology. The consequent identification of pathogenic targets means that the introduction of effective therapies is becoming a realistic prospect. Progress in precision medicine, including genetically targeted therapies, will undoubtedly change the natural history of ALS. The evolution of clinical trial designs combined with improved methods for patient stratification will facilitate the translation of novel therapies into the clinic. In addition, the refinement of emerging biomarkers of therapeutic benefits is critical to the streamlining of care for individuals. In this Review, we synthesize these developments in ALS and discuss the further developments and refinements needed to accelerate the introduction of effective therapeutic approaches.

Smartphone data during the COVID-19 pandemic can quantify behavioral changes in people with ALS

Introduction

Passive data from smartphone sensors may be useful for health‐care research. Our aim was to use the coronavirus disease‐2019 (COVID‐19) pandemic as a positive control to assess the ability to quantify behavioral changes in people with amyotrophic lateral sclerosis (ALS) from smartphone data.

Methods

Eight participants used the Beiwe smartphone application, which passively measured their location during the COVID‐19 outbreak. We used an interrupted time series to quantify the effect of the US state of emergency declaration on daily home time and daily distance traveled.

Results

After the state of emergency declaration, median daily home time increased from 19.4 (interquartile range [IQR], 15.4‐22.0) hours to 23.7 (IQR, 22.2‐24.0) hours and median distance traveled decreased from 42 (IQR, 13‐83) km to 3.7 (IQR, 1.5‐10.3) km. The participant with the lowest functional ability changed behavior earlier. This participant stayed at home more and traveled less than the participant with highest functional ability, both before and after the state of emergency.

Discussion

We provide evidence that smartphone‐based digital phenotyping can quantify the behavior of people with ALS. Although participants spent large amounts of time at home at baseline, the COVID‐19 state of emergency declaration reduced their mobility further. Given participants' high level of daily home time, it is possible that their exposure to COVID‐19 could be less than that of the general population.