Smartphone data during the <scp>COVID</scp>‐19 pandemic can quantify behavioral changes in people with <scp>ALS</scp>

Beukenhorst, Anna L; Collins, Ella; Burke, Katherine; Rahman, Syed Minhajur; Clapp, Mark A.; Konanki, Sai Charan; Paganoni, Sabrina; Miller, Timothy M.; Chan, James; Onnela, Jukka‐Pekka; Berry, James

doi:10.1002/mus.27110

Cited by 23 publications

(23 citation statements)

References 7 publications

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“…It consists of Android and iOS smartphone apps for data collection and an Amazon Web Services cloud-based system back-end for data collection and processing [ 23 ]. It has been used in both observational studies and clinical trials to collect self-administered surveys and various types of passive data [ 8 , 24 ].…”

Section: Methodsmentioning

confidence: 99%

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

Beukenhorst¹,

Burke²,

Scheier³

et al. 2022

JMIR Mhealth Uhealth

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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

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Section: Methodsmentioning

confidence: 99%

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

Beukenhorst¹,

Burke²,

Scheier³

et al. 2022

JMIR Mhealth Uhealth

Self Cite

View full text Add to dashboard Cite

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“…Several surveys aimed at provide a better understanding of this unfortunate phenomenon. They considered patients affected by diabetes, cancer, ALS, hypertension, mental or substance abuse disorders [188] , [189] , [190] , [191] , [192] , [193] , [194] . Admittedly, the research focused on short term effects and on the shift towards telemedicine (i.e., remote care).…”

Section: Surveysmentioning

confidence: 99%

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

2021

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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. Travels 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 of 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.

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“…Recent advances in sensor technology and digital health tools have undoubtedly helped health care providers and investigators to assess disease progression and recovery in a clinical setting as well as outcome measures in trials in a variety of neurological conditions including neuromuscular diseases [ 86 , 87 ]. This has also been helpful in more efficient assessment or management of these patients remotely during televisits [ 88 ]. In 2016, Gidaro et al [ 89 ] introduced ActiMyo (a home-based activity recording device with two sensors) to assess ambulant and non-ambulant patients via recording different types of movements of the upper and/or lower extremities during daily life activity.…”

Section: Outcome Measures In Fshd Clinical Trialsmentioning

confidence: 99%

Outcome Measures in Facioscapulohumeral Muscular Dystrophy Clinical Trials

Ghasemi

Emerson

Hayward

2022

Cells

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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.

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

Cited by 23 publications

References 7 publications

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

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

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

Outcome Measures in Facioscapulohumeral Muscular Dystrophy Clinical Trials

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