Continuous At-Home Monitoring of Nighttime Bed Behavior in Frontotemporal Dementia.

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Published in final edited form as: Neurol Open Access. 2026 Feb 18;2(1):e000068. doi: 10.1212/WN9.0000000000000068 Search in PMC Search in PubMed View in NLM Catalog Add to search Continuous At-Home Monitoring of Nighttime Bed Behavior in Frontotemporal Dementia Emily W Paolillo Emily W Paolillo 1 Weill Neurosciences, Memory and Aging Center, University of California San Francisco Find articles by Emily W Paolillo 1 , Amy Wise Amy Wise 1 Weill Neurosciences, Memory and Aging Center, University of California San Francisco Find articles by Amy Wise 1 , Jeffrey A Kaye Jeffrey A Kaye 2 Department of Neurology, Oregon Health & Science University Find articles by Jeffrey A Kaye 2 , Zachary Beattie Zachary Beattie 2 Department of Neurology, Oregon Health & Science University Find articles by Zachary Beattie 2 , Wan-Tai M Au-Yeung Wan-Tai M Au-Yeung 2 Department of Neurology, Oregon Health & Science University Find articles by Wan-Tai M Au-Yeung 2 , Sreya Dhanam Sreya Dhanam 1 Weill Neurosciences, Memory and Aging Center, University of California San Francisco Find articles by Sreya Dhanam 1 , Kaitlin Blackstone Casaletto Kaitlin Blackstone Casaletto 1 Weill Neurosciences, Memory and Aging Center, University of California San Francisco Find articles by Kaitlin Blackstone Casaletto 1 , Rowan Saloner Rowan Saloner 1 Weill Neurosciences, Memory and Aging Center, University of California San Francisco Find articles by Rowan Saloner 1 , Mario F Mendez Mario F Mendez 3 Department of Neurology, University of California, Los Angeles Find articles by Mario F Mendez 3 , Lawren Vandevrede Lawren Vandevrede 1 Weill Neurosciences, Memory and Aging Center, University of California San Francisco Find articles by Lawren Vandevrede 1 , Peter Alexander Ljubenkov Peter Alexander Ljubenkov 1 Weill Neurosciences, Memory and Aging Center, University of California San Francisco Find articles by Peter Alexander Ljubenkov 1 , William W Seeley William W Seeley 1 Weill Neurosciences, Memory and Aging Center, University of California San Francisco Find articles by William W Seeley 1 , Maria Luisa Gorno-Tempini Maria Luisa Gorno-Tempini 1 Weill Neurosciences, Memory and Aging Center, University of California San Francisco Find articles by Maria Luisa Gorno-Tempini 1 , Bruce L Miller Bruce L Miller 1 Weill Neurosciences, Memory and Aging Center, University of California San Francisco Find articles by Bruce L Miller 1 , Hilary W Heuer Hilary W Heuer 1 Weill Neurosciences, Memory and Aging Center, University of California San Francisco Find articles by Hilary W Heuer 1 , Leah Kathleen Forsberg Leah Kathleen Forsberg 4 Department of Neurology, Mayo Clinic Find articles by Leah Kathleen Forsberg 4 , John Kornak John Kornak 1 Weill Neurosciences, Memory and Aging Center, University of California San Francisco Find articles by John Kornak 1 , Walter K Kremers Walter K Kremers 5 Department of Quantitative Health Sciences, Mayo Clinic Find articles by Walter K Kremers 5 , Bradley F Boeve Bradley F Boeve 6 Department of Neurology, Mayo Clinic, Rochester Find articles by Bradley F Boeve 6 , Adam L Boxer Adam L Boxer 1 Weill Neurosciences, Memory and Aging Center, University of California San Francisco Find articles by Adam L Boxer 1 , Howard J Rosen Howard J Rosen 1 Weill Neurosciences, Memory and Aging Center, University of California San Francisco Find articles by Howard J Rosen 1 , Christine M Walsh Christine M Walsh 1 Weill Neurosciences, Memory and Aging Center, University of California San Francisco Find articles by Christine M Walsh 1 , Adam M Staffaroni Adam M Staffaroni 1 Weill Neurosciences, Memory and Aging Center, University of California San Francisco Find articles by Adam M Staffaroni 1 ; the ALLFTD Consortium Author information Article notes Copyright and License information 1 Weill Neurosciences, Memory and Aging Center, University of California San Francisco 2 Department of Neurology, Oregon Health & Science University 3 Department of Neurology, University of California, Los Angeles 4 Department of Neurology, Mayo Clinic 5 Department of Quantitative Health Sciences, Mayo Clinic 6 Department of Neurology, Mayo Clinic, Rochester # Equal Author Contribution: Christine Walsh and Adam Staffaroni contributed equally to this work and are designated as co-senior authors. Contributions: Emily W Paolillo: Drafting/revision of the manuscript for content, including medical writing for content; Study concept or design; Analysis or interpretation of data Amy Wise: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data Jeffrey A. Kaye: Drafting/revision of the manuscript for content, including medical writing for content; Study concept or design; Analysis or interpretation of data Zachary Beattie: Drafting/revision of the manuscript for content, including medical writing for content; Study concept or design; Analysis or interpretation of data Wan-Tai M. Au-Yeung: Drafting/revision of the manuscript for content, including medical writing for content; Analysis or interpretation of data Sreya Dhanam: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data Kaitlin Blackstone Casaletto: Drafting/revision of the manuscript for content, including medical writing for content; Analysis or interpretation of data Rowan Saloner: Drafting/revision of the manuscript for content, including medical writing for content; Analysis or interpretation of data Mario F. Mendez: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data Lawren Vandevrede: Drafting/revision of the manuscript for content, including medical writing for content; Analysis or interpretation of data Peter Alexander Ljubenkov: Drafting/revision of the manuscript for content, including medical writing for content William W. Seeley: Drafting/revision of the manuscript for content, including medical writing for content Maria Luisa Gorno-Tempini: Drafting/revision of the manuscript for content, including medical writing for content Bruce L. Miller: Drafting/revision of the manuscript for content, including medical writing for content; Analysis or interpretation of data Hilary W. Heuer: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data Leah Kathleen Forsberg: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data John Kornak: Drafting/revision of the manuscript for content, including medical writing for content; Analysis or interpretation of data Walter K Kremers: Drafting/revision of the manuscript for content, including medical writing for content; Analysis or interpretation of data Bradley F. Boeve: Drafting/revision of the manuscript for content, including medical writing for content; Analysis or interpretation of data Adam L. Boxer: Drafting/revision of the manuscript for content, including medical writing for content; Study concept or design Howard J. Rosen: Drafting/revision of the manuscript for content, including medical writing for content; Study concept or design Christine M Walsh: Drafting/revision of the manuscript for content, including medical writing for content; Study concept or design; Analysis or interpretation of data Adam M Staffaroni: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data; Study concept or design; Analysis or interpretation of data ✉ Corresponding Author(s): Dr. Paolillo, [email protected] Issue date 2026 Mar. PMC Copyright notice PMCID: PMC13078744  NIHMSID: NIHMS2139686  PMID: 41987885 The publisher's version of this article is available at Neurol Open Access Abstract Background and Objectives: Sleep and circadian disturbances are common yet understudied in frontotemporal dementia (FTD). Advances in non-invasive digital monitoring technology enable capture of real-time objective nighttime behaviors in naturalistic settings. We examined feasibility and utility of an unobtrusive, under-the-mattress sensor to monitor long-term bed behaviors in a sample of adults with FTD and their study partners. Methods: This longitudinal observational study continuously monitored bed behaviors in participants with FTD recruited from the ALLFTD study for up to two years using an Emfit ™ Movement Monitor. Metrics were derived from each session in bed: duration in bed, time of bed-entry/exit, number of tosses/turns, and number of bed-exits. Bivariate relationships between baseline bed behaviors (aggregated across the first 30 days) and clinical characteristics (FTD vs. control; clinical dementia rating) were tested. Linear mixed-effects regressions examined longitudinal associations between baseline clinical severity and bed behaviors over time. To examine differential seasonal shifts in nightly bed behaviors, Fourier basis functions modeled nightly duration in bed over a full calendar year separately by clinical severity groups (controls vs. mild FTD vs. moderate-to-severe FTD). Results: Participants were 16 adults with FTD (mean age = 67; 69% male) and 12 study partner comparators (mean age = 62; 33% male). At baseline, participants with FTD had higher average durations in bed ( mean difference =1.8, 95%CI [0.03, 3.61], p =0.046), more variability in durations in bed across days ( mean difference =1.8, 95%CI [0.14, 3.37], p =0.035), and more bed exits than study partners (log-transformed; mean difference =0.5, 95%CI [0.02, 1.06], p =0.042). Longitudinally, person-specific linear trajectories of bed behaviors revealed that higher baseline clinical severity associated with significantly steeper increases in tosses/turns over time ( β =0.15, 95%CI [0.05, 0.25], p =0.009). Those with more severe FTD also had an attenuated sleep-behavioral response to seasonal daylight shifts ( β =0.23, 95%CI [0.13, 0.33], p <0.001). Discussion: Findings demonstrate the feasibility of long-term, non-invasive nighttime behavior monitoring using bed sensors, highlighting their utility to monitor disease progression in FTD with potential to detect person-specific changes and assess treatment effects. Further research is needed to explore underlying mechanisms of sleep disturbances in FTD and develop targeted interventions to improve sleep and subsequent quality of life for individuals with FTD. Search Terms: [ 29 ] Frontotemporal dementia, [ 244 ] All Sleep Disorders, [ 14 ] All Clinical Neurology INTRODUCTION Frontotemporal dementia (FTD) is an umbrella term that captures a heterogeneous group of dementia syndromes, including behavioral variant FTD (bvFTD), primary progressive aphasias (PPA), progressive supranuclear palsy syndrome (PSPS), and corticobasal syndrome (CBS) 1 , 2 , that are associated with specific neuropathologies defined as frontotemporal lobar degeneration (FTLD) 3 . In addition to the core defining symptoms of each syndrome, individuals with FTD also often present with significant disruptions in sleep-wake patterns. While there are some known phenotype-specific differences in the sleep disruptions that can manifest (e.g., diminished homeostatic sleep drive in PSP), symptoms often include insomnia, fragmented sleep, excessive daytime sleepiness, and increased nocturnal movements 4 – 8 . These disturbances in sleep and nighttime behaviors may manifest early in the disease 9 and are particularly debilitating to both patients and their caregivers 5 , 10 . Despite their clinical significance, however, characterization of nighttime behavioral disturbances, particularly the temporal course of symptom manifestation and progression in natural settings, remains understudied in FTD. There are several methodological barriers to studying long-term patterns in sleep and nighttime disturbances in FTD. Traditionally, assessment of sleep and nighttime disturbances in individuals with dementia has relied on polysomnography (PSG), self- or informant-report, or wearable actigraphy. PSG does not capture habitual or naturalistic sleep-wake patterns as it requires an overnight stay in a sleep laboratory 11 , and self- and informant-reports can be biased by recall error, state-dependent bias (e.g., mood), and self-desirability bias 12 , 13 . Wrist-worn actigraphy offers an objective, non-invasive, and remote alternative 14 that has been validated in other neurodegenerative disorders canonically associated with sleep disturbances including Parkinson’s and Lewy body disease 15 , 16 . This method, however, still relies on active adherence to wearing the actigraphy device, which can be challenging for individuals with FTD-related cognitive impairment and behavioral changes such as apathy, agitation, and disinhibition 17 . These unique behavioral barriers to wearable actigraphy adherence in FTD warrant the study of objective measures of nighttime disturbances that are even less invasive. Bed pressure sensors, which are placed under the mattress, detect movement, presence, and physiological signals from a person in bed, offering a completely unobtrusive means of assessing changes in sleep-related bed behaviors over long periods of time 18 . These sensors collect data continuously and have shown excellent feasibility and validity in several clinical populations 19 – 25 . There are no reports to date testing feasibility and validity of these bed sensors in FTD, however, and none to our knowledge that has examined their validity to detect independent signals from unique individuals who share a bed. Thus, this study examined the feasibility and utility of an unobtrusive, under-the-mattress sensor to monitor long-term bed behaviors continuously in a sample of adults with FTD and their study partners. We additionally present a case study of a participant who completed in-home monitoring at the end of life, demonstrating individual-level clinical utility of this tool. METHODS Participants Sixteen adults with FTD and 12 study partner comparators were recruited from the parent ARTFL/LEFFTDS Longitudinal FTLD study (ALLFTD; NCT04363684 ) and other parent studies of FTD at the University of California San Francisco (UCSF) and the University of California Los Angeles (UCLA). Inclusion criteria for primary participants included having a clinical syndrome diagnosis of bvFTD, svPPA, PSPS, or CBS per conference consensus with neurologists and neuropsychologists following published criteria 26 – 29 . Additional inclusion criteria included: 1) at least 18 years old; 2) residence in a single or dyad home within 150 miles of UCSF or UCLA; and 3) completion of a comprehensive parent-study visit within 90 days. Primary participants’ study partners served as healthy comparator participants for the purposes of this study. Exclusion criteria included meeting other clinical syndrome diagnoses associated with FTLD such as motor neuron disease. Standard Protocol Approvals, Registrations, and Patient Consents All recruitment procedures and study interventions were approved by the UCSF Institutional Review Board (IRB) with Oregon Health & Science University (OHSU) listed as a relying site. Consent was obtained from all participants, legally authorized representatives, and study partners. Procedures Data were collected between August 2021 and June 2024. After enrollment, a study coordinator assisted participants with the installation of in-home sensors as part of the OHSU Oregon Center for Aging & Technology (ORCATECH) platform 30 . Participants had the option to opt-out of any specific sensor technology. These installation visits were conducted either in-person at participants’ homes or via a remote video visit (N=3) due to restrictions during the COVID-19 pandemic. In addition to the passive monitoring via in-home sensors, participants also completed weekly online surveys asking about changes in living situation or health status, including an item asking about dates when participants were away from home for one or more days. Data were collected for a maximum of 2 years per home. Participants received a $50 Amazon gift code for each month in which data were collected. Measures Bed Sensor. The Emfit Movement Monitor ™ (Emfit Corp., Kuopio, Finland) is a thin flexible mat (32 cm × 62 cm × 0.4 cm) that uses electromechanical film technology to detect changes in pressure from body motion. The sensor was placed under participants’ mattress at about the level of their heart according to manufacturer instructions. For homes with primary participants and study partners who share a bed, two Emfit sensors were placed, one on each side. Data were collected at a frequency of 200 Hz. Data were transferred in concordance with the ORCATECH platform 31 using the Wi-Fi of a home hub computer (Raspberry Pi 3 Model B). These data were first uploaded to Emfit servers and then posted to a secure ORCATECH endpoint where they were encrypted and stored on secure servers at Oregon Health & Science University. Although Emfit computes specific sleep-related metrics from their sensor data (e.g., sleep duration, sleep stages, sleep onset latencies) that correlate with wrist-worn actigraphy, other studies have shown variable agreement with polysomnography 24 , 32 ; instead, the metrics that have been most robustly validated across other clinical populations include those that capture face valid bed movements (henceforth called bed behavior) 32 . Thus, we focused primarily on these bed-behavior metrics derived from Emfit sensor data for each session in bed and examine sleep-related metrics only in exploratory analyses. In-bed sessions were defined as distinct periods of time in which a person was detected as being in bed for at least two hours with no more than 20 minutes out of bed within that period of time. From each in-bed session, the following measures were calculated: start of session clock time (i.e., in-bed clock time; recoded to represent hours from noon), end of session clock time (i.e., out-of-bed clock time; recoded to represent hours from midnight), session duration (i.e., out-of-bed clock time minus in-bed clock time), number of bed exits, duration in bed (i.e., session duration minus duration of bed exits), and total number of tosses/turns. Number of bed exits and number of tosses/turns were log-transformed prior to analysis to achieve approximate normality. FTD Clinical Severity. Informant and participant interviews were conducted at the parent study visit to characterize severity of clinical impairment using the Clinical Dementia Rating plus NACC FTLD scale (CDR ® +NACC-FTLD) 33 . This is a validated, modified version of the of the six-domain CDR that assesses two additional domains affected in FTLD (i.e., language and behavior). Consistent with the CDR, all domains assessed on the CDR ® +NACC-FTLD are rated on a five-point scale 0 to 3 (0, 0.5, 1, 2, 3), with higher scores indicating greater impairment. CDR ® +NACC-FTLD global scores are calculated on the same 5-point scale using a validated algorithm, and the score represents an individual’s overall level of impairment 33 . CDR ® +NACC-FTLD Sum of Boxes score represents the sum of ratings from all eight domains. Neuropsychiatric Symptoms. The Neuropsychiatric Inventory Questionnaire (NPI-Q), a well-validated informant-based interview to assess neuropsychiatric symptoms, was completed for all primary participants during their parent study visit 34 . Each of 12 neuropsychiatric symptoms are rated on a scale from 0 to 3 (0=not present; 1=mild; 2=moderate; and 3=severe). NPI-Q total scores represent the sum of these items, ranging from 0 to 36. Statistical Analyses R Version 4.3.2 was used for all analyses. Descriptive statistics were used to quantify participant characteristics and feasibility, including days of data captured and days of missing data. One potential concern with bed mats is that movement from a bed-sharing partner could affect recordings. We therefore examined the validity of sensor recordings in 11 participant-partner pairs who reported sharing a bed. To examine whether the quantification of sleep behaviors was independent across primary participants and their bed-sharing partners, linear mixed effects models were used to test concurrent associations between participants’ and their partners’ in-bed session data (i.e., tosses/turns, bed exits, and duration in bed) during times that overlapped. Participant-partner pairs (i.e., dyads) were assigned as the clustering variable for which random intercepts were modeled. All participants’ bed behaviors were person-mean centered and models covaried for person-specific average levels of each behavior to appropriately disaggregate within- and between-person effects. The “lme4” and “lmerTest” R packages were used for all linear mixed effects modeling 35 , 36 . Next, we tested associations between clinical characteristics (i.e., FTD status, FTD clinical severity, and neuropsychiatric symptoms) and bed behaviors both cross-sectionally and longitudinally. Cross-sectional analyses using baseline data (i.e., first 30 days of monitoring) were conducted first. Baseline data were aggregated across the first 30 days of monitoring to represent both central tendency (i.e., averages) and day-to-day variability (i.e., root mean square of successive differences [RMSSD]). Pearson correlations and independent t-tests examined relationships of average and variability in bed behaviors with demographic and clinical factors that were continuous and dichotomous, respectively. Next, separate linear mixed effects regressions examined trajectories of change in each bed behavior as a function of baseline clinical severity (0 = study partner comparators; 1 = mild FTD [CDR ® +NACC-FTLD global scores ≤ 1]; 2 = moderate-to-severe FTD [CDR ® +NACC-FTLD global scores > 1]) by modeling the interaction between baseline clinical severity and time (i.e., months since baseline). Subject-specific random intercepts and a random effect of time were modeled. Exploratory analyses examined baseline and longitudinal relationships between clinical characteristics and exploratory sleep-related measures derived from Emfit, including sleep period (i.e., time from when a person falls asleep to when they wake up, including brief awakenings), sleep stage durations (i.e., light, deep, REM, wake after sleep onset), and sleep onset latencies. Finally, to understand non-linear changes in circadian patterns in relation to seasonality over the course of the entire year, three-Fourier-basis functions modeling duration in bed (hours) as a function of day of the year (i.e., 1 = January 1 st to 365 = December 31 st ) were estimated separately for study partners (n=12), participants with mild FTD (CDR ® +NACC-FTLD global scores ≤ 1; n=7), and participants with moderate-to-severe FTD (CDR ® +NACC-FTLD global scores > 1; n=9) using the “fda” package 37 . This basis was selected to capture low-frequency periodic trends in the data, as the Fourier basis is particularly suited for modeling smooth, cyclical patterns. To examine whether differences in these non-linear curves were specifically tied to seasonal shifts in daylight, post-hoc linear mixed effects models examined duration in bed as a function of daily duration of daylight (downloaded from a publicly available database 38 ; specific to each participants’ home location), clinical severity group (study partner; mild FTD; moderate-to-severe FTD), and their interaction. Subject-specific random intercepts and slopes were modeled. Data Availability Deidentified clinical and demographic are available from ALLFTD on request. Investigators are required to complete the Request Clinical Data form on the request portal ( https://www.allftd.org/data ) and to review the data sharing and publication policy. Data that could identify a participant are not provided. Any additional information required to reanalyze the data reported in this paper is available from the lead contact and ALLFTD. RESULTS Participant Characteristics See Table 1 for demographic and clinical characteristics of both primary participants with FTD and study partner controls. Primary participants with FTD were about 67 years old with about 15 years of education on average and 69% male. 9 (56%) of the 16 participants with FTD had a clinical phenotype consistent with bvFTD, two (13%) had PSPS, one (6%) had CBS, one (6%) had nfvPPA, and two (6%) had svPPA. Primary participants’ genetic testing completed through ALLFTD parent study procedures identified three participants with familial forms of FTD, including two with pathogenic expansions of the C9orf72 gene and one with a pathogenic mutation of the MAPT gene. The remaining 13 primary participants had sporadic FTD. Table 1. Baseline Participant Characteristics Primary Participants with FTD (N=16) Study Partner Controls (N=12) Demographics Age 67.4 (7.2) [range = 54–78] 61.8 (12.9) [range = 32–74] a Sex (male) 11 (69%) 4 (33%) Education 14.9 (3.5) 16.4 (2.9) a Clinical Characteristics CDR ® +NACC FTLD Global score 0 0 (0%) - 0.5 3 (19%) - 1 4 (25%) - >1 9 (56%) - CDR ® +NACC FTLD Sum of Boxes 8.1 (4.6) [range = 1.5–17] - NPI-Q Total 9.7 (9.1) - Clinical Phenotypes Controls 0 (0%) 12 (100%) bvFTD 9 (56%) - PSP 2 (13%) - CBS 1 (6%) - nfvPPA 1 (6%) - svPPA 2 (13%) - Open in a new tab Note. Values are mean (SD) or n (%). CDR ® +NACC FTLD = Clinical Dementia Rating plus NACC FTLD module; NPI-Q = Neuropsychiatric Inventory Questionnaire; bvFTD = behavioral variant frontotemporal dementia; PSP = progressive supranuclear palsy; CBS = corticobasal syndrome; nfvPPA = non-fluent variant primary progressive aphasia; svPPA = semantic variant primary progressive aphasia a N=9 out of 12 study partners reported their age and education level Feasibility Participants were in the study for an average of 349.6 days (SD = 182.6; range = 144–700) each. Over the course of the study, 6562 in-bed sessions were captured, equating to 234.4 sessions per participant on average (SD = 126.9; range = 83–503 sessions). This means that there were 115.3 days (31% of days) per person on average (SD = 104.4) for which no in-bed session was captured. 12% of all days with missing Emfit data were accounted for by participants’ responses to the weekly survey indicating that they were away and out of their home. The proportion of days without an in-bed session did not differ between participants with FTD (M=0.32, SD=0.15) and study partners (M=0.31, SD=0.22; p = 0.98) and was not statistically significantly correlated with severity of clinical impairment (CDR ® +NACC-FTLD Sum of Boxes) among the 16 participants with FTD ( r = −0.13, 95%CI [−0.48, 0.25], p = 0.51). Associations between Primary Participants and Study Partners There were 11 participant-partner pairs who reported bed sharing, resulting in 1616 recorded sessions when partner pairs were in bed at the same time. Linear mixed effect regression models revealed that participants’ and study partners’ nightly toss/turn counts were not strongly related ( b = 0.0002, 95%CI [−0.008, 0.009], p = 0.967). In contrast, participants’ nightly bed exit counts ( b = 0.177, 95%CI [0.138, 0.216], p < 0.001) and duration in bed ( b = 0.076, 95%CI [0.052, 0.100], p < 0.001) were positively related to their study partners’ bed exit counts and durations in bed, respectively ( eFigure 1 ). Baseline Associations with Clinical Characteristics Correlations among each bed behavior measure at baseline (i.e., the first 30 days of at-home monitoring) are shown in Figure 1 . At baseline, average duration of each in-bed session was significantly longer among participants with FTD (M=10.6±2.8 hours) compared to study partner controls (M=8.8±1.6 hours; p =0.04; Figure 2 ). Participants with FTD also had significantly more variability in the duration in bed across days (RMSSD; FTD: M=4.0±2.9; Control: 2.2±0.8; p=0.035) and more bed exits on average (log-transformed; FTD: M=1.5±0.8; Control: 1.0±0.5; p=0.042) than study partner controls. These group differences held after covarying for average duration in bed using ANCOVA ( p s < 0.05). Toss/turn count and in-bed/out-of-bed clock times did not differ significantly between participants with FTD and study partner controls ( p s>0.05; eTable 1 ; eFigure 2 ). Exploratory analyses examining baseline group differences in sleep-related metrics showed that compared to study partners, participants with FTD had longer sleep periods (mean difference = 52 minutes, p = 0.046), longer duration of wake after sleep onset (WASO; mean difference = 34 minutes, p = 0.016), and longer sleep onset latencies (mean difference = 6 minutes, p = 0.021; eFigure 3 ). Figure 1. Matrix presenting the correlation between each sleep metric at baseline (first 30 days of monitoring). Open in a new tab Avg = Average; RMSSD = root mean square of successive differences. Figure 2. Baseline group differences between participants with FTD and study partner controls for all bed behaviors captured. Open in a new tab Statistically significant group differences (p<0.05) were observed such that participants with FTD had longer durations in bed, greater variability in duration in bed across days, and more bed exits than study partner controls. Among the subset of 16 participants with FTD, baseline clinical severity (CDR+NACC-FTLD Sum of Boxes) had moderately sized positive correlations with average toss/turn count (r=0.48, 95%CI [−0.02, 0.79], p=0.061), average duration in bed (r=0.44, 95%CI [−0.07, 0.77], p=0.086), and variability in in-bed clock time (r=0.41, 95%CI [−0.10, 0.75], p=0.112), though these did not reach statistical significance. Correlations with other bed behaviors were small to very weak (Pearson r range: |0.05| to |0.27|). NPI-Q Total score strongly correlated with variability in in-bed clock time across days (r=0.76, 95%CI [0.40, 0.92], p<0.001), but not any other bed behaviors (Pearson r range: |0.01| to |0.23|; p s>0.05). The association between NPI-Q Total score and variability in in-bed clock time held with similar effect sizes before ( Std. β = 0.76, 95%CI [0.37, 1.15], p = 0.001; η 2 = 0.58;) and after covarying for average duration in bed ( Std. β = 0.77, 95%CI [0.37, 1.18], p = 0.001; η 2 = 0.59) in linear regression models. Longitudinal Changes in Bed Behaviors Over the entire study duration, participants with FTD continued to have longer average durations in bed (M= 11.0±3.0 hours) than study partners (M= 9.1±1.8 hours; p =0.04). When considering clinical severity among those with FTD and using study partners as unimpaired comparators, greater baseline clinical severity was associated with longitudinal increases in toss/turn count over time even when covarying for duration in bed ( Std. β = 0.15, 95%CI [0.05, 0.25], p = 0.009; Figure 3 ). Baseline clinical severity did not strongly relate to linear trajectories of any other bed behavior ( p s>0.11). Exploratory analyses similarly showed that none of the relationships between baseline clinical severity and trajectories of sleep period, sleep stage durations, nor sleep onset latencies were statistically significant ( p s>0.23). When examining non-linear trends in duration in bed over the course of a calendar year, those with more severe FTD showed an attenuated behavioral response to seasonal daylight shifts ( Figure 4 ). That is, participants with mild FTD and controls demonstrated an expected decrease in duration in bed during summer months whereas participants with moderate-to-severe FTD did not have altered durations in bed relative to seasons. To recapitulate this finding specifically as it relates to environmental exposure to sunlight, post-hoc analyses examined the linear longitudinal relationship between duration in bed and daily hours of daylight across the duration of the study. While the conditional main effect of daylight duration showed a negative association with duration in bed among controls ( Std. β = −0.21, 95%CI [−0.28, −0.14], p < 0.001), there was a statistically significant interaction with clinical group such those with moderate-to-severe FTD showed an attenuated association compared to controls ( Std. β = 0.23, 95%CI [0.13, 0.33], p < 0.001). Additional group contrasts showed that those with moderate-to-severe FTD also had a statistically significantly attenuated association between duration in bed and hours of daylight compared to those with mild FTD ( Std. β = 0.33, 95%CI [0.22, 0.42], p < 0.001). Figure 3. Longitudinal changes in toss/turn count (log-transformed to achieve approximate normality) over time by baseline clinical severity. Open in a new tab Study partners served as controls (represented in blue), participants with mild FTD had CDR+NACC-FTLD Global scores <= 1 (represented in red), and participants with moderate-to-severe FTD had CDR+NACC-FTLD Global scores > 1 (represented in green). Error bands represent 95% confidence intervals. Figure 4. Differential associations between duration in bed and time of year by FTD severity. Open in a new tab Participants with baseline moderate-to-severe FTD (CDR+NACC-FTLD Global score > 1) showed a loss of seasonal shifts in durations in bed compared to those with mild FTD (CDR+NACC-FTLD Global score <= 1) and study partner controls. Data points depicted on this figure represent average durations in bed per calendar day for individuals in each clinical group. End-of-Life Case Example One participant diagnosed with bvFTD was enrolled in this study and followed for nearly 5 months (144 days total) at the end of their life. Data captured on the Emfit Movement Monitor ™ for this participant show notable changes in their bed behaviors in the last month before death due to cardiac arrest, including dramatic increases in the duration spent in bed and toss/turn counts ( Figure 5 ). The first notable spike in bed movement occurred on study day 124 (20 days before death) with corresponding increases in duration in bed within that same timeframe. Although we do not have other data to inform any time-linked disease-related changes, medication changes, or onset of comorbid health conditions, the detection of significant person-specific changes in close temporal proximity to time of death serves as an example of the potential use of a bed sensor as a real-time monitoring tool to support real-time event-based care and interventions. Figure 5. Data from one participant with bvFTD at the end of their life. Open in a new tab Death occurred on study day 144. DISCUSSION By leveraging longitudinal, continuous, real-world data, this study both supports the feasibility and utility of unobtrusive sensors to capture nighttime behaviors in FTD and contributes to a deeper understanding of circadian and nighttime behaviors in this clinical population. With continued validation of this technology in FTD and other dementias, this passive digital monitoring method also has potential to improve clinical care through real-time monitoring of sleep- and bed behavior changes that could inform personalized and precision treatment decisions. Our findings demonstrate that greater impairment at baseline is associated with increased duration in bed, more frequent nighttime movements (tosses/turns), a higher frequency of bed exits, as well as steeper longitudinal increases in nighttime restlessness, suggesting that these nighttime behavioral disturbances worsen as FTD progresses. Notably, we also observed an attenuated behavioral response to seasonal daylight shifts in those with moderate-to-severe FTD, further highlighting circadian dysfunction in FTD. Our findings provide important insights into the potential application of this passive sensing technology in both research and clinical settings. The high level of participant retention and the large number of in-bed sessions recorded suggest that bed sensors are a feasible tool for long-term monitoring of bed behaviors, even when bed sharing. There were some gaps in data capture, with an average of 31% of study days lacking data, though 12% of these missing data days were accounted for by self-reports of being away from home. This is at the high end of missingness per previously published Emfit studies (range 4–31% missing/excluded days) 20 , 23 , 32 . However, our study spans one of the longest monitoring periods and our FTD patient population represents a more impaired sample than those in previously published reports. We also still captured over 230 in-bed sessions per participant on average, representing a rich long-term monitoring period. Other reasons for these data gaps are unclear, as they may represent valid periods of time in which participants were either away from home without a corresponding survey response or sleeping out of their bed, or they may alternatively represent technical issues with the sensor or wireless data transfer. Future studies should aim to address barriers to data capture, potentially by incorporating solutions to monitor missingness in real time. Regarding feasibility and validity to capture two distinct individuals’ data on one bed, we also found that nightly toss/turn data were not correlated between bed-sharing partner-pairs, suggesting adequate differentiation between movements of different individuals on the same mattress and consistent with publicly available information from the manufacturer. Our results showed that participants with FTD exhibited significantly longer and more variable durations in bed, as well as more frequent bed exits, compared to controls. Steeper increases in tosses/turns over time were also observed in those with more severe impairment at baseline, indicating that nighttime movement may worsen as the disease progresses. These findings are consistent with prior research demonstrating sleep fragmentation, disrupted circadian rhythm, and increased nocturnal activity in FTD, likely driven by both behavioral dysregulation and neurodegeneration of neural circuits involved in sleep-wake regulation, including frontal, hypothalamic, and midbrain 4 , 5 , 39 , 40 . The elevated bed exit count observed in those with FTD may additionally reflect several possible symptoms common among people with neurodegenerative disease, including nocturia or nighttime wandering 41 , 42 ; however, these associated behaviors are not quantifiable by the bed sensor alone. Future studies should work to validate the sleep-related metrics derived from Emfit in FTD and neurodegenerative diseases more broadly, as examination of total sleep time could aid diagnosis of clinically relevant conditions such as hypersomnia. While some have previously shown the utility of the Emfit bed sensor to detect periodic limb movements 43 , further validation of its ability to capture dream enactment behavior could aid diagnosis of primary or comorbid alpha-synucleinopathies. Additional work may benefit from integrating multiple sensor technologies (e.g., wearables; motion sensors) and self-reports to contextualize passive sensor data. Additionally, individuals with moderate-to-severe FTD exhibited a blunted behavioral response to seasonal daylight shifts in terms of duration in bed. This novel finding in FTD is consistent with previously published findings in MCI 44 and suggests a potential breakdown in circadian responsiveness to environmental cues. Emerging evidence suggests that the suprachiasmatic nucleus (SCN), a key regulator of circadian rhythms, undergoes neurodegenerative changes in FTD 39 , 45 , which could lead to disrupted melatonin secretion and impaired synchronization to light/dark cycles. From a clinical perspective, this finding warrants further understanding of whether environmental modifications such as increased exposure to natural light during the day or the use of timed bright-light therapy, may help regulate sleep patterns in FTD patients. Finally, the case of our participant with bvFTD at the end of life is a notable example of the potential power to use a passive digital sensing tool like a bed sensor for just-in-time interventions 46 . Although the goals of this study did not include real-time monitoring, these data in combination with larger studies could be used to develop person-specific algorithms to detect significant changes in any bed behavior that may be associated with disease progression, medication changes, or other health status changes, and potentially flag the need for additional clinical support. Previous studies have demonstrated the feasibility and effectiveness of mobile or web-based systems that include both patient-facing and clinician portals linking patients’ real-time data to inform delivery of personalized behavioral interventions for sleep 47 – 49 , as well as real-time support-based dementia care interventions (e.g., Care Ecosystem) 50 . These intervention designs could be extended to include both a passive digital monitoring component and a multidisciplinary clinical team to address emergent health issues detected by the digital device(s). This is the first study to our knowledge to monitor long-term patterns in sleep- and bed-related behaviors in FTD; however, it is not without limitations. First, our sample size was quite small including only 16 participants with FTD, and even smaller subgroups for each FTD syndrome. This limited our ability to examine syndrome-specific nighttime behavioral disturbances, which would be clinically relevant and important for future studies to address. The majority of our primary participants also had sporadic forms of FTD, and future work in genetic mutation carriers is warranted to establish generalizability. Next, the use of bed sensors, while unobtrusive and ecologically valid, does not currently provide valid or robust information about more detailed features of sleep, sleep architecture (e.g., slow-wave sleep), or common primary sleep disorders such as obstructive sleep apnea and dream enactment behavior during sleep which could contribute to movements while in bed. Although we report on Emfit-derived sleep measures in exploratory analyses, future studies should integrate polysomnography to further validate this tool and characterize sleep abnormalities in FTD. In addition, there was a significant amount of missing data and although we had some contextual information from weekly online participant surveys to inform us when participants were away from home, only 12% of missing data were accounted for by these reports. While it is possible that not all days away from home were reported by participants, we are also limited in our ability to determine whether data gaps were due to technical issues with the bed sensor. Future work should incorporate more detailed and timely surveys to gather contextual information from participants as well as metadata from devices to detect technical errors that could be resolved. Additionally, more frequent review and evaluation of incoming data streams could lead to earlier detection of missing data to support more real time troubleshooting and implementation of solutions. In summary, this study demonstrates the feasibility and preliminary utility of an under-the-mattress sensor for monitoring bed behaviors in individuals with FTD. Findings support the potential of this technology as a non-invasive tool for continuous long-term monitoring of nighttime bed behaviors and overall disease progression in both research and clinical contexts, with promising implications for improving real-time, personalized, and precision care for patients with FTD and other dementias. While further validation is needed, this unobtrusive bed activity sensor and its associated measures of objective bed behavior also hold potential as endpoints for evaluating the efficacy of interventions aimed at improving sleep and circadian rhythms in FTD. Supplementary Material eTable 1 NIHMS2139686-supplement-eTable_1.pdf (86.7KB, pdf) eFigure 1 NIHMS2139686-supplement-eFigure_1.pdf (192.9KB, pdf) eFigure 2 NIHMS2139686-supplement-eFigure_2.pdf (503.7KB, pdf) eFigure 3 NIHMS2139686-supplement-eFigure_3.pdf (218.2KB, pdf) Coinvestigator Appendix NIHMS2139686-supplement-Coinvestigator_Appendix.pdf (63.2KB, pdf) Study Funding: This work was supported by: NIH grants U19AG063911, P01AG019724, P30AG062422, RF1AG077557 (PI: AS), K23AG061253 (PI: AS); K23AG084883 (PI: EWP), U2C AG054397, P30 AG066518, P30 AG008017, P30 AG024978, K25AG071841 (PI: WMA); Veterans Administration grant IIR17–144; Alzheimer’s Association grant AARF-23–1145318 (PI: RS); and New Vision Research (CCAD 2024–001-1; PI: RS). Disclosure: E. W. Paolillo has received research support from NIH, the Alzheimer’s Association, and the Shenandoah Foundation; A. Wise reports no disclosures relevant to the manuscript; J. Kaye and OHSU have a financial interest in Life Analytics, Inc., a company that may have a commercial interest in the results of this research and technology. This potential conflict of interest has been reviewed and managed by OHSU. He has also received research support from NIH; Z. Beattie and OHSU have a financial interest in Life Analytics, Inc., a company that may have a commercial interest in the results of this research and technology. This potential conflict of interest has been reviewed and managed by OHSU. He has also received research support from NIH; W-T. M. Au-Yeung has received research support from NIH; S. Dhanam reports no disclosures relevant to the manuscript; K. B. Casaletto has received research support from NIH, Alzheimer’s Association, the Larry L. Hillblom Foundation, and Wellcome Trust Leap; R. Saloner has received research support from NIH, Alzheimer’s Association, the Association for Frontotemporal Degeneration, and the Larry L. Hillblom Foundation; M. Mendez receives research support from NIH; L. Vandevrede has received research support from NIH, Alzheimer’s Association, and Shenandoah Foundation, consulting fees from Roche, Siemens, and Biogen, personal fees from Peerview CME and Haymarket, payment for expert testimony, meeting attendance/travel support from Biogen, Siems, and Tau Consortium, and he serves on a monitoring or advisory board for NIOSILK; P. Ljubenkov receives research support from NIH; W. W. Seeley has received research support from NIH, consulting fees from BridgeBio, Guidepoint Global, Inc., and GLG Council, speaker honoraria from Verge Genomics, and compensation as a member of the scientific advisory board for Lyterian Therapeutics; M. L. Gorno-Tempini receives research support from NIH; B. Miller receives research support from NIH, serves as Medical Director for the John Douglas French Foundation, Scientific Director for the Tau Consortium, Director/Medical Advisory Board of the Larry L. Hillblom Foundation, and Scientific Advisory Board Member for the National Institute for Health Research Cambridge Biomedical Research Center and its subunit, the Biomedical Research Unit in Dementia, UK; H. Heuer reports no disclosures relevant to the manuscript; L. Forsberg receives research support from NIH; J. Kornak has provided expert witness testimony for Teva Pharmaceuticals in Forest Laboratories Inc. et al. v. Teva Pharmaceuticals USA, Inc., Case Nos. 1:14-cv-00121 and 1:14-cv-00686 (D. Del. filed Jan. 31, 2014, and May 30, 2014) regarding the drug Memantine; for Apotex/HEC/Ezra in Novartis AG et al. v. Apotex Inc., No. 1:15-cv-975 (D. Del. filed Oct. 26, 2015), regarding the drug Fingolimod. He has also given testimony on behalf of Puma Biotechnology in Hsingching Hsu et al, vs. Puma Biotechnology, INC., et al. 2018 regarding the drug Neratinib. He receives research support from the NIH; W. K. Kremers receives research funding from AstraZeneca, Biogen, Roche, DOD, and NIH; B. F. Boeve has served as an investigator for clinical trials sponsored by Alector, Biogen and Transposon. He receives royalties from the publication of a book entitled Behavioral Neurology Of Dementia (Cambridge Medicine, 2009, 2017). He serves on the Scientific Advisory Board of the Tau Consortium. He receives research support from NIH, the Mayo Clinic Dorothy and Harry T. Mangurian Jr. Lewy Body Dementia Program, and the Little Family Foundation; A. L. Boxer receives research support from NIH (U19AG063911,R01AG038791, R01AG073482), the Tau Research Consortium, the Association for Frontotemporal Degeneration, Bluefield Project to Cure Frontotemporal Dementia, Corticobasal Degeneration Solutions, the Alzheimer’s Drug Discovery Foundation, and the Alzheimer’s Association. He has served as a consultant for Aeovian, AGTC, Alector,Arkuda, Arvinas, AviadoBio, Boehringer Ingelheim, Denali, GSK, LifeEdit, Humana, Oligomerix, Oscotec, Roche, Transposon, TrueBinding and Wave, and received research support from Biogen, Eisai, and Regeneron. Dr Boxer is a co-inventor of four ALLFTD Mobile App tasks and receives licensing fees, consistent with UCSF institutional policy; H. H. Rosen has received research support from Biogen Pharmaceuticals, has consulting agreements with Wave Neuroscience and Ionis Pharmaceuticals, and receives research support from NIH; C. M. Walsh has received research support from NIH; A. M. Staffaroni received research support from the NIA/NIH, Bluefield Project to Cure FTD, and the Larry L. Hillblom Foundation, and has provided consultation to Alector, Lilly/Prevail, Passage Bio, and Takeda. Dr Staffaroni is a co-inventor of four ALLFTD Mobile App tasks and receives licensing fees, consistent with UCSF institutional policy. References 1. 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Supplementary Materials eTable 1 NIHMS2139686-supplement-eTable_1.pdf (86.7KB, pdf) eFigure 1 NIHMS2139686-supplement-eFigure_1.pdf (192.9KB, pdf) eFigure 2 NIHMS2139686-supplement-eFigure_2.pdf (503.7KB, pdf) eFigure 3 NIHMS2139686-supplement-eFigure_3.pdf (218.2KB, pdf) Coinvestigator Appendix NIHMS2139686-supplement-Coinvestigator_Appendix.pdf (63.2KB, pdf) Data Availability Statement Deidentified clinical and demographic are available from ALLFTD on request. Investigators are required to complete the Request Clinical Data form on the request portal ( https://www.allftd.org/data ) and to review the data sharing and publication policy. Data that could identify a participant are not provided. Any additional information required to reanalyze the data reported in this paper is available from the lead contact and ALLFTD. 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