Feasibility and exploration of remote multimodal sleep measurement in autistic and nonautistic smartphone users

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Remote measurement technologies (RMT), including wearable and smartphone data, offer low‑burden approaches but feasibility in autistic populations is unclear. We evaluated a 28-day protocol combining passive Fitbit sleep staging and smartphone sensor data, and active daily sleep quality ratings in autistic (n = 34) and nonautistic (n = 39) participants (14–35 years). Median data availability was over 70% across modalities. However, usable data (days with valid sleep period and quality rating) were lower (< 60%), particularly for autistic participants, whose tactile sensitivity associated with reduced usable Fitbit data. Autistic participants reported lower sleep quality; however, few differences in passively derived sleep features emerged. Several features (e.g., sleep efficiency, duration, REM proportion) were related to subjective sleep quality, as were passively-derived sleep profile clusters. This study provides foundational work for developing RMT suitable for sleep measurement in autistic populations. Health sciences/Health care Health sciences/Medical research Biological sciences/Neuroscience Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Autism is a neurodevelopmental condition characterised by differences in social interaction and sensory processing, and presence of restricted and repetitive behaviours 1 . Most autistic people experience additional physical and mental health difficulties that affect daily life. Alongside commonly co-occurring conditions, such as anxiety, attention-deficit hyperactivity disorder, and epilepsy, sleep problems are especially prevalent. Sleep difficulties are reported in 71% of autistic children 2 and 79% of autistic adults 3 , and diagnosed sleep disorders occur nearly twice as often in autistic versus nonautistic adults 4 . Although autistic children, adolescents, and adults show similarly high rates of sleep problems 5 , sleep research in autistic adults is sparser 6 . Nevertheless, across age groups, findings from polysomnography, actigraphy, and sleep diaries converge on identifying alterations in initiation and continuity of sleep, as manifested in longer sleep onset latency (SOL), increased wake after sleep onset (WASO), reduced total sleep time (TST), and lower sleep efficiency (SE) 6 – 8 . Possible explanations for elevated sleep problems reported in autism include altered melatonin-related circadian rhythms 9 , heightened autonomic arousal 10 , sensory hyper- or hypo-sensitivity 11 , and co-occurring anxiety 12 . As is the general population, sleep quality in autistic samples is associated with physical and mental health problems and daytime functioning 13 , and predicts later quality of life 14 , 15 . In the general population, successful treatment of sleep problems has been shown to lead to improved mental health 16 , highlighting sleep as a clinically meaningful and modifiable target for intervention. Importantly, sleep is also identified as a research priority by autistic people themselves 17 . Intervention development for sleep difficulties in autism depends on both a deeper understanding of the underlying mechanisms and the rigorous evaluation of emerging treatments. At each of these stages, the use of precise, reliable, and valid sleep measurement methods is essential. However, measuring sleep poses specific challenges that may be amplified in the autistic population. Measures based on polysomnography (PSG), provide important objective data on sleep architecture and are generally considered the diagnostic gold standard for assessment of sleep disorders. However, applying these methods is expensive and may not be representative of everyday sleep. This is because PSG typically involves sleeping in an unfamiliar environment (e.g., the clinic, or lab) and wearing equipment that may cause discomfort and disturb sleep, particularly for autistic individuals with sensory differences 18 , 19 . Further, observation periods are often limited to one or two nights due to high cost 20 . Alternative approaches to sleep measurement, such as actively reported sleep diaries and sleep quality scales provide accounts of sleep in everyday life but are burdensome to complete regularly. Resulting data are subject to recall difficulties and may incorporate measurement error and bias. For example, sleep diaries completed by caregivers (often in paediatric samples) have been shown to underestimate frequency and duration of awakenings and consequently overestimate TST and SE 21 . In adolescents and adults, caregivers may have less involvement in sleep and may not be able to provide accurate information. However, high rates of intellectual disability (ID) and difficulty in reporting on internal states for some autistic individuals 22 mean that self-report methods may not adequately capture information from a broad range of autistic individuals. Wearable devices that integrate accelerometer (movement) and photoplethysmography (PPG; used to estimate pulse rate) data 23 offer a relatively low-cost and unobtrusive means of capturing sleep data remotely. These characteristics enhance their feasibility for extended monitoring and their applicability in populations where reliable self- or proxy-reported sleep information is difficult to gather. Nevertheless, wearables yield indirect estimates of sleep states with varying degrees of validity when compared to PSG 23 . Typically, actigraphy data shows high sensitivity in detecting sleep but lower specificity in correctly identifying wakefulness during a rest period 24 . Due to inferring sleep periods from motion and pulse rate data, sedentary but wakeful behaviour may be mistaken for sleep 25 , leading to errors in the estimation of SOL and WASO. This may be particularly problematic for autism sleep research, given that reported difficulties often centre around initiating and maintaining sleep 6 , 8 . Often, wearable-based methods also still rely on some active input from participants (either through pressing an event marker on the device or via sleep diary) to establish a bedtime and thus infer SOL 26 . This may reintroduce memory burden and accuracy issues. For autistic people in particular, wearable devices may also be less well tolerated due to presence of sensory sensitivities 19 . Smartphones represent another promising tool for capturing sleep‑relevant information. Their increasingly ubiquitous use has led to widespread adoption in health research for both active self‑report and passive sensor‑based data collection 27 , 28 . Because smartphones are already embedded in daily routines, they offer practical advantages for sustained monitoring whilst minimising the need for habituation to new devices or behaviours. Additionally, as users often keep their phones nearby, even at night 29 , sleep‑related signals can be derived from accelerometer data, light sensors, and app‑usage logs. Combined with wearable device data, such sources may indicate bedtime related events such as lights out and reduced activity as well as continued smartphone usage whilst in bed. This may address shortcomings of wearable-only protocols that may have difficulty establishing a bedtime marker without additional active reporting, and in distinguishing wakeful periods of low activity from sleep 23 . Data from smartphones, wearables, and similar devices are often collectively referred to as remote measurement technology (RMT). RMT encompasses active methods, in which participants enter information via a smartphone app, and passive methods, in which data are collected automatically from sensors such as accelerometers, PPG, or smartphone usage logs. Given its affordability, ubiquity, and capacity to capture real world data with minimal participant burden, there is growing interest in applying RMT in autism research, including for sleep 30 . However, little is known about the feasibility of these methods for autistic adolescents and adults reporting on their own experiences, as opposed to parent- or caregiver-reported data in younger samples. This study was implemented as part of a wider project aiming to develop and evaluate an RMT system for capturing a range of clinically relevant measures, suitable for autistic adolescents and adults 31 . For the current study, we focused on integrating both wearable and passive smartphone data to derive commonly investigated sleep features. We had three specific aims: To evaluate the feasibility of these RMT methods and their combination for active and passive sleep capture among autistic and nonautistic participants who were already smartphone users, and to explore participant characteristics relating to feasibility metrics. To explore group differences (autistic versus nonautistic) in passively derived sleep features and actively reported sleep quality. We expected that autistic participants would report lower sleep quality and that passive features commonly indicating sleep quality would be less favourable for autistic sleepers. Specifically, based on previous research, we expected to see evidence of longer SOL, shorter TST, a longer duration of WASO and lower SE among autistic participants. To explore associations between passively derived sleep features and active daily sleep quality reporting. Here we had two sub aims: To examine which individual passively derived sleep features predict next-day sleep quality ratings. To explore whether nights may be grouped into meaningful clusters based on multiple passively derived sleep features, and whether these clusters relate to actively reported sleep quality. Results Participant characteristics Thirty-four autistic and 40 nonautistic participants enrolled in the study. After one nonautistic participant withdrew due to concerns about the pRMT app, this left a total of 73 participants for whom characteristics are described and feasibility assessed. Participants were of similar ages across groups (14-35 years; Table 1). There were significantly ( p =.002) more males in the autistic group and, as expected, the autistic group had a significantly (p=3.0×10⁻8) higher self-reported autism questionnaire score (Social Responsiveness Scale-2). IQ was similar across groups and, while most participants had IQ in the typical range, one autistic and two nonautistic participants met study criteria for mild intellectual disability (IQ score of 50-74). Across both groups, participants were most commonly in full time work (32.4% of autistic and 30.8% of nonautistic participants) or education (44.1% of autistic and 48.7% of nonautistic participants). A small proportion worked part-time (11.8% of autistic and 5.1% of non autistic participants), and 8.8% of autistic participants were unemployed. Table 1. Participant characteristics and feasibility metrics for all participants included in the feasibility analysis. Autistic (N=34) Nonautistic (N=39) Group differences Participant characteristics [N if reduced sample] Mean (SD); range [N if reduced sample] Mean (SD); range Sex (% male) 76.5 38.5 Χ 2 =9.2 p =.002 Age (years) [33] 23.5 (6.5); 14.1 - 35.7 [37] 24.3 (5.2); 14.0 - 33.7 t =0.63; p =.53 SRS-2 total score [26] 83.2 (40.5); 14 - 164 [30] 33 (23.9); 0 - 97 t =-6.81; p =3.0×10⁻ 8 IQ a [33] 111.7 (16.4); 71-139 [13] 112.3 (20.8); 72- 144 t =0.1; p =.92 Occupation (%) Full time work Part time work Full time education Unemployed Do not wish to answer Missing 32.4 11.8 44.1 8.8 0.0 2.9 30.8 5.1 48.7 0.0 2.6 12.8 Descriptive only Operating System b (% Android) 91.2 87.2 Descriptive only Feasibility metrics Median (IQR); range Median (IQR); range Fitbit wear time Hours % 554.4 (353.6); 0 - 642 82.5 (52.6); 0 - 95.5 530.4 (123); 0 - 639.1 78.9 (18.3); 0 - 95.1 U =-99 ; p =0.84 Fitbit wear time (night) Hours % 254.8 (226); 0 - 314 75.8 (67.3); 0 - 93.4 261.6 (50.4); 0 - 318.1 77.9 (15); 0 - 94.7 U =-57; p =.51 Fitbit PSP available Days % 21 (26.8); 0 - 28 75 (95.7); 0 - 100 24 (8.0); 0 - 28 85.7 (28.6); 0 - 100 U =-5.5; p =.21 Sleep rating available Days % 26 (9.8); 0 - 28 92.9 (35.0); 0 - 100 24 (13.5); 0 - 28 85.7 (48.2); 0 – 100 U =-131.5; p=.87 pRMT data availability c Days % [31] 22 (16); 1 - 28 78.6 (57.1); 3.6 - 100 [34] 24 (10); 0 - 28 85.7 (35.7); 0 - 100 U =-75; p = 0.93 Overall eligible days Days % 11.5 (25.8); 0 - 28 41.0 (92.1); 0 - 100 20 (15); 0 - 28 71.4 (53.6); 0 - 100 U =-15.0; p=.26 Notes. PSP Primary Sleep Period; pRMT Passive Remote Monitoring Technology; IQR interquartile range; a available for LEAP participants only; b original eligibility criteria required Android smartphones due to compatibility with RADAR pRMT app, which was later relaxed to accommodate more LEAP participants (see Table S1); c Assessed in Android users only. Objective 1: Feasibility findings Descriptive statistics for key feasibility variables are provided in Table 1. Mann-Whitney U tests revealed no significant differences across autistic and Nonautistic groups for any modality. For Fitbit wear time and PSP availability, median values for both groups lay upwards of 75% across total wear time, night wear time, and availability of a PSP. Similarly, median data availability for the RADAR pRMT app was over 75% for both groups. For SSQS ratings via the RADAR aRMT app, median values were above 85% for both groups. Although feasibility metrics showed favourable median values and no significant group differences, a notable minority of participants contributed zero days of data eligible for the main sleep analysis (Figure 1). Among autistic participants, ten individuals (29.4%) had no eligible days; all exhibited low nighttime Fitbit wear (<25%) and zero or minimal PSP availability, with three additionally showing low SSQS completion rates (Table S5). Among nonautistic participants, five individuals (12.5%) had no eligible days, with four completing no SSQS prompts and three showing low Fitbit night-time wear and minimal PSP availability. As a result, the proportion of analysable data days was substantially reduced, especially in the autistic group. Across all autistic participants, 447 of 952 possible data days (47%) were eligible for analysis (median 41% availability within participants). Across nonautistic participants, 645 of 1092 possible data days (59.1%) were eligible (median 71% availability within participants). Associations between participant characteristics and the key feasibility metrics show that sex was unrelated to any feasibility metrics (see Table 2). Older age was associated with greater aRMT SSQS availability ( ρ = .27; p =.03). Higher autism characteristics score was associated with higher pRMT data availability ( ρ = .31; p =.03). Higher IQ was associated with greater Fitbit primary sleep score availability ( ρ = .31; p =.03). Higher levels of tactile sensitivity among autistic participants were associated with lower Fitbit PSP availability ( ρ = .44; p =.03) and overall eligible data days ( ρ = .42; p =.04). Our sample size and data characteristics did not support running a multiple regression analysis to determine if variance explained in Fitbit PSP availability was shared between IQ and tactile sensitivity. However, a Spearman correlation run between IQ and tactile sensitivity among autistic participants showed a modest association and was not significant ( ρ = .25; p =.27). Table 2. Associations between participant characteristics and feasibility metrics Fitbit primary sleep availability Active sleep quality ratings availability Passive smartphone data availability b Overall days eligible for analysis Sex [n] U ( p ) [73] 134.5 (.78) [73] 221.0 (.21) [65] 33.5 (.22) [73] 177.0 (.46) Age [n] ρ ( p ) [69] .19 (.12) [69] .27 (.03) [61] -.04 (.76) [69] .21 (.07) SRS-2 total score [n] ρ ( p ) [56] -.19 (.16) [56] -.02 (.89) [49] .31 (.03) [56] -.20 (.15) IQ a [n] ρ ( p ) [46] .31; .03 [46] .10 (.50) [38] .03 (.88) [46] 0.24 (.11) Tactile sensitivity a, c [n] ρ ( p ) [23] .44 (.03) N/A N/A [23] .42 (.04) Notes. [n] association statistic (p-value) significant (bold). SRS-2 Social Responsiveness Scale-2; a data on the LEAP sample only; b iOS users excluded (n=8); c higher scores indicate lower tactile sensitivity, assessed using the Short Sensory Profile for autistic participants only. Objective 2: Descriptive sleep data and group differences Table 3 provides descriptive data for the SSQS reported via the aRMT and all passively derived sleep features, and group differences assessed using LMEs. SSQS ratings were statistically higher (LME coefficient=0.45; p =.01) for nonautistic participants (median 73) than autistic participants (median 68.00). According to Fitbit data, participants showed a wide range of sleep onset and offset times. Median sleep onset for both groups lay between midnight and 1am and median sleep offset time was between 8am and 9am for both groups. Median SPP (the interval between final awake marker and sleep onset) was nearly 40 minutes for autistic participants and over 60 minutes for nonautistic participants (LME coefficient = 0.44; p =.005). TST was significantly (LME coefficient = 0.41; p = .005) shorter for autistic participants (Median of 7.0 hours) than for nonautistic participants (median of 7.6 hours). Number of awakenings and duration of WASO was similar across groups with participants waking a median of 3 times per night with median overall duration around 25 minutes. Sleep efficiency was remarkably similar across groups (.84 and .83 for autistic and nonautistic groups, respectively). Relative proportions in each sleep stage also appeared consistent across groups, with light sleep accounting for around three fifths of time spent asleep, and deep and REM sleep accounting for one fifth each. Figure 2 shows effect sizes and 95% confidence intervals for group differences assessed using LME models. Table 3: descriptive statistics of each participant group and their statistical differences Feature Autistic group Median; IQR Nonautistic group Median; IQR Group differences LME Coefficient ( p ); 95% CI SSQS Rating 68.00; 50.00-76.00 73.00; 62.00-81.00 0.447 (.011); 0.101, 0.793 Sleep Onset Time (mins after 00:00) 24.50; -52.25-127.75 3.50; -50.00-76.00 -0.191 (0.344); -0.585, 0.204 Sleep Offset Time (mins after 00:00) 467.00; 412.00-549.75 498.50; 420.00-570.00 0.103 (.569); -0.251, 0.457 Sleep Preparation Period (mins) 37.00; 11.00-65.50 62.74; 23.50-70.00 0.444 (.005); 0.135, 0.754 Number of Awakenings 3.00; 2.00-4.00 3.00; 2.00-5.00 0.064 (0.647); -0.21, 0.338 Wake After Sleep Onset (mins) 23.00; 13.00-35.00 25.00; 14.50-39.50 0.112 (.400); -0.149, 0.372 Latency to Arising (mins) 4.50; 1.00-14.75 3.00; 1.00-15.00 0.006 (.950); -0.185, 0.197 Total Sleep Time (mins) 420.50; 369.25-468.75 457.50; 399.00-514.00 0.408 (.005); 0.124, 0.692 Sleep Efficiency 0.84; 0.81-0.90 0.83; 0.79-0.87 -0.176 (.215); -0.453, 0.102 Proportion of Light Sleep 0.60; 0.55-0.66 0.63; 0.57-0.69 0.260 (.084); -0.035, 0.555 Proportion of REM Sleep 0.22; 0.18-0.26 0.20; 0.16-0.25 -0.212 (.178); -0.521, 0.097 Proportion of Deep Sleep 0.18; 0.14-0.21 0.17; 0.14-0.20 -0.164 (.296); -0.47, 0.143 Notes. a nonautistic is the reference group Objective 3: Correspondence between active and passive data Objective 3. (a) associations for individual sleep features Figure 3 demonstrates the forest plots of LME model estimates, illustrating the associations between passively derived sleep features and SSQS ratings in autistic and nonautistic participants. Here, p aut, p naut, β aut and β naut represent p-values and values effect sizes of autistic and nonautistic participants respectively. For both groups, greater sleep efficiency (p aut =.00014, β aut = 0.1792; p naut = 1.115e-08, β naut = 0.1894), TST (p aut = 5.417e-08, β aut = 0.2504; p naut = 4.731e-21, β naut = 0.3130), proportion of REM sleep (p aut =.0014, β aut = 0.1529; p naut = 8.306e-05, β naut = 0.1436) and sleep offset time (p aut =.0004, β aut = 0.1744; p naut = .0006, β naut = 0.1412) were positively associated with higher SSQS ratings. For autistic participants, higher proportion of light sleep (p aut = .0422, β aut = -0.0963), and for nonautistic participants, later sleep onset time (p naut = 1.468e-08, β naut = -0.2501) were significantly associated with lower SSQS rating. The detailed results of all the LME model estimates are given in Table S4. Figure 3. The forest plot of effect size of the LME models to describe the relationship between passively derived sleep features and SSQS ratings for participants. Objective 3. (b) Clustering results Results of agglomerative clustering performed on the whole dataset are provided in Table S6. For each linkage, the silhouette score decreased with increasing number of clusters. The chosen model for the further clustering models was agglomerative clustering with complete linkage and three clusters based on silhouette Score (0.27), within cluster sum of square (13595.91) and visual inspection of an elbow plot (Figure S2). The number of participant days present in each cluster is provided in Table S7. We examined differences between participant-days belonging to each cluster on all passively derived features (used to derive the clusters) and active SSQS ratings (not involved in clustering). Clusters showed distinct profiles across passively derived features (see Figure 4). Relative to the other clusters, days assigned to cluster A showed features typically associated with high quality sleep (notably, high TST and sleep efficiency, high proportion of deep and REM sleep relative to light sleep, and low number and duration of awakenings). Days assigned to cluster B showed predominantly the opposite pattern, characterised by low TST, low sleep efficiency and low proportion of deep and REM sleep relative to light. Days assigned to cluster C showed certain features associated with poorer sleep quality (e.g., low proportion of deep and REM sleep relative to light sleep); however, TST and sleep efficiency were high. Correspondingly, nights assigned to clusters A (p= 1.299e-09, β= -0.3410) and C (p= 6.524e-04, β= -0.2606) showed significantly higher mean SSQS ratings than cluster B. Distribution of participant-days among clusters was similar across groups (see Table S5). Both autistic and nonautistic participants had a plurality of days assigned to cluster B (48.1% and 46.0% respectively), a substantial minority assigned to cluster A (42.7% and 36.4% respectively), and the smallest percentage assigned to cluster C (9.2% and 17.5% respectively). Discussion Feasibility Remote multimodal sleep measurement was generally feasible across autistic and nonautistic participants, with high median adherence to Fitbit wear (around 80%), daily sleep ratings (more than 85%), and passive smartphone sensing (around 75%) in both groups. However, substantial heterogeneity meant that a notable minority (over one quarter of autistic and one eighth of nonautistic participants) provided little or no sleep data eligible for analysis. This resulted in a marked reduction in usable participant days, especially in the autistic group. Similar discrepancies between overall wear time and valid sleep data have been reported in recent feasibility work on actigraphy in autistic adolescents and adults, including reduced proportions of analysable data for the autistic group despite comparable adherence across groups 32 Our exploration of associations between feasibility metrics and participant characteristics may offer explanations for reduced usable data in the autistic group. Lower Fitbit PSP availability was associated with greater tactile sensitivity in autistic participants, suggesting sensory discomfort as a key barrier to sustained nighttime wearable use. Although our study is, to our knowledge, the first to quantify this relationship directly, the pattern aligns with participant feedback about Fitbit Inspire 3 usage in a similarly aged autistic sample 33 and with prevailing concerns in the autism sleep literature regarding sensory related wearable intolerance 19 . This is notable given that sensory sensitivity is also linked to insomnia severity 34 , meaning individuals with the most severe sleep difficulties may be disproportionately excluded in wearable based studies. The observed relationships with IQ may indicate that the executive or practical demands of managing device wear also constrain feasibility. Remembering to replace a device after unavoidable breaks, e.g., showering or complying with work or school regulations, places organisational demands on participants. Although the Fitbit was chosen partly for its long battery life, reducing the need for frequent charging, even occasional breaks during a 28day period can disrupt data continuity. However, it is also important to consider that the observed IQ associations may reflect shared variance with tactile sensitivity rather than executive demands alone. Sensory sensitivities can vary with cognitive ability, and recent work suggests complex associations between cognitive level and sensory processing in autism 35 . Overall, our findings suggest that wrist worn devices can successfully capture sleep data for many autistic individuals, but a minority may remain systematically underrepresented in wearable based research. This underscores the importance of developing and evaluating alternative contactless approaches, such as mattress based or camera based monitoring, for which emerging evidence in autistic children 36 and autistic adolescents and adults 37 shows some promise. Passive smartphone sensing was available for over threequarters of study days across both groups. Interestingly, higher data availability correlated with higher self-reported autism characteristics. Interpretation of this association is limited by the small, highly selected nature of our sample. However, one possibility is that participants reporting higher autism characteristics in this study represent a subgroup with relatively strong adaptive skills, while those with comparable autism characteristics but with greater support needs (who may face more challenges using smartphones) were underrepresented. Alternatively, higher adherence to this component may reflect autistic strengths. For example, “using technology,” “problem solving,” and “adherence to routines” feature among the most frequently reported autistic strengths 38 , and may reasonably support adherence to this component. Elsewhere, a notable constraint on feasibility arose from operating system restrictions in our study: iOS devices were incompatible with the passive sensing platform, highlighting a broader challenge for scalable RMT given that app functionality is dependent on operating system–level permissions. Group differences Passively derived sleep measures revealed limited differences between autistic and nonautistic participants, and the overall pattern diverged from existing findings in the autism sleep literature. We observed shorter TST in autistic participants but did not replicate commonly reported reductions in sleep efficiency or increases in SOL and WASO 6 . The shorter TST aligns with meta-analytic findings in autistic youth 8 but contrasts with adult meta-analyses showing no group differences 6 . In contrast, subjective sleep quality was lower among autistic participants, aligning with meta-analytic evidence across ages 6 , 8 and longstanding reports of elevated sleep difficulties in autism 3 . Previous research has most consistently characterised autistic sleep by difficulties initiating and maintaining sleep, reflected in longer SOL and lower sleep efficiency 6 , 8 . In our study, time spent attempting to fall sleep was, surprisingly, higher in the nonautistic group. However, we used a multimodal “final awake marker” derived from smartphone and wearable activity, which differs conceptually from conventional measures based on participant-reported bedtime or wearable-only inference. This difference in operational definition may contribute to divergence from prior findings. Alternatively, as discussed above, autistic participants with greater difficulty getting to sleep may have been underrepresented due to sensory-based intolerance to the Fitbit device. The naturalistic design of this study may help explain the attenuated pattern of group differences from passive data. Participants were monitored continuously across an extended period of everyday life, during which sleep is shaped by numerous environmental and contextual factors, including living with children, occupational routines and demands, and noise exposure 39 , that likely affect individuals in both groups. Compared with laboratory-based or short-term observational studies, this real-world variability may make autism-related differences in passively measured sleep patterns harder to detect, whilst preserving differences in subjectively reported sleep experience. Given the exploratory nature of these analyses and the study’s primary focus on feasibility, these findings should be interpreted cautiously. Correspondence between active and passive measures In terms of correspondence between active and passive measures, patterns were similar across groups. For both groups, higher sleep ratings were assigned on participant-nights with higher sleep efficiency, longer total duration, greater proportion of REM sleep, and later sleep offset time. This is in line with findings from the general population, in which total sleep time and sleep efficiency are consistently related to reported sleep quality, though directions of association are inconsistent across studies 40 . Findings from the cluster analysis showed three distinct profiles across passively derived features that appeared to correspond meaningfully to differences in active sleep quality report and were similarly distributed across groups. This correspondence provides some indication as to the validity of our passive sleep data in capturing experienced sleep quality in a relatively low-burden manner. Strengths and limitations This study contributes a novel multimodal remote sleep assessment system, responding to the need for development of scalable, low-burden measurement that is feasible for use in everyday life. Our 28-day measurement period is relatively long compared to other studies investigating sleep in autistic adolescents and adults; thus, we provide important data on sustained adherence to both wearable and active reporting components. Secondly, our implementation within a well characterised sample has allowed us to systematically investigate correlates of feasibility that may guide future studies. Finally, we contribute a novel approach to combining two sources of passive data (wearable and smartphone data), with resulting data corresponding meaningfully to actively reported sleep quality. However, there are several key limitations to note. First, enrolment in this study required participants to be independent smartphone users who were happy to attempt to complete daily questionnaires, to wear the Fitbit, and to allow access to smartphone data. This is likely to exclude important groups, particularly from within the autistic population, e.g., those with lower functional ability, and more significant sensory sensitivities (corroborated by our own findings that tactile sensitivity reduced availability of useable data), as well as those who are more concerned regarding data privacy issues. Regarding device selection, the Fitbit appeared to be broadly acceptable for everyday use in this population, having been chosen with input from autistic community representatives. However, resulting data are processed via proprietary algorithms, reducing insight into validity and flexibility in usage. Including validity assessment against PSG or other reference standard for sleep architecture was beyond the scope of the current study, therefore, our findings drawing on Fitbit data must be interpreted with caution. Conclusion Despite limitations, this study lays important groundwork for developing meaningful remote, passive markers in the autistic population that may be used to characterise sleep profiles, problems (types of which may vary for different subgroups), understand underlying mechanisms, and (ultimately) develop interventions to improve sleep. Our findings may be used to tailor future wearable or nearable technology to enhance tolerance for a wider proportion of the autistic population. Methods Overview Data were collected as part of a 28-day Mobile Measures Month (MMM) RMT study 31 of the AIMS-2-TRIALS research programme 41 . The MMM involved participants wearing a Fitbit Inspire 2 or 3 wristband throughout the 28-day collection period. From this, we remotely collected the Fitbit-derived digital sleep and activity measures (Fig. 5 ). Additionally, participants installed two data-collection smartphone apps supported by the RADAR-base platform 42 . The apps included a passive data collection (pRMT) app to provide sensor data and an active reporting (aRMT) app to collect sleep quality self-reports (Fig. 5 ). We combined the Fitbit and phone sensor data to derive passive sleep measures. The full MMM protocol details are available elsewhere 31 . Participants This study recruited participants between March 2022 and November 2024 from two sources: autistic and nonautistic participants enrolled in the third assessment timepoint of the AIMS Longitudinal European Autism Project (LEAP-3) and an additional sample of nonautistic volunteers recruited at King’s College London (KCL). LEAP is a well-characterised longitudinal cohort of autistic and nonautistic individuals with IQ ≥ 50 43,44 . Autistic participants required a clinical diagnosis (DSM-IV/ICD-10 or DSM-5), and nonautistic participants had no reported psychiatric disorders (see OSF LEAP Protocol). In the present study, only participants recruited at UK sites, KCL and University of Cambridge, were eligible to take part due to app translation constraints. At LEAP-3, 127 autistic and 51 nonautistic participants returned to participate at UK sites. The additional nonautistic sample recruited at KCL (N = 26) included only individuals reporting no known or suspected neurodevelopmental, neurological, mental health, or sleep conditions. For the MMM, all participants were required to be independent smartphone users aged 12 or older (see Table S1 for full eligibility criteria across recruitment sources). Ethics declaration Ethical approval was obtained for the LEAP study at KCL and UCAM from the London-Central and Queen Square Health Research Authority Research Ethics Committee (13/LO/1156). For the comparison sample, approval was granted by the KCL Research Ethics Committee (LRS/DP-23/24-42359). This study was conducted in accordance with the principles expressed in the Declaration of Helsinki. Written informed consent was obtained from participants using the Qualtrics e-platform or in person, depending on how the study information session was conducted for each participant. Procedure Consenting participants were sent a Fitbit device and undertook an enrolment video call with a researcher to set up the Fitbit and smartphone apps. Remote data were then collected for 28 days. Data collection Wearable data We used sleep stage and step count data from Fitbit Inspire 2 (LEAP participants) and Inspire 3 devices (additional nonautistic participants; see Table S1 ), selected in consultation with autistic community representatives. These commercial fitness trackers measure pulse rate using PPG and detect motion via an accelerometer. Validation studies of recent-generation Fitbit models have reported high sensitivity but modest specificity for distinguishing sleep from wakefulness, and a wide range in sleep-stage identification accuracy 45 , 46 . Low measurement errors have been reported when validating Fitbit’s step count measurement 47 . Passive smartphone data The pRMT app was implemented through the RADAR-base platform 42 . It collects a range of Android smartphone sensor data. In this study, phone motion and usage data were used as markers of wakefulness, light level data were used to detect “lights off” events, and battery level data were used for feasibility metrics to establish installation and continued functioning of the app. Active smartphone data Using the RADAR-base aRMT app, participants were prompted daily at 8:30am to complete an adapted Single-item Sleep Quality Scale (SSQS) 48 . The SSQS is a simple measure of overall sleep quality, usually rated on a ten-point scale. It shows a strong association with widely used questionnaire-based sleep assessments (e.g., Pittsburgh Sleep Quality Index) and has been shown to differentiate normal, borderline, and problem sleepers in a study of depressed patients 48 . In this study, participants rated the previous night’s sleep quality on a 0–100 visual analogue scale using a movable slider, aligning the SSQS with the response format used for other measures implemented in the wider study using the aRMT. Other measures Information on participant characteristics was collected via questionnaires and in-person assessments. These measures were used to characterise the sample and to explore associations with key feasibility metrics outlined below. For all participants, available demographic data included age, sex and employment or education status. Self-ratings on autism characteristics were collected via the Social Responsiveness Scale-2 49 , a validated and widely used questionnaire measure quantifying autism-related behaviours. For the LEAP sample only, we also incorporated IQ data collected at LEAP3 using the Wechsler Abbreviated Scale of Intelligence–Second Edition 50 . UK-based LEAP-3 assessment estimated full scale IQ using the two-subtest form, comprising Vocabulary and Matrix Reasoning. Parent‑reported Short Sensory Profile 51 (SSP) data were available for autistic LEAP participants and for nonautistic LEAP participants under 18 years of age. The SSP is a validated parent‑ or caregiver‑reported measure derived from the Sensory Profile 52 . In this study, we focused on the tactile sensitivity subscale to assess sensitivity to touch that might relate to tolerance of wearing the Fitbit. This relationship was examined in autistic participants only, given our specific hypotheses about wearable tolerance in autism and the absence of comparable data for nonautistic adults. Feature extraction Using passive RMT data from Fitbit and the pRMT smartphone app, we extracted 11 sleep architecture features (Table 4 ). These were derived using a novel methodology that combines Fitbit sleep stage data with Fitbit step count and pRMT smartphone data (full pipeline description presented in Table S3). Figure 6 illustrates the feature extraction pipeline. We used Fitbit sleep staging data to identify a Primary Sleep Period (PSP) for each day of the study. We define PSP as the longest continuous sleep period (broken by less than 180-minute wake periods) among all sleep periods with wake-up on the same calendar date 53 . To ensure that anomalously short or long sleep periods were excluded from primary analyses, we operationally defined the PSP as the longest daily sleep episode lasting between 2 and 12 hours 54 . Given the known prevalence of atypical sleep patterns in the autistic population, we applied a relatively broad criterion to balance sensitivity to genuinely unusual sleep with the need to exclude data likely reflecting device malfunction or other artefacts. Sleep Onset Time , Sleep Offset Time , Wake After Sleep Onset (WASO) duration, Number of Awakenings , Total Sleep Time (TST) and proportion of PSP spent in Light , Deep , and REM sleep were also calculated directly from Fitbit sleep staging data. We also derived a novel feature, the Sleep Preparation Period (SPP), defined as the interval between sleep onset and the final awake marker occurring within the two hours preceding estimated sleep onset. This construct differs from the more conventional SOL, which is typically calculated from reported bedtime 23 or inferred solely from wearable data, approaches whose limitations are outlined in the introduction. We estimated SPP using a combination of four ‘awake' markers from Fitbit step count and pRMT data. These were (A) phone pick-up (smartphone motion data), (B) “lights off” (smartphone light meter data), (C) active phone usage (app usage and screen state event logs), and (D) final daily steps (Fitbit data). Precise derivations for these events are provided in Table S2. We also derived Latency to Arising , which we calculated as the interval between estimated sleep offset and first activity of the day according to Fitbit step count. Finally, we calculated Sleep Efficiency as TST divided by total time between the final awake marker and the first activity of the following wake period. An example sleep timeline for one participant-day, showing key sleep features, is provided in Figure S1 . Table 4 Description of passively derived sleep features Variable name Data modality used Unit of measurement Conceptual description Sleep Onset time Fitbit sleep Minutes past midnight The number of minutes past midnight when the PSP started (negative values indicate PSP onset before midnight) Sleep Offset Time Fitbit sleep Minutes past midnight The number of minutes past midnight when the participant woke up from the PSP. Wake After Sleep Onset (WASO) Fitbit sleep Minutes The sum of the durations of each detected awakening during the PSP. Number of Awakenings Fitbit sleep Integer The number awakenings during the PSP. Total Sleep Time (TST) Fitbit sleep Minutes Sum of all sleep stage durations during the PSP Proportion of Light, Deep, and REM sleep, Fitbit sleep Ratio The ratio of total duration of each sleep stage and the sum of all sleep stage durations. Sleep Preparation Period (SPP) pRMT, Fitbit sleep Minutes The time difference between the final awake marker and PSP onset. Latency to Arising Fitbit sleep, Fitbit steps Minutes The time difference between PSP offset and becoming active. Sleep Efficiency pRMT, Fitbit sleep Ratio The proportion of sleep between final awake marker and first activity of the following day. Analysis Analysis Tools Feasibility analyses were conducted using R Statistics. All other processing and analyses were conducted using Python (version 3.11.7) in Jupyter Notebook (version 6.5.4). Statistical analyses and data processing were performed using pandas (version 2.3.3), NumPy (version 1.26.4), and statsmodels (version 0.14.0), including linear mixed-effects models implemented via statsmodels.formula.api.mixedlm. Data visualisation was carried out using Matplotlib (version 3.8.4) and Seaborn (version 0.12.2). Machine-learning and clustering analyses utilised scikit-learn (version 1.4.2), including standardisation via StandardScaler, agglomerative clustering, and silhouette analysis, with knee-point detection implemented using kneed (version 0.8.5). Multiple-testing correction and proportion tests were performed using statsmodels.stats. All analyses were executed on a system running Ubuntu Linux (version 24.04) with 40 GB RAM and a 16-core CPU (model: AMD Ryzen 9 6900HS with Radeon Graphics). Objective 1: Feasibility evaluation Feasibility was assessed by calculating the proportion of participant-days with available and usable data across days 0–28 of enrolment. Data availability was calculated separately for each modality; aRMT, Fitbit, and pRMT data. For aRMT, we calculated number and % of days for which an SSQS rating was available for each participant. For Fitbit data, we examined wear time and availability of a PSP. To calculate Fitbit wear time, we first screened the heart rate data to identify non-wear periods characterised as samples with inter-record gaps greater than 120 seconds or with low physiological variability. Low physiological variability was defined as a rolling variance of heart rate < 1 computed over a 20-sample window. The remaining heart rate data were grouped into 1-minute bins, and any bin containing at least one heart rate data point was considered as a Fitbit-worn minute. The worn minutes aggregated on a calendar day were considered as total daily wear time, and minutes worn between 21:00 and 09:00 were considered “nighttime wear”. The number and % of days with an identifiable PSP also contributed to the feasibility assessment. Finally, for pRMT, we assessed the number and % of days for which pRMT data were available based on phone battery-level records, the presence of which indicates that the app is installed and functioning. For each participant, the mean (µ) and standard deviation (σ) of daily battery sample counts were computed. For a day to be classified as “pRMT data available”, the number of battery records for that day had to exceed the larger value between µ − 2σ and 1. The chosen threshold allowed the inclusion of days with at least some data, while excluding those with very low data availability. To calculate the duration of pRMT data availability, each “pRMT data available” date was divided into 10-minute time windows. Data was considered present in a window if at least one battery record was available in it. Number and % of days with both an available PSP and an associated SSQS score were calculated to indicate data usability for each participant. To be included in the final analysis a participant had to provide at least one day of usable data. Finally, we examined associations between feasibility metrics and participant characteristics. Due to non-normal feasibility data, we used Mann-Whitney U tests to assess associations with categorical variables (sex and autism diagnostic status) and Spearman correlations to assess associations with continuous variables (age, SRS-2, IQ, and tactile sensitivity). Objective 2: Group differences in sleep features and active reporting We employed Linear Mixed-Effects Models (LMEs) to explore group differences in our derived sleep features and self-reported sleep quality between autistic and nonautistic participants. We chose an LME framework because it allows for the inclusion of both fixed effects (e.g., group differences) and random effects (e.g., inter-individual variability) 55 , 56 . Each passive sleep feature and the active sleep score were used as dependent variables to estimate an LME model per feature. The group variable (autistic vs. nonautistic) was included as a fixed effect, allowing for estimation of population-level differences in the dependent variable between the two groups. A random intercept was specified for each participant to account for within-subject correlation due to repeated measures. Objective 3: Associations between passively derived sleep features and active reports Objective 3. (a) Associations between individual passive features and active reports We also employed LMEs to explore associations between our individual sleep features and corresponding SSQS scores. In each of the models, the SSQS ratings served as the dependent variable, and each passive sleep feature (e.g. TST, sleep efficiency, onset latency) was individually used as a fixed-effect predictor and within-subject repeated measures of active sleep score were used as the random-effect predictor. Separate models were estimated for autistic and nonautistic participants to allow for group-specific effect estimates of the associations. Objective 3. (b) Cluster Analysis We employed clustering analysis to explore the relationship between SSQS and passive sleep features among autistic and nonautistic participants. Agglomerative clustering was employed due to the modest dataset size. Agglomerative clustering is an unsupervised machine learning algorithm that detects underlying group structures through repeated, closest cluster pair merges based on a predefined similarity metric 57 . Based on a combination of Silhouette Scores 58 , within-Cluster Sum of Squares (WSS) values 59 , and Elbow plots we determined the optimal total number of clusters to be three (Table S6 and Figure S2). Using the determined clustering parameters (e.g. linkage method, number of clusters), clustering models were applied on passively derived sleep features. The final step involved in this analysis was to investigate differences between the three clusters for the passively derived sleep features. LME models were deployed using each feature individually to test for significant differences in values of the features in three different clusters. In each model, a single sleep feature was specified as the dependent variable, and cluster membership, derived from the unsupervised clustering procedure, was included as a categorical fixed-effect predictor. A random intercept was specified for each participant to account for within-subject correlation arising from repeated nightly measurements. Separate LME models were estimated for each sleep feature. Declarations Data Availability Statement The data that support the findings of this study are available, but restrictions apply. All participants were asked for their consent preferences regarding external data sharing (i.e., beyond the AIMS consortium). For those who indicated 'No' to external data sharing, their data are restricted and cannot be shared. For those who indicated 'Yes' to external data sharing, their coded and processed research data will be hosted via ELIXIR-LU. Those who wish to access AIMS data via ELIXIR-LU will need to submit a project proposal via the ELIXIR-LU platform: Data Catalogue - Home. Alternatively, those wishing to access RMT data from AIMS and the additional comparison sample may contact ES or NC with a reasonable request. Code Availability Statement The underlying code for this study is not publicly available but may be made available to qualified researchers on reasonable request from the corresponding author. Acknowledgements The authors would like to thank all the members of the AIMS-2-TRIALS A-Reps, the Autistica Insight Group, and the Cambridge Autism Research Database who contributed to the protocol design and the selection of devices. We also thank members of the CAMHS Digital Lab who provided feedback on implementation and analysis. The results leading to this publication have received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777394 for the project AIMS-2-TRIALS. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and AUTISM SPEAKS, Autistica, SFARI. The RADAR-base platform is funded by the Innovative Medicines Initiative 2 Joint Undertaking (grant 115902) for the project RADAR-CNS. This study is also funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at the South London and Maudsley Hospital (AC, AF, and NC). Wellcome Trust Grant number 308830/Z/23/Z (AC) and Grant number 316664/Z/24/Z (LT) has also funded this study. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Any views expressed are those of the authors and not necessarily those of the funders (including IHI-JU2 and NIHR) or the Department of Health and Social Care. Author Contributions AC and IY have equal contribution and are joint first authors. NC and ES have equal contribution in supervision and are joint supervisors. IY: Writing - Original Draft, Writing - Review & Editing, Project administration, Investigation, Methodology, Formal analysis, Data curation, Conceptualization, Visualization; AC: Formal analysis, Data curation, Methodology, Writing - Original Draft, Writing - Review & Editing, Investigation, Visualization, Conceptualization; CB: Investigation, Writing - Review & Editing; BO: Investigation, Methodology, Project administration, Writing - Review & Editing; MLL: Investigation, Writing - Review & Editing, Project administration; RH: Investigation, Methodology, Project administration, Writing - Review & Editing; LCM: Data curation, Writing - Review & Editing; NF: Data curation, Writing - Review & Editing; PC: Resources, Software, Writing - Review & Editing; HS: Resources, Software, Writing - Review & Editing; YR: Resources, Software, Writing - Review & Editing; ZR: Resources, Software, Writing - Review & Editing; LT: Project administration, Writing - Review & Editing, Conceptualization; EL: Conceptualization, Writing - Review & Editing, Supervision, Funding acquisition; JB: Conceptualization, Funding acquisition, Supervision, Writing - Review & Editing; DM: Conceptualization, Funding acquisition, Supervision, Writing - Review & Editing; AF: Conceptualization, Resources, Software, Methodology, Supervision, Writing - Review & Editing; RD: Conceptualization, Methodology, Resources, Software, Supervision, Writing - Review & Editing; NC: Conceptualization, Methodology, Project administration, Supervision, Writing - Review & Editing; ES: Conceptualization, Funding acquisition, Supervision, Writing - Review & Editing, Methodology, Project administration. 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Comput. Appl. Math. 20, 53–65 (1987). Thorndike, R. L. Who belongs in the family? Psychometrika 18, 267–276 (1953). Additional Declarations Competing interest reported. YR is the director of Onsentia Ltd. RD is the director of CogStack Ltd and Onsentia Ltd. AF has shares in Google, which acquired Fitbit. All other authors declare no conflicts of interest. Supplementary Files SupplementaryMaterialSleepstudy.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9381221","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":623920767,"identity":"e9f2f09f-305f-4e3a-a441-c6047f469602","order_by":0,"name":"Isabel Yorke","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Isabel","middleName":"","lastName":"Yorke","suffix":""},{"id":623920768,"identity":"acf7e87d-d588-4d0f-8d8a-f5c60de62b0a","order_by":1,"name":"Akash Roy Choudhury","email":"data:image/png;base64,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","orcid":"","institution":"King's College London","correspondingAuthor":true,"prefix":"","firstName":"Akash","middleName":"Roy","lastName":"Choudhury","suffix":""},{"id":623920769,"identity":"75de78c1-7980-437f-a6fb-95a69a088a4e","order_by":2,"name":"Charlotte Boatman","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Charlotte","middleName":"","lastName":"Boatman","suffix":""},{"id":623920772,"identity":"08123853-920c-4b6c-8cd1-cb2245a3d3ba","order_by":3,"name":"Bethany Oakley","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Bethany","middleName":"","lastName":"Oakley","suffix":""},{"id":623920773,"identity":"27ec6463-bab1-4c84-9607-87cd0d41df1f","order_by":4,"name":"Mei Lin Law","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Mei","middleName":"Lin","lastName":"Law","suffix":""},{"id":623920777,"identity":"c53bc940-8022-4807-a81b-69a6f11a24ef","order_by":5,"name":"Rosemary Holt","email":"","orcid":"","institution":"University of Cambridge","correspondingAuthor":false,"prefix":"","firstName":"Rosemary","middleName":"","lastName":"Holt","suffix":""},{"id":623920779,"identity":"5f991140-c06b-4c93-a269-15a48e17c54b","order_by":6,"name":"Laura Colomar Molla","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"Colomar","lastName":"Molla","suffix":""},{"id":623920781,"identity":"fe0e871c-72f1-4e57-956a-52e44f686269","order_by":7,"name":"Natalie J Forde","email":"","orcid":"","institution":"Radboud University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Natalie","middleName":"J","lastName":"Forde","suffix":""},{"id":623920782,"identity":"e85c5d84-7f8d-41fc-853a-4a2205e2c044","order_by":8,"name":"Pauline Conde","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Pauline","middleName":"","lastName":"Conde","suffix":""},{"id":623920784,"identity":"a72d255a-f7c1-4ea6-84aa-f1fe118b1e6b","order_by":9,"name":"Heet Sankesara","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Heet","middleName":"","lastName":"Sankesara","suffix":""},{"id":623920785,"identity":"21b5f511-b093-4051-a0ef-d76e9c74c356","order_by":10,"name":"Yatharth Ranjan","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Yatharth","middleName":"","lastName":"Ranjan","suffix":""},{"id":623920786,"identity":"073dd0eb-3828-4c00-93cf-5f6228f8d563","order_by":11,"name":"Zulqarnain Rashid","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Zulqarnain","middleName":"","lastName":"Rashid","suffix":""},{"id":623920787,"identity":"b2c21e58-372d-4fff-8efe-d21977844520","order_by":12,"name":"Laurence Telesia","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Laurence","middleName":"","lastName":"Telesia","suffix":""},{"id":623920789,"identity":"d9b1994d-5573-4d95-a9cd-09ca39998a96","order_by":13,"name":"Eva Loth","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Eva","middleName":"","lastName":"Loth","suffix":""},{"id":623920790,"identity":"88a12d1b-a7b0-4ed2-a23c-36826f42c974","order_by":14,"name":"Jan Buitelaar","email":"","orcid":"","institution":"Radboud University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"Buitelaar","suffix":""},{"id":623920793,"identity":"c688a353-b1f3-4bf7-8a11-811af57bc567","order_by":15,"name":"Declan Murphy","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Declan","middleName":"","lastName":"Murphy","suffix":""},{"id":623920796,"identity":"6479fd4a-bb1f-46b3-9a13-46fe6dde83fc","order_by":16,"name":"Amos Folarin","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Amos","middleName":"","lastName":"Folarin","suffix":""},{"id":623920798,"identity":"90e4e08e-aaf0-40c2-a3da-e6e36ce8295a","order_by":17,"name":"Richard Dobson","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Dobson","suffix":""},{"id":623920799,"identity":"b1cc57ab-69d4-4588-a5de-b12d1379affd","order_by":18,"name":"Nicholas Cummins","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Nicholas","middleName":"","lastName":"Cummins","suffix":""},{"id":623920800,"identity":"e007379d-1c46-4a6f-8508-2af274c408da","order_by":19,"name":"Emily Simonoff","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Emily","middleName":"","lastName":"Simonoff","suffix":""}],"badges":[],"createdAt":"2026-04-10 15:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9381221/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9381221/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107259082,"identity":"218d2b6e-1fc9-40d1-8412-0e2b8b73370f","added_by":"auto","created_at":"2026-04-19 12:46:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82380,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram to show numbers of eligible and excluded participants at each stage of assessment.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9381221/v1/53af79f27e919243527ea4ce.png"},{"id":107484913,"identity":"62073a59-d09c-43de-8ee3-b0be1e757ff9","added_by":"auto","created_at":"2026-04-22 02:33:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":371282,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe forest plot of effect size of the LME models to describe the relationship between passively derived sleep features and participant group. \u003c/strong\u003eNote. Each red point denotes the fixed effect coefficient (positive coefficients indicate higher values for nonautistic participants), and bars indicate the 95% confidence interval.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9381221/v1/eb3c2d302ff7368dd76e1b24.png"},{"id":107259084,"identity":"908c1d5b-b1f3-4489-b04b-82cce7469cdd","added_by":"auto","created_at":"2026-04-19 12:46:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":388231,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe forest plot of effect size of the LME models to describe the relationship between passively derived sleep features and SSQS ratings for participants.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9381221/v1/66d135c0f9a896bba3c8819b.png"},{"id":107259087,"identity":"093fd80e-bc27-42ec-95a2-2a87acc34485","added_by":"auto","created_at":"2026-04-19 12:46:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3300768,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBox plot of standardized values of active sleep scores and passive sleep parameters.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: descriptions below the parameters describe statistical differences between the clusters with = denoting no significant difference and \u0026lt;/\u0026gt; denoting significant differences. For each cluster where the boxes span the interquartile range from 25\u003csup\u003eth\u003c/sup\u003e to 75\u003csup\u003eth\u003c/sup\u003e percentile and the whiskers extend the values within 1.5 x IQR of the lower and upper quartiles and the points beyond the whiskers denote the outliers.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9381221/v1/2e9eb33929e43a91c428b372.png"},{"id":107259085,"identity":"4c98393f-0d68-43e5-9334-403d557a60c7","added_by":"auto","created_at":"2026-04-19 12:46:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":239644,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelevant components of the Mobile Measures Month\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9381221/v1/338ea322182196791dd49381.png"},{"id":107259086,"identity":"2053b778-cbe8-4db0-a4ff-2fa7ce31c1ef","added_by":"auto","created_at":"2026-04-19 12:46:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2342263,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA flow diagram of the feature extraction pipeline. Raw pRMT data are shown in blue, Fitbit data are shown in green, and final derived features are shown in pink.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9381221/v1/fae66ee3df51cb19f0d98de1.png"},{"id":107705004,"identity":"60738985-a0d8-4b8f-9a33-c0fe95f37feb","added_by":"auto","created_at":"2026-04-24 09:06:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5064021,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9381221/v1/3ce32b37-bae3-4bfd-a917-44c0e5c5350f.pdf"},{"id":107259081,"identity":"fb63c8d8-0ee4-46a1-b407-88f2becb526b","added_by":"auto","created_at":"2026-04-19 12:46:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":430139,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialSleepstudy.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9381221/v1/26f20edb61c8521aa9c5ed57.pdf"}],"financialInterests":"Competing interest reported. YR is the director of Onsentia Ltd. RD is the director of CogStack Ltd and Onsentia Ltd. AF has shares in Google, which acquired Fitbit. All other authors declare no conflicts of interest.","formattedTitle":"Feasibility and exploration of remote multimodal sleep measurement in autistic and nonautistic smartphone users","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAutism is a neurodevelopmental condition characterised by differences in social interaction and sensory processing, and presence of restricted and repetitive behaviours\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Most autistic people experience additional physical and mental health difficulties that affect daily life. Alongside commonly co-occurring conditions, such as anxiety, attention-deficit hyperactivity disorder, and epilepsy, sleep problems are especially prevalent. Sleep difficulties are reported in 71% of autistic children\u003csup\u003e2\u003c/sup\u003e and 79% of autistic adults\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, and diagnosed sleep disorders occur nearly twice as often in autistic versus nonautistic adults\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Although autistic children, adolescents, and adults show similarly high rates of sleep problems\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, sleep research in autistic adults is sparser\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Nevertheless, across age groups, findings from polysomnography, actigraphy, and sleep diaries converge on identifying alterations in initiation and continuity of sleep, as manifested in longer sleep onset latency (SOL), increased wake after sleep onset (WASO), reduced total sleep time (TST), and lower sleep efficiency (SE)\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Possible explanations for elevated sleep problems reported in autism include altered melatonin-related circadian rhythms\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, heightened autonomic arousal\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, sensory hyper- or hypo-sensitivity\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and co-occurring anxiety\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. As is the general population, sleep quality in autistic samples is associated with physical and mental health problems and daytime functioning\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and predicts later quality of life\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In the general population, successful treatment of sleep problems has been shown to lead to improved mental health\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, highlighting sleep as a clinically meaningful and modifiable target for intervention. Importantly, sleep is also identified as a research priority by autistic people themselves\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIntervention development for sleep difficulties in autism depends on both a deeper understanding of the underlying mechanisms and the rigorous evaluation of emerging treatments. At each of these stages, the use of precise, reliable, and valid sleep measurement methods is essential. However, measuring sleep poses specific challenges that may be amplified in the autistic population. Measures based on polysomnography (PSG), provide important objective data on sleep architecture and are generally considered the diagnostic gold standard for assessment of sleep disorders. However, applying these methods is expensive and may not be representative of everyday sleep. This is because PSG typically involves sleeping in an unfamiliar environment (e.g., the clinic, or lab) and wearing equipment that may cause discomfort and disturb sleep, particularly for autistic individuals with sensory differences\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Further, observation periods are often limited to one or two nights due to high cost\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlternative approaches to sleep measurement, such as actively reported sleep diaries and sleep quality scales provide accounts of sleep in everyday life but are burdensome to complete regularly. Resulting data are subject to recall difficulties and may incorporate measurement error and bias. For example, sleep diaries completed by caregivers (often in paediatric samples) have been shown to underestimate frequency and duration of awakenings and consequently overestimate TST and SE\u003csup\u003e21\u003c/sup\u003e. In adolescents and adults, caregivers may have less involvement in sleep and may not be able to provide accurate information. However, high rates of intellectual disability (ID) and difficulty in reporting on internal states for some autistic individuals\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e mean that self-report methods may not adequately capture information from a broad range of autistic individuals.\u003c/p\u003e \u003cp\u003eWearable devices that integrate accelerometer (movement) and photoplethysmography (PPG; used to estimate pulse rate) data\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e offer a relatively low-cost and unobtrusive means of capturing sleep data remotely. These characteristics enhance their feasibility for extended monitoring and their applicability in populations where reliable self- or proxy-reported sleep information is difficult to gather. Nevertheless, wearables yield indirect estimates of sleep states with varying degrees of validity when compared to PSG\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Typically, actigraphy data shows high sensitivity in detecting sleep but lower specificity in correctly identifying wakefulness during a rest period\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Due to inferring sleep periods from motion and pulse rate data, sedentary but wakeful behaviour may be mistaken for sleep\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, leading to errors in the estimation of SOL and WASO. This may be particularly problematic for autism sleep research, given that reported difficulties often centre around initiating and maintaining sleep\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Often, wearable-based methods also still rely on some active input from participants (either through pressing an event marker on the device or via sleep diary) to establish a bedtime and thus infer SOL\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. This may reintroduce memory burden and accuracy issues. For autistic people in particular, wearable devices may also be less well tolerated due to presence of sensory sensitivities\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSmartphones represent another promising tool for capturing sleep‑relevant information. Their increasingly ubiquitous use has led to widespread adoption in health research for both active self‑report and passive sensor‑based data collection\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Because smartphones are already embedded in daily routines, they offer practical advantages for sustained monitoring whilst minimising the need for habituation to new devices or behaviours. Additionally, as users often keep their phones nearby, even at night\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, sleep‑related signals can be derived from accelerometer data, light sensors, and app‑usage logs. Combined with wearable device data, such sources may indicate bedtime related events such as lights out and reduced activity as well as continued smartphone usage whilst in bed. This may address shortcomings of wearable-only protocols that may have difficulty establishing a bedtime marker without additional active reporting, and in distinguishing wakeful periods of low activity from sleep\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eData from smartphones, wearables, and similar devices are often collectively referred to as remote measurement technology (RMT). RMT encompasses active methods, in which participants enter information via a smartphone app, and passive methods, in which data are collected automatically from sensors such as accelerometers, PPG, or smartphone usage logs. Given its affordability, ubiquity, and capacity to capture real world data with minimal participant burden, there is growing interest in applying RMT in autism research, including for sleep\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. However, little is known about the feasibility of these methods for autistic adolescents and adults reporting on their own experiences, as opposed to parent- or caregiver-reported data in younger samples. This study was implemented as part of a wider project aiming to develop and evaluate an RMT system for capturing a range of clinically relevant measures, suitable for autistic adolescents and adults\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. For the current study, we focused on integrating both wearable and passive smartphone data to derive commonly investigated sleep features. We had three specific aims:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo evaluate the feasibility of these RMT methods and their combination for active and passive sleep capture among autistic and nonautistic participants who were already smartphone users, and to explore participant characteristics relating to feasibility metrics.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo explore group differences (autistic versus nonautistic) in passively derived sleep features and actively reported sleep quality. We expected that autistic participants would report lower sleep quality and that passive features commonly indicating sleep quality would be less favourable for autistic sleepers. Specifically, based on previous research, we expected to see evidence of longer SOL, shorter TST, a longer duration of WASO and lower SE among autistic participants.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo explore associations between passively derived sleep features and active daily sleep quality reporting. Here we had two sub aims:\u003c/p\u003e \u003cp\u003e \u003col style=\"list-style-type:lower-alpha;\"\u003e\u003cspan\u003e \u003cli\u003e \u003cp\u003eTo examine which individual passively derived sleep features predict next-day sleep quality ratings.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo explore whether nights may be grouped into meaningful clusters based on multiple passively derived sleep features, and whether these clusters relate to actively reported sleep quality.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eParticipant characteristics\u003c/h2\u003e\n\u003cp\u003eThirty-four autistic and 40 nonautistic participants enrolled in the study. After one nonautistic participant withdrew due to concerns about the pRMT app, this left a total of 73 participants for whom characteristics are described and feasibility assessed. Participants were of similar ages across groups (14-35 years; Table 1). There were significantly (\u003cem\u003ep\u003c/em\u003e=.002) more males in the autistic group and, as expected, the autistic group had a significantly (p=3.0\u0026times;10⁻8) higher self-reported autism questionnaire score (Social Responsiveness Scale-2). IQ was similar across groups and, while most participants had IQ in the typical range, one autistic and two nonautistic participants met study criteria for mild intellectual disability (IQ score of 50-74). Across both groups, participants were most commonly in full time work (32.4% of autistic and 30.8% of nonautistic participants) or education (44.1% of autistic and 48.7% of nonautistic participants). A small proportion worked part-time (11.8% of autistic and 5.1% of non autistic participants), and 8.8% of autistic participants were unemployed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Participant characteristics and feasibility metrics for all participants included in the feasibility analysis. \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eAutistic\u003c/p\u003e\n \u003cp\u003e(N=34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eNonautistic\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(N=39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eGroup differences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipant characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e[N if reduced sample] Mean (SD); range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e[N if reduced sample] Mean (SD); range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eSex (% male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e76.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e38.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026Chi;\u003csup\u003e2\u003c/sup\u003e=9.2 \u003cem\u003ep\u003c/em\u003e=.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e[33] 23.5 (6.5); 14.1 - 35.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e[37] 24.3 (5.2); 14.0 - 33.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e=0.63; \u003cem\u003ep\u003c/em\u003e=.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eSRS-2 total score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e[26] 83.2 (40.5); 14 - 164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e[30] 33 (23.9); 0 - 97\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e=-6.81; \u003cem\u003ep\u003c/em\u003e=3.0\u0026times;10⁻\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eIQ \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e[33] 111.7 (16.4); 71-139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e[13] 112.3 (20.8); 72- 144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e=0.1; \u003cem\u003ep\u003c/em\u003e=.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eOccupation (%)\u003c/p\u003e\n \u003cp\u003eFull time work\u003c/p\u003e\n \u003cp\u003ePart time work\u003c/p\u003e\n \u003cp\u003eFull time education\u003c/p\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003cp\u003eDo not wish to answer\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e32.4\u003c/p\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003cp\u003e44.1\u003c/p\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e30.8\u003c/p\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003cp\u003e48.7\u003c/p\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003cp\u003e12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eDescriptive only\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eOperating System\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e (% Android)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e91.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e87.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eDescriptive only\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeasibility metrics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMedian (IQR); range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eMedian (IQR); range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eFitbit wear time\u003c/p\u003e\n \u003cp\u003eHours\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e554.4 (353.6); 0 - 642\u003c/p\u003e\n \u003cp\u003e82.5 (52.6); 0 - 95.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e530.4 (123); 0 - 639.1\u003c/p\u003e\n \u003cp\u003e78.9 (18.3); 0 - 95.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cem\u003eU\u003c/em\u003e=-99\u003cem\u003e; p\u003c/em\u003e=0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eFitbit wear time (night)\u003c/p\u003e\n \u003cp\u003eHours\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e254.8 (226); 0 - 314\u003c/p\u003e\n \u003cp\u003e75.8 (67.3); 0 - 93.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e261.6 (50.4); 0 - 318.1\u003c/p\u003e\n \u003cp\u003e77.9 (15); 0 - 94.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cem\u003eU\u003c/em\u003e=-57; \u003cem\u003ep\u003c/em\u003e=.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eFitbit PSP available\u003c/p\u003e\n \u003cp\u003eDays\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e21 (26.8); 0 - 28\u003c/p\u003e\n \u003cp\u003e75 (95.7); 0 - 100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24 (8.0); 0 - 28\u003c/p\u003e\n \u003cp\u003e85.7 (28.6); 0 - 100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cem\u003eU\u003c/em\u003e=-5.5; \u003cem\u003ep\u003c/em\u003e=.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eSleep rating available\u003c/p\u003e\n \u003cp\u003eDays\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e26 (9.8); 0 - 28\u003c/p\u003e\n \u003cp\u003e92.9 (35.0); 0 - 100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24 (13.5); 0 - 28\u003c/p\u003e\n \u003cp\u003e85.7 (48.2); 0 \u0026ndash; 100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cem\u003eU\u003c/em\u003e=-131.5; p=.87\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003epRMT data availability \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eDays\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e[31]\u003c/p\u003e\n \u003cp\u003e22 (16); 1 - 28\u003c/p\u003e\n \u003cp\u003e78.6 (57.1); 3.6 - 100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e[34]\u003c/p\u003e\n \u003cp\u003e24 (10); 0 - 28\u003c/p\u003e\n \u003cp\u003e85.7 (35.7); 0 - 100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cem\u003eU\u003c/em\u003e=-75;\u003cem\u003e\u0026nbsp;p\u003c/em\u003e= 0.93\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eOverall eligible days\u003c/p\u003e\n \u003cp\u003eDays\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11.5 (25.8); 0 - 28\u003c/p\u003e\n \u003cp\u003e41.0 (92.1); 0 - 100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20 (15); 0 - 28\u003c/p\u003e\n \u003cp\u003e71.4 (53.6); 0 - 100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cem\u003eU\u003c/em\u003e=-15.0; p=.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes. PSP Primary Sleep Period; pRMT Passive Remote Monitoring Technology; IQR interquartile range; \u003csup\u003ea\u0026nbsp;\u003c/sup\u003eavailable for LEAP participants only; \u003csup\u003eb\u003c/sup\u003e original eligibility criteria required Android smartphones due to compatibility with RADAR pRMT app, which was later relaxed to accommodate more LEAP participants (see Table S1); \u003csup\u003ec\u003c/sup\u003e Assessed in Android users only.\u003c/p\u003e\n\u003ch2\u003eObjective 1: Feasibility findings\u003c/h2\u003e\n\u003cp\u003eDescriptive statistics for key feasibility variables are provided in Table 1. Mann-Whitney U tests revealed no significant differences across autistic and Nonautistic groups for any modality. For Fitbit wear time and PSP availability, median values for both groups lay upwards of 75% across total wear time, night wear time, and availability of a PSP. Similarly, median data availability for the RADAR pRMT app was over 75% for both groups. For SSQS ratings via the RADAR aRMT app, median values were above 85% for both groups.\u003c/p\u003e\n\u003cp\u003eAlthough feasibility metrics showed favourable median values and no significant group differences, a notable minority of participants contributed zero days of data eligible for the main sleep analysis (Figure\u0026nbsp;1). Among autistic participants, ten individuals (29.4%) had no eligible days; all exhibited low nighttime Fitbit wear (\u0026lt;25%) and zero or minimal PSP availability, with three additionally showing low SSQS completion rates (Table\u0026nbsp;S5). Among nonautistic participants, five individuals (12.5%) had no eligible days, with four completing no SSQS prompts and three showing low Fitbit night-time wear and minimal PSP availability. As a result, the proportion of analysable data days was substantially reduced, especially in the autistic group.\u003c/p\u003e\n\u003cp\u003eAcross all autistic participants, 447 of 952 possible data days (47%) were eligible for analysis (median 41% availability within participants). Across nonautistic participants, 645 of 1092 possible data days (59.1%) were eligible (median 71% availability within participants).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAssociations between participant characteristics and the key feasibility metrics show that sex was unrelated to any feasibility metrics (see Table 2). Older age was associated with greater aRMT SSQS availability (\u003cem\u003e\u0026rho;\u003c/em\u003e = .27; \u003cem\u003ep\u003c/em\u003e=.03). Higher autism characteristics score was associated with higher pRMT data availability (\u003cem\u003e\u0026rho;\u003c/em\u003e = .31; \u003cem\u003ep\u003c/em\u003e=.03). Higher IQ was associated with greater Fitbit primary sleep score availability (\u003cem\u003e\u0026rho;\u003c/em\u003e = .31; \u003cem\u003ep\u003c/em\u003e=.03). Higher levels of tactile sensitivity among autistic participants were associated with lower Fitbit PSP availability (\u003cem\u003e\u0026rho;\u003c/em\u003e = .44; \u003cem\u003ep\u003c/em\u003e=.03) and overall eligible data days (\u003cem\u003e\u0026rho;\u003c/em\u003e = .42; \u003cem\u003ep\u003c/em\u003e=.04). Our sample size and data characteristics did not support running a multiple regression analysis to determine if variance explained in Fitbit PSP availability was shared between IQ and tactile sensitivity. However, a Spearman correlation run between IQ and tactile sensitivity among autistic participants showed a modest association and was not significant (\u003cem\u003e\u0026rho;\u003c/em\u003e = .25; \u003cem\u003ep\u003c/em\u003e =.27).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Associations between participant characteristics and feasibility metrics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eFitbit primary sleep availability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eActive sleep quality ratings availability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003ePassive smartphone data availability \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eOverall days eligible for analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eSex\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[n] \u003cem\u003eU\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[73] 134.5 (.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[73] 221.0 (.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[65] 33.5 (.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[73] 177.0 (.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[n] \u003cem\u003e\u0026rho;\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[69] .19 (.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[69] .27 (.03)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[61] -.04 (.76)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[69] .21 (.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eSRS-2 total score\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[n] \u003cem\u003e\u0026rho;\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[56] -.19 (.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[56] -.02 (.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[49] .31 (.03)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[56] -.20 (.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eIQ \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e[n] \u003cem\u003e\u0026rho;\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[46] .31; .03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[46] .10 (.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[38] .03 (.88) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[46] 0.24 (.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eTactile sensitivity \u003csup\u003ea, c\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e[n] \u003cem\u003e\u0026rho;\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[23] .44 (.03)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[23] .42 (.04)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes. [n] association statistic (p-value) significant (bold). \u0026nbsp;SRS-2 Social Responsiveness Scale-2; \u003csup\u003ea\u003c/sup\u003e data on the LEAP sample only; \u003csup\u003eb\u003c/sup\u003e iOS users excluded (n=8); \u003csup\u003ec\u003c/sup\u003e higher scores indicate lower tactile sensitivity, assessed using the Short Sensory Profile for autistic participants only.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eObjective 2: Descriptive sleep data and group differences\u003c/h2\u003e\n\u003cp\u003eTable 3 provides descriptive data for the SSQS reported via the aRMT and all passively derived sleep features, and group differences assessed using LMEs. SSQS ratings were statistically higher (LME coefficient=0.45; \u003cem\u003ep\u003c/em\u003e=.01)\u003cem\u003e\u0026nbsp;\u003c/em\u003efor nonautistic participants (median 73) than autistic participants (median 68.00).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to Fitbit data, participants showed a wide range of sleep onset and offset times. Median sleep onset for both groups lay between midnight and 1am and median sleep offset time was between 8am and 9am for both groups. Median SPP (the interval between final awake marker and sleep onset) was nearly 40 minutes for autistic participants and over 60 minutes for nonautistic participants (LME coefficient = 0.44; \u003cem\u003ep\u003c/em\u003e=.005). TST was significantly (LME coefficient = 0.41; \u003cem\u003ep\u003c/em\u003e= .005) shorter for autistic participants (Median of 7.0 hours) than for nonautistic participants (median of 7.6 hours). Number of awakenings and duration of WASO was similar across groups with participants waking a median of 3 times per night with median overall duration around 25 minutes. Sleep efficiency was remarkably similar across groups (.84 and .83 for autistic and nonautistic groups, respectively). Relative proportions in each sleep stage also appeared consistent across groups, with light sleep accounting for around three fifths of time spent asleep, and deep and REM sleep accounting for one fifth each. Figure 2 shows effect sizes and 95% confidence intervals for group differences assessed using LME models. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: descriptive statistics of each participant group and their statistical differences\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFeature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAutistic group\u003c/p\u003e\n \u003cp\u003eMedian; IQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNonautistic group\u003c/p\u003e\n \u003cp\u003eMedian; IQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGroup differences\u003c/p\u003e\n \u003cp\u003eLME Coefficient (\u003cem\u003ep\u003c/em\u003e); 95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSQS Rating\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e68.00; 50.00-76.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e73.00; 62.00-81.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.447 (.011); 0.101, 0.793\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSleep Onset Time (mins after 00:00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.50; -52.25-127.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.50; -50.00-76.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.191 (0.344); -0.585, 0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSleep Offset Time (mins after 00:00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e467.00; 412.00-549.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e498.50; 420.00-570.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.103 (.569); -0.251, 0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep Preparation Period (mins)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e37.00; 11.00-65.50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e62.74; 23.50-70.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.444 (.005); 0.135, 0.754\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of Awakenings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.00; 2.00-4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.00; 2.00-5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.064 (0.647); -0.21, 0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWake After Sleep Onset (mins)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.00; 13.00-35.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.00; 14.50-39.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.112 (.400); -0.149, 0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLatency to Arising (mins)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.50; 1.00-14.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.00; 1.00-15.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.006 (.950); -0.185, 0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Sleep Time (mins)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e420.50; 369.25-468.75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e457.50; 399.00-514.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.408 (.005); 0.124, 0.692\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSleep Efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.84; 0.81-0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.83; 0.79-0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.176 (.215); -0.453, 0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProportion of Light Sleep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.60; 0.55-0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.63; 0.57-0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.260 (.084); -0.035, 0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProportion of REM Sleep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.22; 0.18-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.20; 0.16-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.212 (.178); -0.521, 0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProportion of Deep Sleep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.18; 0.14-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.17; 0.14-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.164 (.296); -0.47, 0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes. \u003csup\u003ea\u003c/sup\u003e nonautistic is the reference group\u003c/p\u003e\n\u003cp\u003eObjective 3: Correspondence between active and passive data\u003c/p\u003e\n\u003ch3\u003eObjective 3. (a) associations for individual sleep features\u003c/h3\u003e\n\u003cp\u003eFigure 3 demonstrates the forest plots of LME model estimates, illustrating the associations between passively derived sleep features and SSQS ratings in autistic and nonautistic participants. Here, p\u003csub\u003eaut,\u003c/sub\u003e p\u003csub\u003enaut,\u003c/sub\u003e \u0026beta;\u003csub\u003eaut\u003c/sub\u003e and \u0026beta;\u003csub\u003enaut\u003c/sub\u003e represent p-values and values effect sizes of autistic and nonautistic participants respectively. For both groups, greater sleep efficiency (p\u003csub\u003eaut\u003c/sub\u003e=.00014, \u0026beta;\u003csub\u003eaut\u003c/sub\u003e=\u0026nbsp;0.1792; p\u003csub\u003enaut\u003c/sub\u003e=\u0026nbsp;1.115e-08,\u0026nbsp;\u0026beta;\u003csub\u003enaut\u003c/sub\u003e=\u0026nbsp;0.1894), TST (p\u003csub\u003eaut\u003c/sub\u003e=\u0026nbsp;5.417e-08,\u0026nbsp;\u0026beta;\u003csub\u003eaut\u003c/sub\u003e=\u0026nbsp;0.2504; p\u003csub\u003enaut\u003c/sub\u003e=\u0026nbsp;4.731e-21,\u0026nbsp;\u0026beta;\u003csub\u003enaut\u003c/sub\u003e=\u0026nbsp;0.3130), proportion of REM sleep (p\u003csub\u003eaut\u003c/sub\u003e=.0014, \u0026beta;\u003csub\u003eaut\u003c/sub\u003e=\u0026nbsp;0.1529; p\u003csub\u003enaut\u003c/sub\u003e=\u0026nbsp;8.306e-05,\u0026nbsp;\u0026beta;\u003csub\u003enaut\u003c/sub\u003e=\u0026nbsp;0.1436) and sleep offset time (p\u003csub\u003eaut\u003c/sub\u003e=.0004, \u0026beta;\u003csub\u003eaut\u003c/sub\u003e=\u0026nbsp;0.1744;\u0026nbsp;p\u003csub\u003enaut\u003c/sub\u003e=\u0026nbsp;.0006,\u0026nbsp;\u0026beta;\u003csub\u003enaut\u003c/sub\u003e=\u0026nbsp;0.1412) were positively associated with higher SSQS ratings. For autistic participants, higher proportion of light sleep (p\u003csub\u003eaut\u003c/sub\u003e=\u0026nbsp;.0422,\u0026nbsp;\u0026beta;\u003csub\u003eaut\u003c/sub\u003e=\u0026nbsp;-0.0963), and for nonautistic participants, later sleep onset time (p\u003csub\u003enaut\u003c/sub\u003e=\u0026nbsp;1.468e-08,\u0026nbsp;\u0026beta;\u003csub\u003enaut\u003c/sub\u003e=\u0026nbsp;-0.2501) were significantly associated with lower SSQS rating. The detailed results of all the LME model estimates are given in Table S4.\u003cstrong\u003eFigure 3. The forest plot of effect size of the LME models to describe the relationship between passively derived sleep features and SSQS ratings for participants.\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003eObjective 3. (b) Clustering results\u003c/h3\u003e\n\u003cp\u003eResults of agglomerative clustering performed on the whole dataset are provided in Table S6. For each linkage, the silhouette score decreased with increasing number of clusters. The chosen model for the further clustering models was agglomerative clustering with complete linkage and three clusters based on silhouette Score (0.27), within cluster sum of square (13595.91) and visual inspection of an elbow plot (Figure S2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe number of participant days present in each cluster is provided in Table S7. We examined differences between participant-days belonging to each cluster on all passively derived features (used to derive the clusters) and active SSQS ratings (not involved in clustering). Clusters showed distinct profiles across passively derived features (see Figure 4). Relative to the other clusters, days assigned to cluster A showed features typically associated with high quality sleep (notably, high TST and sleep efficiency, high proportion of deep and REM sleep relative to light sleep, and low number and duration of awakenings). Days assigned to cluster B showed predominantly the opposite pattern, characterised by low TST, low sleep efficiency and low proportion of deep and REM sleep relative to light. Days assigned to cluster C showed certain features associated with poorer sleep quality (e.g., low proportion of deep and REM sleep relative to light sleep); however, TST and sleep efficiency were high. Correspondingly, nights assigned to clusters A (p= 1.299e-09, \u0026beta;= -0.3410) and C (p= 6.524e-04, \u0026beta;= -0.2606) showed significantly higher mean SSQS ratings than cluster B. Distribution of participant-days among clusters was similar across groups (see Table S5). Both autistic and nonautistic participants had a plurality of days assigned to cluster B (48.1% and 46.0% respectively), a substantial minority assigned to cluster A (42.7% and 36.4% respectively), and the smallest percentage assigned to cluster C (9.2% and 17.5% respectively).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eFeasibility\u003c/p\u003e \u003cp\u003eRemote multimodal sleep measurement was generally feasible across autistic and nonautistic participants, with high median adherence to Fitbit wear (around 80%), daily sleep ratings (more than 85%), and passive smartphone sensing (around 75%) in both groups. However, substantial heterogeneity meant that a notable minority (over one quarter of autistic and one eighth of nonautistic participants) provided little or no sleep data eligible for analysis. This resulted in a marked reduction in usable participant days, especially in the autistic group. Similar discrepancies between overall wear time and valid sleep data have been reported in recent feasibility work on actigraphy in autistic adolescents and adults, including reduced proportions of analysable data for the autistic group despite comparable adherence across groups\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur exploration of associations between feasibility metrics and participant characteristics may offer explanations for reduced usable data in the autistic group. Lower Fitbit PSP availability was associated with greater tactile sensitivity in autistic participants, suggesting sensory discomfort as a key barrier to sustained nighttime wearable use. Although our study is, to our knowledge, the first to quantify this relationship directly, the pattern aligns with participant feedback about Fitbit Inspire 3 usage in a similarly aged autistic sample\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and with prevailing concerns in the autism sleep literature regarding sensory related wearable intolerance\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. This is notable given that sensory sensitivity is also linked to insomnia severity\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, meaning individuals with the most severe sleep difficulties may be disproportionately excluded in wearable based studies.\u003c/p\u003e \u003cp\u003eThe observed relationships with IQ may indicate that the executive or practical demands of managing device wear also constrain feasibility. Remembering to replace a device after unavoidable breaks, e.g., showering or complying with work or school regulations, places organisational demands on participants. Although the Fitbit was chosen partly for its long battery life, reducing the need for frequent charging, even occasional breaks during a 28day period can disrupt data continuity. However, it is also important to consider that the observed IQ associations may reflect shared variance with tactile sensitivity rather than executive demands alone. Sensory sensitivities can vary with cognitive ability, and recent work suggests complex associations between cognitive level and sensory processing in autism\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOverall, our findings suggest that wrist worn devices can successfully capture sleep data for many autistic individuals, but a minority may remain systematically underrepresented in wearable based research. This underscores the importance of developing and evaluating alternative contactless approaches, such as mattress based or camera based monitoring, for which emerging evidence in autistic children\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e and autistic adolescents and adults\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e shows some promise.\u003c/p\u003e \u003cp\u003ePassive smartphone sensing was available for over threequarters of study days across both groups. Interestingly, higher data availability correlated with higher self-reported autism characteristics. Interpretation of this association is limited by the small, highly selected nature of our sample. However, one possibility is that participants reporting higher autism characteristics in this study represent a subgroup with relatively strong adaptive skills, while those with comparable autism characteristics but with greater support needs (who may face more challenges using smartphones) were underrepresented. Alternatively, higher adherence to this component may reflect autistic strengths. For example, \u0026ldquo;using technology,\u0026rdquo; \u0026ldquo;problem solving,\u0026rdquo; and \u0026ldquo;adherence to routines\u0026rdquo; feature among the most frequently reported autistic strengths\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, and may reasonably support adherence to this component. Elsewhere, a notable constraint on feasibility arose from operating system restrictions in our study: iOS devices were incompatible with the passive sensing platform, highlighting a broader challenge for scalable RMT given that app functionality is dependent on operating system\u0026ndash;level permissions.\u003c/p\u003e \u003cp\u003eGroup differences\u003c/p\u003e \u003cp\u003ePassively derived sleep measures revealed limited differences between autistic and nonautistic participants, and the overall pattern diverged from existing findings in the autism sleep literature. We observed shorter TST in autistic participants but did not replicate commonly reported reductions in sleep efficiency or increases in SOL and WASO\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The shorter TST aligns with meta-analytic findings in autistic youth\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e but contrasts with adult meta-analyses showing no group differences\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In contrast, subjective sleep quality was lower among autistic participants, aligning with meta-analytic evidence across ages\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and longstanding reports of elevated sleep difficulties in autism\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious research has most consistently characterised autistic sleep by difficulties initiating and maintaining sleep, reflected in longer SOL and lower sleep efficiency\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In our study, time spent attempting to fall sleep was, surprisingly, higher in the nonautistic group. However, we used a multimodal \u0026ldquo;final awake marker\u0026rdquo; derived from smartphone and wearable activity, which differs conceptually from conventional measures based on participant-reported bedtime or wearable-only inference. This difference in operational definition may contribute to divergence from prior findings. Alternatively, as discussed above, autistic participants with greater difficulty getting to sleep may have been underrepresented due to sensory-based intolerance to the Fitbit device.\u003c/p\u003e \u003cp\u003eThe naturalistic design of this study may help explain the attenuated pattern of group differences from passive data. Participants were monitored continuously across an extended period of everyday life, during which sleep is shaped by numerous environmental and contextual factors, including living with children, occupational routines and demands, and noise exposure\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, that likely affect individuals in both groups. Compared with laboratory-based or short-term observational studies, this real-world variability may make autism-related differences in passively measured sleep patterns harder to detect, whilst preserving differences in subjectively reported sleep experience. Given the exploratory nature of these analyses and the study\u0026rsquo;s primary focus on feasibility, these findings should be interpreted cautiously.\u003c/p\u003e \u003cp\u003eCorrespondence between active and passive measures\u003c/p\u003e \u003cp\u003eIn terms of correspondence between active and passive measures, patterns were similar across groups. For both groups, higher sleep ratings were assigned on participant-nights with higher sleep efficiency, longer total duration, greater proportion of REM sleep, and later sleep offset time. This is in line with findings from the general population, in which total sleep time and sleep efficiency are consistently related to reported sleep quality, though directions of association are inconsistent across studies\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Findings from the cluster analysis showed three distinct profiles across passively derived features that appeared to correspond meaningfully to differences in active sleep quality report and were similarly distributed across groups. This correspondence provides some indication as to the validity of our passive sleep data in capturing experienced sleep quality in a relatively low-burden manner.\u003c/p\u003e \u003cp\u003eStrengths and limitations\u003c/p\u003e \u003cp\u003eThis study contributes a novel multimodal remote sleep assessment system, responding to the need for development of scalable, low-burden measurement that is feasible for use in everyday life. Our 28-day measurement period is relatively long compared to other studies investigating sleep in autistic adolescents and adults; thus, we provide important data on sustained adherence to both wearable and active reporting components. Secondly, our implementation within a well characterised sample has allowed us to systematically investigate correlates of feasibility that may guide future studies. Finally, we contribute a novel approach to combining two sources of passive data (wearable and smartphone data), with resulting data corresponding meaningfully to actively reported sleep quality.\u003c/p\u003e \u003cp\u003eHowever, there are several key limitations to note. First, enrolment in this study required participants to be independent smartphone users who were happy to attempt to complete daily questionnaires, to wear the Fitbit, and to allow access to smartphone data. This is likely to exclude important groups, particularly from within the autistic population, e.g., those with lower functional ability, and more significant sensory sensitivities (corroborated by our own findings that tactile sensitivity reduced availability of useable data), as well as those who are more concerned regarding data privacy issues.\u003c/p\u003e \u003cp\u003eRegarding device selection, the Fitbit appeared to be broadly acceptable for everyday use in this population, having been chosen with input from autistic community representatives. However, resulting data are processed via proprietary algorithms, reducing insight into validity and flexibility in usage. Including validity assessment against PSG or other reference standard for sleep architecture was beyond the scope of the current study, therefore, our findings drawing on Fitbit data must be interpreted with caution.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDespite limitations, this study lays important groundwork for developing meaningful remote, passive markers in the autistic population that may be used to characterise sleep profiles, problems (types of which may vary for different subgroups), understand underlying mechanisms, and (ultimately) develop interventions to improve sleep. Our findings may be used to tailor future wearable or nearable technology to enhance tolerance for a wider proportion of the autistic population.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eOverview\u003c/p\u003e\u003cp\u003eData were collected as part of a 28-day Mobile Measures Month (MMM) RMT study\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e of the AIMS-2-TRIALS research programme\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The MMM involved participants wearing a Fitbit Inspire 2 or 3 wristband throughout the 28-day collection period. From this, we remotely collected the Fitbit-derived digital sleep and activity measures (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Additionally, participants installed two data-collection smartphone apps supported by the RADAR-base platform\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The apps included a passive data collection (pRMT) app to provide sensor data and an active reporting (aRMT) app to collect sleep quality self-reports (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). We combined the Fitbit and phone sensor data to derive passive sleep measures. The full MMM protocol details are available elsewhere\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eParticipants\u003c/p\u003e\u003cp\u003eThis study recruited participants between March 2022 and November 2024 from two sources: autistic and nonautistic participants enrolled in the third assessment timepoint of the AIMS Longitudinal European Autism Project (LEAP-3) and an additional sample of nonautistic volunteers recruited at King’s College London (KCL).\u003c/p\u003e\u003cp\u003eLEAP is a well-characterised longitudinal cohort of autistic and nonautistic individuals with IQ ≥ 50\u003csup\u003e43,44\u003c/sup\u003e. Autistic participants required a clinical diagnosis (DSM-IV/ICD-10 or DSM-5), and nonautistic participants had no reported psychiatric disorders (see OSF LEAP Protocol). In the present study, only participants recruited at UK sites, KCL and University of Cambridge, were eligible to take part due to app translation constraints. At LEAP-3, 127 autistic and 51 nonautistic participants returned to participate at UK sites. The additional nonautistic sample recruited at KCL (N = 26) included only individuals reporting no known or suspected neurodevelopmental, neurological, mental health, or sleep conditions. For the MMM, all participants were required to be independent smartphone users aged 12 or older (see Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e for full eligibility criteria across recruitment sources).\u003c/p\u003e\u003cp\u003eEthics declaration\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003c/p\u003e\u003cp\u003e was obtained for the LEAP study at KCL and UCAM from the London-Central and Queen Square Health Research Authority Research Ethics Committee (13/LO/1156). For the comparison sample, approval was granted by the KCL Research Ethics Committee (LRS/DP-23/24-42359). This study was conducted in accordance with the principles expressed in the Declaration of Helsinki. Written informed consent was obtained from participants using the Qualtrics e-platform or in person, depending on how the study information session was conducted for each participant.\u003c/p\u003e\u003cp\u003eProcedure\u003c/p\u003e\u003cp\u003eConsenting participants were sent a Fitbit device and undertook an enrolment video call with a researcher to set up the Fitbit and smartphone apps. Remote data were then collected for 28 days.\u003c/p\u003e\u003cp\u003eData collection\u003c/p\u003e\u003cp\u003eWearable data\u003c/p\u003e\u003cp\u003eWe used sleep stage and step count data from Fitbit Inspire 2 (LEAP participants) and Inspire 3 devices (additional nonautistic participants; see Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e), selected in consultation with autistic community representatives. These commercial fitness trackers measure pulse rate using PPG and detect motion via an accelerometer. Validation studies of recent-generation Fitbit models have reported high sensitivity but modest specificity for distinguishing sleep from wakefulness, and a wide range in sleep-stage identification accuracy\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Low measurement errors have been reported when validating Fitbit’s step count measurement\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePassive smartphone data\u003c/p\u003e\u003cp\u003eThe pRMT app was implemented through the RADAR-base platform\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. It collects a range of Android smartphone sensor data. In this study, phone motion and usage data were used as markers of wakefulness, light level data were used to detect “lights off” events, and battery level data were used for feasibility metrics to establish installation and continued functioning of the app.\u003c/p\u003e\u003cp\u003eActive smartphone data\u003c/p\u003e\u003cp\u003eUsing the RADAR-base aRMT app, participants were prompted daily at 8:30am to complete an adapted Single-item Sleep Quality Scale (SSQS)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. The SSQS is a simple measure of overall sleep quality, usually rated on a ten-point scale. It shows a strong association with widely used questionnaire-based sleep assessments (e.g., Pittsburgh Sleep Quality Index) and has been shown to differentiate normal, borderline, and problem sleepers in a study of depressed patients\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. In this study, participants rated the previous night’s sleep quality on a 0–100 visual analogue scale using a movable slider, aligning the SSQS with the response format used for other measures implemented in the wider study using the aRMT.\u003c/p\u003e\u003cp\u003eOther measures\u003c/p\u003e\u003cp\u003eInformation on participant characteristics was collected via questionnaires and in-person assessments. These measures were used to characterise the sample and to explore associations with key feasibility metrics outlined below. For all participants, available demographic data included age, sex and employment or education status. Self-ratings on autism characteristics were collected via the Social Responsiveness Scale-2\u003csup\u003e49\u003c/sup\u003e, a validated and widely used questionnaire measure quantifying autism-related behaviours. For the LEAP sample only, we also incorporated IQ data collected at LEAP3 using the Wechsler Abbreviated Scale of Intelligence–Second Edition\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. UK-based LEAP-3 assessment estimated full scale IQ using the two-subtest form, comprising Vocabulary and Matrix Reasoning. Parent‑reported Short Sensory Profile\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e (SSP) data were available for autistic LEAP participants and for nonautistic LEAP participants under 18 years of age. The SSP is a validated parent‑ or caregiver‑reported measure derived from the Sensory Profile\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. In this study, we focused on the tactile sensitivity subscale to assess sensitivity to touch that might relate to tolerance of wearing the Fitbit. This relationship was examined in autistic participants only, given our specific hypotheses about wearable tolerance in autism and the absence of comparable data for nonautistic adults.\u003c/p\u003e\u003cp\u003eFeature extraction\u003c/p\u003e\u003cp\u003eUsing passive RMT data from Fitbit and the pRMT smartphone app, we extracted 11 sleep architecture features (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). These were derived using a novel methodology that combines Fitbit sleep stage data with Fitbit step count and pRMT smartphone data (full pipeline description presented in Table S3). Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the feature extraction pipeline.\u003c/p\u003e\u003cp\u003eWe used Fitbit sleep staging data to identify a \u003cem\u003ePrimary Sleep Period\u003c/em\u003e (PSP) for each day of the study. We define PSP as the longest continuous sleep period (broken by less than 180-minute wake periods) among all sleep periods with wake-up on the same calendar date\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. To ensure that anomalously short or long sleep periods were excluded from primary analyses, we operationally defined the PSP as the longest daily sleep episode lasting between 2 and 12 hours\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Given the known prevalence of atypical sleep patterns in the autistic population, we applied a relatively broad criterion to balance sensitivity to genuinely unusual sleep with the need to exclude data likely reflecting device malfunction or other artefacts. \u003cem\u003eSleep Onset Time\u003c/em\u003e, \u003cem\u003eSleep Offset Time\u003c/em\u003e, \u003cem\u003eWake After Sleep Onset\u003c/em\u003e (WASO) duration, \u003cem\u003eNumber of Awakenings\u003c/em\u003e, \u003cem\u003eTotal Sleep Time\u003c/em\u003e (TST) and proportion of PSP spent in \u003cem\u003eLight\u003c/em\u003e, \u003cem\u003eDeep\u003c/em\u003e, and \u003cem\u003eREM\u003c/em\u003e sleep were also calculated directly from Fitbit sleep staging data.\u003c/p\u003e\u003cp\u003eWe also derived a novel feature, the Sleep Preparation Period (SPP), defined as the interval between sleep onset and the final awake marker occurring within the two hours preceding estimated sleep onset. This construct differs from the more conventional SOL, which is typically calculated from reported bedtime\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e or inferred solely from wearable data, approaches whose limitations are outlined in the introduction. We estimated SPP using a combination of four ‘awake' markers from Fitbit step count and pRMT data. These were (A) phone pick-up (smartphone motion data), (B) “lights off” (smartphone light meter data), (C) active phone usage (app usage and screen state event logs), and (D) final daily steps (Fitbit data). Precise derivations for these events are provided in Table S2.\u003c/p\u003e\u003cp\u003eWe also derived \u003cem\u003eLatency to Arising\u003c/em\u003e, which we calculated as the interval between estimated sleep offset and first activity of the day according to Fitbit step count. Finally, we calculated \u003cem\u003eSleep Efficiency\u003c/em\u003e as TST divided by total time between the final awake marker and the first activity of the following wake period. An example sleep timeline for one participant-day, showing key sleep features, is provided in Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab4\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of passively derived sleep features\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eVariable name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eData modality used\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eUnit of measurement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eConceptual description\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSleep Onset time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFitbit sleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMinutes past midnight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eThe number of minutes past midnight when the PSP started (negative values indicate PSP onset before midnight)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSleep Offset Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFitbit sleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMinutes past midnight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eThe number of minutes past midnight when the participant woke up from the PSP.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWake After Sleep Onset (WASO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFitbit sleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMinutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eThe sum of the durations of each detected awakening during the PSP.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNumber of Awakenings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFitbit sleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eInteger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eThe number awakenings during the PSP.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTotal Sleep Time (TST)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFitbit sleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMinutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSum of all sleep stage durations during the PSP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eProportion of Light, Deep, and REM sleep,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFitbit sleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eRatio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eThe ratio of total duration of each sleep stage and the sum of all sleep stage durations.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSleep Preparation Period (SPP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003epRMT,\u003c/p\u003e \u003cp\u003eFitbit sleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMinutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eThe time difference between the final awake marker and PSP onset.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLatency to Arising\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFitbit sleep,\u003c/p\u003e \u003cp\u003eFitbit steps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMinutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eThe time difference between PSP offset and becoming active.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSleep Efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003epRMT,\u003c/p\u003e \u003cp\u003eFitbit sleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eRatio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eThe proportion of sleep between final awake marker and first activity of the following day.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAnalysis\u003c/p\u003e\u003cp\u003eAnalysis Tools\u003c/p\u003e\u003cp\u003eFeasibility analyses were conducted using R Statistics. All other processing and analyses were conducted using Python (version 3.11.7) in Jupyter Notebook (version 6.5.4). Statistical analyses and data processing were performed using pandas (version 2.3.3), NumPy (version 1.26.4), and statsmodels (version 0.14.0), including linear mixed-effects models implemented via statsmodels.formula.api.mixedlm. Data visualisation was carried out using Matplotlib (version 3.8.4) and Seaborn (version 0.12.2). Machine-learning and clustering analyses utilised scikit-learn (version 1.4.2), including standardisation via StandardScaler, agglomerative clustering, and silhouette analysis, with knee-point detection implemented using kneed (version 0.8.5). Multiple-testing correction and proportion tests were performed using statsmodels.stats.\u003c/p\u003e\u003cp\u003eAll analyses were executed on a system running Ubuntu Linux (version 24.04) with 40 GB RAM and a 16-core CPU (model: AMD Ryzen 9 6900HS with Radeon Graphics).\u003c/p\u003e\u003cp\u003eObjective 1: Feasibility evaluation\u003c/p\u003e\u003cp\u003eFeasibility was assessed by calculating the proportion of participant-days with available and usable data across days 0–28 of enrolment. Data availability was calculated separately for each modality; aRMT, Fitbit, and pRMT data. For aRMT, we calculated number and % of days for which an SSQS rating was available for each participant. For Fitbit data, we examined wear time and availability of a PSP.\u003c/p\u003e\u003cp\u003eTo calculate Fitbit wear time, we first screened the heart rate data to identify non-wear periods characterised as samples with inter-record gaps greater than 120 seconds or with low physiological variability. Low physiological variability was defined as a rolling variance of heart rate \u0026lt; 1 computed over a 20-sample window. The remaining heart rate data were grouped into 1-minute bins, and any bin containing at least one heart rate data point was considered as a Fitbit-worn minute. The worn minutes aggregated on a calendar day were considered as total daily wear time, and minutes worn between 21:00 and 09:00 were considered “nighttime wear”. The number and % of days with an identifiable PSP also contributed to the feasibility assessment.\u003c/p\u003e\u003cp\u003eFinally, for pRMT, we assessed the number and % of days for which pRMT data were available based on phone battery-level records, the presence of which indicates that the app is installed and functioning. For each participant, the mean (µ) and standard deviation (σ) of daily battery sample counts were computed. For a day to be classified as “pRMT data available”, the number of battery records for that day had to exceed the larger value between µ − 2σ and 1. The chosen threshold allowed the inclusion of days with at least some data, while excluding those with very low data availability. To calculate the duration of pRMT data availability, each “pRMT data available” date was divided into 10-minute time windows. Data was considered present in a window if at least one battery record was available in it.\u003c/p\u003e\u003cp\u003eNumber and % of days with both an available PSP and an associated SSQS score were calculated to indicate data usability for each participant. To be included in the final analysis a participant had to provide at least one day of usable data.\u003c/p\u003e\u003cp\u003eFinally, we examined associations between feasibility metrics and participant characteristics. Due to non-normal feasibility data, we used Mann-Whitney U tests to assess associations with categorical variables (sex and autism diagnostic status) and Spearman correlations to assess associations with continuous variables (age, SRS-2, IQ, and tactile sensitivity).\u003c/p\u003e\u003cp\u003eObjective 2: Group differences in sleep features and active reporting\u003c/p\u003e\u003cp\u003eWe employed Linear Mixed-Effects Models (LMEs) to explore group differences in our derived sleep features and self-reported sleep quality between autistic and nonautistic participants. We chose an LME framework because it allows for the inclusion of both fixed effects (e.g., group differences) and random effects (e.g., inter-individual variability)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Each passive sleep feature and the active sleep score were used as dependent variables to estimate an LME model per feature. The group variable (autistic vs. nonautistic) was included as a fixed effect, allowing for estimation of population-level differences in the dependent variable between the two groups. A random intercept was specified for each participant to account for within-subject correlation due to repeated measures.\u003c/p\u003e\u003cp\u003eObjective 3: Associations between passively derived sleep features and active reports\u003c/p\u003e\u003ch3\u003eObjective 3. (a) Associations between individual passive features and active reports\u003c/h3\u003e\u003cp\u003eWe also employed LMEs to explore associations between our individual sleep features and corresponding SSQS scores. In each of the models, the SSQS ratings served as the dependent variable, and each passive sleep feature (e.g. TST, sleep efficiency, onset latency) was individually used as a fixed-effect predictor and within-subject repeated measures of active sleep score were used as the random-effect predictor. Separate models were estimated for autistic and nonautistic participants to allow for group-specific effect estimates of the associations.\u003c/p\u003e\u003ch3\u003eObjective 3. (b) Cluster Analysis\u003c/h3\u003e\u003cp\u003eWe employed clustering analysis to explore the relationship between SSQS and passive sleep features among autistic and nonautistic participants.\u003c/p\u003e\u003cp\u003eAgglomerative clustering was employed due to the modest dataset size. Agglomerative clustering is an unsupervised machine learning algorithm that detects underlying group structures through repeated, closest cluster pair merges based on a predefined similarity metric\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Based on a combination of Silhouette Scores\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, within-Cluster Sum of Squares (WSS) values\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, and Elbow plots we determined the optimal total number of clusters to be three (Table S6 and Figure S2). Using the determined clustering parameters (e.g. linkage method, number of clusters), clustering models were applied on passively derived sleep features. The final step involved in this analysis was to investigate differences between the three clusters for the passively derived sleep features. LME models were deployed using each feature individually to test for significant differences in values of the features in three different clusters. In each model, a single sleep feature was specified as the dependent variable, and cluster membership, derived from the unsupervised clustering procedure, was included as a categorical fixed-effect predictor. A random intercept was specified for each participant to account for within-subject correlation arising from repeated nightly measurements. Separate LME models were estimated for each sleep feature.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eData Availability Statement\u003c/h3\u003e\n\u003cp\u003eThe data that support the findings of this study are available, but restrictions apply. All participants were asked for their consent preferences regarding external data sharing (i.e., beyond the AIMS consortium). For those who indicated \u0026apos;No\u0026apos; to external data sharing, their data are restricted and cannot be shared. For those who indicated \u0026apos;Yes\u0026apos; to external data sharing, their coded and processed research data will be hosted via ELIXIR-LU. Those who wish to access AIMS data via ELIXIR-LU will need to submit a project proposal via the ELIXIR-LU platform: Data Catalogue - Home. Alternatively, those wishing to access RMT data from AIMS and the additional comparison sample may contact ES or NC with a reasonable request.\u003c/p\u003e\n\u003ch3\u003eCode Availability Statement\u003c/h3\u003e\n\u003cp\u003eThe underlying code for this study is not publicly available but may be made available to qualified researchers on reasonable request from the corresponding author.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors would like to thank all the members of the AIMS-2-TRIALS A-Reps, the Autistica Insight Group, and the Cambridge Autism Research Database who contributed to the protocol design and the selection of devices. We also thank members of the CAMHS Digital Lab who provided feedback on implementation and analysis.\u003c/p\u003e\n\u003cp\u003eThe results leading to this publication have received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777394 for the project AIMS-2-TRIALS. This Joint Undertaking receives support from the European Union\u0026apos;s Horizon 2020 research and innovation programme and EFPIA and AUTISM SPEAKS, Autistica, SFARI.\u003c/p\u003e\n\u003cp\u003eThe RADAR-base platform is funded by the Innovative Medicines Initiative 2 Joint Undertaking (grant 115902) for the project RADAR-CNS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study is also funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at the South London and Maudsley Hospital (AC, AF, and NC).\u003c/p\u003e\n\u003cp\u003eWellcome Trust Grant number 308830/Z/23/Z (AC) and Grant number 316664/Z/24/Z (LT) has also funded this study.\u003c/p\u003e\n\u003cp\u003eThe funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Any views expressed are those of the authors and not necessarily those of the funders (including IHI-JU2 and NIHR) or the Department of Health and Social Care.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eAC and IY have equal contribution and are joint first authors.\u0026nbsp;NC and ES have equal contribution in supervision and are joint supervisors.\u0026nbsp;IY: Writing - Original Draft, Writing - Review \u0026amp; Editing, Project administration, Investigation, Methodology, Formal analysis, Data curation, Conceptualization, Visualization; AC: Formal analysis, Data curation, Methodology, Writing - Original Draft, Writing - Review \u0026amp; Editing, Investigation, Visualization, Conceptualization; CB: Investigation, Writing - Review \u0026amp; Editing; BO: Investigation, Methodology, Project administration, Writing - Review \u0026amp; Editing; MLL: Investigation, Writing - Review \u0026amp; Editing, Project administration; \u0026nbsp;RH: Investigation, Methodology, Project administration, Writing - Review \u0026amp; Editing; LCM: Data curation, Writing - Review \u0026amp; Editing; NF: Data curation, Writing - Review \u0026amp; Editing; PC: Resources, Software, Writing - Review \u0026amp; Editing; HS: Resources, Software, Writing - Review \u0026amp; Editing; YR: Resources, Software, Writing - Review \u0026amp; Editing; ZR: Resources, Software, Writing - Review \u0026amp; Editing; \u0026nbsp;LT: Project administration, Writing - Review \u0026amp; Editing, Conceptualization; EL: Conceptualization, Writing - Review \u0026amp; Editing, Supervision, Funding acquisition; JB: Conceptualization, Funding acquisition, Supervision, Writing - Review \u0026amp; Editing; DM: Conceptualization, Funding acquisition, Supervision, Writing - Review \u0026amp; Editing; AF: Conceptualization, Resources, Software, Methodology, Supervision, Writing - Review \u0026amp; Editing; \u0026nbsp;RD: Conceptualization, Methodology, Resources, Software, Supervision, Writing - Review \u0026amp; Editing; NC: Conceptualization, Methodology, Project administration, Supervision, Writing - Review \u0026amp; Editing; ES: Conceptualization, Funding acquisition, Supervision, Writing - Review \u0026amp; Editing, Methodology, Project administration. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eYR is the director of Onsentia Ltd. RD is the director of CogStack Ltd and Onsentia Ltd. AF has shares in Google, which acquired Fitbit. All other authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmerican Psychiatric Association. \u003cem\u003eDiagnostic and Statistical Manual of Mental Disorders (DSM-5)\u003c/em\u003e. (American Psychiatric Publishing, 2013). doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1176/appi.books.9780890425596.744053\u003c/span\u003e\u003cspan address=\"10.1176/appi.books.9780890425596.744053\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalow, B. 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Who belongs in the family? \u003cem\u003ePsychometrika\u003c/em\u003e 18, 267\u0026ndash;276 (1953).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9381221/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9381221/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSleep difficulties represent a health priority for autistic adults, yet scalable real-world sleep measurement tools remain limited. Remote measurement technologies (RMT), including wearable and smartphone data, offer low‑burden approaches but feasibility in autistic populations is unclear. We evaluated a 28-day protocol combining passive Fitbit sleep staging and smartphone sensor data, and active daily sleep quality ratings in autistic (n\u0026thinsp;=\u0026thinsp;34) and nonautistic (n\u0026thinsp;=\u0026thinsp;39) participants (14\u0026ndash;35 years). Median data availability was over 70% across modalities. However, usable data (days with valid sleep period and quality rating) were lower (\u0026lt;\u0026thinsp;60%), particularly for autistic participants, whose tactile sensitivity associated with reduced usable Fitbit data. Autistic participants reported lower sleep quality; however, few differences in passively derived sleep features emerged. Several features (e.g., sleep efficiency, duration, REM proportion) were related to subjective sleep quality, as were passively-derived sleep profile clusters. This study provides foundational work for developing RMT suitable for sleep measurement in autistic populations.\u003c/p\u003e","manuscriptTitle":"Feasibility and exploration of remote multimodal sleep measurement in autistic and nonautistic smartphone users","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-19 12:45:57","doi":"10.21203/rs.3.rs-9381221/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8966526f-23e0-46b2-9d02-6a81f154dd8f","owner":[],"postedDate":"April 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66401658,"name":"Health sciences/Health care"},{"id":66401659,"name":"Health sciences/Medical research"},{"id":66401660,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-04-20T13:57:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-19 12:45:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9381221","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9381221","identity":"rs-9381221","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}