A summary of participant characteristics is presented in Table 2. The participant sample size varied across studies (10-376 people) and decreased over longitudinal measurement time points due to loss to follow-up and progression of the disease. However, it was often unclear (or not described) how many participants were included at each measurement point. Overall, there were more male participants (523/762, 68.6%) in the MND population compared to female (239/762, 31.4%). The studies reported age as either mean (7/12, 58%) or median (2/12, 17%). The overall mean age of participants in the studies reporting the mean was 59.7 years, while the median was 58.8 years. In total, 3 (30%) studies [21,26,28] included healthy controls, but the sample size was much smaller (25-58 individuals) and was not age or sex matched to the clinical population with median age of 51 years and had more female (61/113, 54%) participants compared to male (52/113, 46%) participants. The reported means and medians of the baseline ALSFRS-R scores ranged from 31.4 to 41.6 points. MND subtype or ALS phenotype at onset were reported in 7 (58%) studies. The most common subtype of MND was ALS, which represented 50% (5/10 participants) [30], 93% (39/42 participants) [24], 93% (87/94 participants) [26] and 97% (94/97 participants) [29] of the study’s baseline population, while the most common phenotype at onset was upper limb, which represented 60% (15/25) of the baseline population [20]. Of the 7 (70%) studies that reported symptom duration at baseline, 2 (29%) predominantly included those who had symptoms for ≤18 months, although it should be noted these 2 (29%) reports used the same dataset [20,22]. In the other 5 (5/7, 71%) studies, participants had predominantly experienced symptoms for >20 months [24-26,28,29].

Follow-up refers to the overall duration of monitoring completed in a study. Measurement frequency refers to how often participants were invited to wear or use a device (eg, every 3 months), and the measurement duration refers to the period over which measures were recorded on each device deployment (examples are provided in Table 3). The participant follow-up ranged between 12 weeks and 24 months and the measurement frequency was mostly every 1 to 4 months (Table 3). The type of measurement was either periodic (7/12, 58%) or continuous for the duration of the follow-up (3/12, 25%). When the type of measurement was periodic, the duration of each measurement ranged from 1 to 8 consecutive days, with the most common choice being 7 days (3/12, 25%).

Most studies (8/12, 67%) used commercially available triaxial accelerometers (Table 3). Only 1 (8%) study used a biaxial accelerometer and 1 (8%) study used the participant’s personal smartphone (GPS and triaxial accelerometer). Most (8/12, 67%) studies reported the sampling frequency (Hz) of the device, which ranged from 10 to 128 Hz; however, there was little justification for the choice. The most used frequency was 30 Hz (4/12, 33%). The device wear location varied and included wrist, chest, hip, and ankle. Most studies mounted the device in one place (6/12, 50%), while some studies (n=2, 16%) used 2 cohorts with different wear locations. In total, 2 (16%) studies mounted the devices to several locations, with Gupta et al [28] simultaneously comparing 4 devices (one on each ankle and wrist) per participant and Beswick et al [30] comparing 2 devices (right wrist and right ankle) per participant (Table 3).

All studies used clinician or self-administered ALSFRS-R to track disease progression and used it as a correlation point when assessing the validity of the physical behavior end points. Either a total score or subdomain scores of ALSFRS-R were used (gross motor, fine motor, bulbar, and respiratory). In total, 6 (50%) studies included other outcome measures, such as additional questionnaires (Hospital Anxiety and Depression Scale, Rasch-built Overall ALS Disability Scale, and study specific questionnaires; 4/12, 33%), respiratory data (2/12, 17%), cardiac data (1/12, 8%), speech data (2/12, 17%), muscle strength (1/12, 8%), mobility tests (1/12, 8%), and survival status (1/12, 8%; Table 3). Additional outcome measures were predominantly used as stand-alone measures, although their relationship with the physical behavior end points was assessed in 2 (17%) studies. van Unnik et al [29] found that participants with a lower vertical movement index (Table 4) also experienced a significantly lower probability of survival compared to participants who had a higher vertical movement index during follow-up. Beswick et al [30] assessed the relationship between a mobility test and physical behavior end points from the devices. They found a significant correlation between the distance walked during the 6-minute walking test and the 6-minute walking test total vector magnitude counts from ankle-mounted devices.

The reported research findings consistently demonstrated that physical activity levels decreased longitudinally with MND progression. Moreover, physical behavior end points were associated with total ALSFRS-R score with correlation coefficients (r) ranging from 0.31 to 0.78. This was also true for correlation with the gross motor and fine motor domains of ALSFRS-R. In addition, van Unnik et al [29] demonstrated high correlation coefficients of changes in the fine motor domain (Pearson r=0.86, 95% CI 0.80-0.90) and gross motor subdomain (Pearson r=0.79, 95% CI 0.70–0.85). However, while certain end points (daily vector magnitude and variation in vertical axis; Table 4) resulted in reduced between-patient variability (measured as coefficient of variation), [24] some (eg, average daytime active [min] and percentage daytime active [%]) showed greater variability compared to ALSFRS-R [22].

Device placement influenced reported outcomes. Specifically, wrist-derived outcome measures consistently correlated with functional loss in “fine motor” domain in ALSFRS-R, while measures from hip or ankle-mounted devices were strongly associated with a change in gross motor function [24,26,28] and most recently shown to also correlate with the fine motor domain [29]. In addition, Gupta et al [28] demonstrated that when monitoring all 4 limbs, there was good agreement between right and left limbs for physical behavior submovement outcome measures, with agreement between the left and right ankle stronger (r=0.81-0.97) than between the left and right wrists (r=0.65-0.82). Moreover, taking the score of a limb with the maximum progression rate produces a motor outcome measure consistent with, but more sensitive than, ALSFRS-R [28]. None of the studies investigated the effects of disease phenotype on physical behavior end points nor the most optimal wear location for each phenotype.

In total, 5 (42%) studies investigated the effects of using physical behavior end points, including increased measurement frequency, on sample size requirements of hypothetical clinical trials. In total, 4 (33%) studies found that a reduction in sample size would be related to the increased sensitivity of their proposed outcome measures [23,24,28,29]. This was determined either through increasing measurement frequency (daily monitoring) [23], taking the score of a limb with maximum progression rate in a study monitoring all 4 limbs [28], or reduced between-patient variability (and thus increase sensitivity) of end point based on the variation of daily activities [24]. For example, van Eijk et al [24] demonstrated that when recording 7 days of data every 2 to 3 months, end point, such as daily vector magnitude and variation in vertical axis, outperform ALSFRS-R at 9 months and lead to 30% reduction in required sample size at 12 months. Similarly, van Unnik et al [29] demonstrated that for a study with 7-day recordings at monthly intervals with a 6-month follow-up, 50 participants would be required (80% power) to detect differential progression rates of vertical movement index. In addition, van Unnik et al [29] found that if the follow-up duration is increased to 12 months, the sample size can be reduced by 50%. In contrast, Kelly et al [22] found that their physical behavior end point (average daytime active [min] and percentage daytime active [%]) resulted in increased sample size requirement for a hypothetical clinical trial compared to ALSFRS-R total score (500-700 participants for physical activity end points vs 290 participants for ALSFRS-R). This was explained by greater end point variability toward the end of the study compared to ALSFRS-R, possibly due to the relatively small sample size in the reviewed study (n=18) [22].

In total, 9 (75%) studies assessed at least 1 or more aspects of feasibility in implementing accelerometer-derived measures of physical behavior in people living with MND. Feasibility was typically assessed via Likert-type, dichotomous or numerical rating scale questionnaires. The assessment of feasibility reported focused on perceptions of participants and did not include input from other individuals, such as clinicians, caregivers, or family members. The overall impression was positive, participants found procedures acceptable [20,30], and reported it improved their sense of control of the disease [23].

Device cost was reported by Gupta et al [28] as US $234-US $433 over the course of the study and by van Unnik et al [29] at US $315 (as of 2021). Garcia-Gancedo et al [20] reported adverse events that occurred during the study, all of which related to skin sensitivity to the adhesive used to secure the device to the participant. In terms of technical challenges, Garcia-Gancedo et al [20] reported 1 electrical failure of a device while it was being charged. Rutkove et al [21] reported challenges regarding manufacturers stopping production of devices used during their study. In the study by Beswick et al [30], no participants reported side effects, nor did they have any concerns about remembering to charge the device or the device interfering with daily activities. They also found that 90% of participants would be happy to wear the devices for longer than 12 weeks, and 70% felt positive about the suggestion that using the device may result in needing to attend fewer clinic appointments.

In total, 2 (17%) studies invited participants to visit the study site, where devices were introduced to participants at setup [20,22]. In contrast, 6 (50%) studies were mostly conducted remotely, with varying levels of details reported regarding whether devices and their instructions were posted to participants and the level of support provided over telephone or videoconference calls [21,23-25,27,31]. The study by van Unnik et al [29] had in-person visits, but for some participants the device was mailed out. Beswick et al [30] carried out in-person visits and used videoconferencing to do the study assessments. Rutkove et al [21] was the only study to report issues related to participants being unable to successfully work the device.

Adherence was assessed in 8 (67%) studies, based on the number of valid wear days (days when the minimum wear threshold of the device was achieved) against the total number of recording days. Overall, adherence was good, ranging from 91.8% to 93% for hip-worn devices [24,29], 92% for chest-worn devices [20], 86% to 95.7% for wrist-worn devices [26,30], and 87.3% for ankle-worn devices [30]. Overall, the number of valid days was higher for wrist-worn devices compared to ankle-worn devices in studies that assessed multiple devices in 1 participant cohort [28,30]. Adherence for chest-mounted monitors reduced longitudinally, from 92% at baseline to 56% at the last measurement, which was explained by physical inability to meet protocol requirements for attaching the device, increased reliance on caregivers to facilitate device use, or decreased willingness to comply with study procedures [20].

Van Eijk et al [24] assessed the wear burden using a Likert rating scale where 0 indicated no burden and 10 indicated high burden. The mean score was 1.3, indicating a low rate of burden for the hip-worn device. Similarly, Garcia-Gancedo et al [20] reported that participants found the chest-mounted device comfortable to wear; however, 24% (6/25) of participants reported symptoms of local skin irritation (itching and skin reaction potentially due to allergy to the adhesive). Beswick et al [30] explored patients’ expectations of wearing devices. They found that 90% of participants thought wearing the devices would be useful for tracking changes in their symptoms.

Currently, considerable heterogeneity exists across studies monitoring physical behavior in MND. This is not surprising given the nascent use of these methods in MND research (~5 years), and something that is also seen in other research areas where such methods are much more common [35-38]. Given the rareness of MND (and MND subtypes) and the challenges this presents for accruing large longitudinal datasets, there is value in establishing a consensus approach in MND, and the development of standardized methods of data collection and analysis that would enable harmonization across datasets. This would facilitate data sharing, comparability of findings, and support better phenotyping of MND subtypes. From the results presented here, it seems particularly important to consider developing a consensus across aspects of data acquisition, analysis, and reporting. Therefore, the following subsections summarize key elements of these factors found in the reviewed studies that require further consideration.

Across the reviewed studies, there were notable differences in total participant follow-up time, frequency, and duration of each measurement period (Table 3). It is important that the total follow-up time should allow observation of clinically relevant changes. The total participant follow-up of 6 to 24 months in the studies captured such changes and sits well within the average MND survival of 2 to 3 years after symptom onset [2]. Studies tended to record data either monthly or every 2 to 3 months, and the latter would coincide with routine clinical assessment or appointment frequency currently recommended by National Institute for Health and Care Excellence [3]. Once meaningful changes in physical behavior is known, remote monitoring could facilitate personalized visit schemes that could reduce the travel burden and cost. However, it is important to note that studies tended to be biased toward more slowly progressing and predominantly limb-onset disease phenotypes (Table 2), and optimal data recording frequency could differ between slower and faster progressors. This concern is not unique to studies on movement, as a bias toward the inclusion of slower progressing patients is well-documented in traditional epidemiological studies. This will, in part, be addressed by the release of data from clinical trials (where inclusion is generally biased toward more rapidly progressing patients) that include measures of movement as part of study outcomes.

To capture accurate information on the participant’s current functional ability, the duration of each measurement period should capture day-to-day variations in behavior [39]. Larger day-to-day variations necessitate longer monitoring periods to be robustly captured; however, day-to-day variations were not reported in any of the reviewed studies. This makes it difficult to identify the optimal duration of recording. Most included studies were recorded over 7 consecutive days (Table 3). This duration is considered adequate to capture most physical behavior variables [39,40] and accounts for variations in social and work activities that occur over a week yet does not exceed the battery life for most commercially available accelerometers [41-43]. However, if future MND specific research establishes that there is little day-to-day variability for MND specific end points, the duration of each measurement could be reduced, which could provide several benefits, including reduced wear burden for participants.

While accelerometers were the most used devices, the method of their attachment and the wear location varied (Table 3). The studies suggest that the wear location has the potential to influence the outcomes [26]. However, the most appropriate location is yet to be determined and will likely depend on the aspect of physical function of main interest (ie, fine vs gross motor skill). Moreover, optimal wear location and the end point most sensitively reflecting physical function may differ across MND presentation and phenotype. Participants living with different MND subtypes were reported to be included in 4 (33%) studies, and ALS site of symptom onset was reported in 3 (25%) studies (Table 2). It is not clear whether people living with different MND subtypes participated in the other studies, and these details were not reported, or whether all participants had the same subtype. Either way, this means the influence of disease-specific factors on the suitability of different physical behavior end point measures has not yet been assessed in detail and represents a gap in current knowledge.

An additional parameter of importance in data acquisition is the device sampling frequency. This varied greatly between the studies (10-128 Hz, Table 3), with limited justification for the frequency used. The major frequency components of human movement are low, occurring up to 20 Hz [44-46]. In gait, most of the energy is contained below 15 Hz; therefore, to conserve 99% of the signal power, the sampling frequency must be a minimum of 30 Hz [44,47], and this was the most used sampling frequency within the reviewed studies (Table 3). However, Khan et al [48] eloquently demonstrated that datasets of different activities (eg, Parkinson disease and walking and physical monitoring) each have different optimal sampling frequency ranges (26-63 Hz). Therefore, the optimal sampling frequency for sensitively detecting changes in physical function in MND, while avoiding battery drain and large storage requirements may warrant further assessment. The MND community will therefore benefit from establishing standardized means of collecting accelerometer data, in a similar manner to that developed by the Mobilise-D consortium [49].

Reviewing the data analysis approaches used across studies revealed large differences in data preprocessing undertaken to derive physical behavior metrics, which have implications for end point comparison across studies. For example, ActiGraph devices were the most used in reviewed studies (7/10, 70%), and 5 (71%) studies used the manufacturer’s proprietary software (ActiLife) to derive activity counts from the raw data to construct physical behavior end point. However, activity counts can be calculated in different ways (not universal) with a complex relationship between raw data and counts that differs between device models [48]. This will influence the generalizability of results and could also pose challenges when comparing studies that have used different versions of the same software. Therefore, it seems important for researchers to consider the implications of data preprocessing via proprietary algorithms, a point that must be balanced against the availability of technical expertise in the research team. There is likely value in the MND research community working toward the provision of transparent and accessible processing tools (eg, through an Open Science framework, as used to share activity index analysis code) to facilitate replication of findings and standardization across study sites.

A further important feature of data analysis that warrants consideration is the reduction of data into epochs. This approach of smoothing data was routinely used across studies, with 10- or 60-seconds epochs commonly used. Despite this being routine practice, there is a lack of consensus on the most appropriate epoch length for specific, measurable outcomes. Because epoch length determines data resolution (Figure 2), the choice should likely be guided by the behavior of interest. End points relating to subtle movement variations, moving from sitting to standing or reaching and grasping, may warrant shorter epoch lengths (eg, 1 second). In contrast, end points relating to physical behaviors, such as walking, may warrant longer epoch lengths (eg, ≥10 seconds). Therefore, future research should consider and justify the choices of data smoothing to balance the difference in information against the feasibility and demands of processing large quantities of uncompressed data.

Most (7/12, 58%) of the studies only reported traditional physical behavior end points, which typically summarize the overall frequency, intensity, and duration of physical activity. In addition, changes to end points specific to sedentary behavior patterns could also be explored [37]. However, these measures represent a broad overview of physical behavior but do not provide information on movement characteristics. In addition, it seems inevitable that these measures will decline with MND progression, and some (eg, average daytime active [min] and percentage daytime active [%]) even lead to greater data variability compared to the ALSFRS-R [22]. Thus, when used in isolation, some outputs may not be optimally suited as clinical end points. In contrast, end points that go beyond measuring the volume of physical behavior and focus more on movement characteristics, quality, or complexity (ie, how people move rather than how much they move) may have greater potential value. Examples of such measures include the daily vector magnitude [24] and submovement analysis [28], with vector magnitude having been shown to reduce between-patient variability compared to traditional measures, such as the percentage of time spent active and thereby increasing sensitivity to disease progression [24,26].

The growing interest in studying free-living physical behavior end points in MND means there is value to ensuring transparent and clear reporting. This would facilitate the identification of problems in conducting studies as well as the clarity and quality of reporting and hence accelerate consensus around optimal study designs. For example, a clear representation of participants at each stage of the study, potentially in the form of a flowchart, would allow a clear reflection of longitudinal attrition rates and their causes. In addition, there should be justification for design choice, including a clear description of the type of follow-up, accelerometer location, method of attachment, and sampling frequency. Consideration should also be made of the requirements for reporting data preprocessing, as this is something that differed significantly between studies. Consequently, work to develop standard reporting guidelines would be particularly timely and valuable to the MND community. Examples of such efforts in other fields that could provide a useful foundation for the MND community include the recommendations for assessing and reporting human joint kinematics using inertial measurement units recently reported in the biomechanics community but has yet to demonstrate its impact [50].

When recorded, reviewed studies consistently found that participants had positive attitudes toward remote monitoring of physical behavior, excellent use, and reported a low rate of burden. Despite limited evidence specific to accelerometry, other studies of telehealth tools in MND also found that people living with MND are accepting toward using remote monitoring approaches [12,51]. However, it is clear that participants face challenges using digital devices over the course of study. For example, the physical challenge of removing and reattaching a device may, coupled with the progression of MND, influence adherence [20]. Gupta et al [28] and Beswick et al [30] have also shown that the amount of use differs between wear locations. However, there was very limited information on factors that reduced use (including consideration of family member or caregiver burden) provided in the articles included. This restricts the evidence base on which future study protocols can be optimized to maintain participant involvement as the disease progresses and avoid missing data. Further research is needed, and we are currently working to expand our knowledge of the influencing factors.

To support the development of a consensus approach for the quantification of physical function from digital devices in MND and enhance the opportunity for data harmonization, we present currently unanswered questions and recommendations for future research in this area (Table 5). None of the reviewed studies evaluated the use of devices for clinical care, and research has not explored the implications of remotely monitoring physical behavior on clinical care. Further research is warranted, and it is likely that further considerations will need to be made to mature and translate the technology for clinical practice. The focus of this review was to explore monitoring of free-living physical behaviors, where individuals are completing tasks of their choice. It should be noted that digital devices can also be used in clinics or home-based assessments of defined functional tests, such as sit-to-stand tests [14,52]. These approaches are especially useful when standardizing data acquisition, analysis, and reporting protocols.