Abstract
Introduction
Actigraphy-quantified physical activity (PA) allows for continuous measurements of PA that are reflective of real-world day-to-day functioning and morbidity in persons living with cardiomyopathy. This analysis reports the results of actigraphy monitoring and relates these to other clinical outcome assessments in the phase 3, multinational REALM-dilated cardiomyopathy (DCM) (NCT03439514) clinical trial in LMNA-related DCM.
Methods
Between 2020 and 2022, REALM-DCM randomized 37 patients with actigraphy worn on the nondominant wrist continuously to monitor daily PA. Of those, 35 participants had analyzable data for this analysis.
Results
The median duration of actigraphy monitoring for all participants was 293 days across 120 patient visits. Over 85% of the visits met a predefined threshold of wear-time compliance of 10 h of awake wear time for at least 4 days within the 2-week monitoring period prior to and after clinic visits. Kansas City Cardiomyopathy Questionnaire (KCCQ) physical limitation scores were positively associated with several actigraphy-quantified PA metrics, including moderate-to-vigorous physical activity (MVPA), moderate activity, non-sedentary behavior, total step counts, total activity counts (all 3 axes and their vector magnitude). Six-minute walk time distance was positively associated with time spent in MVPA and moderate activity, and total step counts. Patient Global Impression (PGI) Symptom Heart Failure Severity was negatively associated with non-sedentary behavior, total activity counts (vector magnitude, X- and Y-axes), and light activity. Actigraphy endpoints also distinguished between NYHA class II and class III patients. Actigraphy endpoints did not correlate with the KCCQ total score.
Conclusion
This is the largest and most longitudinal dataset of LMNA-DCM patients collected and reported to date using wearable sensors to gain understanding of PA patterns in these patients. These data help understand the potential use of actigraphy monitoring and wearable technologies in genetic cardiomyopathy and heart failure clinical trials.
Keywords: Wearable physical activity monitoring, Heart failure, Dilated cardiomyopathy, Digital biomarkers, Clinical study
Plain Language Summary
This study looked at how physical activity levels in patients with a specific type of heart disease, called LMNA-related dilated cardiomyopathy (DCM), can be measured using wearable devices. These devices, worn on the wrist, track movements and provide data on how active the patients are in their daily lives. The study involved 37 patients in the clinical trial REALM-DCM who each wore these devices continuously on average for 293 days. The data collected showed that patients who were more physically active had better scores on certain heart disease questionnaires and walked longer distances in a six-minute walk test. The study also found that patients with more severe heart disease (classified as NYHA class III) were less active compared to those with less severe disease (NYHA class II). The findings suggest that wearable devices can be useful in monitoring physical activity in patients with heart failure and could help in designing future clinical trials. However, more research is needed to establish the best ways to use these devices and interpret the data they provide.
Introduction
Lamin A/C gene (LMNA)-related dilated cardiomyopathy (DCM) is a rare autosomal dominant disease characterized by chronic heart failure (CHF), conduction disorders, and life-threatening arrhythmias [1]. Despite guideline-recommended therapies, patients with LMNA-related DCM usually progress, highlighting an unmet treatment need [1].
In 2019, the US Food and Drug Administration released the draft guidance “Treatment for Heart Failure: Endpoints for Drug Development,” suggesting patients’ subjective symptoms and function (e.g., measures of daily activity) as potential efficacy endpoints in clinical trials [2, 3]. Limitations in physical activity (PA) are features of HF associated with poor prognosis [3]. Digital health technologies (DHTs; e.g., accelerometry, actigraphy) can provide granular and real-time insights on patient functioning across multiple dimensions [4]. Actigraphy is a noninvasive and objective method of assessing PA by monitoring movements using sensors and accompanying algorithms. Although trials incorporating actigraphy-quantified PA (AQPA) outcomes have increased in recent years, studies on patients with genetic cardiomyopathies, standardization of device and analytical approach, and validated endpoints and patient populations are lacking.
This paper assessed associations between AQPA metrics and clinical endpoints using data from the randomized, double-blind, placebo-controlled, phase 3 REALM-DCM trial (NCT03439514), investigating efficacy and safety of ARRY-371797 in patients with symptomatic LMNA-related DCM [1]. Compliance, data completeness, and the association between actigraphy measured PA metrics with other clinical points including 6-min walk time (6MWT), Kansas City Cardiomyopathy Questionnaire (KCCQ), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) is examined.
Methods
The REALM-DCM trial design, ethics statement, and outcomes have been previously published [1]. Actigraphy was added as an exploratory endpoint after a protocol amendment (February 2021). REALM-DCM was terminated early after a planned interim analysis suggested futility [1].
Patients continuously wore the E.200.4004 ActiGraph CentrePoint Insight Watch (ActiGraph, Pensacola, FL; https://theactigraph.com/cpiw) on their nondominant wrist deploying firmware E.208. Real-time data collection (sample rate: 64 Hz), monitoring, and automated alerts about device malfunction and noncompliance were performed through device-to-cloud data transfer.
AQPA metrics included time spent for light, moderate, moderate-to-vigorous, and vigorous activity; non-sedentary and sedentary behavior; total step counts; total activity counts (combined or individual x-, y-, z-axis); mean activity count/h during the most active 10 hours (M10hr), 1 hour (M60min), 15 minutes (M15min), and 6 minutes (M6min) of the day; and mean activity count/h for the least active 5 hours of the day (L5hr). Data were derived using algorithms in the ActiGraph CentrePoint Version 3 API (https://github.com/actigraph/CentrePoint3APIDocumentation) [5]. AQPA assessments were performed throughout the study (baseline, weeks 4, 12, and every 12 weeks thereafter), reflecting patient follow-up visits for clinical assessments. Daily AQPA measurements were considered valid with ≥10 h of awake wear time. During each assessment period, the 14-day analytical window of interest included 7 days before and 7 days after clinic visit, excluding the day of visit as nonrepresentative of patients’ daily routine. Patients with ≥4 days of valid AQPA measurements within the 14-day period were considered compliant and included in the analysis.
Associations between each clinical outcome (6MWT distance, Kansas City Cardiomyopathy Questionnaire physical limitation/total symptom scores [KCCQ-PLS/-TSS], NT-proBNP concentration, and Patient Global Impression of Severity-PA Limitation/Severity of HF Symptom [PGIS-PAL/-SHFS] score) and AQPA metric were evaluated using a mixed-effects model. Each AQPA metric was considered the response and each clinical outcome as fixed effect, and a random patient intercept. Standardized coefficients from the mixed-effects models for the clinical outcome will be reported to represent the magnitude and direction of the linear association. Differences between subgroups based on age (18–49, 50–64, ≥65 years) and New York Heart Association (NYHA) class (II, III) were assessed using a mixed-effects model with both age groups and NYHA class as the fixed effects, and a random patient intercept. For significant between-group differences, Cohen’s effect sizes will be reported. Analyses were conducted using R 4.2.2 (R Foundation, Vienna, Austria).
Results
In REALM-DCM, 77 patients received ARRY-371797 (n = 40) or placebo (n = 37) (shown in Fig. 1) [1]. From February 2021 to October 2022, 35 of 37 patients who consented to actigraphy had evaluable data and contributed to a combined total of 120 patient visits. Patients (mean age, 53 years) were mostly male (60%) and white (97%) with NYHA class II HF (86%) (shown in Table 1).
Fig. 1.
Patient disposition. *As there were no treatment differences observed in REALM-DCM, patients who received ARRY-371797 or placebo were combined in this study.
Table 1.
Baseline demographics and clinical characteristics
| Variable | Patients (n = 35) |
|---|---|
| Age, years | |
| Mean (SD) | 52.97 (10.46) |
| Median (range) | 60.00 (24.00–70.00) |
| Categories, n (%) | |
| 18–49 | 13 (37.14) |
| 50–64 | 18 (51.43) |
| ≥65 | 4 (11.43) |
| Sex, n (%) | |
| Male | 21 (60.00) |
| Female | 14 (40.00) |
| Treatment group, n (%) | |
| ARRY-371797 | 15 (42.86) |
| Placebo | 20 (57.14) |
| NYHA functional class, n (%) | |
| II | 30 (85.71) |
| III | 5 (14.29) |
| NT-proBNP, pg/mL | |
| Mean (SD) | 137.29 (105.96) |
| Median (range) | 111.98 (20.06–363.44) |
| 6MWT distance, m | |
| Mean (SD) | 390.69 (54.41) |
| Median (range) | 402.50 (238.47–460.25) |
| KCCQ-PLS | |
| Mean (SD) | 69.48 (20.78) |
| Median (range) | 70.83 (20.83–100.00) |
| KCCQ-TSS | |
| Mean (SD) | 71.96 (16.67) |
| Median (range) | 70.83 (35.42–100.00) |
| PGIS-PAL score | |
| Mean (SD) | 1.49 (0.89) |
| Median (range) | 2.00 (0.00–3.00) |
| PGIS-SHFS score | |
| Mean (SD) | 1.40 (0.65) |
| Median (range) | 1.00 (0.00–2.00) |
| PA measuresa | n = 7 |
| Light activity, min/day | |
| Mean (SD) | 604.87 (257.18) |
| Median (range) | 718.15 (186.06–845.21) |
| Moderate activity, min/day | |
| Mean (SD) | 82.99 (56.23) |
| Median (range) | 93.58 (4.19–162.04) |
| MVPA, min/day | |
| Mean (SD) | 87.53 (59.35) |
| Median (range) | 101.71 (4.19–170.00) |
| Vigorous activity, min/day | |
| Mean (SD) | 4.54 (4.00) |
| Median (range) | 4.98 (0.00–8.89) |
| Total activity counts | |
| Mean (SD) | 1,618,044.65 (1,029,877.14) |
| Median (range) | 1,780,759.10 (53,260.28–2,955,624.30) |
| Total activity counts at x-axis | |
| Mean (SD) | 873,648.50 (535,625.53) |
| Median (range) | 993,256.93 (32,391.50–1,501,949.93) |
| Total activity counts at y-axis | |
| Mean (SD) | 878,722.61 (587,005.37) |
| Median (range) | 963,461.04 (28,366.75–1,708,638.00) |
| Total activity counts at z-axis | |
| Mean (SD) | 1,036,188.76 (659,876.66) |
| Median (range) | 1,115,569.07 (31,063.00–1,883,516.79) |
| Total step count | |
| Mean (SD) | 4,569.44 (2,897.74) |
| Median (range) | 4,342.90 (269.50–8,159.50) |
| Non-sedentary behavior, min/day | |
| Mean (SD) | 321.79 (173.22) |
| Median (range) | 387.96 (38.62–502.36) |
| Sedentary behavior, min/day | |
| Mean (SD) | 370.61 (129.42) |
| Median (range) | 410.85 (151.62–517.55) |
| M6min, activity level/day | |
| Mean (SD) | 8,559.48 (1,837.86) |
| Median (range) | 8,760.39 (5,968.30–10,901.75) |
| M15min, activity level/day | |
| Mean (SD) | 7,000.84 (1,614.23) |
| Median (range) | 7,293.28 (5,007.16–9,226.52) |
| M60min, activity level/day | |
| Mean (SD) | 5,190.25 (1,186.56) |
| Median (range) | 5,440.83 (3,775.85–6,540.63) |
| M10hr, activity level/h | |
| Mean (SD) | 5,248.36 (1,104.23) |
| Median (range) | 5,440.83 (4,066.40–6,540.63) |
| L5hr, activity level/h | |
| Mean (SD) | 1,500.01 (240.57) |
| Median (range) | 1,519.07 (1,243.11–1,737.56) |
6MWT, six-minute walk test; KCCQ-PLS, Kansas City Cardiomyopathy Questionnaire – physical limitation score; KCCQ-TSS, Kansas City Cardiomyopathy Questionnaire – total symptom score; L5hr, least active 5 h of the day; M6min, most active 6 min of the day; M15min, most active 15 min of the day; M60min, most active 60 min of the day; M10hr, most active 10 h of the day; MVPA, moderate-to-vigorous physical activity; NT-proBNP, N-terminal pro-B-type natriuretic peptide; NYHA, New York Heart Association; PA, physical activity; PAL, physical activity limitation; PGIS, Patient Global Impression of Severity; SD, standard deviation; SHFS, severity of heart failure symptom.
aData 14 days before to day 1 clinical site visit were used for baseline PA. Only 7 patients were recruited after the protocol amendment and therefore had PA measurements at baseline visits.
Across all patient visits, 85% had ≥4 days of valid AQPA data and on average (mean [standard deviation]), 7.9 (4.46) compliant days/visit. The median duration for actigraphy monitoring was 293 days. On average (mean [standard deviation]), patients wore the device for 18.63 (7.08) h/day and 11.42 (4.35) awake h/day.
Among clinical outcomes, KCCQ-PLS was positively associated with many AQPA metrics, including moderate-to-vigorous PA (MVPA), moderate activity, non-sedentary behavior, and total step and activity counts (shown in Fig. 2a in terms of the standardized coefficients for the clinical outcomes from the mixed-effects model). 6MWT was positively associated with MVPA, moderate activity, and total step counts. PGIS-SHFS was negatively associated with non-sedentary behavior, total activity counts, and light activity, whereas NT-proBNP was negatively associated with L5hr. KCCQ-TSS and PGIS-PAL were not associated with any AQPA metric.
Fig. 2.
Standardized estimated coefficients from the mixed-effects models assessing the association between AQPA and clinical measures (a) and estimated differences in AQPA between NYHA classes (b). In a, the T-score was used as a standardized representation to facilitate clearer visualization of the strength of the association. A positive standardized coefficient implies a positive relationship, while a negative standardized coefficient implies a negative relationship. Empty squares indicate that the association is not statistically significant (p > 0.05). No imputations were conducted for missing data. Total activity counts refer to the sum of daily total activity counts for all vector magnitudes (x-, y-, and z-axes). 6MWT, 6-min walk test; AQPA, actigraphy-quantified physical activity; CI, confidence interval; KCCQ-PLS, Kansas City Cardiomyopathy Questionnaire – physical limitation score; KCCQ-TSS, Kansas City Cardiomyopathy Questionnaire – total symptom score; L5hr, least active 5 h of the day; LS, least squares; M6min, most active 6 min of the day; M15min, most active 15 min of the day; M60min, most active 60 min of the day; M10hr, most active 10 h of the day; MVPA, moderate-to-vigorous physical activity; NT-proBNP, N-terminal pro-B-type natriuretic peptide; NYHA, New York Heart Association; PA, physical activity; PAL, physical activity limitation; PGIS, Patient Global Impression of Severity; SHFS, severity of heart failure symptom.
Least squares means for several AQPA metrics were significantly different between patients with NYHA class II vs. III HF (shown in Fig. 2b), and Cohen’s effect sizes were estimated to be ranged from 1.84 to 2.68 which were considered large (online suppl. Table 2; for all online suppl. material, see https://doi.org/10.1159/000550259). AQPA metrics were not statistically different between age groups.
Discussion
This analysis, to our knowledge, provides the first data using actigraphy to measure PA in patients with genetic cardiomyopathies (e.g., LMNA-related DCM). The findings are broadly similar to studies in patients with generic HF [4, 6–8]. KCCQ-PLS and 6MWT were positively associated with several AQPA metrics, including time spent in moderate and moderate-to-vigorous activity, non-sedentary behavior, and total step or activity counts, whereas KCCQ-TSS was not associated with any metric. NT-proBNP was negatively associated with L5hr. PGIS-SHFS was negatively associated with non-sedentary behavior, total activity counts, and light activity, whereas PGIS-PAL was not associated with any metric. These data suggest that individual AQPA metrics may not fully capture the granularity of meaningful PA and therefore may incompletely represent subjective clinical symptoms in patients with LMNA-related DCM or HF. Nevertheless, decreased AQPA distinguished patients with NYHA class III vs. II HF. Additional work is necessary to derive rigorous objective metrics on the range of daily PAs integrated in subjective symptom scores for disorders with varying neuromuscular and metabolic effects.
Although only 35 patients had AQPA data in REALM-DCM, the median duration of PA monitoring was considerably longer than that of earlier studies (42 vs. 12 weeks) [6, 8]. In the CHIEF-HF trial, results from patients wearing Fitbit Versa 2 showed association between increasing 25-point ranges of KCCQ-PLS/-TSS had progressively higher daily step counts and greater number of mean floors climbed up to an inflection point of approximately 2,000 steps [6]. In our analysis of the LMNA-DCM population in REALM-DCM, the total step count was also positively correlated with KCCQ-PLS as were most of the PA measures assessed including MVPA, moderate activity, non-sedentary behavior, and total activity counts (all 3 axes and vector magnitude). 6MWD was also positively associated with time spent in MVPA, moderate activity, and total step count. Interestingly, M6min which is the mean activity level during the maximum daily 6 min of activity was not correlated with the 6MWD in this patient population. Previously, a study in elderly HF patients (≥65 years of age) has shown a correlation between the maximum 6 min of daily activity with 6MWD although that correlation was lower in younger (<65 years of age) HF patients [4].
In 2021, the OUTSTEP-HF trial reported 6MWT distance improved (35.09 m) after 12 weeks of sacubitril/valsartan vs. enalapril treatment, without improvements in average daytime non-sedentary activity and M6min [8]. In the mixed-effects model for the REALM-DCM trial, there was no correlation between daily non-sedentary activity and 6MWT although there was a positive correlation with daily non-sedentary activity and KCCQ-PL. Further longitudinal studies will be needed to better understand the reproducibility and clinical meaning of AQPA measures over time and further understanding of which of these measures are clinically meaningful and by what degree.
This exploratory analysis has several limitations. REALM-DCM was not powered to analyze actigraphy data and excluded patients with impaired ability to perform the 6MWT. Therefore, findings may not represent the general population with LMNA-related DCM and must be interpreted with caution. Some AQPA metrics and algorithms were developed in healthy individuals and require independent validation in patients with HF, which has well-known effects on multiple physiologic systems. Moreover, some of the algorithms deployed in this study were previously validated in healthy individuals but used in this study for a HF population. Dibben et al. [9] previously have shown that patients with HF may expend more energy as measured by metabolic equivalents (METs and VO2) to accomplish the same activity as a healthy volunteer. To potentially overcome this limitation, other measures that are agnostic to normative values were used including total activity counts and mean activity levels. Despite these limitations, to our knowledge, this analysis presents the largest AQPA dataset for patients with LMNA-related DCM and the longest overall duration for DHT monitoring in a phase 3 study of CHF.
Actigraphy provides objective assessment of PA that could help in designing and conducting clinical trials. There is an ongoing need for therapies to treat persons living with different forms of HF including LMNA-related DCM and a growing trend toward obtaining continuous longitudinal data using DHTs to complement traditional episodic clinical assessments. However, further studies are needed to establish clinically relevant AQPA metrics that can detect treatment differences. While this analysis did not evaluate treatment differences, our findings support future efforts to establish the validity of AQPA as a complement or alternative to traditional clinical efficacy measures including subjective symptom scores. Combined information gathered from qualitative patient interviews and quantitative data analysis from future clinical trials where efficacy is demonstrated will help develop a range of values representing meaningful within-patient change for the AQPA endpoints and other digital metrics. Growing this body of evidence around content validity, convergent validity, predictive validity, reproducibility, and responsiveness to intervention as well as the relevant costs will support inclusion of these novel endpoints in future trials.
Conclusions
This is the largest wearable dataset collected from LMNA-DCM patients and reported to date using wearable sensors to gain understanding of PA patterns in these patients. This dataset also describes the longest overall duration of wearable monitoring in CHF patients over the course of a phase 3 study. NYHA classes were distinguished by all of the measures examined over time using actigraphy. Time spent in MVPA, moderate activity, and total step count were positively correlated with 6MWT and KCCQ-PL. Additionally, total activity count was positively correlated with KCCQ-PL. In contrast, only L5hr on actigraphy correlated with NT-proBNP and no actigraphy measures examined in this population correlated with KCCQ-TS, suggesting that these wearable metrics are better correlated to PA limitations as captured by the KCCQ questionnaire. These data add to the growing body of work of actigraphy in HF studies and understanding the potential uses of these and many other wearable or sensing technologies in future clinical trials.
Acknowledgments
The authors thank all patients, clinical investigators, and staff who took part in REALM-DCM and Dr. Wenyi Lin for checking the quality of the statistical analyses. Medical writing support was provided by April Cabang of Engage Scientific Solutions and was funded by Pfizer.
Statement of Ethics
The REALM-DCM study protocol was approved by the independent review board or Ethics Committee at each participating site and was conducted in accordance with the Declaration of Helsinki and the International Conference on Harmonization-Good Clinical Practice guidelines. All patients provided written, informed consent. A listing of all sites where the study was approved by independent review board or Ethics Committee was previously published with the main study results in Garcia-Pavia et al. [1].
Conflict of Interest Statement
P.G.-P. reports speaking fees from Alnylam Pharmaceuticals, AstraZeneca, BMS, BridgeBio, Intellia, Ionis Pharmaceuticals, Novo Nordisk, and Pfizer; consulting fees from Alexion, Alnylam Pharmaceuticals, AstraZeneca, Attralus, Bayer, BioMarin, Bristol Myers Squibb, BridgeBio, Cytokinetics, Daiichi Sankyo, Edgewise, General Electric, IDOVEN, Intellia, Ionis Pharmaceuticals, Lexeo, Neuroimmune, Novo Nordisk, Pfizer, and Rocket Pharmaceuticals; and research/educational support to his institution from Alnylam, AstraZeneca, BridgeBio, Intellia, Novo Nordisk, and Pfizer. X.C., J.D., H.L., C.D., J.B., T.M.H., and F.S.A. are employees of Pfizer and hold stock and/or stock options. C.A.M. reports consultancy fees from Affinia, Array BioPharma, AstraZeneca, Bayer, Bristol Myers Squibb, Design Therapeutics, Dewpoint, DiNAQOR, Merck, MyoKardia, Novartis, and Pfizer; and grant support from Bayer, Merck, Novartis, and Sanofi.
Funding Sources
This study was sponsored by Pfizer.
Author Contributions
P.G.-P., X.C., J.D., H.L., C.D., J.B., T.M.H., F.S.A., C.A.M., and FSA: conceptualization and design. P.G.-P., T.M.H., F.S.A., and C.A.M: data collection and study execution. J.D., H.L., and C.D: analysis. X.C., J.D., H.L., J.B., and F.S.A: drafting of manuscript. P.G.-P., X.C., J.D., H.L., C.D., J.B., T.M.H., F.S.A., and C.A.M.: review and revision of the manuscript. All authors had full access to the study data and approved this manuscript.
Funding Statement
This study was sponsored by Pfizer.
Data Availability Statement
The data that support the findings of this study are not publicly available due to privacy reasons. Upon reasonable request and subject to review, Pfizer will provide the data that support the findings of this study. Subject to certain criteria, conditions, and exceptions, Pfizer may also provide access to the related individual de-identified participant data. To make a data access request, go to https://www.pfizer.com/science/clinical-trials/trial-data-and-results for more information.
Supplementary Material.
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Associated Data
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Supplementary Materials
Data Availability Statement
The data that support the findings of this study are not publicly available due to privacy reasons. Upon reasonable request and subject to review, Pfizer will provide the data that support the findings of this study. Subject to certain criteria, conditions, and exceptions, Pfizer may also provide access to the related individual de-identified participant data. To make a data access request, go to https://www.pfizer.com/science/clinical-trials/trial-data-and-results for more information.