Dec 05, 2023
Valutazione dello stato di salute fisica oltre il conteggio dei passi giornalieri utilizzando un sensore di attività indossabile
npj Medicina Digitale volume 5, Numero articolo: 164 (2022) Cita questo articolo 5201 Accessi 1 Citazioni 136 Dettagli metriche altmetriche Lo stato di salute fisica definisce la capacità di un individuo di eseguire prestazioni
npj Medicina Digitale volume 5, numero articolo: 164 (2022) Citare questo articolo
5201 accessi
1 Citazioni
136 Altmetrico
Dettagli sulle metriche
Lo stato di salute fisica definisce la capacità di un individuo di svolgere le normali attività della vita quotidiana e viene solitamente valutato in ambito clinico mediante questionari e/o test validati, ad esempio test del cammino cronometrato. Queste misurazioni hanno un contenuto informativo relativamente basso e sono solitamente limitate nella frequenza. I sensori indossabili, come i monitor dell’attività, consentono la misurazione remota dei parametri associati all’attività fisica, ma non sono stati ampiamente esplorati oltre la misurazione del conteggio dei passi giornalieri. Qui riportiamo i risultati di una coorte di 22 individui con ipertensione arteriosa polmonare (PAH) a cui è stato fornito un monitor di attività Fitbit (Fitbit Charge HR®) tra due visite cliniche (18,4 ± 12,2 settimane). Ad ogni visita clinica sono state registrate un massimo di 26 misurazioni (19 categoriche e 7 continue). Dall'analisi della frequenza dei passi minuto per minuto e della frequenza cardiaca ricaviamo diversi parametri associati all'attività fisica e alla funzione cardiovascolare. Questi parametri vengono utilizzati per identificare i sottogruppi all'interno della coorte e per confrontarli con i parametri clinici. Diverse metriche Fitbit sono fortemente correlate ai parametri clinici continui. Utilizzando un approccio basato su soglie, mostriamo che molte metriche Fitbit determinano differenze statisticamente significative nei parametri clinici tra i sottogruppi, inclusi quelli associati allo stato fisico, alla funzione cardiovascolare, alla funzione polmonare, nonché ai biomarcatori derivanti dagli esami del sangue. Questi risultati evidenziano il fatto che il conteggio dei passi giornalieri è solo uno dei tanti parametri che possono essere derivati dai monitoraggi delle attività.
I sensori di attività indossabili consentono il monitoraggio remoto dell'attività fisica di un individuo, ma sono stati in gran parte limitati alla valutazione del conteggio medio dei passi giornalieri. Camminare, o deambulare, è un movimento fondamentale della vita quotidiana ed è diventato un parametro importante nella promozione della salute umana1. Ad esempio, l’aumento del numero di passi giornalieri (da <4.000 a ≥12.000) è associato a una diminuzione della mortalità per tutte le cause2,3. Nei pazienti ospedalizzati, le soglie giornaliere di conteggio dei passi (tipicamente < 1000 passi al giorno) sono state associate a scarsi risultati, come riammissioni4,5,6. Anche i parametri di deambulazione correlati, come la velocità dell'andatura7,8,9 e i test del cammino temporizzato10,11, si sono rivelati predittivi di risultati clinicamente rilevanti.
Storicamente, il monitoraggio remoto dello stato fisico di un individuo è stato impegnativo, tuttavia, i progressi nella tecnologia indossabile hanno consentito una valutazione continua dopo un intervento chirurgico o tra una visita clinica e l'altra per i pazienti con malattie croniche. Le unità di misurazione inerziale indossabili (IMU), come i dispositivi Fitbit, registrano il conteggio dei passi insieme ad altri parametri derivati dai segnali IMU (ad esempio il sonno) che possono essere visualizzati nell'app dello smartphone associata. Inoltre, molti dispositivi indossabili, come Fitbit, utilizzano la fotopletismografia per misurare la frequenza cardiaca.
Il conteggio dei passi, e in particolare il conteggio dei passi giornalieri, rimane la metrica più comune per la valutazione remota dell'attività fisica, tuttavia, il conteggio dei passi minuto per minuto e i dati sulla frequenza cardiaca possono essere scaricati dal server Fitbit utilizzando l'interfaccia di programmazione dell'applicazione (API). Pertanto, per un individuo che indossa Fibit in modo continuativo, è possibile ottenere in una settimana 10.080 valori di frequenza di passi (unità: passi al minuto, SPM) e frequenza cardiaca (unità: battiti al minuto, BPM), ciascun punto rappresenta il valore medio della frequenza dei passi e della frequenza cardiaca per quel minuto. Sebbene l’accuratezza delle misurazioni del conteggio dei passi in ambienti di vita libera e in popolazioni di pazienti con andatura atipica rimanga una preoccupazione12,13, studi condotti su individui affetti da cancro, malattie cardiovascolari, ipertensione arteriosa polmonare e sclerosi multipla suggeriscono che questi dispositivi possono fornire misurazioni accurate e affidabili. dati clinicamente rilevanti14,15,16,17. Allo stesso modo, in studi comparativi, le misurazioni della frequenza cardiaca effettuate dai dispositivi Fitbit mostrano in generale un buon accordo con gli elettrocardiogrammi per individui a riposo o con bassi livelli di attività18,19. Tuttavia, anche altri fattori, come la pigmentazione della pelle, possono influenzare l’accuratezza della misurazione20.
0. Red line shows a normal fit. d Weekly activity map: scatter plot showing heart rate versus step rate. Each point represents one minute where a physiological heart rate was recorded. The grey lines show the upper and lower envelopes of the activity map. The blue line shows a linear least squares fit to the data./p>5000 steps (14/22) to those with <5000 steps (8/22). This arbitrary threshold resulted in 6 statistically significant clinical parameters (Supplementary Table 2 and Supplementary Fig. 2). Subjects with <5000 steps per day had lower 6MWD at visit 1, lower hemoglobin levels at visit 2, poorer pulmonary health (higher physician-assessed WHO FC) at visit 1, and experienced more pedal edema (Pedal Edema score) at visit 2. Two subjects had average daily step counts >10,000 steps per day (PAH 1, 19), but had no other similarities. Sensitivity analysis of threshold values and the number of statistically significant clinical parameters for all Fitbit metrics are provided in Supplementary Figs. 3 and 4./p> 0 (HR(SR = 0), i.e. active). Histograms for HR(SR = 0) (Fig. 1b) and HR(SR > 0) (Fig. 1c) were described by normal distributions, from which we obtained the mean, standard deviation, and skewness. The range of mean HR(SR = 0) was 66.2–111.8 BPM, with standard deviations of 6.4–13.7 BPM (Supplementary Fig. 5). The skewness varied from −0.75 to 2.30, highlighting a broad range of behavior with relatively large tails to the left and right of the peak (Supplementary Figs. 6 and 7)./p>82 BPM (8/22). This resulted in 8 statistically significant clinical parameters (Supplementary Table 2 and Supplementary Fig. 8). Subjects with lower mean values of HR(SR = 0) had lower RHR at visits 1 and 2, and lower peak heart rate at visit 2, but experienced more pedal edema (Pedal Edema score) and more palpitations (Palpitation score) at visit 1, were less able to perform usual activities (lower EQ-5D Usual Activity scores) at visit 1, and experienced more pain/discomfort (lower EQ-5D pain/discomfort scores) at visit 1./p>100 BPM. Both subjects had low fitness slopes (see below), suggesting that they did not access a wide range of heart rate during daily activities. However, PAH 1 had the highest average daily step count in the dataset. We note that 3 subjects (PAH 4, 20, 27) removed the device overnight (see below), which may have resulted in higher mean HR(SR = 0) values since heart rate values during sleeping were likely not included./p>90,000 individuals over 35 weeks, reported that the RHR (assumed to be the true RHR) was dependent on age, BMI and sleep duration, with daily values of RHR from 40–108 BPM25, although 95% of men and women had RHR values between 50–80 BPM, similar to the range found here./p>1. This resulted in 4 statistically significant clinical parameters (Supplementary Table 2 and Supplementary Fig. 9). Subjects with lower skewness values were more likely to have higher resting heart rate at visits 1 and 2, experienced less pain/discomfort (lower EQ-5D pain/discomfort scores) at visit 1 and were more likely to be in better health (higher EQ-5D Index) at visit 1. Two subjects had skewness of HR(SR = 0) values >1.9 (PAH 27, 28): both subjects also had relatively low resting heart rates, longer free-living 6MWD, and higher fitness plot slopes./p> 0 represents HR values while subjects were active. The mean values of HR(SR > 0) were 78.6–121.0 BPM (mean 94.4 BPM), and the standard deviation was 6.5–14.0 BPM (Supplementary Fig. 10). The mean values were only slightly higher than the mean values of HR(SR = 0), although the standard deviations were similar. The mean skewness values for HR(SR > 0) were from −0.57 to 1.35, similar to the range for HR(SR = 0). We compared individuals with mean values of HR(SR > 0) <95 BPM (12/22) to those with >95 BPM, resulting in 4 statistically significant clinical parameters (Supplementary Table 2 and Supplementary Fig. 11). Subjects with lower mean values of HR(SR > 0) had lower RHR at visits 1 and 2, lower albumin levels at visit 1, and experienced more palpitations (lower Palpitation score) at visit 1./p> 0, the mean HR at SR = 0, and the fraction of time inactive (Fig. 2a). The data points for each week for most subjects were tightly clustered in distinct regions. From the loading plot (Fig. 2b), PC1 is dominated by the step rate parameters (+PC1) and the fraction of time inactive (−PC1). PC2 is dominated by the mean heart rate at SR = 0 (+PC2) and the standard deviation of the heart rate for SR > 0 (−PC2). The group of subjects in the fourth quadrant (PAH 3, 9, 12, 19, 23, 27) are characterized by high mean and standard deviation of the step rate, and a high value of the standard deviation of the heart rate at SR > 0. This implies that these individuals exhibit a wide range of step rates and a wide range of heart rates during normal activities of daily life. The group of subjects along the positive y-axis (PAH 1, 10, 14, 17) are characterized by high mean heart rate at SR = 0. High values of HR(SR = 0) imply that these individuals have a high resting heart rate and are unlikely to access a wide range of heart rates during normal activities, even if they have the capacity for moderate or high step rates. The group of subjects along the negative x-axis (PAH 2, 7, 11, 13, 20, 21, 30) are characterized by a large fraction of time inactive. Three subjects (PAH 15, 26, 28) are clustered around the origin. The PCA plot suggests a range of behavior with distinct combinations of metrics associated with heart rate and step rate. To explore these relationships in more detail, we assessed several derived parameters. Distinct groupings of subjects were found for mean HR(SR = 0) >82 BPM, skewness of HR(SR = 0) <1, ambulation product, P > 1000, and fitness slope >0.15 (Supplementary Fig. 12)./p> 0):SD is the standard deviation of the heart rate at SR > 0; SR(SR > 0):mean is the mean step count for SR>0; SR(SR>0):SD is the standard deviation of the step rate for SR > 0; time active is the fraction of minutes with SR = 0./p>0.15 (11/22) to those with slope <0.15, resulted in 3 statistically significant clinical parameters (Supplementary Table 2 and Supplementary Fig. 13). Notably, subjects with slopes >0.15 had lower NT-proBNP levels at visits 1 and 2. B-type natriuretic peptide (BNP) and N-terminal pro b-type natriuretic peptide (NT-proBNP) are biomarkers for cardiac stress, and PAH patients with NT-proBNP levels below about 300 pg L−1 are considered low risk for heart failure26. The mean levels for subjects with slope >0.15 at visits 1 and 2 were 188 ± 180 and 145 ± 165 pg mL−1, respectively. These results suggest that the fitness slope may be a useful indicator of NT-proBNP levels and risk for heart failure. Comparison of subjects with fitness intercepts above (10/22) and below (12/22) the mean (91 BPM) were similar to results for subgroups with HR(SR = 0) above and below 95 BPM./p> 1000 (12/22) to those with P < 1000, resulted in 7 statistically significant clinical parameters (Supplementary Table 2 and Supplementary Fig. 14). An ambulation product value of 1000 was selected as it was close to the median value (1079), and represented a well-defined separation between the two groups (Fig. 4d). Subjects with P < 1000 had lower 6MWD at visits 1 and 2, and experienced more pedal edema (Pedal Edema score) at visit 1. Two subjects had ambulation product values > 5000 (PAH 9, 19). Both subjects had a high ambulation frequency and walked more than 5000 steps per day on average. Both subjects also had relatively lower resting heart rates, longer free-living 6MWD (see below), and higher fitness plot slopes. PAH 1, despite having the highest step count, ranked fourth in ambulation product value as a result of having relatively lower endurance and intensity values./p> 0) for analysis. In this study the average weekly usage was 0.44–0.97. Note that charging the device overnight (e.g. 8 h) once a week results in a weekly usage of 0.95. We also defined the maximum off-time as the longest continuous time during the week that the device was not worn, which varied from less than 1 h to more than 12 h. From heat maps of usage and the maximum off-times for all subjects (Supplementary Figs. 15 and 16) we can further infer how the device was used./p> 0. Yellow cells indicate that the device was worn continuously for the full hour. White cells indicate that the device was not worn (no HR recorded) for the full hour. a Heat map for PAH27 (13 weeks of data), showing low usage (average = 0.49) with the device not worn overnight. b The maximum off time for each week for PAH27 is consistently around 12 h overnight. Each point represents the maximum off-time for each week in the trial. c Heat map for PAH30 (22 weeks of data), showing relatively high usage (0.90), with the device removed for several hours every few days. d The maximum off time for PAH30 is typically 8–20 h and includes overnight hours. e Heat map for PAH10 (13 weeks of data), showing high usage (0.97). For the first 10 weeks the maximum off-time is less than 1 h. f The maximum off time for PAH10 is usually less than 1 h./p>0.94, which corresponds approximately to the 75th percentile. Comparison of usage, resulted in 4 statistically significant clinical parameters (Supplementary Table 2 and Supplementary Fig. 18). Subjects with average weekly usage < 0.94 (15/22) were more likely to have more severe PAH (higher EQ VAS score) at visit 1, worse pulmonary health (higher physician assessed WHO FC score) at visit 1, and experienced more difficulty breathing (modified Borg dyspnea score) at visit 2. Two subjects had average usage < 0.5 (PAH 4, 27), however, both of these subjects removed the device overnight. The third subject who removed the device overnight (PAH 20) also had low average usage (0.60). (Changes in device usage over time are summarized in Supplementary Figs. 19 and 20)./p>320 m (PAH1, 3, 9, 10, 11, 12, 14, 17, 19, 22, 23, 26, 27, 28). Comparison of FL6MWD resulted in 6 statistically significant clinical parameters (Supplementary Table 2 and Supplementary Fig. 23). Notably, subjects with average FL6MWD < 320 m had lower 6MWD at visit 1 and visit 2, experienced more pedal edema (Pedal Edema score) at visit 2, had worse pulmonary health (higher physician-assessed WHO FC) at visit 1, and had lower hemoglobin at visit 2./p> 480 m (PAH3, 23). These subjects were in the fourth quadrant of the PCA plot, which implies that they had a wide range of step rates and heart rates during normal weekly activity, and had ambulation product P values > 1000./p> 400 m (12/22) had higher 6MWD at visit 2, lower NTpro-BNP at visit 2, experienced less chest pain (Angina score) at visit 1, and had better pulmonary health (lower physician-assessed WHO FC) at visit 2 (Supplementary Table 2 and Supplementary Fig. 24)./p>4.0 m/week) (PAH3, 10, 20), and four subjects had a large negative slope (<4.0 m/week) (PAH1, 13, 21, 23)./p> 1 but, as described previously, this subject recorded high FL6MWD values during the first 13 weeks, but then maintained a much lower value in subsequent weeks. It is evident that there is no correlation between the FL6MWD in week 1 and the predicted 6MWD (H6MWD) for an equivalent healthy individual (Fig. 7a)./p> 0. Three subjects (PAH 30, 2, 20, 11) had health state values below 0.52 in their first and last weeks. These subjects were located along the negative x-axis of the PCA plot, characterized by a large fraction of time inactive./p> 0), ambulation P value, fitness slope. Based on the maximum Bayesian Information Criterion (BIC) (Supplementary Table 3), the subjects were categorized into three groups (Supplementary Fig. 28). Group 1 had high ambulation metrics (steps/day, ambulation product P, and FL6MWD), high HR(SR > 0), and high fitness slope (Supplementary Table 4). Group 2 were characterized by the lowest ambulation metrics (steps/day, ambulation product P, FL6MWD), the lowest HR(SR = 0) and HR(SR > 0), and the highest HR(SR = 0)sk. Group 3 had the highest HR(SR = 0) and HR(SR > 0), the lowest HR(SR = 0)sk and fitness slope. The three groups identified from LPA analysis occupied distinct regions of the PCA plot, with the exception of PAH 10 who was in Group 2 (Supplementary Fig. 29)./p> ±0.5). Albumin was correlated with HR(SR = 0) and HR(SR > 0) at visit 1 (r = 0.565 and 0.627, respectively). NT-proBNP was also correlated with HR(SR = 0) at visit 1 (r = 0.585), and was inversely correlated with fitness slope at visit 1 (r = −0.585). RHR at visits 1 and 2 were correlated with HR(SR = 0), HR(SR = 0)sk, and HR(SR > 0). 6MWD at visits 1 and 2 were correlated with FL6MWD. RVSP at visit 1 was inversely correlated with fitness slope. Notably, steps/day and ambulation P did not have strong correlations to the continuous clinical parameters./p> 0.15 had lower NT-proBNP levels, an important biomarker of cardiac stress, at visits 1 and 2. In addition, this approach may contribute to identification of individuals who would benefit from more frequent clinic visits or specific medications./p> 0), i.e. active). From the distributions of these three metrics we obtained the mean, standard deviation, and the skewness. The heart rates were fit to a normal distribution, and the step rate was fit to a log normal distribution. A scatter plot of step rate versus heart rate provided a weekly signature of cardiovascular activity for each individual. From a linear least-squares fit to the data we obtained the slope (heart rate per step rate (BPM/SPM)). The effective area of the heart rate versus step rate (HR vs. SR) plot was determined by first calculating the upper (lower) envelopes. Each point in the upper and lower envelopes represents the average of the maximum (or minimum) HR values at each value of step count in a bin width of 10 SPM. The envelope point is located at the average step rate for all values with HR values. Step rates with no HR values are omitted from the calculation. Bins with no HR values do not have an envelope point. We then performed a linear least-squares fit to the envelopes to determine the area of the HR-SC plot./p> \,1.0\) is considered large./p> 0):SD, SR(SR > 0):mean, SR(SR > 0):SD, time inactive (fraction of minutes with SR = 0). These parameters were selected to represent heart rate and ambulation metrics and to avoid redundancy. For each parameter we used the average weekly value. The variance for the first two principal components were 48.6% and 30.0%, respectively. For 100 independent runs where we randomly selected different weeks, the mean variance of PC1 and PC2 was 77.5 ± 0.58%./p> 0), ambulation P value, fitness slope, FL6MWD, and usage. LPA was performed through package ‘mclust’ (version 5.4.10) in R (version 4.2.1). The optimal number of clusters was determined based on the maximum Bayesian Information Criterion (BIC) through the function ‘mclustBIC’./p>
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