Abstract
Objective
The purposes of this study were to (1) evaluate heart rate variability (HRV) monitoring for assessing morning, mid-day, evening, and sleep sample periods; (2) assess study-related procedures, personnel, and physical resources available at our institution; (3) identify potential problems and solutions associated with the use of 24-hour HRV at our institution.
Methods
Six participants were fitted with 24-hour HRV recording devices (DMS myPatch-sl) and evaluated under 3 conditions; normal-daily-activity, treadmill-walking, and paced-deep-breathing. In addition, each participant maintained a written event-diary during the evaluation period. Tachograms for sample time periods were reported with representative Fast Fourier Transform Power Analyses.
Results
We found that proper ECG electrode patch placement on study participants required specific training and written instructions. The DMS Holter monitor provided good-quality data over the 24-hour period without interfering with normal daily activities. However, there was data loss for 1 hour immediately following exposure of the device to water while showering. Also, there was a need to stabilize the V5 electrode wire to minimize disconnections during sleep. Participant diaries varied considerably in useful information content.
Conclusion
The findings for the 6 participants in this study support the feasibility of and inform applying 24-hour HRV assessment in research studies at our institution.
Key Indexing Terms: Heart Rate; Observer Variation; Electrocardiography, Ambulatory; Biomarkers; Feasibility Studies; Health; Chiropractic
Introduction
Chiropractic clinicians strive to provide healthcare that is wellness-focused and supports optimal health and performance.1 While numerous biomarkers assess health status in the disease state, their performance in the nondisease portion of the healthcare continuum is largely unknown (Fig 1).2,3 In 1946, the World Health Organization asserted that, “Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity.”4 This was a paradigm shift, acknowledging that absence of disease is an important, but not sufficient, feature of “wellness.”5,6 In a 2017 commentary on assessing health in aging populations, Cesari et al7 noted that dichotomous variables frequently used to describe disease conditions support the construction of clinical decision algorithms but are relatively insensitive clinical endpoints when used to determine therapeutic effectiveness. While dichotomous variables can signal clinical manifestation of disease, provide a medical alert, and be associated with a therapeutic response, wellness-focused variables that signal reduced intrinsic health capacity can prompt needed interventions in the absence of overt disease signs and symptoms.7,8
Fig 1.
Wellness-focused biomarkers on the healthcare continuum.
Wellness-focused care requires validated psychomodulated (subjective) outcome assessments and biomarkers (objective assessments) that are sensitive to changes within the wellness end of the health spectrum. These assessments need to be easy to measure, low cost, and practicable to assess in the clinician’s office.9 The overarching goal of our institution’s research effort has been to identify potential wellness-focused assessments that can inform general chiropractic practice and, more specifically, chiropractic wellness care. Consequently, we conducted an extensive literature search to identify outcome measures that are not disease-specific but those that may inform a wide range of health conditions, which is an essential feature for an operational definition of health-focused biomarkers. Heart rate variability (HRV) was one of the well-documented biomarkers.10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20
HRV as a Wellness Biomarker
HRV is a measure of variation in the time intervals between normal sinus heartbeats, the interbeat intervals. An optimal level of variability is critical to the inherent flexibility and adaptability that characterizes healthy function and well-being (intrinsic health capacity).3 As noted by Shaffer et al,21 “a healthy heart is not a metronome”; the time intervals between beats are not normally of equal lengths. For example, a heart beating at 60 beats per minute does not beat once every second—or at 1-second intervals like a clock. Rather, there is a normal variation between heartbeats. In electrocardiogram tracings, HRV is observed as changing time intervals between the R-R peaks of adjacent QRS complexes (Fig 2).22
Fig 2.
Heart rate variability.
The heart rate demonstrates a circadian pattern during each 24-hour day.20 The circadian system regulates daily modulation of synchronized physiological activity, conserving energy expenditure and internal resources.23,24 Among healthy persons, this optimizes body functions in relation to demand loads associated with the time of day, thereby coordinating physiological functions with behavior. Because HRV is under moment-to-moment autonomic control, it provides a noninvasive way to monitor dynamic changes in sympathetic and parasympathetic activity.12,25 Consequently, HRV may provide a means for studying autonomic functioning in health and disease.
Rapid and appropriate autonomic responses enable adaptation to challenges that occur frequently in the environment. Moreover, autonomic activity influences most body systems and functions within the full range of the health spectrum. Consistent with this role, HRV is a sensitive biomarker for a number of health conditions: chronic tension-type headache,26 fibromyalgia and chronic fatigue syndrome,27 obstructive sleep apnea,28 anxiety,29 depression,30 asthma,31 inflammatory bowel disease,32 increased risk of obstructive angiographic coronary disease,33 stroke,34 and sudden infant death syndrome.35 And, HRV may be used to assess the impact of lifestyle changes within the wellness/optimal-vitality range of the health spectrum14,36, 37, 38, 39, 40 and correlates well with global measures of self-rated health.41
The purposes of this feasibility study were to (1) evaluate DMS myPatch-sl 24-hour HRV monitoring; (2) assess prospective study procedures, personnel, and physical resources available when applying 24-hour HRV monitoring; and (3) identify potential problems and possible solutions associated with the use of 24-hour HRV monitoring.
Methods
The Life Chiropractic College West Research Board assigned the 45 CFR 46.102 determination, “Not Human Subjects Research” (Project ID: NHSR 2019-A.IRB). Each volunteer participant provided written affirmation of informed consent. Six research staff/faculty members volunteered to be participants in this feasibility exercise. Three of the 4 authors participated as subjects in this study (CH, DJ, MS).
Participants were fitted with a 24-hour HRV recording device (Fig 3) and evaluated under 3 conditions: normal daily activity, treadmill-walking, and paced-deep-breathing. In addition, each participant maintained a written event-diary during the 24-hour evaluation period. Two research team members received 2-days of 1-on-1 technical training with the DMS myPatch-sl Holter device from Phyllis K. Stein, PhD, director of the Washington University School of Medicine’s HRV Lab.
Fig 3.
The DMS myPatch-sl monitor. The water-resistant patch system has 3 surface electrodes with a mounted Holter recorder that can store up to 72 hours of high-resolution 3-channel ECG data.
Data Collection
Beat-to-beat files from the Holter Recorder were provided via DropBox to research technicians at the Washington University School of Medicine Heart Rate Variability Laboratory. They processed the files using a GE Marquette MARS 8000 Holter Analyzer (GE-Marquette, Milwakee, Wisconsin) and then imported the data into a custom-designed program for calculation of HRV parameters (HRVInteractive). All Holter analyses were reviewed in detail by study investigator PKS, with special attention paid to ensuring that only beats with uniformly detected onsets were labeled as normal. The longest and shortest true normal-to-normal intervals were identified for each recording, and intervals outside of these limits, including blocked atrial premature contractions, were excluded from all HRV calculations. Time domain, frequency domain, nonlinear HRV, and ectopy counts were calculated from the annotated beat-to-beat files exported to the HRVInteractive software (Fig. 4).
Fig 4.
HRV conditions and parameters.
Results
The 6 volunteer participants all completed the study (Table 1). Heart rate showed changes across the day, as illustrated in Fig. 5A to D (case 3), with a low heart rate in the morning after the participant arises from sleep, at 07:25 (Fig. 5A), a moderate increase during the mid-day and evening (Fig. 5B and C), and a precipitous heart rate drop with substantially less variation during sleep (Fig. 5D). See also the heart rate and HRV metrics reported across the 4 time periods for case 3 (Table 2).
Table 1.
Participant Demographics
| Case | Sex | Age (y) |
|---|---|---|
| 1 | F | 46 |
| 2 | M | 69 |
| 3 | M | 61 |
| 4 | F | 42 |
| 5 | F | 61 |
| 6 | M | 75 |
Fig 5.
Time-period tachograms: Heart rate vs time (second). Four representative 1-hour time periods are reported for comparison. The time marks immediately above and below each plot frame report minutes. The lowermost time marks report hours on the 24-hour clock (hh:mm:ss). At the right margin of each plot, values for the highest, mean, and lowest heart rates are reported: (A) Morning (0700), (B) mid-day (1200), (C) evening (1900), and (D) sleep (0200). Case 3.
Table 2.
Heart Rate Variability Metrics (2-Hour Day-Periods, Case 3)
| Morn | Mid | Eve | Sleep | |
|---|---|---|---|---|
| HR Mean (bpm) | 68.2 | 75.9 | 81.3 | 59.2 |
| NN Mean (ms) | 901.9 | 796.5 | 745.4 | 1017.2 |
| SDNN (ms) | 142.9 | 71.2 | 77.3 | 52.8 |
| RMSSD (ms) | 18.6 | 16.4 | 13.6 | 18.2 |
| VHF (0.4-0.5 Hz) | 7.8 | 13.0 | 8.7 | 8.3 |
| HF (0.15-0.4 Hz) | 81.5 | 38.8 | 35.0 | 76.4 |
| LF (0.04-0.15 Hz) | 608.8 | 361.4 | 159.2 | 333.8 |
| VLF (0.003-0.04 Hz) | 3899.1 | 1550.0 | 528.8 | 2106.6 |
| ULF (0-0.003 Hz) | 3337.5 | 2123.0 | 2311.1 | 398.6 |
| TP (0-0.5 Hz) | 7867.1 | 4086.2 | 3042.8 | 2923.6 |
From the 24-hour collection of ECG data, HRV parameter averages were calculated for four 2-hour time periods as the participants went about their normal daily activities (24-hour clock): Morning, 07:00 to 09:00 hours. Mid-day, 12:00 to 14:00 hours. Evening, 19:00 to 21:00 hours. Sleep, 02:00 to 04:00 hours.
During the treadmill-walking challenge, there was an increase in heart rate and decreased HRV (Fig. 6A). The paced-breathing challenge displayed cyclic increased heart rate with inspiration and decreased rate with expiration (Fig. 6B). Representative power spectral analysis plots (Fast Fourier Transforms)42 are shown for the morning and sleep time periods (Fig. 7). The power spectrum for morning (Fig. 7A) shows almost all power in the lower frequencies (LF, VLF, and ULF), while during sleep (Fig. 7B), a small high frequency peak was observed in 5 of the 6 cases.
Fig 6.
Challenge tachograms: Heart rate vs time (second). (A) Treadmill-walking—10-minute period shown. Increased heart rate is sustained during treadmill walking. (B) Paced-deep-breathing (5 breaths/min)—2-minute period shown. Heart rate increases with each inspiration and decreases with each expiration. Plot time marks are explained in the legend for Figure 3. Case 3.
Fig 7.
Power spectral analysis, fast Fourier transforms. (A) Morning (07:00-08:00) time period was typical of 1-hour plots in all time periods except sleep. Almost all power was distributed in the lower frequencies (Hz, reported below the plots). HF, high frequency; LF, low frequency; ULF, ultralow frequency; VHF, very-high frequency; VLF, very-low frequency. (B) Sleep (02:00-03:00), an HF peak was common during sleep (small green peak at approx. 0.17 Hz).
Description of Study Procedure and Resource Feasibility
The findings from the 6 participants provided observations concerning our study procedures, personnel, and the physical resources available at our institution for assessing 24-hour HRV. The following describes problems and possible solutions.
-
1.
Proper placement of the ECG electrode patch on study participants required specific training and written instructions. For female participants, a female assistant was needed for electrode placement. The consideration to respect modesty preferences may be especially important in the case of culturally diverse participant populations.
-
2.
The V5 electrode (Fig 3) became disconnected during sleep for 2 of our 6 participants. Taping the wire proximal to the electrode may provide greater stability.
-
3.
Although the DMS myPatch-sl Holter device is water-resistant (showering is permitted with the device), the signal was corrupted for approximately 1 hour after participants showered.
-
4.
Participant event-diaries varied in quantity and usefulness of recorded information. Few participants reported the times for critical events such as bedtime, awake time, meals, exercise periods, shower/bath, etc. No participants reported on the presence or absence of stressful events during the assessment period. Guidelines with individual instruction and examples may be required in future studies to reduce variance. Multimedia collection procedures should also be considered (eg, online survey or smartphone utilization). The use of smartphone input capability may make it easier for participants to provide time-specific information (eg, bedtime, awake time, meal times, and the beginning and ending of exercise periods). This solution would be limited to participants with smartphones and would require that those participants are comfortable using smartphone input procedures. The use of an online questionnaire would permit collecting study-specific information, but it would be limited to participants with online access and comfort using that format. At a minimum, participants would need a diary template and instruction on its use.
-
5.
The clinical exam room provided by the college was adequate, but improved electromotive force shielding and better room temperature control is needed.
-
6.
Participant compliance was poor regarding abstention from coffee and alcohol consumption prior to and during the assessment period. Explaining the effects of coffee and alcohol consumption on study outcome measures may improve compliance with this restriction.
Discussion
Figure 1 demonstrates the widely held disease-focused perspective. The large double arrow presents 2 healthcare approaches within a dynamic range of health outcomes: (1) a disease-focused approach that acts primarily to move patients from the symptomatic disease region (red) into the “no symptoms” region of patient experience and (2) a wellness-focused approach (green) that strives to help individuals achieve and maintain wellness, expressed as optimal vitality. HRV is simply a measure of the variation in time between heartbeats (R-R intervals). Note the 828 ms interval between the first 2 beats and the varying time intervals between the other beats, 845 ms, 754 ms, and 742 ms (Fig. 2).
This feasibility study was completed in preparation for future research at our institution that will evaluate 24-hour HRV as a potential wellness-focused biomarker. In the present feasibility study, we evaluated use of the DMS myPatch-sl 24-hour HRV monitoring device in collaboration with a researcher and support staff at the Washington University School of Medicine’s HRV Lab (PKS). In addition, we tested our prospective study procedures and personnel, reviewed our institution’s pertinent physical resources, and identified potential study-related problems and solutions.
The DMS myPatch-sl Holter Monitor provided quality data over the 24-hour period, albeit with loss of usable data for 1 hour immediately after a participant exposed the device to water when showering. This suggests an issue to be considered when designing future studies with this device: How will the DMS myPatch-sl Holter Monitor perform if research participants are exposed to rain showers or sweating during a vigorous workout? In addition, we learned that a brief training session was needed for our staff with written instructions regarding electrode placement. And adhesive tape stabilization of the V5 electrode wire is needed to minimize disconnection during the participant’s sleep period.
Study participants did not adequately record events on the diary sheets. We believe this information source will be improved by emphasizing its importance during the consent process and on the day that participants have the 24-hour Holter monitors attached. Future diaries should have “text-fill-in” fields for specific events of interest (eg, time to bed and arise, shower/bath time, and physical exercise time) with examples of what would constitute other relevant events. There are also multimedia approaches that could be taken, such as using online survey platforms (eg, SurveyMonkey or Qualtrics). It is possible that study participants could be reminded periodically by cell phone text to record events. In addition, they could be trained to use their smartphones to vocally record diary data. These approaches should increase the quality and quantity of diary information. The cell phone method would only apply to participants that have smartphones. Finally, participants were not fully compliant with the instructions that they refrain from coffee consumption immediately prior to and during the 24-hour HRV monitoring period. Research participants are commonly asked to refrain from coffee and alcohol consumption in 24-hour HRV studies, but it can be argued that this may introduce a confounding modification of their typical daily activities, conflicting with the objective to record HRV during a participant’s normal activities.43, 44, 45 In addition, it has been argued that the effect of study-related behavior modification is likely to be more marked in binge and chronic drinkers, possibly triggering a physiological withdrawal response.43 This would be an important issue to consider in studies that are particularly likely to include binge and chronic drinkers.
Limitations and Future Directions
The primary limitations of this study were the size and selection of participants. The small sample size limits generalizability. A larger, randomized sample with participants from diverse backgrounds, ages, and ethnicities would provide a better representation of issues and opportunities with this technology at our institution. Nevertheless, this small study was instructive, and we believe it provides adequate information to proceed with a small-scale exploratory study that will initiate our evaluation of 24-hour HRV as a wellness-focused biomarker. In future work, we will conduct convergence validation investigations with additional measures of autonomic function such as pupillometry and electrodermal activity.46,47
Conclusions
The findings of this 6-case study support the feasibility of applying 24-hour HRV assessment in research studies at our institution. The DMS myPatch-sl Holter Monitor provided quality data over the 24-hour period without interfering with normal daily activities, but with loss of useable data for 1 hour immediately following a participant’s exposure of the device to water while showering. In addition, we observed a need to stabilize the V5 electrode wire to minimize disconnection during participant sleep periods. Lastly, our experience with the study diaries suggested modifications are needed to increase both the quantity and quality of data obtained from the diaries.
Funding Sources and Conflicts of Interest
No funding sources or conflicts of interest were reported for this study.
Contributorship Information
Concept development (provided idea for the research): C.H., M.S. Design (planned the methods to generate the results): C.H. Supervision (oversight, organization, and implementation): C.H. Data collection/processing (experiments, organization, or reporting data): C.H., D.J. Analysis/interpretation (analysis, evaluation, presentation of results): C.H., P.K.S., D.J. Literature search (performed the literature search): C.H. Writing (responsible for writing a substantive part of the manuscript): C.H. Critical review (revised manuscript for intellectual content): C.H., D.J., M.S., P.K.S.
Practical Applications.
-
•
This feasibility study evaluated heart rate variability (HRV) monitoring for assessing morning, mid-day, evening, and sleep sample periods for 6 participants.
-
•
We found that proper ECG electrode patch placement on study participants required specific training and written instructions.
-
•
The DMS Holter monitor provided good-quality data over the 24-hour period without interfering with normal daily activities.
-
•
This study supports the feasibility of and informs applying 24-hour HRV assessment in research studies at our institution.
Alt-text: Unlabelled box
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