Systemic arterial hypertension in adults is generally defined as a systolic blood pressure (SBP) of >140 mmHg and/or a diastolic blood pressure (DBP) of >90 mmHg [] and represents a major challenge worldwide owing to a rapid year-by-year increase in prevalence [,,]. There are well-recognised challenges in blood pressure (BP) measurement by physicians in the clinical setting []. As such, methods to evaluate systemic hypertension have evolved considerably over the last two decades, moving away from a conventional office-based approach towards integrated, automated techniques that can be performed unattended. The emergence of enhanced smartphone technologies shows great promise in this space.
In this issue of Diagnostics, “Comparability of a Blood-Pressure-Monitoring Smartphone Application with Conventional Measurements” [], Vischer et al. compare a smartphone application-based algorithm (termed AppBP) with an office-based BP monitoring approach (termed CuffBP). This study enrolled consecutive patients with an indication for ambulatory BP monitoring. Smartphone app technology (RIVA digital) was custom designed to acquire the pulse wave in the fingers’ arterial bed using the smartphone’s camera and then estimate BP based on an analysis of photoplethysmographic (PPG) waveforms [].
Measurements were alternatingly taken with the AppBP or CuffBP on two consecutive days. Four measurements per day resulted in four hundred valid CuffBP values. After calibration, each smartphone BP recording was compared to the mean of the previous or following CuffBP recording. A total of 50 patients were analysed (of which 48% were female). Thirty-eight participants (76%) had a known history of hypertension, and thirty (60%) were receiving antihypertensive treatment at the time of the study []. The systolic BP readings ranged from 89 to 202 mmHg for all 400 CuffBP values, with a mean of 126 ± 16 mmHg. The diastolic BP ranged from 48 to 96 mmHg, with a mean of 79 ± 8.7 mmHg. A total of 4% of the systolic CuffBP values were ≤100 mmHg, 15% were ≥140 mmHg, and 3% were ≥160 mmHg []. Additionally, 3% of the diastolic CuffBP values were ≤60 mmHg, 28% were ≥85 mmHg, but none were ≥100 mmHg [].
Notably, the post-recording quality threshold was not met in 225 AppBP-based measurements, which had to be excluded. This left 175 uncalibrated values and 83 calibrated values. Importantly, the uncalibrated systolic AppBP values were significantly lower than the CuffBP values (p < 0.0005); as a result, only the AppBP sets with a valid first AppBP measurement were included for subsequent analysis. Reassuringly, the calibrated systolic AppBP values (median 127.6 [IQR 113.8–135.3] mmHg) did not significantly differ from the corresponding systolic CuffBP values (median 125.0 [IQR 115.0–132.0] mmHg, p-value 0.234, z-value 1.190); however, the calibrated diastolic AppBP values (median 79.3 (IQR 74.0–83.6) mmHg) were significantly higher than the corresponding diastolic CuffBP values (78.5 [IQR 72.0–82.5] mmHg, p-value 0.041, z-value 2.039) [].
Overall, Vischer et al. found that an AppBP assessment alone without calibration was insufficient to meet the quality threshold in more than 50% of the participants, as there were significant differences between the AppBP and CuffBP values. After calibration, however, the agreement between AppBP and CuffBP values was improved (especially for systolic BP measurement) []. The author(s) concluded that the PPG-based algorithm with single-point calibration was of sufficient quality to meet established international standards [,].
One limitation is that in both arms of the study, patients were asked to attend the office. White-coat hypertension is a well-documented phenomenon, and the digital era has facilitated the uptake of home-based strategies []. Home and ambulatory monitoring have proven to be better prognostically in predicted end-organ damage []. Moreover, telemedicine has been shown to reduce SBP and DBP when compared to usual care []. In addition, the self-monitoring of BP has been shown to increase patient autonomy and compliance with therapy [].
Vischer et al. demonstrate that an AppBP model that utilises PPG technology can record BP with sufficient accuracy. The authors should be commended for their innovative work in bringing together an array of technologies. In so doing, the authors have elegantly demonstrated a PPG model that may be applied in future population studies where the diagnosis of hypertension is in question.
The diagnosis of hypertension can be, at times, challenging and imprecise in the clinical setting. Advanced smartphone technology systems are indispensable tools in this regard. Though this is a pilot study with a relatively small dataset, it is the first to demonstrate that a PPG-based app approach is viable. Undoubtedly, the quality of data was improved with the addition of calibration, and the high PPG data rejection rates are a clear indication that a post-recording check is also crucial. Nevertheless, this study provides insight into the prognostic value of integrating PPG-based smartphone applications into the hypertension diagnostics space []. This technology can only be strengthened with home-based monitoring, continuous monitoring to identify stressors, and the potential incorporation of artificial intelligence.
Overall, Vischer et al. demonstrate that an AppBP model that utilises PPG technology can record BP with sufficient accuracy. The author(s) should be commended for their innovative work in bringing together an array of technologies. In so doing, the author(s) have elegantly demonstrated a PPG model that may be applied in future population studies where the diagnosis of hypertension is in question. Future prospective studies of this model with other systems are warranted.
Author Contributions
S.H. and N.B. were equally responsible for all aspects of editorial manuscript formulation and writing. All authors have read and agreed to the published version of the manuscript.
Funding
All authors have reported that they have no relationships relevant to the contents of this editorial to disclose.
Conflicts of Interest
All authors have reported that they have no relationships relevant to the contents of this editorial to disclose.
References
- Vischer, A.S.; Rosania, J.; Socrates, T.; Blaschke, C.; Eckstein, J.; Proust, Y.-M.; Bonnier, G.; Proença, M.; Lemay, M.; Burkard, T. Comparability of a Blood-Pressure-Monitoring Smartphone Application with Conventional Measurements—A Pilot Study. Diagnostics 2022, 12, 749. [Google Scholar] [CrossRef] [PubMed]
- Zanchetti, A. Challenges in hypertension: Prevalence, definition, mechanisms and management. J. Hypertens. 2014, 32, 451–453. [Google Scholar] [CrossRef] [PubMed]
- Bloch, M.J. Worldwide prevalence of hypertension exceeds 1.3 billion. J. Am. Soc. Hypertens 2016, 10, 753–754. [Google Scholar] [CrossRef] [PubMed]
- Mills, K.T.; Bundy, J.D.; Kelly, T.N.; Reed, J.; Kearney, P.M.; Reynolds, K.; Chen, J.; He, J. Global Disparities of Hypertension Prevalence and Control: A Systematic Analysis of Population-Based Studies From 90 Countries. Circulation 2016, 134, 441–450. [Google Scholar] [CrossRef] [PubMed]
- Mancia, G.; Facchetti, R.; Bombelli, M.; Cuspidi, C.; Grassi, G. White-Coat Hypertension: Pathophysiological and Clinical Aspects: Excellence Award for Hypertension Research 2020. Hypertension 2021, 78, 1677–1688. [Google Scholar] [CrossRef] [PubMed]
- NCD Risk Factor Collaboration (NCD-RisC). Worldwide Trends in Blood Pressure from 1975 to 2015: A Pooled Analysis of 1479 Population-Based Measurement Studies with 19·1 Million Participants; Lancet: London, UK, 2017; Volume 389, pp. 37–55. [Google Scholar]
- Stergiou, G.S.; Alpert, B.; Mieke, S.; Asmar, R.; Atkins, N.; Eckert, S.; Frick, G.; Friedman, B.; Graßl, T.; Ichikawa, T.; et al. A Universal Standard for the Validation of Blood Pressure Measuring Devices: Association for the Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization (AAMI/ESH/ISO) Collaboration Statement. Hypertension 2018, 71, 368–374. [Google Scholar] [CrossRef] [PubMed]
- Stergiou, G.S.; Palatini, P.; Asmar, R.; Ioannidis, J.P.; Kollias, A.; Lacy, P.; McManus, R.J.; Myers, M.G.; Parati, G.; Shennan, A.; et al. Recommendations and Practical Guidance for performing and reporting validation studies according to the Universal Standard for the validation of blood pressure measuring devices by the Association for the Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization (AAMI/ESH/ISO). J. Hypertens. 2019, 37, 459–466. [Google Scholar] [PubMed]
- Kario, K. Management of Hypertension in the Digital Era: Small Wearable Monitoring Devices for Remote Blood Pressure Monitoring. Hypertension 2020, 76, 640–650. [Google Scholar] [CrossRef] [PubMed]
- Hoshide, S.; Yano, Y.; Haimoto, H.; Yamagiwa, K.; Uchiba, K.; Nagasaka, S.; Matsui, Y.; Nakamura, A.; Fukutomi, M.; Eguchi, K.; et al. Morning and Evening Home Blood Pressure and Risks of Incident Stroke and Coronary Artery Disease in the Japanese General Practice Population: The Japan Morning Surge-Home Blood Pressure Study. Hypertension 2016, 68, 54–61. [Google Scholar] [CrossRef] [PubMed]
- McLean, G.; Band, R.; Saunderson, K.; Hanlon, P.; Murray, E.; Little, P.; McManus, R.J.; Yardley, L.; Mair, F.S. Digital interventions to promote self-management in adults with hypertension systematic review and meta-analysis. J. Hypertens 2016, 34, 600–612. [Google Scholar] [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).