8.3 Lifestyle interventions 132
8.3.2 Guided home-based resistance exercise
For the last contribution of this thesis, presented in Chapter 7, the goal was to go beyond lifestyle recommendations. Rather, the focus was on creating technology that can help individuals in executing a lifestyle intervention effectively and safely. The intervention that was selected for this was resistance exercise training performed in a home setting. A system was proposed and evaluated which leveraged consumer devices in a home setting to track the exercise and provide guiding auditory and visual feedback. The aim was to improve the execution, safety, engagement and enjoyment of resistance exercise at home for individuals with (pre-)hypertension.
Participants showed signs of better engagement, less perceived effort and more correct execution in comparison to receiving no feedback. At the same time, the system did not show better results than a human tele-coach which was used as a reference. This intuitively makes sense as human guidance is intelligent and non-automated, however one major advantage over human coaching is that such a system is cheaper and always available at home. Not everyone can afford or is willing to pay for a real coach, neither are there enough coaches for all people who need them. This technology provides a surrogate that can potentially reach many more people who can benefit from guidance during home-based resistance exercise.
As sensory technology evolves, such techniques are likely to improve. For example, virtual reality technology could provide a much more immersive experience, making resistance exercise more enjoyable. Adapting such systems for specific patient groups, closely following international guidelines such as those of the ACSM, while simultaneously making them more engaging and accessible, will likely have a positive impact on population health and help combat the chronic disease epidemic.
8.4 Towards preventing and managing lifestyle disease
In this PhD project, a holistic view has been taken towards the prevention and management of lifestyle diseases, with the ambition to propose technologies that can assist people with measuring and monitoring their health comfortably and unobtrusively, helping in navigating the different lifestyle interventions through personalised recommendations and even providing assistance while individuals perform interventions. To those ends, several contributions have been made. Method-ologies have been evaluated to improve PPG-based monitoring during sleep. This resulted in two validated algorithms for PPG: one for the measurement of sleep stages and another for the measurement of the nocturnal blood pressure dip. These methods can be used in conjunction with
earlier innovations in PPG measurement, such as respiration rate monitoring [258, 233] or atrial fibrillation detection [26] to name a few. Combining all these intelligent algorithms will result in a holistic and comprehensive view of an individual’s health, that can be unobtrusively measured through PPG over very long terms. However it remains to be seen how this rich set of physiological data points can then be leveraged into actionable insights for individuals and care givers to act upon.
The next milestone is to combine these building blocks and deploy them in large populations to understand how all of this data may be appropriated in health management. The vision is that individuals (and their care providers) can track their health over time to to acquire continuous insights about their personal health to facilitate building up the right intentions to improve lifestyle behavior. However, turning an intention into actual behavior is difficult. There are several barriers that may demotivate the individual and make their attempts unsuccessful. To that end, a model of behavior feasibility was presented with which individualised predictions of behavior feasibility could be made with respect to the individual’s ability level. This model could also be combined with other systems, such as for example multi-armed bandit recommenders [277] or insight mining from behavioral data [262]. Using a combination of modelling techniques could help to narrow down to recommendations that are even more personalised, beneficial and appealing to the user.
Finally, there are many technologies available to assist people in executing the recommended lifestyle interventions. An example are the many physical exercise [121] or diet [228] tracking apps.
Also to this end, a solution was proposed to a less-catered for intervention: home-based resistance exercise for patients or at-risk populations. As pervasive devices and sensing technologies become more standardised and commonplace across the world, there is a clear opportunity to leverage the sensing and actuation mechanisms for well-being. Again, combining this building block with other existing tools and methods will help in guiding and supporting people in performing a wide spectrum of lifestyle interventions effectively and safely.
The big unanswered question remains how all these things would come together in practice.
There is still extensive research needed to understand how the potential of these technologies can be utilised. Creating holistic systems that encompass the full spectrum of measurement technologies, lifestyle coaching and intervention guidance laid out here requires significant investments. However, there is no incentive for industry to develop such systems as wellbeing, preventive self-care are in most cases not insured. Correct government policies that embrace and prioritise the data-driven future of well-being are needed to either directly invest in such programs or incentivise commercial development through changes in insurance policies. Such a shift in priorities could accelerate the research in this area, moving beyond the stage of individual concept validation such as presented here and in related research, into an integrated systems approach where multiple enablers are connected to form holistic, intelligent systems that can solve some of the biggest health challenges of our day.
9. Bibliography
Articles
[1] I. Abubakar, T. Tillmann, and A. Banerjee, “Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: A systematic analysis for the global burden of disease study 2013”, Lancet, volume 385, number 9963, pages 117–171, 2015 (cited on page 16).
[2] U. R. Acharya, K. P. Joseph, N. Kannathal, C. M. Lim, and J. S. Suri, “Heart rate variability:
A review”, Medical and biological engineering and computing, volume 44, number 12, pages 1031–1051, 2006 (cited on pages 17, 21).
[3] U. R. Acharya, K. P. Joseph, N. Kannathal, L. C. Min, and J. S. Suri, “Heart rate variability”, Advances in cardiac signal processing, pages 121–165, 2007 (cited on page 35).
[4] A. Afshin, D. Babalola, M. Mclean, Z. Yu, W. Ma, C.-Y. Chen, M. Arabi, and D. Mozaf-farian, “Information technology and lifestyle: A systematic evaluation of internet and mobile interventions for improving diet, physical activity, obesity, tobacco, and alcohol use”, Journal of the american heart association, volume 5, number 9, e003058, 2016 (cited on page 29).
[5] J. Allen, “Photoplethysmography and its application in clinical physiological measurement”, Physiological measurement, volume 28, number 3, R1, 2007 (cited on pages 17, 18).
[6] D. G. Altman and J. M. Bland, “Improving doctors’ understanding of statistics”, Journal of the royal statistical society: Series a (statistics in society), volume 154, number 2, pages 223–248, 1991 (cited on page 58).
[7] A. J. Amorose and T. S. Horn, “Intrinsic motivation: Relationships with collegiate athletes’
gender, scholarship status, and perceptions of their coaches’ behavior”, Journal of sport and exercise psychology, volume 22, number 1, pages 63–84, 2000 (cited on page 126).
[8] J. van Andel, C. Ungureanu, R. M. Aarts, F. Leijten, and J. Arends, “Using photoplethys-mography in heart rate monitoring of patients with epilepsy”, Epilepsy & behavior, volume 45, pages 142–145, 2015 (cited on pages 17, 69).
[9] Ankle Brachial Index Collaboration, “Ankle-Brachial index combined with Framingham Risk Score to predict cardiovascular events and mortality: a meta-analysis”, Jama: The journal of the american medical association, volume 300, number 2, page 197, 2008 (cited on page 70).
[10] F. Bagala, C. Becker, A. Cappello, L. Chiari, K. Aminian, J. M. Hausdorff, W. Zijlstra, and J. Klenk, “Evaluation of accelerometer-based fall detection algorithms on real-world falls”, Plos one, volume 7, number 5, e37062, 2012 (cited on page 16).
[11] A. Bandura, “Self-efficacy: Toward a unifying theory of behavioral change”, Psychological review, volume 84, number 2, page 191, 1977 (cited on pages 100, 111).
[12] ——, “Perceived self-efficacy in cognitive development and functioning”, Educational psychologist, volume 28, number 2, pages 117–148, 1993 (cited on page 102).
[13] ——, “Health promotion by social cognitive means”, Health education & behavior, volume 31, number 2, pages 143–164, 2004 (cited on pages 29, 30, 100).
[14] M. Basner, B. Griefahn, U. Müller, G. Plath, and A. Samel, “An ecg-based algorithm for the automatic identification of autonomic activations associated with cortical arousal”, Sleep, volume 30, number 10, pages 1349–1361, 2007 (cited on pages 41, 55).
[15] Z. Beattie, Y. Oyang, A. Statan, A. Ghoreyshi, A. Pantelopoulos, A. Russell, and C.
Heneghan, “Estimation of sleep stages in a healthy adult population from optical plethys-mography and accelerometer signals”, Physiological measurement, volume 38, pages 1968–
79, 2017 (cited on page 36).
[16] Z. Beattie, Y. Oyang, A. Statan, A. Ghoreyshi, A. Pantelopoulos, A. Russell, and C.
Heneghan, “Estimation of sleep stages in a healthy adult population from optical plethys-mography and accelerometer signals”, Physiological measurement, volume 38, number 11, page 1968, 2017 (cited on pages 36, 51, 63).
[17] A. Beratarrechea, A. G. Lee, J. M. Willner, E. Jahangir, A. Ciapponi, and A. Rubinstein,
“The impact of mobile health interventions on chronic disease outcomes in developing coun-tries: A systematic review”, Telemedicine and e-health, volume 20, number 1, pages 75–82, 2014 (cited on page 29).
[18] R. J. Berger and N. H. Phillips, “Energy conservation and sleep”, Behavioural brain research, volume 69, number 1-2, pages 65–73, 1995 (cited on page 35).
[19] R. B. Berry, R. Brooks, C. E. Gamaldo, S. M. Harding, C. L. Marcus, B. V. Vaughn, et al.,
“The aasm manual for the scoring of sleep and associated events”, Rules, terminology and technical specifications, darien, illinois, american academy of sleep medicine, volume 176, 2012 (cited on pages 20, 35, 49, 54).
[20] H. Bettermann, D. Cysarz, and P. Van Leeuwen, “Detecting Cardiorespiratory Coordination By Respiratory Pattern Analysis of Heart Period Dynamics - the Musical Rhythm Ap-proach”, International journal of bifurcation and chaos, volume 10, number 10, pages 2349–
60, Oct. 2000,ISSN: 0218-1274.DOI: 10.1142/S021812740000150X (cited on pages 40, 41).
[21] S. J. Black and M. R. Weiss, “The relationship among perceived coaching behaviors, perceptions of ability, and motivation in competitive age-group swimmers”, Journal of sport and exercise psychology, volume 14, number 3, pages 309–325, 1992 (cited on page 126).
[22] G. Bobrie, P. Clerson, J. Menard, N. Postel-Vinay, G. Chatellier, and P.-F. Plouin, “Masked hypertension: A systematic review”, Journal of hypertension, volume 26, number 9, pages 1715–1725, 2008 (cited on page 23).
137 [23] J. Boggia, Y. Li, L. Thijs, T. W. Hansen, M. Kikuya, K. Björklund-Bodegård, T. Richart, T. Ohkubo, T. Kuznetsova, C. Torp-Pedersen, et al., “Prognostic accuracy of day versus night ambulatory blood pressure: A cohort study”, The lancet, volume 370, number 9594, pages 1219–1229, 2007 (cited on page 24).
[24] B. M. Bolstad, R. A. Irizarry, M. Åstrand, and T. P. Speed, “A comparison of normal-ization methods for high density oligonucleotide array data based on variance and bias”, Bioinformatics, volume 19, number 2, pages 185–193, 2003 (cited on page 87).
[25] M. H. Bonnet, “Effect of sleep disruption on sleep, performance, and mood”, Sleep, volume 8, number 1, pages 11–19, 1985 (cited on page 36).
[26] A. G. Bonomi, G. Plasqui, A. H. Goris, and K. R. Westerterp, “Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerome-ter”, Journal of applied physiology, volume 107, number 3, pages 655–661, 2009 (cited on pages 17, 134).
[27] A. G. Bonomi, F. Schipper, L. M. Eerikäinen, J. Margarito, R. Van Dinther, G. Muesch, H. M. De Morree, R. M. Aarts, S. Babaeizadeh, D. D. McManus, et al., “Atrial fibrillation detection using a novel cardiac ambulatory monitor based on photo-plethysmography at the wrist”, Journal of the american heart association, volume 7, number 15, e009351, 2018 (cited on pages 17–19).
[28] A. A. Borbély, “A two process model of sleep regulation”, Hum neurobiol, volume 1, number 3, pages 195–204, 1982 (cited on page 21).
[29] S. Bozkurt and G. Ertas, “Comparison of left and right fingertip ppg signals using signal power estimates and poincare indexes”, Journal of biomedical and life sciences, volume 3, number 2, pages 6–10, 2015 (cited on page 85).
[30] J. C. Bramwell and A. V. Hill, “The velocity of pulse wave in man”, Proceedings of the royal society of london. series b, containing papers of a biological character, volume 93, number 652, pages 298–306, 1922 (cited on pages 28, 50).
[31] M. A. Brodie, M. J. Coppens, S. R. Lord, N. H. Lovell, Y. J. Gschwind, S. J. Redmond, M. B. Del Rosario, K. Wang, D. L. Sturnieks, M. Persiani, et al., “Wearable pendant device monitoring using new wavelet-based methods shows daily life and laboratory gaits are different”, Medical & biological engineering & computing, volume 54, number 4, pages 663–674, 2016 (cited on page 16).
[32] A. Buchs, Y. Slovik, M. Rapoport, C. Rosenfeld, B. Khanokh, and M. Nitzan, “Right-left correlation of the sympathetically induced fluctuations of photoplethysmographic signal in diabetic and non-diabetic subjects”, Medical and biological engineering and computing, volume 43, number 2, pages 252–257, 2005 (cited on page 85).
[33] L. E. Burke, J. Ma, K. M. Azar, G. G. Bennett, E. D. Peterson, Y. Zheng, W. Riley, J.
Stephens, S. H. Shah, B. Suffoletto, et al., “Current science on consumer use of mobile health for cardiovascular disease prevention a scientific statement from the american heart association”, Circulation, volume 132, number 12, pages 1157–1213, 2015 (cited on page 99).
[34] P. Bušek, J. Vaˇnková, J. Opavský, J. Salinger, and S. Nevšímalová, “Spectral analysis of the heart rate variability in sleep”, Physiological research, volume 54, number 4, pages 369–76, 2005,ISSN: 0862-8408 (cited on page 41).
[35] D. Buxi, J.-M. Redouté, and M. R. Yuce, “A survey on signals and systems in ambulatory blood pressure monitoring using pulse transit time”, Physiological measurement, volume 36, R1–R26, 2015.DOI: 10.1088/0967-3334/36/3/R1 (cited on page 82).
[36] K. Byrka and F. G. Kaiser, “Health performance of individuals within the campbell paradigm”, International journal of psychology, volume 48, number 5, pages 986–999, 2013 (cited on pages 100, 103, 110).
[37] J. G. Chase, C. Starfinger, Z. Lam, F. Agogue, and G. M. Shaw, “Quantifying agitation in sedated ICU patients using heart rate and blood pressure”, Physiological measurement, volume 25, number 4, page 1037, 2004 (cited on page 67).
[38] S. W. Cheatham, K. R. Stull, M. Fantigrassi, and I. Motel, “The efficacy of wearable activity tracking technology as part of a weight loss program: A systematic review.”, The journal of sports medicine and physical fitness, volume 58, number 4, pages 534–548, 2018 (cited on page 16).
[39] Y. C. Chiu, P. W. Arand, S. G. Shroff, T. Feldman, and J. D. Carroll, “Determination of pulse wave velocities with computerized algorithms”, American heart journal, volume 121, number 5, pages 1460–1470, 1991 (cited on page 87).
[40] A. V. Chobanian, G. L. Bakris, H. R. Black, W. C. Cushman, L. A. Green, J. L. Izzo, D. W.
Jones, B. J. Materson, S. Oparil, J. T. Wright, et al., “Seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure”, Hypertension, volume 42, number 6, pages 1206–1252, 2003 (cited on page 103).
[41] C. K. Chow, J. Redfern, G. S. Hillis, J. Thakkar, K. Santo, M. L. Hackett, S. Jan, N. Graves, L. de Keizer, T. Barry, et al., “Effect of lifestyle-focused text messaging on risk factor modification in patients with coronary heart disease: A randomized clinical trial”, Jama, volume 314, number 12, pages 1255–1263, 2015 (cited on pages 99, 111).
[42] E. C.-P. Chua, S. J. Redmond, G. McDarby, and C. Heneghan, “Towards using photo-plethysmogram amplitude to measure blood pressure during sleep”, Annals of biomedical engineering, volume 38, number 3, pages 945–954, 2010 (cited on page 19).
[43] L. E. Costa, C. H. G. Uchôa, R. R. Harmon, L. A. Bortolotto, G. Lorenzi-Filho, and L. F.
Drager, “Potential underdiagnosis of obstructive sleep apnoea in the cardiology outpatient setting”, Heart, volume 101, number 16, pages 1288–1292, 2015 (cited on page 21).
[44] M. Costa, A. L. Goldberger, and C.-K. Peng, “Multiscale entropy analysis of biological signals”, Physical review e, volume 71, number 2, page 021 906, 2005 (cited on page 87).
[45] M. Costa, A. Goldberger, and C.-K. Peng, “Multiscale Entropy Analysis of Complex Physiologic Time Series”, Physical review letters, volume 89, number 6, page 068 102, Jul.
2002,ISSN: 0031-9007 (cited on page 41).
[46] R. Couceiro, P. Carvalho, R. Paiva, J. Henriques, I. Quintal, M. Antunes, J. Muehlsteff, C. Eickholt, C. Brinkmeyer, M. Kelm, et al., “Assessment of cardiovascular function from multi-gaussian fitting of a finger photoplethysmogram”, Physiological measurement, volume 36, number 9, page 1801, 2015 (cited on page 17).
[47] J. A. Cutler, P. D. Sorlie, M. Wolz, T. Thom, L. E. Fields, and E. J. Roccella, “Trends in hypertension prevalence, awareness, treatment, and control rates in united states adults between 1988–1994 and 1999–2004”, Hypertension, volume 52, number 5, pages 818–827, 2008 (cited on page 67).
[48] D. Cysarz, H. Bettermann, and P. van Leeuwen, “Entropies of short binary sequences in heart period dynamics”, American journal of physiology - heart and circulatory physiology, volume 278, number 6, pages 2163–72, 2000 (cited on page 41).
139 [49] D. Cysarz, H. Bettermann, S. Lange, D. Geue, and P. van Leeuwen, “A quantitative comparison of different methods to detect cardiorespiratory coordination during night-time sleep”, Biomedical engineering online, volume 3, number 1, page 44, 2004,ISSN: 1475-925X (cited on pages 40, 41).
[50] C. A. Czeisler, E. M. Wickwire, L. K. Barger, W. C. Dement, K. Gamble, N. Hartenbaum, M. M. Ohayon, R. Pelayo, B. Phillips, K. Strohl, et al., “Sleep-deprived motor vehicle operators are unfit to drive: A multidisciplinary expert consensus statement on drowsy driving”, Sleep health, volume 2, number 2, pages 94–99, 2016 (cited on page 49).
[51] E. Dafna, A. Tarasiuk, and Y. Zigel, “Sleep-wake evaluation from whole-night non-contact audio recordings of breathing sounds”, Plos one, volume 10, number 2, e0117382, 2015 (cited on page 35).
[52] ——, “Sleep staging using nocturnal sound analysis”, Scientific reports, volume 8, number 1, page 13 474, 2018 (cited on page 35).
[53] H. Danker-hopfe, P. Anderer, J. Zeitlhofer, M. Boeck, H. Dorn, G. Gruber, E. Heller, E.
Loretz, D. Moser, S. Parapatics, et al., “Interrater reliability for sleep scoring according to the rechtschaffen & kales and the new AASM standard”, Journal of sleep research, volume 18, number 1, pages 74–84, 2009 (cited on page 63).
[54] R. Davies, N. Jenkins, and J. Stradling, “Effect of measuring ambulatory blood pressure on sleep and on blood pressure during sleep”, Bmj, volume 308, number 6932, pages 820–823, 1994 (cited on pages 19, 24).
[55] M. L. Day, M. R. McGuigan, G. Brice, and C. Foster, “Monitoring exercise intensity during resistance training using the session rpe scale”, The journal of strength & conditioning research, volume 18, number 2, pages 353–358, 2004 (cited on page 118).
[56] R. E. De Meersman, “Aging as a modulator of respiratory sinus arrhythmia”, Journal of gerontology, volume 48, number 2, B74–B78, 1993 (cited on page 47).
[57] H. O. Dickinson, J. M. Mason, D. J. Nicolson, F. Campbell, F. R. Beyer, J. V. Cook, B.
Williams, and G. A. Ford, “Lifestyle interventions to reduce raised blood pressure: A systematic review of randomized controlled trials”, Journal of hypertension, volume 24, number 2, pages 215–233, 2006 (cited on pages 29, 99, 113).
[58] W. J. Dixon and K. K. Yuen, “Trimming and winsorization: A review”, Statistische hefte, volume 15, number 2-3, pages 157–170, 1974 (cited on page 87).
[59] A. Domingues, T. Paiva, and J. M. Sanches, “Hypnogram and sleep parameter computation from activity and cardiovascular data”, IEEE transactions on biomedical engineering, volume 61, number 6, pages 1711–1719, 2014 (cited on page 35).
[60] M. J. Drinnan, J. Allen, and A. Murray, “Relation between heart rate and pulse transit time during paced respiration”, Physiological measurement, volume 22, number 3, page 425, 2001 (cited on page 79).
[61] R. van Ee, S. Van de Cruys, L. J. Schlangen, and B. N. Vlaskamp, “Circadian-time sickness:
Time-of-day cue-conflicts directly affect health”, Trends in neurosciences, volume 39, number 11, pages 738–749, 2016 (cited on page 23).
[62] M. D. Ekstrand, F. M. Harper, M. C. Willemsen, and J. a. Konstan, “User perception of differences in recommender algorithms”, Proceedings of the 8th ACM conference on recommender systems - RecSys ’14, pages 161–168, 2014. DOI: 10 . 1145 / 2645710 . 2645737. [Online]. Available: http://dl.acm.org/citation.cfm?doid=2645710.
2645737 (cited on page 108).
[63] M. Elgendi, “On the Analysis of Fingertip Photoplethysmogram Signals”, Current cardiol-ogy reviews, volume 8, number 1, pages 14–25, 2012.DOI: 10.2174/157340312801215782 (cited on pages 17, 24, 82, 87).
[64] ——, “On the analysis of fingertip photoplethysmogram signals”, Current cardiology reviews, volume 8, number 1, pages 14–25, 2012 (cited on page 17).
[65] P. J. Elmer, R. Grimm, B. Laing, G. Grandits, K. Svendsen, N. Vanheel, E. Betz, J. Raines, M. Link, J. Stamler, et al., “Lifestyle intervention: Results of the treatment of mild hyper-tension study (tomhs)”, Preventive medicine, volume 24, number 4, pages 378–388, 1995 (cited on page 29).
[66] D. W. Esliger, A. Probert, S. G. Connor, S. Bryan, M. Laviolette, and M. S. Tremblay,
“Validity of the actical accelerometer step-count function.”, Medicine and science in sports and exercise, volume 39, number 7, pages 1200–1204, 2007 (cited on page 16).
[67] C. Fava, P. Burri, P. Almgren, G. Arcaro, L. Groop, U. L. Hulthén, and O. Melander,
“Dipping and variability of blood pressure and heart rate at night are heritable traits”, American journal of hypertension, volume 18, number 11, pages 1402–1407, 2005 (cited on page 90).
[68] J. S. Floras, “Blood pressure variability: A novel and important risk factor”, Canadian journal of cardiology, volume 29, number 5, pages 557–563, 2013 (cited on pages 23, 81).
[69] P. Fonseca, R. M. Aarts, J. Foussier, and X. Long, “A novel low-complexity post-processing algorithm for precise QRS localization”, Springerplus, volume 3, number 1, page 1, 2014 (cited on pages 40, 55, 73).
[70] P. Fonseca, X. Long, M. Radha, R. Haakma, R. M. Aarts, and J. Rolink, “Sleep stage classification with ecg and respiratory effort”, Physiological measurement, volume 36, number 10, page 2027, 2015 (cited on pages 35, 37, 38, 41, 50).
[71] P. Fonseca, N. den Teuling, X. Long, and R. M. Aarts, “Cardiorespiratory sleep stage detection using conditional random fields”, IEEE journal of biomedical health informatics, volume 21, number 4, pages 956–66, 2017,ISSN: 2168-2194 (cited on pages 35, 40).
[72] P. Fonseca, N. den Teuling, X. Long, and R. M. Aarts, “A comparison of probabilistic classifiers for sleep stage classification”, Physiological measurement, volume 39, number 5, page 055 001, 2018 (cited on pages 36, 37, 46, 55).
[73] P. Fonseca, T. Weysen, M. S. Goelema, E. I. Møst, M. Radha, C. Lunsingh Scheurleer, L. van den Heuvel, and R. M. Aarts, “Validation of photoplethysmography-based sleep staging compared with polysomnography in healthy middle-aged adults”, Sleep, volume 40, number 7, zsx097, 2017 (cited on pages 17, 19, 51, 54, 63, 85, 87).
[74] J. Y. A. Foo, C. S. Lim, S. J. Wilson, G. R. Williams, M. A. Harris, and D. M. Cooper,
“Pulse transit time ratio as a potential marker for paediatric crural and brachial blood pressure index”, Journal of human hypertension, volume 21, pages 415–417, 2007,ISSN: 0950-9240 (cited on page 70).
[75] L. Fraiwan, K. Lweesy, N. Khasawneh, H. Wenz, and H. Dickhaus, “Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier”, Computer methods and programs in biomedicine, volume 108, number 1, pages 10–19, 2012,ISSN: 01692607.DOI: 10.1016/j.cmpb.2011.11.005.
[Online]. Available: http://dx.doi.org/10.1016/j.cmpb.2011.11.005 (cited on page 21).
141 [76] P. M. Franssen and B. P. Imholz, “Evaluation of the mobil-o-graph new generation abpm device using the esh criteria”, Blood pressure monitoring, volume 15, number 4, pages 229–
231, 2010 (cited on page 84).
[77] K. Fujimoto, Y. Ding, and E. Takahashi, “Sleep stage detection using a wristwatch-type physiological sensing device”, Sleep and biological rhythms, volume 16, number 4, pages 449–456, 2018 (cited on pages 37, 51, 63).
[78] R. Gal, A. M. May, E. J. van Overmeeren, M. Simons, and E. M. Monninkhof, “The effect of physical activity interventions comprising wearables and smartphone applications on physical activity: A systematic review and meta-analysis”, Sports medicine-open, volume 4, number 1, page 42, 2018 (cited on page 16).
[79] R. George, T. D. Chung, S. S. Vedam, V. Ramakrishnan, R. Mohan, E. Weiss, and P. J. Keall,
“Audio-visual biofeedback for respiratory-gated radiotherapy: Impact of audio instruction and audio-visual biofeedback on respiratory-gated radiotherapy”, International journal of radiation oncology* biology* physics, volume 65, number 3, pages 924–933, 2006 (cited on page 114).
[80] H. Gesche, D. Grosskurth, G. Küchler, and A. Patzak, “Continuous blood pressure measure-ment by using the pulse transit time: Comparison to a cuff-based method”, European journal of applied physiology, volume 112, number 1, pages 309–315, 2012,ISSN: 14396319 (cited on pages 68, 73, 82).
[81] D. Giavarina, “Understanding bland altman analysis”, Biochemia medica: Biochemia medica, volume 25, number 2, pages 141–151, 2015 (cited on page 58).
[82] E. Gil, M. Mendez, J. M. Vergara, S. Cerutti, A. M. Bianchi, and P. Laguna, “Discrimination of sleep-apnea-related decreases in the amplitude fluctuations of ppg signal in children by hrv analysis”, IEEE transactions on biomedical engineering, volume 56, number 4, pages 1005–1014, 2009 (cited on page 19).
[83] P. Greenland, J. S. Alpert, G. A. Beller, E. J. Benjamin, M. J. Budoff, Z. A. Fayad, E.
Foster, M. A. Hlatky, J. M. Hodgson, F. G. Kushner, et al., “2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults”, Journal of the american college of cardiology, volume 56, number 25, e50–e103, 2010 (cited on page 67).
[84] R. Guthold, G. A. Stevens, L. M. Riley, and F. C. Bull, “Worldwide trends in insufficient physical activity from 2001 to 2016: A pooled analysis of 358 population-based surveys with 1· 9 million participants”, The lancet global health, volume 6, number 10, e1077–
e1086, 2018 (cited on page 16).
[85] M. Hafner, M. Stepanek, J. Taylor, W. M. Troxel, and C. van Stolk, “Why sleep matters—the economic costs of insufficient sleep: A cross-country comparative analysis”, Rand health
[85] M. Hafner, M. Stepanek, J. Taylor, W. M. Troxel, and C. van Stolk, “Why sleep matters—the economic costs of insufficient sleep: A cross-country comparative analysis”, Rand health