Early Screening of Sleep-Disordered Breathing Using a Smartphone-Based Portable System in Stroke Patients and Its Relevance for Rehabilitation: A Prospective Observational Study
Highlights
- The smartphone-based portable monitoring system enabled detection of previously undiagnosed sleep apnea among post-stroke patients undergoing rehabilitation.
- Greater sleep-disordered respiratory events and nocturnal hypoxemia were associated with worse baseline disability and lower rehabilitation metrics.
- The portable system was easy to use, facilitating sleep apnea detection after stroke and supporting broader implementation in rehabilitation settings.
- Routine screening for sleep-disordered breathing at admission may enable earlier diagnosis and management in patients with substantial hypoxemia/event burden that could slow functional recovery.
Abstract
1. Introduction
2. Materials and Methods
2.1. Participants
2.2. Equipment and Instruments
2.3. Evaluation of SDB
2.4. Signal Processing and Analysis
2.5. Demographic and Stroke-Related Variables
2.6. Cardiovascular Risk Factors and Medication
2.7. Sleep Assessment Questionnaires
2.8. Rehabilitation-Related Indices
2.9. Statistics
3. Results
3.1. Prevalence and Severity of SDB Using Smartphone-Based Portable Systems
3.2. Sleep Questionnaires
3.3. Clinical and Stroke-Related Characteristics Across SA Severity Groups
3.4. Exploratory Correlation Analysis Between Smartphone-Based Respiratory Metrics, Stroke Severity, and Rehabilitation Metrics
4. Discussion
4.1. Sleep Apnea Prevalence and Severity in Stroke Patients
4.2. Role of Portable Systems in Sleep-Disordered Breathing Screening
4.3. Questionnaires and Smartphone-Based Sleep Measures
4.4. Role of Stroke Characteristics and Associated Risk Factors on Sleep Apnea
4.5. Sleep-Disordered Breathing and Rehabilitation-Related Metrics
4.6. Study Relevance
4.7. Study Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
| AHI | Apnea–Hypopnea Index |
| AI | Apnea Index |
| BMI | Body Mass Index |
| c-FIM | Cognitive Functional Independence Measure |
| CPAP | Continuous Positive Airway Pressure |
| CT90 | Cumulative time with SpO2 < 90% |
| CT94 | Cumulative time with SpO2 < 94% |
| FIM | Functional Independence Measure |
| HI | Hypopnea Index |
| LOS | Length of hospital stay |
| m-FIM | Motor Functional Independence Measure |
| MinSpO2 | Minimum oxygen saturation |
| mRS | Modified Rankin Scale |
| NIHSS | National Institutes of Health Stroke Scale |
| ODI3 | Oxygen Desaturation Index (drops of SpO2 ≥ 3% per hour) |
| ODI4 | Oxygen Desaturation Index (drops of SpO2 ≥ 4% per hour) |
| PSG | Polysomnography |
| PROMIS | Patient-Reported Outcomes Measurement Information System |
| PSQI | Pittsburgh Sleep Quality Index |
| SA | Sleep apnea |
| SDB | Sleep-disordered breathing |
| SPSS | Statistical Package for the Social Sciences |
| SpO2 | Peripheral capillary oxygen saturation |
| STROBE | Strengthening the Reporting of Observational Studies in Epidemiology |
References
- Kapur, V.K.; Auckley, D.H.; Chowdhuri, S.; Kuhlmann, D.C.; Mehra, R.; Ramar, K.; Harrod, C.G. Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline. J. Clin. Sleep Med. 2017, 13, 479–504. [Google Scholar] [CrossRef]
- Seiler, A.; Camilo, M.; Korostovtseva, L.; Haynes, A.G.; Brill, A.K.; Horvath, T.; Egger, M.; Bassetti, C.L. Prevalence of sleep-disordered breathing after stroke and TIA: A meta-analysis. Neurology 2019, 92, e648–e654. [Google Scholar] [CrossRef]
- Brown, D.L.; McDermott, M.; Mowla, A.; De Lott, L.; Morgenstern, L.B.; Kerber, K.A.; Hegeman, G., 3rd; Smith, M.A.; Garcia, N.M.; Chervin, R.D.; et al. Brainstem infarction and sleep-disordered breathing in the BASIC sleep apnea study. Sleep Med. 2014, 15, 887–891. [Google Scholar] [CrossRef]
- Ho, L.Y.W.; Lai, C.K.Y.; Ng, S.S.M. Contribution of sleep quality to fatigue following a stroke: A cross-sectional study. BMC Neurol. 2021, 21, 151. [Google Scholar] [CrossRef]
- Byun, E.; Kohen, R.; Becker, K.J.; Kirkness, C.J.; Khot, S.; Mitchell, P.H. Stroke impact symptoms are associated with sleep-related impairment. Heart Lung 2020, 49, 117–122. [Google Scholar] [CrossRef]
- Ding, Q.; Whittemore, R.; Redeker, N. Excessive Daytime Sleepiness in Stroke Survivors: An Integrative Review. Biol. Res. Nurs. 2016, 18, 420–431. [Google Scholar] [CrossRef]
- Siarnik, P.; Jurik, M.; Veverka, J.; Klobucnikova, K.; Kollar, B.; Turcani, P.; Sykora, M. Pulse oximetric routine examination of sleep apnea in acute stroke (PRESS). Sleep Med. 2020, 73, 208–212. [Google Scholar] [CrossRef] [PubMed]
- Boulos, M.I.; Kamra, M.; Colelli, D.R.; Kirolos, N.; Gladstone, D.J.; Boyle, K.; Sundaram, A.; Hopyan, J.J.; Swartz, R.H.; Mamdani, M.; et al. SLEAP SMART (Sleep Apnea Screening Using Mobile Ambulatory Recorders After TIA/Stroke): A Randomized Controlled Trial. Stroke 2022, 53, 710–718. [Google Scholar] [CrossRef] [PubMed]
- Castillo-Escario, Y.; Ferrer-Lluis, I.; Montserrat, J.M.; Jané, R. Entropy Analysis of Acoustic Signals Recorded With a Smartphone for Detecting Apneas and Hypopneas: A Comparison With a Commercial System for Home Sleep Apnea Diagnosis. IEEE Access 2019, 7, 18. [Google Scholar] [CrossRef]
- Ferrer-Lluis, I.; Castillo-Escario, I.; Montserrat, J.M.; Jané, R. Analysis of Smartphone Triaxial Accelerometry for Monitoring Sleep-Disordered Breathing and Sleep Position at Home. IEEE Access 2020, 8, 14. [Google Scholar] [CrossRef]
- Castillo-Escario, Y.; Kumru, H.; Ferrer-Lluis, I.; Vidal, J.; Jane, R. Detection of Sleep-Disordered Breathing in Patients with Spinal Cord Injury Using a Smartphone. Sensors 2021, 21, 7182. [Google Scholar] [CrossRef] [PubMed]
- Castillo-Escario, Y.; Albu, S.; Kumru, H.; Jane, R. Evaluation of Sleep Apnea in Stroke Patients Using a Portable Smartphone-Based System. IEEE Trans. Neural Syst. Rehabil. Eng. 2025, 33, 4546–4557. [Google Scholar] [CrossRef] [PubMed]
- Ferrer-Lluis, I.; Castillo-Escario, Y.; Montserrat, J.M.; Jane, R. Enhanced Monitoring of Sleep Position in Sleep Apnea Patients: Smartphone Triaxial Accelerometry Compared with Video-Validated Position from Polysomnography. Sensors 2021, 21, 3689. [Google Scholar] [CrossRef] [PubMed]
- Boll, S. Suppression of acoustic noise in speech using spectral subtraction. IEEE Trans. Acoust. Speech Signal Process. 1979, 27, 8. [Google Scholar] [CrossRef]
- American Academy of Sleep Medicine. International Classification of Sleep Disorders, 3rd ed.; American Academy of Sleep Medicine: Darien, IL, USA, 2014. [Google Scholar]
- Piraino, T.; Madden, M.; Roberts, K.J.; Lamberti, J.; Ginier, E.; Strickland, S.L. AARC Clinical Practice Guideline: Management of Adult Patients With Oxygen in the Acute Care Setting. Respir. Care 2022, 67, 115–128. [Google Scholar] [CrossRef]
- Henriquez-Beltran, M.; Dreyse, J.; Jorquera, J.; Weissglas, B.; Del Rio, J.; Cendoya, M.; Jorquera-Diaz, J.; Salas, C.; Fernandez-Bussy, I.; Labarca, G. Is the time below 90% of SpO2 during sleep (T90%) a metric of good health? A longitudinal analysis of two cohorts. Sleep Breath. 2024, 28, 281–289. [Google Scholar] [CrossRef]
- Duncan, P.W.; Zorowitz, R.; Bates, B.; Choi, J.Y.; Glasberg, J.J.; Graham, G.D.; Katz, R.C.; Lamberty, K.; Reker, D. Management of Adult Stroke Rehabilitation Care: A clinical practice guideline. Stroke 2005, 36, e100–e143. [Google Scholar] [CrossRef]
- Banks, J.L.; Marotta, C.A. Outcomes validity and reliability of the modified Rankin scale: Implications for stroke clinical trials: A literature review and synthesis. Stroke 2007, 38, 1091–1096. [Google Scholar] [CrossRef]
- Macías Fernández, J.A.; Royuela Rico, A. La versión española del índice de la calidad de sueño de Pittsburgh. Inf. Psiquiátricas 1996, 146, 465–472. [Google Scholar]
- Buysse, D.J.; Yu, L.; Moul, D.E.; Germain, A.; Stover, A.; Dodds, N.E.; Johnston, K.L.; Shablesky-Cade, M.A.; Pilkonis, P.A. Development and validation of patient-reported outcome measures for sleep disturbance and sleep-related impairments. Sleep 2010, 33, 781–792. [Google Scholar] [CrossRef]
- Yu, L.; Buysse, D.J.; Germain, A.; Moul, D.E.; Stover, A.; Dodds, N.E.; Johnston, K.L.; Pilkonis, P.A. Development of short forms from the PROMIS sleep disturbance and Sleep-Related Impairment item banks. Behav. Sleep Med. 2011, 10, 6–24. [Google Scholar] [CrossRef]
- Stineman, M.G.; Shea, J.A.; Jette, A.; Tassoni, C.J.; Ottenbacher, K.J.; Fiedler, R.; Granger, C.V. The Functional Independence Measure: Tests of scaling assumptions, structure, and reliability across 20 diverse impairment categories. Arch. Phys. Med. Rehabil. 1996, 77, 1101–1108. [Google Scholar] [CrossRef] [PubMed]
- Benjafield, A.V.; Ayas, N.T.; Eastwood, P.R.; Heinzer, R.; Ip, M.S.M.; Morrell, M.J.; Nunez, C.M.; Patel, S.R.; Penzel, T.; Pepin, J.L.; et al. Estimation of the global prevalence and burden of obstructive sleep apnoea: A literature-based analysis. Lancet Respir. Med. 2019, 7, 687–698. [Google Scholar] [CrossRef] [PubMed]
- Hasan, F.; Gordon, C.; Wu, D.; Huang, H.C.; Yuliana, L.T.; Susatia, B.; Marta, O.F.D.; Chiu, H.Y. Dynamic Prevalence of Sleep Disorders Following Stroke or Transient Ischemic Attack: Systematic Review and Meta-Analysis. Stroke 2021, 52, 655–663. [Google Scholar] [CrossRef] [PubMed]
- Bassetti, C.L.A.; Randerath, W.; Vignatelli, L.; Ferini-Strambi, L.; Brill, A.K.; Bonsignore, M.R.; Grote, L.; Jennum, P.; Leys, D.; Minnerup, J.; et al. EAN/ERS/ESO/ESRS statement on the impact of sleep disorders on risk and outcome of stroke. Eur. J. Neurol. 2020, 27, 1117–1136. [Google Scholar] [CrossRef]
- Tayade, K.; Vibha, D.; Singh, R.K.; Pandit, A.K.; Ramanujam, B.; Das, A.; Elavarasi, A.; Agarwal, A.; Srivastava, A.K.; Tripathi, M. Polysomnographic correlates of self-and caregiver-reported sleep problems in post-stroke patients. Front. Neurol. 2025, 16, 1587378. [Google Scholar] [CrossRef]
- Skarpsno, E.S.; Mork, P.J.; Nilsen, T.I.L.; Holtermann, A. Sleep positions and nocturnal body movements based on free-living accelerometer recordings: Association with demographics, lifestyle, and insomnia symptoms. Nat. Sci. Sleep 2017, 9, 267–275. [Google Scholar] [CrossRef]
- Baillieul, S.; Dekkers, M.; Brill, A.K.; Schmidt, M.H.; Detante, O.; Pepin, J.L.; Tamisier, R.; Bassetti, C.L.A. Sleep apnoea and ischaemic stroke: Current knowledge and future directions. Lancet Neurol. 2022, 21, 78–88. [Google Scholar] [CrossRef]
- Battaglia, E.; Poletti, V.; Volpato, E.; Banfi, P. Positional Therapy: A Real Opportunity in the Treatment of Obstructive Sleep Apnea? An Update from the Literature. Life 2025, 15, 1175. [Google Scholar] [CrossRef]
- Wu, M.N.; Lai, C.L.; Liu, C.K.; Liou, L.M.; Yen, C.W.; Chen, S.C.; Hsieh, C.F.; Hsieh, S.W.; Lin, F.C.; Hsu, C.Y. More severe hypoxemia is associated with better subjective sleep quality in obstructive sleep apnea. BMC Pulm. Med. 2015, 15, 117. [Google Scholar] [CrossRef]
- Scarlata, S.; Pedone, C.; Curcio, G.; Cortese, L.; Chiurco, D.; Fontana, D.; Calabrese, M.; Fusiello, R.; Abbruzzese, G.; Santangelo, S.; et al. Pre-polysomnographic assessment using the Pittsburgh Sleep Quality Index questionnaire is not useful in identifying people at higher risk for obstructive sleep apnea. J. Med. Screen. 2013, 20, 220–226. [Google Scholar] [CrossRef] [PubMed]
- Thompson, C.; Legault, J.; Moullec, G.; Baltzan, M.; Cross, N.; Dang-Vu, T.T.; Martineau-Dussault, M.E.; Hanly, P.; Ayas, N.; Lorrain, D.; et al. A portrait of obstructive sleep apnea risk factors in 27,210 middle-aged and older adults in the Canadian Longitudinal Study on Aging. Sci. Rep. 2022, 12, 5127. [Google Scholar] [CrossRef] [PubMed]
- Yaggi, H.K.; Concato, J.; Kernan, W.N.; Lichtman, J.H.; Brass, L.M.; Mohsenin, V. Obstructive sleep apnea as a risk factor for stroke and death. N. Engl. J. Med. 2005, 353, 2034–2041. [Google Scholar] [CrossRef] [PubMed]
- Jehan, S.; Farag, M.; Zizi, F.; Pandi-Perumal, S.R.; Chung, A.; Truong, A.; Jean-Louis, G.; Tello, D.; McFarlane, S.I. Obstructive sleep apnea and stroke. Sleep Med. Disord. 2018, 2, 120–125. [Google Scholar]
- Sanders, C.B.; Knisely, K.; Edrissi, C.; Rathfoot, C.; Poupore, N.; Wormack, L.; Nathaniel, T. Obstructive sleep apnea and stroke severity: Impact of clinical risk factors. Brain Circ. 2021, 7, 92–103. [Google Scholar] [CrossRef]
- Liu, X.; Lam, D.C.; Chan, K.P.F.; Chan, H.Y.; Ip, M.S.; Lau, K.K. Prevalence and Determinants of Sleep Apnea in Patients with Stroke: A Meta-Analysis. J. Stroke Cerebrovasc. Dis. 2021, 30, 106129. [Google Scholar] [CrossRef]
- Andrade, J.B.C.; Mohr, J.P.; Lima, F.O.; de Carvalho, J.J.F.; Barros, L.C.M.; Nepomuceno, C.R.; Ferrer, J.; Silva, G.S. The Role of Hemorrhagic Transformation in Acute Ischemic Stroke Upon Clinical Complications and Outcomes. J. Stroke Cerebrovasc. Dis. 2020, 29, 104898. [Google Scholar] [CrossRef]
- Messineo, L.; Sands, S.A.; Labarca, G. Hypnotics on Obstructive Sleep Apnea Severity and Endotypes: A Systematic Review and Meta-Analysis. Am. J. Respir. Crit. Care Med. 2024, 210, 1461–1474. [Google Scholar] [CrossRef]
- Chen, C.Y.; Chen, C.L.; Yu, C.C. Trazodone improves obstructive sleep apnea after ischemic stroke: A randomized, double-blind, placebo-controlled, crossover pilot study. J. Neurol. 2021, 268, 2951–2960. [Google Scholar] [CrossRef]
- Ott, S.R.; Fanfulla, F.; Miano, S.; Horvath, T.; Seiler, A.; Bernasconi, C.; Cereda, C.W.; Brill, A.K.; Young, P.; Nobili, L.; et al. SAS Care 1: Sleep-disordered breathing in acute stroke an transient ischaemic attack—Prevalence, evolution and association with functional outcome at 3 months, a prospective observational polysomnography study. ERJ Open Res. 2020, 6, 00334-2019. [Google Scholar] [CrossRef]
- Moon, H.I.; Yoon, S.Y.; Jeong, Y.J.; Cho, T.H. Sleep disturbances negatively affect balance and gait function in post-stroke patients. NeuroRehabilitation 2018, 43, 211–218. [Google Scholar] [CrossRef]
- Zhang, Y.; Xia, X.; Zhang, T.; Zhang, C.; Liu, R.; Yang, Y.; Liu, S.; Li, X.; Yue, W. Relationship between sleep disorders and the prognosis of neurological function after stroke. Front. Neurol. 2022, 13, 1036980. [Google Scholar] [CrossRef]
- Toh, Z.A.; Cheng, L.J.; Wu, X.V.; De Silva, D.A.; Oh, H.X.; Ng, S.X.; He, H.G.; Pikkarainen, M. Positive airway pressure therapy for post-stroke sleep disordered breathing: A systematic review, meta-analysis and meta-regression. Eur. Respir. Rev. 2023, 32, 220169. [Google Scholar] [CrossRef]
- Dromerick, A.W.; Geed, S.; Barth, J.; Brady, K.; Giannetti, M.L.; Mitchell, A.; Edwardson, M.A.; Tan, M.T.; Zhou, Y.; Newport, E.L.; et al. Critical Period After Stroke Study (CPASS): A phase II clinical trial testing an optimal time for motor recovery after stroke in humans. Proc. Natl. Acad. Sci. USA 2021, 118, e2026676118. [Google Scholar] [CrossRef]
- Perez, L.M.; Inzitari, M.; Quinn, T.J.; Montaner, J.; Gavalda, R.; Duarte, E.; Coll-Planas, L.; Cerda, M.; Santaeugenia, S.; Closa, C.; et al. Rehabilitation Profiles of Older Adult Stroke Survivors Admitted to Intermediate Care Units: A Multi-Centre Study. PLoS ONE 2016, 11, e0166304. [Google Scholar] [CrossRef]


| Variables | Mild/no-SA (N = 12) | Moderate-SA (N = 21) | Severe-SA (N = 23) | p Value, One-Way ANCOVA | p Value, Chi Squared Test |
|---|---|---|---|---|---|
| Age | 43 [34, 51] a | 53 [47, 59] | 60 [55, 65] | 0.001 | |
| Sex: Male (%) | 5 (42%) | 16 (76%) | 15 (65%) | 0.14 | |
| Stroke Type | |||||
| 5 (42%) | 12 (57%) | 13 (57%) | 0.21 | |
| 7 (58%) | 8 (38%) | 6 (26%) | ||
| 0 (0%) | 1 (5%) | 4 (17%) | ||
| Stroke to admission time (days) | 38 [25, 51] | 48 [39, 57] | 53 [44, 62] | 0.21 | |
| Stroke Location | |||||
| 4 (33%) | 10 (48%) | 7 (30%) | 0.54 | |
| 5 (42%) | 6 (29%) | 10 (43%) | ||
| 1 (8%) | 0 (0%) | 3 (13%) | ||
| 2 (17%) | 5 (24%) | 3 (13%) | ||
| NIHSS at admission | 13 [9, 18] | 11 [8, 14] | 13 [10, 15] | 0.72 | |
| mRS at admission | |||||
| 3 (25%) | 2 (10%) | 4 (17%) | 0.16 | |
| 7 (58%) | 14 (67%) | 8 (35%) | ||
| 2 (17%) | 5 (24%) | 11 (48%) | ||
| m-FIM at admission | 50 [36, 63] | 46 [37, 55] | 39 [30, 49] | 0.43 | |
| c-FIM at admission | 26 [21, 31] | 23 [19, 26] | 25 [21, 29] | 0.48 | |
| Smoker status | |||||
| 1 (8%) | 2 (10%) | 6 (26%) | 0.17 | |
| 7 (58%) | 6 (29%) | 9 (39%) | ||
| 4 (33%) | 13 (62%) | 8 (35%) | ||
| Hypertension | 5 (42%) | 12 (57%) | 17 (74%) | 0.16 | |
| Arrhythmia | 0 (0%) | 0 (0%) | 4 (17%) b | 0.045 | |
| Diabetes Mellitus | 1 (8%) | 4 (19%) | 8 (35%) | 0.18 | |
| Dyslipidemia | 0 (0%) | 5 (24%) | 12 (52%) b | 0.004 | |
| Ischemic heart disease | 1 (8%) | 2 (10%) | 1 (4%) | 0.79 | |
| Obesity | 2 (17%) | 6 (29%) | 10 (43%) | 0.25 | |
| BMI | 25 [22, 28] | 27 [25, 29] | 27 [25, 29] | 0.44 |
| Variables | Mild/no-SA (N = 12) | Moderate-SA (N = 21) | Severe-SA (N = 23) | p Value, One-Way ANCOVA |
|---|---|---|---|---|
| AHI (events/h) | 6 [0, 12] ab | 22 [18, 26] c | 50 [46, 54] | <0.001 |
| AI (events/h) | 2 [−3, 7] ab | 9 [6, 13] c | 19 [16, 22] | <0.001 |
| HI (events/h) | 6 [1, 12] ab | 13 [9, 17] c | 27 [24, 31] | <0.001 |
| ODI3 | 8 [0, 15] ab | 22 [17, 27] c | 45 [40, 50] | <0.001 |
| ODI4 | 4 [−3, 12] ab | 13 [7, 18] c | 34 [29, 39] | <0.001 |
| CT94 | 26 [8, 44] d | 45 [33, 58] | 48 [35, 61] | 0.03 |
| CT90 | 2 [−9, 12] ef | 7 [0, 14] | 17 [9, 24] | 0.002 |
| Average SpO2 | 95 [94, 97] g | 93 [92, 94] | 93 [92, 94] | 0.03 |
| Minimum SpO2 | 84 [78, 91] h | 82 [77, 86] | 74 [69, 78] | 0.015 |
| Oral Breathing (%) | 27 [15, 38] | 30 [22, 38] | 31 [22, 39] | 0.94 |
| Time in supine position (%) | 65 [47, 83] | 56 [43, 69] | 77 [64, 89] | 0.19 |
| AHI in supine position (%) | 10 [2, 17] ab | 22 [17, 27] c | 48 [43, 53] | <0.001 |
| Variables | Mild-No SA (N = 12) | Moderate-SA (N = 21) | Severe-SA (N = 23) | p Value, One-Way ANCOVA | p Value, Chi Squared Test |
|---|---|---|---|---|---|
| Sleep medication (%) | 10 (83%) | 15 (71%) | 14 (61%) | 0.38 | |
| Suspected sleep apneas (%) | 1 (8%) | 9 (43%) | 11 (48%) | 0.06 | |
| Time spent in bed (hours) ¥ | 9 [8, 10] | 9 [9, 10] | 9 [9, 10] | 0.55 | |
| Self-reported sleep time (hours) ¥ | 6 [5, 7] | 8 [7, 8] | 7 [7, 8] | 0.13 | |
| Sleep efficiency (%) ¥ | 72 [60, 84] | 83 [74, 92] | 80 [70, 89] | 0.39 | |
| PROMIS sleep impairment ¥ | 18 [14, 22] | 14 [11, 17] | 13 [10, 16] | 0.21 | |
| PROMIS sleep disturbance ¥ | 19 [14, 23] | 19 [15, 22] | 17 [13, 20] | 0.67 | |
| Pittsburgh total score ¥ | 11 [8, 14] | 7 [5, 9] | 8 [5, 10] | 0.20 |
| Variables | Mild/no-SA (N = 12) | Moderate-SA (N = 21) | Severe-SA (N = 23) | p Value, One-Way ANCOVA |
|---|---|---|---|---|
| m-FIM at admission | 50 [36, 63] | 46 [37, 55] | 39 [30, 49] | 0.43 |
| c-FIM at admission | 26 [21, 31] | 23 [19, 26] | 25 [21, 29] | 0.48 |
| m-FIM at discharge | 75 [60, 89] | 63 [53, 73] | 61 [51, 71] | 0.26 |
| c-FIM at discharge | 30 [25, 34] | 26 [23, 30] | 28 [25, 32] | 0.50 |
| m-FIM gain | 25 [16, 34] | 17 [11, 24] | 21 [15, 28] | 0.18 |
| c-FIM gain | 4 [1, 6] | 4 [2, 6] | 3 [1, 5] | 0.77 |
| m-FIM effectiveness | 65 [46, 83] | 53 [40, 65] | 45 [32, 57] | 0.16 |
| c-FIM effectiveness | 48 [25, 71] | 35 [19, 50] | 54 [38, 70] | 0.11 |
| m-FIM efficacy | 40 [24, 56] | 29 [18, 40] | 30 [18, 41] | 0.33 |
| c-FIM efficacy | 5 [−1, 10] | 6 [3, 10] | 6 [3, 10] | 0.48 |
| LOS | 72 [55, 90] | 65 [53, 77] | 70 [58, 83] | 0.62 |
| Age | BMI | NIHSS | mRS | m-FIM Admission | c-FIM Admission | m-FIM Gain | c-FIM Gain | m-FIM Effectiveness | c-FIM Effectiveness | m-FIM Efficacy | c-FIM Efficacy | PROMIS Impairment | PROMIS Disturbance | PSQ Total Score | 0.6 | Spearman rho (only significant FDR shown) | |
| AHI | 0.42 ** | 0.30 | −0.04 | 0.18 | −0.17 | 0.18 | 0.06 | −0.10 | −0.20 | 0.12 | 0.08 | −0.12 | −0.33 | −0.29 | −0.19 | 0.5 | |
| AI | 0.38 * | 0.33 * | −0.08 | 0.27 | −0.25 | 0.07 | −0.02 | −0.07 | −0.27 | 0.00 | −0.01 | −0.06 | −0.32 | −0.27 | −0.19 | 0.4 | |
| HI | 0.52 *** | 0.36 * | 0.04 | 0.16 | −0.17 | 0.13 | 0.12 | −0.04 | −0.09 | 0.06 | 0.04 | −0.07 | −0.16 | −0.18 | −0.13 | 0.3 | |
| ODI3 | 0.49 *** | 0.41 ** | 0.07 | 0.32 | −0.30 | 0.08 | −0.01 | −0.05 | −0.31 | 0.05 | −0.07 | −0.09 | −0.28 | −0.25 | −0.17 | 0.2 | |
| ODI4 | 0.52 *** | 0.44 ** | 0.05 | 0.33 * | −0.28 | 0.07 | −0.02 | −0.06 | −0.31 | 0.02 | −0.10 | −0.10 | −0.30 | −0.25 | −0.18 | 0.1 | |
| CT94 | 0.47 *** | 0.48 *** | 0.10 | 0.37 * | −0.26 | 0.09 | −0.07 | −0.38 * | −0.28 | −0.17 | −0.12 | −0.39 * | −0.23 | −0.07 | −0.09 | 0 | |
| CT90 | 0.52 *** | 0.49 *** | 0.11 | 0.40 ** | −0.28 | 0.11 | −0.09 | −0.22 | −0.37 * | −0.04 | −0.13 | −0.23 | −0.18 | −0.20 | −0.04 | −0.1 | |
| AvgSpO2 | −0.46 ** | −0.51 *** | −0.12 | −0.40 ** | 0.25 | −0.15 | 0.10 | 0.40 ** | 0.31 | 0.14 | 0.15 | 0.40 ** | 0.23 | 0.13 | 0.14 | −0.2 | |
| MinSpO2 | −0.45 ** | −0.41 ** | −0.04 | −0.27 | 0.25 | −0.16 | 0.00 | 0.06 | 0.27 | −0.11 | 0.13 | 0.14 | 0.08 | 0.26 | 0.10 | −0.3 | |
| Oral Breathing | 0.24 | 0.03 | 0.19 | 0.25 | −0.35 * | −0.04 | −0.12 | −0.10 | −0.37 * | 0.00 | −0.16 | −0.12 | −0.02 | −0.03 | 0.12 | −0.4 | |
| Supine time (%) | −0.11 | −0.05 | 0.15 | 0.04 | −0.11 | 0.09 | 0.22 | 0.10 | −0.04 | 0.19 | 0.17 | 0.01 | −0.08 | −0.30 | −0.06 | −0.5 | |
| AHI in supine | 0.50 *** | 0.28 | −0.01 | 0.23 | −0.18 | 0.11 | 0.01 | −0.05 | −0.16 | 0.02 | −0.01 | −0.04 | −0.22 | −0.20 | −0.19 | −0.6 |
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Albu, S.; Castillo-Escario, Y.; Romero Marquez, A.; López Andurell, M.; Jané, R.; Kumru, H. Early Screening of Sleep-Disordered Breathing Using a Smartphone-Based Portable System in Stroke Patients and Its Relevance for Rehabilitation: A Prospective Observational Study. Sensors 2026, 26, 794. https://doi.org/10.3390/s26030794
Albu S, Castillo-Escario Y, Romero Marquez A, López Andurell M, Jané R, Kumru H. Early Screening of Sleep-Disordered Breathing Using a Smartphone-Based Portable System in Stroke Patients and Its Relevance for Rehabilitation: A Prospective Observational Study. Sensors. 2026; 26(3):794. https://doi.org/10.3390/s26030794
Chicago/Turabian StyleAlbu, Sergiu, Yolanda Castillo-Escario, Alicia Romero Marquez, Mónica López Andurell, Raimon Jané, and Hatice Kumru. 2026. "Early Screening of Sleep-Disordered Breathing Using a Smartphone-Based Portable System in Stroke Patients and Its Relevance for Rehabilitation: A Prospective Observational Study" Sensors 26, no. 3: 794. https://doi.org/10.3390/s26030794
APA StyleAlbu, S., Castillo-Escario, Y., Romero Marquez, A., López Andurell, M., Jané, R., & Kumru, H. (2026). Early Screening of Sleep-Disordered Breathing Using a Smartphone-Based Portable System in Stroke Patients and Its Relevance for Rehabilitation: A Prospective Observational Study. Sensors, 26(3), 794. https://doi.org/10.3390/s26030794

