| Literature DB >> 34575234 |
Onintza Garmendia1, Ramon Farré2,3,4, Concepción Ruiz1, Monique Suarez-Girón1, Marta Torres3,5,6, Raisa Cebrian7, Laura Saura7, Carmen Monasterio8, Miguel A Negrín9, Josep M Montserrat1,3,4.
Abstract
Patients with sleep apnea are usually treated with continuous positive airway pressure (CPAP). This therapy is very effective if the patient's adherence is satisfactory. However, although CPAP adherence is usually acceptable during the first months of therapy, it progressively decreases, with a considerable number of patients accepting average treatment duration below the effectiveness threshold (4 h/night). Herein, our aim was to describe and evaluate a novel telemedicine strategy for rescuing CPAP treatment in patients with low adherence after several months/years of treatment. This two-week intervention includes (1) patient support using a smartphone application, phone and voice recorder messages to be answered by a nurse, and (2) daily transmission and analysis of signals from the CPAP device and potential variation of nasal pressure if required. On average, at the end of the intervention, median CPAP adherence considerably increased by 2.17 h/night (from 3.07 to 5.24 h/night). Interestingly, the procedure was able to markedly rescue CPAP adherence: the number of patients with poor adherence (<4 h/night) was considerably reduced from 38 to 7. After one month, adherence improvement was maintained (median 5.09 h/night), and only 13 patients had poor adherence (<4 h/night). This telemedicine intervention (103€ per included patient) is a cost-effective tool for substantially increasing the number of patients with CPAP adherence above the minimum threshold for achieving positive therapeutic effects.Entities:
Keywords: compliance; nasal pressure; obstructive sleep apnea; patient adherence; sleep breathing disorders; telemedicine interventions
Year: 2021 PMID: 34575234 PMCID: PMC8470548 DOI: 10.3390/jcm10184123
Source DB: PubMed Journal: J Clin Med ISSN: 2077-0383 Impact factor: 4.241
Patient characteristics.
| Number | 56 |
| Gender (male; %) | 78.6 |
| Age (yr; m ± SD) | 57.9 ± 8.9 |
| Apnea-hypopnea index (events/h; m ± SD) | 45.8 ± 20.1 |
| Time on CPAP therapy (yr; m ± SD) | 2.46 ± 0.90 |
| Main Comorbidity: | |
| Cardiovascular (%) | 41.1 |
| Metabolic (%) | 39.3 |
| Neurological (%) | 1.8 |
| Respiratory (%) | 19.6 |
| Depression (%) | 12.5 |
| Neurological (%) | 1.8 |
Figure 1Flow chart of the study.
Figure 2Adherence of CPAP is expressed as the number of hours per night on treatment (median, 25–75% percentiles and smallest and largest values). Data are shown for the whole group of patients (red), for those patients with preintervention (PRE) adherence of <4 h/night (blue), and for those with preintervention adherence between 4 and 5.5 h/night (green). Labels “POST” and “1-MONTH” indicate values measured immediately at the end of the intervention and 1 month later, respectively. All changes from PRE to POST and from PRE to 1-MONTH were statistically significant, and none of the minor changes from POST to 1-MONTH were statistically significant. ***, **, and * indicate p < 0.001, p < 0.01 and p < 0.05, respectively.
Figure 3Number of patients within three different levels of adherence: poor and thus poor adherence (<4 h/night) (red), good adherence (4–5.5 h/night) (yellow) and excellent adherence (>5.5 h/night) (green) at three different time points: prior to the telemedicine intervention (Pre), after the intervention (Post) and one month later (1 month). The p values refer to differences in the number of patients within three different levels of adherence when comparing PRE vs. POST and PRE vs. 1-MONTH.
Figure 4Patient satisfaction with the telemedicine intervention. Left section (A) shows the percentage of patient’s responses when asked whether the App was totally, partially or not useful. (B) and (C), on the right, show the percentage of patients who would recommend using the App to other patients and who would like to use the App regularly along their CPAP treatment, respectively.
Figure 5Cost distribution of the proposed telemedicine intervention.