Clinical and economic assessments of a personalized support plan to improve adherence to positive airway pressure therapy in patients with obstructive sleep apnea: a randomized controlled trial in Portugal

Millions of adults worldwide struggle with a condition where their breathing stops and starts repeatedly during sleep, a disorder known as obstructive sleep apnea. This happens when the muscles in the throat relax too much, blocking the airway and cutting off oxygen. The most common and effective treatment involves a machine that delivers a gentle stream of air through a mask, acting like a pneumatic splint to keep the throat open. This therapy, called positive airway pressure, is highly effective at preventing dangerous health events like heart attacks and strokes, but only if patients use it consistently every night. The challenge is that many people find the masks uncomfortable or the routine difficult to maintain, leading them to stop using the machine or use it for only a short time each night. Without consistent use, the health benefits disappear, and the risks return.

Researchers in Portugal recently investigated whether a more tailored approach could help patients stick with this life-saving therapy. They tested a system where home support providers, rather than just following a standard schedule, created a unique plan for each person based on their lifestyle, motivation, and early struggles with the device. The study, published in a peer-reviewed journal, sought to determine if this personalized attention would lead to better usage rates and whether the extra effort required to provide it would be worth the cost to the healthcare system. The findings suggest that while the immediate results were mixed, a long-term strategy of personalized support significantly improves how many people use their machines enough to stay healthy, and that this improvement could save the health system a substantial amount of money over time.

The study took place across five medical centers in Portugal, involving adults who had just been diagnosed with sleep apnea and were starting treatment for the first time. The researchers divided these patients into two groups. One group received the standard care that is typical in the region, which includes a few scheduled visits and basic instructions. The other group was assigned to a personalized support plan. For these patients, a team of nurses and technicians assessed their daily habits, their job schedules, and their feelings about the therapy at the start of treatment. Based on this information, each person was placed on a specific plan that dictated how often they would be contacted and through which method, such as a phone call, a video chat, a mobile app, or a home visit. The plan was flexible; if a patient was struggling, the team could increase the frequency of their check-ins. If a patient was doing well, the team could step back. Crucially, the machines used by the patients could send data wirelessly, allowing the support team to see exactly how many hours the patient used the device each night and to spot any problems immediately.

The primary goal of the study was to see if the personalized group used their machines more often during the first three months compared to the standard group. In this initial period, the difference was small and not statistically significant. The personalized group averaged about 4.6 hours of use per day, while the standard group averaged about 3.9 hours. While the personalized group did use the machine slightly more, the gap was not large enough to be considered a definitive victory in the first few months. However, the story changed when the researchers looked at the full year. Over twelve months, the personalized group showed a clear and significant advantage. They used the machine for a greater proportion of days, and a much higher percentage of them met the clinical goal of using it for at least four hours a night. Conversely, far fewer people in the personalized group fell into the category of very low usage, defined as less than two hours a day.

The data revealed that the personalized approach was particularly effective at preventing patients from giving up entirely. In the standard group, roughly thirty-five percent of patients used the machine for less than two hours a day after a year, whereas in the personalized group, that number dropped to about sixteen percent. This shift meant that nearly one in five more patients in the personalized group were using the therapy enough to reap its health benefits. The study also tracked how the patients felt. Those in the personalized group reported significantly less daytime sleepiness after a year compared to the standard group. This suggests that the extra support helped them sleep better and feel more alert during the day. Interestingly, the intensity of the support was not the same for everyone; the team provided more frequent visits and calls to those who were struggling the most, while those who adapted quickly received less intervention. This targeted approach meant that resources were directed where they were needed most.

While the personalized plan required more work from the support teams, with an average of about five interventions per patient compared to two for the standard group, the researchers modeled the financial impact to see if it made economic sense. They calculated that the extra visits and calls were offset by the money saved when patients used their machines consistently. By preventing the health complications associated with untreated sleep apnea, such as heart disease and accidents, the system could save money. The model estimated that for every person managed with this personalized plan, the health system could save approximately two hundred and fifty-five euros in direct medical costs and an additional two hundred and twenty euros in indirect costs, such as lost workdays. When applied to the thousands of patients in Portugal who start this therapy each year, the potential annual savings could reach millions of euros.

The researchers acknowledged that their study had limitations, including a relatively small number of participants and a follow-up period that might have been too short to capture the full effect of the intervention in the very first months. They noted that the personalized plan seemed to work best for patients with mild to moderate sleep apnea, while those with severe cases did not show the same immediate improvement in usage rates. Despite these caveats, the results point toward a clear conclusion: a flexible, data-driven support system that adapts to the individual needs of patients can significantly improve long-term adherence to sleep apnea therapy. This approach not only helps patients feel better and sleep safer but also offers a financially sustainable way to manage a widespread health condition. The study suggests that moving away from a one-size-fits-all model toward a system that listens and responds to the patient's daily reality could be the key to unlocking the full potential of this therapy.