The rise of Parkinson's disease is a global challenge, but efforts to tackle this must begin at a national level: a protocol for national digital screening and "eat, move, sleep" lifestyle interventions to prevent or slow the rise of non-communicable diseases in Thailand

Abstract

The rising prevalence of Parkinson's disease (PD) globally presents a significant public health challenge for national healthcare systems, particularly in low-to-middle income countries, such as Thailand, which may have insufficient resources to meet these escalating healthcare needs. There are also many undiagnosed cases of early-stage PD, a period when therapeutic interventions would have the most value and least cost. The traditional "passive" approach, whereby clinicians wait for patients with symptomatic PD to seek treatment, is inadequate. Proactive, early identification of PD will allow timely therapeutic interventions, and digital health technologies can be scaled up in the identification and early diagnosis of cases. The Parkinson's disease risk survey (TCTR20231025005) aims to evaluate a digital population screening platform to identify undiagnosed PD cases in the Thai population. Recognizing the long prodromal phase of PD, the target demographic for screening is people aged ≥ 40 years, approximately 20 years before the usual emergence of motor symptoms. Thailand has a highly rated healthcare system with an established universal healthcare program for citizens, making it ideal for deploying a national screening program using digital technology. Designed by a multidisciplinary group of PD experts, the digital platform comprises a 20-item questionnaire about PD symptoms along with objective tests of eight digital markers: voice vowel, voice sentences, resting and postural tremor, alternate finger tapping, a "pinch-to-size" test, gait and balance, with performance recorded using a mobile application and smartphone's sensors. Machine learning tools use the collected data to identify subjects at risk of developing, or with early signs of, PD. This article describes the selection and validation of questionnaire items and digital markers, with results showing the chosen parameters and data analysis methods to be robust, reliable, and reproducible. This digital platform could serve as a model for similar screening strategies for other non-communicable diseases in Thailand.

Keywords: Parkinson’s disease; Thailand; digital interventions; lifestyle interventions; non-communicable diseases; prevention.

Figure 1

The natural history of Parkinson’s disease (PD) showing the progression of motor and nonmotor symptoms from the prodromal to advanced stage. There is commonly a delay between onset of motor symptoms and a confirmed clinical diagnosis of PD. Boxes shaded in red/pink are clinical parameters that can be screened either by using a questionnaire or digital application while those in gray cannot. Due to the long prodromal period of PD, the recommended age for screening is 40 years.

Figure 2

Rationale and objectives of the Parkinson’s disease risk survey screening program.

Figure 3

The Parkinson’s disease risk survey project implemented in honor of Her Royal Highness Princess Maha Chakri Sirindhorn, Executive Vice President of Thai Red Cross Society. Top: Her Royal Highness and the multidisciplinary team involved in the study’s design, validation, and implementation at the official opening event; bottom: subjects being screened with the digital tools.

Figure 4

Application interface of the digital assessment package and how the assessment was undertaken.

Figure 5

The procedure for splitting the training and testing sets for PD classification using ensemble learning.

Figure 6

Receiver operating characteristic curve of classifiers using voice vowel data (left) and voice sentence data (right).

Figure 7

Receiver operating characteristic curve of classifiers using rest tremor data (left) and postural tremor data (right).

Figure 8

Receiver operating characteristic of classifiers using alternate finger tapping data (left) and pinch-to-size data (right).

Figure 9

Receiver operating characteristic of classifiers using gait data (left) and balance data (right).