A qualitative study on the facilitators and barriers to adopting the N-of-1 trial methodology as part of clinical practice: potential versus implementation challenges.

PURPOSE: To investigate opinions among healthcare stakeholders whether implementation of the N-of-1 trial approach in clinical practice is a feasible way to optimize evidence-based treatment results for unique patients. METHODS: We interviewed clinicians, researchers, and a patient advocate (n = 13) with an interest in or experience with N-of-1 trials on the following topics: experience with N-of-1, measurement, validity and reliability, informally gathered data usability, and influence on physician-patient relationship. Interviews were analysed using qualitative, thematic analysis. RESULTS: The N-of-1 approach has the potential to shift the current healthcare system towards embracing personalized medicine. However, its application in clinical practice carries significant challenges in terms of logistics, time investment and acceptability. New skills will be required from patients and healthcare providers, which may alter the patient-physician relationship. The rise of consumer technology enabling self-measurement may leverage the uptake of N-of-1 approaches in clinical practice. CONCLUSIONS: There is a strong belief that the N-of-1 approach has the potential to play a prominent role in transitioning the current healthcare system towards embracing personalized medicine. However, there are many barriers deeply ingrained in our healthcare system that hamper the uptake of the N-of-1 approach, making it momentarily only interesting for research purposes.

Key findings The potential merits of adopting N-of-1 trials into clinical practice (in terms of efficacy and participation) was acknowledged by all participants.The trade-off between methodological rigidity and practical application for the patient was mentioned by clinicians as an important barrier for the use of N-of-1 trials in clinical practice.There appears to be substantial dissensus on the usefulness of "informal/pragmatic" N-of-1 trials in clinical practice; clinicians appear the strongest advocates for strict methodological rigour.What this adds to what is known Previous research suggests that lack of knowledge by researchers, clinicians, and patients on the topic of N-of-1, operational complexity, and costs are primary barriers for adoption of N-of-1 trials in clinical practice.Our work confirms the abovementioned barriers and adds to this list: the current design of the healthcare system and the lack of consensus on methodological requirements.The Quantified Self movement as well as the advances in the wearable technology were mentioned by (patient)researchers as facilitators for the adoption of N-of-1 methodologies in clinical practice.What is the implication, what should change now Education on N-of-1 trials need to be included in the medical (and thus not only the biomedical sciences) curriculum.The N-of-1 approach might help promote shared decision making in which patient can lead using their own data.Best practices of N-of-1 adoption in clinical practice need to be identified and used as examples to further inform communication between medical stakeholders and policymakers.

Fusion of three spectroscopic techniques for prediction of fatty acid in processed lamb.

The application of individual spectroscopic techniques for meat analysis has been widely explored. Attempts to fuse data from multiple spectroscopic instruments for meat analysis are still lacking. Comparative assessment of the performance of mid infrared (MIR), near infrared (NIR) and Raman spectroscopy to estimate fatty acid (FA) composition in processed lamb was investigated. The acquired data from these individual techniques were then utilised in estimating similar parameters using a multi-block partial least square data fusion approach. Model performance was assessed with respect to the determination coefficient and ratio of predictive deviation upon cross-validation of the model. The fused data had slight improvements for the prediction of four FA parameters including MUFA, C18:0, C18:1 c9 and C9, t11- CLA), suggesting possible information enhancement with use of multiple instruments. However, MIR offered better predictability (RPD values) across the FA parameters considered.

Evaluating low- mid- and high-level fusion strategies for combining Raman and infrared spectroscopy for quality assessment of red meat.

The implementation of Raman and infrared spectroscopy with three data fusion strategies to predict pH and % IMF content of red meat was investigated. Raman and FTIR systems were utilized to assess quality parameters of intact red meat. Quantitative models were built using PLS, with model performances assessed with respect to the determination coefficient (R2), root mean square error and normalized root mean square error (NRMSEP). Results obtained on validation against an independent test set show that the high-level fusion strategy had the best performance in predicting the observed pH; with RP2 and NRMSEP values of 0.73 and 12.9% respectively, whereas low-level fusion strategy showed promise in predicting % IMF (NRMSEP = 8.5%). The fusion of data from more than one technique at low and high level resulted in improvement in the model performances; highlighting the possibility of information enhancement.

Rapid discrimination of intact beef, venison and lamb meat using Raman spectroscopy.

With increasing demand for fast and reliable techniques for intact meat discrimination, we explore the potential of Raman spectroscopy in combination with three chemometric techniques to discriminate beef, lamb and venison meat samples. Ninety (90) intact red meat samples were measured using Raman spectroscopy, with the acquired spectral data preprocessed using a combination of rubber-band baseline correction, Savitzky-Golay smoothing and standard normal variate transformation. PLSDA and SVM classification were utilized in building classification models for the meat discrimination, whereas PCA was used for exploratory studies. Results obtained using linear and non-linear kernel SVM models yielded sensitivities of over 87 and 90 % respectively, with the corresponding specificities above 88 % on validation against a test set. The PLSDA model yielded over 80 % accuracy in classifying each of the meat specie. PLSDA and SVM classification models in combination with Raman spectroscopy posit an effective technique for red meat discrimination.