Forecasting migraine with machine learning based on mobile phone diary and wearable data

INTRODUCTION: Triggers, premonitory symptoms and physiological changes occur in the preictal migraine phase and may be used in models for forecasting attacks. Machine learning is a promising option for such predictive analytics. The objective of this study was to explore the utility of machine learning to forecast migraine attacks based on preictal headache diary entries and simple physiological measurements. METHODS: In a prospective development and usability study 18 patients with migraine completed 388 headache diary entries and self-administered app-based biofeedback sessions wirelessly measuring heart rate, peripheral skin temperature and muscle tension. Several standard machine learning architectures were constructed to forecast headache the subsequent day. Models were scored with area under the receiver operating characteristics curve. RESULTS: Two-hundred-and-ninety-five days were included in the predictive modelling. The top performing model, based on random forest classification, achieved an area under the receiver operating characteristics curve of 0.62 in a hold-out partition of the dataset. DISCUSSION: In this study we demonstrate the utility of using mobile health apps and wearables combined with machine learning to forecast headache. We argue that high-dimensional modelling may greatly improve forecasting and discuss important considerations for future design of forecasting models using machine learning and mobile health data

Mental health, pain and tiredness in adults born very preterm or with very low birthweight

Aim: Adults born preterm have increased risk of mental health problems and other neurodevelopmental conditions. We aimed to investigate associations of mental health with pain and tiredness in adults born very preterm (VP; <32 weeks) or very low birthweight (VLBW; <1500 g) and at term, and whether these associations are influenced by physical activity. Methods: As part of an EU Horizon 2020 project, individual participant data from six prospective cohort studies were harmonised for 617 VP/VLBW and 1122 term‐born participants. Mental health was assessed by the Achenbach System of Empirically Based Assessment Adult Self‐Report. Pain and tiredness were harmonised based on specific items from self‐reported questionnaires. Associations between mental health and pain or tiredness were explored by linear regression. Results: An increase in the mental health scales internalising, externalising and total problems was associated with increased pain and tiredness in the preterm and term group alike. Results were maintained when adjusting for physical activity. Conclusion: The findings indicate that associations between mental health, pain and tiredness in adults are independent of gestation or birthweight. Future research should explore other potential mechanisms that may underlie the increased risk of mental health problems in the preterm population

QEEG and Infra-Low Frequency Neurofeedback Training in Fibromyalgia: A pilot Study

Bakgrunn: Fibromyalgi (FM) er en kompleks kronisk smertesykdom karakterisert av diffus smerte, fatigue (utmattelse), og fibrotåke (kognitive vanskeligheter) som i betydelig grad påvirker pasientenes livskvalitet. Årsaken til og effekten av fibromyalgi er uklar og gjør det utfordrende for legepersonell å gi en korrekt diagnose. I tillegg finnes det ingen behandling for fibromyalgi som fokuserer på sykdommen som en helhet. Forskning indikerer at sentrale sensitiviserings mekanismer er en del av symptombildet, i tillegg til abnormal smerteprosessering i hjernens «dynamic pain connectome» (DPC). Dette pilotstudie hadde to formål; (a) å sammenligne FM pasienters hjerneaktivitet med friske kontroller for å se om hjerneområdene som viser avvik er tilknyttet DPC, og videre vurdere om disse kortikale regionene kan bidra til å avdekke nevrobiologiske markører for fibromyalgi; (b) å undersøke effekten av infra-low frekvens nevrofeedback-trening (ILF NFT) på FM symptomer for å evaluere ILF NFT som en potensiell intervensjon for fibromyalgi. Metode: Pasientene gjennomførte 10-15 treninger med ILF NFT, i tillegg til pre- og post-test undersøkelser (EEG-opptak og utfylling av spørreskjemaer relatert til grad av symptomer og livskvalitet). Resultater: Wilcoxon Signed-Rank Tester indikerte signifikant symptom-reduksjon og normalisering av hjerneaktivitet blant pasientene etter ILF NFT. Posisjoneringsanalyse indikerte at alle pasientene viste avvik i et eller flere av hjerneområdene inkludert i DPC. Begrensninger ved studien og tolkninger av resultatene er diskutert

Visuopathy of prematurity: is retinopathy just the tip of the iceberg?

Background: Chronic brain pathology and pre-stroke cognitive impairment (PCI) is predictive of post-stroke dementia. The aim of the current study was to measure pre-stroke neurodegenerative and vascular disease burden found on brain MRI and to assess the association between pre-stroke imaging pathology and PCI, whilst also looking for potential sex differences. Methods: This prospective brain MRI cohort is part of the multicentre Norwegian cognitive impairment after stroke (Nor-COAST) study. Patients hospitalized with acute ischemic or hemorrhagic stroke were included from five participating stroke units. Visual rating scales were used to categorize baseline MRIs (N = 410) as vascular, neurodegenerative, mixed, or normal, based on the presence of pathological imaging findings. Pre-stroke cognition was assessed by interviews of patients or caregivers using the Global Deterioration Scale (GDS). Stroke severity was assessed with the National Institute of Health Stroke Scale (NIHSS). Univariate and multiple logistic regression analyses were performed to investigate the association between imaging markers, PCI, and sex. Results: Patients’ (N = 410) mean (SD) age was 73.6 (±11) years; 182 (44%) participants were female, the mean (SD) NIHSS at admittance was 4.1 (±5). In 68% of the participants, at least one pathological imaging marker was found. Medial temporal lobe atrophy (MTA) was present in 30% of patients, white matter hyperintensities (WMH) in 38% of patients and lacunes in 35% of patients. PCI was found in 30% of the patients. PCI was associated with cerebrovascular pathology (OR 2.5; CI = 1.4 to 4.5, p = 0.001) and mixed pathology (OR 3.4; CI = 1.9 to 6.1, p = 0.001) but was not associated with neurodegeneration (OR 1.0; CI = 0.5 to 2.2; p = 0.973). Pathological MRI markers, including MTA and lacunes, were more prevalent among men, as was a history of clinical stroke prior to the index stroke. The OR of PCI for women was not significantly increased (OR 1.2; CI = 0.8 to 1.9; p = 0.3)

sj-jpg-1-cep-10.1177_03331024231169244 - Supplemental material for Forecasting migraine with machine learning based on mobile phone diary and wearable data

Supplemental material, sj-jpg-1-cep-10.1177_03331024231169244 for Forecasting migraine with machine learning based on mobile phone diary and wearable data by Anker Stubberud, Sigrid Hegna Ingvaldsen, Eiliv Brenner, Ingunn Winnberg, Alexander Olsen, Gøril Bruvik Gravdahl, Manjit Singh Matharu, Parashkev Nachev and Erling Tronvik in Cephalalgia</p

sj-pdf-2-cep-10.1177_03331024231169244 - Supplemental material for Forecasting migraine with machine learning based on mobile phone diary and wearable data

Supplemental material, sj-pdf-2-cep-10.1177_03331024231169244 for Forecasting migraine with machine learning based on mobile phone diary and wearable data by Anker Stubberud, Sigrid Hegna Ingvaldsen, Eiliv Brenner, Ingunn Winnberg, Alexander Olsen, Gøril Bruvik Gravdahl, Manjit Singh Matharu, Parashkev Nachev and Erling Tronvik in Cephalalgia</p

Mental health, pain, and tiredness in adults born very preterm or with very low birthweight

AimAdults born preterm have increased risk of mental health problems and other neurodevelopmental conditions. We aimed to investigate associations of mental health with pain and tiredness in adults born very preterm (VP; <32 weeks) or very low birthweight (VLBW; <1500 g) and at term, and whether these associations are influenced by physical activity.MethodsAs part of an EU Horizon 2020 project, individual participant data from six prospective cohort studies were harmonised for 617 VP/VLBW and 1122 term-born participants. Mental health was assessed by the Achenbach System of Empirically Based Assessment Adult Self-Report. Pain and tiredness were harmonised based on specific items from self-reported questionnaires. Associations between mental health and pain or tiredness were explored by linear regression.ResultsAn increase in the mental health scales internalising, externalising and total problems was associated with increased pain and tiredness in the preterm and term group alike. Results were maintained when adjusting for physical activity.ConclusionThe findings indicate that associations between mental health, pain and tiredness in adults are independent of gestation or birthweight. Future research should explore other potential mechanisms that may underlie the increased risk of mental health problems in the preterm population.</p