Segmenting accelerometer data from daily life with unsupervised machine learning

Purpose: Accelerometers are increasingly used to obtain valuable descriptors of physical activity for health research. The cut-points approach to segment accelerometer data is widely used in physical activity research but requires resource expensive calibration studies and does not make it easy to explore the information that can be gained for a variety of raw data metrics. To address these limitations, we present a data-driven approach for segmenting and clustering the accelerometer data using unsupervised machine learning. Methods: The data used came from five hundred fourteen-year-old participants from the Millennium cohort study who wore an accelerometer (GENEActiv) on their wrist on one weekday and one weekend day. A Hidden Semi-Markov Model (HSMM), configured to identify a maximum of ten behavioral states from five second averaged acceleration with and without addition of x, y, and z-angles, was used for segmenting and clustering of the data. A cut-points approach was used as comparison. Results: Time spent in behavioral states with or without angle metrics constituted eight and five principal components to reach 95% explained variance, respectively; in comparison four components were identified with the cut-points approach. In the HSMM with acceleration and angle as input, the distributions for acceleration in the states showed similar groupings as the cut-points categories, while more variety was seen in the distribution of angles. Conclusion: Our unsupervised classification approach learns a construct of human behavior based on the data it observes, without the need for resource expensive calibration studies, has the ability to combine multiple data metrics, and offers a higher dimensional description of physical behavior. States are interpretable from the distributions of observations and by their duration

Baseline characteristics and balance for 2-year follow-up analysis sample.

<p>Baseline characteristics and balance for 2-year follow-up analysis sample.</p

Study flow diagram.

<p>^aPotential disability classed as child scoring less than 3 SD below the mean on the cognitive scale of the Bayley-III at baseline, relative to external norms. Bayley-III, Bayley Scales of Infant and Toddler Development–Third Edition; MN, micronutrient; PS, psychosocial stimulation.</p

Estimated treatment effects for child behaviour.

<p>Estimated treatment effects for child behaviour.</p

Estimated treatment effects for stimulation in the home environment and maternal depressive symptoms.

<p>Estimated treatment effects for stimulation in the home environment and maternal depressive symptoms.</p

Estimated treatment effects on child cognition, language, school readiness, and executive functioning.

<p>Estimated treatment effects on child cognition, language, school readiness, and executive functioning.</p

Additional file 4 of The UK Coronavirus Job Retention Scheme and smoking, alcohol consumption and vaping during the COVID-19 pandemic: evidence from eight longitudinal population surveys

Additional file 4. Stratified Analysis (Forest plots). Figure set 1 16 31. Currently drinks 4+ days/week or 5+ drinks/occasion. Figure set 2 17 32. Increased alcohol consumption. Figure set 3 18 33. Reduced alcohol consumption. Figure set 4 19 34. Drinks 5+ drinks/occasion. Figure set 5 20 35. Drinks more alcohol units per occasion. Figure set 6 21 36. Drinks fewer alcohol units per occasion. Figure set 7 22 37. Currently drinks 4+ days/week. Figure set 8 23 38. Drinks more frequently. Figure set 9 24 39: Drinks less frequently. Figure set 10 25 40. Current smoker. Figure set 11 26 41. Smoking more. Figure set 12 27 42. Smoking less. Figure set 13 28 43. Current vaper. Figure set 14 29 44. Vaping more. Figure set 15 30 45. Vaping less

Additional file 3 of The UK Coronavirus Job Retention Scheme and smoking, alcohol consumption and vaping during the COVID-19 pandemic: evidence from eight longitudinal population surveys

Additional file 3. Meta-Analysis. Table 1. Main analysis excluding studies with ≤5 cell counts for exposure-outcome. Table 2. Main analysis excluding studies with ≤2 cell counts for exposure-outcome. Table 3. Main analysis excluding studies with zero cell counts for exposure-outcome. Table 4. Analysis of change excluding studies with ≤ 5 cell counts. Table 5. Analysis of change excluding studies with ≤ 2 cell counts. Table 6. Analysis of change excluding studies with zero cell counts. Figure set 1. Currently drinks 4+ days/week or 5+ drinks/occasion. Figure set 2. Increased alcohol consumption. Figure set 3. Reduced alcohol consumption. Figure set 4. Currently drinks 5+ drinks/occasion. Figure set 5. Drinks more alcohol units per occasion. Figure set 6. Drinks fewer alcohol units per occasion. Figure set 7. Currently drinks 4+ days/week. Figure set 8. Drinks more frequently. Figure set 9. Drinks less frequently. Figure set 10. Current smoker. Figure set 11. Smoking more. Figure set 12. Smoking less. Figure set 13. Current vaper. Figure set 14. Vaping more. Figure set 15. Vaping less

Additional file 1 of The UK Coronavirus Job Retention Scheme and smoking, alcohol consumption and vaping during the COVID-19 pandemic: evidence from eight longitudinal population surveys

Additional file 1: Table S1. Description of Studies. Table S2. Ethics and data access statements for each study. Table S3. Sample characteristics by study. Table S4. Employment status change by sex, education, and age-group. Table S5. Meta-analysed risk ratios and heterogeneity estimates for associations between changes in employment status and drinking behaviour: unadjusted, basic & full adjustment results. Table S6. Meta-analysed risk ratios and heterogeneity estimates for associations between changes in employment status and smoking: unadjusted, basic & full adjustment results. Table S7. Meta-analysed risk ratios and heterogeneity estimates for associations between changes in employment status and vaping: unadjusted, basic & full adjustment results. Figure S8. Causal pathways blocked under differing levels of adjustment

Additional file 2 of The UK Coronavirus Job Retention Scheme and smoking, alcohol consumption and vaping during the COVID-19 pandemic: evidence from eight longitudinal population surveys

Additional file 2. Variable Coding