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Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things–Based Thermostat Data and Google Mobility Insights

Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things–Based Thermostat Data and Google Mobility Insights

Further, a 1-way ANOVA test (Table 3) showed that the day of the week had a statistically significant impact on residential mobility for all 4 provinces (Google; all P Analysis of the (A) Google residential and (B) ecobee mobility data in days of a week in Ontario, Quebec, Alberta, and British Columbia. A high resolution version is available in Multimedia Appendix 1. The ANOVA test compares the day of the week’s impact on Google and ecobee mobility for the 4 provinces of Canada.

Kirti Sundar Sahu, Joel A Dubin, Shannon E Majowicz, Sam Liu, Plinio P Morita

JMIR Public Health Surveill 2024;10:e46903

Characterization of False or Misleading Fluoride Content on Instagram: Infodemiology Study

Characterization of False or Misleading Fluoride Content on Instagram: Infodemiology Study

For all analyses, P As shown in Table 1, in general, the posts were predominantly commercial, produced by regular users, expressing positive sentiment, and published as an album/photo. The types of interests identified among the 500 selected posts were social (n=500, 100.0%), psychological (n=492, 98.4%), financial (n=421, 84.2%), and political (n=79, 15.8%).

Matheus Lotto, Tamires Sá Menezes, Irfhana Zakir Hussain, Shu-Feng Tsao, Zahid Ahmad Butt, Plinio P Morita, Thiago Cruvinel

J Med Internet Res 2022;24(5):e37519

Intervention and Evaluation of Mobile Health Technologies in Management of Patients Undergoing Chronic Dialysis: Scoping Review

Intervention and Evaluation of Mobile Health Technologies in Management of Patients Undergoing Chronic Dialysis: Scoping Review

Four studies that examined IWG had inconclusive results, showing nonsignificant differences between the baseline and follow-up period [33], a strong trend for relatively lower IWG (P=.06) in the intervention groups during follow-up [16], a significant mean reduction in weight gain between the groups at the end of the study [26], and a superimposed linear regression of the reduction of average daily IWG for 1 participant during the follow-up in a case study [20].

Yang Yang, Helen Chen, Hammad Qazi, Plinio P Morita

JMIR Mhealth Uhealth 2020;8(4):e15549