Detecting Acute Otitis Media Symptom Episodes Using a Mobile App: Cohort Study

Abstract

BackgroundPopulation cohort studies are useful to study infectious diseases episodes not attended by health care services, but conventional paper diaries and questionnaires to capture cases are prone to noncompliance and recall bias. Use of smart technology in this setting may improve case finding.ObjectiveThe objective of our study was to validate an interactive mobile app for monitoring occurrence of acute infectious diseases episodes in individuals, independent of health care seeking, using acute otitis media (AOM) symptom episodes in infants as a case study. We were interested in determining participant compliance and app performance in detecting and ascertaining (parent-reported) AOM symptom episodes with this novel tool compared with traditional methods used for monitoring study participants.MethodsWe tested the InfectieApp research app to detect AOM symptom episodes. In 2013, we followed 155 children aged 0 to 3 years for 4 months. Parents recorded the presence of AOM symptoms in a paper diary for 4 consecutive months and completed additional disease questionnaires when AOM symptoms were present. In 2015 in a similar cohort of 69 children, parents used an AOM diary and questionnaire app instead.ResultsDuring conventional and app-based recording, 93.13% (17,244/18,516) and 94.56% (7438/7866) of symptom diaries were returned, respectively, and at least one symptom was recorded for 32.50% (n=5606) and 43.99% (n=3272) of diary days (P<.01). The incidence of AOM symptom episodes was 605 and 835 per 1000 child-years, respectively. Disease questionnaires were completed for 59% (17/29) of episodes when participants were using conventional recording, compared with 100% (18/18) for app-based recording.ConclusionsThe use of the study’s smart diary app improved AOM case finding and disease questionnaire completeness. For common infectious diseases that often remain undetected by health care services, use of this technology can substantially improve the accurateness of disease burden estimates.

Study protocol for a randomised controlled trial evaluating the clinical effect of antibiotic prophylaxis in children with recurrent respiratory tract infections: the Approach study

“…The use of our Diary-App for symptom recording costs parents less than 1 min per day and has shown to improve case-finding and questionnaire completeness from 60% to ≥90%. 37 Also, parents will be asked to answer extra questions once a month. These include questions about the use of other antibiotics (see online supplemental file 1 for the monthly questionnaire).…”

Incorporating patient generated health data into pharmacoepidemiological research

“…The accuracy and consistency of wearable devices must be assessed, and few available devices have been robustly validated 18 . For collecting PGHD on people's experience of disease and treatment, such as symptom burden or medication side effects, digital tools (see Figure 1) are equivalent to their paper‐based counterparts, 19‐21 and potentially improve the timeliness, completeness and accuracy of data collection 22,23 …”

“…18 For collecting PGHD on people's experience of disease and treatment, such as symptom burden or medication side effects, digital tools (see Figure 1) are equivalent to their paper-based counterparts, [19][20][21] and potentially improve the timeliness, completeness and accuracy of data collection. 22,23 Social media platforms such as Facebook, 29 However, some comparisons between social media data and established datasets have been reported. 30,31 Comparisons of Facebook and Twitter with FDA data for adverse event identification showed mixed results.…”

“…18 For collecting PGHD on people's experience of disease and treatment, such as symptom burden or medication side effects, digital tools (see Figure 1) are equivalent to their paper-based counterparts, [19][20][21] and potentially improve the timeliness, completeness and accuracy of data collection. 22,23 Social media platforms such as Facebook, Twitter and health forums like PatientsLikeMe and HealthUnlocked offer large volumes of public posts where people share their experiences of living with disease. Incorporating such data into pharmacoepidemiological studies provides major opportunities, but concerns remain about generalizability and validity.…”

Advances in Artificial Intelligence to Diagnose Otitis Media: State of the Art Review

“…This is similar in concept to current techniques to augment diagnostic medicine, such as biosensor technology to monitor vitals and the use of smartphones to conduct portable ultrasounds. 41,47,48 In conclusion, the last decade has seen a rise in the number of ML advances and automated computer algorithms to help diagnose OM. Even though the bulk of the current literature is at an infancy stage in terms of practical implementation, the reported outcomes have diagnostic accuracies comparable to those of clinicians.…”