Design, implementation and validation of a novel open framework for agile development of mobile health applications

Baños, Oresti; Villalonga, Claudia; García, Rafael B.; Saez, Alejandro; Damas, Miguel; Holgado-Terriza, Juan A.; Lee, Sung Yong; Pomares, H.; Rojas, Ignacio

doi:10.1186/1475-925x-14-s2-s6

Cited by 224 publications

(134 citation statements)

References 35 publications

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“…Given the increased interest of health care organizations in the potential of mobile technology to address service gaps for mental health [45], the data from this study provide important information on the impact apps can have in depression care. Our findings suggest that apps designed to engage cognitive correlates of depression had the strongest effect on depressed mood for people with more moderate levels of depression.…”

Section: Discussionmentioning

confidence: 99%

The Use and Effectiveness of Mobile Apps for Depression: Results From a Fully Remote Clinical Trial

Areán¹,

Hallgren²,

Jordan³

et al. 2016

J Med Internet Res

306

290

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BackgroundMobile apps for mental health have the potential to overcome access barriers to mental health care, but there is little information on whether patients use the interventions as intended and the impact they have on mental health outcomes.ObjectiveThe objective of our study was to document and compare use patterns and clinical outcomes across the United States between 3 different self-guided mobile apps for depression.MethodsParticipants were recruited through Web-based advertisements and social media and were randomly assigned to 1 of 3 mood apps. Treatment and assessment were conducted remotely on each participant’s smartphone or tablet with minimal contact with study staff. We enrolled 626 English-speaking adults (≥18 years old) with mild to moderate depression as determined by a 9-item Patient Health Questionnaire (PHQ-9) score ≥5, or if their score on item 10 was ≥2. The apps were (1) Project: EVO, a cognitive training app theorized to mitigate depressive symptoms by improving cognitive control, (2) iPST, an app based on an evidence-based psychotherapy for depression, and (3) Health Tips, a treatment control. Outcomes were scores on the PHQ-9 and the Sheehan Disability Scale. Adherence to treatment was measured as number of times participants opened and used the apps as instructed.ResultsWe randomly assigned 211 participants to iPST, 209 to Project: EVO, and 206 to Health Tips. Among the participants, 77.0% (482/626) had a PHQ-9 score >10 (moderately depressed). Among the participants using the 2 active apps, 57.9% (243/420) did not download their assigned intervention app but did not differ demographically from those who did. Differential treatment effects were present in participants with baseline PHQ-9 score >10, with the cognitive training and problem-solving apps resulting in greater effects on mood than the information control app (χ22=6.46, P=.04).ConclusionsMobile apps for depression appear to have their greatest impact on people with more moderate levels of depression. In particular, an app that is designed to engage cognitive correlates of depression had the strongest effect on depressed mood in this sample. This study suggests that mobile apps reach many people and are useful for more moderate levels of depression.ClinicalTrialClinicaltrials.gov NCT00540865; https://www.clinicaltrials.gov/ct2/show/NCT00540865 (Archived by WebCite at http://www.webcitation.org/6mj8IPqQr)

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Section: Discussionmentioning

confidence: 99%

The Use and Effectiveness of Mobile Apps for Depression: Results From a Fully Remote Clinical Trial

Areán¹,

Hallgren²,

Jordan³

et al. 2016

J Med Internet Res

306

290

View full text Add to dashboard Cite

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“…Smartphone passive sensing has been explored in research to measure daily behaviours, which could in principle give insight into cognitive disorders. For example, various works have exploited the smartphone's inertial sensors, GPS and microphones for detecting indoor and outdoor physical activities (Ouchi and Doi 2012;Banos et al 2015;Hur et al 2017), which may relate to the cognitive state of a person (Hayes et al 2008;Hagler et al 2010). In a similar fashion, Bluetooth scans, photo captures and ambient audio recordings are used to measure levels of sociability (Lane et al 2011;Vu et al 2015), that may be linked to cognitive functioning (Akl et al 2016).…”

Section: Related Workmentioning

confidence: 99%

MobileCogniTracker

Wohlfahrt-Laymann

Hermens

Villalonga

et al. 2018

J Ambient Intell Human Comput

Self Cite

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As the population ages, cognitive decline is becoming a worldwide threat to older adults' independence and quality of life.Cognitive decline involves problems with memory, language, thinking and judgement, thus severely compromising multiple aspects of people's everyday life. Diagnosis of cognitive disorders is currently performed through clinical questionnairebased assessments, which are typically conducted by medical experts once symptoms appear. Digital technologies can help providing more immediate, pervasive and seamless assessment, which could, in turn, allow for much earlier diagnosis of cognitive disorders and decline. In this work, we present MobileCogniTracker, a digital tool for facilitating momentary, seamless and ubiquitous clinically-validated cognitive measurements. The proposed tool develops digital cognitive tests in the form of multimedia experience sampling questionnaires, which can run on a smartphone and can be scheduled and assessed remotely. The tool further integrates the digital cognitive experience sampling with passive smartphone sensor data streams that may be used to study the interplay of cognition and physical, social and emotional behaviours. The Mini-Mental State Examination test, a clinical questionnaire extensively used to measure cognitive disorders, has been particularly implemented here to showcase the possibilities offered by our tool. A usability test showed the tool to be usable for performing digital cognitive examinations, and that cognitively unimpaired persons in the relevant age-group are capable of performing such digital examination. A qualitative expert-driven validation also shows a high inter-reliability between the digital and pencil-and-paper version of the test.

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“…As discussed in Section 1, we considered the datasets acquired from 2012 to be compliant with the year of the older smartphone-based dataset. This set includes, sorted by year of creation from the oldest the most recent, the following datasets: DLR v2 [37], Ugulino [38], USC HAD [39], DaLiAc [10], EvAAL [40], MHEALTH [41], UCI ARSA [32], BaSA [42], UR Fall Detection [43], MMsys [9], SisFall [44], UMA Fall (UMA Fall contains samples from both smartphones and ad-hoc wearable devices.) [23], and REALDISP [45].…”

Section: Adls and Fallsmentioning

confidence: 99%

UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones

2017

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Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are being increasingly used to monitor human activities. Data acquired by the hosted sensors are usually processed by machine-learning-based algorithms to classify human activities. The success of those algorithms mostly depends on the availability of training (labeled) data that, if made publicly available, would allow researchers to make objective comparisons between techniques. Nowadays, there are only a few publicly available data sets, which often contain samples from subjects with too similar characteristics, and very often lack specific information so that is not possible to select subsets of samples according to specific criteria. In this article, we present a new dataset of acceleration samples acquired with an Android smartphone designed for human activity recognition and fall detection. The dataset includes 11,771 samples of both human activities and falls performed by 30 subjects of ages ranging from 18 to 60 years. Samples are divided in 17 fine grained classes grouped in two coarse grained classes: one containing samples of 9 types of activities of daily living (ADL) and the other containing samples of 8 types of falls. The dataset has been stored to include all the information useful to select samples according to different criteria, such as the type of ADL performed, the age, the gender, and so on. Finally, the dataset has been benchmarked with four different classifiers and with two different feature vectors. We evaluated four different classification tasks: fall vs. no fall, 9 activities, 8 falls, 17 activities and falls. For each classification task, we performed a 5-fold cross-validation (i.e., including samples from all the subjects in both the training and the test dataset) and a leave-one-subject-out cross-validation (i.e., the test data include the samples of a subject only, and the training data, the samples of all the other subjects). Regarding the classification tasks, the major findings can be summarized as follows: (i) it is quite easy to distinguish between falls and ADLs, regardless of the classifier and the feature vector selected. Indeed, these classes of activities present quite different acceleration shapes that simplify the recognition task; (ii) on average, it is more difficult to distinguish between types of falls than between types of activities, regardless of the classifier and the feature vector selected. This is due to the similarity between the acceleration shapes of different kinds of falls. On the contrary, ADLs acceleration shapes present differences except for a small group. Finally, the evaluation shows that the presence of samples of the same subject both in the training and in the test datasets, increases the performance of the classifiers regardless of the feature vector used. This happens because each human subject differs from other subjects in performing activities even if she shares with them the same physical characteristics.

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Design, implementation and validation of a novel open framework for agile development of mobile health applications

Cited by 224 publications

References 35 publications

The Use and Effectiveness of Mobile Apps for Depression: Results From a Fully Remote Clinical Trial

The Use and Effectiveness of Mobile Apps for Depression: Results From a Fully Remote Clinical Trial

MobileCogniTracker

UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones

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