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Tell me your apps and I will tell you your mood
Abstract
Bipolar Disorder is a disease that is manifested with cycling periods of polar episodes, namely mania and depression. Depressive episodes are manifested through disturbed mood, psychomotor retardation, behaviour change, decrease in energy levels and length of sleep. Manic episodes are manifested through elevated mood, psychomotor acceleration and increase in intensity of social interactions. In this paper we report results of a clinical trial with bipolar patients that amongst other aspects, investigated whether changes in general behaviour of patients due to onset of a bipolar episode, can be captured through the analysis of smartphone usage. We have analysed changes in smartphone usage, specifically app usage and how these changes correlate with the self-reported patient state. We also used psychiatric evaluation scores provided by the clinic to understand correlation of the patient smartphone behaviour before the psychiatric evaluation and after the psychiatric evaluation. The results show that patients have strong correlation of patterns of app usage with different aspects of their self-reported state including mood, sleep and irritability. While, on the other hand, the patients' application usage shows discernable changes in the period before and after psychiatric evaluation.
... Literature so far published on the topic refers to a really limited timeframe ranging from April 2013 to 2020, by suggesting that the research in digital phenotyping, particularly in the field of BD, is still at its earliest stages. Overall, literature here retrieved may be ideally classified, according to the type of data collection and processing, alongside six research key-points, as follows: (a) studies investigating the development of mood prediction algorithms by using a digital phenotyping approach [19,21]; (b) studies investigating the association between mobile phone keyboard metadata and mood disorders [20,25]; (c) studies evaluating the relationship between specific patterns of speech features and mood disturbances [18,[26][27][28]; (d) studies investigating the correlation (if any) between automatically generated objective smartphone data and the mood [22,[29][30][31][32]; (e) studies investigating the development of a healthcare app for BD [33][34][35][36][37] and all further future applications of the digital phenotyping in the treatment of BD [38]. The main findings of each paper here retrieved are summarized in Table 1. ...
... In a 14-week clinical trial involving 12 adult participants affected with BD, the authors found that the patient's adherence to the digital self-assessment improved compared to using paper-based forms, the MONARCA system was overly considered very easy to use, by reaching a very highperceived usefulness by patients. Since the development of the MONARCA system by Bardram and Faurholt-Jepsen et al. [33], further studies were performed by using the MONARCA system as a research tool [34][35][36][37]. ...
... Alvarez-Lozano et al. [34] in a clinical trial recruiting 18 BD subjects, investigated app usage patterns within the smartphone and their correlation with the episode of BD patients. Patients were provided with a smartphone containing MONARCA system and were monitored over a period of 5 months. ...
Bipolar disorder (BD) is a complex neurobiological disorder characterized by a pathologic mood swing. Digital phenotyping, defined as the 'moment-by-moment quantification of the individual-level human phenotype in its own environment', represents a new approach aimed at measuring the human behavior and may theoretically enhance clinicians' capability in early identification, diagnosis, and management of any mental health conditions, including BD. Moreover, a digital phenotyping approach may easily introduce and allow clinicians to perform a more personalized and patient-tailored diagnostic and therapeutic approach, in line with the framework of precision psychiatry. The aim of the present paper is to investigate the role of digital phenotyping in BD. Despite scarce literature published so far, extremely heterogeneous methodological strategies, and limitations, digital phenotyping may represent a grounding research and clinical field in BD, by owning the potentialities to quickly identify, diagnose, longitudinally monitor, and evaluating clinical response and remission to psychotropic drugs. Finally, digital phenotyping might potentially constitute a possible predictive marker for mood disorders.
... The smartphone is one possibility to collect behavioral data in everyday life [Alvarez-Lozano et al., 2014;Canzian and Musolesi, 2015;LiKamWa et al., 2013;Mehrotra et al., 2014;Saeb et al., 2015;Suhara et al., 2017]. Currently, around 36% of individuals all over the world and 81% of Germans own at least one smartphone, which is used by youths and young adults for an average of 162 min a day [Montag et al., 2015]. ...
... Previous studies differ in their findings [Alvarez-Lozano et al., 2014;Canzian and Musolesi, 2015;Elhai et al., 2017;Ferdous et al., 2015;Rozgonjuk et al., 2018;Saeb et al., 2015;Seabrook et al., 2016;Servia-Rodríguez et al., 2017;Suhara et al., 2017]. According to Saeb and colleagues [2015], an association between depression severity and smartphone usage behavior was found. ...
... Another comparably small study (n = 18) provides first insights into a relationship between daily usage time as well as the usage of social applications (e.g., Facebook, Whats-App) and negative mood [Alvarez-Lozano et al., 2014]. ...
Background: Due to the ubiquitous use of smartphones in daily life, they offer unique opportunities to study human behavior. This study sheds light on associations between self-reported stress, drive, and mood levels and smartphone usage behavior. Methods: A total of 157 students installed the Insights app on their personal smartphone and tracked smartphone usage behavior. Furthermore, students assessed self-reported levels of stress, drive, and mood for 8 weeks. Results: Three multilevel models were used to associate smartphone usage behavior and self-reported mood, drive, and stress levels. Results indicate a negative association of self-reported stress and number of SMS (–3.539, SE = 0.937) as well as a positive relation of stress and call duration (0.018, SE = 0.937). Mood was linked negatively with total usage time (–0.019, SE = 0.004) and call duration (–0.016, SE = 0.007). Moreover, drive was negatively associated with Facebook usage time (–0.127, SE = 0.041). Discussion: Overall smartphone usage behavior is negatively associated with measurements of well-being. Conclusion: Passive smartphone tracking could assist in the standardized assessment of behavioral data in real life in the future. Due to the risk of data misuse, ethical, legal, and clinical guidelines have to be developed.
... Eine Möglichkeit Verhaltensdaten aufzuzeichnen stellt das Smartphone dar [LiKamWa et al., 2013;Alvarez-Lozano et al., 2014;Mehrotra et al., 2014;Canzian und Musolesi, 2015;Saeb et al., 2015;Suhara et al., 2017]. Derzeit besitzen rund 36% weltweit und 81% der Deutschen ein Smartphone, welches von Jugendlichen und jungen Erwachsenen im Schnitt 162 Minuten pro Tag aktiv genutzt wird [Montag et al., 2015]. ...
... Bisherige Studien kommen zu unterschiedlichen Ergebnissen [Alvarez-Lozano et al., 2014;Canzian und Musolesi, 2015;Ferdous et al., 2015;Saeb et al., 2015;Seabrook et al., 2016;Servia-Rodríguez et al., 2017;Suhara et al., 2017;Elhai et al., 2018;Rozgonjuk et al., 2018]. Korrelationen zwischen Depressionsschwere und Smartphone-Nutzungsverhalten konnten von Saeb et al. [2015] gefunden werden. ...
... Eine weitere, deutlich kleinere Studie (n = 18) liefert erste Hinweise darauf, dass die Gesamtnutzungsdauer und der Gebrauch sozialer Apps (z.B. Facebook, Whats-App) negativ mit der Stimmung assoziiert ist [Alvarez-Lozano et al., 2014]. ...
Hintergrund: Die Einbindung von Smartphones in den Alltag ermöglicht es, longitudinale Echtzeit-Daten aufzuzeichnen, die keine aktive Eingabe der Nutzer benötigen. Mithilfe der vorliegenden Studie soll gezeigt werden, dass das aktuelle Befinden (Stimmung, Antrieb, Stress) mit Smartphone-Nutzungsvariablen assoziiert ist. Methoden: An der Studie nahmen 157 Studierende teil, welche für 8 Wochen die App Insights auf ihrem Smartphone installierten. Insightszeichnete das Smartphone-Nutzungsverhalten (z.B. Gesamtnutzungsdauer, Anrufdauer, Anzahl der SMS, Facebook-Nutzung) auf und erfasste täglich mittels Selbstbericht Stimmung, Antrieb und Stress. Ergebnisse: In 3 Mehrebenenmodellen wurde das aktuelle Befinden über Smartphone-Nutzungsverhalten vorhergesagt. Die Ergebnisse demonstrieren, dass Stress negativ mit der Anzahl der SMS (–3,539, SE = 0,937) und positiv mit der Anrufdauer (0,018, SE = 0,937) assoziiert ist. Stimmung ist negativ mit der Gesamtnutzungsdauer (–0,019, SE = 0,004) und der Anrufdauer (–0,016, SE = 0,007) verbunden. Ebenso ist der Antrieb negativ mit der Facebook-Nutzung (–0,127, SE = 0,041) korreliert. Diskussion: In zukünftigen Studien sollte der Kausalität des negativen Zusammenhanges zwischen Befindlichkeitsparametern und der Smartphone-Nutzung nachgegangen werden. Schlussfolgerung: In Zukunft könnte passives Smartphone-Tracking eingesetzt werden, um standardisiert Verhaltensdaten von Personen mit psychischen Problemen zu sammeln. Aufgrund der immanenten Gefahr des Datenmissbrauchs sind ethische, rechtliche und berufspolitische Leitlinien zu entwickeln.
... Examples include the characterization of application usage behavior [18], users' attentiveness and receptivity to notifications [28] and mobile communication (via calls and SMSs) patterns [42]. However, until now, only a handful of studies have investigated the relationship between users' mood and their mobile phone interaction behavior [7,22,29,34]. Indeed, going beyond the study of physical interactions with smartphones and exploring the emotional interactions with them is a fascinating emerging area in ubiquitous computing. ...
... Previous studies, such as [22], have demonstrated that communication and application usage patterns can be exploited to statistically infer the daily mood of a user. Another study [7] investigated the correlation between the application usage patterns and users' mood, sleep and irritability states. Some studies [29,34] have also shown that users' cognitive states (such as feeling bored and engagement with other tasks) correlate with their receptivity to information delivered through mobile phones. ...
... These metrics represent the basis of our correlation and causality analysis that we will present in the following section. Some metrics are indeed classic indicators widely used for this class of studies in the ubiquitous computing community [7,22], while others, such as the metrics related to phone usage in terms of notification and screen interaction, are introduced for the first time in this work. ...
Most of the existing work concerning the analysis of emotional states and mobile phone interaction has been based on correlation analysis. In this paper, for the first time, we carry out a causality study to investigate the causal links between users’ emotional states and their interaction with mobile phones, which could provide valuable information to practitioners and researchers. The analysis is based on a dataset collected in-the-wild. We recorded 5,118 mood reports from 28 users over a period of 20 days.
Our results show that users’ emotions have a causal impact on different aspects of mobile phone interaction. On the other hand, we can observe a causal impact of the use of specific applications, reflecting the external users’ context, such as socializing and traveling, on happiness and stress level. This study has profound implications for the design of interactive mobile systems since it identifies the dimensions that have causal effects on users’ interaction with mobile phones and vice versa. These findings might lead to the design of more effective computing systems and services that rely on the analysis of the emotional state of users, for example for marketing and digital health applications.
... However, cognitive context is inferred mostly by employing ESM techniques, according to which users are prompted with a series of questions that are required to be responded repeatedly [16]. Past studies have shown the potential of exploiting mobile sensing data to learn and, potentially, predict the user's cognitive context [1,2,3,4,11,8,12]. For example, Canzian et al. have used mobility data to monitor depressive states [4] and Alvarez-Lozano at al. have exploited application usage logs to monitor patients affected by bipolar disorder [2]. ...
... Past studies have shown the potential of exploiting mobile sensing data to learn and, potentially, predict the user's cognitive context [1,2,3,4,11,8,12]. For example, Canzian et al. have used mobility data to monitor depressive states [4] and Alvarez-Lozano at al. have exploited application usage logs to monitor patients affected by bipolar disorder [2]. ...
... Past studies have used mobility, activity, application usage and communication data for inferring depressive state of users [8,2,4]. We hypothesize that there are additional features (which can be captured via mobile phones) that are associated with the changes in the user's depressive state. ...
Remarkable advances in smartphone technology, especially in terms of passive sensing, have enabled researchers to passively monitor user behavior in real-time and at a granularity that was not possible just a few years ago. Recently , different approaches have been proposed to investigate the use of different sensing and phone interaction features , including location, call, SMS and overall application usage logs, to infer the depressive state of users. In this paper , we propose an approach for monitoring of depressive states using multi-modal sensing via smartphones. Through a brief literature review we show the sensing modalities that have been exploited in the past studies for monitoring depression. We then present the initial results of an ongoing study to demonstrate the association of depressive states with the smartphone interaction features. Finally, we discuss the challenges in predicting depression through multi-modal mobile sensing.
... In 2021, two out of three teenagers and young adults reported that they had previously used a mental health application [76]. According to Caldeira et al. [22], users of publicly available mood tracking and inference apps use them to (1) learn about their idiosyncratic mood patterns, (2) improve their mood, and (3) monitor and manage mental illness. These apps are also often used to track and manage stress [22]. ...
... There is evidence that dropout rates are not purely based on frequency, but also on the complexity of mood input. Alvarez-Lozano et al. [2] required just one mood input per day, however, the mood report included four scales, five yes/no questions, and three numerical inputs. While this study ran for five months, they were not able to access large amounts of data for most users due to instances where self-assessment tests weren't provided. ...
Due to their abundance of sensors, today’s smartphones can act as a scientific tool to collect contextual information on users’ emotional, social, and physical behaviour. With the continuously growing amount of data that can be unobtrusively extracted from smartphones, mood-tracking and inference methods have become more feasible. However, this does raise critical implications for end-users, including accessibility and privacy. Following a structured selection process, we reviewed 32 papers from the ACM Digital Library on mood inference and tracking using smartphones. We conducted an in-depth analysis of used sensors, platform and accessibility, study designs, privacy, self-reporting methods, and accuracy. Based on our analysis, we provide a detailed discussion of the opportunities for research and practice that arise from our findings and outline recommendations for future research within the area of smartphone-based mood tracking and inference.
KeywordsMood trackingMood inferenceSmartphones
... No; other severe mood disorders N/A c ; this is a protocol [25] Users involved in prototype design and evaluation stage with iteration MoodRhythm: smartphone app that can track social rhythms Yes 7 [26,27] Users involved in prototype development and evaluation stage with iteration MONARCA system: combination of passive and active self-monitoring smartphone app Yes 42 (all papers combined) [28][29][30] Users involved in prototype development and evaluation stages with iteration OpenSIMPLE: smartphone-based psychoeducation program Yes 303 (all papers combined) [31][32][33] ...
... Through this design process, users were "involved" in making decisions regard-Patient-Clinician Designer Framework using principles of user-centered design [28][29][30] • Prototype design and deployment stage: 3hour workshops were held for design and iterative prototyping where feedback was incorporated into design ing system features using collaborative design workshops. The design of the MONARCA c system uses a mobile phone app as the main component. ...
... No; other severe mood disorders N/A c ; this is a protocol [25] Users involved in prototype design and evaluation stage with iteration MoodRhythm: smartphone app that can track social rhythms Yes 7 [26,27] Users involved in prototype development and evaluation stage with iteration MONARCA system: combination of passive and active self-monitoring smartphone app Yes 42 (all papers combined) [28][29][30] Users involved in prototype development and evaluation stages with iteration OpenSIMPLE: smartphone-based psychoeducation program Yes 303 (all papers combined) [31][32][33] ...
... Through this design process, users were "involved" in making decisions regard-Patient-Clinician Designer Framework using principles of user-centered design [28][29][30] • Prototype design and deployment stage: 3hour workshops were held for design and iterative prototyping where feedback was incorporated into design ing system features using collaborative design workshops. The design of the MONARCA c system uses a mobile phone app as the main component. ...
BACKGROUND
Self-monitoring applications for bipolar disorder are increasing in numbers. The application of user-centred design (UCD) is becoming standardised to optimise the reach, adoption and sustained use of this type of technology.
OBJECTIVE
This paper presents the current landscape regarding UCD and evaluation of self-monitoring applications for bipolar disorder by investigating two specific questions a) are the design principles of UCD being implemented? and b) if so, how?
METHODS
To explore these practices, we review available literature for self-tracking technology for bipolar disorder and make an overall assessment of the level of user-involvement in design. The findings from this review are used to form best practice “ingredients” for design of mental health technology, which combines the already existing practices of patient and public involvement and human-computer interaction in order to evolve from the generic guidelines of UCD and to ones that are tailored towards mental health technology.
RESULTS
For question a), it was found that out of the 13 novel smartphone applications included in this review, 4 self-monitoring applications were classified as having no mention of user involvement in design, 3 self-monitoring applications were classified as having low user-involvement, 4 self-monitoring applications were classified was having medium user involvement and 2 self-monitoring applications were classified as high user involvement. In regards to question b), it was found that despite the presence of recommended standards for the involvement of the user in the process of design and evaluation, there is large variability in whether the user is involved, how they are involved and to what extent there is a genuine empowerment in the voice of the user, which is the ultimate aim of design approaches involved in mental health technology.
CONCLUSIONS
The findings of this study were reviewed by an expert panel, including an individual with lived experience of bipolar disorder. It is recommended that users are involved in all stages of design with the ultimate goal to empower and create empathy for the user. Users should be involved early in the process of design and this should not just be limited to design itself, but also associated research ensuring end-to-end involvement. The communities in healthcare based design and human-computer interaction design need to work together to increase awareness of the different methods available and to encourage the use and mixing of the methods, as well as establish better mechanisms to reach the target user group. Future research using systematic literature search methods should explore this further.
... No; other severe mood disorders N/A c ; this is a protocol [25] Users involved in prototype design and evaluation stage with iteration MoodRhythm: smartphone app that can track social rhythms Yes 7 [26,27] Users involved in prototype development and evaluation stage with iteration MONARCA system: combination of passive and active self-monitoring smartphone app Yes 42 (all papers combined) [28][29][30] Users involved in prototype development and evaluation stages with iteration OpenSIMPLE: smartphone-based psychoeducation program Yes 303 (all papers combined) [31][32][33] ...
... Through this design process, users were "involved" in making decisions regard-Patient-Clinician Designer Framework using principles of user-centered design [28][29][30] • Prototype design and deployment stage: 3hour workshops were held for design and iterative prototyping where feedback was incorporated into design ing system features using collaborative design workshops. The design of the MONARCA c system uses a mobile phone app as the main component. ...
Bipolar disorder (BD) is a chronic mental illness with each relapse causing permanent neurological damage. Self-tracking behaviors and symptoms such as early warning signs (EWS) are effective in improving relapse and functioning [1]. The design of current self-tracking pervasive technology for BD is not considering the needs of the user and the complexities behind self-tracking. The aims of this study are to develop and test a prototype design approach which combines the principles of user-centered design and participatory design to explore self-tracking in BD, EWS and pervasive technology. This research was designed alongside two individuals with BD and workshops were ran with a further 16 individuals with BD to explore reasons for self-tracking, methods of self-tracking and challenges/opportunities for pervasive technology. The social aspect of tracking was found to be the most important and process of doing this tracking was complex, as participants described 50 methods with further descriptions on how these are used to count and keep track of self-management factors. When exploring challenges, we found that participants were reluctant to leave a paper trial of their health and found that too much insight can be counterproductive. The literature to date has not explored specific views towards pervasive technology and self-tracking for BD. Participants displayed openness towards this technology with some specific cautions, as well as guidance on how to make the technology usable. These findings will be used to design further workshops with those with BD which will be focused on designing a mobile based tracking tool.
... In order to explore the impact of the network structure on the performance of our models to predict depressive states, we optimize the number of hidden layers (denoted by h in this paper) to construct autoencoders. More specically, we construct autoencoders with the dierent number of hidden layers (i.e., excluding the input and output layers) such that h 2 [1, 3,5,7]. We use 1 as a lower bound because that is a minimum number of hidden layer an autoencoder should have. ...
... The authors evaluated their approach with 32 participants over two months and demonstrated that their system could predict 93.1% of the daily pleasure averages and 92.7% of the activeness averages with less than 0.25 MSE (mean squared error). Similarly, in [3], Alvarez-Lozano at al. examined the potential of exploiting the mobile app usage logs for predicting the bipolar state of users. In particular, the author proposed to quantify the changes in app usage behavior and exploit them to predict variations in self-reported bipolar states. ...
Recent studies have shown the potential of exploiting GPS data for passively inferring people's mental health conditions. However, feature extraction for characterizing human mobility remains a heuristic process that relies on the domain knowledge of the condition under consideration. Moreover, we do not have guarantees that these "hand-crafted" metrics are able to effectively capture mobility behavior of users. Indeed, informative emerging patterns in the data might not be characterized by them. This is also a complex and often time-consuming task, since it usually consists of a lengthy trial-and-error process.
In this paper, we investigate the potential of using autoencoders for automatically extracting features from the raw input data. Through a series of experiments we show the effectiveness of autoencoder-based features for predicting depressive states of individuals compared to "hand-crafted" ones. Our results show that automatically extracted features lead to an improvement of the performance of the prediction models, while, at the same time, reducing the complexity of the feature design task. Moreover, through an extensive experimental performance analysis, we demonstrate the optimal configuration of the key parameters at the basis of the proposed approach.
... Several mobile solutions have been proposed to utilize a self-monitoring and intervention based treatment of depression (see [2][3][4][5] for a review). One particular research approach taken by many research groups has been to investigate how objectively measured behavioral features such as 'location' and 'social interaction' correlate with depression, and thereby try to differentiate euthymic and depressed state (e.g., [6][7][8][9][10][11]). For example, by using a smartphone application passively recording information from sensors in the phone, Saeb et al. [7] could show a statistically significant correlation between six different objective features, including smartphone usage frequency and self-assessed mood using the Patient Health Questionnaire 9 (PHQ-9) scale [12] in non-clinical samples. ...
... Digital behavioral markers have been defined as higher-level features, reflecting behaviors, cognitions, and emotions, which are measured using low-level features and sensor data collected from digital technology, including mobile and wearable computing [13]. Many studies have found statistically significant correlations between objective behavioral features collected from mobile and wearable technology and mood symptoms in non-clinical samples of participants without psychiatric illness (e.g., [14][15][16][17]) as well as in clinical samples of patients diagnosed with psychiatric disorders (e.g., [11,[18][19][20]). ...
Background:
Several studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms in patients with affective disorders (unipolar and bipolar disorders). However, individual studies have reported on different and sometimes contradicting results, and no quantitative systematic review of the correlation between objective behavioral features and depressive mood symptoms has been published.
Objective:
The objectives of this systematic review were to (1) provide an overview of the correlations between objective behavioral features and depressive mood symptoms reported in the literature and (2) investigate the strength and statistical significance of these correlations across studies. The answers to these questions could potentially help identify which objective features have shown most promising results across studies.
Methods:
We conducted a systematic review of the scientific literature, reported according to the preferred reporting items for systematic reviews and meta-analyses guidelines. IEEE Xplore, ACM Digital Library, Web of Sciences, PsychINFO, PubMed, DBLP computer science bibliography, HTA, DARE, Scopus, and Science Direct were searched and supplemented by hand examination of reference lists. The search ended on April 27, 2017, and was limited to studies published between 2007 and 2017.
Results:
A total of 46 studies were eligible for the review. These studies identified and investigated 85 unique objective behavioral features, covering 17 various sensor data inputs. These features were divided into 7 categories. Several features were found to have statistically significant and consistent correlation directionality with mood assessment (eg, the amount of home stay, sleep duration, and vigorous activity), while others showed directionality discrepancies across the studies (eg, amount of text messages [short message service] sent, time spent between locations, and frequency of mobile phone screen activity).
Conclusions:
Several studies showed consistent and statistically significant correlations between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms. Hence, continuous and everyday monitoring of behavioral aspects in affective disorders could be a promising supplementary objective measure for estimating depressive mood symptoms. However, the evidence is limited by methodological issues in individual studies and by a lack of standardization of (1) the collected objective features, (2) the mood assessment methodology, and (3) the statistical methods applied. Therefore, consistency in data collection and analysis in future studies is needed, making replication studies as well as meta-analyses possible.
... The MONARCA system, a personal smartphone-based monitoring system for bipolar disorder patients, collected different subjective self-reported data and objective sensor data, including mood, sleep, activity, and therapy adherence (Bardram et al., 2013;Alvarez-Lozano et al., 2014) (Fig. 2). The MONARCA system has been generally demonstrated to be an effective tool for early recognizing warning patients with a bipolar disorder (Faurholt-Jepsen et al., 2015, 2019. ...
... Alvarez- Lozano et al. (2014) state that OAs use their smartphones more rarely than other age demographics. Senior folks have seen the greatest surge in smartphone adoption in recent years (Hong et al., 2016). ...
Recent technological advancements show that mobile phones are becoming an increasingly significant part of our daily lives. Older adults (OAs) [60+] constitute a key demographic for this study. This study aims to determine the features of smartphone technologies used by OAs in Windhoek and to analyse the possible factors that may influence their adoption as well as to assess OAs perceptions of smartphones. A quantitative research method was adopted. A structured questionnaire was used to collect data from 99 OAs in Windhoek through convenience sampling method. Data were analysed employing SPSS. The study revealed that OAs do not fully adopt smartphones. However, they believe that smartphones provide benefits such as giving entertainment and curbing loneliness. The findings suggest there is a relationship between the smart technology (ST) features and perceptions towards the adoption of STs by OAs in Windhoek. This study can educate smartphone manufacturers and developers about elements that should be considered when designing communication devices and applications for OAs.
... The MONARCA system, a personal smartphone-based monitoring system for bipolar disorder patients, collected different subjective self-reported data and objective sensor data, including mood, sleep, activity, and therapy adherence (Bardram et al., 2013;Alvarez-Lozano et al., 2014) (Fig. 2). The MONARCA system has been generally demonstrated to be an effective tool for early recognizing warning patients with a bipolar disorder (Faurholt-Jepsen et al., 2015, 2019. ...
... smart homes), which consider affect as one of the parameters, the adaptation is carried out through tailored music [3,4], colour [5][6][7], and lighting conditions [8,9]. Other emotionbased AAL/ELE solutions, for example, mobile application 'Optimism App' [10], are designed to track patient's daily mood and to help them with mood disorders. A social application named 'SocialConnector' [11] monitors the mood of the elderly to allow caregivers or family members to react on time in supporting the person in need. ...
... Through patients' mobile phones and other wearable devices, continuous sensor data can be collected in a noninvasive manner, providing valuable information about everyday activity patterns. The possibility of inferring emotional states by analyzing smartphone use data [7][8][9], GPS traces of movement [10,11], social media data [12], and even sound recordings [13,14] has become a growing research focus over the past decade. Such approaches can be used to analyze individuals' emotional patterns, enabling the better self-management of one's activities and behavioral choices. ...
Background:
Mental health disorders affect multiple aspects of patients’ lives, including mood, cognition, and behavior. eHealth and mobile health (mHealth) technologies enable rich sets of information to be collected noninvasively, representing a promising opportunity to construct behavioral markers of mental health. Combining such data with self-reported information about psychological symptoms may provide a more comprehensive and contextualized view of a patient’s mental state than questionnaire data alone. However, mobile sensed data are usually noisy and incomplete, with significant amounts of missing observations. Therefore, recognizing the clinical potential of mHealth tools depends critically on developing methods to cope with such data issues.
Objective:
This study aims to present a machine learning–based approach for emotional state prediction that uses passively collected data from mobile phones and wearable devices and self-reported emotions. The proposed methods must cope with high-dimensional and heterogeneous time-series data with a large percentage of missing observations.
Methods:
Passively sensed behavior and self-reported emotional state data from a cohort of 943 individuals (outpatients recruited from community clinics) were available for analysis. All patients had at least 30 days’ worth of naturally occurring behavior observations, including information about physical activity, geolocation, sleep, and smartphone app use. These regularly sampled but frequently missing and heterogeneous time series were analyzed with the following probabilistic latent variable models for data averaging and feature extraction: mixture model (MM) and hidden Markov model (HMM). The extracted features were then combined with a classifier to predict emotional state. A variety of classical machine learning methods and recurrent neural networks were compared. Finally, a personalized Bayesian model was proposed to improve performance by considering the individual differences in the data and applying a different classifier bias term for each patient.
Results:
Probabilistic generative models proved to be good preprocessing and feature extractor tools for data with large percentages of missing observations. Models that took into account the posterior probabilities of the MM and HMM latent states outperformed those that did not by more than 20%, suggesting that the underlying behavioral patterns identified were meaningful for individuals’ overall emotional state. The best performing generalized models achieved a 0.81 area under the curve of the receiver operating characteristic and 0.71 area under the precision-recall curve when predicting self-reported emotional valence from behavior in held-out test data. Moreover, the proposed personalized models demonstrated that accounting for individual differences through a simple hierarchical model can substantially improve emotional state prediction performance without relying on previous days’ data.
Conclusions:
These findings demonstrate the feasibility of designing machine learning models for predicting emotional states from mobile sensing data capable of dealing with heterogeneous data with large numbers of missing observations. Such models may represent valuable tools for clinicians to monitor patients’ mood states.
... Anhedonia and low energy level can manifest as reduced physical activity (19,20), which can be measured using GPS and motion sensors embedded in smartphones or wearable sensors. It has also been documented that more severe depressive symptoms and worse mood levels are negatively correlated with a higher amount of time the phone screen is on, a higher number of entertainment applications (apps) used, and an overall time of interaction with the smartphone (18,21). Finally, dysregulated sleep, a common problem in depression, can be measured relatively well by wearable activity sensors (22). ...
Background: While preliminary evidence suggests that sensors may be employed to detect presence of low mood it is still unclear whether they can be leveraged for measuring depression symptom severity. This study evaluates the feasibility and performance of assessing depressive symptom severity by using behavioral and physiological features obtained from wristband and smartphone sensors.
Method: Participants were thirty-one individuals with Major Depressive Disorder (MDD). The protocol included 8 weeks of behavioral and physiological monitoring through smartphone and wristband sensors and six in-person clinical interviews during which depression was assessed with the 17-item Hamilton Depression Rating Scale (HDRS-17).
Results: Participants wore the right and left wrist sensors 92 and 94% of the time respectively. Three machine-learning models estimating depressive symptom severity were developed–one combining features from smartphone and wearable sensors, one including only features from the smartphones, and one including features from wrist sensors–and evaluated in two different scenarios. Correlations between the models' estimate of HDRS scores and clinician-rated HDRS ranged from moderate to high (0.46 [CI: 0.42, 0.74] to 0.7 [CI: 0.66, 0.74]) and had moderate accuracy with Mean Absolute Error ranging between 3.88 ± 0.18 and 4.74 ± 1.24. The time-split scenario of the model including only features from the smartphones performed the best. The ten most predictive features in the model combining physiological and mobile features were related to mobile phone engagement, activity level, skin conductance, and heart rate variability.
Conclusion: Monitoring of MDD patients through smartphones and wrist sensors following a clinician-rated HDRS assessment is feasible and may provide an estimate of changes in depressive symptom severity. Future studies should further examine the best features to estimate depressive symptoms and strategies to further enhance accuracy.
... Through patients' mobile phones and other wearable devices, continuous sensor data can be collected in a non-invasive manner, which can provide valuable information about everyday activity patterns. The possibility of inferring emotional states by analysing smartphone usage data [4][5][6], GPS traces of movement [7,8], social media data [9], and even sound recordings [10,11] has become a growing research focus over the past decade. Such approaches can be used to analyse emotional patterns of individuals, enabling better self-management of one's activity and behavioural choices. ...
BACKGROUND
Mental health disorders affect multiple aspects of patients' lives, including mood, cognition, and behaviour. The advent of eHealth and mHealth technologies enables rich sets of information to be collected from individuals in a non-invasive way presenting a promising opportunity for the construction of behavioural markers of mental health. Importantly, combining such data with self-reported information about psychological symptoms may provide a more comprehensive and contextualised view of a patient's mental state than questionnaire data alone. However, in the real world, this kind of data is usually noisy and incomplete - with significant numbers of missing observations. Realising the clinical potential of mHealth tools, therefore depends critically upon the development of methods to cope with such data.
OBJECTIVE
Here, we present a machine learning-based approach for emotional valence (mood) analysis using passively-collected data from mobile phones and wearable devices.
METHODS
Passively-sensed behaviour and self-reported emotional state data from an international cohort of N=943 individuals (psychiatric outpatients recruited from community clinics) were available for analysis. All study participants had at least 30 days worth of observations of naturally-occurring behaviour, which included information about physical activity, geolocation, sleep, and smartphone app usage. These regularly sampled, but frequently missing and heterogeneous time series data were analysed using a semi-supervised Hidden Markov Model (HMM) for data averaging and feature extraction, which was then combined with a classifier to provide emotional valence predictions. We examined the performance of both a variety of classical machine learning methods and recurrent neural networks.
RESULTS
The best-performing models achieved greater than 0.80 Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) and 0.75 Area Under the Precision-Recall Curve (AUC-PRC) when predicting self-reported emotional valence from behaviour in held-out test data. Models which took into account the posterior probabilities of latent states identified by the HMM analysis outperformed those which did not - suggesting that the underlying behavioural patterns identified were meaningful with respect to individuals' overall emotional state.
CONCLUSIONS
These findings demonstrate the feasibility of designing machine learning models for predicting emotional state from mobile sensing data that are capable of dealing with heterogeneous data with large numbers of missing observations. Such models may represent a valuable tool for clinicians in the monitoring of mood states of their patients.
... Because of its ubiquity and its use in many daily tasks, it serves as an ideal platform for collecting data streams that can be used in models of psychiatric functioning. A nonexhaustive list of the types of data that have been investigated with regard to their utility for building such models includes typing kinematics (17)(18)(19)(20), acoustic characteristics of speech (21), number of phone calls (22,23), number of text messages (22,23), pattern of phone calls to contacts stored on the smartphone (23), locations visited as measured via global positioning system signal (23,24), and patterns of app usage (23,25). Stand-alone wearable sensors have also been investigated, including off-the-shelf activity trackers (26,27) and custom devices to measure electrodermal activity and galvanic skin response (28,29); however, the majority of the research published to date has focused on smartphone-derived sensor data. ...
Current management of psychiatric disorders relies heavily on retrospective, subjective reports provided by patients and their families. Consequently, psychiatric services are often provisioned inefficiently and with suboptimal outcomes. Recent advances in computing and sensor technologies have enabled the development of real-time monitoring systems for the diagnosis and management of psychiatric disorders. The state of these technologies is rapidly evolving, with passive monitoring and predictive modeling as two areas that have great potential to affect psychiatric care. Although outpatient psychiatry probably stands to benefit the most from the use of real-time monitoring technologies, there are also several ways in which inpatient psychiatry may also benefit. As the capabilities of these technologies increase and their use becomes more common, many ethical and legal issues will need to be considered. The role of governmental regulatory bodies and nongovernmental organizations in providing oversight of the implementation of these technologies is an active area of discussion.
... Shared use is difficult to grasp, and it becomes more relevant when logs are used for psychometric predictions, e.g. [76][77][78], as they refer to a single user. Therefore, similarly to the questioning of self-reported use not being 'objective data,' tracked use also faces interpretive challenges as it is a proxy of usage not fully representing actual human use. ...
Digital practices in later life are not yet well understood. Therefore, this paper discusses the framework for a research design project that aims at tracing differences and similarities in how older adults use their smartphones in circumstances in and outside their homes in Spain, the Netherlands, Sweden, and Canada. The research questions of this international research project focus on the extent to which digital mobile practices relate to perceived social connectedness among older adults aged 55–79 years old. While studies have shown that the subjective experience of ‘being connected’ supports continued wellbeing in later life, there remains an insufficient understanding of the processes through which digital mediated social interaction is effective for social connectedness. The analytical framework of the project prioritizes the co-constituency of (digital) technology and ageing, and takes digital practices in everyday life as its entry point. The main data collection tool will be the tracking of smartphone activity of 600 older adults (150 per country) during four weeks. An online survey and qualitative interviews will gather data about the meanings of the quantified digital practices, and how they shape (if they do) the participants’ connection to the world. This approach will allow us not only to get insight into what older adults say how they used their smartphone but also to gain insight into their real-life daily use. The assessment of the challenges, strengths, and weaknesses of the methods contributes towards an accurate and appropriate interpretation of empirical results and their implications.
... Smartphone logs are key input for research aimed at understanding different dimensions of mobile communication, from smartphone use (Böhmer et al., 2011) or general communication behaviours (Wagner et al., 2013) to the way in which given contexts shape them (Karikoski & Soikkeli, 2013). At the intersection of social sciences and computer sciences, mobile logs help to predict selected human behaviour dimensions, including human stress (Ferdous et al., 2015), bipolar disorder states (Alvarez-Lozano et al., 2014) and other personality traits (De Montjoye et al., 2013). In the following, we describe the biases identified in this area. ...
Digital systems can track every activity. Their logs are the fundamental raw material of intelligent systems in big data approaches. However, big data approaches mainly use predictions and correlations that often fail in the prediction of minorities or invisibilize collectives, causing discriminatory decisions. While this discrimination has been documented regarding, sex, race and sexual orientation, age has received less attention. A critical review of the academic literature confirms that structural ageism also shapes big data approaches. The article identifies some instances in which ageism is in operation either implicitly or explicitly. Concretely, biased samples and biased tools tend to exclude the habits, interests and values of older people from algorithms and studies, which contributes to reinforcing structural ageism.
... They feature sophisticated sensors capable of capturing different types of contextual information such as location, movement, audio environment, proximity to other objects, collocation with other devices and many others [3,5,10,15]. Recent studies have demonstrated the potential of exploiting mobile sensing data to learn and, potentially, predict users' mood and well-being [1,4,11,12,21,23,25]. Indeed, smartphones and wearables are increasingly seen as very powerful tools for research in social and clinical psychology [17]. ...
In recent years, numerous studies have explored the use of machine learning algorithms for supporting applications in social and clinical psychology. In particular, there is an increasing prevalence of smartphone-based techniques for collecting data through embedded sensors and efficient in-situ questionnaires. Models are then built to explore the patterns between these data types. In this paper, we study the application of machine learning for the task of predicting mental states of adverse valence, based on the Photographic Affect Meter data. We present a technique for daily aggregation, which is designed to detect significant negative events. A variety of features is used as input, including GPS-based metrics and features assessing social interactions, sleep and phone usage. Experimental evidence is presented, which suggests that machine learning algorithms could successfully be employed for such a prediction task.
... Smartphones also use logs as part of their intelligent systems, e.g., for fast app launching ( Yan et al. 2012;Huangfu et al. 2015), to improve battery life ( Ferreira et al. 2011), to suggest proactive tasks ( Banovic et al. 2014) or to offer contextual information ( Maggiore et al. 2014). Logs are also used to predict stress ( Ferdous et al. 2015), bipolar disorder ( Alvarez-Lozano et al. 2014), spending behaviour ( Singh et al. 2013) and friendship ( Ikebe et al. 2012), among others (De Montjoye et al. 2013). ...
Older people are a minority in digital media, in terms of both access and use. While the divide in access has decreased, this is not the case with the divide in use. In this chapter, we go deeper into the divide in use, by studying the diversity of smartphone usage among older people. We have used three complementary perspectives: tracked use, reported use, and reflections on use. According to our study, between 2014 and 2016 the divide in smartphone use increased between younger individuals and older people. Moreover, older smartphone users in Spain are a diverse user group, which includes basic, proficient and advanced users. Proficient users are the most common group. Basic users are often new users with little experience of digital technologies who usually achieve their communication goals by other means. We used a triangulation of qualitative and quantitative methods. This approach allowed us to show the limited and at the same time diverse use of smartphones by older people. These results question the stereotypes that only associate older people with a limited use of digital technologies. They also help to raise awareness of the importance of taking the particular characteristics of older proficient smartphone users into account in the design of intelligent systems, in order to fight structural ageism.
... Initiatives such as the personalized cancer therapy program [4] have utilized EMR resources to tailor treatment regimens to participating patients. Moreover, additional data parameters from EMR are used to augment current traditional Modified Early Warning Score (MEWS) algorithms, which "track-and-"trigger" warnings of patient condition deterioration based on six cardinal vital signs [5], especially when combined with continuous monitoring of patients [6][7][8][9][10] Furthermore, discoveries from data-driven, EMR-based research can lead to actionable findings, such as identifying medication adverse reactions [11] [12] and predicting future disease risk [13]. In addition to enabling the provision of clinical care, EMR are also a powerful tool to assist fundamental research [14]. ...
Background. Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data generated from health and wellbeing devices (such as personal trackers), a significant contribution to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms.
... Several applications have been developed to track personalized mood [18,9,1,4,2]. MoA 2 developed by Bachmann et al. [2] is a context-aware smartphone application which provides the ambulatory assessment of mood, tiredness and stress level. The main features of this tool are (1) mood assessment and (2) mood recognition. ...
Humor appreciation is one of the determinants of individual's mood and can be assessed through jokes. We have developed a functional prototype called Humoris which asks users to select the funniest punchline and register their affective response to the jokes. Based on users' responses, the application predicts and displays their short-term mood using emoticons. Our smartphone prototype is evaluated using the 'think-aloud' method with 9 participants. Usability of Humoris was examined by System Usability Scale questionnaire which gave an average score of 79.44 (SD=8.08). Based on our findings, participants liked the application interface as well as the mood prediction but some of them found some jokes difficult to understand.
... Furthermore, within-patient Naïve Bayes classification results revealed 81% mean accuracy in recognising patients' state, and 82% recall. Further recent studies have begun to emerge, supporting these findings for serious and severe mental illness populations (Grunerbl et al., 2015;Alvarez-Lozano et al., 2014;Mayora et al., 2013;Beiwinkel et al., 2016). ...
Abstract
Background
Monitoring is integral to adequately recognise and track mental health indicators of symptoms and functioning. Early identification of warning signs from digital footprints could facilitate adaptive and dynamic just in-time monitoring and care for individuals with common mental disorders.
Methods
Self-report data on mental health and lifestyle behaviour from 120 male and female Australian young adults experiencing psychological distress were collected online. API software was used to download participant's daily activity duration measurements over eight months from linked commercial activity tracker apps and wearables in real time. An independent samples t-test was conducted to compare the differences in daily durations of recorded physical activity between wearable devises and smartphone apps. Entropy techniques using R interpol package were used to analyse volatility in daily activity duration.
Results
DASS-21 depression, stress and anxiety sub-scale scores indicated the study sample on average, had a moderate level of psychological distress. Daily activity duration was significantly greater from wearable devices when compared with smartphone apps (t-test = 25.4, p < 0.001). Entropy indices were not related with any of the DASS-21 measures. However, significant correlation between DASS-21 anxiety subscale scores and entropy of those with over 45 days measurements (r = 0.58, p = 0.02) was observed.
Limitations
The observational nature of this study prohibits causal inference. As a convenience sample was used, the results may lack generalisability to the wider population.
Conclusions
Continuous monitoring using commercial apps and wearables as a resource to help clinicians augment clinical care for common mental disorders appears viable.
Keywords
Common mental disorders;
Digital foot prints;
Daily activity duration;
Wearables;
Smartphone apps
... Initiatives such as the personalized cancer therapy program [4] have utilized EMR resources to tailor treatment regimens to participating patients. Moreover, additional data parameters from EMR are used to augment current traditional Modified Early Warning Score (MEWS) algorithms, which "track-and-"trigger" warnings of patient condition deterioration based on six cardinal vital signs [5], especially when combined with continuous monitoring of patients [6][7][8][9][10] Furthermore, discoveries from data-driven, EMR-based research can lead to actionable findings, such as identifying medication adverse reactions [11] [12] and predicting future disease risk [13]. In addition to enabling the provision of clinical care, EMR are also a powerful tool to assist fundamental research [14]. ...
Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data generated from health and wellbeing devices (such as personal trackers), a significant contribution to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms.
... Authors of [26] have shown that aggregated features obtained from smartphone usage data (e.g., app usage, phone call records, location information, etc) are indicators of the Big-Five personality traits (i.e., extraversion, agreeableness, conscientiousness, emotional stability and openness to experience). Authors of [27] have reported that the changes in general behaviour of patients due to onset of a bipolar episode can be captured through the differences in their smartphone usage pattern, while a study investigating the stress and strain of student life is reported in [28]. ...
Explosion of number of smartphone apps and their diversity has created a fertile ground to study behaviour of smartphone users. Patterns of app usage, specifically types of apps and their duration are influenced by the state of the user and this information can be correlated with the self-reported state of the users. The work in this paper is along the line of understanding patterns of app usage and investigating relationship of these patterns with the perceived stress level within the workplace context. Our results show that using a subject-centric behaviour model we can predict stress levels based on smartphone app usage. The results we have achieved, of average accuracy of 75% and precision of 85.7%, can be used as an indicator of overall stress levels in work environments and in turn inform stress reduction organisational policies, especially when considering interrelation between stress and productivity of workers.
... Several studies have monitored phone call and SMS logs (for frequency, duration, and incoming/ outgoing interactions) and data from apps or online social networks (eg, Facebook, Twitter and emails) and have found that usage patterns and language choices can be used to infer mood changes. [26][27][28] The real-time and longitudinal data collected from these devices may provide some necessary information for investigating the interaction between affect, mood and temperament. ...
The growing understanding that mood disorders are dynamic in nature and fluctuate over variable epochs of time has compelled researchers to develop innovative methods of monitoring mood. Technological advancement now allows for the detection of minute-to-minute changes while also capturing a longitudinal perspective of an individual’s illness. Traditionally, assessments of mood have been conducted by means of clinical interviews and paper surveys. However, these methods are often inaccurate due to recall bias and compliance issues, and are limited in their capacity to collect and process data over long periods of time. The increased capability, availability and affordability of digital technologies in recent decades has offered a novel, non-invasive alternative to monitoring mood and emotion in daily life. This paper reviews the emerging literature addressing the use of digital mood tracking technologies, primarily focusing on the strengths and inherent limitations of using these new methods including electronic self-report, behavioural data collection and wearable physiological biosensors. This developing field holds great promise in generating novel insights into the mechanistic processes of mood disorders and improving personalised clinical care. However, further research is needed to validate many of these novel approaches to ensure that these devices are indeed achieving their purpose of capturing changes in mood.
... Attempts at passive mobile sensing have found high correlations between smartphone use patterns and BD patients' self-reported mood, sleep, and irritability (Alvarez-Lozano et al., 2014). Frost et al. developed a smartphone app to provide better disease insights by collecting data relevant to the behavioral trends of bipolar disorder (Frost et al., 2013). ...
Human behavior is increasingly reflected or acted out through technology. This is of particular salience when it comes to changes in behavior associated with serious mental illnesses including schizophrenia and bipolar disorder. Early detection is crucial for these conditions but presently very challenging to achieve. Potentially, characteristics of these conditions' traits and symptoms, at both idiosyncratic and collective levels, may be detectable through technology use patterns. In bipolar disorder specifically, initial evidence associates changes in mood with changes in technology-mediated communication patterns. However much less is known about how people with bipolar disorder use technology more generally in their lives, how they view their technology use in relation to their illness, and, perhaps most crucially, the causal relationship (if any exists) between their technology use and their disease. To address these uncertainties, we conducted a survey of people with bipolar disorder (N = 84). Our results indicate that technology use varies markedly with changes in mood and that technology use broadly may have potential as an early warning signal of mood episodes. We also find that technology for many of these participants is a double-edged sword: acting as both a culprit that can trigger or exacerbate symptoms as well as a support mechanism for recovery. These findings have implications for the design of both early warning systems and technology-mediated interventions.
... The backend collects sensor and self-assessment data (blue components) and use this to build features representing behavior (such as mobility, physical activity, social activity, sleep, mood, etc.) and to train a machine learning model that can predicting upcoming affective episodes (depression and/or mania) 3 (red components). 3 Some preliminary research have shown that affective episodes in bipolar disorder may be predicted using machine learning methods [9, 5, 1, 10] The recommender engine (yellow component) will take as input these behavior and predictive models and use this to suggest CBT content adapted to the current context (including behavior) and upcoming affective state. The CBT content component (green component) will contain different CBT methods, as outlined above in the sidebar. ...
This position paper presents our preliminary design of context-aware cognitive behavioral therapy for unipolar and bipolar disorders. We report on the background for this study and the methods applied in the ongoing design process. The paper ends by presenting and discussing different design options. We hope this will be useful input for further discussion at the workshop.
Smartphones and personal sensing technologies have made collecting data continuously and in real time feasible. The promise of pervasive sensing technologies in the realm of mental health has recently garnered increased attention. Using Artificial Intelligence methods, it is possible to forecast a person's emotional state based on contextual information such as their current location, movement patterns, and so on. As a result, conditions like anxiety, stress, depression, and others might be tracked automatically and in real‐time. The objective of this research was to survey the state‐of‐the‐art autonomous psychological health monitoring (APHM) approaches, including those that make use of sensor data, virtual chatbot communication, and artificial intelligence methods like Machine learning and deep learning algorithms. We discussed the main processing phases of APHM from the sensing layer to the application layer and an observation taxonomy deals with various observation devices, observation duration, and phenomena related to APHM. Our goal in this study includes research works pertaining to working of APHM to predict the various mental disorders and difficulties encountered by researchers working in this sector and potential application for future clinical use highlighted.
This article is categorized under: Technologies > Machine Learning
Technologies > Prediction
Application Areas > Health Care
Technology plays an increasingly pivotal role in mediating mental health support in people's everyday lives. However, it is not clear how that mediation is occurring, to what end, and what technologies are implicated. In this study, we examine these questions with a mixed-methods analysis of conversations among participants in several Bipolar Disorder (BD) communities on Reddit. Analyzing posts produced over four years, we identify a wide variety of technologies that people employ to manage their mental conditions, such as communication technologies, online communities and tracking tools. Using this taxonomy of technologies as a framework, we then summarize three technology-mediated management strategies that these technologies enable, including serving as community, episode, and information mediators. We argue that with a comprehensive and nuanced understanding of people's in situ technology use, we can identify research and design opportunities for designing human-centered technologies to help people manage mental health challenges more effectively.
Background
Smartphones can facilitate patients completing surveys and collecting sensor data to gain insight into their mental health conditions. However, the utility of sensor data is still being explored. Prior studies have reported a wide range of correlations between passive data and survey scores.
Aims
To explore correlations in a large data-set collected with the mindLAMP app. Additionally, we explored whether passive data features could be used in models to predict survey results.
Method
Participants were asked to complete daily and weekly mental health surveys. After screening for data quality, our sample included 147 college student participants and 270 weeks of data. We examined correlations between six weekly surveys and 13 metrics derived from passive data features. Finally, we trained logistic regression models to predict survey scores from passive data with and without daily surveys.
Results
Similar to other large studies, our correlations were lower than prior reports from smaller studies. We found that the most useful features came from GPS, call, and sleep duration data. Logistic regression models performed poorly with only passive data, but when daily survey scores were included, performance greatly increased.
Conclusions
Although passive data alone may not provide enough information to predict survey scores, augmenting this data with short daily surveys can improve performance. Therefore, it may be that passive data can be used to refine survey score predictions and clinical utility may be derived from the combination of active and passive data.
Efforts to infer personality from digital footprints have focused on behavioral stability at the trait level without considering situational dependency. We repeated a classic study of intraindividual consistency with secondary data (five data sets) containing 28,692 days of smartphone usage from 780 people. Using per-app measures of pickup frequency and usage duration, we found that profiles of daily smartphone usage were significantly more consistent when taken from the same user than from different users ( d > 1.46). Random-forest models trained on 6 days of behavior identified each of the 780 users in test data with 35.8% accuracy for pickup frequency and 38.5% accuracy for duration frequency. This increased to 73.5% and 75.3%, respectively, when success was taken as the user appearing in the top 10 predictions (i.e., top 1%). Thus, situation-dependent stability in behavior is present in our digital lives, and its uniqueness provides both opportunities and risks to privacy.
Background:
Binge eating is a subjective loss of control while eating, leading to the consumption of large amounts of food. It can cause significant emotional distress and is often accompanied by purging behaviours (eg, meal skipping, over-exercising or vomiting).
Objective:
The aim of this study was to explore the potential for mobile sensing to detect indicators for binge eating episodes, with a view toward informing the design of future context-aware mobile interventions.
Methods:
Our study was conducted in two stages. The first involved the development of the DeMMI app. As part of this, we conducted a consultation session to explore whether the types of sensor data we were proposing to capture were seen to be useful and appropriate, as well as gathering feedback on some specific app features relating to self-report. The second stage involved carrying out a 6-week period of data collection with 10 participants experiencing binge eating (logging both their mood and episodes of binge eating) and 10 comparison participants (logging only mood). An optional interview was conducted post-study discussing their experience with using the app, 8 participants (3 binge eating and 5 comparisons) consented.
Results:
Findings showed unique differences in the types of sensor data that were triangulated with individuals' episodes (with nearby Bluetooth devices, screen and app usage features, mobility features, and mood scores showing relevance). Participants had a largely positive opinion about the app, its unobtrusive role, and its ease of use. Interacting with the app increased their awareness of and reflection around mood and their phone usage patterns. Moreover, they expressed no privacy concerns as the study information sheet alleviated these.
Conclusions:
In this study, we contribute a series of recommendations for future studies wishing to scale our approach, and for the design of bespoke mobile interventions to support this population.
Clinicaltrial:
Background
Passive measures collected using smartphones have been suggested to represent efficient proxies for depression severity, but the performance of such measures across diagnoses has not been studied.
Methods
We enrolled a cohort of 45 individuals (11 with major depressive disorder, 11 with bipolar disorder, 11 with schizophrenia or schizoaffective disorder, and 12 individuals with no axis I psychiatric disorder). During the 8‐week study period, participants were evaluated with a rater‐administered Montgomery–Åsberg Depression Rating Scale (MADRS) biweekly, completed self‐report PHQ‐8 measures weekly on their smartphone, and consented to collection of smartphone‐based GPS and accelerometer data in order to learn about their behaviors. We utilized linear mixed models to predict depression severity on the basis of phone‐based PHQ‐8 and passive measures.
Results
Among the 45 individuals, 38 (84%) completed the 8‐week study. The average root‐mean‐squared error (RMSE) in predicting the MADRS score (scale 0–60) was 4.72 using passive data alone, 4.27 using self‐report measures alone, and 4.30 using both.
Conclusions
While passive measures did not improve MADRS score prediction in our cross‐disorder study, they may capture behavioral phenotypes that cannot be measured objectively, granularly, or over long‐term via self‐report.
Background
Mobile mental health systems (MMHS) have been increasingly developed and deployed in support of monitoring, management, and intervention with regard to patients with mental disorders. However, many of these systems rely on patient data collected by smartphones or other wearable devices to infer patients’ mental status, which raises privacy concerns. Such a value-privacy paradox poses significant challenges to patients’ adoption and use of MMHS; yet, there has been limited understanding of it.
Objective
To address the significant literature gap, this research aims to investigate both the antecedents of patients’ privacy concerns and the effects of privacy concerns on their continuous usage intention with regard to MMHS.
Methods
Using a web-based survey, this research collected data from 170 participants with MMHS experience recruited from online mental health communities and a university community. The data analyses used both repeated analysis of variance and partial least squares regression.
Results
The results showed that data type (P=.003), data stage (P<.001), privacy victimization experience (P=.01), and privacy awareness (P=.08) have positive effects on privacy concerns. Specifically, users report higher privacy concerns for social interaction data (P=.007) and self-reported data (P=.001) than for biometrics data; privacy concerns are higher for data transmission (P=.01) and data sharing (P<.001) than for data collection. Our results also reveal that privacy concerns have an effect on attitude toward privacy protection (P=.001), which in turn affects continuous usage intention with regard to MMHS.
Conclusions
This study contributes to the literature by deepening our understanding of the data value-privacy paradox in MMHS research. The findings offer practical guidelines for breaking the paradox through the design of user-centered and privacy-preserving MMHS.
Depression and anxiety are critical public health issues affecting millions of people around the world. To identify individuals who are vulnerable to depression and anxiety, predictive models have been built that typically utilize data from one source. Unlike these traditional models, in this study, we leverage a rich heterogeneous dataset from the University of Notre Dame’s NetHealth study that collected individuals’ (student participants’) social interaction data via smartphones, health-related behavioral data via wearables (Fitbit), and trait data from surveys. To integrate the different types of information, we model the NetHealth data as a heterogeneous information network (HIN). Then, we redefine the problem of predicting individuals’ mental health conditions (depression or anxiety) in a novel manner, as applying to our HIN a popular paradigm of a recommender system (RS), which is typically used to predict the preference that a person would give to an item (e.g., a movie or book). In our case, the items are the individuals’ different mental health states. We evaluate four state-of-the-art RS approaches. Also, we model the prediction of individuals’ mental health as another problem type—that of node classification (NC) in our HIN, evaluating in the process four node features under logistic regression as a proof-of-concept classifier. We find that our RS and NC network methods produce more accurate predictions than a logistic regression model using the same NetHealth data in the traditional non-network fashion as well as a random-approach. Also, we find that the best of the considered RS approaches outperforms all considered NC approaches. This is the first study to integrate smartphone, wearable sensor, and survey data in a HIN manner and use RS or NC on the HIN to predict individuals’ mental health conditions.
Telepsychiatry refers to the use of technology to support the remote provision of psychiatric services. Discussions of this technology have often focussed on the use of video conferencing in place of in-person visits and how such care is found to be non-inferior to traditional care. New developments in the fields of remote-sensing and digital phenotyping have the potential to overcome the limitations inherent in remote visits as well as the limitations of current outpatient care models more generally. Such technologies may enable the collection of more relevant, objective clinical data which could lead to improved care quality and transformed care delivery models. The development and implementation of these new technologies raise important ethical questions.
There has been an increasing interest in the problem of inferring emotional states of individuals using sensor and user-generated information as diverse as GPS traces, social media data and smartphone interaction patterns. One aspect that has received little attention is the use of visual context information extracted from the surroundings of individuals and how they relate to it. In this paper, we present an observational study of the relationships between the emotional states of individuals and objects present in their visual environment automatically extracted from smartphone images using deep learning techniques.
We developed MyMood, a smartphone application that allows users to periodically log their emotional state together with pictures from their everyday lives, while passively gathering sensor measurements. We conducted an in-the-wild study with 22 participants and collected 3,305 mood reports with photos. Our findings show context-dependent associations between objects surrounding individuals and self-reported emotional state intensities. The applications of this work are potentially many, from the design of interior and outdoor spaces to the development of intelligent applications for positive behavioral intervention, and more generally for supporting computational psychology studies.
Längst hat die Digitalisierung auch Eingang in die Gesundheitsversorgung psychisch Kranker gefunden, wo entsprechende Ansätze unter dem Begriff E-Mental Health zusammengefasst werden. In diesem Kapitel sollen exemplarisch E-Mental-Health-Angebote für Menschen mit depressiven Störungen vorgestellt werden, da Depressionen aufgrund ihrer Häufigkeit und Schwere sowie auch wegen bestehender Versorgungsdefizite zu den Erkrankungen mit dem größten Optimierungsspielraum zählen und hier digitale Angebote wichtige Ergänzungen zur Leitlinienbehandlung bieten können. Am Beispiel von drei konkreten Anwendungsbereichen (Online-Diskussionsforen, Digitale Psychotherapie- und Selbstmanagement-Programme sowie Smartphone-basiertes Selbstmonitoring) sollen Chancen, aber auch mögliche Risiken und Nebenwirkungen dieser E-Mental-Health-Angebote diskutiert werden.
Objective::
Currently, the diagnosis in bipolar disorder relies on patient information and careful clinical evaluations and judgements with a lack of objective tests. Core clinical features of bipolar disorder include changes in behaviour. We aimed to investigate objective smartphone data reflecting behavioural activities to classify patients with bipolar disorder compared with healthy individuals.
Methods::
Objective smartphone data were automatically collected from 29 patients with bipolar disorder and 37 healthy individuals. Repeated measurements of objective smartphone data were performed during different affective states in patients with bipolar disorder over 12 weeks and compared with healthy individuals.
Results::
Overall, the sensitivity of objective smartphone data in patients with bipolar disorder versus healthy individuals was 0.92, specificity 0.39, positive predictive value 0.88 and negative predictive value 0.52. In euthymic patients versus healthy individuals, the sensitivity was 0.90, specificity 0.56, positive predictive value 0.85 and negative predictive value 0.67. In mixed models, automatically generated objective smartphone data (the number of text messages/day, the duration of phone calls/day) were increased in patients with bipolar disorder (during euthymia, depressive and manic or mixed states, and overall) compared with healthy individuals. The amount of time the smartphone screen was 'on' per day was decreased in patients with bipolar disorder (during euthymia, depressive state and overall) compared with healthy individuals.
Conclusion::
Objective smartphone data may represent a potential diagnostic behavioural marker in bipolar disorder and may be a candidate supplementary method to the diagnostic process in the future. Further studies including larger samples, first-degree relatives and patients with other psychiatric disorders are needed.
Mental health is an urgent global issue. Around 450 million people suffer from serious mental illnesses worldwide, which results in devastating personal outcomes and huge societal burden. Effective symptom monitoring and personalized interventions can significantly improve mental health care across different populations. However, traditional clinical methods often fall short when it comes to real-time monitoring of symptoms. Sensing technologies can address these issues by enabling granular tracking of behavioral, physiological, and social signals relevant to mental health. In this article, we describe how sensing technologies can be used to diagnose and monitor patient states for numerous serious mental illnesses. We also identify current limitations and potential future directions. We believe that the multimedia community can build on sensing technologies to enable efficient clinical decision-making in mental health care. Specifically, innovative multimedia systems can help identify and visualize personalized early-warning signs from complex multimodal signals, which could lead to effective intervention strategies and better preemptive care.
In 2001, the WHO stated that: "The use of mobile and wireless technologies to support the achievement of health objectives (mHealth) has the potential to transform the face of health service delivery across the globe". Within mental health, interventions and monitoring systems for depression, anxiety, substance abuse, eating disorder, schizophrenia and bipolar disorder have been developed and used. The present paper presents the status and findings from studies using automatically generated objective smartphone data in the monitoring of bipolar disorder, and addresses considerations on the current literature and methodological as well as clinical aspects to consider in the future studies.
Background
Electronic mental health interventions for mood disorders have increased rapidly over the past decade, most recently in the form of various systems and apps that are delivered via smartphones.
Objective
We aim to provide an overview of studies on smartphone-based systems that combine subjective ratings with objectively measured data for longitudinal monitoring of patients with affective disorders. Specifically, we aim to examine current knowledge on: (1) the feasibility of, and adherence to, such systems; (2) the association of monitored data with mood status; and (3) the effects of monitoring on clinical outcomes.
Methods
We systematically searched PubMed, Web of Science, PsycINFO, and the Cochrane Central Register of Controlled Trials for relevant articles published in the last ten years (2007-2017) by applying Boolean search operators with an iterative combination of search terms, which was conducted in February 2017. Additional articles were identified via pearling, author correspondence, selected reference lists, and trial protocols.
Results
A total of 3463 unique records were identified. Twenty-nine studies met the inclusion criteria and were included in the review. The majority of articles represented feasibility studies (n=27); two articles reported results from one randomized controlled trial (RCT). In total, six different self-monitoring systems for affective disorders that used subjective mood ratings and objective measurements were included. These objective parameters included physiological data (heart rate variability), behavioral data (phone usage, physical activity, voice features), and context/environmental information (light exposure and location). The included articles contained results regarding feasibility of such systems in affective disorders, showed reasonable accuracy in predicting mood status and mood fluctuations based on the objectively monitored data, and reported observations about the impact of monitoring on clinical state and adherence of patients to the system usage.
Conclusions
The included observational studies and RCT substantiate the value of smartphone-based approaches for gathering long-term objective data (aside from self-ratings to monitor clinical symptoms) to predict changes in clinical states, and to investigate causal inferences about state changes in patients with affective disorders. Although promising, a much larger evidence-base is necessary to fully assess the potential and the risks of these approaches. Methodological limitations of the available studies (eg, small sample sizes, variations in the number of observations or monitoring duration, lack of RCT, and heterogeneity of methods) restrict the interpretability of the results. However, a number of study protocols stated ambitions to expand and intensify research in this emerging and promising field.
Mobile sensing technologies and machine learning techniques have been successfully exploited to build effective systems for mental health monitoring and intervention. Various approaches have recently been proposed to effectively exploit contextual information such as mobility, communication and mobile usage patterns for quantifying users' emotional states and wellbeing. In particular, it has been shown that location information collected by means of smartphones can be successfully used to monitor and predict depression levels, as measured by means of standard scores such as PHQ-8. In this paper, we investigate the design of novel digital biomarkers based on the fine-grained characterization of the mobility patterns of a user, also considering the temporal dimension of their movements (e.g., sequence of places visited by them). We show that the proposed biomarkers have a statistically significant association with emotional states. We also demonstrate that emotional states have a stronger relationship with mobility patterns of weekdays compared to all days of a week. Finally, we discuss the challenges in using these biomarkers in the implementation of "emotion-aware" systems for digital health.
In this paper, we report on a responsive web-based application that we have been developing for the cancer hospital in the city of Vejle, Denmark.
The application administers and handles systematic frailty scoring of patients with multiple myeloma (bone marrow cancer) and thereby helps the doctor make a more efficient and more effective treatment choice.
The application is currently being tested with a small number of patients and is to replace the frailty measurement system used until now, which is usually done by the doctor on a per patient basis.
The delivery of psychiatric care is changing with a new emphasis on integrated care, preventative measures, population health, and the biological basis of disease. Fundamental to this transformation are big data and advances in the ability to analyze these data. The impact of big data on the routine treatment of bipolar disorder today and in the near future is discussed, with examples that relate to health policy, the discovery of new associations, and the study of rare events. The primary sources of big data today are electronic medical records (EMR), claims, and registry data from providers and payers. In the near future, data created by patients from active monitoring, passive monitoring of Internet and smartphone activities, and from sensors may be integrated with the EMR. Diverse data sources from outside of medicine, such as government financial data, will be linked for research. Over the long term, genetic and imaging data will be integrated with the EMR, and there will be more emphasis on predictive models. Many technical challenges remain when analyzing big data that relates to size, heterogeneity, complexity, and unstructured text data in the EMR. Human judgement and subject matter expertise are critical parts of big data analysis, and the active participation of psychiatrists is needed throughout the analytical process.
We describe a real life study of the use of smart phone based sensors for state monitoring of bipolar disorder (manic-depressive disorder). The study was conducted with 10 patients in a rural area of Austria under the supervision of a local psychiatric hospital. It shows initial evidence that relatively simple features derived from location, motion and phone call patterns are a good indication of state transitions.
In this paper we demonstrate how smart phone sensors, specifically acceleration sensor and GPS traces, can be used as an objective “measurement device” for aiding psychiatric diagnosis. In a trial with 12 bipolar disorder patients con- ducted over a total (summed over all patients) of over 1000 days (on average 12 weeks per patient) we have achieved state change detection with a precision/recall of 96%/94% and state recognition accuracy of 80%. The paper describes the data collection, which was conducted as a medical trial in a real life every day environment in a rural area, outlines the recognition methods, and discusses the results.
Mobile computing is changing the landscape of clinical monitoring and self-monitoring. One of the major impacts will be in healthcare, where increase in number of sensing modalities is providing more and more information on the state of overall wellbeing, behaviour and health. There are numerous applications of mobile computing that range from wellbeing applications, such as physical fitness, stress or burnout up to applications that target mental disorders including bipolar disorder. Use of information provided by mobile computing devices can track the state of the subjects and also allow for experience sampling in order to gather subjective information. This paper reports on the results obtained from a medical trial with monitoring of bipolar disorder patients and how the episodes of the diseases correlate to the analysis of the data sampled from mobile phone acting as a monitoring device.
Obstructive Sleep Apnea (OSA) is a sleeping disorder characterized by the repetitive reduction of airflow during sleep. In this paper, we discuss the design and implementation of a user-friendly mobile application developed on multiple platforms (Windows and Android) to monitor and detect symptoms of sleep apnea using the smart phone's built-in sensors. The purpose of the application, Sleep Apnea Monitor (SAM), is to allow users to get a sense of whether or not they are likely to have sleep apnea, before continuing with more expensive and advanced sleep tests. In addition, SAM provides doctors and sleep specialists with remote access to patients' records and allows them to confirm their initial diagnosis. The parameters measured by this application are breathing patterns and movement patterns, which are recorded respectively using the built-in microphone and accelerometer. The recorded data is sent to a server for analysis in order to diagnose patients and maintain geographical studies of areas with sleep apnea patterns. The application is successfully tested among a number of users in the UAE. The system diagnoses and reports the level of the user's sleep apnea. In addition, doctors can remotely monitor users through the website, which is interfaced with Google Maps to keep track of user locations, and keep track of their analysed records.
Equipment of mobile phones with various kinds of sensors is transforming these devices from mere capabilities of voice and internet access to devices capable of sensing a number of phenomena pertaining to their users. In this paper we make use of these capabilities of phones to detect social interactions between people and analyze social context by using embedded sensors found in typical smart phones. Work carried out in this area has typically used dedicated hardware to establish social interactions, and we contend on the suitability of mobile phone, since additional devices that user is not familiar with influence natural user behavior and thus their social interaction patterns. Our work shows that two parameters that can be detected through mobile phone sensing, namely interpersonal distance and relative body orientation, provide a solid basis for inferring social interactions. We describe how these factors are acquired using smart phones and describe our analysis. The experiments demonstrate that we can recognize not only whether a social interaction is taking place, but also the type of social interaction, distinguishing between formal and informal social settings.
In this paper we present a personal, cost-effective, multi-parametric monitoring system based on textile platforms and portable sensing devices for the long term and short term acquisition of data from bipolar patients affected by mood disorders. The system allows the early indication and prevention of bipolar state relapse situations. The bipolar mood state of the patients is etimated from several physiogical and physical cues such as biochemical markers, voice analysis and a behavioural index correlated to patient state.
Long-term monitoring of patients with affective disorders has been shown to reduce certain types of episodes and improve social functioning. However, most of the research to date has been based on patient-reported data, which exhibits certain deficiencies including failure to detect depressive aspects of the illness. Questions have also been raised about the accuracy and completeness of patient-reported data. Enhancing patient- reported data with environmental and physiological recordings could improve the accuracy and completeness that current solutions are missing. This paper describes procedures for the fusion of patient-reported data with remotely-sensed patient- centred data collected using small wireless devices placed on patients and in their homes.
In this study, we demonstrate that we can use non-invasive physiology to diagnose, monitor, and trend clinically significant depression state over long periods of time. Toward these ends, a clinical study in collaboration with the MGH Psychiatry De-partment is described. In this study, we monitor the long-term continuous physiology and behavior of clinically depressed patients throughout their stay at an in-ward treatment center as they undergo electro-convulsive therapy. We show how non-invasive physiological measures are correlated to depression state and can bed used to track trends in depression state through the course of treatment.
Mobile and environmental sensing technology can be used to assess human behaviour and mental health trajectories outside of laboratories and in ecologically-relevant settings. To achieve maximum benefit, the set of equipment and the monitoring patterns must be personalised to respect individual needs and fit into individual lifestyles. We have developed a sensor network infrastructure for mobile phones and homecare using a rule-oriented programming architecture to monitor the activity signatures of people with Bipolar Disorder (BD). We believe that the use of this rule-based paradigm within the network for a mental health setting to be a contribution of this work. We are evaluating the effectiveness of the technology in an ongoing technical trial with control participants as a precursor to studying the effectiveness of the system for use with people with BD. In this paper, we report the design and development of the monitoring system along with preliminary findings from the technical trial of the system, and discuss future developments.
As advances in technology accelerate, sensors and recording devices are increasingly being integrated into homes. Although the added benefit of sensing is often clear (e.g., entertainment, security, encouraging sustainable behaviors, etc.), the home is a private and intimate place, with multiple stakeholders who may have competing priorities and tolerances for what is acceptable and useful. In an effort to develop systems that account for the needs and concerns of householders, we conducted an anonymous survey (N = 475) focusing on the activities and habits that people do at home that they would not want to be recorded. In this paper, we discuss those activities and where in the home they are performed, and offer suggestions for the design of UbiComp systems that rely on sensing and recording.
Objective long-term health monitoring can improve the clinical management of several medical conditions ranging from cardiopulmonary diseases to motor disorders. In this paper, we present our work toward the development of a home-monitoring system. The system is currently used to monitor patients with Parkinson's disease who experience severe motor fluctuations. Monitoring is achieved using wireless wearable sensors whose data are relayed to a remote clinical site via a web-based application. The work herein presented shows that wearable sensors combined with a web-based application provide reliable quantitative information that can be used for clinical decision making.
Reliable smartphone app prediction can strongly benefit both users and phone system performance alike. However, real-world smartphone app usage behavior is a complex phenomena driven by a number of competing factors. In this pa- per, we develop an app usage prediction model that leverages three key everyday factors that affect app usage decisions -- (1) intrinsic user app preferences and user historical patterns; (2) user activities and the environment as observed through sensor-based contextual signals; and, (3) the shared aggregate patterns of app behavior that appear in various user communities. While rapid progress has been made recently in smartphone app prediction, existing prediction models tend to focus on only one of these factors. We evaluate a multi-faceted approach to prediction using (1) a 3-week 35-user field trial, along with (2) analysis of app usage logs of 4,606 smartphone users worldwide. We find our app usage model can not only produce more robust app predictions than conventional techniques, but it can also enable significant smartphone system optimizations.
Equipped with cutting-edge sensing technology and high-end processors, smartphones can unobtrusively sense human behavior and deliver feedback and behavioral therapy. The authors discuss two applications for behavioral monitoring and change and present UBhave, the first holistic platform for large-scale digital behavior change intervention.
This paper investigates the prediction of two aspects of human behavior using smartphones as sensing devices. We present a framework for predicting where users will go and which app they will use in the next ten minutes by exploiting the rich contextual information from smartphone sensors. Our first goal is to understand which smartphone sensor data types are important for the two prediction tasks. Secondly, we aim at extracting generic (i.e., user-independent) behavioral patterns and study how generic behavior models can improve the predictive performance of personalized models. Experimental validation was conducted on the Lausanne Data Collection Campaign (LDCC) dataset, with longitudinal smartphone data collected over a period of 17 months from 71 users.
One of the areas of great demand for the need of continuous monitoring, patient participation and medical prediction is that of mood disorders, more specifically bipolar disorders. Due to the unpredictable and episodic nature of bipolar disorder, it is necessary to take the traditional standard procedures of mood assessment through the administration of rating scales and questionnaires and integrate this with tangible data found in emerging research on central and peripheral changes in brain function that may be associated to the clinical status and response to treatment throughout the course of bipolar disorder. This paper presents PSYCHE system, a personal, cost-effective, multi-parametric monitoring system based on textile platforms and portable sensing devices for the long term and short term acquisition of data from selected class of patients affected by mood disorders. The acquired data will be processed and analyzed in the established platform that takes into account the Electronic Health Records (EHR) of the patient, a personalized data referee system, as well as medical analysis in order to verify the diagnosis and help in prognosis of the illness. Constant feedback and monitoring will be used to manage the illness, to give patients support, to facilitate interaction between patient and physician as well as to alert professionals in case of patients relapse and depressive or manic episodes income, as the ultimate goal is to identify signal trends indicating detection and prediction of critical events.
Mobile phones are becoming more and more widely used nowadays, and people do not use the phone only for communication: there is a wide variety of phone applications allowing users to select those that fit their needs. Aggregated over time, application usage patterns exhibit not only what people are consistently interested in but also the way in which they use their phones, and can help improving phone design and personalized services. This work aims at mining automatically usage patterns from apps data recorded continuously with smartphones. A new probabilistic framework for mining usage patterns is proposed. Our methodology involves the design of a bag-of-apps model that robustly represents level of phone usage over specific times of the day, and the use of a probabilistic topic model that jointly discovers patterns of usage over multiple applications and describes users as mixtures of such patterns. Our framework is evaluated using 230 000+ hours of real-life app phone log data, demonstrates that relevant patterns of usage can be extracted, and is objectively validated on a user retrieval task with competitive performance.
To examine the feasibility of collecting course of illness data from patients with bipolar I and II disorder, using weekly text-messaged mood ratings, and to examine the time trajectory of symptom ratings based on this method of self-report.
A total of 62 patients with bipolar I (n = 47) or II (n = 15) disorder provided mood data in response to weekly cell phone text messages (n = 54) or e-mail prompts (n = 8). Participants provided weekly ratings using the Altman Self-Rating Mania Scale and the Quick Inventory of Depressive Symptoms-Self Report. Patients with bipolar I and II disorder, and men and women, were compared on percentages of time in depressive or manic mood states over up to two years.
Participants provided weekly ratings over an average of 36 (range 1-92) weeks. Compliance with the procedure was 75%. Overall, participants reported depressive symptoms 47.7% of the time compared to 7% of entries reflecting manic symptoms, 8.8% reflecting both depressive and manic symptoms, and 36.5% reflecting euthymic mood. Participants with bipolar I disorder reported more days of depression and were less likely to improve with time than participants with bipolar II disorder. Gender differences observed at the beginning of the study were not observed at follow-up.
The results are similar to those of other longitudinal studies of bipolar disorder that use traditional retrospective, clinician-gathered mood data. Text-message-based symptom monitoring during routine follow-up may be a reliable alternative to in-person interviews.
Frequent symptom self-monitoring protocols have become popular tools in the addiction field. Interactive Voice Response (IVR) is a telephone monitoring system that has been shown to be feasible for collecting frequent self-reports from a variety of research populations. Little is known, however, about the feasibility of using IVR monitoring in clinical samples, and few controlled trials exist assessing the impact of any type of frequent self-report monitoring on the behaviors monitored. This pilot study with patients in early recovery from an alcohol use disorder (n=98) evaluated compliance with two IVR monitoring protocols, subjective experiences with monitoring, and change in symptoms associated with monitoring (i.e., measurement reactivity). Participants were randomly assigned to call an IVR system daily for 28 days, once per week for 4 weeks, or only to complete 28-day follow-up assessment including retrospective drinking reports. Monitoring calls assessed alcohol craving, substance use, emotional well-being, and PTSD symptoms. Most monitoring participants completed calls on at least 75% of scheduled days (72.2% and 59.2% for daily and weekly, respectively). Including reconstructed data from follow-up of missed calls yielded 77.8% and 74.1% of maximum data points, respectively. Most monitoring participants indicated the protocol was manageable and reported positive or no effects of monitoring on urges to use alcohol, actual drinking, and PTSD symptoms. Analyses of measurement reactivity based on assessment one month after randomization found no significant group differences on drinking, craving for alcohol, or PTSD-related symptoms. Results suggest that IVR technology is feasible and appropriate for telephone symptom monitoring in similar clinical samples.
Cardiovascular disease, obesity, and lack of physical fitness are increasingly common and negatively affect people's health, requiring medical assistance and decreasing people's wellness and productivity. In the last years, researchers as well as companies have been increasingly investigating wearable devices for fitness applications with the aim of improving user's health, in terms of cardiovascular benefits, loss of weight or muscle strength. Dedicated GPS devices, accelerometers, step counters and heart rate monitors are already commercially available, but they are usually very limited in terms of user interaction and artificial intelligence capabilities. This significantly limits the training and motivation support provided by current systems, making them poorly suited for untrained people who are more interested in fitness for health rather than competitive purposes. To better train and motivate users, we propose the mobile personal trainer (MOPET) system.
MOPET is a wearable system that supervises a physical fitness activity based on alternating jogging and fitness exercises in outdoor environments. By exploiting real-time data coming from sensors, knowledge elicited from a sport physiologist and a professional trainer, and a user model that is built and periodically updated through a guided autotest, MOPET can provide motivation as well as safety and health advice, adapted to the user and the context. To better interact with the user, MOPET also displays a 3D embodied agent that speaks, suggests stretching or strengthening exercises according to user's current condition, and demonstrates how to correctly perform exercises with interactive 3D animations.
By describing MOPET, we show how context-aware and user-adaptive techniques can be applied to the fitness domain. In particular, we describe how such techniques can be exploited to train, motivate, and supervise users in a wearable personal training system for outdoor fitness activity.
With the widespread recognition of the value of active patient participation in their care, ChronoRecord software was developed to automate daily self-reporting by patients with bipolar disorder. A prior study demonstrated concurrent validity between self-ratings on ChronoRecord and clinician ratings on the Hamilton Depression Rating Scale (HAMD), but validity with the Young Mania Rating Scale (YMRS) could not be shown due to a lack of data when the outpatients were manic (Bauer et al., Bipolar Disorders 6, 67-74, 2004). This study expanded upon the prior validation study to include inpatients with mania. Self-reported mood ratings on ChronoRecord and clinician ratings on the YMRS were obtained on the same day from 27 inpatients (57 ratings); these data were also combined with the ratings from the 80 outpatients (total 107 patients, 340 ratings). Using Pearson correlation, the self-reported ratings on ChronoRecord were significantly correlated with the YMRS. The accuracy of ChronoRecord to discriminate hypomania and mania was high, as described by the area under the receiver operating characteristic curve. Post-hoc analysis of the level of agreement between ChronoRecord and YMRS ratings was excellent or good in all cases using the kappa statistic. These data demonstrate concurrent validity between ChronoRecord and YMRS.
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