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It consists of a single mount with three swiveling hooks that can Top 3 Best Free Android Application to Track Mobile Phone Location fold flat against the wall when not in use.
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Mobile tracker software helps its users with tracking and spying , which is its main function. Often it is considered important because it helps to recover a stolen or lost device without signals. Modern society continues to embrace new technologies daily. Young children can access a huge variety of devices, using smartphones and tablets from of different brands and manufacturers.
Their primary focus is protection against harm for their children. After reading this review of the top tracking app, you can decide which one you think is the best. It is a perfect cell phone spy without access to target phone. Over the years, many people have preferred to use mSpy. This is due to its functionality.
Apple iPads, iPhones and iPods, and other gadgets. You can anonymously get data about target device activities.
You can view phone book numbers and calendar plans within the targeted gadgets. Out of all software used to monitor devices from this review, FlexiSpy is among the best.
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You can use this cell tracker app with every kind of electronic tool. FlexiSpy will impress you and provide an enjoyable experience, while spying and monitoring targeted device easily. SpyBubble mobile tracking app is recommended for parents to use this software and ensure that their children do not get into trouble. SpyBubble cell tracker app has GPS location feature. It allows seeing the location of the target device for parents to understand where their children are at any given moment without calling them.
SpyEra tracker app has one of the best tracking and spying features. It is efficient and effective. Business owners can use this smart cell phone tracking app to track and monitor online actions of their employees while they use company gadgets. The remaining 11 teens had an average PHQ-9 score of Their parents included 11 women and 1 man there was 1 family with 2 teens and had an average age of In fact, 5 of the 12 parents were also diagnosed with mild depression and 2 showed moderate-to-severe depression.
A total of 8 families completed the entire 8-week trial, and the others enrolled in the study between 4 and 6 weeks. The smartphone sensor data were represented by mobility, social interaction, and living context—related features extracted using methods in Section Methods-Feature Extraction and Statistical Analysis.
Pearson correlation coefficients between self-reports, parental inputs, and psychometric scores for the adolescent patients. Pearson correlation coefficients between social interactions, living context, and psychometric scores for the adolescent patients. Therefore, daily responses submitted through a customized smartphone app by either teens or their parents can be used as a reliable approach for monitoring depression.
Figure 5 shows the correlations between mobility and psychometric scores, where mobility is captured by steps taken and daily trajectory.
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From the figure, we observed that subjects with higher depression scores tend to have lower mobility, as indicated by fewer steps taken. Patients with more severe depression also visit fewer places and have lower location variance, yet they spend time more uniformly across different places, as reflected by higher normalized entropy.
Similar results were also reported in previous studies with depressed adult patients, thus further validating our observations [ 25 ]. Although the overall interpretation matches our understanding that depression generally reduces mobility, higher entropy does not seem to have a clear interpretation, and thus it needs further investigation. Pearson correlation coefficients between mobility and psychometric scores for the adolescent patients.
The correlations between social interactions, other living context, and psychometric scores are shown in Figure 6. The results indicate that a higher depression score is significantly correlated with lower social interaction level, such as shorter phone call durations and fewer text messages.
Conversely, there is no significant correlation between other living contexts, that is, ambient light intensity, smartphone screen usage, and psychometric scores. Thus, as expected, communication patterns are affected by depression severity, but our dataset did not indicate any significant patterns regarding screen usage and ambient light intensity, with the latter being a proxy for sleep duration. Depressive symptoms assessed by clinical instruments were also significantly correlated with mobility level and social interactions captured by passive smartphone sensor data.
However, we did not observe significant correlations between other daily living contexts ie, ambient light intensity and smartphone screen usage and depressive symptoms. We further fitted 2 regression models to predict the psychometric scores from the smartphone data, specifically a linear regressor and a support vector regressor with a polynomial kernel. The linear regressor fits a linear model to the data, whereas the support vector regressor fits a nonlinear model.
To evaluate the model performance in the prediction of PHQ-9 scores, we divided the entire dataset into training and test subsets with a ratio of Then, we computed the root mean square error RMSE of the predicted PHQ-9 scores on the test set and experimented with different selections of feature subsets to decide which combination gave the highest accuracy.
The results are shown in Table 1. Root mean square error and variance of predicting Patient Health Questionnaire-9 scores using different subsets of features extracted from smartphone data. Conversely, if we use only smartphone sensor and usage data, the RMSE is 2. The major significance of the result is that there appears to be significant predictability of clinical measures from smartphone data.
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Besides, we could achieve similar PHQ-9 prediction accuracy by using only background sensor data, compared with using both active self-reports and passive sensor data, thereby reducing the efforts of manual inputs. This clearly shows the potential of mobile-based measurements for tracking depressive states and the importance of introducing someone close to the subjects eg, their parent as a human sensor to provide an additional dimension of data.
The SOLVD-Teen is a first-of-its-kind study that investigated the feasibility of evaluating depressive symptoms of adolescents using smartphone sensor data, daily self-reports, and parental inputs through a customized smartphone app. The proposed approach worked well for teenagers who are typically heavy smartphone users. Submitting the responses through the app only took less than 15 s per day.
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Both the teens and their participating parents showed a high adherence rate to submitting daily self-evaluations. All the study participants were comfortable with the use of the technology, and neither the teens nor their parents perceived the app as invasive or burdensome. Besides, the data collection of the study was completely through the smartphone app; hence, no additional sensor or cost was needed. Lower levels of mobility and fewer social interactions were predictive of higher depression symptoms, which was consistent with a decline in mobility and social communications in individuals with depression.
However, other daily living contexts such as light intensity and smartphone screen usage were not significantly correlated with depressive symptoms. Our study expanded on prior studies by suggesting that introducing other people as human sensors could further increase the accuracy in depression monitoring. The prediction accuracy could be improved by adding the evaluations from parents apart from self-inputs from the teens.
Our study also showed evidence that by using only passive sensor loggings, we could achieve comparable prediction accuracy by using both sensor loggings and self-inputs. Therefore, the SOLVD-Teen app could further reduce user effort by collecting only passive data, while maintaining a comparable depression monitoring effect. The study conclusion is preliminary given the relatively small sample size of 13 families. Study recruitment ended as the program from which the patients were recruited shut down for logistic reasons as the AIM program closed. In addition, 8 out of the 13 families completed the entire 8-week trial: the program necessitated biweekly attendance, which created a burden time spent at the program and transportation costs and effort , but the treatment team noted that families tended to drop out once their child did better, and intensive treatment was not felt to be as crucial.
We plan to extend the study with the reopening of the AIM program in a different location. In college-aged students, increased ruminations were found to relate to increasing depressive symptoms [ 26 ].
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Similarly, nonsuicidal self-injury correlates with self-criticism and feeling criticized by others and usually links to a negative affect of brief durations [ 27 ]. Such brief moments of ruminations and self-blame are the epitome of what smartphone apps can help us capture and leverage into treatment opportunities. Thus, the aim of mobile health interventions is to move the treatment of adolescents with mood disorders from reactive to proactive and personalized, thus paving the way to truly individualized treatments [ 28 ].
Its key novelty lies in the recruitment of adolescent depression patients and the introduction of parental evaluations as an additional source of inputs. The study was limited by the relatively small sample size and the constraint of using only Android phones because of the lack of permission to access certain sensors on the iOS platform. We would like the thank Ben Taub Hospital and Rice Scalable Health Labs for their support in the conduction of the clinical trial described in this paper.
All authors participated in the clinical trial design and manuscript preparation. Conflicts of Interest: None declared. National Center for Biotechnology Information , U. Published online Jan Reviewed by Sofian Berrouiguet and Georgina Cox. Author information Article notes Copyright and License information Disclaimer. Corresponding author.
Corresponding Author: Nidal Moukaddam ude. This article has been cited by other articles in PMC. Abstract Background Depression carries significant financial, medical, and emotional burden on modern society. Methods We recruited 13 families with adolescent patients diagnosed with MDD with or without comorbid anxiety disorder. Conclusions Smartphone apps such as SOLVD represent a useful way to monitor depressive symptoms in clinically depressed adolescents, and these apps correlate well with current gold-standard psychometric instruments.
Introduction Background Depression has a global lifetime prevalence of Objective and Results In this paper, we report the results from a Smartphone- and OnLine usage—based eValuation for Depression SOLVD -Teen trial with the aim to quantify the use of smartphones in monitoring depression in a clinically depressed adolescent population. Related Work Although our results are a promising step toward developing new tools and methods for managing adolescent conditions, the usage of apps to monitor adolescents remains a challenge.
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