Identifying associations between smartphone usage and mental health during depression, anxiety and stress

Mestry, Madhura; Mehta, Jigesh; Mishra, Ajit; Gawande, Kiran

doi:10.1109/iccict.2015.7045656

Cited by 6 publications

(12 citation statements)

References 6 publications

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“…Similarly, subjective-based mobile phone use has been studied in relation to depression, where Thomée et al found that high mobile phone use was associated with symptoms of depression [69]. These finding were replicated in this review with only a single statistically non-significant contradictive result by a two sample study by Mestry et al [14] (r = -.03, p = .79).…”

Section: Secondary Featuressupporting

confidence: 52%

“…As an example, from data provided by the corresponding author [14], we see statistically significant results in communication app usage (r = -0.33, p < .01) calculated by a within subjects ANCOVA. The low variability with device-based features could indicate that there is a general tendency for participants to use their phone more, but at the same time withdraw from the social context by lowering the communicative apps usage.…”

Section: Primary Featuresmentioning

confidence: 99%

“…The final list was critically investigated by all authors which led to the exclusion of 16 papers due to outcome measures that did not represent mood assessment (e.g., happiness scales [31][32][33], Quality of Life [34] , or Satisfaction With Life Scale [35]), or wearables that was not deemed consumer-based, e.g., an Holter monitor [36], or multisensory clothing [37][38][39]. Several studies did not include correlation results between the objective features and the outcome assessment (e.g., [9,14,33,40]). Contact via email was made with the corresponding author of these, and the relevant data was acquired in all cases.…”

Section: Study Selectionmentioning

confidence: 99%

“…A digital marker has been defined as a consumer generated physiological and behavioral measure collected from digital tool that can be used to explain, influence and/or predict health related outcomes [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]), and this has raised great enthusiasm in terms of using mobile and wearable technology in the treatment and monitoring of depression and other affective disorders. It has been argued that such an approach may provide an easy and objective way to monitor illness activity and could serve as a digital marker of mood symptoms in affective disorders [18].…”

Section: Introductionmentioning

confidence: 99%

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Correlations between objective behavioral features collected from mobile and wearable devices, and depressive mood symptoms in affective disorders: A systematic review (Preprint)

Rohani¹,

Faurholt‐Jepsen²,

Kessing³

et al. 2017

Preprint

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Background: A number of studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable technologies and mood symptoms in affective disorders (unipolar disorder and bipolar disorder). However, individual studies have reported on different and sometimes contradicting results and no quantitative systematic review of the correlation between objective behavioral features and mood symptoms has been published. Objective: The objectives of this systematic review were to 1) provide an overview of correlations between objective behavioral features and 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 in the identification on which objective features have shown most promising results across studies. Methods: A systematic review of the scientific literature, reported according to the Preferred Reporting items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines was conducted. 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 was limited to studies published 2007-2017. Results: A total of 34 studies were eligible for the present review. They investigated 71 unique objective features divided into six different types covering 16 various sensor data inputs. Several objective features were found to have consistent correlation directionality with mood assessment (e.g., the amount of home stay, sleep duration, vigorous activity), while others showed directionality discrepancies across the studies (e.g., amount of SMS sent, time you spend between locations, frequency of smartphone screen activity).

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Section: Secondary Featuressupporting

confidence: 52%

Section: Primary Featuresmentioning

confidence: 99%

Section: Study Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Correlations between objective behavioral features collected from mobile and wearable devices, and depressive mood symptoms in affective disorders: A systematic review (Preprint)

Rohani¹,

Faurholt‐Jepsen²,

Kessing³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…Many studies have found statistically significant correlations between objective behavioral features collected from mobile and wearable devices and mood symptoms in nonclinical samples of participants without psychiatric illnesses [14-17] as well as in clinical samples of patients diagnosed with psychiatric disorders [11,18-20]. …”

Section: Introductionmentioning

confidence: 99%

Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review

Rohani

Faurholt‐Jepsen

Kessing

et al. 2018

JMIR Mhealth Uhealth

163

146

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BackgroundSeveral 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.ObjectiveThe 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.MethodsWe 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.ResultsA 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).ConclusionsSeveral 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.

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Predictive modeling in e-mental health: A common language framework

Becker

Breda

Funk

et al. 2018

Internet Interventions

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Recent developments in mobile technology, sensor devices, and artificial intelligence have created new opportunities for mental health care research. Enabled by large datasets collected in e-mental health research and practice, clinical researchers and members of the data mining community increasingly join forces to build predictive models for health monitoring, treatment selection, and treatment personalization. This paper aims to bridge the historical and conceptual gaps between the distant research domains involved in this new collaborative research by providing a conceptual model of common research goals. We first provide a brief overview of the data mining field and methods used for predictive modeling. Next, we propose to characterize predictive modeling research in mental health care on three dimensions: 1) time, relative to treatment (i.e., from screening to post-treatment relapse monitoring), 2) types of available data (e.g., questionnaire data, ecological momentary assessments, smartphone sensor data), and 3) type of clinical decision (i.e., whether data are used for screening purposes, treatment selection or treatment personalization). Building on these three dimensions, we introduce a framework that identifies four model types that can be used to classify existing and future research and applications. To illustrate this, we use the framework to classify and discuss published predictive modeling mental health research. Finally, in the discussion, we reflect on the next steps that are required to drive forward this promising new interdisciplinary field.

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Identifying associations between smartphone usage and mental health during depression, anxiety and stress

Cited by 6 publications

References 6 publications

Correlations between objective behavioral features collected from mobile and wearable devices, and depressive mood symptoms in affective disorders: A systematic review (Preprint)

Correlations between objective behavioral features collected from mobile and wearable devices, and depressive mood symptoms in affective disorders: A systematic review (Preprint)

Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review

Predictive modeling in e-mental health: A common language framework

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