Enriching Mental Health Mobile Assessment and Intervention with Situation Awareness

Abstract

Current mobile devices allow the execution of sophisticated applications with the capacity for identifying the user situation, which can be helpful in treatments of mental disorders. In this paper, we present SituMan, a solution that provides situation awareness to MoodBuster, an ecological momentary assessment and intervention mobile application used to request self-assessments from patients in depression treatments. SituMan has a fuzzy inference engine to identify patient situations using context data gathered from the sensors embedded in mobile devices. Situations are specified jointly by the patient and mental health professional, and they can represent the patient’s daily routine (e.g., “studying”, “at work”, “working out”). MoodBuster requests mental status self-assessments from patients at adequate moments using situation awareness. In addition, SituMan saves and displays patient situations in a summary, delivering them for consultation by mental health professionals. A first experimental evaluation was performed to assess the user satisfaction with the approaches to define and identify situations. This experiment showed that SituMan was well evaluated in both criteria. A second experiment was performed to assess the accuracy of the fuzzy engine to infer situations. Results from the second experiment showed that the fuzzy inference engine has a good accuracy to identify situations.

Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations

“…Two other studies developed their monitoring solution including a recommendation system to support patients with depression to cope with their diagnosis [59,60]. Mental health systems have also been used as a tool by caregivers to access the summary of situations experienced by patients with depression [61] or to alert physicians and families if an abnormal behavior is detected in patients with mood disorders [62].…”

Recognizing Context-Aware Human Sociability Patterns Using Pervasive Monitoring for Supporting Mental Health Professionals

“…Therefore, we used fuzzy logic due to its ability to model expert knowledge through fuzzy sets and fuzzy rules and its notation that is easy to understand. Moreover, fuzzy logic has already been used to model situations of interest to mental health professionals [ 50 ].…”

Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations (Preprint)

“…In fact, 4 studies aimed to monitor health conditions related to a specific disease, such as detecting sleep abnormalities in patients with schizophrenia or hand tremors in those with Parkinson disease, and another 12 opted to use smartphones to sense users' daily lives to improve their general health and well-being. Among the studies that targeted a specific population, 6 [55,[60][61][62]75]. Senior population and workers were targeted by 3 studies each [1, 3,9,30,35,69].…”

“…Reference Concerns [2,3,11,13,15,29,31,35,51,61,65,66,70,[77][78][79]83,91,93] Battery levels [2,3,34,35,51,61,65,69,70,78,79,87,91] Privacy [1,2,15,35,70,76,88,94] Developed models [1][2][3][6][7][8][9][10][11][12][13]15,26,…”

“…GPS[36][37][38][39]42,44,46,47,[50][51][52][58][59][60][61]72,[105][106][107]; smartphone and app usage[36,39,41,42,52,53,59,60,72,106,[108][109][110]; accelerometer[35,36,[39][40][41][42]44,51,57,58,60,72,106]; cell-ID/calls[45,49,51,72,[105][106][107]; text messages…”