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In many countries, the demand for healthcare services has been increasing due to growing and aging populations. This situation has put unprecedented strains on health service providers to find economical ways of offering high-quality and sustainable health services. Moreover, there exists a gap today between physical healing, which hospitals strive to provide, and emotional and psychological healing, which is still lagging. This gap is one among many other reasons why patients do not welcome extended stays in hospitals. What is needed today is a more engaged healthcare service where healing is not confined to the immediate physical wellbeing of patients but takes into account their emotional and psychological states as well. Recently, connected health (aka technology-enabled care), which leverages the proliferation of mobile technology and health apps, has been positioned as a strong enabler to address this challenge by creating new potential opportunities for clinicians to access near real-time information about patients’ mood and emotional states.
In psychology, mood refers to a positive or negative emotional state of varying intensity that can evolve in response to life’s conditions and which reflects the balance between an individual’s resources and his/her personal life challenges (Polak et al., 2015; Desmet, 2015). In contrast to emotion, which refers to an acute and reactionary feeling state that is triggered in response to an event or a situation, mood is slower to change and “objectless” in the sense that people might not know the cause of mood swings (Russell, 2003). For example, in the case of depression (mood), a person might feel down for days or even weeks without knowing the reason. In contrast, a person might feel angry (emotion) in response to a particular situation, but this feeling can dissipate quickly (Polak et al., 2015). In most emotion detection approaches, scientists have relied on Ekman’s discrete emotional model (Ekman, 1999) or its variants, including the Lang arousal model (Lang, 1995). Ekman's model distinguishes among six basic emotions, namely: sadness, joy; anger; fear; disgust; and surprise.
As highlighted by many earlier studies (e.g., Velden & Sommervold, 2016; Desmet, 2015), the words emotion and mood are often used interchangeably in everyday conversation and e-health design research. However, although both terms relate to the wellbeing of a person, a distinction is warranted when designing mood tracking applications. This distinction is because emotions (e.g., anger, panic, fear, worry) are evoked through external events, some of which constitute perceived threats that require immediate attention.
Previous research has shown that hospital stays, whether for long or short periods, can potentially affect patients’ mood and emotional state. In particular, mood swings are commonly observed phenomena among patients in both medical and behavioral healthcare environments (Foreman et al., 2011).
Several factors can afflict the mood and the mental state (ability to think and concentrate) of hospital patients. These include feeling unwell, suffering from pain or discomfort, worrying about one’s health, a low-level of daytime light exposure, unpleasant interactions with the hospital’s staff, poor environmental factors, and feelings of boredom and loneliness. Furthermore, some older people have exhibited signs of delirium, confusion, and depression during their stay in hospitals.
Delirium is a severe disturbed state of mind that often targets older people during their stay in hospitals, leading to changes in mood and behavior. It is characterized by sudden confusion, restlessness, illusions, agitation, and incoherence. Delirium is often attributed to dehydration, fever, particular medication, illness (such as urinary tract infection), and other disorders.
Depression is an emotional state that is marked by low mood, loss of emotional expression, social withdrawal, feelings of sadness, anxiety, pessimism, low self-worth or guilt, a change in appetite, and a reduced interest in life.