Possible Application of Ecological Momentary Assessment to Older Adults’ Daily Depressive Mood: Integrative Literature Review

Abstract

Background Ecological momentary assessment is a method of investigating individuals’ real-time experiences, behaviors, and moods in their natural environment over time. Despite its general usability and clinical value for evaluating daily depressive mood, there are several methodological challenges when applying ecological momentary assessment to older adults. Objective The aims of this integrative literature review were to examine possible uses of the ecological momentary assessment methodology with older adults and to suggest strategies to increase the feasibility of its application in geriatric depression research and practice. Methods We searched 4 electronic databases (MEDLINE, CINAHL, PsycINFO, and EMBASE) and gray literature; we also hand searched the retrieved articles’ references. We limited all database searches to articles published in peer-reviewed journals from 2009 to 2019. Search terms were “ecological momentary assessment,” “smartphone assessment,” “real time assessment,” “electronic daily diary,” “mHealth momentary assessment,” “mobile-based app,” and “experience sampling method,” combined with the relevant terms of depression. We included any studies that enrolled older adults even as a subgroup and that reported depressive mood at least once a day for more than 2 days. Results Of the 38 studies that met the inclusion criteria, only 1 study enrolled adults aged 65 years or older as the entire sample; the remainder of the reviewed studies used mixed samples of both younger and older adults. Most of the analyzed studies (18/38, 47%) were quantitative, exploratory (descriptive, correlational, and predictive), and cohort in design. Ecological momentary assessment was used to describe the fluctuating pattern of participants’ depressive moods primarily and to examine the correlation between mood patterns and other health outcomes as a concurrent symptom. We found 3 key methodological issues: (1) heterogeneity in study design and protocol, (2) issues with definitions of dropout and adherence, and (3) variation in how depressive symptoms were measured with ecological momentary assessment. Some studies (8/38, 21%) examined the age difference of participants with respect to dropout or poor compliance rate. Detailed participant burden was reported, such as technical problems, aging-related health problems, or discomfort while using the device. Conclusions Ecological momentary assessment has been used for comprehensive assessment of multiple mental health indicators in relation to depressive mood. Our findings provide methodological considerations for further studies that may be implemented using ecological momentary assessment to assess daily depressive mood in older adults. Conducting more feasibility studies focusing on older adults with standardized data collection protocols and mixed-methods research is required to reflect users’ experiences. Further telepsychiatric evaluation and diagnosis based on ecological momentary assessment data should involve standardized and sophisticated strategies to maximize the potential of ecological momentary assessment for older adults with depression in the community setting.

Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives

“…Several studies have highlighted the issue of recall bias with self-report depression questionnaires conducted every 4 to 6 week during patients’ clinic visits[ 9 , 11 ]. Evidence indicates that patients with depression tend to judge their symptoms to be more severe or remember negative experiences more prominently when asked to recall them retrospectively[ 9 , 42 ]. Active EMA via mobile devices allows the collection of information in real-time, minimizing recall bias[ 9 , 11 , 42 ].…”

“…Evidence indicates that patients with depression tend to judge their symptoms to be more severe or remember negative experiences more prominently when asked to recall them retrospectively[ 9 , 42 ]. Active EMA via mobile devices allows the collection of information in real-time, minimizing recall bias[ 9 , 11 , 42 ]. Obtaining this information in real-time also allows clinicians to put variations of mood in patients’ situational and social context.…”

“…For both active and passive EMA, the degree of patients’ technical knowledge can be a critical factor affecting compliance. Technical problems and inappropriate operating systems have been cited as among the most common reasons for participant drop out in EMA studies[ 41 , 42 ]. For active EMA, this may include technical issues with data entry and uploading of data.…”

“…For active EMA, this may include technical issues with data entry and uploading of data. For passive EMA, it usually involves uploading of passive sensory data to the servers[ 41 , 42 ].…”

Ecological Momentary Assessment of Depression in People With Advanced Dementia: Longitudinal Pilot Study

“…EMA helps to address various methodological limitations of conventional tools, such as reducing recall bias and enhancing the ecological validity of the data collected [9]. EMA studies in older adults have demonstrated its feasibility, enhanced precision of outcome measurement, and the ability to identify clinically significant depressive symptoms, although most studies exclude people with dementia and are typically self-reported [16][17][18]. Informant-rated EMA studies are less common than self-reported EMA studies but have been used in the population of people with dementia.…”