Health App Use Among Individuals With Symptoms of Depression and Anxiety: A Survey Study With Thematic Coding
Bibliographic record
Abstract
BACKGROUND: Researchers have largely turned to commercial app stores, randomized trials, and systematic reviews to make sense of the mHealth landscape. Few studies have approached understanding by collecting information from target end users. The end user perspective is critical as end user interest in and use of mHealth technologies will ultimately drive the efficacy of these tools. OBJECTIVE: The purpose of this study was to obtain information from end users of mHealth technologies to better understand the physical and mental health apps people use and for what purposes. METHODS: People with depressive or anxious symptoms (N=176) seeking entry into a trial of mental health and well-being apps for Android devices completed online questionnaires assessing depression and anxiety (Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7), past and current mental health treatment-seeking behavior, overall mobile device use, and use of mobile health apps. Participants reported the physical health and mental health apps on their devices and their reasons for using them. Data were extracted from the participant self-reports and apps and app purposes were coded in order to categorize them. RESULTS: Participants were largely white, middle-aged females from the Midwest region of the United States recruited via a health care organization and Web-based advertising (135 female, 41 male, mean age 38.64 years, age range 19-75 years.) Over three-quarters (137/176, 77.8%) of participants indicated having a health app on their device. The top 3 kinds of apps were exercise, fitness, and pedometers or heart rate monitoring apps (93/176, 52.8%); diet, food, or calorie counting apps (65/177, 36.9%); and mental health/wellness apps (46/177, 26.1%). The mean number of mobile physical and mental health apps on a participant's phone was 2.15 (SD 3.195). Of 176 participants, 107 (60.8%) specifically reported the top 5 health apps that they used and their purposes. Across the 107 participants, a total of 285 apps were reported, with 139 being unique apps. The majority of these apps were free (129/139, 92.8%). Almost two-thirds of participants (67/107, 62.6%) reported using health apps at least on a daily basis. CONCLUSIONS: Among those seeking support for their well-being via physical and mental health apps, people are using a variety of health apps. These people use health apps on a daily basis, especially free apps. The most common reason for using a health app is to track some health-related data; for mental health apps specifically, training or habit building was the most popular reason. Understanding the end user perspective is important because it allows us to build on the foundation of previously established mHealth research and may help guide future work in mHealth. TRIAL REGISTRATION: Clinicaltrials.gov NCT02176226; https://clinicaltrials.gov/ct2/show/NCT02176226 (Archived by WebCite at http://www.webcitation.org/6rGc1MGyM).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".