Objective:This study examined changes in mental health symptoms and weight during weight loss treatment. It was hypothesized that worsening mental health would negatively impact weight loss. Methods:Data were analyzed from a trial of 92 Hispanic women with overweight/obesity and prediabetes, who were randomized to receive intensive lifestyle intervention (ILI), metformin 1,700 mg daily, or standard care. Depression, anxiety and perceived stress were assessed at 0, 6 and 12 months. Six- and 12-month weight change was compared among participants whose symptom scores worsened on any mental health measure (W) vs. improved or remained stable on all three (I/S). Results:Among ILI participants, the 12-month difference in weight loss between I/S and W groups was statistically significant: -5.1 kg (P = 0.001). From baseline to 6 months, ILI participants in I/S and W groups experienced comparable weight loss. However, from 6 to 12 months, W participants regained weight, whereas I/S participants experienced continued weight loss. In the metformin and standard care arms, there was no weight difference between I/S and W groups. Conclusions:In ILI, 12-month improvement or stability in mental health was significantly associated with weight loss. Weight trajectories between I/S and W groups diverged at 6 months.

Copyright is held by the owner/author(s). Publication rights licensed to ACM. The increasing ubiquity of health sensing technology holds promise to enable patients and health care providers to make more informed decisions based on continuously-captured data. The use of sensor-captured patient-generated data (sPGD) has been gaining greater prominence in the assessment of physical health, but we have little understanding of the role that sPGD can play in mental health. To better understand the use of sPGD in mental health, we interviewed care providers in an intensive treatment program (ITP) for veterans with post-traumatic stress disorder. In this program, patients were given Fitbits for their own voluntary use. Providers identified a number of potential benefits from patients’ Fitbit use, such as patient empowerment and opportunities to reinforce therapeutic progress through collaborative data review and interpretation. However, despite the promise of sensor data as offering an “objective” view into patients’ health behavior and symptoms, the relationships between sPGD and therapeutic progress are often ambiguous. Given substantial subjectivity involved in interpreting data from commercial wearables in the context of mental health treatment, providers emphasized potential risks to their patients and were uncertain how to adjust their practice to effectively guide collaborative use of the FitBit and its sPGD. We discuss the implications of these findings for designing systems to leverage sPGD in mental health care.

Background

Cognitive behavioral therapy (CBT) can be delivered efficaciously through various modalities, including telephone (T-CBT) and face-to-face (FtF-CBT). The purpose of this study was to explore predictors of outcome in T-CBT and FtF-CBT for depression.

Method

A total of 325 depressed participants were randomized to receive eighteen 45-min sessions of T-CBT or FtF-CBT. Depression severity was measured using the Hamilton Depression Rating Scale (HAMD) and the Patient Health Questionnaire-9 (PHQ-9). Classification and regression tree (CART) analyses were conducted with baseline participant demographics and psychological characteristics predicting depression outcomes, HAMD and PHQ-9, at end of treatment (week 18).

Results

The demographic and psychological characteristics accurately identified 85.3% and 85.0% of treatment responders and 85.7% and 85.0% of treatment non-responders on the HAMD and PHQ-9, respectively. The Coping self-efficacy (CSE) scale predicted outcome on both the HAMD and PHQ-9; those with moderate to high CSE were likely to respond with no other variable influencing that prediction. Among those with low CSE, depression severity influenced response. Social support, physical functioning, and employment emerged as predictors only for the HAMD, and sex predicted response on the PHQ-9. Treatment delivery method (i.e. telephone or face-to-face) did not impact the prediction of outcome.

Conclusions

Findings suggest that the predictors of improved depression are similar across treatment modalities. Most importantly, a moderate to high level of CSE significantly increases the chance of responding in both T-CBT and FtF-CBT. Among patients with low CSE, those with lower depressive symptom severity are more likely to do well in treatment.