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Digital phenotyping adherence, feasibility, and tolerability in outpatients with schizophrenia.

ABSTRACT: Digital phenotyping has potential for use as an objective and ecologically valid form of symptom assessment in clinical trials for schizophrenia. However, there are critical methodological factors that must be addressed before digital phenotyping can be used for this purpose. The current study evaluated levels of adherence, feasibility, and tolerability for active (i.e., signal and event contingent ecological momentary assessment surveys) and passive (i.e., geolocation, accelerometry, and ambulatory psychophysiology) digital phenotyping methods recorded from smartphone and smartband devices. Participants included outpatients diagnosed with schizophrenia (SZ: n = 54) and demographically matched healthy controls (CN: n = 55), who completed 6 days of digital phenotyping. Adherence was significantly lower in SZ than CN for active recordings, but not markedly different for passive recordings. Some forms of passive recordings had lower adherence (ambulatory psychophysiology) than others (accelerometry and geolocation). Active digital phenotyping adherence was predicted by higher psychosocial functioning, whereas passive digital phenotyping adherence was predicted by education, positive symptoms, negative symptoms, and psychosocial functioning in people with SZ. Both groups found digital phenotyping methods tolerable and feasibility was supported by low frequency of invalid responding, brief survey completion times, and similar impediments to study completion. Digital phenotyping methods can be completed by individuals with SZ with good adherence, feasibility, and tolerability. Recommendations are provided for using digital phenotyping methods in clinical trials for SZ.

SUBMITTER: Raugh IM

PROVIDER: S-EPMC8192468 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Digital phenotyping adherence, feasibility, and tolerability in outpatients with schizophrenia.

Raugh Ian M IM James Sydney H SH Gonzalez Cristina M CM Chapman Hannah C HC Cohen Alex S AS Kirkpatrick Brian B Strauss Gregory P GP

Journal of psychiatric research 20210430

Digital phenotyping has potential for use as an objective and ecologically valid form of symptom assessment in clinical trials for schizophrenia. However, there are critical methodological factors that must be addressed before digital phenotyping can be used for this purpose. The current study evaluated levels of adherence, feasibility, and tolerability for active (i.e., signal and event contingent ecological momentary assessment surveys) and passive (i.e., geolocation, accelerometry, and ambula ...[more]

PMID: 33964681

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Project description:The promise of machine learning successfully exploiting digital phenotyping data to forecast mental states in psychiatric populations could greatly improve clinical practice. Previous research focused on binary classification and continuous regression, disregarding the often ordinal nature of prediction targets derived from clinical rating scales. In addition, mental health ratings typically show important class imbalance or skewness that need to be accounted for when evaluating predictive performance. Besides it remains unclear which machine learning algorithm is best suited for forecast tasks, the eXtreme Gradient Boosting (XGBoost) and long short-term memory (LSTM) algorithms being 2 popular choices in digital phenotyping studies. The CrossCheck dataset includes 6,364 mental state surveys using 4-point ordinal rating scales and 23,551 days of smartphone sensor data contributed by patients with schizophrenia. We trained 120 machine learning models to forecast 10 mental states (e.g., Calm, Depressed, Seeing things) from passive sensor data on 2 predictive tasks (ordinal regression, binary classification) with 2 learning algorithms (XGBoost, LSTM) over 3 forecast horizons (same day, next day, next week). A majority of ordinal regression and binary classification models performed significantly above baseline, with macro-averaged mean absolute error values between 1.19 and 0.77, and balanced accuracy between 58% and 73%, which corresponds to similar levels of performance when these metrics are scaled. Results also showed that metrics that do not account for imbalance (mean absolute error, accuracy) systematically overestimated performance, XGBoost models performed on par with or better than LSTM models, and a significant yet very small decrease in performance was observed as the forecast horizon expanded. In conclusion, when using performance metrics that properly account for class imbalance, ordinal forecast models demonstrated comparable performance to the prevalent binary classification approach without losing valuable clinical information from self-reports, thus providing richer and easier to interpret predictions.

Project description:Importance:Antipsychotic medications for the treatment of schizophrenia have limitations, and new treatments are needed. A prior pilot investigation suggested that adjunctive sodium nitroprusside (SNP) administered intravenously had rapid efficacy in the treatment of patients with schizophrenia. Objective:To determine the efficacy and tolerability of intravenous SNP infused at a rate of 0.5 μg/kg/min for 4 hours in patients with schizophrenia with some degree of treatment resistance. Design, Setting, and Participants:Multicenter, randomized, double-blind acute treatment study using a sequential parallel comparison design conducted in two 2-week phases at 4 academic medical centers beginning May 20, 2015, and ending March 31, 2017. Participants were adults 18 to 65 years of age with a diagnosis of schizophrenia as confirmed by the Structured Clinical Interview for DSM-IV, taking antipsychotic medication for at least 8 weeks, and had at least 1 failed trial of an antipsychotic medication within the past year. A total of 90 participants consented, 60 participants enrolled, and 52 participants were included in the analyses. A modified intent-to-treat analysis was used. Interventions:Participants were randomized in a 1:1:1 ratio to 1 of 3 treatment sequences: SNP and SNP, placebo and SNP, and placebo and placebo. The SNP and SNP group received SNP in phase 1 and SNP in phase 2 for the purpose of blinding, but the data from phase 2 were not included in the results. The placebo and SNP group received placebo in phase 1 and SNP in phase 2. If there was no response to placebo in phase 1, data from phase 2 were included in the analyses. The placebo and placebo group received placebo in both phases; if there was no response to placebo in phase 1, data from phase 2 were included in the analyses. Main Outcomes and Measures:Effectiveness of SNP compared with placebo in improving Positive and Negative Syndrome Scale (PANSS) total, positive, and negative scores across each 2-week phase. Results:Fifty-two participants (12 women and 40 men) were included in the study. In the SNP and SNP group, the mean (SD) age was 47.1 (10.5) years. In the placebo and SNP group, the mean (SD) age was 45.9 (12.3) years. In the placebo and placebo group, the mean (SD) age was 40.4 (11.0) years. There were no significant differences between the SNP and placebo groups at baseline or in change from baseline for PANSS-total (weighted β = -1.04; z = -0.59; P = .57), PANSS-positive (weighted β = -0.62; z = -0.93; P = .35), or PANSS-negative (weighted β = -0.12; z = -0.19; P = .85) scores. No significant differences in safety or tolerability measures were identified. Conclusions and Relevance:Although intravenous SNP is well tolerated, it was not an efficacious adjunctive treatment of positive or negative symptoms of psychosis among outpatients with schizophrenia with prior history of treatment resistance. Trial Registration:ClinicalTrials.gov identifier: NCT02164981.

Project description:BackgroundDigital phenotyping, the in-situ collection of passive (phone sensor) and active (daily surveys) data using a digital device, may provide new insights into the complex relationship between daily behaviour and mood for people with type 2 diabetes. However, there are critical knowledge gaps regarding its use in people with type 2 diabetes. This study assessed feasibility, tolerability, and user experience of digital phenotyping in people with and without type 2 diabetes after participation in a 2-month digital phenotyping study in Ireland. At study completion, participants rated methodology elements from "not a problem" to a "serious problem" on a 5-point scale and reported their comfort with the potential future use of digital phenotyping in healthcare, with space for qualitative expansion.ResultsEighty-two participants completed baseline. Attrition was 18.8%. Missing data ranged from 9-44% depending on data stream. Sixty-eight participants (82.9%) completed the user experience questionnaire (51.5% with type 2 diabetes; 61.8% female; median age-group 50-59). Tolerability of digital phenotyping was high, with "not a problem" being selected 76.5%-89.7% of the time across questions. People with type 2 diabetes (93.9%) were significantly more likely to be comfortable with their future healthcare provider having access to their digital phenotyping data than those without (53.1%), χ2 (1) = 14.01, p = < .001. Free text responses reflected a range of positive and negative experiences with the study methodology.ConclusionsAn uncompensated, 2-month digital phenotyping study was feasible among people with and without diabetes, with low attrition and reasonable missing data rates. Participants found digital phenotyping to be acceptable, and even enjoyable. The potential benefits of digital phenotyping for healthcare may be more apparent to people with type 2 diabetes than the general population.Supplementary informationThe online version contains supplementary material available at 10.1186/s44247-024-00116-6.

Project description:BackgroundAntipsychotics are well-known to cause potentially serious and life-threatening adverse drug reactions (ADRs) that have been reported to be also one of the major reasons for non-adherence. In Eritrea, shortage of psychiatrists and physicians, inadequacy of laboratory setups and unavailability of second-generation antipsychotics in the national list of medicines would seem to amplify the problem. This study's objective is to determine the impact of adverse effects of first-generation antipsychotics on treatment adherence in outpatients with schizophrenia at Saint Mary Neuro-Psychiatric National Referral Hospital.MethodsA cross-sectional study design was employed. All eligible adult patients with diagnosed schizophrenia (n = 242) who visited the hospital during the study period were enrolled. Data on ADRs, adherence and other variables were collected from patients using a self-administered questionnaire, interview and through medical cards review. The collected variables were analyzed using SPSS 22.0 with descriptive and multivariable logistic regression analysis. Statistical significance was tested at p value < 0.05.ResultsGreater than one-third (35.5%) of the patients with schizophrenia were non-adherent to treatment. The odds of non-adherence increased 1.06 times for each unit increase in the total ADR score (AOR = 1.06, 95% CI 1.04, 1.09). Patients with extrapyramidal (AOR = 44.69, 95% CI 5.98, 334.30), psychic (AOR = 14.90, 95% CI 1.90, 116.86), hormonal (AOR = 2.60, 95% CI 1.41, 4.80), autonomic (AOR = 3.23, 95% CI 1.37, 7.57) and miscellaneous reactions (AOR = 2.16, 95% CI 1.13, 4.13) were more likely to be non-adherent compared to their counterparts.ConclusionPoor treatment adherence was found to be substantial which was attributed to total ADR score, extrapyramidal, hormonal, psychic, autonomic and miscellaneous categories of reactions of the LUNSERS. To improve treatment adherence, early detection and management of adverse effects and inclusion of second-generation antipsychotics are recommended.

Project description:Once-daily oral HIV pre-exposure prophylaxis (PrEP) is an effective strategy to prevent HIV, but is highly dependent on adherence. Men who have sex with men (MSM) who use substances face unique challenges maintaining PrEP adherence. Digital pill systems (DPS) allow for real-time adherence measurement through ingestible sensors. Integration of DPS technology with other digital health tools, such as digital phenotyping, may improve understanding of nonadherence triggers and development of personalized adherence interventions based on ingestion behavior. This study explored the willingness of MSM with substance use to share digital phenotypic data and interact with ancillary systems in the context of DPS-measured PrEP adherence. Adult MSM on PrEP with substance use were recruited through a social networking app. Participants were introduced to DPS technology and completed an assessment to measure willingness to participate in DPS-based PrEP adherence research, contribute digital phenotyping data, and interact with ancillary systems in the context of DPS-based research. Medical mistrust, daily worry about PrEP adherence, and substance use were also assessed. Participants who identified as cisgender male and were willing to participate in DPS-based research (N = 131) were included in this subsample analysis. Most were White (76.3%) and non-Hispanic (77.9%). Participants who reported daily PrEP adherence worry had 3.7 times greater odds (95% CI: 1.03, 13.4) of willingness to share biometric data via a wearable device paired to the DPS. Participants with daily PrEP adherence worry were more likely to be willing to share smartphone data (p = 0.006) and receive text messages surrounding their daily activities (p = 0.003), compared to those with less worry. MSM with substance use disorder, who worried about PrEP adherence, were willing to use DPS technology and share data required for digital phenotyping in the context of PrEP adherence measurement. Efforts to address medical mistrust can increase advantages of this technology for HIV prevention.

Dataset Information

Digital phenotyping adherence, feasibility, and tolerability in outpatients with schizophrenia.

Publications

Digital phenotyping adherence, feasibility, and tolerability in outpatients with schizophrenia.

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