Frailty and depression predict instrumental activities of daily living in older adults: A population-based longitudinal study using the CARE75+ cohort.

OBJECTIVES: To evaluate if depression contributes, independently and/or in interaction with frailty, to loss of independence in instrumental activities of daily living (ADL) in older adults with frailty.
METHODS: Longitudinal cohort study of people aged ≥75 years living in the community. We used multi-level linear regression model to quantify the relationship between depression (≥5 Geriatric Depression Scale) and frailty (electronic frailty index), and instrumental activities of daily living (Nottingham Extended Activities of Daily Living scale; range: 0-66; higher score implies greater independence). The model was adjusted for known confounders (age; gender; ethnicity; education; living situation; medical comorbidity).
RESULTS: 553 participants were included at baseline; 53% were female with a mean age of 81 (5.0 SD) years. Depression and frailty (moderate and severe levels) were independently associated with reduced instrumental activities of daily living scores. In the adjusted analysis, the regression coefficient was -6.4 (95% CI: -8.3 to -4.5, p<0.05) for depression, -1.5 (95% CI: -3.8 to 0.9, p = 0.22) for mild frailty, -6.1 (95% CI: -8.6 to -3.6, p<0.05) for moderate frailty, and -10.1 (95% CI: -13.5 to -6.8, p<0.05) for severe frailty. Moreover, depression interacted with frailty to further reduce instrumental activities of daily living score in individuals with mild or moderate frailty. These relationships remained significant after adjusting for confounders.
CONCLUSION: Frailty and depression are independently associated with reduced independence in instrumental activities of daily living. Also, depression interacts with frailty to further reduce independence for mild to moderately frail individuals, suggesting that clinical management of frailty should integrate physical and mental health care.

Introduction

Globally, populations are rapidly ageing owing to increase in life expectancy and falling fertility rates. The fastest growing population is the oldest old, those aged 85 years and over. In the UK, the population of those aged ≥85 years is set to double to 3.2 million or 4% of the total population by mid-2041, and treble by 2066 with profound implications for health and social care services [1]. The likelihood of disability and/or experiencing multiple long term conditions, so-called multimorbidity, increases with age among those aged ≥65 years, leading to the possibility of increased time spent in poor health [2]. While there is some evidence that morbidity has become compressed into later life, in high income countries 50% of disease burden in those aged ≥60 years is attributable to long term conditions and there is considerable uncertainty about the health of future generations of older adults [3].

Using disease based approaches to understanding and managing the complex health needs of older adults has limited value and using frailty to characterise health status in older adults has emerged as a worthwhile alternative approach. There are a number of frailty definitions but there is broad consensus that it is characterised by loss of biological reserves, failure of homeostatic mechanisms and heightened vulnerability to adverse health outcomes, including falls, hospitalisations and mortality [4]. Frailty is common in later life and the risk of frailty increases substantially with age, affecting 1 in 10 people aged ≥65 years and between 25% and 50% of adults aged ≥85 years. The urgency to address this growing problem is reflected in UK health policy which has highlighted the need to develop and implement innovative care models to support older people with frailty [5].

A recent systematic review with meta-analysis (48 studies; n = 78122 participants) demonstrated that multimorbidity increased the risk of frailty two fold and in pooled analysis multimorbidity was significantly associated with frailty in community-dwelling people (OR 2.27; 95%CI 1.97–2.62) [6]. However, while most people with frailty will have multimorbidity, not all people with multimorbidity will become frail. While physical and biological factors are preeminent in explaining vulnerability to poor health among those with frailty, mental health is also implicated in adverse outcomes in older adults with disabling long-term conditions [7, 8]. Depression is longitudinally associated with frailty and those with frailty have a four-fold increased odds of having depression [9, 10]. Likewise, those with depression have similar odds of having frailty, pointing to the reciprocal nature between depressive states and frailty. Furthermore, the clinical manifestations of depression overlap with some of the key phenotypic components of frailty, such as exhaustion, low energy expenditure, weight loss and possibly slow walking speed owing to lack of motivation. Indeed there is a strong argument to consider depression and frailty as distinct but over-lapping constructs and as such these two inter-related syndromes should be evaluated together in efforts to understand how to optimally manage health and reduce disability in older adults [11].

Frailty predicts functioning and disability in activities of daily living (ADL) in community dwelling older adults and functioning and disability are better markers of survival and future health outcomes than disease status and comorbidity [12, 13]. However, it is not known if depression contributes either independently or in combination with frailty and multimorbidity to disability in older adults with frailty. IADL represent functional competence in everyday higher level tasks such as shopping or preparing a meal and are known to decline before basic ADLs associated with self-care. In this sense IADLs are appropriate markers of loss of independence and disability in older adults with a broad range of frailty. We therefore evaluated whether depression predicts instrumental ADL (IADL) disability in a unique population-based cohort of adults aged ≥75 years with a focus on frailty status and frailty trajectories.

Materials and methods

Ethical approval

The Bradford and Leeds Research Ethics Committee granted ethical approval for the CARE 75+ study (ref: 14/YH/1120). The CARE 75+ study is registered at ISRCTN16588124.

Study population

The Community Ageing Research 75+ Study (CARE75+) (Trial registration number ISRCTN16588124) is an on-going, longitudinal population based cohort study of older people. The data that comprise the analytic sample in this study were collected between January 2014 and December 2018. Community dwelling older people aged ≥75 years were eligible for inclusion. Exclusion criteria were: care home residents at point of recruitment; bedbound at home; have terminal cancer; in receipt of the Amber Care Bundle and estimated life expectancy of three months or less; and in receipt of palliative care services. Potential participants were recruited via their general practices. Participants undergo a range of cognitive, physical and psychosocial assessments at baseline, 6 and 12 months. Assessment were conducted face-to-face in the person’s home, with an optional modified follow-up at 6 months via telephone or the internet. Full details of the CARE75+ protocol including recruitment, consent and data collection, entry, coding, security and storage have previously been reported [14]. The protection and security of CARE75+ data was ensured through an information sharing agreement between Bradford Teaching Hospitals NHS Foundation Trust and the University of York in accordance with the Data Protection Act 1998.

Outcomes and predictors

The primary outcome was disability in IADL, measured using the Nottingham Extended ADL (NEADL) scale [15]. The NEADL scale was first validated in stroke patients and includes sub-scales for mobility, kitchen and domestic ability, and leisure activity. It is scored between 0 and 66 with higher scores indicating greater independence and less disability.

Our list of potential predictors were based on the frailty literature: age, gender, ethnicity (white; mixed white/black Caribbean; black Caribbean; Asian Pakistani; Asian Indian; Asian Bangladeshi; other), education, self-reported medical comorbidities (using Katz index). Depression was measured using the 15-item Geriatric Depression Scale (GDS) Short-Form [16]. The GDS-15 is scored from 0 to 15, with a score of ≥5 is suggestive of depressive symptoms and a score ≥ 10 is almost always indicative of depression. In meta-analysis the GDS-15 has an average sensitivity of 0.805 and specificity of 0.750 for identifying cases of major depressive disorder [17]. Frailty was measured using the electronic frailty index (eFI) which is based on the cumulative deficit model of frailty [18]. The eFI includes 36 equally weighted deficit variables based on Read codes and recorded routinely in the primary care electronic health record. The eFI score is derived from the number of deficits present relative to the proportion of the total possible and identifies four frailty categories: 0–0.12 = fit; 0.13–0.24 = mild frailty; 0.25–0.36 = moderate frailty; >0.36 = severe frailty. The eFI has good convergent validity with other research standard frailty measures [14]. We also explored if comorbidity predicted instrumental ADL given their strong association with functional decline in community dwelling older adults [19, 20]. Comorbidity data were collected using the self-reported Katz comorbidity questionnaire that asks questions about the presence of absence of long term physical and mental conditions that are given a “yes” or “no” response, giving a total number of comorbidities [21]. Variables were measured at baseline, 6-months and 12-months [22].

Statistical analysis

Regression analyses were conducted with NEADL score as a continuous dependent variable (range: 0–66, with higher score implying greater independence) and frailty levels (categorical: no, mild, moderate or severe frailty), depression status (binary variable, based on GDS-15 score of ≥5) and the interaction between frailty and depression. Other covariates included age, gender, ethnicity (white vs non-white), level of education (no education; GCSE or AS/A levels; Higher National Qualification (HNQ), diploma or University degree), living situation (living alone, with partner/spouse only, with family), comorbidity (measured using self-assessed Katz scale) and time (baseline, 6-months and 12-months). A multi-level regression model with linear mixed-effects was used which allows use of all available data at each time point. The model included individual-level random intercepts to account for repeated measurements nested within an individual (i.e. repeated observations nested within individual-level). Models were fitted with increasing complexity, starting first with main effects for frailty and depression and then introducing interaction effects. The interaction term evaluates whether the effect of frailty on instrumental daily activities of living is moderated by the level of depression, i.e. does depression influence the relationship between frailty and daily activities. Finally, based on this model, marginal (population-averaged) effects were estimated. The analysis was conducted in Stata v.15.1.

Results

This study is reported in accordance with the Strengthening The Reporting of Observational Studies in Epidemiology (STROBE) checklist (S1 Checklist). From a total of 1875 potentially eligible participants we identified baseline data for 553 participants (Fig 1). We recorded a total of 14 deaths; half of these participants, i.e. 7/14, completed all three waves of the study (i.e. they died after the 12-month follow-up period). As a result, deaths accounted for only 7 cases of missing data during the study follow-up. We did not collect data on possible transition to a care home. Of the 553 participants included at baseline just over half (53%) were female with a mean age of 81 (5.0 SD) years. The majority (89%) were of white ethnicity and 57% had no formal educational qualifications. A high proportion were either living alone (41%) or living with a partner or spouse (44%). The mean baseline total score on the NEADL (50.95; SD = 15.47) indicated that participants generally had good global instrumental functioning; this score was lower at follow-up time points (6 months: mean = 46.19; SD = 19.31) and 12 months: mean = 46.27; SD = 18.42). All participants had multimorbidity with a mean of five long term health conditions. Fifty eight participants had GDS scores that were suggestive of depression (Table 1). Table 2 shows the number of people with depression at each level of frailty over time.

Fig 1

STROBE flow diagram.

Table 1

Characteristics of participants at baseline.

	Mean (continuous variables); no. of observations (categorical variables)	SD (continuous variables); percentage (categorical variables)
Females, n (%)	296	53.5
Mean (SD) age, years	81.0	5.05
Ethnicity, n (%)
White	494	89.49
Asian Bangladeshi	3	0.54
Asian Indian	3	0.54
Asian Pakistani	50	9.06
Mixed white/black Caribbean	1	0.18
Black Caribbean	1	0.18
Missing	1	0.18
Education, n (%)
No qualifications	316	57.2
GCSE	79	14.3
AS and A levels	17	3.0
Higher National Certificate	36	6.5
Diploma	35	6.3
Bachelor’s degree	37	6.7
Postgraduate	15	2.7
Missing	17	3.0
Living situation, n (%)
Alone	225	40.8
With family	81	14.7
With partner/spouse	245	44.4
Missing	1	0.18
Nottingham Extended ADL score (baseline), mean (SD)	50.95	15.5
Geriatric Depression Scale score (baseline), mean (SD)	2.4	2.5
Comorbidity (based on Katz comorbidity index), mean (SD)	5.27	1.55

ADL: activities of daily living; AS/A Level: advanced subsidiary/advanced level; GCSE: general certificate of education; SD; standard deviation

Table 2

Cross-tabulation of depression and frailty at baseline, 6 months, and 12 months.

Baseline	Electronic Frailty Index (EFI)
	No frailty	Mild frailty	Moderate frailty	Severe frailty
Not depressed	84	127	116	28
Depressed	0	15	30	20
	Electronic Frailty Index (EFI)
6-months	No frailty	Mild frailty	Moderate frailty	Severe frailty
Not depressed	47	79	74	21
Depressed	0	8	20	11
	Electronic Frailty Index (EFI)
12-months	No frailty	Mild frailty	Moderate frailty	Severe frailty
Not depressed	33	52	68	20
Depressed	2	2	10	8

Regression results for model 1: Unadjusted main effects analysis

Instrumental ADL, measured by the NEADL scale, was significantly lower at 6 and 12 months compared to the baseline. Both frailty and depression were independently and negatively associated with NEADL score, i.e. depressed and more frail individuals had lower levels of independence and higher level of disability (Table 3).

Table 3

Results of linear multilevel models with NEADL score as the dependent variable and individual-level random intercepts.

	Unadjusted models				Adjusted models
	Model 1: unadjusted, no interaction		Model 2: unadjusted, with interaction		Model 3: adjusted, no interaction		Model 4: adjusted, with interaction
VARIABLES	Coefficients (95% CI)	p-value	Coefficients (95% CI)	p-value	Coefficients (95% CI)	p-value	Coefficients (95% CI)	p-value
Time point (reference: baseline)
6-months	-1.017	0.063	-0.991	0.068	-0.966	0.078	-0.961	0.078
	(-2.087 to 0.053)		(-2.053 to 0.071)		(-2.040 to 0.107)		(-2.028 to 0.107)
12-months	-1.163	0.053	-1.289**	0.032	-1.282*	0.033	-1.392*	0.020
	(-2.343 to 0.017)		(-2.466 to -0.112)		(-2.461 to -0.103)		(-2.568 to -0.215)
EFI: reference (reference: fit)
EFI: mild frailty	-3.653**	0.010	-3.313*	0.019	-1.464	0.223	-1.153	0.340
	(-6.423 to -0.883)		(-6.088 to -0.538)		(-3.818 to 0.891)		(-3.519 to 1.213)
EFI: moderate frailty	-10.507**	<0.001	-10.407**	<0.001	-6.104**	<0.001	-5.836**	<0.001
	(-13.441 to -7.572)		(-13.354 to -7.461)		(-8.606 to -3.601)		(-8.365 to -3.308)
EFI: severe frailty	-17.290**	<0.001	-18.058**	<0.001	-10.138**	<0.001	-11.053**	<0.001
	(-20.946 to -13.634)		(-21.924 to -14.192)		(-13.525 to -6.751)		(-14.610 to -7.496)
Depressed (based on GDS-15)	-6.710**	<0.001	4.213	0.434	-6.411**	<0.001	3.958	0.505
	(-8.684 to -4.735)		(-6.343 to 14.769)		(-8.274 to -4.549)		(-7.677 to 15.592)
Interaction: Frailty X Depression
Interaction: mild frailty X depressed			-14.189**	0.014			-12.526**	0.045
			(-25.526 to -2.851)				(-24.790 to -0.262)
Interaction: moderate frailty X depressed			-11.143*	0.045			-11.200	0.065
			(-22.042 to -0.244)				(-23.115 to 0.714)
Interaction: severe frailty X depressed			-8.469	0.144			-7.281	0.244
			(-19.829 to 2.892)				(-19.536 to 4.974)
Age (in years)					-0.703**	<0.001	-0.713**	<0.001
					(-0.899 to -0.507)		(-0.909 to -0.517)
Female					-0.634	0.517	-0.772	0.430
					(-2.550 to 1.283)		(-2.688 to 1.145)
Ethnicity: white					15.274**	<0.001	15.315**	<0.001
					(12.189 to 18.359)		(12.233 to 18.396)
Education (reference: no formal qualification)
GCSE or AS/A levels					3.795**	0.002	3.705**	0.003
					(1.351 to 6.240)		(1.255 to 6.156)
Higher National Certificate or diploma					4.251**	0.003	4.205**	0.003
					(1.453 to 7.048)		(1.409 to 7.001)
University degree					3.120	0.062	2.876	0.086
					(-0.156 to 6.395)		(-0.405 to 6.156)
Living situation (reference: living alone)
With family					-7.143*	<0.001	-7.132**	<0.001
					(-10.108 to -4.178)		(-10.095 to -4.168)
With partner/spouse					-2.284*	0.036	-2.460*	0.024
					(-4.415 to -0.154)		(-4.591 to -0.330)
Comorbidity (Katz comorbidity index)					-0.930**	<0.001	-0.936**	<0.001
					(-1.428 to -0.433)		(-1.432 to -0.439)
Constant	58.426***	<0.001	58.388***	<0.001	43.033***	<0.001	43.083***	<0.001
	(56.020 to 60.831)		(55.987 to 60.789)		(39.018 to 47.048)		(39.072 to 47.095)
Random effects parameters
Patient-level (SD)	11.690		11.693		8.099		8.097
	(10.820–12.631)		(10.824–12.631)		(7.360 to 8.912)		(7.360–8.909)
Residual	6.040		5.990		6.140		6.099
	(5.637–6.472)		(5.591–6.418)		(5.719 to 6.592)		(5.680–6.548)

AS/A Level: advanced subsidiary/advanced level; CI: confidence interval; EFI: electronic frailty index; GCSE: general certificate of education; GDS: geriatric depression scale; SD; standard deviation

*** p<0.001

** p < .001

* p<0.05

Regression results for model 2: Unadjusted main and interaction effects analysis

In model 2, an interaction term for depression and frailty was introduced. This model shows that there was a statistically significant interaction between depression and mild frailty (-14.2, 95% CI -25.5 to -2.8, p<0.05) and moderate frailty levels (-11.1, 95% CI -22.0 to -0.24, p<0.05) but not severe frailty. This implies that, in patients with mild or moderate frailty, depression is associated with even lower levels of independence in terms of instrumental activities of daily living.

Regression results for model 3: Adjusted main effects analysis

Model 3 adjusted for potential confounders and found that older age, non-white ethnicity, lower education level, living situation, and higher comorbidity were all significantly associated with impaired instrumental ADL (Table 2). Coefficients for the association between frailty and instrumental ADL were smaller after adjusting for confounders but remained significant for all levels except mild frailty. Compared with no frailty, moderate and severe frailty levels were associated with about 6 and 10 points lower scores on NEADL, respectively. The coefficient for depression was also statistically significant and indicate that depressed individuals had6.4 points (95% CI -8.274 to -4.549) lower NEADL score compared with non-depressed individuals).

Regression results for model 4: Adjusted main effects and interaction analysis

Finally, model 4 evaluated interaction effect in the adjusted model (i.e. same as model 3 but with interaction terms). The results showed that after adjusting for potential confounders the interaction between mild frailty and depression was still statistically significant (-12.5, 95% CI -24.8 to -0.26, p<0.05). The interaction between moderate frailty and depression was weakly significant (-11.2, 95% CI -23.1 to 0.71, p<0.1). The interaction term was not significant for severe frailty (-7.3, 95% CI -19.5 to 5.0).

The results imply that individuals with mild or moderate frailty who also experience depressive symptoms are likely to have higher disability in instrumental ADL compared with non-depressed individuals with the same level of frailty. This relationship is represented in the margins plot which shows that the predicted NEADL score in depressed individuals is lower than non-depressed individuals (Fig 2).

Fig 2

Margins plot showing interaction between depression status (measured by GDS-15) and electronic frailty index in predicting NEADL score.

Discussion

Using a large population based cohort of older adults aged ≥75 years we showed that frailty status and depression independently predicted disability in instrumental ADL. More severe frailty and depressive symptoms predicted poorer instrumental ADL respectively. Beta-coefficients were slightly attenuated when frailty and depression were modelled together but both variables remained significant predictors even when adjusted for comorbidity and other known confounders. There was evidence that depression moderates the relationship between frailty and instrumental ADL, but only in those with mild and moderate frailty. The absence of an effect in those with more severe frailty might stem from an underpowered analysis owing to smaller numbers of people with severe frailty with above threshold depressive symptoms. Another possible explanation is that severe frailty in itself is so debilitating for ADL that no moderating effects are observed.

Previously, Lohman et al. have shown that rapid increases in both frailty and depression predict nursing home admission and serious falls in community dwelling adults aged 51 years and over [23]. Furthermore, as with our analysis, when modelled together the effects of depression and frailty were attenuated suggesting that both frailty and depression explain vulnerability to adverse health outcomes. Indeed there is accumulating evidence to suggest that depressive symptoms contribute significantly to observed vulnerability to poor health in older people with frailty and that targeting depression might mitigate the negative effects of frailty on functioning and other health outcomes [24]. While the precise mechanisms that might explain such mediation are not yet clear, it is possible that disability in instrumental ADL is exacerbated in older people with frailty because depression is a disabling condition in older adults and is associated with increased number and severity of medical comorbidities and clusters of health-risk behaviours such as sedentary lifestyles [25, 26]. There is also the prospect that the relationship between depression and functioning is bi-directional. Low grip strength, a marker of physical functioning, is associated both cross-sectionally and longitudinally with depression [27]. Grip strength is highly correlated with upper body strength and predicts future ADLs [28]. Considered together, treating depression is likely to be an effective way to delay or reduce the likelihood of frailty progression in older adults.

Implications for research and practice

There is a persuasive argument that the management of frailty can be optimised if it is conceptualised as a long-term condition. In this sense, frailty can be used to identify target groups of people with multimorbidity with complex needs such as those with combinations of mental and physical conditions and functional impairment [29]. Here, engaging proactive approaches that draw on the chronic care model and behaviour change interventions may have some utility [30]. While there is good evidence that integrated collaborative care interventions that target depression in people with long term conditions are effective, even in people with high levels of disability and multimorbidity, these approaches have yet to be proven effective in older adults with frailty [31, 32]. Similarly, there is evidence that behavioural activation for depression in older adults is effective but trials to date have historically been small with significant methodological limitations [33]. Additionally, trials in behavioural activation have narrowly focused on depression rather than looking to impact both physical functioning and mental health outcomes in older adults.

There is also scope to go beyond traditional medical approaches and consider public health strategies that can delay or slow physical decline in older adults with frailty and depression. Here the natural environment as a community health asset could play a critical role in the management of frailty by making a positive impact on functioning and mental health. Green spaces in rural and urban areas have been shown in the general population to be highly beneficial to health and wellbeing and enhance social interaction [34]. There is also emerging evidence that exposure to the natural environment can confer health benefits in older adults. Higher coverage of urban green space is associated with reduced risk of all cause mortality, circulatory system-caused mortality, and stroke-caused mortality in community dwelling older adults aged ≥65 years [35]. Moreover, the frailty status of older adults living in neighbourhoods with higher levels of green space is more likely to improve over two years than in those living in neighbourhoods with less green space [36]. The pathway to such frailty improvements might be through improving mental health. Recent analysis of the Whitehall II cohort has shown that higher residential greenness and proximity to any natural environment slows decline in walking speed and grip strength and this association is party mediated by mental health [37]. Nature based interventions can benefit both physical and mental health and could confer health benefits for older adults across the frailty trajectory. Well designed and robust studies are needed to test the effectiveness of public mental health interventions that can be mapped to frailty status and promote healthy ageing in older people.

Strengths and limitations

A key strength of this study is the use of the CARE75+ cohort as a highly phenotyped primary care cohort with a recruitment rate of approximately 40%, comparable with other contemporary UK-based cohorts recruiting similar populations [38]. Findings are therefore likely to be representative of older adults recruited from primary care populations. However the sample was predominately white precluding exploration of whether ethnicity potentially explains variation in frailty and depression in older adults. Additionally, while the CARE75+ cohort includes an extensive range of health, social and economic outcome data it does not actively recruit older people from care homes, limiting opportunities to explore the impact of frailty and depression in older adults with the worst functioning. However, if people transitioned to a care home during the course of the study attempts are made to follow these people up. Furthermore, because we found that depression moderated the impact of frailty on instrumental ADL in only those with mild and moderate frailty it is likely that therapeutic interventions to manage depression in these groups will be delivered outside of care homes.

The CARE 75+ cohort study is an on-going study that started in January 2014 and not all participants in this study had reached the 6 month and 12 month follow-up assessment point at the time of the data extraction and analysis. As a result 214 participants were not included at 6-months, and an additional 117 participants were not included at 12 moths. These do not qualify as loss to follow-up but represent a lag in data accrual which is a feature of longitudinal studies. However we were able to collect sufficient outcomes on cases at follow-up to undertake suitably powered analyses. Other limitations relate to the instruments used to collect outcome data. The GDS-15 is not diagnostic but it does have proven capacity to screen for depressive symptoms across genders and age groups, from the young-old to the oldest-old [39]. Furthermore, the GDS has been shown to have predictive validity for mortality in populations aged ≥65 years with chronic heart failure, disability and cognitive impairments, suggesting it does have some utility in measuring depression in older adults [40]. However, there remains a debate about the performance of the GDS in people with cognitive impairment [41]. Depression and cognitive impairment often overlap. The study did not include a measure of cognitive impairment. Future studies should seek to clarify the independent contribution of depressive symptoms to outcome controlling for cognitive impairment. Finally, while grip strength is included in measures of frailty captured by CARE75+ we chose to use instrumental ADL as a proxy for physical functioning. Instrumental ADLs are essential to the maintenance of autonomy and independence and decrements in instrumental ADL are critical markers of progression of disability [42].

Conclusion

We have shown using data from a large population-based cohort of older adults that frailty and depression independently predict disability in instrumental ADL. Depression moderated the impact of frailty on instrumental ADL pointing to the potential for innovative solutions that target both physical and mental health in the management of frailty.

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13 May 2020

PONE-D-20-09338

Frailty and depression predict instrumental activities of daily living in older adults: a population-based longitudinal study using the CARE75+ cohort

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Additional Editor Comments (if provided):

According to Reviewer's comments the manuscripts needs a major revision.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors evaluate if depression contributes, independently and/or in interaction with frailty, to loss of independence in instrumental activities of daily living (ADL) in older adults with frailty. They studied Longitudinal cohort study of people aged ≥75 years living in the community by usying multi-level linear regression model to quantify the relationship between depression (≥5 Geriatric Depression Scale) and frailty (electronic frailty index), and instrumental activities of daily living (Nottingham Extended Activities of Daily Living scale; range: 0-66; higher score implies greater independence). They included 553 participants at baseline; 53% were female with a mean age of 81 (5.0 SD) years. Depression and frailty (moderate and severe levels) were independently associated with reduced instrumental activities of daily living scores. Moreover, depression interacted with frailty to further reduce instrumental activities of daily living score in individuals with mild or moderate frailty. These relationships remained significant after adjusting for confounders.

The manuscript is interesting. However I have some concerns about the number of patients. What about the sample size and the power of the study? What about the comorbidities such as heart failure? Please see and discuss Liguori I et al. Depression and chronic heart failure in the elderly: an intriguing relationship. J Geriatr Cardiol. 2018 Jun;15(6):451-459.

Reviewer #2: The data has to be requested from the data controller's website. Technically the authors cannot make it fully available.

The authors used a subsample of participants in the CARE75+ cohort to examine the association between depression, frailty and instrumental activities of daily living (IADL) from baseline to 12 months of follow-up. The analytic approach is a linear mixed model with random intercept. The presentation and presentation of results can be improved. Answers to the following questions can improve a reader’s understanding of the validity of the results and conclusions.

1). Based on the flow chart, 106 participants were excluded from the study due to “delay between participant consent and baseline data being uploaded.” Were their baseline data available? If not, they probably would have been counted as “lost to study entry.” So my guess is their data were available. Then, what is the justification for this exclusion?

2). There was substantial amount of attrition (39% at 6 months and 60% at 6 months). It is known that linear mixed effects models are consistent under the assumption of missing at random. However, given this cohort’s age, it is unlikely the attrition was at random. The authors were not clear how many died, transitioned to a care home and was not followed, and was lost to follow-up due to other reasons. When attrition is not at random, the mixed effects model estimates are biased.

3). After estimating a linear mixed model, the authors presented marginal effects in Figure 2. Given that the analysis was done in Stata, I assume the authors used the margins statement after the linear mixed model for the marginal effects. However, the margins command after a mixed model in Stata only calculates the fixed effects part of the model, thus is not a real marginalization. Granted that if the missing at random assumption holds, the fixed part of the mixed model is equivalent to the population-averaged effects; however, given 2) this is unlikely to be true. The authors could take a look at the article by Rouanet et al. (2019) “Interpretation of mixed models and marginal models with cohort attrition due to death and drop-out” in Statistical Methods in Medical Research.

4). The authors speculated that “the absence of an effect in those with severe frailty stem from an underpowered analysis owing to smaller numbers.” However, the authors did not present the number of people with depression and severe frailty in each time period. A tabulate of sample size over time will be helpful.

Reviewer #3: The authors in this study evaluate if depression contribute, independently and/or in interaction with frailty, to loss of independence in instrumental activities of daily living (IADL) in older adults with frailty in a longitudinal cohort study of people aged ≥75 years living in the community by using multi-level linear regression model. They included 553 participants at baseline. Depression and frailty (moderate and severe levels) were independently associated with reduced instrumental activities of daily living scores. Moreover, depression interacted with frailty to further reduce instrumental activities of daily living score in individuals with mild or moderate frailty. These relationships remained significant after adjusting for confounders.

The manuscript is very interesting. However I have some concerns about adjusting confounders: given that cognitive impairment is an independent factor for ADL and IADL loss and that depression and dementia are often overlapping in the elderly population, has cognitive impairment been evaluated in the sample? Furthermore, GDS is not a validated tool for the evaluation of depression in patients with cognitive impairment. Indeed I think that some clarifications are necessary about effects of depression symptoms on comorbidities, please see and discuss “Depressive symptoms predict mortality in elderly subjects with chronic heart failure. Testa G, Cacciatore F, Galizia G, Della-Morte D, Mazzella F, Gargiulo G, Langellotto A, Raucci C, Ferrara N, Rengo F, Abete P. Eur J Clin Invest. 2011 Dec;41(12):1310-7”.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Submitted filename: Review PONE-D-20-09338.docx

Click here for additional data file.

10 Sep 2020

We have uploaded a response to the reviewers table as part of our revised submission.

As requested, in the cover letter, we have addressed the issue about data availability and given details about how requests for data access can be made.

Submitted filename: Responses to the reviewers_v2.docx

Click here for additional data file.

23 Sep 2020

PONE-D-20-09338R1

Frailty and depression predict instrumental activities of daily living in older adults: a population-based longitudinal study using the CARE75+ cohort

PLOS ONE

Dear Dr. COVENTRY,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Nov 07 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Pasquale Abete

Academic Editor

PLOS ONE

Additional Editor Comments (if provided):

According to Reviewer's comments the manuscript needs a major revision.

Sincerely,

P. Abete

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

Reviewer #3: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: All comments have been addressed. The manuscript presented in an intelligible fashion and written in standard English. No further comments.

Reviewer #2: The authors responded to my first question by changing the description of why the 106 patients were excluded.

The authors responded to my second question by saying “The study recorded a total of 14 deaths; half of these participants, i.e. 7/14, completed all three waves of the study (i.e. they died after the 12-month follow-up period).” In the main text this information should be given. In the follow chart and in the main text, the reason for loss to follow up (not death) should be given, at six months 214 (7 died) patients were not included ; and at 12 months 331 (7 died) patients were not included; what are the reason for the exclusions?

The model for 12-month loss to follow up should not be among only those patients with non-missing 6-month data, it should be among all 553 patients. The authors should show in their response letter the logistic regression for loss to follow up at 6 months with age and baseline frailty and depression as predictors, although I do not believe this necessarily mean anything because we still don’t know if other variables are related to loss to follow up. Descriptive statistics of all variables in Table 1 should be given for those with and those without loss to follow up, one table for 6-month; one table for 12-month. We can from the descriptive statistics get a glimpse of how different the two groups are.

The authors added Table 2 (cross-tabulation of depression and frailty at baseline, 6 months and 12 months) in response to my question about their speculation that “the absence of an effect in those with severe frailty stem from an underpowered analysis owing to smaller numbers”, we can see that there are only 2 patients with depression and no frailty in 12 months. This fact makes the models with interaction between depression and frailty very questionable. No descriptive data were given for the outcome NEAL at baseline, 6 months or 12 months. One cannot get a sense of how big the effects (coefficients) are relative to the distribution of the outcome.

Reviewer #3: All comments has been addressed. The manuscript is improved and is suitable for pubblication in present form

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

10 Nov 2020

We have compiled a response to reviewer comments table that includes author responses to reviewer number 2. These responses are in a table that we have included in this revised submission.

Submitted filename: Responses to the reviewers_revision_v3.docx

Click here for additional data file.

2 Dec 2020

Frailty and depression predict instrumental activities of daily living in older adults: a population-based longitudinal study using the CARE75+ cohort

PONE-D-20-09338R2

Dear Dr. COVENTRY,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Pasquale Abete

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

No further comments.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #2: No

4 Dec 2020

PONE-D-20-09338R2

Frailty and depression predict instrumental activities of daily living in older adults: a population-based longitudinal study using the CARE75+ cohort

Dear Dr. Coventry:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Prof. Pasquale Abete

Academic Editor

PLOS ONE